diff --git a/Bonus/.gitignore b/Bonus/.gitignore new file mode 100644 index 0000000..6e60522 --- /dev/null +++ b/Bonus/.gitignore @@ -0,0 +1,5 @@ +/Bonus 2/data +/Bonus 3/data +/Bonus 4/data +/Bonus 4/runs +/Bonus 5/archive \ No newline at end of file diff --git a/Bonus/2_Bonusaufgabe_Neifeld_Medved.ipynb b/Bonus/2_Bonusaufgabe_Neifeld_Medved.ipynb new file mode 100644 index 0000000..c6c5798 --- /dev/null +++ b/Bonus/2_Bonusaufgabe_Neifeld_Medved.ipynb @@ -0,0 +1,1074 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gruppenabgabe\n", + "### Michel Medved - 9303634\n", + "### Jan Neifeld - 8722662" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vorverarbeitung der Daten\n", + "Zunächst wird der Pfad des Ordners der die einzelnen Dateien enthält eingegeben (hier: WINDOWS format)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "### WINDOWS path to directory containing training and testing data files\n", + "input_dir = \"C:\\\\DHBW WWI DS(A) Studium\\\\S5\\\\Advanced Machine Learning\\\\Bonusaufgaben\\\\2_Bonusaufgabe_DimensionalityReduction\\\\\"\n", + "RANDOM_SEED = 42" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mit der Funktion format_data werden die einzelnen Training und Testdaten (PEMS_test; PEMS_testlabels; PEMS_train; PEMS_trainlabels) eingelesen und verarbeitet.\n", + "- Ersetze \";\" durch Leerzeichen \" \"\n", + "- Entferne Klammerung \"[\" und \"]\"\n", + "- speichere Datei als .csv Datei im selben Ordner ab\n", + "\n", + "Die Dateien werden im folgenden mit Leerzeichen als Separator eingelesen." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file: PEMS_test\n", + "Processing file: PEMS_testlabels\n", + "Processing file: PEMS_train\n", + "Processing file: PEMS_trainlabels\n" + ] + } + ], + "source": [ + "def format_data(input_dir):\n", + " ### Iteriere über Ordner und prozessiere alle Dateien mit \"train\" oder \"test\" string im Namen\n", + " for filename in os.listdir(input_dir):\n", + " if filename.endswith(\".csv\"):\n", + " continue\n", + "\n", + " if \"test\" in filename or \"train\" in filename:\n", + " print(f\"Processing file: {filename}\")\n", + " \n", + " ### Prozessierung\n", + " with open(filename, \"r\") as file:\n", + " filedata = file.read()\n", + " filedata = filedata.replace(';', ' ')\n", + " filedata = filedata.replace('[', '')\n", + " filedata = filedata.replace(']', '')\n", + " ### Speichern in separate .csv file\n", + " with open(filename+\".csv\", 'w') as file:\n", + " file.write(filedata)\n", + " else:\n", + " continue\n", + "format_data(input_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Die generierten .csv Dateien werden ohne Header eingelesen mit Leerzeichen als Separator:\n", + "- X_train = PEMS_train.csv\n", + "- y_train = PEMS_trainlabels.csv\n", + "- X_test = PEMS_test.csv\n", + "- y_test = PEMS_testlabels.csv\n", + "\n", + "Die Dimensionen der Trainingsets stimmen mit Angaben im Internet überein --> 440 Instanzen (267 train + 173 test) und 138672 einzelne Attribute
\n", + "Allerdings müssen die Labels (y_train und y_test) transponiert werden von (1, 267) --> (267,)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (267, 138672)\n", + "y_train shape: (1, 267)\n", + "X_test shape: (173, 138672)\n", + "y_test shape: (1, 173)\n" + ] + } + ], + "source": [ + "X_train = pd.read_csv(\"PEMS_train.csv\", sep=\" \", header=None)\n", + "y_train = pd.read_csv(\"PEMS_trainlabels.csv\", sep=\" \", header=None)\n", + "X_test = pd.read_csv(\"PEMS_test.csv\", sep=\" \", header=None)\n", + "y_test = pd.read_csv(\"PEMS_testlabels.csv\", sep=\" \", header=None)\n", + "\n", + "print(f\"X_train shape: {X_train.shape}\")\n", + "print(f\"y_train shape: {y_train.shape}\")\n", + "print(f\"X_test shape: {X_test.shape}\")\n", + "print(f\"y_test shape: {y_test.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correction of the dimensionality shape of the y_train and y_test data\n", + "X_train shape: (267, 138672)\n", + "y_train shape: (267,)\n", + "X_test shape: (173, 138672)\n", + "y_test shape: (173,)\n" + ] + } + ], + "source": [ + "print(\"Correction of the dimensionality shape of the y_train and y_test data\")\n", + "y_train = y_train.transpose().values.ravel()\n", + "y_test=y_test.transpose().values.ravel()\n", + "print(f\"X_train shape: {X_train.shape}\")\n", + "print(f\"y_train shape: {y_train.shape}\")\n", + "print(f\"X_test shape: {X_test.shape}\")\n", + "print(f\"y_test shape: {y_test.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Verteilung der vorherzusagenden Zielvariable (Wochentage 1-7) mit leichter Ungleichverteilung" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PCA und explained variance ratio\n", + "\n", + "Zunächst wurde eine PCA Pipeline ohne Klassifizierung erstellt. Die kumulative erklärte Varianz enthalten in den einzelnen Principal Components wurde explorative geplottet und die Anzahl der einzelnen Components für jeweil 75, 90, 95, 99 % der enthaltenen Varianz wurden ermittelt." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.86618722e-01 1.24870908e-01 9.07014104e-02 7.50306281e-02\n", + " 7.06214534e-02 2.87593323e-02 1.72911161e-02 1.61515590e-02\n", + " 1.20610014e-02 1.16451132e-02 1.15532656e-02 9.07745571e-03\n", + " 7.96032095e-03 7.71581260e-03 6.31439661e-03 5.27858493e-03\n", + " 5.10904903e-03 4.86969409e-03 4.76957350e-03 4.51124692e-03\n", + " 4.39664376e-03 4.22116405e-03 4.12826242e-03 4.03838214e-03\n", + " 3.79778867e-03 3.74390091e-03 3.66294476e-03 3.51056112e-03\n", + " 3.49491397e-03 3.37491637e-03 3.33157893e-03 3.21232651e-03\n", + " 3.13255092e-03 3.10946288e-03 3.06810482e-03 2.99926282e-03\n", + " 2.95616885e-03 2.90204425e-03 2.89611969e-03 2.83565734e-03\n", + " 2.81239104e-03 2.78298359e-03 2.75033340e-03 2.72492911e-03\n", + " 2.68007342e-03 2.64644141e-03 2.61574235e-03 2.58631481e-03\n", + " 2.57163891e-03 2.55348312e-03 2.52663661e-03 2.51831546e-03\n", + " 2.47218515e-03 2.45942738e-03 2.41823659e-03 2.41018364e-03\n", + " 2.37553646e-03 2.35999286e-03 2.34012965e-03 2.33079805e-03\n", + " 2.28513361e-03 2.24015813e-03 2.22619297e-03 2.20324778e-03\n", + " 2.18990761e-03 2.15878137e-03 2.12608881e-03 2.11308562e-03\n", + " 2.09750439e-03 2.06415844e-03 2.04657907e-03 2.02327831e-03\n", + " 2.01605667e-03 1.98948255e-03 1.95383166e-03 1.92326356e-03\n", + " 1.91929247e-03 1.89604502e-03 1.89037689e-03 1.87056068e-03\n", + " 1.84978746e-03 1.83830689e-03 1.82717387e-03 1.80362491e-03\n", + " 1.76743507e-03 1.75819154e-03 1.73905296e-03 1.73590289e-03\n", + " 1.71625946e-03 1.69513714e-03 1.68601903e-03 1.67360641e-03\n", + " 1.65930447e-03 1.64809434e-03 1.64401844e-03 1.62584383e-03\n", + " 1.61511171e-03 1.60906454e-03 1.59390885e-03 1.58979369e-03\n", + " 1.55561423e-03 1.55135045e-03 1.54729389e-03 1.51902075e-03\n", + " 1.51454748e-03 1.49265322e-03 1.48861581e-03 1.47973433e-03\n", + " 1.47495158e-03 1.46846927e-03 1.46086745e-03 1.45180533e-03\n", + " 1.42819507e-03 1.41409197e-03 1.40978440e-03 1.40341505e-03\n", + " 1.39022535e-03 1.38307463e-03 1.37050503e-03 1.35649912e-03\n", + " 1.35021824e-03 1.34646196e-03 1.33245162e-03 1.33173603e-03\n", + " 1.31657735e-03 1.30834496e-03 1.28817377e-03 1.27664810e-03\n", + " 1.27111883e-03 1.26680557e-03 1.25304168e-03 1.25208764e-03\n", + " 1.24778781e-03 1.22651426e-03 1.21883044e-03 1.21319716e-03\n", + " 1.20316045e-03 1.18767621e-03 1.18129685e-03 1.17955878e-03\n", + " 1.17224237e-03 1.16325300e-03 1.15771417e-03 1.15708536e-03\n", + " 1.14697163e-03 1.13742275e-03 1.12458309e-03 1.11893641e-03\n", + " 1.11254273e-03 1.09850651e-03 1.08735970e-03 1.08348586e-03\n", + " 1.07498761e-03 1.07323084e-03 1.06184496e-03 1.04719208e-03\n", + " 1.04307983e-03 1.02877889e-03 1.02694310e-03 1.01910649e-03\n", + " 1.01084335e-03 9.94197408e-04 9.88308306e-04 9.76472590e-04\n", + " 9.73088535e-04 9.62871088e-04 9.53791110e-04 9.30349244e-04\n", + " 9.12936130e-04 8.87684164e-04 8.67410126e-04 8.51187553e-04\n", + " 8.45593562e-04 8.19380018e-04 8.10785274e-04 8.02135772e-04\n", + " 7.96551621e-04 7.85499273e-04 7.67817292e-04 7.62525285e-04\n", + " 7.36789150e-04 7.34388946e-04 7.24181278e-04 7.15008666e-04\n", + " 7.04832860e-04 7.00148737e-04 6.91223893e-04 6.85675288e-04\n", + " 6.68747445e-04 6.45209679e-04 6.43733082e-04 6.22782928e-04\n", + " 6.12499478e-04 5.77332582e-04 5.22514344e-04 1.32510563e-31\n", + " 6.85734187e-32 5.92915771e-32 5.45527328e-32 4.95430651e-32\n", + " 4.05251605e-32 3.01952032e-32 2.63262129e-32 2.61480426e-32\n", + " 2.31809913e-32 1.69034806e-32 1.59341478e-32 1.58165865e-32\n", + " 1.47412554e-32 1.46910113e-32 1.27456159e-32 1.20599985e-32\n", + " 1.04502876e-32 7.74317651e-33 7.31833628e-33 6.97670674e-33\n", + " 6.78949318e-33 6.14218637e-33 5.23769887e-33 4.74291871e-33\n", + " 4.72668931e-33 4.47294940e-33 4.25778765e-33 4.01847124e-33\n", + " 3.79031049e-33 3.49979043e-33 3.43967888e-33 3.36036539e-33\n", + " 2.54910571e-33 2.15453315e-33 2.13080103e-33 1.92501451e-33\n", + " 1.74762911e-33 1.63567783e-33 1.44839992e-33 1.30703396e-33\n", + " 1.04222081e-33 1.03017874e-33 9.11155481e-34 8.14493810e-34\n", + " 8.14493810e-34 8.14493810e-34 8.14493810e-34 8.14493810e-34\n", + " 8.14493810e-34 8.14493810e-34 8.14493810e-34 8.14493810e-34\n", + " 8.14493810e-34 8.14493810e-34 8.14493810e-34 8.14493810e-34\n", + " 8.14493810e-34 8.14493810e-34 8.14493810e-34 8.14493810e-34\n", + " 8.14493810e-34 7.43196643e-34 4.62281645e-34 3.58754041e-34\n", + " 3.07909009e-34 2.07218626e-34 2.04482263e-34 1.45760992e-34\n", + " 8.25715965e-35 6.65060164e-35 2.26819967e-36]\n", + "Number of Components: 267\n" + ] + }, + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline as Pipeline\n", + "from sklearn.preprocessing import StandardScaler, RobustScaler\n", + "from sklearn.decomposition import PCA\n", + "\n", + "pipeline = Pipeline(\n", + " steps=[\n", + " (\"scaler\", StandardScaler()),\n", + " (\"pca\", PCA(random_state=RANDOM_SEED))\n", + " ]\n", + ")\n", + "pipeline.fit(X_train)\n", + "\n", + "### Show the number of the explained variance ratio and the number of components\n", + "print(pipeline.named_steps[\"pca\"].explained_variance_ratio_)\n", + "print(\"Number of Components: \", pipeline.named_steps[\"pca\"].n_components_)\n", + "\n", + "### Plot explained variance as a function of number of Components\n", + "fig, ax = plt.subplots(1,1, figsize=(15,10), squeeze=False)\n", + "ax = plt.plot(np.cumsum(pipeline.named_steps[\"pca\"].explained_variance_ratio_))\n", + "### Draw horizontal lines at 0.75, 0.9, 0.95, 0.99\n", + "plt.axhline(y = 0.75, color='y', linestyle='--', label = '75% Explained Variance')\n", + "plt.axhline(y = 0.90, color='c', linestyle='--', label = '90% Explained Variance')\n", + "plt.axhline(y = 0.95, color='k', linestyle='--', label = '95% Explained Variance')\n", + "plt.axhline(y = 0.99, color='r', linestyle='--', label = '99% Explained Variance')\n", + "\n", + "plt.xlabel('number of components')\n", + "plt.ylabel('cumulative explained variance')\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "75.0 % explained variance: 34\n", + "90.0 % explained variance: 103\n", + "95.0 % explained variance: 140\n", + "99.0 % explained variance: 180\n", + "[34, 103, 140, 180]\n" + ] + } + ], + "source": [ + "pca_var_lst = [0.75, 0.90, 0.95, 0.99]\n", + "n_comp_lst = []\n", + "for i in pca_var_lst:\n", + " pipeline = Pipeline(\n", + " steps=[\n", + " (\"scaler\", StandardScaler()),\n", + " (\"pca\", PCA(i, random_state=RANDOM_SEED))\n", + " ])\n", + " pipeline.fit(X_train)\n", + " n_comp = pipeline.named_steps[\"pca\"].n_components_\n", + " n_comp_lst.append(n_comp)\n", + " print(f\"{i*100} % explained variance: {n_comp}\")\n", + "print(n_comp_lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Classification Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import RocCurveDisplay, PrecisionRecallDisplay\n", + "from sklearn.metrics import roc_curve, auc, precision_recall_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=80; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=80; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=80; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=100; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=100; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=100; total time= 5.1s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=200; total time= 4.9s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=200; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=10, rforest__n_estimators=200; total time= 5.0s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=80; total time= 4.6s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=80; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=80; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=100; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=100; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=100; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=200; total time= 4.9s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=200; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=30, rforest__n_estimators=200; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=80; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=80; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=80; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=100; total time= 4.6s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=100; total time= 4.7s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=100; total time= 4.8s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=200; total time= 4.9s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=200; total time= 4.9s\n", + "[CV] END pca__n_components=34, rforest__max_depth=50, rforest__n_estimators=200; total time= 4.9s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=80; total time= 8.7s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=80; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=80; total time= 8.7s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=100; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=100; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=100; total time= 9.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=200; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=200; total time= 8.6s\n", + "[CV] END pca__n_components=103, rforest__max_depth=10, rforest__n_estimators=200; total time= 8.6s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=80; total time= 8.3s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=80; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=80; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=100; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=100; total time= 8.3s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=100; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=200; total time= 8.8s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=200; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=30, rforest__n_estimators=200; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=80; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=80; total time= 8.3s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=80; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=100; total time= 8.5s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=100; total time= 8.3s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=100; total time= 8.4s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=200; total time= 8.6s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=200; total time= 8.6s\n", + "[CV] END pca__n_components=103, rforest__max_depth=50, rforest__n_estimators=200; total time= 8.6s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=80; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=80; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=80; total time= 9.9s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=100; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=100; total time= 9.9s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=100; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=200; total time= 10.3s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=200; total time= 10.2s\n", + "[CV] END pca__n_components=140, rforest__max_depth=10, rforest__n_estimators=200; total time= 10.2s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=80; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=80; total time= 9.9s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=80; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=100; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=100; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=100; total time= 10.7s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=200; total time= 10.4s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=200; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=30, rforest__n_estimators=200; total time= 10.2s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=80; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=80; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=80; total time= 9.9s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=100; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=100; total time= 10.1s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=100; total time= 10.0s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=200; total time= 10.2s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=200; total time= 10.2s\n", + "[CV] END pca__n_components=140, rforest__max_depth=50, rforest__n_estimators=200; total time= 10.2s\n", + "The best parameters: {'pca__n_components': 34, 'rforest__max_depth': 30, 'rforest__n_estimators': 100}\n" + ] + } + ], + "source": [ + "pca_rf_pipeline = Pipeline(\n", + " steps=[\n", + " (\"scaler\", StandardScaler()),\n", + " (\"pca\", PCA(random_state=RANDOM_SEED)),\n", + " (\"rforest\", RandomForestClassifier(random_state=RANDOM_SEED))\n", + " ]\n", + ")\n", + "\n", + "pca_rf_estimator = GridSearchCV(\n", + " estimator=pca_rf_pipeline,\n", + " param_grid = {\n", + " \"pca__n_components\":[34, 103, 140],\n", + " \"rforest__max_depth\":[10, 30, 50],\n", + " \"rforest__n_estimators\":[80, 100, 200],\n", + " # \"rforest__bootstrap\":[True, False],\n", + " # # \"rforest__criterion\":[\"gini\", \"entropy\", \"log_loss\"]\n", + " },\n", + " cv=3,\n", + " verbose=2,\n", + " scoring=\"f1_weighted\",\n", + " )\n", + "\n", + "pca_rf_estimator.fit(X_train, y_train)\n", + "print(f\"The best parameters: {pca_rf_estimator.best_params_}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Die besten Parameter werden in die pipeline integriert und das Modell wird evaluiert" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 1 1.00 1.00 1.00 30\n", + " 2 1.00 0.92 0.96 25\n", + " 3 0.79 0.73 0.76 26\n", + " 4 0.66 0.83 0.73 23\n", + " 5 0.95 0.91 0.93 22\n", + " 6 0.96 0.93 0.94 27\n", + " 7 1.00 1.00 1.00 20\n", + "\n", + " accuracy 0.90 173\n", + " macro avg 0.91 0.90 0.90 173\n", + "weighted avg 0.91 0.90 0.90 173\n", + "\n", + "TESTING RESULTS: \n", + "===============================\n", + "CONFUSION MATRIX:\n", + "[[30 0 0 0 0 0 0]\n", + " [ 0 23 2 0 0 0 0]\n", + " [ 0 0 19 7 0 0 0]\n", + " [ 0 0 3 19 1 0 0]\n", + " [ 0 0 0 1 20 1 0]\n", + " [ 0 0 0 2 0 25 0]\n", + " [ 0 0 0 0 0 0 20]]\n", + "ACCURACY SCORE:\n", + "0.9017\n" + ] + } + ], + "source": [ + "### Setze die besten Parameter in die Pipeline ein und fitte erneut auf Trainingsdaten\n", + "pca_rf_pipeline.set_params(**pca_rf_estimator.best_params_)\n", + "pca_rf_pipeline.fit(X_train, y_train)\n", + "\n", + "### Klassifierungsevaluation\n", + "pca_rf_pred = pca_rf_pipeline.predict(X_test)\n", + "print(\"Classification Report:\\n\", classification_report(y_test, pca_rf_pred))\n", + "print(\"TESTING RESULTS: \\n===============================\")\n", + "print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_test, pca_rf_pred)}\")\n", + "print(f\"ACCURACY SCORE:\\n{accuracy_score(y_test, pca_rf_pred):.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Classification without PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n", + "[CV] END ....rforest__max_depth=10, rforest__n_estimators=80; total time= 3.7s\n", + "[CV] END ....rforest__max_depth=10, rforest__n_estimators=80; total time= 3.6s\n", + "[CV] END ....rforest__max_depth=10, rforest__n_estimators=80; total time= 3.6s\n", + "[CV] END ...rforest__max_depth=10, rforest__n_estimators=100; total time= 3.9s\n", + "[CV] END ...rforest__max_depth=10, rforest__n_estimators=100; total time= 3.8s\n", + "[CV] END ...rforest__max_depth=10, rforest__n_estimators=100; total time= 4.0s\n", + "[CV] END ...rforest__max_depth=10, rforest__n_estimators=200; total time= 5.5s\n", + "[CV] END ...rforest__max_depth=10, rforest__n_estimators=200; total time= 5.4s\n", + "[CV] END ...rforest__max_depth=10, rforest__n_estimators=200; total time= 5.3s\n", + "[CV] END ....rforest__max_depth=30, rforest__n_estimators=80; total time= 3.5s\n", + "[CV] END ....rforest__max_depth=30, rforest__n_estimators=80; total time= 3.6s\n", + "[CV] END ....rforest__max_depth=30, rforest__n_estimators=80; total time= 3.6s\n", + "[CV] END ...rforest__max_depth=30, rforest__n_estimators=100; total time= 4.0s\n", + "[CV] END ...rforest__max_depth=30, rforest__n_estimators=100; total time= 4.0s\n", + "[CV] END ...rforest__max_depth=30, rforest__n_estimators=100; total time= 3.9s\n", + "[CV] END ...rforest__max_depth=30, rforest__n_estimators=200; total time= 6.1s\n", + "[CV] END ...rforest__max_depth=30, rforest__n_estimators=200; total time= 5.8s\n", + "[CV] END ...rforest__max_depth=30, rforest__n_estimators=200; total time= 5.5s\n", + "[CV] END ....rforest__max_depth=50, rforest__n_estimators=80; total time= 3.7s\n", + "[CV] END ....rforest__max_depth=50, rforest__n_estimators=80; total time= 3.8s\n", + "[CV] END ....rforest__max_depth=50, rforest__n_estimators=80; total time= 4.0s\n", + "[CV] END ...rforest__max_depth=50, rforest__n_estimators=100; total time= 4.0s\n", + "[CV] END ...rforest__max_depth=50, rforest__n_estimators=100; total time= 4.1s\n", + "[CV] END ...rforest__max_depth=50, rforest__n_estimators=100; total time= 4.0s\n", + "[CV] END ...rforest__max_depth=50, rforest__n_estimators=200; total time= 6.3s\n", + "[CV] END ...rforest__max_depth=50, rforest__n_estimators=200; total time= 5.9s\n", + "[CV] END ...rforest__max_depth=50, rforest__n_estimators=200; total time= 5.7s\n", + "The best parameters: {'rforest__max_depth': 10, 'rforest__n_estimators': 200}\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 1 1.00 1.00 1.00 30\n", + " 2 1.00 1.00 1.00 25\n", + " 3 1.00 1.00 1.00 26\n", + " 4 1.00 1.00 1.00 23\n", + " 5 0.92 1.00 0.96 22\n", + " 6 1.00 0.93 0.96 27\n", + " 7 1.00 1.00 1.00 20\n", + "\n", + " accuracy 0.99 173\n", + " macro avg 0.99 0.99 0.99 173\n", + "weighted avg 0.99 0.99 0.99 173\n", + "\n", + "TESTING RESULTS: \n", + "===============================\n", + "CONFUSION MATRIX:\n", + "[[30 0 0 0 0 0 0]\n", + " [ 0 25 0 0 0 0 0]\n", + " [ 0 0 26 0 0 0 0]\n", + " [ 0 0 0 23 0 0 0]\n", + " [ 0 0 0 0 22 0 0]\n", + " [ 0 0 0 0 2 25 0]\n", + " [ 0 0 0 0 0 0 20]]\n", + "ACCURACY SCORE:\n", + "0.9884\n" + ] + } + ], + "source": [ + "rf_pipeline = Pipeline(\n", + " steps=[\n", + " (\"scaler\", StandardScaler()),\n", + " (\"rforest\", RandomForestClassifier(random_state=RANDOM_SEED))\n", + " ]\n", + ")\n", + "\n", + "rf_estimator = GridSearchCV(\n", + " estimator=rf_pipeline,\n", + " param_grid = {\n", + " \"rforest__max_depth\":[10, 30, 50],\n", + " \"rforest__n_estimators\":[80, 100, 200],\n", + " # \"rforest__bootstrap\":[True, False],\n", + " # # \"rforest__criterion\":[\"gini\", \"entropy\", \"log_loss\"]\n", + " },\n", + " cv=3,\n", + " verbose=2,\n", + " scoring=\"f1_weighted\",\n", + " )\n", + "\n", + "rf_estimator.fit(X_train, y_train)\n", + "print(f\"The best parameters: {rf_estimator.best_params_}\")\n", + "\n", + "### Setze die besten Parameter in die Pipeline ein und fitte erneut auf Trainingsdaten\n", + "rf_pipeline.set_params(**rf_estimator.best_params_)\n", + "rf_pipeline.fit(X_train, y_train)\n", + "\n", + "### Klassifierungsevaluation\n", + "rf_pred = rf_pipeline.predict(X_test)\n", + "print(\"Classification Report:\\n\", classification_report(y_test, rf_pred))\n", + "print(\"TESTING RESULTS: \\n===============================\")\n", + "print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_test, rf_pred)}\")\n", + "print(f\"ACCURACY SCORE:\\n{accuracy_score(y_test, rf_pred):.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ROC and Precision/Recall Curves für jede Klasse\n", + "Die Kurven müssen für jede Klasse einzeln angefertigt werden. Dabei müssen die Labels zunächst binariziert werden." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import label_binarize\n", + "from itertools import cycle\n", + "\n", + "y_score = pca_rf_pipeline.fit(X_train, y_train).predict_proba(X_test)\n", + "y_test_bin = label_binarize(y_test, classes=[1, 2, 3, 4, 5, 6, 7])\n", + "n_classes = y_test_bin.shape[1]\n", + "\n", + "# ROC CURVE\n", + "#########################\n", + "fpr = dict()\n", + "tpr = dict()\n", + "roc_auc = dict()\n", + "### Generate ROC_AUC Score for each class for each instance\n", + "for i in range(n_classes):\n", + " fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_score[:, i])\n", + " roc_auc[i] = auc(fpr[i], tpr[i])\n", + "colors = cycle(['blue', 'red', 'green', 'orange', 'magenta', 'yellow', 'cyan'])\n", + "for i, color in zip(range(n_classes), colors):\n", + " plt.plot(fpr[i], tpr[i], color=color,\n", + " label=f\"ROC curve of class {i+1} ({round(roc_auc[i],2)})\"\n", + " )\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.xlim([-0.05, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('ROC for multi-class data')\n", + "plt.legend(loc=\"best\")\n", + "plt.show()\n", + "\n", + "\n", + "# PRECISION RECALL CURVE\n", + "#########################\n", + "precision = dict()\n", + "recall = dict()\n", + "precrec_auc = dict()\n", + "for i in range(n_classes):\n", + " precision[i], recall[i], _ = precision_recall_curve(y_test_bin[:, i], y_score[:, i])\n", + " precrec_auc[i] = auc(recall[i], precision[i])\n", + "for i, color in zip(range(n_classes), colors):\n", + " plt.plot(precision[i], recall[i], color=color,\n", + " label=f\"PRRE curve of class {i+1} ({round(precrec_auc[i],2)})\"\n", + " )\n", + "plt.plot([1, 0], [0, 1], 'k--')\n", + "plt.xlabel(\"Recall\")\n", + "plt.ylabel(\"Precision\")\n", + "plt.title('Precsion Recall curve for multi-class data')\n", + "plt.legend(loc=\"best\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Andere Klassifizierungsmodelle zum Vergleich" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.neighbors import KNeighborsClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", + "[CV] END ...............lr__penalty=l1, pca__n_components=34; total time= 4.4s\n", + "[CV] END ...............lr__penalty=l1, pca__n_components=34; total time= 3.9s\n", + "[CV] END ...............lr__penalty=l1, pca__n_components=34; total time= 3.9s\n", + "[CV] END ...............lr__penalty=l2, pca__n_components=34; total time= 5.4s\n", + "[CV] END ...............lr__penalty=l2, pca__n_components=34; total time= 5.0s\n", + "[CV] END ...............lr__penalty=l2, pca__n_components=34; total time= 6.1s\n", + "[CV] END .......lr__penalty=elasticnet, pca__n_components=34; total time= 3.5s\n", + "[CV] END .......lr__penalty=elasticnet, pca__n_components=34; total time= 3.4s\n", + "[CV] END .......lr__penalty=elasticnet, pca__n_components=34; total time= 3.7s\n", + "[CV] END .............lr__penalty=None, pca__n_components=34; total time= 4.4s\n", + "[CV] END .............lr__penalty=None, pca__n_components=34; total time= 4.4s\n", + "[CV] END .............lr__penalty=None, pca__n_components=34; total time= 4.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n", + "9 fits failed out of a total of 12.\n", + "The score on these train-test partitions for these parameters will be set to nan.\n", + "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", + "\n", + "Below are more details about the failures:\n", + "--------------------------------------------------------------------------------\n", + "3 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\pipeline.py\", line 382, in fit\n", + " self._final_estimator.fit(Xt, y, **fit_params_last_step)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1091, in fit\n", + " solver = _check_solver(self.solver, self.penalty, self.dual)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 61, in _check_solver\n", + " raise ValueError(\n", + "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "3 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\pipeline.py\", line 382, in fit\n", + " self._final_estimator.fit(Xt, y, **fit_params_last_step)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1091, in fit\n", + " solver = _check_solver(self.solver, self.penalty, self.dual)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 61, in _check_solver\n", + " raise ValueError(\n", + "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "3 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\pipeline.py\", line 382, in fit\n", + " self._final_estimator.fit(Xt, y, **fit_params_last_step)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1091, in fit\n", + " solver = _check_solver(self.solver, self.penalty, self.dual)\n", + " File \"c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 55, in _check_solver\n", + " raise ValueError(\n", + "ValueError: Logistic Regression supports only penalties in ['l1', 'l2', 'elasticnet', 'none'], got None.\n", + "\n", + " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", + "c:\\Users\\miche\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [ nan 0.83521292 nan nan]\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best parameters: {'lr__penalty': 'l2', 'pca__n_components': 34}\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 1 1.00 1.00 1.00 30\n", + " 2 1.00 0.80 0.89 25\n", + " 3 0.72 0.81 0.76 26\n", + " 4 0.70 0.70 0.70 23\n", + " 5 0.84 0.95 0.89 22\n", + " 6 0.96 0.93 0.94 27\n", + " 7 1.00 1.00 1.00 20\n", + "\n", + " accuracy 0.88 173\n", + " macro avg 0.89 0.88 0.88 173\n", + "weighted avg 0.89 0.88 0.89 173\n", + "\n", + "TESTING RESULTS: \n", + "===============================\n", + "CONFUSION MATRIX:\n", + "[[30 0 0 0 0 0 0]\n", + " [ 0 20 5 0 0 0 0]\n", + " [ 0 0 21 5 0 0 0]\n", + " [ 0 0 3 16 4 0 0]\n", + " [ 0 0 0 0 21 1 0]\n", + " [ 0 0 0 2 0 25 0]\n", + " [ 0 0 0 0 0 0 20]]\n", + "ACCURACY SCORE:\n", + "0.8844\n" + ] + } + ], + "source": [ + "lr_pipeline = Pipeline(\n", + " steps=[\n", + " (\"scaler\", StandardScaler()),\n", + " (\"pca\", PCA(random_state=RANDOM_SEED)),\n", + " (\"lr\", LogisticRegression(max_iter=10000))\n", + " ]\n", + ")\n", + "\n", + "lr_estimator = GridSearchCV(\n", + " estimator=lr_pipeline,\n", + " param_grid = {\n", + " \"pca__n_components\":[34],\n", + " \"lr__penalty\": [\"l1\", \"l2\", \"elasticnet\", None]\n", + " },\n", + " cv=3,\n", + " verbose=2,\n", + " scoring=\"f1_weighted\",\n", + " )\n", + "\n", + "lr_estimator.fit(X_train, y_train)\n", + "print(f\"The best parameters: {lr_estimator.best_params_}\")\n", + "\n", + "### Setze die besten Parameter in die Pipeline ein und fitte erneut auf Trainingsdaten\n", + "lr_pipeline.set_params(**lr_estimator.best_params_)\n", + "lr_pipeline.fit(X_train, y_train)\n", + "\n", + "### Klassifierungsevaluation\n", + "lr_pred = lr_pipeline.predict(X_test)\n", + "print(\"Classification Report:\\n\", classification_report(y_test, lr_pred))\n", + "print(\"TESTING RESULTS: \\n===============================\")\n", + "print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_test, lr_pred)}\")\n", + "print(f\"ACCURACY SCORE:\\n{accuracy_score(y_test, lr_pred):.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# kNN" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n", + "[CV] END ...........kNN__n_neighbors=3, pca__n_components=34; total time= 5.9s\n", + "[CV] END ...........kNN__n_neighbors=3, pca__n_components=34; total time= 4.9s\n", + "[CV] END ...........kNN__n_neighbors=3, pca__n_components=34; total time= 5.2s\n", + "[CV] END ...........kNN__n_neighbors=5, pca__n_components=34; total time= 5.4s\n", + "[CV] END ...........kNN__n_neighbors=5, pca__n_components=34; total time= 5.0s\n", + "[CV] END ...........kNN__n_neighbors=5, pca__n_components=34; total time= 5.8s\n", + "[CV] END ..........kNN__n_neighbors=10, pca__n_components=34; total time= 5.1s\n", + "[CV] END ..........kNN__n_neighbors=10, pca__n_components=34; total time= 5.0s\n", + "[CV] END ..........kNN__n_neighbors=10, pca__n_components=34; total time= 5.1s\n", + "[CV] END ..........kNN__n_neighbors=20, pca__n_components=34; total time= 5.1s\n", + "[CV] END ..........kNN__n_neighbors=20, pca__n_components=34; total time= 5.0s\n", + "[CV] END ..........kNN__n_neighbors=20, pca__n_components=34; total time= 4.9s\n", + "[CV] END ..........kNN__n_neighbors=50, pca__n_components=34; total time= 4.8s\n", + "[CV] END ..........kNN__n_neighbors=50, pca__n_components=34; total time= 5.0s\n", + "[CV] END ..........kNN__n_neighbors=50, pca__n_components=34; total time= 5.4s\n", + "The best parameters: {'kNN__n_neighbors': 3, 'pca__n_components': 34}\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 1 1.00 0.87 0.93 30\n", + " 2 0.81 0.84 0.82 25\n", + " 3 0.44 0.31 0.36 26\n", + " 4 0.28 0.48 0.35 23\n", + " 5 0.70 0.32 0.44 22\n", + " 6 0.80 0.89 0.84 27\n", + " 7 0.83 1.00 0.91 20\n", + "\n", + " accuracy 0.68 173\n", + " macro avg 0.70 0.67 0.67 173\n", + "weighted avg 0.70 0.68 0.67 173\n", + "\n", + "TESTING RESULTS: \n", + "===============================\n", + "CONFUSION MATRIX:\n", + "[[26 0 0 0 0 0 4]\n", + " [ 0 21 1 3 0 0 0]\n", + " [ 0 3 8 15 0 0 0]\n", + " [ 0 2 8 11 2 0 0]\n", + " [ 0 0 1 8 7 6 0]\n", + " [ 0 0 0 2 1 24 0]\n", + " [ 0 0 0 0 0 0 20]]\n", + "ACCURACY SCORE:\n", + "0.6763\n" + ] + } + ], + "source": [ + "kNN_pipeline = Pipeline(\n", + " steps=[\n", + " (\"scaler\", StandardScaler()),\n", + " (\"pca\", PCA(random_state=RANDOM_SEED)),\n", + " (\"kNN\", KNeighborsClassifier())\n", + " ]\n", + ")\n", + "\n", + "kNN_estimator = GridSearchCV(\n", + " estimator=kNN_pipeline,\n", + " param_grid = {\n", + " \"pca__n_components\":[34],\n", + " \"kNN__n_neighbors\": [3, 5, 10, 20, 50],\n", + " },\n", + " cv=3,\n", + " verbose=2,\n", + " scoring=\"f1_weighted\",\n", + " )\n", + "\n", + "kNN_estimator.fit(X_train, y_train)\n", + "print(f\"The best parameters: {kNN_estimator.best_params_}\")\n", + "\n", + "### Setze die besten Parameter in die Pipeline ein und fitte erneut auf Trainingsdaten\n", + "kNN_pipeline.set_params(**kNN_estimator.best_params_)\n", + "kNN_pipeline.fit(X_train, y_train)\n", + "\n", + "### Klassifierungsevaluation\n", + "kNN_pred = kNN_pipeline.predict(X_test)\n", + "print(\"Classification Report:\\n\", classification_report(y_test, kNN_pred))\n", + "print(\"TESTING RESULTS: \\n===============================\")\n", + "print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_test, kNN_pred)}\")\n", + "print(f\"ACCURACY SCORE:\\n{accuracy_score(y_test, kNN_pred):.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NLP", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Bonus 1/Bonus1.ipynb b/Bonus/Bonus 1/Bonus1.ipynb similarity index 100% rename from Bonus 1/Bonus1.ipynb rename to Bonus/Bonus 1/Bonus1.ipynb diff --git a/Bonus 1/ames.csv b/Bonus/Bonus 1/ames.csv similarity index 99% rename from Bonus 1/ames.csv rename to Bonus/Bonus 1/ames.csv index 19107f2..0991a6d 100644 --- a/Bonus 1/ames.csv +++ b/Bonus/Bonus 1/ames.csv @@ -1,2931 +1,2931 @@ -MS_SubClass,MS_Zoning,Lot_Frontage,Lot_Area,Street,Alley,Lot_Shape,Land_Contour,Utilities,Lot_Config,Land_Slope,Neighborhood,Condition_1,Condition_2,Bldg_Type,House_Style,Overall_Qual,Overall_Cond,Year_Built,Year_Remod_Add,Roof_Style,Roof_Matl,Exterior_1st,Exterior_2nd,Mas_Vnr_Type,Mas_Vnr_Area,Exter_Qual,Exter_Cond,Foundation,Bsmt_Qual,Bsmt_Cond,Bsmt_Exposure,BsmtFin_Type_1,BsmtFin_SF_1,BsmtFin_Type_2,BsmtFin_SF_2,Bsmt_Unf_SF,Total_Bsmt_SF,Heating,Heating_QC,Central_Air,Electrical,First_Flr_SF,Second_Flr_SF,Low_Qual_Fin_SF,Gr_Liv_Area,Bsmt_Full_Bath,Bsmt_Half_Bath,Full_Bath,Half_Bath,Bedroom_AbvGr,Kitchen_AbvGr,Kitchen_Qual,TotRms_AbvGrd,Functional,Fireplaces,Fireplace_Qu,Garage_Type,Garage_Finish,Garage_Cars,Garage_Area,Garage_Qual,Garage_Cond,Paved_Drive,Wood_Deck_SF,Open_Porch_SF,Enclosed_Porch,Three_season_porch,Screen_Porch,Pool_Area,Pool_QC,Fence,Misc_Feature,Misc_Val,Mo_Sold,Year_Sold,Sale_Type,Sale_Condition,Sale_Price,Longitude,Latitude -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,141,31770,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Hip,CompShg,BrkFace,Plywood,Stone,112,Typical,Typical,CBlock,Typical,Good,Gd,BLQ,2,Unf,0,441,1080,GasA,Fair,Y,SBrkr,1656,0,0,1656,1,0,1,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,528,Typical,Typical,Partial_Pavement,210,62,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,215000,-93.619754,42.054035 -One_Story_1946_and_Newer_All_Styles,Residential_High_Density,80,11622,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,144,270,882,GasA,Typical,Y,SBrkr,896,0,0,896,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,730,Typical,Typical,Paved,140,0,0,0,120,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,105000,-93.619756,42.053014 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,14267,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,108,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,406,1329,GasA,Typical,Y,SBrkr,1329,0,0,1329,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,312,Typical,Typical,Paved,393,36,0,0,0,0,No_Pool,No_Fence,Gar2,12500,6,2010,WD ,Normal,172000,-93.6193873,42.052659 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,11160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1968,1968,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,1045,2110,GasA,Excellent,Y,SBrkr,2110,0,0,2110,1,0,2,1,3,1,Excellent,8,Typ,2,Typical,Attchd,Fin,2,522,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,244000,-93.61732,42.051245 -Two_Story_1946_and_Newer,Residential_Low_Density,74,13830,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,137,928,GasA,Good,Y,SBrkr,928,701,0,1629,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,482,Typical,Typical,Paved,212,34,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,189900,-93.638933,42.060899 -Two_Story_1946_and_Newer,Residential_Low_Density,78,9978,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,20,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,324,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,470,Typical,Typical,Paved,360,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,195500,-93.638925,42.060779 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,4920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2001,2001,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,722,1338,GasA,Excellent,Y,SBrkr,1338,0,0,1338,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,582,Typical,Typical,Paved,0,0,170,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,213500,-93.633792,42.062978 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,5005,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,1017,1280,GasA,Excellent,Y,SBrkr,1280,0,0,1280,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,506,Typical,Typical,Paved,0,82,0,0,144,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,191500,-93.633826,42.060728 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,39,5389,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1996,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,415,1595,GasA,Excellent,Y,SBrkr,1616,0,0,1616,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,237,152,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,236500,-93.632852,42.06112 -Two_Story_1946_and_Newer,Residential_Low_Density,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,994,994,GasA,Good,Y,SBrkr,1028,776,0,1804,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,442,Typical,Typical,Paved,140,60,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,189000,-93.639068,42.059193 -Two_Story_1946_and_Newer,Residential_Low_Density,75,10000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,763,763,GasA,Good,Y,SBrkr,763,892,0,1655,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,157,84,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,175900,-93.636947,42.05848 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7980,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Good,1992,2007,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,PConc,Good,Typical,No,ALQ,1,Unf,0,233,1168,GasA,Excellent,Y,SBrkr,1187,0,0,1187,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,483,21,0,0,0,0,No_Pool,Good_Privacy,Shed,500,3,2010,WD ,Normal,185000,-93.635951,42.057419 -Two_Story_1946_and_Newer,Residential_Low_Density,63,8402,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,789,789,GasA,Good,Y,SBrkr,789,676,0,1465,0,0,2,1,3,1,Typical,7,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,180400,-93.638647,42.058151 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10176,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,663,1300,GasA,Good,Y,SBrkr,1341,0,0,1341,1,0,1,1,2,1,Good,5,Typ,1,Poor,Attchd,Unf,2,506,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,171500,-93.634626,42.057268 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,6820,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1985,1985,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,BLQ,1120,0,1488,GasA,Typical,Y,SBrkr,1502,0,0,1502,1,0,1,1,1,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,0,54,0,0,140,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,212000,-93.632913,42.059169 -Two_Story_1946_and_Newer,Residential_Low_Density,47,53504,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,CulDSac,Mod,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Hip,CompShg,CemntBd,Wd Shng,BrkFace,603,Excellent,Typical,PConc,Good,Typical,Gd,ALQ,1,Unf,0,234,1650,GasA,Excellent,Y,SBrkr,1690,1589,0,3279,1,0,3,1,4,1,Excellent,12,Mod,1,Good,BuiltIn,Fin,3,841,Typical,Typical,Paved,503,36,0,0,210,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,538000,-93.62655,42.061239 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,152,12134,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Gilbert,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Good,1988,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,Wood,Good,Typical,Av,GLQ,3,Unf,0,132,559,GasA,Good,Y,SBrkr,1080,672,0,1752,0,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Basment,RFn,2,492,Typical,Typical,Paved,325,12,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,164000,-93.6235954,42.0603514 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11394,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Poor,2010,2010,Hip,CompShg,VinylSd,VinylSd,Stone,350,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,411,1856,GasA,Excellent,Y,SBrkr,1856,0,0,1856,1,0,1,1,1,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,834,Typical,Typical,Paved,113,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,394432,-93.628804,42.05885 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,140,19138,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Below_Average,Average,1951,1951,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,744,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,141000,-93.622971,42.056673 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,13175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1978,1988,Gable,CompShg,Plywood,Plywood,Stone,119,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Rec,163,589,1542,GasA,Typical,Y,SBrkr,2073,0,0,2073,1,0,2,0,3,1,Typical,7,Min1,2,Typical,Attchd,Unf,2,500,Typical,Typical,Paved,349,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,210000,-93.636655,42.054453 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,11751,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,480,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,1139,1844,GasA,Excellent,Y,SBrkr,1844,0,0,1844,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,546,Typical,Typical,Paved,0,122,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2010,COD,Normal,190000,-93.633962,42.050346 -Split_Foyer,Residential_Low_Density,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Good,Above_Average,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,81,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,LwQ,168,0,1053,GasA,Typical,Y,SBrkr,1173,0,0,1173,1,0,2,0,3,1,Good,6,Typ,2,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2010,WD ,Family,170000,-93.636372,42.05027 -Two_Story_1946_and_Newer,Floating_Village_Residential,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,281,814,GasA,Excellent,Y,SBrkr,814,860,0,1674,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,663,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,216000,-93.639366,42.049297 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11241,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1970,1970,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,426,1004,GasA,Excellent,Y,SBrkr,1004,0,0,1004,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,700,3,2010,WD ,Normal,149000,-93.626231,42.055147 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12537,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1971,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,344,1078,GasA,Excellent,Y,SBrkr,1078,0,0,1078,1,0,1,1,3,1,Typical,6,Typ,1,Fair,Attchd,Fin,2,500,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,149900,-93.626537,42.054592 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,281,1056,GasA,Excellent,Y,SBrkr,1056,0,0,1056,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,Unf,1,304,Typical,Typical,Paved,0,85,184,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,142000,-93.628806,42.055227 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,78,0,882,GasA,Typical,Y,SBrkr,882,0,0,882,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,2,525,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,126000,-93.627112,42.053395 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,432,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,1,Poor,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLI,Normal,115000,-93.622769,42.056375 -One_Story_PUD_1946_and_Newer,Residential_High_Density,26,5858,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Good,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,BLQ,0,354,1405,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Fair,Attchd,Fin,2,511,Typical,Typical,Paved,203,68,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,184000,-93.624373,42.055318 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,327,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,275,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,COD,Normal,96000,-93.627271,42.051835 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,492,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,225,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,320,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,105500,-93.627536,42.051684 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,ImStucc,BrkFace,381,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,525,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Family,88000,-93.62728,42.051685 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4043,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,709,1069,GasA,Typical,Y,SBrkr,1069,0,0,1069,0,0,2,0,2,1,Typical,4,Typ,1,Fair,Attchd,RFn,2,440,Typical,Typical,Paved,0,55,0,0,165,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,127500,-93.627232,42.049672 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,341,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,173,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,149900,-93.625924,42.050683 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Above_Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,836,836,GasA,Excellent,Y,SBrkr,836,0,0,836,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,120000,-93.625966,42.050681 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,544,855,GasA,Fair,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,26,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,146000,-93.625848,42.049811 -Two_Story_1946_and_Newer,Residential_Low_Density,102,12858,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,162,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1590,1590,GasA,Excellent,Y,SBrkr,1627,707,0,2334,0,0,2,1,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,751,Typical,Typical,Paved,144,133,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,New,Partial,376162,-93.653201,42.062352 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,11478,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,200,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,486,1704,GasA,Excellent,Y,SBrkr,1704,0,0,1704,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,772,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,306000,-93.654645,42.062109 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,10159,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2010,Hip,CompShg,VinylSd,VinylSd,Stone,450,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,284,1930,GasA,Excellent,Y,SBrkr,1940,0,0,1940,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,606,Typical,Typical,Paved,168,95,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,New,Partial,395192,-93.6537329,42.0611331 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,12883,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1544,1544,GasA,Excellent,Y,SBrkr,1544,0,0,1544,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,868,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,290941,-93.6528306,42.0612885 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,12182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,226,Good,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,340,1541,GasA,Excellent,Y,SBrkr,1541,0,0,1541,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,532,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,New,Partial,220000,-93.652713,42.060872 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,Norm,OneFam,One_Story,Excellent,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,615,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1588,1698,GasA,Excellent,Y,SBrkr,1698,0,0,1698,0,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,730,Typical,Typical,Paved,192,74,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,275000,-93.65332,42.0608079 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,14122,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,240,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1794,1822,GasA,Excellent,Y,SBrkr,1822,0,0,1822,0,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,678,Typical,Typical,Paved,0,119,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,259000,-93.652336,42.060879 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10171,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,168,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1515,1517,GasA,Excellent,Y,SBrkr,1535,0,0,1535,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,532,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,214000,-93.652307,42.060298 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,12919,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2010,Hip,CompShg,VinylSd,VinylSd,Stone,760,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,142,2330,GasA,Excellent,Y,SBrkr,2364,0,0,2364,1,0,2,1,2,1,Excellent,11,Typ,2,Good,Attchd,Fin,3,820,Typical,Typical,Paved,0,67,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,New,Partial,611657,-93.655051,42.059617 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,44,6371,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,128,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,625,1358,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,224000,-93.650436,42.058388 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14300,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,1095,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1473,2846,GasA,Excellent,Y,SBrkr,2696,0,0,2696,1,0,2,1,3,1,Excellent,10,Typ,2,Good,Attchd,Fin,3,958,Typical,Typical,Paved,220,150,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,500000,-93.656067,42.058599 -Two_Story_1946_and_Newer,Residential_Low_Density,105,13650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,232,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1093,1671,GasA,Excellent,Y,SBrkr,1687,563,0,2250,1,0,2,1,3,1,Good,7,Typ,1,Excellent,Attchd,Fin,3,756,Typical,Typical,Paved,238,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,320000,-93.654853,42.057102 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,7658,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,412,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1296,1752,GasA,Excellent,Y,SBrkr,1752,0,0,1752,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,196,82,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,319900,-93.654121,42.057164 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,7132,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,178,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1346,1370,GasA,Excellent,Y,SBrkr,1370,0,0,1370,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,120,49,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,205000,-93.649447,42.058252 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2628,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,764,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,0,0,2,1,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,175500,-93.651013,42.057181 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,18494,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1324,1324,GasA,Excellent,Y,SBrkr,1324,0,0,1324,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,430,Typical,Typical,Paved,36,23,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,199500,-93.644356,42.061928 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3203,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1129,1145,GasA,Excellent,Y,SBrkr,1145,0,0,1145,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,437,Typical,Typical,Paved,100,116,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,160000,-93.641635,42.06257 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1232,1256,GasA,Excellent,Y,SBrkr,1269,0,0,1269,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,2,430,Typical,Typical,Paved,146,20,0,0,144,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,192000,-93.642252,42.062303 -Split_or_Multilevel,Residential_Low_Density,67,13300,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,58,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,1,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,184500,-93.644076,42.061443 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7851,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,235,860,GasA,Excellent,Y,SBrkr,860,1100,0,1960,1,0,2,1,4,1,Good,8,Typ,2,Typical,BuiltIn,Fin,2,440,Typical,Typical,Paved,288,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,216500,-93.641487,42.060977 -Two_Story_1946_and_Newer,Residential_Low_Density,63,8577,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,886,0,1733,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,433,Typical,Typical,Paved,144,48,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,185088,-93.642447,42.061193 -Split_or_Multilevel,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,134,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,180000,-93.641325,42.060936 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9505,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,884,1151,0,2035,0,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,144,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,222500,-93.641337,42.057103 -Two_Story_1946_and_Newer,Residential_Low_Density,108,14774,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,165,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1393,1393,GasA,Excellent,Y,SBrkr,1422,1177,0,2599,0,0,2,1,4,1,Good,10,Typ,1,Typical,BuiltIn,Fin,3,779,Typical,Typical,Paved,668,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,333168,-93.651381,42.054386 -Two_Story_1946_and_Newer,Residential_Low_Density,60,17433,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,114,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1629,1629,GasA,Excellent,Y,SBrkr,1645,830,0,2475,0,0,2,1,4,1,Good,7,Typ,1,Typical,Attchd,Fin,3,962,Typical,Typical,Paved,23,172,0,0,256,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,355000,-93.652334,42.053243 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,10593,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Good,Average,1996,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,338,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,801,1720,GasA,Excellent,Y,SBrkr,1720,0,0,1720,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,527,Typical,Typical,Paved,240,56,154,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,260400,-93.654206,42.051721 -Two_Story_1946_and_Newer,Residential_Low_Density,98,12256,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1995,Gable,CompShg,HdBoard,HdBoard,BrkFace,362,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,431,1463,GasA,Excellent,Y,SBrkr,1500,1122,0,2622,1,0,2,1,3,1,Good,9,Typ,2,Typical,Attchd,RFn,2,712,Typical,Typical,Paved,186,32,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,325000,-93.653616,42.051674 -Two_Story_1946_and_Newer,Residential_Low_Density,92,11764,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1999,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,348,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,628,1152,GasA,Excellent,Y,SBrkr,1164,1106,0,2270,0,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Fin,3,671,Typical,Typical,Paved,132,57,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,290000,-93.650356,42.052457 -Two_Story_1946_and_Newer,Residential_Low_Density,58,16770,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,30,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1195,1195,GasA,Good,Y,SBrkr,1195,644,0,1839,0,0,2,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,486,Typical,Typical,Paved,0,81,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,221000,-93.650578,42.052437 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,56,14720,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Average,1995,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,579,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1217,2033,GasA,Excellent,Y,SBrkr,2053,1185,0,3238,1,0,2,1,4,1,Good,9,Typ,1,Excellent,Attchd,Fin,3,666,Typical,Typical,Paved,283,86,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,410000,-93.652972,42.050909 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8987,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,226,Good,Typical,PConc,Good,Typical,No_Basement,Unf,7,Unf,0,1595,1595,GasA,Excellent,Y,SBrkr,1595,0,0,1595,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,880,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,221500,-93.642368,42.05306 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,92,9215,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2009,2010,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1218,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,0,0,2,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,676,Typical,Typical,Paved,0,136,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,New,Partial,204500,-93.649976,42.051827 -Two_Story_1946_and_Newer,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,732,756,GasA,Excellent,Y,SBrkr,764,783,0,1547,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,614,Typical,Typical,Paved,169,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,Con,Normal,215200,-93.650318,42.051653 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,36,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,488,1566,GasA,Excellent,Y,SBrkr,1566,0,0,1566,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,750,Typical,Typical,Paved,144,168,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,262500,-93.647105,42.050084 -Two_Story_1946_and_Newer,Floating_Village_Residential,100,12552,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,769,991,GasA,Excellent,Y,SBrkr,991,956,0,1947,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,678,Typical,Typical,Paved,0,136,0,0,0,0,No_Pool,Good_Wood,None,0,5,2010,WD ,Normal,254900,-93.642773,42.052347 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,84,10440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,54,1468,GasA,Excellent,Y,SBrkr,1468,0,0,1468,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,528,Typical,Typical,Paved,0,102,0,0,216,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,271500,-93.642224,42.051429 -Two_Story_1946_and_Newer,Residential_Low_Density,76,10142,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,300,956,GasA,Excellent,Y,SBrkr,956,1128,0,2084,1,0,2,1,4,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,618,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,233000,-93.692309,42.036255 -Two_Story_1946_and_Newer,Residential_Low_Density,70,11920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,122,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,831,831,GasA,Excellent,Y,SBrkr,831,828,0,1659,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,181000,-93.692412,42.036113 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8880,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1994,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,253,948,GasA,Excellent,Y,SBrkr,1222,888,0,2110,1,0,2,1,3,1,Good,8,Typ,2,Fair,Attchd,RFn,2,463,Typical,Typical,Paved,0,130,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,205000,-93.68981,42.036026 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,BLQ,119,261,923,GasA,Typical,Y,SBrkr,923,0,0,923,0,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,1,264,Typical,Typical,Paved,80,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,143000,-93.686417,42.03548 -Two_Story_1946_and_Newer,Residential_Low_Density,70,11218,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1055,1055,GasA,Excellent,Y,SBrkr,1055,790,0,1845,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,462,Typical,Typical,Paved,635,104,0,0,0,0,No_Pool,Good_Privacy,Shed,400,5,2010,WD ,Normal,189000,-93.687694,42.036659 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,55,7892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,918,918,GasA,Typical,Y,SBrkr,918,0,0,918,0,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,Unf,1,264,Typical,Typical,Paved,28,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,99500,-93.686263,42.034589 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1984,1984,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,121,0,744,GasA,Typical,Y,SBrkr,752,0,0,752,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,353,0,0,0,90,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,125000,-93.686264,42.0347 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9453,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,RRNe,Norm,OneFam,Two_Story,Good,Good,1993,2003,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,594,996,GasA,Excellent,Y,SBrkr,1014,730,0,1744,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,457,Typical,Typical,Paved,370,70,0,238,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,194500,-93.68628,42.036633 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,9672,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1984,1985,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,702,1040,GasA,Typical,Y,SBrkr,1097,0,0,1097,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2010,WD ,Normal,152000,-93.685028,42.034555 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1980,1981,Gable,CompShg,HdBoard,HdBoard,BrkFace,130,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,650,650,GasA,Typical,Y,SBrkr,888,676,0,1564,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,476,Typical,Typical,Paved,0,50,0,0,204,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,171000,-93.685029,42.034678 -One_Story_1945_and_Older,Residential_High_Density,70,9800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_Story,Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,N,FuseA,1012,0,0,1012,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,429,Typical,Typical,Paved,121,0,80,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,67500,-93.6790694,42.0360754 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,68,8930,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,One_and_Half_Fin,Above_Average,Average,1978,1978,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1318,584,0,1902,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,539,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,112000,-93.677205,42.036535 -Split_Foyer,Residential_Low_Density,88,11782,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Good,1961,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,210,1109,GasA,Typical,Y,SBrkr,1155,0,0,1155,1,0,1,0,3,1,Good,6,Min2,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,6,2010,WD ,Normal,148000,-93.673669,42.034793 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Very_Good,1965,2009,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,BLQ,117,224,894,GasA,Excellent,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,1,Good,Detchd,Unf,1,336,Typical,Typical,Paved,416,144,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,138500,-93.670956,42.035685 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9819,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,31,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,432,882,GasA,Typical,Y,SBrkr,900,0,0,900,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,122000,-93.672096,42.034856 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,216,1040,GasA,Fair,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,308,Typical,Typical,Paved,0,0,220,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,133000,-93.672067,42.03457 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6897,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Very_Good,1962,2010,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,381,1040,GasA,Excellent,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,260,Typical,Typical,Paved,0,104,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,127000,-93.669688,42.035333 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,39,15410,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRNe,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,2002,Hip,CompShg,Plywood,Plywood,BrkCmn,250,Typical,Good,CBlock,Typical,Typical,Gd,BLQ,2,GLQ,859,223,1208,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Typical,7,Typ,2,Fair,Attchd,Fin,2,461,Typical,Typical,Paved,296,0,186,0,0,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Abnorml,169000,-93.660672,42.034603 -Two_Story_1946_and_Newer,Residential_Low_Density,107,10186,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,76,750,GasA,Excellent,Y,SBrkr,1061,862,0,1923,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,240,39,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,190000,-93.646749,42.048253 -Two_Story_1946_and_Newer,Residential_Low_Density,85,13143,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1993,Gable,CompShg,HdBoard,ImStucc,BrkFace,504,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,981,0,1231,GasA,Excellent,Y,SBrkr,1251,1098,0,2349,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,762,Typical,Typical,Paved,32,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,362500,-93.651773,42.046825 -Two_Story_1946_and_Newer,Residential_Low_Density,88,11134,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,180,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,261,1390,GasA,Excellent,Y,SBrkr,1402,823,0,2225,1,0,2,1,4,1,Good,7,Typ,1,Typical,Attchd,RFn,3,713,Typical,Typical,Paved,198,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,285000,-93.652773,42.045773 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,25,4835,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,190,1488,GasA,Excellent,Y,SBrkr,1488,0,0,1488,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,506,Typical,Typical,Paved,168,50,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,260000,-93.647988,42.047452 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,39,3515,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,840,0,1680,0,0,2,1,2,1,Good,3,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,0,111,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,190000,-93.64614,42.048086 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3215,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,BrkFace,120,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,320,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLD,Normal,155000,-93.645599,42.048566 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3182,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,151000,-93.644889,42.047899 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,42,190,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,149500,-93.644889,42.047876 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,224,600,GasA,Excellent,Y,SBrkr,600,636,0,1236,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,152000,-93.64489,42.047782 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,0,4403,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2009,2009,Gable,CompShg,MetalSd,MetalSd,Stone,432,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,892,1470,GasA,Excellent,Y,SBrkr,1478,0,0,1478,1,0,2,1,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,484,Typical,Typical,Paved,0,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,222000,-93.6468879,42.0471748 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,378,756,GasA,Excellent,Y,SBrkr,769,804,0,1573,0,0,2,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,177500,-93.645606,42.046145 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2980,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,1159,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,290,756,GasA,Excellent,Y,SBrkr,756,756,0,1512,1,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,177000,-93.645585,42.046145 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2572,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,1999,1999,Hip,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,92,696,GasA,Excellent,Y,SBrkr,696,720,0,1416,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,155000,-93.645482,42.046424 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2403,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,286,530,GasA,Excellent,Y,SBrkr,530,550,0,1080,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,496,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,147110,-93.645478,42.046114 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,57,12853,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2010,2010,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Poor,No,GLQ,3,Unf,0,610,1642,GasA,Excellent,Y,SBrkr,1418,0,0,1418,1,0,1,1,1,1,Good,6,Typ,1,Good,Attchd,RFn,3,852,Typical,Typical,Paved,160,192,0,224,0,0,No_Pool,No_Fence,None,0,4,2010,New,Partial,267916,-93.641253,42.0470316 -Two_Story_1946_and_Newer,Floating_Village_Residential,68,7379,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,491,975,GasA,Excellent,Y,SBrkr,975,873,0,1848,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,592,Typical,Typical,Paved,280,184,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,254000,-93.643556,42.04638 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,30,4420,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,728,GasA,Typical,Y,SBrkr,788,0,0,788,1,0,1,0,1,1,Good,3,Typ,1,Typical,Detchd,Fin,2,484,Typical,Typical,Paved,133,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,155000,-93.649361,42.042565 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3880,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Above_Average,1978,1978,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,174,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,223,78,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,206000,-93.648143,42.043704 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13517,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,Two_Story,Above_Average,Very_Good,1976,2005,Gable,CompShg,HdBoard,Plywood,BrkFace,289,Good,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,192,725,GasA,Excellent,Y,SBrkr,725,754,0,1479,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,475,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,130500,-93.65921,42.034561 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,659,1492,GasA,Excellent,Y,SBrkr,1492,0,0,1492,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,596,Typical,Typical,Paved,277,137,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,230000,-93.639516,42.048606 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10456,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,1323,1829,GasA,Good,Y,SBrkr,1829,0,0,1829,1,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,RFn,2,535,Typical,Typical,Paved,0,76,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,218500,-93.633351,42.046322 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10791,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,143,1280,GasA,Excellent,Y,SBrkr,1280,1215,0,2495,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,660,Typical,Typical,Paved,224,32,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,243500,-93.636879,42.043038 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10603,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1977,2001,Gable,CompShg,Plywood,Plywood,BrkFace,28,Typical,Typical,PConc,Typical,Typical,Mn,ALQ,1,Unf,0,410,1610,GasA,Good,Y,SBrkr,1610,0,0,1610,1,0,2,0,3,1,Good,6,Typ,2,Typical,Attchd,RFn,2,480,Typical,Typical,Paved,168,68,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,205000,-93.637463,42.043306 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,18837,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1978,1978,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,ALQ,1,LwQ,46,491,1224,GasA,Typical,Y,SBrkr,1287,604,0,1891,0,1,3,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,678,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,212500,-93.637531,42.043306 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10421,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,42,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,586,980,GasA,Typical,Y,SBrkr,980,734,0,1714,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,496,Typical,Typical,Paved,228,66,156,0,0,0,No_Pool,Minimum_Privacy,Shed,500,3,2010,WD ,Normal,196500,-93.637788,42.043329 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1972,2006,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,179,1161,GasA,Typical,Y,SBrkr,1381,0,0,1381,1,0,1,1,3,1,Good,5,Typ,1,Typical,Attchd,RFn,2,676,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,197500,-93.631435,42.043822 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1971,1971,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,386,715,GasA,Typical,Y,SBrkr,930,715,0,1645,0,0,1,2,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,441,Typical,Typical,Paved,0,78,0,0,0,0,No_Pool,Good_Wood,None,0,6,2010,WD ,Normal,171000,-93.632554,42.043821 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Hip,CompShg,VinylSd,VinylSd,BrkFace,172,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,534,1232,GasA,Typical,Y,SBrkr,1232,0,0,1232,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,490,Typical,Typical,Paved,0,224,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,142250,-93.626137,42.046574 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,BrkFace,451,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,81,678,1328,GasA,Typical,Y,SBrkr,1328,0,0,1328,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,528,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,143000,-93.625926,42.042205 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,9320,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1959,1959,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,381,950,GasA,Fair,Y,SBrkr,1225,0,0,1225,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,352,0,0,0,0,0,No_Pool,No_Fence,Shed,400,1,2010,WD ,Normal,128950,-93.623383,42.043363 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,150,1209,GasA,Good,Y,SBrkr,1209,0,0,1209,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Typical,Paved,0,0,120,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLD,Normal,159000,-93.626939,42.039085 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,9680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,Wd Sdng,Plywood,BrkFace,268,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,500,1510,GasA,Excellent,Y,SBrkr,1510,0,0,1510,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,517,Typical,Typical,Paved,0,40,0,0,204,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Normal,178900,-93.627536,42.038971 -Split_or_Multilevel,Residential_Low_Density,0,10600,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,533,533,GasA,Typical,Y,SBrkr,1131,644,0,1775,0,0,2,0,3,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,COD,Family,136300,-93.626785,42.040326 -Split_or_Multilevel,Residential_Low_Density,0,14112,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1964,1964,Hip,CompShg,Wd Sdng,HdBoard,BrkFace,86,Typical,Typical,PConc,Typical,Typical,Av,GLQ,3,Unf,0,138,1152,GasA,Typical,Y,SBrkr,1152,0,0,1152,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,227,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,180500,-93.627809,42.041146 -Split_Foyer,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,SFoyer,Above_Average,Good,1963,1963,Gable,CompShg,MetalSd,MetalSd,BrkFace,156,Typical,Good,PConc,Typical,Typical,Gd,GLQ,3,Unf,0,173,936,GasA,Excellent,Y,SBrkr,1054,0,0,1054,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,480,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Abnorml,137500,-93.625728,42.040391 -Duplex_All_Styles_and_Ages,Residential_Low_Density,98,13260,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Above_Average,1962,2001,Hip,CompShg,HdBoard,HdBoard,BrkFace,144,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,228,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,2,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,Oth,Abnorml,84900,-93.622904,42.0418387 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9717,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1950,1996,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,GLQ,3,Rec,1029,0,1078,GasA,Good,Y,FuseA,1078,0,0,1078,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,366,0,112,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,142125,-93.622864,42.039211 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,68,9724,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1952,2002,Gable,CompShg,MetalSd,MetalSd,BrkFace,265,Good,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,470,1140,GasA,Good,Y,SBrkr,1929,532,0,2461,0,0,2,0,3,1,Typical,7,Min2,2,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2010,WD ,Normal,197600,-93.620651,42.039211 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,120,17360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1949,1950,Gable,CompShg,MetalSd,MetalSd,Stone,340,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,482,782,GasA,Typical,Y,SBrkr,1019,537,0,1556,0,0,2,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,2,470,Typical,Typical,Paved,0,0,150,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,172500,-93.620504,42.038538 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,7207,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1958,2008,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,Gd,BLQ,2,Unf,0,162,858,GasA,Good,Y,SBrkr,858,0,0,858,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,117,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,116500,-93.629287,42.035256 -One_Story_1945_and_Older,Residential_Low_Density,55,5350,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Fair,Poor,1940,1966,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Poor,CBlock,Typical,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,1306,0,0,1306,0,0,1,0,3,1,Fair,6,Mod,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,263,0,0,0,0,0,No_Pool,Good_Wood,Shed,450,5,2010,WD ,Normal,76500,-93.629313,42.035482 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,HdBoard,HdBoard,Stone,110,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,290,412,1056,GasA,Typical,Y,SBrkr,1063,0,0,1063,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,280,Typical,Typical,Paved,0,0,164,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,128000,-93.62475,42.035943 -Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1955,1972,Gable,CompShg,AsbShng,AsbShng,BrkFace,164,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,LwQ,132,350,1156,GasA,Excellent,Y,SBrkr,1520,0,0,1520,1,0,1,0,3,1,Typical,7,Typ,2,Good,Basment,RFn,1,364,Typical,Typical,Paved,0,0,189,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,153000,-93.624601,42.036095 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,728,1268,GasA,Good,Y,SBrkr,1268,0,0,1268,0,0,1,0,2,1,Typical,7,Typ,1,Good,Attchd,Fin,1,244,Typical,Typical,Paved,0,98,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,132000,-93.621431,42.037424 -Split_Foyer,Residential_Low_Density,75,11380,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Above_Average,Very_Good,1966,2008,Gable,CompShg,HdBoard,HdBoard,BrkFace,216,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,136,1080,GasA,Good,Y,SBrkr,1128,0,0,1128,1,0,1,0,2,1,Good,5,Typ,1,Good,Attchd,Unf,1,315,Typical,Typical,Paved,238,0,0,0,0,0,No_Pool,No_Fence,Shed,1500,1,2010,WD ,Normal,178000,-93.618221,42.048494 -Duplex_All_Styles_and_Ages,Residential_Low_Density,80,11600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,Duplex,Two_Story,Average,Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkFace,361,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,662,1105,GasA,Typical,Y,FuseA,1105,1169,0,2274,0,0,2,0,5,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,154300,-93.6184183,42.0459375 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,19900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Good,Average,1970,1989,Gable,CompShg,Plywood,Plywood,BrkFace,287,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,1035,1947,GasA,Typical,Y,SBrkr,2207,0,0,2207,1,0,2,0,3,1,Typical,7,Min1,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,301,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,180000,-93.614307,42.049514 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,137,16492,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Above_Average,Above_Average,1966,2002,Gable,CompShg,BrkFace,Plywood,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,713,557,1517,GasA,Excellent,Y,SBrkr,1888,0,0,1888,0,0,2,1,2,1,Good,6,Mod,1,Good,Attchd,Fin,2,578,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,190000,-93.614244,42.047447 -Duplex_All_Styles_and_Ages,Residential_Low_Density,70,8267,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,Duplex,One_Story,Average,Average,1958,1958,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1604,1604,GasA,Typical,Y,SBrkr,1604,0,0,1604,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Paved,42,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,135000,-93.617693,42.044895 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8197,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,2003,2009,Gable,CompShg,VinylSd,VinylSd,BrkFace,506,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,292,1480,GasA,Excellent,Y,SBrkr,1480,0,0,1480,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,620,Typical,Typical,Paved,252,73,0,0,0,0,No_Pool,Minimum_Privacy,Shed,300,2,2010,WD ,Normal,214000,-93.617553,42.043984 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8050,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,150,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,162,125,1143,GasA,Typical,Y,SBrkr,1143,0,0,1143,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2010,WD ,Normal,136000,-93.617117,42.042908 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10552,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,380,1398,GasA,Good,Y,SBrkr,1700,0,0,1700,0,1,1,1,4,1,Good,6,Typ,1,Good,Attchd,RFn,2,447,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,165500,-93.6177041,42.0430839 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1957,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,392,1314,GasA,Typical,Y,SBrkr,1314,0,0,1314,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,145000,-93.618626,42.044847 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,220,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1194,1194,GasA,Typical,Y,SBrkr,1194,0,0,1194,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,0,120,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,148000,-93.616582,42.043969 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12160,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,Plywood,Plywood,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,188,1188,GasA,Fair,Y,SBrkr,1188,0,0,1188,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,531,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,COD,Abnorml,142000,-93.614882,42.044052 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,9300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1960,1960,Gable,CompShg,MetalSd,HdBoard,BrkFace,324,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,571,1268,GasA,Typical,Y,SBrkr,1264,0,0,1264,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,461,Typical,Typical,Paved,0,0,0,0,143,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,167500,-93.613301,42.044176 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10725,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,91,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,270,1206,GasA,Fair,Y,SBrkr,1206,0,0,1206,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,108538,-93.61513,42.044883 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10032,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1959,1959,Gable,CompShg,Wd Sdng,Wd Sdng,Stone,432,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,510,1244,GasA,Excellent,Y,SBrkr,1580,0,0,1580,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,440,Typical,Typical,Paved,0,28,0,0,160,0,No_Pool,Good_Wood,None,0,6,2010,WD ,Normal,159500,-93.614241,42.0435919 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8382,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,FuseA,832,505,0,1337,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,263,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,108000,-93.613624,42.042308 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1952,1952,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,525,864,GasA,Typical,Y,SBrkr,1064,0,0,1064,0,1,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,318,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,135000,-93.615622,42.040396 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,119,10895,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,324,972,GasA,Typical,Y,SBrkr,972,0,0,972,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,305,Typical,Typical,Paved,0,0,205,0,0,0,No_Pool,Good_Wood,None,0,6,2010,WD ,Normal,122500,-93.617092,42.04137 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,13587,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwoFmCon,One_Story,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,456,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Abnorml,119000,-93.61722,42.038515 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,7922,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1953,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,326,1057,GasA,Typical,Y,SBrkr,1057,0,0,1057,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,246,Typical,Typical,Paved,0,52,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Abnorml,109000,-93.618428,42.039841 -One_Story_1945_and_Older,Residential_Low_Density,65,7898,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1920,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,576,576,GasA,Good,Y,SBrkr,985,0,0,985,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,676,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,105000,-93.615475,42.038443 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1955,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,827,0,0,827,0,0,1,0,2,1,Typical,5,Mod,1,Poor,Detchd,Unf,1,392,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,107500,-93.611642,42.038521 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1954,2003,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,104,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,362,404,1086,GasA,Good,Y,SBrkr,1086,0,0,1086,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,490,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2010,WD ,Normal,144900,-93.618788,42.038249 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,5868,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1956,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,240,448,936,GasA,Excellent,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,308,Typical,Typical,Paved,0,0,80,0,160,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,129000,-93.6192014,42.0381282 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,17503,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,912,912,GasA,Typical,Y,SBrkr,912,546,0,1458,0,1,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,330,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Abnorml,97500,-93.620341,42.035869 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,6979,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,0,1056,GasA,Good,Y,SBrkr,1056,0,0,1056,2,0,0,0,0,2,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,264,56,0,0,0,0,No_Pool,Good_Privacy,Shed,600,6,2010,WD ,Normal,144000,-93.6116762,42.0372097 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9830,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Rec,258,733,1063,GasA,Excellent,Y,SBrkr,1287,0,0,1287,1,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Fin,2,576,Typical,Typical,Paved,364,17,0,0,182,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,162000,-93.607275,42.040085 -Split_or_Multilevel,Residential_Low_Density,96,11275,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,PosN,Norm,OneFam,SLvl,Good,Good,1967,2007,Mansard,WdShake,Wd Sdng,Wd Sdng,BrkFace,300,Good,Good,CBlock,Good,Typical,No,Unf,7,Unf,0,710,710,GasA,Excellent,Y,SBrkr,1898,1080,0,2978,0,0,2,1,5,1,Good,11,Typ,1,Good,BuiltIn,Fin,2,564,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,242000,-93.606905,42.041119 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1948,2004,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,174,161,816,GasA,Typical,Y,SBrkr,816,408,0,1224,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,414,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2010,WD ,Normal,132000,-93.61061,42.035867 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1994,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,906,0,1246,GasA,Excellent,Y,SBrkr,1246,0,0,1246,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,305,Typical,Typical,Paved,218,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2010,WD ,Normal,154000,-93.605969,42.03594 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,71,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1952,1952,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,No,Rec,6,Unf,0,403,910,GasA,Fair,Y,SBrkr,910,475,0,1385,0,0,2,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,166000,-93.609218,42.0347571 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,486,180,900,GasA,Typical,Y,SBrkr,900,0,0,900,0,1,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,222,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,134800,-93.607801,42.034848 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,261,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1116,1116,GasA,Typical,Y,SBrkr,1116,0,0,1116,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,385,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,160000,-93.605943,42.034748 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,7635,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1960,1960,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,350,237,1175,GasA,Excellent,Y,SBrkr,1175,0,0,1175,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,148000,-93.606742,42.034851 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1963,1984,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,218,Typical,Typical,CBlock,Typical,Typical,Gd,BLQ,2,LwQ,263,415,1395,GasA,Typical,Y,SBrkr,1395,0,0,1395,1,0,1,0,2,1,Typical,7,Min1,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,657,0,113,0,240,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,192000,-93.604835,42.036873 -Two_Story_1946_and_Newer,Residential_Low_Density,70,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1966,1966,Gable,CompShg,MetalSd,MetalSd,BrkFace,351,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,756,756,GasA,Typical,Y,SBrkr,1051,788,0,1839,0,0,1,1,4,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,442,Typical,Typical,Paved,0,124,216,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,155000,-93.603705,42.037093 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,4800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1900,1954,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,771,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,661,709,GasA,Typical,Y,SBrkr,1157,687,0,1844,1,0,1,0,3,1,Typical,9,Min2,2,Good,Basment,Unf,1,240,Typical,Typical,Paved,84,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,COD,Abnorml,80400,-93.617014,42.033313 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,55,8800,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1910,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,576,576,GasA,Good,Y,SBrkr,792,348,0,1140,0,0,1,0,3,1,Typical,7,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,160,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,96500,-93.617135,42.032134 -One_Story_1945_and_Older,Residential_Medium_Density,56,4485,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Good,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,357,936,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,5,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Partial_Pavement,51,0,135,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,109500,-93.62024,42.031363 -One_Story_1945_and_Older,Residential_Medium_Density,56,8960,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Above_Average,1927,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1008,1008,GasA,Good,Y,FuseA,1028,0,0,1028,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,130,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,115000,-93.620241,42.031381 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,69,5805,Pave,Gravel,Regular,Bnk,AllPub,Inside,Mod,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1957,1957,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,1073,0,1347,GasA,Good,Y,SBrkr,1347,0,0,1347,1,1,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,551,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,143000,-93.6179112,42.0303783 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Fair,Above_Average,1915,1950,Gambrel,CompShg,VinylSd,VinylSd,None,0,Good,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,840,840,GasA,Good,N,SBrkr,840,765,0,1605,0,0,2,0,3,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,379,Typical,Typical,Paved,0,0,202,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,107400,-93.614002,42.033279 -One_Story_1945_and_Older,Residential_Medium_Density,47,4608,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Below_Average,Above_Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,747,747,GasA,Typical,Y,SBrkr,747,0,0,747,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,220,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,80000,-93.615431,42.033644 -One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1940,1992,Gable,CompShg,MetalSd,MetalSd,Stone,294,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,278,788,GasA,Typical,Y,SBrkr,804,0,0,804,1,0,1,0,2,1,Good,4,Typ,2,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,154,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Abnorml,119000,-93.615446,42.032391 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1929,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,ALQ,692,0,926,GasA,Typical,Y,SBrkr,926,0,390,1316,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,130000,-93.608954,42.03128 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,6900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1938,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,827,827,GasA,Good,Y,SBrkr,827,424,0,1251,0,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,119000,-93.6090761,42.0303113 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,69,11851,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1948,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,247,1027,GasA,Excellent,Y,SBrkr,1027,606,0,1633,0,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,100,126,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,129000,-93.610466,42.030383 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,8239,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1920,1962,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,832,1008,GasA,Typical,Y,SBrkr,1060,185,0,1245,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,315,Typical,Typical,Paved,0,0,334,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,100000,-93.610468,42.030467 -One_Story_1945_and_Older,Residential_Medium_Density,68,9656,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Poor,Poor,1923,1970,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,BrkTil,Fair,Fair,No,Unf,7,Unf,0,678,678,GasA,Typical,N,SBrkr,832,0,0,832,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,2,780,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Abnorml,12789,-93.606789,42.030388 -One_Story_1945_and_Older,Residential_Medium_Density,60,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1928,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Good,Y,SBrkr,720,0,0,720,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,106,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,105900,-93.606789,42.030331 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1900,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,930,930,GasW,Typical,N,SBrkr,930,636,0,1566,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,54,228,246,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Abnorml,150000,-93.616887,42.028099 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Fair,Typical,No,LwQ,4,Unf,0,0,686,GasA,Typical,Y,SBrkr,966,686,0,1652,1,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,416,Typical,Typical,Paved,0,0,196,0,0,0,No_Pool,No_Fence,Shed,1200,6,2010,WD ,Normal,139000,-93.610588,42.029146 -Two_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1890,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,Stone,Fair,Fair,No,Unf,7,Unf,0,346,346,GasA,Excellent,Y,SBrkr,1157,1111,0,2268,0,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Dirt_Gravel,0,108,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,240000,-93.61221,42.028145 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,100,9045,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Fair,1910,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,840,840,Grav,Fair,N,FuseF,1128,1128,0,2256,0,0,2,0,4,2,Fair,12,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,18,18,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Abnorml,76500,-93.608878,42.028083 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,90,7407,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Good,1957,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,312,912,GasA,Typical,Y,FuseA,1236,0,0,1236,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,923,Typical,Typical,Paved,0,158,158,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,149700,-93.608246,42.03005 -Two_Story_1945_and_Older,Residential_Medium_Density,60,7740,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Good,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,622,622,GasA,Good,Y,SBrkr,741,622,0,1363,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,168,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,125500,-93.607238,42.028164 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,11340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Above_Average,1885,1950,Gable,CompShg,VinylSd,AsbShng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,777,777,GasA,Good,Y,SBrkr,1246,1044,0,2290,0,0,2,0,4,2,Typical,11,Typ,0,No_Fireplace,Detchd,Unf,2,560,Typical,Typical,Dirt_Gravel,0,0,114,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,122500,-93.608752,42.02916 -Two_Story_1945_and_Older,Residential_Medium_Density,60,10560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1922,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,Rec,6,Unf,0,455,738,GasA,Excellent,Y,SBrkr,868,602,0,1470,0,0,1,1,2,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,140750,-93.607088,42.028165 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,53,5830,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Feedr,OneFam,One_and_Half_Fin,Average,Above_Average,1950,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,200,988,GasA,Excellent,Y,SBrkr,1030,582,0,1612,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,363,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,128500,-93.625737,42.033642 -Two_and_Half_Story_All_Ages,Residential_Low_Density,0,7793,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Good,1922,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,BLQ,2,Unf,0,634,1108,GasA,Typical,N,FuseA,1160,908,0,2068,0,0,1,1,3,1,Good,8,Typ,1,Good,Detchd,Unf,1,315,Typical,Typical,Paved,0,0,60,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,209500,-93.625825,42.030187 -One_Story_1945_and_Older,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Below_Average,Good,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,577,765,GasA,Typical,N,FuseF,765,0,0,765,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Partial_Pavement,135,0,41,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,87000,-93.625586,42.033621 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Good,No,BLQ,2,LwQ,12,144,608,GasA,Typical,Y,SBrkr,608,524,0,1132,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,128,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Abnorml,134000,-93.624566,42.033563 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,Unf,0,308,572,GasA,Excellent,Y,FuseA,848,348,0,1196,0,1,1,1,3,1,Typical,6,Typ,2,Good,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,128000,-93.622597,42.0336 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,53,6360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1942,1950,Gable,CompShg,MetalSd,MetalSd,Stone,300,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,159,316,835,GasA,Typical,Y,FuseA,955,498,0,1453,0,0,1,1,3,1,Good,7,Min2,2,Fair,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,35,0,148,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,132000,-93.623523,42.033665 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,480,780,GasA,Typical,Y,SBrkr,780,636,0,1416,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,312,Typical,Typical,Partial_Pavement,221,0,48,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,139900,-93.622448,42.033631 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1936,1980,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,252,528,GasA,Good,Y,SBrkr,548,492,0,1040,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Fin,2,624,Typical,Typical,Partial_Pavement,306,0,32,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,123900,-93.623483,42.031556 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1930,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,480,928,GasA,Typical,Y,FuseF,928,608,0,1536,0,0,2,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,2,480,Typical,Typical,Paved,0,10,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,138400,-93.621482,42.031337 -One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Average,1923,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Fair,No,ALQ,1,Unf,0,164,1124,GasA,Typical,Y,SBrkr,1068,0,0,1068,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,128,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,109500,-93.620409,42.032302 -Two_Story_1945_and_Older,Residential_Medium_Density,50,10300,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,RRAn,Feedr,OneFam,Two_Story,Good,Above_Average,1921,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,576,576,GasA,Good,Y,SBrkr,902,808,0,1710,0,0,2,0,3,1,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,12,11,64,0,0,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Normal,140000,-93.62338,42.027986 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,57,8094,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,888,888,GasA,Excellent,Y,SBrkr,888,1074,0,1962,0,0,1,1,4,1,Typical,9,Typ,1,Typical,Detchd,Unf,2,572,Typical,Typical,Paved,160,0,364,0,0,0,No_Pool,Good_Privacy,None,0,6,2010,WD ,Normal,149500,-93.621674,42.028888 -Two_Story_1945_and_Older,Residential_Medium_Density,60,12900,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1912,2009,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Excellent,Y,SBrkr,780,780,0,1560,0,0,1,1,3,1,Good,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,344,0,0,0,168,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,159900,-93.621835,42.028875 -Two_Story_1945_and_Older,Residential_Medium_Density,52,3068,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1920,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,662,662,GasA,Excellent,Y,SBrkr,662,662,0,1324,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,180,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,Good_Privacy,None,0,2,2010,WD ,Normal,122000,-93.621632,42.026914 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,110,8472,Grvl,No_Alley_Access,Moderately_Irregular,Bnk,AllPub,Corner,Mod,Iowa_DOT_and_Rail_Road,RRNn,Norm,Duplex,One_Story,Average,Average,1963,1963,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,CBlock,Good,Typical,Gd,LwQ,4,GLQ,712,0,816,GasA,Typical,N,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,CarPort,Unf,2,516,Typical,Typical,Paved,106,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,110000,-93.629495,42.020693 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,70,5600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Average,1930,1950,Hip,CompShg,VinylSd,Wd Shng,None,0,Fair,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,SBrkr,372,720,0,1092,0,0,2,0,3,2,Fair,7,Mod,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,Othr,3500,7,2010,WD ,Normal,55000,-93.626876,42.024141 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,76,7630,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,TwoFmCon,Two_Story,Average,Excellent,1900,1996,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Good,Typical,No,Unf,7,Unf,0,360,360,GasA,Good,Y,SBrkr,1032,780,0,1812,0,0,2,0,4,2,Good,8,Typ,1,Poor,Detchd,Unf,2,672,Typical,Typical,Dirt_Gravel,344,0,40,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,140000,-93.620386,42.026741 -Two_Story_1945_and_Older,Residential_Low_Density,0,24090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Good,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1032,1032,GasA,Excellent,Y,SBrkr,1207,1196,0,2403,0,0,2,0,4,1,Typical,10,Typ,2,Typical,Attchd,Unf,1,349,Typical,Typical,Paved,56,0,318,0,0,0,No_Pool,No_Fence,None,0,6,2010,COD,Normal,244400,-93.656231,42.033454 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,15263,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Feedr,Norm,OneFam,One_Story,Average,Average,1959,1959,Gable,CompShg,HdBoard,HdBoard,BrkFace,90,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,656,1422,GasA,Good,Y,SBrkr,1675,0,0,1675,0,0,2,0,3,1,Typical,8,Typ,2,Good,Attchd,Unf,1,365,Typical,Typical,Paved,0,132,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,173000,-93.655538,42.032432 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,72,10632,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Average,Fair,1917,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Fair,No,Unf,7,Unf,0,689,689,GasA,Good,N,SBrkr,725,499,0,1224,0,0,1,1,3,1,Poor,6,Mod,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,0,0,248,0,0,0,No_Pool,No_Fence,None,0,1,2010,COD,Normal,107500,-93.658265,42.028256 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Below_Average,1915,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Fair,Fair,BrkTil,Typical,Typical,No,Rec,6,Unf,0,186,1212,GasA,Typical,N,SBrkr,1212,180,0,1392,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,168,0,0,0,No_Pool,No_Fence,None,0,2,2010,ConLD,Normal,100000,-93.656924,42.026752 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,65,6001,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,LwQ,4,Unf,0,232,600,GasA,Excellent,N,SBrkr,600,319,0,919,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,231,Typical,Typical,Paved,0,0,45,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,95000,-93.658081,42.02616 -Two_Story_1945_and_Older,C_all,0,6449,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Below_Average,Very_Poor,1907,1950,Gambrel,CompShg,Wd Sdng,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,634,707,GasW,Typical,N,SBrkr,942,942,0,1884,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,239,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Abnorml,93369,-93.657573,42.025255 -Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,60,6048,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Artery,Norm,TwoFmCon,One_and_Half_Fin,Average,Good,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,Mn,LwQ,4,Unf,0,120,856,GasA,Good,Y,SBrkr,936,744,0,1680,1,0,2,0,2,2,Typical,7,Typ,1,Good,Detchd,Unf,2,450,Typical,Fair,Partial_Pavement,56,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,COD,Normal,114900,-93.655849,42.022825 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,54,6342,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1875,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Good,N,SBrkr,780,240,0,1020,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,176,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,94000,-93.672036,42.034451 -Duplex_All_Styles_and_Ages,Residential_Low_Density,72,10773,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Below_Average,Fair,1967,1967,Gable,Tar&Grv,Plywood,Plywood,BrkFace,72,Fair,Fair,CBlock,Typical,Typical,No,ALQ,1,Unf,0,1128,1832,GasA,Typical,N,SBrkr,1832,0,0,1832,2,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,58,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,136000,-93.671021,42.033386 -Duplex_All_Styles_and_Ages,Residential_Low_Density,72,10778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Below_Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1768,1768,GasA,Typical,N,SBrkr,1768,0,0,1768,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,136500,-93.671076,42.033387 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,75,11625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,TwoFmCon,One_Story,Average,Below_Average,1965,1965,Hip,CompShg,Plywood,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,Mn,BLQ,2,Unf,0,198,1039,GasA,Excellent,Y,SBrkr,1039,0,0,1039,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,131500,-93.673401,42.033008 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11341,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1996,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,180,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,90,1392,GasA,Typical,Y,SBrkr,1392,0,0,1392,1,0,1,1,3,1,Typical,5,Mod,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,95,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,121500,-93.672381,42.03237 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8521,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,70,912,GasA,Typical,Y,SBrkr,912,0,0,912,0,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,125000,-93.678141,42.029867 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8246,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Very_Good,1968,2001,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Rec,6,ALQ,668,204,1060,GasA,Excellent,Y,SBrkr,1060,0,0,1060,1,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,1,270,Typical,Typical,Paved,406,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,154000,-93.676659,42.03069 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1966,2008,Hip,CompShg,HdBoard,HdBoard,BrkFace,47,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,474,150,864,GasA,Excellent,Y,SBrkr,892,0,0,892,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,416,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,137900,-93.677992,42.029924 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9364,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1969,1969,Gable,CompShg,HdBoard,HdBoard,Stone,143,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,292,663,GasA,Typical,Y,SBrkr,663,689,0,1352,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,299,Typical,Typical,Paved,379,36,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,158000,-93.676654,42.029731 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7832,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,89,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,226,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,137250,-93.676674,42.030519 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7424,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,SFoyer,Average,Average,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1319,GasA,Typical,Y,SBrkr,1373,0,0,1373,1,0,1,0,3,1,Typical,5,Typ,2,Typical,Attchd,Fin,2,591,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,160250,-93.671511,42.031187 -Two_Story_1946_and_Newer,Residential_Low_Density,86,11227,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Good,No,Rec,6,ALQ,453,0,720,GasA,Excellent,Y,SBrkr,720,720,0,1440,0,0,1,1,4,1,Typical,7,Typ,2,Typical,Attchd,Unf,2,480,Typical,Typical,Paved,192,38,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,163000,-93.673565,42.03136 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11616,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkCmn,328,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,234,672,GasA,Typical,Y,SBrkr,672,714,0,1386,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,335,0,0,0,0,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Abnorml,158900,-93.672523,42.03128 -Two_Story_1946_and_Newer,Residential_Low_Density,80,14000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,105,1306,GasA,Excellent,Y,SBrkr,1306,954,0,2260,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,533,Typical,Typical,Paved,296,44,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,328000,-93.670503,42.030082 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20062,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Good,1977,2001,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,328,1420,GasA,Good,Y,SBrkr,1483,0,0,1483,1,0,1,1,1,1,Typical,4,Typ,2,Typical,Attchd,RFn,2,690,Typical,Typical,Paved,496,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,270000,-93.670114,42.03009 -One_Story_1945_and_Older,Residential_Low_Density,94,9259,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1927,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,660,660,GasA,Typical,N,SBrkr,756,0,0,756,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Dirt_Gravel,80,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,85000,-93.663619,42.03387 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Good,1950,1995,Gable,CompShg,VinylSd,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,625,1067,GasA,Typical,Y,SBrkr,1067,0,0,1067,0,0,2,0,2,1,Good,4,Min2,0,No_Fireplace,Attchd,Unf,2,436,Typical,Typical,Paved,290,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,128000,-93.668378,42.032164 -One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,0,23595,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,1979,1979,Shed,WdShake,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,74,1332,GasA,Typical,Y,SBrkr,1332,192,0,1524,2,0,0,1,0,1,Good,4,Typ,1,Typical,Attchd,Fin,2,586,Typical,Typical,Paved,268,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,260000,-93.667904,42.026547 -Two_Story_1946_and_Newer,Residential_Low_Density,0,17082,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Mod,Clear_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1978,1992,Gable,CompShg,VinylSd,VinylSd,BrkFace,288,Typical,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,153,1117,GasA,Excellent,Y,SBrkr,1117,864,0,1981,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,2,522,Typical,Typical,Paved,336,104,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,230000,-93.675038,42.024673 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,124,18600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Below_Average,1938,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,LwQ,684,0,972,GasA,Typical,Y,FuseA,1052,558,0,1610,0,1,2,0,4,1,Fair,8,Typ,1,Good,Attchd,RFn,1,480,Typical,Typical,Paved,0,0,60,0,0,0,No_Pool,No_Fence,Shed,450,6,2010,WD ,Normal,124000,-93.671178,42.023079 -One_Story_1945_and_Older,Residential_Low_Density,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,350,458,GasA,Fair,N,SBrkr,835,0,0,835,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,1,366,Fair,Typical,Paved,0,0,77,0,0,0,No_Pool,No_Fence,Shed,400,5,2010,COD,Abnorml,83000,-93.662762,42.028226 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,11479,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Good,1950,1987,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,387,172,663,GasA,Excellent,Y,SBrkr,1074,0,0,1074,1,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,1,467,Typical,Typical,Paved,0,52,52,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,144500,-93.66357,42.026533 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,405,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,596,Typical,Typical,Paved,44,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,129000,-93.667708,42.024208 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,9350,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1947,1979,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,564,756,GasA,Excellent,Y,SBrkr,1169,0,362,1531,0,0,1,0,3,1,Typical,8,Typ,1,Typical,Detchd,Unf,1,209,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,127000,-93.66526,42.025173 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1954,1998,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,218,1172,GasA,Typical,Y,SBrkr,1172,0,0,1172,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,366,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,128000,-93.666097,42.02526 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,10420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1176,1212,GasA,Excellent,Y,SBrkr,1212,0,0,1212,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,460,Typical,Typical,Paved,100,22,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,186000,-93.6658876,42.0247497 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,44,17485,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,96,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,162,1508,GasA,Excellent,Y,SBrkr,1508,0,0,1508,1,0,1,0,1,1,Good,5,Typ,2,Typical,Attchd,RFn,2,572,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,Con,Partial,308030,-93.665799,42.024126 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11200,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Fair,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1250,1250,GasA,Excellent,Y,SBrkr,1298,0,0,1298,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,504,Typical,Fair,Dirt_Gravel,0,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,COD,Normal,114000,-93.663367,42.025019 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11100,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Good,1946,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,930,0,0,930,0,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Abnorml,84900,-93.659808,42.024162 -Two_Story_1946_and_Newer,Residential_Low_Density,77,9206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1985,1985,Gable,CompShg,HdBoard,HdBoard,BrkFace,336,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,741,741,GasA,Typical,Y,SBrkr,977,755,0,1732,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,476,Typical,Typical,Paved,192,46,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,178000,-93.685001,42.03374 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,11980,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,1987,1987,Gable,CompShg,Plywood,Plywood,BrkFace,177,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,1433,GasA,Excellent,Y,SBrkr,1433,0,0,1433,1,0,1,1,1,1,Good,4,Typ,2,Typical,Attchd,RFn,2,528,Good,Good,Paved,0,278,0,0,266,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,270000,-93.685988,42.032076 -Two_Story_1946_and_Newer,Residential_Low_Density,87,12361,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,85,Good,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,86,946,GasA,Excellent,Y,SBrkr,964,838,0,1802,0,1,2,1,3,1,Good,8,Typ,1,Good,More_Than_Two_Types,RFn,4,1017,Typical,Typical,Paved,450,92,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,218000,-93.683167,42.033783 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9938,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,MetalSd,MetalSd,BrkFace,246,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,300,1050,GasA,Excellent,Y,SBrkr,1062,887,0,1949,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,574,Typical,Typical,Paved,156,90,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2010,WD ,Normal,236000,-93.682026,42.03068 -Split_Foyer,Residential_Low_Density,73,9069,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,189,936,GasA,Excellent,Y,SBrkr,996,0,0,996,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,564,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,147000,-93.6801568,42.0311198 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,10475,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,72,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1694,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,776,Typical,Typical,Paved,160,33,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,245350,-93.69019,42.025747 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,24,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,501,1187,GasA,Excellent,Y,SBrkr,1208,0,0,1208,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,105,61,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,206000,-93.691269,42.024614 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,10402,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1226,1226,GasA,Excellent,Y,SBrkr,1226,0,0,1226,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,740,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,198900,-93.69245,42.024604 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2010,2010,Gable,CompShg,VinylSd,VinylSd,Stone,80,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1198,1222,GasA,Excellent,Y,SBrkr,1222,0,0,1222,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,615,Typical,Typical,Paved,0,54,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,187000,-93.692521,42.024604 -Two_Story_1946_and_Newer,Residential_Low_Density,90,12376,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1990,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,203,1673,GasA,Good,Y,SBrkr,1699,1523,0,3222,1,0,3,0,5,1,Good,11,Typ,2,Typical,Attchd,Unf,3,594,Typical,Typical,Paved,367,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,320000,-93.68329,42.030601 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,82,14235,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1900,1993,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,PConc,Fair,Good,No,Unf,7,Unf,0,676,676,GasA,Typical,Y,SBrkr,831,614,0,1445,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Dirt_Gravel,0,59,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,138500,-93.6801805,42.0298532 -Split_or_Multilevel,Residential_Low_Density,80,8816,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Good,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,80,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,506,1010,GasA,Good,Y,SBrkr,1052,0,0,1052,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,155000,-93.682138,42.024318 -Split_Foyer,Residential_Low_Density,82,11105,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Fair,Av,GLQ,3,Unf,0,0,870,GasA,Good,Y,SBrkr,965,0,0,965,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,580,Good,Typical,Paved,71,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2010,WD ,Normal,159000,-93.69188,42.021321 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9337,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,525,878,GasA,Excellent,Y,SBrkr,892,800,0,1692,0,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,513,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,191000,-93.690542,42.018411 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,116,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,299,804,GasA,Excellent,Y,SBrkr,804,878,0,1682,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,523,Typical,Typical,Paved,0,77,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,200500,-93.688258,42.01867 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,38,15240,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1977,2004,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Good,CBlock,Good,Typical,No,GLQ,3,Rec,688,140,1026,GasA,Excellent,Y,SBrkr,1026,0,0,1026,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,316,85,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,150000,-93.68773,42.022186 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10900,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1977,1977,Gable,CompShg,HdBoard,HdBoard,BrkFace,153,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,311,689,GasA,Excellent,Y,SBrkr,689,703,0,1392,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,299,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Minimum_Privacy,Shed,450,3,2010,WD ,Normal,161750,-93.687807,42.022045 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Good,Av,LwQ,4,ALQ,712,0,894,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,365,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,128200,-93.687745,42.021395 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,7480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1972,1972,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,396,876,GasA,Typical,Y,SBrkr,876,0,0,876,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,127000,-93.683727,42.021133 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10389,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,CemntBd,CmentBd,BrkFace,320,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,296,1978,GasA,Excellent,Y,SBrkr,1978,0,0,1978,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,850,Typical,Typical,Paved,188,25,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,318000,-93.687859,42.018662 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11423,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,479,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,223,1581,GasA,Excellent,Y,SBrkr,1601,0,0,1601,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,670,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,2000,5,2010,WD ,Normal,272000,-93.685523,42.019069 -Two_Story_1946_and_Newer,Residential_Low_Density,44,9548,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Above_Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,223,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,458,941,GasA,Excellent,Y,SBrkr,941,888,0,1829,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,613,Typical,Typical,Paved,192,39,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,237000,-93.687764,42.016121 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1040,1040,GasA,Excellent,Y,SBrkr,1044,1054,0,2098,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,2,621,Typical,Typical,Paved,331,38,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,240000,-93.680864,42.018721 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,48,12137,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,442,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1649,1649,GasA,Excellent,Y,SBrkr,1661,0,0,1661,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,598,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,224900,-93.682686,42.017749 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,176,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,143750,-93.681227,42.016275 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,RFn,2,420,Typical,Typical,Paved,160,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,143000,-93.6837,42.016253 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,48,10635,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,171,Good,Typical,PConc,Good,Typical,Av,BLQ,2,GLQ,972,315,1657,GasA,Excellent,Y,SBrkr,1668,0,0,1668,1,0,2,0,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,502,Typical,Typical,Paved,0,262,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,232000,-93.687922,42.01404 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,109,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,761,1473,GasA,Excellent,Y,SBrkr,1484,0,0,1484,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,606,Typical,Typical,Paved,0,35,0,144,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,213000,-93.684015,42.014092 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8773,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,98,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1390,1414,GasA,Excellent,Y,SBrkr,1414,0,0,1414,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,494,Typical,Typical,Paved,132,105,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,185500,-93.685961,42.014023 -One_Story_1945_and_Older,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Feedr,Norm,OneFam,One_Story,Fair,Above_Average,1945,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,640,0,0,640,0,0,1,0,2,1,Typical,5,Min1,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLD,Normal,84900,-93.6783313,42.0203585 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8842,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,381,381,GasA,Excellent,Y,SBrkr,992,0,0,992,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,319,Typical,Typical,Paved,60,0,56,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2010,Oth,Abnorml,155891,-93.677482,42.021245 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,9825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1965,1965,Hip,CompShg,AsphShn,AsphShn,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1664,0,0,1664,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,100000,-93.677553,42.021041 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10044,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,126,1196,GasA,Typical,Y,SBrkr,1196,0,0,1196,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,336,Typical,Typical,Paved,257,0,168,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,144000,-93.677545,42.020635 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,89,11792,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,744,744,GasA,Excellent,N,FuseF,792,328,0,1120,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Fair,Partial_Pavement,0,0,0,0,160,0,No_Pool,No_Fence,None,0,6,2010,WD ,Abnorml,64000,-93.676372,42.021163 -Split_or_Multilevel,Residential_Low_Density,65,6305,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,480,1008,GasA,Typical,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,125200,-93.675268,42.020306 -Split_or_Multilevel,Residential_Low_Density,94,7819,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,BLQ,127,480,1029,GasA,Typical,Y,SBrkr,1117,0,0,1117,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Detchd,Unf,2,672,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Abnorml,107000,-93.675175,42.019849 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6410,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1958,1958,Hip,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Excellent,Y,SBrkr,960,0,0,960,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,90000,-93.6663375,42.019629 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,80,8546,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1121,1121,GasA,Excellent,Y,SBrkr,1121,0,0,1121,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,132,64,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,140000,-93.66751,42.020196 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,52,8741,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Below_Average,1945,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,641,735,GasA,Typical,Y,FuseA,798,689,0,1487,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,220,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,113000,-93.664841,42.021793 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,12180,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Below_Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Good,Typical,No,BLQ,2,Unf,0,324,672,Grav,Fair,N,FuseA,672,252,0,924,1,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2010,WD ,Normal,80000,-93.660168,42.021583 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8562,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,2002,Hip,CompShg,HdBoard,HdBoard,Stone,145,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,833,1216,GasA,Excellent,Y,FuseA,1526,0,0,1526,0,0,1,0,4,1,Typical,7,Min2,1,Good,Basment,Unf,1,364,Typical,Typical,Paved,116,78,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,144500,-93.661944,42.017726 -One_Story_1945_and_Older,Residential_Low_Density,67,4853,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Artery,Norm,OneFam,One_Story,Average,Above_Average,1924,1999,Gable,CompShg,MetalSd,VinylSd,BrkFace,203,Typical,Typical,BrkTil,Typical,Typical,Mn,Rec,6,Unf,0,974,1107,GasA,Fair,N,FuseA,1296,0,0,1296,0,0,2,0,2,1,Fair,5,Typ,1,Good,Detchd,Unf,1,260,Typical,Typical,Paved,0,0,36,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,104000,-93.655762,42.022674 -Two_Story_1945_and_Older,Residential_Low_Density,66,6858,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Above_Average,Below_Average,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,806,806,GasA,Typical,N,FuseF,841,806,0,1647,1,0,1,1,4,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,0,66,136,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,128000,-93.655653,42.022702 -One_Story_1945_and_Older,Residential_Low_Density,45,8212,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Fair,1914,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Fair,BrkTil,Typical,Fair,No,Rec,6,Unf,0,661,864,GasA,Typical,N,FuseF,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Fair,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,58500,-93.656718,42.022089 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,5000,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1924,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,808,1026,GasA,Typical,Y,SBrkr,1026,665,0,1691,0,0,2,0,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,242,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,127000,-93.650321,42.017737 -One_Story_1945_and_Older,Residential_Low_Density,0,7890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,618,856,GasA,Typical,Y,SBrkr,856,0,0,856,1,0,1,0,2,1,Typical,4,Typ,1,Good,Detchd,Unf,2,399,Typical,Typical,Paved,0,0,0,0,166,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,126000,-93.6514413,42.0177987 -Duplex_All_Styles_and_Ages,Residential_High_Density,60,7200,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,Duplex,Two_Story,Below_Average,Above_Average,1967,1967,Flat,Tar&Grv,Plywood,CBlock,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,862,1788,0,2650,0,0,3,0,6,2,Typical,10,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,500,2,2010,WD ,Normal,160000,-93.6507568,42.0163944 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,9839,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Poor,1931,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Fair,No,Unf,7,Unf,0,894,894,GasA,Excellent,Y,SBrkr,894,772,0,1666,1,0,1,0,3,1,Typical,7,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,156,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,100000,-93.64678,42.019091 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,9638,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1919,1990,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,804,804,GasA,Excellent,Y,SBrkr,1699,748,0,2447,0,0,2,0,4,1,Good,10,Min2,1,Good,Detchd,Unf,1,336,Typical,Typical,Paved,272,0,42,0,116,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,169000,-93.646683,42.019093 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,10452,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Above_Average,1941,1985,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,371,Good,Good,BrkTil,Good,Typical,No,ALQ,1,BLQ,252,850,1528,GasA,Excellent,Y,SBrkr,1225,908,0,2133,1,0,1,1,4,1,Typical,8,Typ,2,Typical,Attchd,Unf,1,312,Typical,Typical,Paved,0,0,86,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,257500,-93.640041,42.016226 -Duplex_All_Styles_and_Ages,Residential_Low_Density,78,15600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1950,1991,Gable,CompShg,VinylSd,VinylSd,BrkFace,430,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,657,1032,GasA,Excellent,Y,SBrkr,1102,1075,0,2177,0,0,2,1,5,2,Typical,11,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,215000,-93.639781,42.014937 -Two_Story_1945_and_Older,Residential_Low_Density,66,9042,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Excellent,1941,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Excellent,Good,Stone,Typical,Good,No,GLQ,3,Unf,0,877,1152,GasA,Excellent,Y,SBrkr,1188,1152,0,2340,0,0,2,0,4,1,Good,9,Typ,2,Good,Attchd,RFn,1,252,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,Good_Privacy,Shed,2500,5,2010,WD ,Normal,266500,-93.642793,42.014682 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,17500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,PosA,Norm,OneFam,One_Story,Good,Very_Good,1959,2002,Gable,CompShg,BrkFace,HdBoard,None,0,Good,Good,PConc,Good,Typical,Av,GLQ,3,Unf,0,496,1902,GasA,Typical,Y,SBrkr,1902,0,0,1902,1,0,2,0,3,1,Excellent,7,Typ,2,Typical,Attchd,Fin,2,567,Typical,Typical,Paved,0,207,162,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,335000,-93.639678,42.011005 -Split_or_Multilevel,Residential_Low_Density,85,19645,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Crawford,Norm,Norm,OneFam,SLvl,Good,Above_Average,1994,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,44,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,80,423,GasA,Excellent,Y,SBrkr,896,756,0,1652,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,473,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,203135,-93.645014,42.010606 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,35,3907,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Blueste,Norm,Norm,TwnhsE,One_Story,Very_Good,Above_Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,235,982,GasA,Good,Y,SBrkr,1034,0,0,1034,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,598,Typical,Typical,Paved,141,36,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,185000,-93.645701,42.009345 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,35,3907,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Blueste,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,1115,1191,GasA,Good,Y,SBrkr,1191,0,0,1191,0,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Unf,2,531,Typical,Typical,Paved,112,81,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,162500,-93.645729,42.009337 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,15602,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Good,Very_Good,1959,1997,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,254,1501,GasA,Typical,Y,SBrkr,1801,0,0,1801,1,0,2,0,1,1,Typical,6,Typ,2,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,0,54,0,0,161,0,No_Pool,Good_Wood,None,0,3,2010,WD ,Normal,289000,-93.641677,42.010797 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,40,5436,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Very_Good,1922,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,61,796,GasA,Good,Y,SBrkr,796,358,0,1154,1,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,96,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,125500,-93.625291,42.022772 -One_Story_1945_and_Older,Residential_Medium_Density,58,8154,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Poor,Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,0,480,GasA,Typical,Y,SBrkr,540,0,0,540,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLw,Normal,82000,-93.629496,42.021414 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,9140,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1921,1975,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,BLQ,2,Unf,0,321,629,GasA,Fair,Y,SBrkr,727,380,0,1107,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,625,Typical,Typical,Paved,0,56,0,0,200,0,No_Pool,Minimum_Privacy,None,0,4,2010,COD,Normal,110000,-93.626689,42.021474 -One_and_Half_Story_Finished_All_Ages,C_all,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Typical,Y,FuseA,698,430,0,1128,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,30,0,164,0,0,0,No_Pool,No_Fence,None,0,4,2010,COD,Abnorml,68400,-93.615272,42.021456 -One_and_Half_Story_Finished_All_Ages,C_all,66,8712,Pave,Paved,Regular,HLS,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Stone,Typical,Typical,Mn,Unf,7,Unf,0,859,859,GasA,Good,Y,SBrkr,859,319,0,1178,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,RFn,1,384,Typical,Typical,Dirt_Gravel,68,0,98,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Abnorml,102776,-93.606593,42.022653 -One_Story_1946_and_Newer_All_Styles,C_all,66,8712,Grvl,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Average,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,540,540,GasA,Typical,N,FuseA,1044,0,0,1044,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Basment,Unf,2,504,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,Shed,54,6,2010,WD ,Alloca,55993,-93.608271,42.021327 -One_Story_1945_and_Older,C_all,66,8712,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Good,1896,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,756,756,GasA,Good,Y,SBrkr,952,0,0,952,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,330,Typical,Typical,Dirt_Gravel,0,0,265,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Alloca,50138,-93.60775,42.02152 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3811,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Crawford,Artery,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,186,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,221,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,525,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,246000,-93.615993,42.008687 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,434,1049,GasA,Excellent,Y,SBrkr,1036,880,0,1916,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Unf,3,741,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,254900,-93.604947,41.99706 -Split_or_Multilevel,Residential_Low_Density,74,9620,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Good,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,564,1243,GasA,Typical,Y,SBrkr,1285,0,0,1285,0,1,2,0,3,1,Good,6,Typ,1,Fair,Attchd,Unf,2,473,Typical,Typical,Paved,375,26,0,0,0,0,No_Pool,Good_Privacy,Shed,80,5,2010,WD ,Normal,190000,-93.602823,41.997052 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,9196,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1560,1560,GasA,Excellent,Y,SBrkr,1560,0,0,1560,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,573,Typical,Typical,Paved,100,150,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,201000,-93.603972,41.996065 -Split_or_Multilevel,Residential_Low_Density,0,12328,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,335,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,473,1012,GasA,Typical,Y,SBrkr,1034,0,0,1034,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,3,888,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,169900,-93.600797,41.994784 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,12760,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1958,1958,GasA,Typical,Y,SBrkr,2048,0,0,2048,0,0,3,0,5,2,Typical,9,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,2,776,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,ConLD,Normal,170000,-93.602002,41.994013 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,57200,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Sev,Timberland,Norm,Norm,OneFam,One_Story,Average,Average,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Rec,334,60,747,GasA,Typical,Y,SBrkr,1687,0,0,1687,1,0,1,0,3,1,Typical,7,Min1,2,Typical,Detchd,Unf,2,572,Typical,Typical,Dirt_Gravel,0,0,50,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,160000,-93.657894,41.997741 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11896,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,60,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1258,1336,GasA,Excellent,Y,SBrkr,1346,0,0,1346,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,3,660,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,220000,-93.649203,41.995346 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,9803,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1214,1214,GasA,Excellent,Y,SBrkr,1214,0,0,1214,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,0,25,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,New,Partial,179781,-93.649201,41.995278 -Two_Story_1946_and_Newer,Residential_Low_Density,73,9802,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,744,700,0,1444,0,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,174000,-93.649096,41.994284 -Two_Story_1946_and_Newer,Residential_Low_Density,92,12003,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,BrkFace,84,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,774,774,GasA,Excellent,Y,SBrkr,774,1194,0,1968,0,0,2,1,4,1,Excellent,8,Typ,1,Good,BuiltIn,Fin,3,680,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,New,Partial,269500,-93.649471,41.99353 -Two_Story_1946_and_Newer,Residential_Low_Density,80,11316,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,44,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,193,817,GasA,Excellent,Y,SBrkr,824,1070,0,1894,1,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,510,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,214900,-93.647645,41.995489 -Two_Story_1946_and_Newer,Residential_Low_Density,85,14191,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,967,967,GasA,Excellent,Y,SBrkr,993,915,0,1908,0,0,2,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,431,Typical,Typical,Paved,135,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,202900,-93.646814,41.99435 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,13214,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2009,Hip,CompShg,Stucco,CmentBd,None,0,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,2002,2002,GasA,Excellent,Y,SBrkr,2018,0,0,2018,0,0,2,0,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,746,Typical,Typical,Paved,144,76,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,378500,-93.652495,41.992989 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,15300,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1965,1977,Hip,CompShg,Plywood,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,1026,1068,GasA,Typical,Y,SBrkr,1264,0,0,1264,1,0,1,0,2,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,169000,-93.60743,41.99321 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,10114,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1430,1430,GasA,Excellent,Y,SBrkr,1430,0,0,1430,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,173500,-93.608372,41.990934 -Duplex_All_Styles_and_Ages,Residential_Low_Density,94,9400,Pave,No_Alley_Access,Regular,Low,AllPub,Corner,Gtl,Mitchell,Norm,Norm,Duplex,Two_Story,Above_Average,Average,1971,1971,Mansard,CompShg,MetalSd,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,912,912,GasA,Typical,Y,SBrkr,912,912,0,1824,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,128,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,139000,-93.607622,41.993253 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1344,1344,GasA,Excellent,Y,SBrkr,1344,0,0,1344,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,686,Typical,Typical,Paved,328,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,166500,-93.608223,41.991004 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,1974,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,212,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,120,96,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,83500,-93.60359,41.991861 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,31,2394,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Meadow_Village,Norm,Norm,Twnhs,One_Story,Average,Above_Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,30,945,GasA,Excellent,Y,SBrkr,945,0,0,945,1,1,1,0,2,1,Typical,5,Typ,1,Poor,Attchd,RFn,1,253,Typical,Typical,Paved,174,0,56,0,108,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,119500,-93.604318,41.991758 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,36,2592,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Fair,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,No,Rec,6,BLQ,232,175,536,GasA,Typical,Y,SBrkr,536,576,0,1112,0,0,1,1,3,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,182,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,85000,-93.604425,41.991876 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1476,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Good,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,370,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,200,26,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,76000,-93.603435,41.992212 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1491,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,SFoyer,Below_Average,Above_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,LwQ,4,GLQ,480,0,630,GasA,Excellent,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,96,24,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,75500,-93.60359,41.992119 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Below_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,88250,-93.601806,41.9917 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Above_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,252,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,64,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,85500,-93.603398,41.991824 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,6953,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1971,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,395,864,GasA,Excellent,Y,SBrkr,874,0,0,874,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,ConLD,Normal,130000,-93.601449,41.991558 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,26142,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1962,1962,Gable,CompShg,HdBoard,HdBoard,BrkFace,189,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,595,1188,GasA,Typical,Y,SBrkr,1188,0,0,1188,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,312,Typical,Typical,Partial_Pavement,261,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,157900,-93.606856,41.988729 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,12887,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1984,1984,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,Rec,6,GLQ,590,36,833,GasA,Typical,Y,SBrkr,833,0,0,833,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,495,Typical,Typical,Paved,431,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,149900,-93.6035354,41.9889358 -Duplex_All_Styles_and_Ages,Residential_Low_Density,70,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,Two_Story,Average,Poor,1985,1986,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Poor,PConc,Typical,Typical,No,Unf,7,Unf,0,1216,1216,GasA,Good,Y,SBrkr,1216,1216,0,2432,0,0,4,2,4,2,Typical,10,Typ,0,No_Fireplace,Attchd,Unf,2,616,Typical,Fair,Paved,200,0,0,0,0,0,No_Pool,No_Fence,Shed,600,2,2010,WD ,Normal,159000,-93.6030339,41.98766 -Two_Story_1946_and_Newer,Residential_Low_Density,63,10475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,166,624,GasA,Good,Y,SBrkr,624,650,0,1274,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,22,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2010,WD ,Normal,136000,-93.600582,41.986647 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,68,10544,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,388,864,GasA,Typical,Y,SBrkr,864,615,0,1479,0,0,2,0,5,1,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,1,275,Typical,Typical,Paved,287,0,280,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,161000,-93.599791,41.9913 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,Gd,GLQ,3,LwQ,284,54,1679,GasA,Excellent,Y,SBrkr,1803,0,0,1803,1,1,2,1,3,1,Good,6,Typ,2,Typical,Attchd,Unf,2,482,Typical,Typical,Paved,129,64,222,0,0,0,No_Pool,Good_Wood,None,0,2,2010,WD ,Normal,285000,-93.600006,41.989848 -Two_Story_1946_and_Newer,Residential_Low_Density,74,12961,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Typical,Mn,GLQ,3,Unf,0,208,1152,GasA,Excellent,Y,SBrkr,1152,645,0,1797,1,0,2,1,3,1,Good,7,Typ,1,Fair,Attchd,Fin,2,616,Typical,Typical,Paved,162,312,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,231000,-93.600147,41.989185 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,13008,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Fair,No,Rec,6,Unf,0,318,882,GasA,Typical,Y,SBrkr,882,0,0,882,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,502,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,124500,-93.6188955,42.0530363 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1974,1974,Hip,CompShg,Plywood,Plywood,BrkFace,440,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,590,1434,GasA,Typical,Y,SBrkr,1434,0,0,1434,1,0,2,0,4,1,Typical,7,Typ,1,Good,Attchd,RFn,2,528,Typical,Typical,Paved,80,21,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,157000,-93.619562,42.05139 -Split_or_Multilevel,Residential_Low_Density,75,13860,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Very_Good,Good,1972,1995,Gable,CompShg,Plywood,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,542,1952,GasA,Good,Y,SBrkr,2000,704,0,2704,1,0,2,1,4,1,Excellent,9,Typ,3,Typical,Attchd,Fin,2,538,Typical,Typical,Paved,269,111,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,345000,-93.615524,42.049322 -Two_Story_1946_and_Newer,Residential_Low_Density,88,10179,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,98,945,GasA,Excellent,Y,SBrkr,945,663,0,1608,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,470,Typical,Typical,Paved,252,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,189500,-93.639388,42.059956 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11792,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,188,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,158,1008,GasA,Excellent,Y,SBrkr,1008,1275,0,2283,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,632,Typical,Typical,Paved,120,46,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,270000,-93.639915,42.063294 -Split_or_Multilevel,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1996,1997,Gable,CompShg,HdBoard,HdBoard,BrkFace,32,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,100,384,GasA,Good,Y,SBrkr,958,670,0,1628,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,390,Typical,Typical,Paved,48,72,0,0,0,0,No_Pool,No_Fence,Shed,490,6,2009,WD ,Normal,189000,-93.6364,42.060896 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,42,14892,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,160,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,426,1746,GasA,Excellent,Y,SBrkr,1746,0,0,1746,1,0,2,0,3,1,Excellent,7,Typ,2,Good,Attchd,Fin,3,758,Typical,Typical,Paved,201,39,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,377500,-93.635838,42.062956 -Split_or_Multilevel,Residential_Low_Density,0,8530,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,22,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,804,670,0,1474,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,120,72,0,0,0,0,No_Pool,No_Fence,Shed,700,5,2009,WD ,Normal,168500,-93.637442,42.060598 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,28,7296,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,243,2208,GasA,Excellent,Y,SBrkr,2522,0,0,2522,1,0,2,0,1,1,Good,8,Typ,1,Good,Attchd,Fin,2,564,Typical,Typical,Paved,182,57,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,375000,-93.634265,42.062903 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,5664,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2000,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,343,1501,GasA,Excellent,Y,SBrkr,1659,0,0,1659,1,0,2,0,2,1,Excellent,5,Typ,1,Excellent,Attchd,Fin,2,499,Typical,Typical,Paved,212,59,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,278000,-93.633739,42.061981 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,7380,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,1998,1998,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1077,1418,GasA,Excellent,Y,SBrkr,1478,0,0,1478,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,495,Typical,Typical,Paved,168,43,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,240000,-93.633547,42.061633 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,57,8013,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1996,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,846,1587,GasA,Excellent,Y,SBrkr,1734,0,0,1734,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,52,50,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,239500,-93.633024,42.061178 -Split_or_Multilevel,Residential_Low_Density,57,8923,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,195,384,GasA,Good,Y,SBrkr,751,631,0,1382,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,396,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,177500,-93.63921,42.059283 -Two_Story_1946_and_Newer,Residential_Low_Density,74,10141,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,832,832,GasA,Good,Y,SBrkr,885,833,0,1718,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,427,Typical,Typical,Paved,0,94,0,0,291,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,185000,-93.639069,42.059129 -Two_Story_1946_and_Newer,Residential_Low_Density,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,476,952,GasA,Good,Y,SBrkr,952,684,0,1636,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,191000,-93.639069,42.059161 -Two_Story_1946_and_Newer,Residential_Low_Density,59,7837,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,799,799,GasA,Good,Y,SBrkr,799,772,0,1571,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,178000,-93.636955,42.058274 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9765,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Excellent,Good,PConc,Good,Good,No,ALQ,1,Unf,0,370,680,GasA,Good,Y,SBrkr,680,790,0,1470,0,0,2,1,3,1,Typical,6,Typ,1,Typical,BuiltIn,Fin,2,420,Typical,Typical,Paved,232,63,0,0,0,0,No_Pool,No_Fence,Shed,480,4,2009,WD ,Normal,185000,-93.637046,42.058059 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8803,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1994,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,107,707,GasA,Good,Y,SBrkr,707,809,0,1516,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,409,Typical,Typical,Paved,0,46,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,181316,-93.637671,42.059645 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,58,7250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,45,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1181,1181,GasA,Excellent,Y,SBrkr,1190,0,0,1190,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,430,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,166000,-93.635824,42.058194 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9636,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,808,808,GasA,Good,Y,SBrkr,808,785,0,1593,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,RFn,2,389,Typical,Typical,Paved,342,40,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2009,WD ,Normal,178000,-93.635753,42.05793 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1994,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,702,702,GasA,Good,Y,SBrkr,702,779,0,1481,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,343,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,174000,-93.636822,42.058173 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9248,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,106,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,598,1158,GasA,Good,Y,SBrkr,1167,0,0,1167,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,400,Typical,Typical,Paved,120,26,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,173000,-93.636047,42.057854 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10762,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,344,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,284,978,GasA,Excellent,Y,SBrkr,1005,978,0,1983,0,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Fin,2,490,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,225000,-93.639325,42.058201 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,99,11851,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1424,1424,GasA,Excellent,Y,SBrkr,1442,0,0,1442,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,500,Typical,Typical,Paved,0,34,0,508,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,180500,-93.634644,42.058747 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,5814,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,184,1220,GasA,Good,Y,SBrkr,1360,0,0,1360,1,0,1,0,1,1,Good,4,Typ,1,Excellent,Attchd,RFn,2,565,Typical,Typical,Paved,63,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,COD,Abnorml,187500,-93.633245,42.059242 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,17423,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,748,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,312,2216,GasA,Excellent,Y,SBrkr,2234,0,0,2234,1,0,2,0,1,1,Excellent,9,Typ,1,Good,Attchd,Fin,3,1166,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,501837,-93.627751,42.060265 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11844,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,464,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,2046,2046,GasA,Excellent,Y,SBrkr,2046,0,0,2046,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,834,Typical,Typical,Paved,322,82,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,372500,-93.628639,42.059817 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,124,16158,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Stone_Brook,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,Stone,16,Good,Typical,PConc,Excellent,Typical,Av,ALQ,1,Unf,0,256,1530,GasA,Excellent,Y,SBrkr,1530,0,0,1530,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,430,Typical,Typical,Paved,168,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,260000,-93.629357,42.058439 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1977,1977,Gable,CompShg,HdBoard,Wd Sdng,BrkFace,157,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,722,1122,GasA,Excellent,Y,SBrkr,946,988,0,1934,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,567,Typical,Typical,Partial_Pavement,0,176,0,0,200,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,185000,-93.623975,42.056796 -Two_Story_1946_and_Newer,Residential_Low_Density,94,13005,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1980,1980,Gable,CompShg,CemntBd,CmentBd,BrkFace,278,Good,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,153,845,GasA,Typical,Y,SBrkr,1153,1200,0,2353,1,0,2,1,4,1,Excellent,10,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,288,195,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2009,WD ,Normal,260000,-93.637343,42.053532 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,48,17043,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1979,1998,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Good,Fair,No,Unf,7,Unf,0,1362,1362,GasA,Typical,Y,SBrkr,1586,0,0,1586,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,435,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,181000,-93.636512,42.055408 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,209,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,564,1386,GasA,Typical,Y,SBrkr,1411,0,0,1411,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,544,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Family,82500,-93.635056,42.055081 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16635,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1977,2000,Gable,CompShg,CemntBd,CmentBd,Stone,126,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,356,1602,GasA,Good,Y,SBrkr,1602,0,0,1602,0,1,2,0,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,529,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,215000,-93.632417,42.055572 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRNn,Norm,OneFam,Two_Story,Good,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,256,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,SBrkr,1154,896,0,2050,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,529,Typical,Typical,Paved,192,192,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Abnorml,154000,-93.637774,42.05184 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10928,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1978,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,101,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,1064,1427,GasA,Typical,Y,SBrkr,1671,0,0,1671,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,252,55,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,200000,-93.63745,42.052507 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12388,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1980,1991,Gable,CompShg,Plywood,Plywood,BrkFace,229,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,441,1043,GasA,Typical,Y,SBrkr,1539,1134,0,2673,0,0,2,1,4,1,Good,9,Typ,1,Typical,BuiltIn,RFn,2,441,Typical,Typical,Paved,178,84,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,249000,-93.638366,42.052102 -Split_or_Multilevel,Residential_Low_Density,0,14115,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Good,Average,1980,1980,Gable,CompShg,Plywood,Plywood,BrkFace,225,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,336,1372,GasA,Typical,Y,SBrkr,1472,0,0,1472,1,0,2,0,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,2,588,Typical,Typical,Paved,233,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,187500,-93.634212,42.052719 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11088,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1978,1998,Gable,CompShg,HdBoard,HdBoard,BrkFace,144,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,308,1140,GasA,Good,Y,SBrkr,1707,0,0,1707,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,479,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,184000,-93.637043,42.05288 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Very_Good,Average,1981,1981,Hip,WdShngl,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,1420,2524,GasA,Typical,Y,SBrkr,2524,0,0,2524,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Fin,2,542,Typical,Typical,Paved,474,120,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,278000,-93.633796,42.052915 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,11880,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Good,Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,206,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,567,1271,GasA,Typical,Y,SBrkr,1601,0,0,1601,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,COD,Abnorml,157000,-93.637289,42.050345 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,189,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1090,1090,GasA,Typical,Y,SBrkr,1370,0,0,1370,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,479,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Family,152000,-93.634694,42.0496539 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10304,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Average,Good,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,44,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,399,780,GasA,Excellent,Y,SBrkr,1088,780,0,1868,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,448,96,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,197500,-93.635919,42.049394 -Two_Story_1946_and_Newer,Floating_Village_Residential,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,304,926,GasA,Excellent,Y,SBrkr,1016,868,0,1884,1,0,2,1,3,1,Excellent,7,Typ,1,Excellent,Attchd,RFn,2,581,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,240900,-93.639516,42.049106 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,8004,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,Two_Story,Very_Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,110,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,288,832,GasA,Excellent,Y,SBrkr,832,1103,0,1935,1,0,2,1,3,1,Typical,8,Typ,0,No_Fireplace,BuiltIn,Fin,2,552,Typical,Typical,Paved,0,150,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,New,Partial,263435,-93.639511,42.051619 -Two_Story_1946_and_Newer,Floating_Village_Residential,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,RRNn,Norm,OneFam,Two_Story,Very_Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1058,1058,GasA,Excellent,Y,SBrkr,1058,816,0,1874,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,588,Typical,Typical,Paved,0,134,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,220000,-93.6392308,42.0503771 -Two_Story_1946_and_Newer,Floating_Village_Residential,0,8470,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,276,471,972,GasA,Excellent,Y,SBrkr,972,839,0,1811,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,565,Typical,Typical,Paved,225,48,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,235000,-93.639366,42.049406 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9373,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Average,Good,1975,1975,Gable,CompShg,HdBoard,HdBoard,BrkFace,161,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,168,120,1621,GasA,Typical,Y,SBrkr,1621,0,0,1621,1,0,2,0,3,1,Typical,7,Typ,2,Fair,Attchd,RFn,2,478,Typical,Typical,Paved,0,0,0,0,490,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,213000,-93.632915,42.053166 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,1974,Hip,CompShg,Plywood,Plywood,BrkFace,196,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,228,1116,GasA,Excellent,Y,SBrkr,1116,0,0,1116,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2009,WD ,Normal,167900,-93.632178,42.052355 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11050,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1975,1975,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,540,1176,GasA,Fair,Y,SBrkr,1193,0,0,1193,0,0,2,0,3,1,Typical,5,Typ,1,Typical,Attchd,Unf,2,506,Typical,Typical,Paved,40,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,158000,-93.630253,42.052448 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1974,1974,Hip,CompShg,HdBoard,HdBoard,BrkFace,174,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1064,1064,GasA,Typical,Y,SBrkr,1350,0,0,1350,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,165000,-93.631432,42.05267 -Split_or_Multilevel,Residential_Low_Density,0,10448,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,333,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,689,689,GasA,Typical,Y,SBrkr,1378,741,0,2119,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,583,Typical,Typical,Paved,0,104,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2009,COD,Abnorml,158000,-93.631791,42.04891 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,Duplex,Two_Story,Average,Average,1974,1974,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,896,896,GasA,Typical,Y,SBrkr,896,896,0,1792,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,32,45,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,136000,-93.625856,42.056398 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1970,1970,Gable,CompShg,MetalSd,MetalSd,BrkFace,76,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,174,1002,GasA,Typical,Y,SBrkr,1002,0,0,1002,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,902,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,148500,-93.626554,42.054626 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7930,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1969,2005,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,LwQ,472,115,1026,GasA,Good,Y,SBrkr,1026,0,0,1026,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,440,Typical,Typical,Paved,171,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,156000,-93.628707,42.055228 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7830,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1180,1180,GasA,Typical,Y,SBrkr,1180,0,0,1180,0,0,1,1,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,477,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,COD,Normal,128000,-93.626603,42.055184 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8510,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1971,1971,Gable,CompShg,Plywood,Plywood,BrkFace,178,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,543,1043,GasA,Excellent,Y,SBrkr,1050,0,0,1050,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,143000,-93.62655,42.055154 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7038,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1970,1970,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,138,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2009,WD ,Abnorml,76500,-93.627037,42.05337 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6960,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1970,1970,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,239,250,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,660,Typical,Typical,Paved,96,0,0,0,0,0,No_Pool,No_Fence,Shed,500,11,2009,WD ,Normal,120500,-93.625657,42.053306 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1970,1970,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,673,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,463,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,124500,-93.625542,42.053307 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,180,160,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,216,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Abnorml,97000,-93.6234986,42.0564419 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1971,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,624,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,200,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,130000,-93.6223289,42.056461 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,229,483,GasA,Typical,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,111000,-93.627396,42.052758 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2368,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,None,312,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,765,GasA,Typical,Y,SBrkr,765,600,0,1365,0,0,1,1,3,1,Typical,7,Min1,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,125000,-93.628119,42.052338 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,142,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,COD,Abnorml,112000,-93.627565,42.051683 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,425,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,294,79,483,GasA,Typical,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,97000,-93.629456,42.051661 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Very_Good,1972,2007,Gable,CompShg,HdBoard,HdBoard,BrkFace,510,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,321,483,GasA,Good,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,118000,-93.629851,42.051823 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Good,1972,1972,Gable,CompShg,CemntBd,CmentBd,BrkFace,268,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,399,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,185,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,119500,-93.629881,42.051822 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,4928,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Unf,0,0,958,GasA,Typical,Y,SBrkr,958,0,0,958,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,143750,-93.627244,42.050615 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,498,804,GasA,Typical,Y,SBrkr,804,744,0,1548,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Detchd,RFn,2,440,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,146000,-93.624729,42.050705 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,289,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,87,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,148500,-93.624748,42.050705 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,622,0,1057,GasA,Typical,Y,SBrkr,1055,0,0,1055,0,1,2,0,2,1,Typical,4,Typ,1,Fair,Attchd,RFn,2,440,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,123000,-93.625945,42.050682 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1974,1974,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,495,103,855,GasA,Typical,Y,SBrkr,855,467,0,1322,0,1,2,1,3,1,Typical,6,Typ,1,Fair,Attchd,Unf,2,440,Typical,Typical,Paved,260,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,147000,-93.625918,42.050533 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2349,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,466,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,137900,-93.625846,42.050213 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2364,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,484,804,GasA,Typical,Y,SBrkr,804,744,0,1548,0,1,2,1,3,1,Typical,7,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,147000,-93.625688,42.05009 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2289,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,544,855,GasA,Typical,Y,SBrkr,855,586,0,1441,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,28,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,148500,-93.62569,42.050134 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2364,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,576,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,138000,-93.625696,42.050222 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2104,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,300,836,GasA,Typical,Y,SBrkr,836,0,0,836,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,345,Typical,Typical,Paved,150,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,128500,-93.625707,42.050397 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,60,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,460,892,GasA,Typical,Y,SBrkr,892,0,0,892,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,100000,-93.6256036,42.0488934 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1966,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,165,Good,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,220,864,GasA,Excellent,Y,SBrkr,1120,0,0,1120,0,1,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,656,Typical,Typical,Paved,0,162,0,0,0,0,No_Pool,No_Fence,Shed,1200,7,2009,WD ,Normal,148800,-93.626521,42.048486 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,96,12456,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,Stone,230,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,528,1700,GasA,Excellent,Y,SBrkr,1718,0,0,1718,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,786,Typical,Typical,Paved,216,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,337500,-93.658877,42.0623099 -Two_Story_1946_and_Newer,Residential_Low_Density,110,14257,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,Norm,OneFam,Two_Story,Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,726,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,416,1776,GasA,Excellent,Y,SBrkr,1794,978,0,2772,1,0,3,1,4,1,Excellent,10,Typ,3,Good,BuiltIn,Fin,3,754,Typical,Typical,Paved,135,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,462000,-93.6562749,42.063304 -Two_Story_1946_and_Newer,Residential_Low_Density,104,13518,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,860,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1926,1926,GasA,Excellent,Y,SBrkr,1966,1174,0,3140,0,0,3,1,4,1,Excellent,11,Typ,2,Good,BuiltIn,Fin,3,820,Typical,Typical,Paved,144,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,485000,-93.652716,42.063025 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,15431,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,200,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,ALQ,539,788,3094,GasA,Excellent,Y,SBrkr,2402,0,0,2402,1,0,2,0,2,1,Excellent,10,Typ,2,Good,Attchd,Fin,3,672,Typical,Typical,Paved,0,72,0,0,170,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,555000,-93.657828,42.061902 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,108,13173,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,300,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,80,1652,GasA,Excellent,Y,SBrkr,1652,0,0,1652,1,0,2,0,2,1,Excellent,6,Typ,2,Excellent,Attchd,Fin,2,840,Typical,Typical,Paved,404,102,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,325000,-93.657483,42.06128 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14230,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,640,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1566,1566,GasA,Excellent,Y,SBrkr,1600,0,0,1600,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,890,Typical,Typical,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,256300,-93.654241,42.062991 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,12704,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,BrkFace,306,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,2042,2042,GasA,Excellent,Y,SBrkr,2042,0,0,2042,0,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,1390,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,New,Partial,253293,-93.654144,42.062992 -Two_Story_1946_and_Newer,Residential_Low_Density,95,12350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,379,1365,GasA,Excellent,Y,SBrkr,1365,1325,0,2690,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,864,Typical,Typical,Paved,0,197,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,398800,-93.653955,42.062994 -Two_Story_1946_and_Newer,Residential_Low_Density,96,11308,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,154,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,168,1104,GasA,Excellent,Y,SBrkr,1130,1054,0,2184,1,0,2,1,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,836,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,335000,-93.652863,42.062942 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,12350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,450,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,788,2020,GasA,Excellent,Y,SBrkr,2020,0,0,2020,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,RFn,3,896,Typical,Typical,Paved,192,98,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,404000,-93.654439,42.062258 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,12220,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2009,2009,Hip,CompShg,CemntBd,CmentBd,BrkFace,305,Excellent,Typical,CBlock,Excellent,Typical,No,GLQ,3,Unf,0,570,2006,GasA,Excellent,Y,SBrkr,2020,0,0,2020,1,0,2,1,3,1,Excellent,9,Typ,1,Good,Attchd,Fin,3,900,Typical,Typical,Paved,156,54,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,402861,-93.654567,42.062257 -Two_Story_1946_and_Newer,Residential_Low_Density,97,13478,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2008,2008,Gable,CompShg,CemntBd,CmentBd,Stone,420,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,384,1722,GasA,Excellent,Y,SBrkr,1728,568,0,2296,1,0,2,1,3,1,Excellent,10,Typ,1,Good,BuiltIn,RFn,3,842,Typical,Typical,Paved,382,274,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,ConLI,Normal,451950,-93.658224,42.062776 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,13693,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosA,PosA,OneFam,One_Story,Very_Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,472,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,342,2630,GasA,Excellent,Y,SBrkr,2674,0,0,2674,2,0,2,1,2,1,Excellent,8,Typ,2,Good,Attchd,Fin,3,762,Typical,Typical,Paved,360,50,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,610000,-93.657835,42.062789 -Two_Story_1946_and_Newer,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,424,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1734,1734,GasA,Excellent,Y,SBrkr,1734,1088,0,2822,0,0,3,1,4,1,Excellent,12,Typ,1,Good,BuiltIn,RFn,3,1020,Typical,Typical,Paved,52,170,0,0,192,0,No_Pool,No_Fence,None,0,1,2009,New,Partial,582933,-93.657049,42.0623985 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,11578,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,302,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1736,1736,GasA,Excellent,Y,SBrkr,1736,0,0,1736,0,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,RFn,3,834,Typical,Typical,Paved,319,90,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,360000,-93.654437,42.062108 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,16870,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,238,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,251,1782,GasA,Excellent,Y,SBrkr,1782,0,0,1782,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,Fin,3,932,Typical,Typical,Paved,99,82,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,296000,-93.658854,42.060934 -Two_Story_1946_and_Newer,Residential_Low_Density,59,23303,Pave,No_Alley_Access,Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,20,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,278,1508,GasA,Excellent,Y,SBrkr,1508,1012,0,2520,1,0,2,1,5,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,640,Typical,Typical,Paved,192,273,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Family,409900,-93.656737,42.060353 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,11146,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,250,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1709,1709,GasA,Excellent,Y,SBrkr,1717,0,0,1717,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,908,Typical,Typical,Paved,169,39,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,255500,-93.653152,42.061382 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10367,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,284,Excellent,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,724,1739,GasA,Excellent,Y,SBrkr,1743,0,0,1743,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,927,Typical,Typical,Paved,168,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,ConLI,Normal,335000,-93.652884,42.061437 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9591,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,262,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,625,1713,GasA,Excellent,Y,SBrkr,1713,0,0,1713,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,856,Typical,Typical,Paved,0,26,0,0,170,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,274900,-93.654724,42.060576 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,10872,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,122,Good,Typical,PConc,Excellent,Good,Av,GLQ,3,Unf,0,467,1504,GasA,Excellent,Y,SBrkr,1531,0,0,1531,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,700,Typical,Typical,Paved,184,52,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,300000,-93.65476,42.060575 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,13514,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,None,285,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,632,1774,GasA,Excellent,Y,SBrkr,1808,0,0,1808,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,850,Typical,Typical,Paved,200,26,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,324000,-93.652084,42.061678 -Two_Story_1946_and_Newer,Residential_Low_Density,74,8834,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,Stone,216,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,292,1462,GasA,Excellent,Y,SBrkr,1462,762,0,2224,1,0,2,1,4,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,738,Typical,Typical,Paved,184,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,350000,-93.654652,42.060426 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,11362,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,Stone,42,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,797,1836,GasA,Excellent,Y,SBrkr,1836,0,0,1836,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,862,Typical,Typical,Paved,125,185,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,280000,-93.652724,42.060178 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10655,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,296,Good,Typical,PConc,Good,Typical,No,GLQ,3,No_Basement,479,1603,3206,GasA,Excellent,Y,SBrkr,1629,0,0,1629,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,880,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,284000,-93.6541267,42.0599123 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,12878,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,418,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,498,1760,GasA,Excellent,Y,SBrkr,1760,0,0,1760,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,583,Typical,Typical,Paved,165,190,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,269500,-93.6547754,42.0598989 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,9541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,268,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1502,1502,GasA,Excellent,Y,SBrkr,1502,0,0,1502,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,644,Typical,Typical,Paved,0,114,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,233170,-93.643771,42.061404 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,103,13472,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,922,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,2336,2392,GasA,Excellent,Y,SBrkr,2392,0,0,2392,0,0,2,0,3,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,968,Typical,Typical,Paved,248,105,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,386250,-93.65604,42.059241 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,15274,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,724,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,480,2452,GasA,Excellent,Y,SBrkr,2452,0,0,2452,2,0,2,0,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,886,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,445000,-93.65486,42.058782 -Two_Story_1946_and_Newer,Residential_Low_Density,96,13262,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,Stone,186,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1082,1082,GasA,Excellent,Y,SBrkr,1105,1295,0,2400,0,0,3,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,730,Typical,Typical,Paved,114,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,290000,-93.6545189,42.058886 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9658,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,383,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1598,1598,GasA,Good,Y,SBrkr,1606,0,0,1606,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,871,Typical,Typical,Paved,230,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,255900,-93.65048,42.0592 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,47,6904,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,240,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,522,1358,GasA,Excellent,Y,SBrkr,1358,0,0,1358,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,213000,-93.649779,42.059177 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5381,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,135,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,406,1306,GasA,Excellent,Y,SBrkr,1306,0,0,1306,1,0,2,0,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,624,Typical,Typical,Paved,170,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,196000,-93.649743,42.059179 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5122,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,135,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,425,1306,GasA,Excellent,Y,SBrkr,1306,0,0,1306,1,0,2,0,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,624,Typical,Typical,Paved,170,63,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,184500,-93.649723,42.059183 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,80,10307,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,474,1350,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,212500,-93.650258,42.058339 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5001,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,166,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,410,1314,GasA,Excellent,Y,SBrkr,1314,0,0,1314,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,626,Typical,Typical,Paved,172,62,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,230000,-93.650244,42.058336 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,14836,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,730,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,346,2492,GasA,Excellent,Y,SBrkr,2492,0,0,2492,1,0,2,1,2,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,949,Typical,Typical,Paved,226,235,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Abnorml,552000,-93.655483,42.057177 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,117,15262,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,470,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,643,2200,GasA,Excellent,Y,SBrkr,2200,0,0,2200,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,685,Typical,Typical,Paved,208,55,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,382500,-93.6552449,42.05851 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,44,7390,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2009,Hip,CompShg,MetalSd,MetalSd,BrkFace,308,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1084,1884,GasA,Excellent,Y,SBrkr,1884,0,0,1884,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,649,Typical,Typical,Paved,231,90,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,New,Partial,320000,-93.654204,42.057164 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6472,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,BrkFace,500,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1451,1451,GasA,Excellent,Y,SBrkr,1456,0,0,1456,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,539,Typical,Typical,Paved,192,42,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,248500,-93.654215,42.057089 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,16770,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,270,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,516,1712,GasA,Excellent,Y,SBrkr,1712,0,0,1712,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,701,Typical,Typical,Paved,218,183,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,286500,-93.654405,42.057019 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,461,1324,GasA,Excellent,Y,SBrkr,1324,0,0,1324,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,192,38,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,254000,-93.6514849,42.056983 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,3480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,Stone,163,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1405,1405,GasA,Excellent,Y,SBrkr,1405,0,0,1405,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,148,36,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,173000,-93.651014,42.057277 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2268,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,197,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,173000,-93.650238,42.057467 -Two_Story_1946_and_Newer,Residential_Low_Density,63,10928,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,184000,-93.645752,42.06232 -Two_Story_1946_and_Newer,Residential_Low_Density,57,8918,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,745,745,GasA,Excellent,Y,SBrkr,745,745,0,1490,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,392,Typical,Typical,Paved,36,20,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,167800,-93.64466,42.063183 -Two_Story_1946_and_Newer,Residential_Low_Density,149,12589,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,742,742,GasA,Excellent,Y,SBrkr,742,742,0,1484,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,36,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,174000,-93.644457,42.062058 -Two_Story_1946_and_Newer,Residential_Low_Density,122,11911,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,684,684,GasA,Excellent,Y,SBrkr,684,876,0,1560,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,174000,-93.645627,42.062404 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3684,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,130,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1373,1373,GasA,Excellent,Y,SBrkr,1555,0,0,1555,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,660,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,174000,-93.641561,42.062562 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,51,3635,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,130,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,398,1386,GasA,Excellent,Y,SBrkr,1569,0,0,1569,0,1,2,0,1,1,Good,7,Typ,1,Typical,Attchd,RFn,3,660,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,175900,-93.641515,42.062557 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1357,1373,GasA,Excellent,Y,SBrkr,1555,0,0,1555,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,430,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,192500,-93.642094,42.062305 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1330,1346,GasA,Excellent,Y,SBrkr,1504,0,0,1504,0,0,2,0,1,1,Good,7,Typ,1,Good,Attchd,Fin,2,457,Typical,Typical,Paved,156,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,181000,-93.642597,42.062266 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1204,1220,GasA,Excellent,Y,SBrkr,1220,0,0,1220,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,397,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,180000,-93.642911,42.062081 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,11,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1129,1145,GasA,Excellent,Y,SBrkr,1145,0,0,1145,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,397,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,160200,-93.642914,42.062086 -Two_Story_1946_and_Newer,Residential_Low_Density,71,7795,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,291,716,GasA,Excellent,Y,SBrkr,716,716,0,1432,1,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,432,Typical,Typical,Paved,100,51,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,188500,-93.642749,42.061562 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8068,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1010,1010,GasA,Excellent,Y,SBrkr,1010,1257,0,2267,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,RFn,2,390,Typical,Typical,Paved,120,46,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,ConLI,Normal,200000,-93.643312,42.059377 -Split_or_Multilevel,Residential_Low_Density,59,9434,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,170000,-93.644106,42.061739 -Two_Story_1946_and_Newer,Residential_Low_Density,62,7984,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,868,868,GasA,Excellent,Y,SBrkr,868,762,0,1630,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,436,Typical,Typical,Paved,120,48,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,189500,-93.641772,42.061149 -Split_or_Multilevel,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,55,408,GasA,Excellent,Y,SBrkr,779,640,0,1419,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,527,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,184100,-93.641488,42.061114 -Two_Story_1946_and_Newer,Residential_Low_Density,61,10125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,846,846,GasA,Excellent,Y,SBrkr,846,748,0,1594,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,434,Typical,Typical,Paved,300,48,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,195500,-93.642626,42.061656 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8965,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,130,782,GasA,Excellent,Y,SBrkr,806,683,0,1489,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,192000,-93.640051,42.061365 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8174,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,204,698,GasA,Excellent,Y,SBrkr,698,644,0,1342,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,393,Typical,Typical,Paved,100,56,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,178000,-93.640065,42.063283 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5063,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,166,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,410,1314,GasA,Excellent,Y,SBrkr,1314,0,0,1314,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,626,Typical,Typical,Paved,172,62,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,ConLw,Normal,207500,-93.649649,42.058368 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8795,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,652,952,GasA,Excellent,Y,SBrkr,980,1276,0,2256,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,2,554,Typical,Typical,Paved,224,54,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,236000,-93.642813,42.05859 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12891,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,219,870,GasA,Excellent,Y,SBrkr,878,1126,0,2004,1,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,3,644,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,257500,-93.642932,42.059481 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12224,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,297,992,GasA,Excellent,Y,SBrkr,1022,1032,0,2054,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,RFn,2,390,Typical,Typical,Paved,24,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,244000,-93.641919,42.059068 -Split_or_Multilevel,Residential_Low_Density,61,9734,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,SLvl,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Rec,113,30,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,167000,-93.644251,42.059605 -Two_Story_1946_and_Newer,Residential_Low_Density,60,8123,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,982,982,GasA,Excellent,Y,SBrkr,1007,793,0,1800,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,463,Typical,Typical,Paved,100,63,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,179000,-93.641377,42.057839 -Two_Story_1946_and_Newer,Residential_Low_Density,42,8433,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,111,794,GasA,Excellent,Y,SBrkr,819,695,0,1514,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,394,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,190000,-93.641313,42.057834 -Split_or_Multilevel,Residential_Low_Density,62,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,RFn,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,156000,-93.641303,42.057833 -Two_Story_1946_and_Newer,Residential_Low_Density,0,15896,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,RRNn,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,264,1177,GasA,Excellent,Y,SBrkr,1223,1089,0,2312,1,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,3,658,Typical,Typical,Paved,298,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,245000,-93.641367,42.057098 -Two_Story_1946_and_Newer,Residential_Low_Density,0,24682,Pave,No_Alley_Access,Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,841,841,GasA,Excellent,Y,SBrkr,892,783,0,1675,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,502,Typical,Typical,Paved,0,103,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,181000,-93.641397,42.057093 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8755,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,RRNn,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,298,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,220,992,GasA,Excellent,Y,SBrkr,1022,1038,0,2060,1,0,2,1,3,1,Good,8,Typ,1,Typical,BuiltIn,RFn,2,390,Typical,Typical,Paved,0,0,0,168,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,214000,-93.641312,42.056955 -Split_or_Multilevel,Residential_Low_Density,64,7848,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Above_Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,410,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,168000,-93.640879,42.058668 -Two_Story_1946_and_Newer,Residential_Low_Density,82,9430,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,673,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,89,1252,GasA,Excellent,Y,SBrkr,1268,1097,0,2365,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,856,Typical,Typical,Paved,0,128,0,0,180,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,337000,-93.65429,42.053749 -Two_Story_1946_and_Newer,Residential_Low_Density,174,15138,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,506,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,773,1462,GasA,Excellent,Y,SBrkr,1490,1304,0,2794,1,0,2,1,4,1,Excellent,9,Typ,1,Typical,Attchd,Fin,3,810,Typical,Typical,Paved,0,146,202,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,403000,-93.657163,42.053911 -Two_Story_1946_and_Newer,Residential_Low_Density,106,12720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,282,1455,GasA,Excellent,Y,SBrkr,1466,1221,0,2687,1,0,2,1,4,1,Good,10,Typ,2,Typical,BuiltIn,RFn,3,810,Typical,Typical,Paved,252,30,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,327000,-93.650957,42.055626 -Two_Story_1946_and_Newer,Residential_Low_Density,0,16545,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,731,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,503,1284,GasA,Excellent,Y,SBrkr,1310,1140,0,2450,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,1069,Typical,Typical,Paved,0,126,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,340000,-93.652608,42.053441 -Two_Story_1946_and_Newer,Residential_Low_Density,98,12203,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Hip,CompShg,VinylSd,VinylSd,BrkFace,975,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,371,1225,GasA,Excellent,Y,SBrkr,1276,1336,0,2612,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,3,676,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,336000,-93.65238,42.053041 -Two_Story_1946_and_Newer,Residential_Low_Density,79,10208,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,921,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1264,1264,GasA,Excellent,Y,SBrkr,1277,1067,0,2344,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,889,Typical,Typical,Paved,220,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,265000,-93.655555,42.052668 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,634,Good,Typical,PConc,Good,Typical,Av,BLQ,2,GLQ,1526,262,2024,GasA,Excellent,Y,SBrkr,2063,0,0,2063,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,Fin,3,815,Typical,Typical,Paved,182,56,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,315000,-93.655988,42.050411 -Two_Story_1946_and_Newer,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,256,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,360,1347,GasA,Excellent,Y,SBrkr,1372,1274,0,2646,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,656,Typical,Typical,Paved,340,60,144,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,260000,-93.654048,42.049701 -Two_Story_1946_and_Newer,Residential_Low_Density,79,9085,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,286,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,254,1070,GasA,Excellent,Y,SBrkr,1094,967,0,2061,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,647,Typical,Typical,Paved,296,102,209,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,260000,-93.651503,42.05161 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,372,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,549,1173,GasA,Excellent,Y,SBrkr,1215,1017,0,2232,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,623,Typical,Typical,Paved,173,165,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,263550,-93.652117,42.050215 -Two_Story_1946_and_Newer,Residential_Low_Density,52,46589,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1994,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,528,Good,Typical,PConc,Good,Good,No,GLQ,3,Rec,180,88,1629,GasA,Excellent,Y,SBrkr,1686,762,0,2448,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,711,Typical,Typical,Paved,517,76,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,402000,-93.650529,42.049921 -Two_Story_1946_and_Newer,Residential_Low_Density,0,29959,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,378,973,GasA,Excellent,Y,SBrkr,979,871,0,1850,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,467,Typical,Typical,Paved,168,98,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,248000,-93.650822,42.050971 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,9612,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Gable,CompShg,VinylSd,VinylSd,Stone,72,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1468,1468,GasA,Excellent,Y,SBrkr,1468,0,0,1468,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,3,898,Typical,Typical,Paved,210,150,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,New,Partial,244600,-93.644121,42.054282 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,11194,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,PosN,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,240,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1696,1696,GasA,Excellent,Y,SBrkr,1696,0,0,1696,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,972,Typical,Typical,Paved,120,56,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,275000,-93.64247,42.055036 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,PosN,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,294,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1614,1614,GasA,Excellent,Y,SBrkr,1658,0,0,1658,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,726,Typical,Typical,Paved,144,44,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,257500,-93.642553,42.054302 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9262,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Gable,CompShg,CemntBd,CmentBd,Stone,194,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1573,1573,GasA,Excellent,Y,SBrkr,1578,0,0,1578,0,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,840,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,New,Partial,287090,-93.643944,42.054152 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,260,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,408,1702,GasA,Excellent,Y,SBrkr,1702,0,0,1702,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,844,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,275500,-93.642579,42.054151 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9139,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,206,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1043,1422,GasA,Excellent,Y,SBrkr,1432,0,0,1432,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,492,Typical,Typical,Paved,297,50,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,245000,-93.64236,42.053212 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,772,1113,GasA,Excellent,Y,SBrkr,1113,858,0,1971,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,689,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,253000,-93.641251,42.053258 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11128,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,PosN,PosN,OneFam,One_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,Stone,198,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,300,2458,GasA,Excellent,Y,SBrkr,2490,0,0,2490,1,0,2,0,2,1,Excellent,9,Typ,2,Good,Attchd,Fin,3,795,Typical,Typical,Paved,70,226,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2009,WD ,Normal,468000,-93.641296,42.053108 -Two_Story_1946_and_Newer,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,78,900,GasA,Excellent,Y,SBrkr,932,920,0,1852,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,644,Typical,Typical,Paved,168,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,252678,-93.650321,42.051804 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,786,804,0,1590,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,676,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,210000,-93.6504,42.051797 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,7862,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1191,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,0,0,2,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,676,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,208300,-93.65044,42.051793 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,90,7993,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2008,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1436,1436,GasA,Excellent,Y,SBrkr,1436,0,0,1436,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,529,Typical,Typical,Paved,0,121,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,New,Partial,225000,-93.650417,42.051645 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2009,2009,Gable,CompShg,CemntBd,CmentBd,Stone,72,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,364,1300,GasA,Excellent,Y,SBrkr,1314,0,0,1314,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,552,Typical,Typical,Paved,135,112,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,229456,-93.650377,42.051648 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1402,1402,GasA,Excellent,Y,SBrkr,1402,0,0,1402,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,625,Typical,Typical,Paved,205,126,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,229800,-93.650338,42.051652 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,112,12606,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,120,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1530,1530,GasA,Excellent,Y,SBrkr,1530,0,0,1530,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,984,Typical,Typical,Paved,212,136,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,250000,-93.648275,42.050406 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,85,9187,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2009,Gable,CompShg,CemntBd,CmentBd,Stone,162,Excellent,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,645,1766,GasA,Excellent,Y,SBrkr,1766,0,0,1766,1,0,2,1,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,478,Typical,Typical,Paved,195,130,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,New,Partial,370878,-93.648809,42.051717 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,238,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1348,1372,GasA,Excellent,Y,SBrkr,1448,0,0,1448,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,692,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,238500,-93.648778,42.051795 -Two_Story_1946_and_Newer,Floating_Village_Residential,85,11003,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,160,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,252,1017,GasA,Excellent,Y,SBrkr,1026,981,0,2007,1,0,2,1,3,1,Excellent,10,Typ,1,Excellent,Attchd,Fin,3,812,Typical,Typical,Paved,168,52,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,310000,-93.643001,42.052337 -Two_Story_1946_and_Newer,Floating_Village_Residential,84,10603,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,121,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,218,900,GasA,Excellent,Y,SBrkr,909,886,0,1795,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,782,Typical,Typical,Paved,168,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,270000,-93.64287,42.052346 -Two_Story_1946_and_Newer,Floating_Village_Residential,85,10574,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1082,1082,GasA,Excellent,Y,SBrkr,1082,871,0,1953,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,RFn,3,1043,Typical,Typical,Paved,160,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,252000,-93.643842,42.052127 -Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2007,Gable,CompShg,CemntBd,CmentBd,Stone,100,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,280,1092,GasA,Excellent,Y,SBrkr,1112,438,0,1550,1,0,2,0,2,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,438,Typical,Typical,Paved,0,168,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,241000,-93.642474,42.05142 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,BrkFace,288,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1836,1836,GasA,Excellent,Y,SBrkr,1836,0,0,1836,0,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,517,Typical,Typical,Paved,0,175,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,New,Partial,264500,-93.643752,42.051378 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,222,1652,GasA,Excellent,Y,SBrkr,1662,0,0,1662,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,711,Typical,Typical,Paved,168,120,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,291000,-93.640847,42.052122 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,912,912,GasA,Excellent,Y,SBrkr,912,1182,0,2094,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,615,Typical,Typical,Paved,182,182,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,263000,-93.641746,42.051414 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,62,7500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,811,1221,GasA,Excellent,Y,SBrkr,1221,0,0,1221,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,400,Typical,Typical,Paved,0,113,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,185000,-93.643131,42.051247 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,68,8736,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,360,422,1553,GasA,Excellent,Y,SBrkr,1553,0,0,1553,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,192,88,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,234500,-93.641501,42.051251 -Two_Story_1946_and_Newer,Floating_Village_Residential,65,8127,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,402,812,GasA,Excellent,Y,SBrkr,812,841,0,1653,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,628,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,209000,-93.639832,42.050895 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9605,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1218,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,RFn,2,576,Typical,Typical,Paved,0,178,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,159000,-93.69153,42.037557 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,1087,1141,GasA,Excellent,Y,SBrkr,1141,0,0,1141,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,RFn,2,484,Typical,Typical,Paved,182,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,152000,-93.692017,42.037611 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1158,1158,GasA,Excellent,Y,SBrkr,1158,0,0,1158,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,143500,-93.692472,42.037661 -Two_Story_1946_and_Newer,Residential_Low_Density,96,10628,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,835,835,GasA,Excellent,Y,SBrkr,871,941,0,1812,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,478,Typical,Typical,Paved,146,91,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,193000,-93.69143,42.036001 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,10141,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2004,Gable,Tar&Grv,VinylSd,VinylSd,BrkFace,264,Good,Typical,PConc,Good,Typical,Gd,BLQ,2,Rec,774,222,1512,GasA,Excellent,Y,SBrkr,1512,0,0,1512,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,845,Typical,Typical,Paved,210,36,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,203000,-93.691506,42.036011 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,10083,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,343,1176,GasA,Excellent,Y,SBrkr,1200,0,0,1200,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,555,Typical,Typical,Paved,0,41,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,184900,-93.691707,42.035282 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1114,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,248,102,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,159000,-93.691868,42.037923 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1114,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,142000,-93.691068,42.038127 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,12450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,278,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,153000,-93.690159,42.037756 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7328,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2008,2009,Gable,CompShg,VinylSd,VinylSd,BrkFace,140,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1450,1450,GasA,Excellent,Y,SBrkr,1450,0,0,1450,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,788,Typical,Typical,Paved,0,93,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,New,Partial,224243,-93.690402,42.037434 -Two_Story_1946_and_Newer,Residential_Low_Density,43,11492,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,132,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,276,913,GasA,Excellent,Y,SBrkr,913,1209,0,2122,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,559,Typical,Typical,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,220000,-93.689136,42.036852 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,10994,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,366,Good,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,868,1844,GasA,Excellent,Y,SBrkr,1844,0,0,1844,1,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,620,Typical,Typical,Paved,165,44,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,COD,Abnorml,257000,-93.69139,42.03607 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8529,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1434,1454,GasA,Excellent,Y,SBrkr,1434,0,0,1434,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,527,Typical,Typical,Paved,290,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,189000,-93.6888,42.036905 -Two_Story_1946_and_Newer,Residential_Low_Density,70,7703,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Rec,364,400,816,GasA,Excellent,Y,SBrkr,833,897,0,1730,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,0,91,0,0,168,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,171500,-93.687843,42.036576 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,75,10762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,626,626,GasA,Typical,Y,SBrkr,626,591,0,1217,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,288,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,120000,-93.686263,42.034533 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1990,1991,Gable,CompShg,Plywood,ImStucc,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1332,1332,GasA,Good,Y,SBrkr,1332,0,0,1332,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,542,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,145000,-93.686267,42.035518 -Two_Story_1946_and_Newer,Residential_Low_Density,70,9109,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,Two_Story,Good,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,596,122,754,GasA,Excellent,Y,SBrkr,754,786,0,1540,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,495,Typical,Typical,Paved,140,32,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,184000,-93.687804,42.037607 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10274,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1986,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,141,Typical,Good,CBlock,Good,Typical,No,Rec,6,Unf,0,345,676,GasA,Typical,Y,SBrkr,698,702,0,1400,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,465,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,162000,-93.685037,42.035968 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,Two_Story,Above_Average,Good,1981,1981,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,964,918,0,1882,0,0,2,0,4,2,Typical,8,Typ,2,Typical,Attchd,Unf,2,612,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,160000,-93.683957,42.035756 -One_Story_1946_and_Newer_All_Styles,Residential_High_Density,60,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1950,1950,Gable,CompShg,Wd Sdng,AsbShng,None,0,Fair,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,721,721,GasA,Good,Y,SBrkr,841,0,0,841,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,CarPort,Unf,1,294,Typical,Typical,Dirt_Gravel,250,0,24,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,82000,-93.6790752,42.0365385 -One_and_Half_Story_Unfinished_All_Ages,Residential_High_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Fair,1928,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Fair,No,Unf,7,Unf,0,784,784,GasA,Typical,N,FuseA,784,0,0,784,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,360,Fair,Fair,Dirt_Gravel,0,0,91,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,76000,-93.680198,42.035287 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,9750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,884,28,980,GasA,Good,Y,SBrkr,980,0,0,980,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,WD ,Normal,110000,-93.676364,42.035806 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,7064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,153,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,420,980,GasA,Typical,Y,SBrkr,980,0,0,980,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,135000,-93.677008,42.034747 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8499,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,204,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,732,Typical,Typical,Paved,0,312,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,141000,-93.674051,42.034615 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9079,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,0,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,440,Typical,Typical,Paved,158,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,122000,-93.675716,42.035289 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9316,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,480,1024,GasA,Good,Y,SBrkr,1020,0,0,1020,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,171,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,Oth,Family,124100,-93.669554,42.03466 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7791,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Very_Good,1963,1995,Gable,CompShg,Plywood,Plywood,None,0,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,288,912,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,10,2009,WD ,Normal,129000,-93.669538,42.035331 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1983,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,270,864,GasA,Excellent,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,264,Typical,Typical,Paved,165,0,0,0,0,0,No_Pool,Good_Wood,Shed,400,3,2009,WD ,Normal,131400,-93.673518,42.034595 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8281,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,311,0,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,1,Poor,Detchd,Unf,1,360,Typical,Typical,Paved,0,0,236,0,0,0,No_Pool,Good_Wood,None,0,12,2009,WD ,Normal,62383,-93.669707,42.034602 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1962,1962,Gable,CompShg,VinylSd,VinylSd,BrkFace,60,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,590,182,912,GasA,Good,Y,SBrkr,912,0,0,912,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,252,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,123000,-93.669845,42.034581 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15676,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Very_Good,1980,1980,Gable,CompShg,VinylSd,VinylSd,BrkFace,115,Good,Good,CBlock,Good,Good,Gd,ALQ,1,Rec,92,189,2014,GasA,Good,Y,SBrkr,2014,0,0,2014,1,0,2,0,2,1,Good,6,Maj1,2,Good,Attchd,RFn,3,864,Typical,Typical,Paved,462,0,0,255,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,275000,-93.660327,42.037236 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11949,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1991,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Good,PConc,Good,Typical,No,GLQ,3,ALQ,216,158,975,GasA,Excellent,Y,SBrkr,975,780,0,1755,0,1,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,524,Typical,Typical,Paved,502,60,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2009,WD ,Normal,235000,-93.650333,42.047669 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,32,2880,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1376,1376,GasA,Excellent,Y,SBrkr,1376,1629,0,3005,0,0,2,1,3,1,Good,9,Mod,1,Typical,BuiltIn,Fin,3,704,Typical,Typical,Paved,0,177,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,280750,-93.647765,42.047627 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,40,3951,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,76,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,612,612,GasA,Excellent,Y,SBrkr,612,612,0,1224,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,0,234,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,164500,-93.646316,42.048416 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3000,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,76,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,318,612,GasA,Excellent,Y,SBrkr,612,612,0,1224,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,0,234,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,173733,-93.646338,42.048488 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,3830,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,280,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1726,1726,GasA,Excellent,Y,SBrkr,1726,0,0,1726,0,0,2,1,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,561,Typical,Typical,Paved,0,254,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,New,Partial,222000,-93.647039,42.047338 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4217,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,252,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,183,1145,GasA,Excellent,Y,SBrkr,1256,0,0,1256,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,Fin,2,641,Typical,Typical,Paved,0,169,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,195000,-93.646916,42.047253 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3230,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,894,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,348,729,GasA,Good,Y,SBrkr,742,729,0,1471,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,172500,-93.644205,42.047101 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,2998,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,249,756,GasA,Excellent,Y,SBrkr,756,756,0,1512,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,180000,-93.64566,42.046144 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,3768,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,1999,1999,Hip,CompShg,MetalSd,MetalSd,BrkFace,218,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,142,691,GasA,Excellent,Y,SBrkr,713,739,0,1452,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,156000,-93.645482,42.046418 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,321,970,GasA,Excellent,Y,SBrkr,983,756,0,1739,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,480,Typical,Typical,Paved,115,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,172500,-93.641799,42.047171 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14694,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Excellent,1977,2008,Gable,CompShg,MetalSd,MetalSd,BrkFace,450,Excellent,Excellent,CBlock,Good,Good,Gd,GLQ,3,ALQ,136,306,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,1,0,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,642,Typical,Typical,Paved,501,120,0,225,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,318750,-93.645923,42.043782 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,3782,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1981,1981,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,266,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,2,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,133,78,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,211500,-93.649358,42.042652 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,15417,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Good,Average,1981,1981,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Typical,Mn,LwQ,4,Unf,0,1619,1740,GasA,Typical,Y,SBrkr,1740,0,0,1740,0,0,1,1,2,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,540,Typical,Typical,Paved,228,20,218,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,241600,-93.648057,42.043553 -Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Low,AllPub,FR2,Mod,Veenker,Feedr,Norm,OneFam,SLvl,Very_Good,Average,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,200,Good,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,392,392,GasA,Excellent,Y,SBrkr,1487,1012,0,2499,0,0,2,1,4,1,Typical,5,Typ,1,Good,Attchd,Unf,2,527,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Abnorml,180500,-93.645546,42.043019 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9991,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1976,1993,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,165,1281,GasA,Excellent,Y,SBrkr,1620,0,0,1620,1,0,2,0,3,1,Typical,8,Min1,1,Typical,Attchd,Unf,2,490,Typical,Typical,Paved,120,78,0,0,0,0,No_Pool,Good_Wood,None,0,6,2009,WD ,Normal,150000,-93.65921,42.034509 -Two_Story_1946_and_Newer,Residential_Low_Density,78,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,429,727,GasA,Excellent,Y,SBrkr,829,727,0,1556,0,0,1,1,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,441,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,154000,-93.635342,42.047708 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11717,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,One_Story,Above_Average,Above_Average,1970,1970,Hip,CompShg,HdBoard,HdBoard,BrkFace,571,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1442,1442,GasA,Typical,Y,SBrkr,1442,0,0,1442,0,0,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,615,Typical,Typical,Paved,371,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,185000,-93.634634,42.048009 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9156,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Above_Average,Good,1968,1968,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1489,1489,GasA,Good,Y,SBrkr,1489,0,0,1489,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,185750,-93.634803,42.04896 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10382,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Good,Above_Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,Stone,240,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,BLQ,32,216,1107,GasA,Excellent,Y,SBrkr,1107,983,0,2090,1,0,2,1,3,1,Typical,7,Typ,2,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,235,204,228,0,0,0,No_Pool,No_Fence,Shed,350,11,2009,WD ,Normal,200000,-93.631971,42.048761 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12732,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Good,Above_Average,1974,1974,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,GLQ,3,LwQ,42,150,752,GasA,Typical,Y,SBrkr,1285,782,0,2067,0,0,2,1,3,1,Good,7,Typ,2,Typical,Attchd,RFn,2,784,Typical,Typical,Paved,297,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,206000,-93.631881,42.04876 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12936,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Good,No,BLQ,2,Unf,0,130,723,GasA,Typical,Y,SBrkr,735,660,0,1395,0,1,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,497,Typical,Typical,Paved,294,116,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,162000,-93.631519,42.047652 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Above_Average,Good,1967,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,256,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,932,932,GasA,Good,Y,SBrkr,1271,1369,0,2640,0,0,2,1,5,1,Good,8,Typ,1,Typical,Attchd,RFn,2,515,Typical,Typical,Paved,0,120,0,0,168,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,256900,-93.633163,42.046476 -Split_or_Multilevel,Residential_Low_Density,0,17871,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1967,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,359,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,1152,1680,GasA,Fair,Y,SBrkr,1724,0,0,1724,1,0,1,1,3,1,Typical,7,Typ,1,Good,Attchd,RFn,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,197900,-93.631851,42.046476 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,128,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Rec,147,588,1288,GasA,Typical,Y,SBrkr,1336,0,0,1336,0,1,2,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,502,Typical,Typical,Paved,312,11,0,0,0,0,No_Pool,No_Fence,Shed,650,8,2009,WD ,Normal,163000,-93.632537,42.045806 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,261,1216,GasA,Typical,Y,SBrkr,1216,0,0,1216,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Abnorml,113000,-93.632823,42.045799 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13774,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1977,1992,Hip,CompShg,HdBoard,HdBoard,BrkFace,283,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,476,908,GasA,Excellent,Y,SBrkr,1316,972,0,2288,0,0,1,2,4,1,Good,8,Typ,2,Typical,Attchd,RFn,2,520,Typical,Typical,Paved,321,72,0,0,156,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,230000,-93.637964,42.043236 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,360,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,664,1350,GasA,Typical,Y,SBrkr,1334,0,0,1334,0,1,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,630,Typical,Typical,Paved,0,16,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,167900,-93.637905,42.043842 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Good,Average,1998,1999,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,LwQ,4,GLQ,1127,379,1650,GasA,Excellent,Y,SBrkr,1680,0,0,1680,1,0,2,0,3,1,Good,7,Maj1,1,Typical,Attchd,Fin,2,583,Typical,Typical,Paved,78,73,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,213250,-93.632647,42.043673 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Very_Good,Above_Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,167,810,GasA,Excellent,Y,SBrkr,810,855,0,1665,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,528,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,227000,-93.631899,42.044693 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,7130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,216,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,130000,-93.628903,42.048796 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1568,1568,GasA,Typical,Y,SBrkr,1568,0,0,1568,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,160,40,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,COD,Normal,143000,-93.627047,42.0467129 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,466,121,925,GasA,Excellent,Y,SBrkr,925,0,0,925,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,429,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,117500,-93.624612,42.046635 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Average,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,252,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,467,1165,GasA,Good,Y,SBrkr,1165,896,0,2061,0,1,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,498,Typical,Typical,Paved,0,77,0,0,196,0,No_Pool,No_Fence,None,0,5,2009,COD,Abnorml,168500,-93.630714,42.044821 -Split_Foyer,Residential_Low_Density,0,16500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Above_Average,Average,1971,1971,Hip,CompShg,HdBoard,HdBoard,BrkFace,509,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,270,1232,GasA,Fair,Y,SBrkr,1320,0,0,1320,0,1,2,0,3,1,Typical,5,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,172500,-93.627416,42.043935 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1967,1967,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,630,491,1372,GasA,Typical,Y,SBrkr,1342,0,0,1342,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,457,Typical,Typical,Paved,0,0,0,0,197,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,161500,-93.625775,42.043145 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7436,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1960,1960,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,160,894,GasA,Good,Y,SBrkr,894,0,0,894,1,0,1,0,2,1,Typical,5,Typ,1,Poor,Detchd,Unf,2,396,Typical,Typical,Paved,0,0,0,360,0,0,No_Pool,Good_Wood,None,0,8,2009,WD ,Normal,141500,-93.622991,42.043135 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1959,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,461,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,118000,-93.622465,42.042241 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,252,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,300,Excellent,Excellent,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,450,10,2009,WD ,Normal,127500,-93.623485,42.044843 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,160,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,265,1040,GasA,Typical,Y,SBrkr,1362,0,0,1362,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,3,768,Typical,Typical,Paved,0,0,84,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,140000,-93.622635,42.044774 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13495,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,70,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,LwQ,201,222,1048,GasA,Fair,Y,SBrkr,1728,0,0,1728,1,0,2,0,3,1,Typical,7,Min1,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,0,99,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,177625,-93.622476,42.044926 -Duplex_All_Styles_and_Ages,Residential_Low_Density,85,11475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1958,1958,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1584,1584,GasA,Typical,Y,SBrkr,1776,0,0,1776,1,0,2,0,4,2,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,3,888,Typical,Typical,Paved,0,25,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,COD,Abnorml,110000,-93.623411,42.04488 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,10004,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,Plywood,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,345,975,1516,GasA,Typical,Y,SBrkr,1516,0,0,1516,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,472,Typical,Typical,Paved,0,0,0,0,152,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,167000,-93.629415,42.040645 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1995,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,217,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,512,491,1313,GasA,Typical,Y,SBrkr,1313,0,0,1313,1,0,1,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,610,Typical,Typical,Paved,172,28,0,0,121,0,No_Pool,Minimum_Privacy,None,0,2,2009,WD ,Normal,153000,-93.628658,42.038492 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,115,10500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,Stone,144,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,Unf,0,294,1292,GasA,Typical,Y,SBrkr,1292,0,0,1292,1,0,1,1,3,1,Typical,6,Typ,2,Good,Detchd,Unf,2,520,Typical,Typical,Paved,0,32,0,0,92,0,No_Pool,No_Fence,None,0,7,2009,COD,Abnorml,145100,-93.6273127,42.0407264 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,1966,Gable,CompShg,Plywood,Plywood,BrkFace,200,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,230,184,1154,GasA,Excellent,Y,SBrkr,1154,0,0,1154,0,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,2,480,Typical,Typical,Paved,0,58,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,WD ,Normal,154000,-93.627836,42.040011 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8970,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,356,744,GasA,Typical,Y,SBrkr,825,1315,0,2140,0,0,2,1,4,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,549,Typical,Typical,Paved,0,40,264,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,177500,-93.627985,42.039902 -Split_or_Multilevel,Residential_Low_Density,0,12095,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1964,1964,Gable,CompShg,MetalSd,HdBoard,BrkFace,115,Typical,Good,CBlock,Typical,Typical,Gd,Rec,6,Unf,0,563,1127,GasA,Typical,Y,SBrkr,1445,0,0,1445,0,0,1,1,3,1,Typical,7,Typ,1,Fair,Attchd,RFn,2,645,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,158000,-93.626979,42.040888 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,MetalSd,MetalSd,BrkFace,132,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,50,1041,GasA,Excellent,Y,SBrkr,1041,0,0,1041,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,270,Typical,Typical,Paved,224,88,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,124500,-93.624581,42.041104 -Split_or_Multilevel,Residential_Low_Density,85,11475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1961,1961,Hip,CompShg,HdBoard,HdBoard,BrkFace,90,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,640,1208,GasA,Excellent,Y,SBrkr,1576,0,0,1576,1,0,1,0,4,1,Good,7,Typ,1,Poor,BuiltIn,Fin,2,368,Typical,Typical,Paved,85,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,174500,-93.622685,42.039463 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9768,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,247,613,960,GasA,Good,Y,SBrkr,960,0,0,960,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,330,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2009,WD ,Normal,122000,-93.621699,42.038596 -One_Story_1945_and_Older,Residential_Low_Density,50,5330,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Below_Average,Good,1940,1950,Hip,CompShg,VinylSd,VinylSd,None,0,Fair,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,140,420,GasA,Good,Y,SBrkr,708,0,0,708,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,164,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,82500,-93.629499,42.035489 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,7015,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,BrkCmn,161,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,524,709,GasA,Typical,Y,SBrkr,979,224,0,1203,1,0,1,0,3,1,Good,5,Typ,1,Typical,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,248,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,110000,-93.62919,42.035443 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,128,12160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Below_Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,90,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,481,1505,GasA,Excellent,Y,SBrkr,1505,0,0,1505,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,505,Typical,Typical,Paved,0,0,0,162,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,149500,-93.625633,42.03464 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1967,1967,Gable,CompShg,BrkComm,Brk Cmn,None,0,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,507,1680,GasA,Typical,Y,SBrkr,1691,0,0,1691,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,550,Good,Typical,Paved,0,67,260,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,175000,-93.62568,42.036182 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1953,1953,Hip,CompShg,HdBoard,HdBoard,Stone,238,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,131,1416,GasA,Typical,Y,SBrkr,1644,0,0,1644,1,0,1,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,418,Typical,Typical,Paved,110,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,167000,-93.623705,42.034741 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,546,1050,GasA,Good,Y,SBrkr,1050,0,0,1050,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,338,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,128900,-93.624612,42.037452 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1950,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,736,856,GasA,Excellent,Y,SBrkr,1112,556,0,1668,0,0,1,1,3,1,Typical,6,Min2,0,No_Fireplace,Attchd,Unf,1,271,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2009,WD ,Normal,140000,-93.622644,42.035951 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1950,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,506,1113,GasA,Good,Y,SBrkr,1113,0,0,1113,0,0,1,0,3,1,Good,5,Typ,1,Good,Attchd,Unf,1,264,Typical,Typical,Paved,0,80,120,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,147000,-93.622494,42.036178 -Duplex_All_Styles_and_Ages,Residential_Low_Density,65,8944,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1584,1584,GasA,Typical,Y,SBrkr,1584,0,0,1584,0,0,2,0,4,2,Typical,8,Mod,0,No_Fireplace,Detchd,Unf,3,792,Typical,Typical,Paved,0,152,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,124000,-93.619673,42.049306 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,92,10573,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1961,1961,Hip,CompShg,MetalSd,MetalSd,BrkFace,3,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,141,1453,GasA,Excellent,Y,SBrkr,1453,0,0,1453,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,530,Typical,Typical,Paved,0,49,0,0,288,0,No_Pool,Good_Privacy,None,0,4,2009,WD ,Normal,187500,-93.617963,42.048294 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1963,1963,Gable,CompShg,Plywood,Plywood,BrkFace,247,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,785,1394,GasA,Good,Y,SBrkr,1394,0,0,1394,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,514,Typical,Typical,Paved,0,76,0,0,185,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,159000,-93.615686,42.046827 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,14695,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1966,2008,Gable,CompShg,MetalSd,MetalSd,BrkFace,210,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,175,1562,GasA,Good,Y,SBrkr,1567,0,0,1567,1,0,2,0,2,1,Good,5,Typ,2,Good,Attchd,Unf,2,542,Typical,Typical,Paved,0,110,0,0,342,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,256000,-93.615602,42.048608 -Two_Story_1946_and_Newer,Residential_Low_Density,85,13600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1965,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,314,768,GasA,Typical,Y,SBrkr,1186,800,0,1986,0,0,2,1,3,1,Typical,7,Typ,3,Fair,Attchd,Unf,2,486,Typical,Typical,Paved,0,42,0,0,189,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,205000,-93.615558,42.048724 -Two_Story_1946_and_Newer,Residential_Low_Density,100,13000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,VinylSd,VinylSd,BrkFace,576,Typical,Good,CBlock,Good,Typical,No,Rec,6,Unf,0,448,896,GasA,Typical,Y,SBrkr,1182,960,0,2142,0,0,2,1,4,1,Good,8,Typ,1,Good,Attchd,Fin,1,509,Typical,Typical,Paved,0,72,0,0,252,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,193500,-93.614619,42.04806 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,13560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Fair,1968,1968,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,216,Typical,Typical,CBlock,Fair,Fair,No,Unf,7,Unf,0,1392,1392,GasA,Good,Y,SBrkr,1392,0,0,1392,1,0,1,0,2,1,Typical,5,Maj2,2,Typical,Attchd,RFn,2,576,Typical,Typical,Paved,0,0,240,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,110000,-93.614216,42.04579 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,12513,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1920,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Fair,No,Unf,7,Unf,0,715,715,GasA,Good,Y,SBrkr,1281,457,0,1738,0,0,2,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,1,368,Typical,Typical,Paved,55,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,104900,-93.619029,42.044886 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,164,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,313,1169,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,257,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,150000,-93.618784,42.043966 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Fair,1957,1957,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,661,628,1478,GasA,Good,Y,SBrkr,1478,0,0,1478,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,442,Typical,Typical,Paved,114,0,0,0,216,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,156500,-93.61864,42.044125 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12285,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1960,1960,Gable,CompShg,Plywood,Plywood,BrkFace,128,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,785,1329,GasA,Good,Y,SBrkr,1329,0,0,1329,0,0,1,1,3,1,Typical,5,Typ,2,Good,Attchd,Unf,2,441,Typical,Typical,Paved,0,0,203,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,176000,-93.61488,42.043991 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,9240,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1959,1998,Hip,CompShg,MetalSd,MetalSd,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,280,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,297,Typical,Typical,Paved,156,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,149500,-93.616434,42.044083 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1988,Hip,CompShg,Wd Sdng,Wd Sdng,BrkCmn,183,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,620,0,1240,GasA,Good,Y,SBrkr,1632,0,0,1632,1,0,2,0,3,1,Typical,6,Min1,1,Good,Attchd,RFn,1,338,Typical,Typical,Paved,289,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,139000,-93.614751,42.045473 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,12400,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Good,1958,1998,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,176,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,630,1215,GasA,Typical,Y,FuseA,1215,0,0,1215,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,297,Typical,Typical,Paved,0,0,0,0,234,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,155000,-93.614059,42.044914 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,202,565,1202,GasA,Typical,Y,SBrkr,1202,0,0,1202,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,304,Typical,Typical,Paved,0,35,120,0,0,0,No_Pool,Good_Wood,None,0,11,2009,COD,Abnorml,120000,-93.614768,42.04297 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,483,727,1382,GasA,Good,Y,FuseA,1382,0,0,1382,0,1,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,350,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,153000,-93.61588,42.042233 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1956,1956,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,750,295,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,294,Typical,Typical,Paved,0,189,140,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Family,144000,-93.614543,42.043238 -Split_or_Multilevel,Residential_Low_Density,120,19296,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,SLvl,Above_Average,Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,399,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,ALQ,690,0,1362,GasA,Typical,Y,SBrkr,1382,0,0,1382,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,884,Typical,Typical,Paved,0,0,252,0,0,0,No_Pool,Good_Wood,None,0,5,2009,WD ,Normal,176000,-93.620016,42.040931 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8532,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1990,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,650,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,84,1297,GasA,Good,Y,SBrkr,1297,0,0,1297,0,1,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,498,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,153000,-93.618478,42.040857 -Duplex_All_Styles_and_Ages,Residential_Low_Density,76,9482,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Below_Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,657,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1866,1866,GasA,Excellent,Y,SBrkr,1866,0,0,1866,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,495,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,135000,-93.6165473,42.0404571 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,8128,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1954,1954,Hip,CompShg,MetalSd,MetalSd,BrkFace,80,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,572,1062,GasA,Good,Y,SBrkr,1062,0,0,1062,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,297,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,131000,-93.618549,42.039674 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10634,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,LwQ,180,0,608,GasA,Typical,Y,SBrkr,1319,0,0,1319,1,0,1,0,3,1,Typical,5,Min2,0,No_Fireplace,Attchd,Unf,1,270,Typical,Typical,Paved,66,0,0,0,0,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Normal,123000,-93.6190376,42.0385265 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,13070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1951,1951,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,323,631,GasA,Typical,Y,FuseA,1112,0,0,1112,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,2,480,Typical,Typical,Paved,0,0,0,0,255,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,126000,-93.617219,42.038356 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10434,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1005,1005,GasA,Typical,Y,SBrkr,1005,0,0,1005,0,0,1,0,2,1,Fair,5,Typ,1,Typical,Detchd,Unf,2,672,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,115000,-93.6124,42.038398 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,14559,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,2000,Hip,CompShg,Wd Sdng,Wd Sdng,BrkCmn,70,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,180,178,1008,GasA,Excellent,Y,SBrkr,1363,0,0,1363,1,0,1,0,2,1,Typical,6,Min1,2,Typical,CarPort,Unf,1,288,Typical,Typical,Paved,324,42,0,0,168,0,No_Pool,No_Fence,Shed,2000,6,2009,WD ,Normal,164900,-93.617207,42.037122 -One_Story_1945_and_Older,Residential_Low_Density,80,8480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,93,793,GasA,Typical,Y,SBrkr,793,0,0,793,1,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,113000,-93.6167912,42.0372232 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7626,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1952,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,100,1031,GasA,Good,Y,SBrkr,1031,0,0,1031,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,1,230,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,145500,-93.618605,42.034918 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,140,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1210,0,0,1210,0,0,1,1,2,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,616,Typical,Typical,Paved,208,0,100,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,ConLD,Normal,102900,-93.618607,42.034686 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,107,10615,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Mod,Old_Town,Artery,Artery,TwoFmCon,Two_Story,Fair,Average,1900,1970,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,BLQ,2,Unf,0,538,978,GasA,Typical,Y,SBrkr,1014,685,0,1699,1,0,2,0,3,2,Typical,7,Typ,0,No_Fireplace,CarPort,Unf,2,420,Fair,Fair,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,95000,-93.62008,42.034754 -Two_Story_1946_and_Newer,Residential_Low_Density,78,11419,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Good,Good,1948,1999,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,0,699,GasA,Excellent,Y,FuseA,801,726,0,1527,1,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,Unf,2,410,Typical,Typical,Paved,0,0,134,0,0,0,No_Pool,Good_Wood,None,0,12,2009,WD ,Normal,152500,-93.620336,42.036223 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,810,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,246,Typical,Typical,Paved,126,0,0,0,0,0,No_Pool,Good_Wood,None,0,8,2009,WD ,Normal,129900,-93.61719,42.035829 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1948,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,292,972,GasA,Excellent,Y,SBrkr,972,0,0,972,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,132000,-93.617182,42.034746 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,5470,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,792,792,GasA,Good,Y,FuseA,792,0,0,792,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,366,Fair,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,99900,-93.6146206,42.0380659 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10410,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1916,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,660,660,GasA,Excellent,Y,SBrkr,808,704,144,1656,0,0,2,1,3,1,Typical,8,Min2,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,0,0,0,140,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,135000,-93.6144209,42.037679 -Two_Story_1946_and_Newer,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1939,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,676,676,GasA,Excellent,Y,SBrkr,676,676,0,1352,0,1,2,0,4,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,187,0,128,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,149000,-93.61239,42.037144 -Two_Story_1945_and_Older,Residential_Low_Density,80,8146,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Very_Good,1900,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,BrkTil,Fair,Typical,No,Unf,7,Unf,0,405,405,GasA,Good,Y,SBrkr,717,322,0,1039,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,281,Typical,Typical,Dirt_Gravel,0,0,168,0,111,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,114000,-93.612241,42.037328 -One_Story_1945_and_Older,Residential_Low_Density,52,9022,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Very_Good,1924,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,792,0,0,792,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Fair,Dirt_Gravel,316,0,120,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,109500,-93.614049,42.036015 -One_Story_1945_and_Older,Residential_Low_Density,60,10230,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1054,1054,GasA,Excellent,Y,SBrkr,1078,0,0,1078,0,0,1,0,3,1,Excellent,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Dirt_Gravel,0,0,0,0,112,0,No_Pool,Good_Wood,None,0,12,2009,WD ,Normal,125000,-93.615465,42.036002 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10410,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1313,1313,GasA,Typical,Y,SBrkr,1313,0,1064,2377,0,0,2,0,3,1,Good,8,Min2,1,Typical,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,432,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,142900,-93.614049,42.034668 -Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Good,Excellent,1910,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,560,560,GasA,Excellent,Y,SBrkr,930,760,0,1690,0,0,2,0,4,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,156500,-93.613899,42.034574 -One_Story_1945_and_Older,Residential_Low_Density,90,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Poor,Average,1940,1950,Gable,CompShg,Stucco,Stucco,None,0,Fair,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,416,416,GasA,Good,N,FuseA,599,0,0,599,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,81,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,59000,-93.6117397,42.0347572 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,90,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1920,1950,Gable,CompShg,CBlock,CBlock,None,0,Fair,Typical,PConc,Typical,Typical,No,ALQ,1,Rec,105,420,840,GasA,Excellent,Y,SBrkr,840,534,0,1374,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,1,338,Typical,Typical,Paved,0,0,198,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,105000,-93.61203,42.0347398 -One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,720,720,GasA,Typical,N,SBrkr,846,0,0,846,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Fin,2,576,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,106000,-93.612253,42.035257 -One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Average,1890,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Typical,Y,FuseA,725,0,0,725,0,0,1,1,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,320,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,78500,-93.612254,42.03528 -Duplex_All_Styles_and_Ages,Residential_Low_Density,81,9671,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,Duplex,Two_Story,Above_Average,Average,1969,1969,Gable,CompShg,MetalSd,MetalSd,Stone,480,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1248,1248,GasA,Typical,Y,SBrkr,1248,1296,0,2544,0,0,2,2,6,2,Typical,12,Typ,0,No_Fireplace,Attchd,RFn,3,907,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,190000,-93.610355,42.0417879 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,10143,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,295,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,996,1380,GasA,Fair,Y,SBrkr,1380,0,0,1380,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,364,Typical,Typical,Paved,0,0,0,0,216,0,No_Pool,Good_Wood,None,0,6,2009,WD ,Normal,154000,-93.607655,42.04015 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Below_Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Fair,Typical,CBlock,Typical,Fair,No,BLQ,2,Rec,60,108,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,6,Min1,1,Poor,Attchd,RFn,2,480,Typical,Typical,Paved,0,0,156,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,163000,-93.610588,42.040295 -Duplex_All_Styles_and_Ages,Residential_Low_Density,91,11643,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,Duplex,Two_Story,Average,Average,1969,1969,Gable,CompShg,MetalSd,MetalSd,BrkFace,368,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,748,1248,GasA,Typical,Y,SBrkr,1338,1296,0,2634,1,1,2,2,6,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,4,968,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,200000,-93.610649,42.04124 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,8010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Excellent,1958,2007,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,206,951,GasA,Good,Y,SBrkr,951,0,0,951,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,42,0,0,0,No_Pool,No_Fence,Shed,450,9,2009,WD ,Normal,143500,-93.608196,42.038509 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,10454,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1957,Hip,CompShg,Plywood,Plywood,Stone,143,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,559,1105,GasA,Good,Y,FuseA,1105,0,0,1105,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,135000,-93.608927,42.038349 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,8712,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1957,2000,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,132,992,GasA,Typical,Y,SBrkr,1306,0,0,1306,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,756,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,153000,-93.609226,42.039047 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17600,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,30,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,208,1478,GasA,Excellent,Y,FuseA,1478,0,0,1478,1,0,2,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,498,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,157500,-93.606573,42.040766 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9000,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Flat,Tar&Grv,Wd Sdng,Wd Sdng,BrkFace,82,Typical,Typical,CBlock,Good,Typical,Gd,Unf,7,Unf,0,160,160,GasA,Fair,Y,SBrkr,1142,0,0,1142,0,0,1,0,2,1,Typical,5,Typ,1,Good,Basment,RFn,1,384,Typical,Typical,Paved,0,28,64,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,113500,-93.60552,42.038811 -One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,60,12144,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1949,1950,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,457,832,GasA,Good,Y,SBrkr,1036,0,232,1268,0,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,288,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,Othr,0,9,2009,WD ,Normal,133000,-93.609059,42.037129 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1950,1950,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseA,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,92900,-93.608977,42.035712 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,174,795,GasA,Good,N,SBrkr,765,368,0,1133,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,900,Typical,Typical,Paved,0,0,0,0,231,0,No_Pool,No_Fence,None,0,12,2009,COD,Abnorml,128500,-93.61061,42.035787 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Below_Average,Average,1949,1950,Gable,CompShg,BrkFace,Stone,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,420,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,90000,-93.608903,42.035841 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1950,1982,Gable,CompShg,VinylSd,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,352,676,1208,GasA,Good,Y,FuseA,1136,768,0,1904,1,0,1,1,3,1,Typical,7,Min1,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,168,0,0,0,No_Pool,Good_Privacy,None,0,5,2009,WD ,Normal,138000,-93.60891,42.037233 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,7350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1958,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1041,1041,GasA,Good,Y,SBrkr,1041,0,0,1041,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2009,WD ,Normal,128000,-93.606891,42.03602 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1965,Hip,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,525,1029,GasA,Typical,Y,SBrkr,1339,0,0,1339,0,0,1,0,3,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,1,294,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,COD,Abnorml,139000,-93.606889,42.035968 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1952,1952,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Good,No,ALQ,1,BLQ,102,0,732,GasA,Typical,Y,SBrkr,732,0,0,732,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,118900,-93.607814,42.03572 -Split_or_Multilevel,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1959,1959,Gable,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Rec,95,0,528,GasA,Typical,Y,SBrkr,1183,0,0,1183,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,138000,-93.605968,42.035706 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,0,1148,GasA,Typical,Y,SBrkr,1148,0,0,1148,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,672,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2009,WD ,Normal,132500,-93.606786,42.037214 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1949,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,FuseF,832,629,0,1461,0,0,2,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,204,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,133500,-93.609945,42.0347542 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1948,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,744,864,GasA,Typical,Y,SBrkr,1064,0,431,1495,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,180,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,COD,Abnorml,135000,-93.610605,42.034858 -Two_Story_1946_and_Newer,Residential_Low_Density,76,7570,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,420,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Typical,Y,SBrkr,993,813,0,1806,0,0,1,1,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,483,Typical,Typical,Paved,0,55,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,144750,-93.604987,42.037049 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8604,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,SFoyer,Average,Good,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,124,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,941,GasA,Good,Y,SBrkr,941,0,0,941,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,2,564,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,145000,-93.604047,42.038948 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,7936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,219,1045,GasA,Typical,Y,SBrkr,1045,0,0,1045,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,127000,-93.603879,42.038257 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,894,894,GasA,Good,N,SBrkr,894,0,0,894,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,109500,-93.603701,42.035287 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,3950,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1926,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,350,818,GasA,Typical,Y,SBrkr,818,406,0,1224,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,210,Typical,Typical,Dirt_Gravel,0,0,116,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,115000,-93.6179738,42.0342224 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10440,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1910,1981,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,440,440,GasA,Good,Y,SBrkr,682,548,0,1230,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,74,0,128,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,110000,-93.615583,42.033273 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,68,4080,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1935,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,901,901,GasA,Good,Y,SBrkr,861,517,0,1378,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,162,Fair,Fair,Partial_Pavement,54,0,40,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,128900,-93.619013,42.0320748 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,57,10307,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,Two_Story,Above_Average,Average,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,LwQ,4,Unf,0,339,972,GasA,Good,N,FuseA,972,972,0,1944,1,0,2,0,4,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,2,324,Fair,Typical,Dirt_Gravel,0,28,169,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,103500,-93.6195025,42.0323259 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,5720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,676,676,GasA,Excellent,Y,SBrkr,676,455,0,1131,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Typical,Paved,26,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,66500,-93.62026,42.032396 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,90,15660,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1910,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,240,240,GasA,Typical,Y,SBrkr,810,496,0,1306,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,472,Fair,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,130000,-93.615598,42.032202 -Two_Story_1945_and_Older,Residential_Medium_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1910,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,504,504,GasA,Excellent,Y,SBrkr,764,700,0,1464,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,176,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,129000,-93.615599,42.032108 -Two_Story_1945_and_Older,Residential_Medium_Density,57,6406,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,269,690,GasA,Typical,Y,FuseA,868,690,0,1558,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,36,0,0,182,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,150000,-93.6195361,42.0319053 -Two_Story_1945_and_Older,Residential_Medium_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Below_Average,1892,1965,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Stone,Typical,Typical,No,Unf,7,Unf,0,994,994,GasA,Typical,N,SBrkr,1378,994,0,2372,0,0,2,0,4,2,Typical,11,Min2,0,No_Fireplace,Attchd,RFn,1,432,Typical,Typical,Paved,0,287,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,107500,-93.618486,42.031305 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,63,7627,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,TwoFmCon,Two_Story,Below_Average,Above_Average,1920,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fair,Typical,BrkTil,Fair,Poor,No,Unf,7,Unf,0,600,600,GasA,Good,N,SBrkr,1101,600,0,1701,0,0,2,0,4,2,Fair,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,148,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,94550,-93.6200683,42.0304742 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,60,7596,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,Duplex,Two_Story,Average,Average,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,360,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Good,Y,SBrkr,960,1000,0,1960,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,COD,Normal,124500,-93.620216,42.030327 -Two_Story_1946_and_Newer,Residential_Medium_Density,60,3378,Pave,Gravel,Regular,HLS,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1946,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,651,651,GasA,Good,Y,SBrkr,707,682,0,1389,0,0,1,1,3,1,Typical,6,Typ,2,Good,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,0,126,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,135000,-93.6176367,42.0303538 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,56,10134,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1910,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,801,801,GasA,Good,N,SBrkr,801,646,0,1447,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,80,0,244,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,103000,-93.615381,42.030282 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,60,6000,Pave,Paved,Regular,Bnk,AllPub,Inside,Mod,Old_Town,Norm,Norm,TwoFmCon,One_Story,Below_Average,Below_Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Fair,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,936,936,GasA,Typical,N,SBrkr,936,0,0,936,0,0,1,0,2,1,Typical,4,Min2,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,32,112,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,93000,-93.6129105,42.0310603 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1950,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Excellent,CBlock,Typical,Typical,No,BLQ,2,Unf,0,384,768,GasA,Typical,Y,FuseA,768,560,0,1328,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,12,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,129500,-93.608866,42.033381 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Below_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,811,811,GasA,Typical,Y,FuseA,811,576,0,1387,0,0,2,0,3,2,Good,7,Typ,0,No_Fireplace,BuiltIn,Unf,1,256,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,93000,-93.607776,42.033579 -One_Story_1945_and_Older,Residential_Medium_Density,62,7404,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,861,861,GasA,Typical,Y,SBrkr,861,0,0,861,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,288,Typical,Typical,Dirt_Gravel,0,0,128,0,0,0,No_Pool,No_Fence,None,0,11,2009,Oth,Normal,80000,-93.608829,42.032048 -One_Story_1945_and_Older,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Poor,Below_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,FuseA,612,0,0,612,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Fair,Dirt_Gravel,0,0,25,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,45000,-93.606842,42.032301 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Above_Average,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,600,600,Grav,Fair,N,SBrkr,600,368,0,968,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2009,WD ,Abnorml,37900,-93.606841,42.032227 -One_Story_1945_and_Older,Residential_Medium_Density,50,5784,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Very_Good,1938,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,190,190,GasA,Good,Y,FuseA,886,0,0,886,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,273,Typical,Typical,Paved,144,20,80,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,91300,-93.610469,42.030523 -One_Story_1945_and_Older,Residential_Medium_Density,60,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Very_Good,1923,1950,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,624,624,GasA,Typical,Y,SBrkr,792,0,0,792,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,287,Typical,Typical,Paved,0,0,81,0,0,0,No_Pool,Good_Wood,None,0,2,2009,WD ,Normal,99500,-93.606788,42.030196 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,33,4456,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Average,1920,2008,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,736,736,GasA,Good,Y,SBrkr,736,716,0,1452,0,0,2,0,2,3,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,102,0,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,113000,-93.618648,42.0296722 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Below_Average,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,BrkTil,Fair,Typical,No,Unf,7,Unf,0,677,677,GasA,Typical,Y,SBrkr,833,677,0,1510,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Dirt_Gravel,0,0,160,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,87500,-93.617054,42.029007 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,3500,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1947,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,408,720,GasA,Typical,Y,SBrkr,720,564,0,1284,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2009,WD ,Normal,110000,-93.6178679,42.0290352 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,90,8100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Average,Average,1898,1965,Hip,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,849,849,GasA,Typical,N,FuseA,1075,1063,0,2138,0,0,2,0,2,3,Typical,11,Typ,0,No_Fireplace,Detchd,Unf,2,360,Fair,Poor,Dirt_Gravel,40,156,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,WD ,Normal,106000,-93.613887,42.028957 -Two_Story_1945_and_Older,Residential_Medium_Density,65,11700,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1880,2003,Mansard,CompShg,Stucco,Stucco,None,0,Good,Typical,Stone,Typical,Fair,No,Unf,7,Unf,0,1240,1240,GasW,Typical,N,SBrkr,1320,1320,0,2640,0,0,1,1,4,1,Good,8,Typ,1,Good,Detchd,Unf,4,864,Typical,Typical,Dirt_Gravel,181,0,386,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,265979,-93.612245,42.029305 -Two_Story_1945_and_Older,Residential_Medium_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Above_Average,Good,1900,1950,Gable,CompShg,Stucco,BrkFace,None,0,Typical,Typical,BrkTil,Fair,Good,Mn,Unf,7,Unf,0,917,917,GasA,Good,Y,FuseA,1090,917,0,2007,0,0,2,0,3,1,Excellent,8,Typ,0,No_Fireplace,Detchd,Unf,1,357,Typical,Typical,Paved,0,235,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,160000,-93.610588,42.029001 -Two_Story_1945_and_Older,Residential_Medium_Density,60,3600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1917,2007,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Excellent,Y,SBrkr,664,624,0,1288,1,0,1,0,3,1,Excellent,5,Typ,1,Good,Attchd,Unf,1,280,Typical,Typical,Dirt_Gravel,0,103,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,119000,-93.6130761,42.0281106 -Two_Story_1945_and_Older,Residential_Medium_Density,121,17671,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Very_Good,Excellent,1882,1986,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,700,916,GasA,Good,Y,SBrkr,916,826,0,1742,0,0,1,1,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,424,Typical,Typical,Partial_Pavement,0,169,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,168000,-93.610582,42.028104 -One_Story_1945_and_Older,C_all,0,3300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1910,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Excellent,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,33,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,58500,-93.6148612,42.0276972 -Two_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Good,Excellent,1920,1988,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,ALQ,1,Unf,0,26,650,GasA,Excellent,Y,SBrkr,832,650,0,1482,0,1,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,324,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,143000,-93.610044,42.028556 -One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Below_Average,1910,2006,Hip,CompShg,MetalSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Rec,465,310,1022,GasW,Typical,N,SBrkr,1022,0,0,1022,1,0,1,0,2,1,Typical,4,Maj2,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,30,226,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,85000,-93.6076506,42.0282044 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,98,8820,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1890,1996,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,0,1088,GasA,Typical,Y,SBrkr,1188,561,120,1869,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,456,Typical,Typical,Paved,48,0,244,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,9,2009,WD ,Normal,124900,-93.608704,42.027062 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Fair,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,972,972,GasA,Excellent,Y,SBrkr,1044,0,436,1480,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,207,Fair,Typical,Paved,0,0,176,0,0,0,No_Pool,No_Fence,None,0,9,2009,ConLI,Family,119000,-93.608707,42.0272 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9720,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1910,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,741,741,GasA,Excellent,Y,SBrkr,780,741,0,1521,0,0,1,0,4,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,3,640,Typical,Typical,Paved,0,0,238,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2009,WD ,Normal,146500,-93.607058,42.027227 -One_Story_1945_and_Older,C_all,60,7879,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,225,720,GasA,Typical,N,FuseA,720,0,0,720,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,523,115,0,0,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Abnorml,34900,-93.605207,42.023218 -One_Story_1945_and_Older,C_all,72,9392,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Fair,Fair,1900,1950,Mansard,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,CBlock,Fair,Typical,No,Unf,7,Unf,0,245,245,GasA,Typical,N,SBrkr,797,0,0,797,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,36,94,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Abnorml,44000,-93.605421,42.023222 -Two_Story_1945_and_Older,Residential_Low_Density,144,21384,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1923,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,15,1324,GasA,Excellent,Y,SBrkr,1072,504,0,1576,2,0,1,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,312,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,223500,-93.6289369,42.033996 -One_Story_1945_and_Older,Residential_Low_Density,0,6615,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1923,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1022,1022,GasA,Typical,N,FuseA,1432,0,0,1432,0,0,1,0,3,1,Good,6,Typ,1,Good,BuiltIn,Unf,1,216,Fair,Typical,Paved,266,61,0,0,0,0,No_Pool,Good_Wood,None,0,9,2009,WD ,Normal,149000,-93.628848,42.033558 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,55,7264,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1925,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,952,952,GasW,Good,N,SBrkr,952,596,0,1548,0,0,2,1,3,1,Excellent,5,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,74,0,0,0,144,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,205000,-93.6279815,42.0333765 -Two_Story_1945_and_Older,Residential_Low_Density,50,4960,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1982,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,297,297,GasA,Excellent,Y,SBrkr,1001,653,0,1654,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,336,Typical,Typical,Dirt_Gravel,244,60,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,137000,-93.625968,42.031564 -One_Story_1945_and_Older,Residential_Low_Density,0,8854,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Above_Average,1916,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,952,952,Grav,Fair,N,FuseF,952,0,0,952,0,0,1,0,2,1,Fair,4,Typ,1,Good,Detchd,Unf,1,192,Fair,Poor,Partial_Pavement,0,98,0,0,40,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,121000,-93.626116,42.031542 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,0,672,GasA,Typical,Y,SBrkr,757,567,0,1324,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,128000,-93.624564,42.033406 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Excellent,1937,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Typical,Y,SBrkr,786,390,0,1176,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Dirt_Gravel,210,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,134900,-93.624567,42.033616 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1925,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,884,464,0,1348,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,0,0,208,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,117000,-93.622513,42.03329 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Excellent,Y,FuseF,825,587,0,1412,0,0,1,0,4,1,Typical,6,Typ,1,Good,Detchd,Unf,1,280,Typical,Typical,Paved,45,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,132500,-93.623515,42.033347 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1924,1950,Gable,CompShg,Stucco,Stucco,BrkFace,444,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,248,468,GasA,Good,Y,SBrkr,822,320,0,1142,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,320,Typical,Typical,Paved,0,0,98,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,93000,-93.621677,42.033496 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Good,1926,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Good,Y,SBrkr,904,0,0,904,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,180,Typical,Typical,Paved,0,0,105,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,119000,-93.622443,42.033474 -One_Story_1945_and_Older,Residential_Medium_Density,60,6911,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Average,Average,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,LwQ,4,Unf,0,740,1145,GasA,Typical,Y,SBrkr,1301,0,0,1301,0,0,1,0,2,1,Fair,5,Min1,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,100000,-93.624689,42.031395 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1936,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,927,927,GasA,Typical,Y,SBrkr,1067,472,0,1539,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,112,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,141500,-93.623645,42.03252 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1935,1995,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,989,584,0,1573,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,54,0,120,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,133000,-93.623644,42.03246 -One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Poor,Fair,1936,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fair,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,264,264,Grav,Fair,N,FuseA,800,0,0,800,0,0,1,0,1,1,Fair,4,Maj1,1,Poor,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,ConLw,Normal,60000,-93.623643,42.032359 -One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1931,1993,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,506,715,GasA,Typical,Y,FuseA,875,0,0,875,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Typical,Paved,48,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,105000,-93.623629,42.031292 -One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Above_Average,1925,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Good,Y,SBrkr,995,0,0,995,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,15,51,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Abnorml,115000,-93.622565,42.032375 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1938,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,LwQ,4,Unf,0,679,952,GasA,Typical,Y,FuseA,994,588,0,1582,0,0,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,250,Fair,Typical,Paved,189,0,34,150,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,150000,-93.621351,42.03235 -One_Story_1945_and_Older,Residential_Medium_Density,50,8635,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Average,1925,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,938,1072,GasA,Typical,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,5,1184,Fair,Typical,Partial_Pavement,0,0,105,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,126500,-93.6213592,42.0310861 -Two_and_Half_Story_All_Ages,Residential_Low_Density,0,11888,Pave,Paved,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,PosN,Norm,OneFam,Two_and_Half_Unf,Above_Average,Above_Average,1916,1994,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,844,844,GasA,Good,N,FuseA,1445,689,0,2134,0,0,2,0,5,1,Good,10,Typ,0,No_Fireplace,Detchd,Unf,2,441,Typical,Typical,Paved,0,60,268,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,214500,-93.62726,42.030062 -Two_Story_1945_and_Older,Residential_Low_Density,60,11414,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Brookside,RRAn,Feedr,OneFam,Two_Story,Good,Very_Good,1910,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,BrkTil,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Typical,N,SBrkr,1136,883,0,2019,0,0,1,0,3,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,2,532,Typical,Typical,Paved,509,135,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2009,WD ,Normal,167500,-93.625362,42.026759 -Two_Story_1945_and_Older,Residential_Medium_Density,57,8094,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1048,1048,GasA,Good,Y,FuseA,1048,720,0,1768,0,0,2,0,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,576,Fair,Fair,Paved,0,0,150,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,155000,-93.621525,42.028948 -Two_Story_1945_and_Older,Residential_Medium_Density,50,2500,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1915,2005,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,611,910,GasA,Excellent,Y,SBrkr,916,910,0,1826,1,0,1,1,4,1,Excellent,7,Min2,1,Good,Attchd,Unf,1,164,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,155000,-93.621504,42.027854 -Two_Story_1945_and_Older,Residential_Medium_Density,68,9928,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1915,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Fair,Y,SBrkr,1272,672,0,1944,0,0,2,0,3,1,Typical,8,Min2,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,24,28,0,0,0,0,No_Pool,No_Fence,Shed,400,6,2009,WD ,Normal,179900,-93.621506,42.027998 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,92,5520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Fin,Above_Average,Above_Average,1912,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,755,755,GasA,Excellent,Y,SBrkr,929,929,371,2229,0,0,1,0,5,1,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,198,30,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Abnorml,104000,-93.621635,42.027039 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,50,3000,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,Two_Story,Average,Very_Poor,1922,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1040,1040,GasA,Typical,N,SBrkr,1088,1040,0,2128,0,0,2,0,4,2,Typical,11,Sev,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Abnorml,62500,-93.621633,42.026956 -Two_Story_1945_and_Older,Residential_Medium_Density,57,6876,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1927,1950,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,198,720,GasA,Fair,Y,SBrkr,1146,784,0,1930,1,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,1,316,Typical,Typical,Paved,0,0,213,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,149000,-93.628299,42.025306 -One_Story_1945_and_Older,Residential_Medium_Density,52,6240,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,628,780,GasA,Typical,Y,FuseA,848,0,360,1208,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,539,Typical,Typical,Paved,0,23,112,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,103000,-93.626716,42.023085 -Two_Story_1945_and_Older,Residential_Medium_Density,0,5775,Pave,No_Alley_Access,Moderately_Irregular,Bnk,AllPub,Corner,Mod,Old_Town,Feedr,Norm,OneFam,Two_Story,Above_Average,Good,1915,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,483,483,GasA,Excellent,Y,SBrkr,741,686,0,1427,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,1,379,Typical,Typical,Paved,0,24,112,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,123000,-93.621862,42.025796 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,41,5852,Pave,No_Alley_Access,Irregular,Bnk,AllPub,Corner,Gtl,Old_Town,Feedr,Norm,TwoFmCon,Two_and_Half_Unf,Good,Average,1902,2000,Gable,CompShg,MetalSd,MetalSd,Stone,188,Typical,Fair,BrkTil,Typical,Fair,No,Rec,6,Unf,0,851,1020,GasA,Typical,N,FuseF,978,886,0,1864,0,0,2,1,6,1,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,188,102,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,97500,-93.6217097,42.0260296 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1204,1204,GasA,Typical,Y,FuseA,1204,462,0,1666,0,0,1,0,3,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,384,Fair,Typical,Paved,0,0,148,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,135000,-93.6250433,42.0242487 -Split_Foyer,Residential_Medium_Density,86,5160,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,SFoyer,Below_Average,Above_Average,1923,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Good,Fair,Av,BLQ,2,Rec,63,46,858,GasA,Typical,Y,SBrkr,892,0,0,892,1,0,1,0,1,1,Good,5,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,105,0,160,0,No_Pool,No_Fence,None,0,7,2009,COD,Abnorml,70000,-93.625154,42.024143 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,TwoFmCon,Two_Story,Above_Average,Good,1915,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,ALQ,262,160,698,GasA,Excellent,Y,FuseF,754,649,0,1403,1,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,288,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,116000,-93.599575,42.022828 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,4280,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Excellent,1946,2001,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,560,560,GasA,Excellent,Y,FuseA,704,0,0,704,0,1,1,0,2,1,Fair,4,Typ,0,No_Fireplace,CarPort,Unf,1,220,Typical,Typical,Paved,0,0,24,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,88750,-93.659205,42.032681 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1987,1988,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Good,Gd,GLQ,3,Unf,0,0,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,3,0,3,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Alloca,179000,-93.658207,42.028101 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1987,1988,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Good,Good,Gd,GLQ,3,Unf,0,0,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,3,0,3,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Alloca,179000,-93.658173,42.028098 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,10547,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Average,Average,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Good,Gd,GLQ,3,Unf,0,0,1152,GasA,Typical,Y,SBrkr,1152,0,0,1152,2,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,252,Typical,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,159900,-93.657107,42.028049 -One_Story_1945_and_Older,Residential_Low_Density,0,10020,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Sev,Edwards,Norm,Norm,OneFam,One_Story,Very_Poor,Very_Poor,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,BrkTil,Fair,Poor,Gd,BLQ,2,Unf,0,333,683,GasA,Good,N,FuseA,904,0,0,904,1,0,0,1,1,1,Fair,4,Maj1,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,61000,-93.6601886,42.0269586 -Two_Story_1945_and_Older,Residential_High_Density,54,6629,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,Two_Story,Above_Average,Above_Average,1925,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,121,672,GasA,Typical,N,SBrkr,697,672,0,1369,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,300,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,103600,-93.657241,42.022825 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Fair,1934,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Typical,N,FuseA,687,425,0,1112,1,0,2,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,226,Poor,Poor,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,63000,-93.658381,42.022978 -Split_or_Multilevel,Residential_Low_Density,70,12886,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Above_Average,1963,1999,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,76,520,GasA,Excellent,Y,SBrkr,1464,0,0,1464,0,1,2,0,3,1,Typical,6,Min2,1,Typical,Attchd,RFn,2,480,Typical,Typical,Paved,302,0,0,0,100,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,175000,-93.669839,42.033376 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8816,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,470,1121,GasA,Typical,Y,SBrkr,1121,0,0,1121,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,80,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,139000,-93.675401,42.033633 -Two_Story_1946_and_Newer,Residential_Low_Density,70,11184,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkFace,92,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,500,192,918,GasA,Good,Y,SBrkr,918,765,0,1683,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,243,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,172500,-93.672288,42.032367 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,172,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,525,1052,GasA,Typical,Y,SBrkr,1052,0,0,1052,0,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,Fin,1,668,Typical,Typical,Paved,0,215,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Abnorml,113500,-93.673382,42.032388 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1967,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,89,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,414,864,GasA,Excellent,Y,SBrkr,899,0,0,899,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,1,288,Typical,Typical,Paved,64,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,130000,-93.677991,42.029863 -Split_Foyer,Residential_Low_Density,0,8014,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,HdBoard,BrkFace,23,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,456,GasA,Typical,Y,SBrkr,1034,0,0,1034,0,1,1,0,3,1,Typical,5,Typ,1,Fair,Basment,Fin,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,149900,-93.672651,42.031342 -Split_Foyer,Residential_Low_Density,0,7252,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Average,1982,1982,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,173,858,GasA,Typical,Y,SBrkr,858,0,0,858,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,134900,-93.671849,42.030628 -Split_Foyer,Residential_Low_Density,74,8740,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1982,1982,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,168,840,GasA,Typical,Y,SBrkr,860,0,0,860,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,137000,-93.671369,42.031138 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11616,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,116,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,670,252,1092,GasA,Typical,Y,SBrkr,1092,0,0,1092,0,1,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,0,20,144,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,139000,-93.666432,42.033427 -Split_or_Multilevel,Residential_Low_Density,88,15400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Average,1961,1961,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Good,No,Unf,7,Unf,0,552,552,GasA,Typical,Y,SBrkr,904,611,259,1774,0,0,2,0,4,1,Typical,8,Typ,1,Typical,Attchd,Unf,1,384,Typical,Typical,Paved,290,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,165000,-93.66636,42.033277 -Split_or_Multilevel,Residential_Low_Density,88,15312,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Average,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,54,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,550,1138,GasA,Excellent,Y,SBrkr,1138,0,0,1138,0,1,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,480,Typical,Typical,Paved,0,0,0,0,140,0,No_Pool,Minimum_Privacy,None,0,3,2009,COD,Normal,148000,-93.666813,42.032302 -Split_or_Multilevel,Residential_Low_Density,0,15584,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,SLvl,Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,366,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,825,825,GasA,Excellent,Y,SBrkr,2071,0,0,2071,0,1,1,1,4,1,Typical,9,Typ,1,Typical,Attchd,Unf,1,336,Typical,Typical,Paved,131,28,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,165000,-93.662126,42.032652 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Poor,Poor,1947,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,SBrkr,660,0,0,660,0,0,1,0,2,1,Fair,5,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,100,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,63900,-93.659279,42.032564 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1957,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,1104,1104,GasA,Excellent,Y,FuseA,1104,684,0,1788,1,0,1,0,5,1,Typical,8,Min2,2,Typical,Attchd,Unf,1,304,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,161500,-93.660877,42.032503 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,25095,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1968,2003,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,113,1437,GasA,Excellent,Y,SBrkr,1473,0,0,1473,2,0,1,0,1,1,Excellent,5,Typ,2,Good,Attchd,Unf,1,452,Typical,Typical,Paved,0,48,0,0,60,0,No_Pool,No_Fence,None,0,6,2009,WD ,Partial,143000,-93.66798,42.026548 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,75,1134,GasA,Excellent,Y,FuseA,1229,0,0,1229,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,284,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,135000,-93.671177,42.023229 -One_Story_1945_and_Older,Residential_Low_Density,0,12342,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,599,861,GasA,Excellent,Y,SBrkr,861,0,0,861,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,539,Typical,Typical,Paved,158,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,82500,-93.6721573,42.0240278 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10708,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1955,1993,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,768,470,1617,GasA,Excellent,Y,FuseA,1867,0,0,1867,1,0,1,0,2,1,Typical,7,Typ,3,Good,Attchd,Fin,1,303,Typical,Typical,Paved,476,0,0,0,142,0,No_Pool,Good_Wood,None,0,11,2009,COD,Normal,190000,-93.666242,42.028468 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13680,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Average,1955,1955,Hip,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,FuseA,1733,0,0,1733,0,0,2,0,4,1,Typical,8,Min2,1,Good,Attchd,Unf,2,452,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,139600,-93.665996,42.027656 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15635,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1383,0,0,1383,0,0,1,0,2,1,Typical,6,Mod,0,No_Fireplace,Attchd,Unf,2,498,Fair,Typical,Paved,0,0,90,0,110,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,122000,-93.665994,42.027506 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,9855,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1436,1436,GasA,Fair,Y,SBrkr,1689,0,0,1689,0,0,1,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,COD,Normal,127500,-93.667776,42.024483 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,816,1073,GasA,Typical,Y,FuseA,1073,0,0,1073,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,340,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,2,2009,WD ,Normal,121500,-93.668212,42.023982 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Fair,1958,1958,Gable,CompShg,BrkComm,Brk Cmn,None,0,Typical,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,1276,1276,GasA,Typical,Y,FuseA,1276,0,0,1276,0,0,1,0,3,1,Typical,5,Mod,0,No_Fireplace,Attchd,Unf,1,350,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,COD,Abnorml,60000,-93.667209,42.023916 -Two_Story_1946_and_Newer,Residential_Low_Density,50,9350,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1946,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,405,747,GasA,Excellent,Y,SBrkr,892,747,0,1639,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,154400,-93.665271,42.026133 -One_Story_1945_and_Older,Residential_Low_Density,50,9340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1941,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,328,672,GasA,Typical,Y,SBrkr,672,0,0,672,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,234,Typical,Typical,Dirt_Gravel,0,113,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,113000,-93.66527,42.025996 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,7440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,916,1089,GasW,Typical,Y,SBrkr,1089,0,0,1089,1,0,1,0,3,1,Typical,5,Typ,1,Good,Detchd,RFn,1,252,Typical,Typical,Partial_Pavement,328,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2009,WD ,Normal,125000,-93.660147,42.026307 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,N,SBrkr,845,0,0,845,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,290,Typical,Typical,Dirt_Gravel,186,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,84000,-93.663366,42.024993 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,42,4235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,One_Story,Average,Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,BrkFace,149,Good,Typical,CBlock,Good,Typical,Mn,GLQ,3,ALQ,393,104,1049,GasA,Typical,Y,SBrkr,1049,0,0,1049,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,1,266,Typical,Typical,Paved,0,105,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,139500,-93.6641939,42.0246851 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,11409,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1949,2008,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,476,768,GasA,Good,Y,SBrkr,1148,568,0,1716,0,0,1,1,3,1,Typical,8,Min2,1,Good,Attchd,Unf,1,281,Typical,Typical,Paved,0,0,0,0,160,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,131000,-93.66104,42.0247212 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1939,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Rec,6,Unf,0,356,560,GasA,Typical,Y,SBrkr,698,560,0,1258,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,105000,-93.661005,42.023548 -Two_Story_1945_and_Older,Residential_Low_Density,60,9084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,Two_Story,Below_Average,Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,755,755,GasA,Typical,Y,SBrkr,755,755,0,1510,1,0,1,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,1,296,Fair,Poor,Partial_Pavement,120,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,108000,-93.659855,42.022754 -Split_or_Multilevel,Residential_Low_Density,74,10778,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,SLvl,Good,Above_Average,1990,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,286,308,1054,GasA,Good,Y,SBrkr,1061,0,0,1061,1,0,1,1,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,2,462,Typical,Typical,Paved,114,36,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,162000,-93.684708,42.034183 -Split_or_Multilevel,Residential_Low_Density,66,19255,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,SLvl,Above_Average,Average,1983,1983,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,100,Good,Typical,CBlock,Good,Typical,Av,Rec,6,GLQ,450,0,520,GasA,Good,Y,SBrkr,1338,0,0,1338,0,0,1,1,2,1,Good,5,Min2,1,Poor,Attchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,600,9,2009,WD ,Normal,156500,-93.685733,42.033714 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,79,12327,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Very_Good,1983,2009,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,55,1496,GasA,Excellent,Y,SBrkr,1496,636,0,2132,1,0,1,1,1,1,Good,5,Min2,1,Good,BuiltIn,Fin,2,612,Good,Typical,Paved,349,40,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,316600,-93.685986,42.0318449 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,14684,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Good,1990,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,234,Good,Typical,CBlock,Good,Typical,Mn,ALQ,1,BLQ,177,1496,2158,GasA,Good,Y,SBrkr,2196,0,0,2196,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,701,Typical,Typical,Paved,84,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,271900,-93.68341,42.03254 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9317,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,137,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,227,740,GasA,Excellent,Y,SBrkr,1006,769,0,1775,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,425,Typical,Typical,Paved,234,72,192,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,213000,-93.681063,42.030625 -Two_Story_1946_and_Newer,Residential_Low_Density,85,10560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,605,1079,GasA,Excellent,Y,SBrkr,1079,800,0,1879,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,473,Typical,Typical,Paved,400,100,144,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,239900,-93.682025,42.030623 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9828,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,544,1128,GasA,Excellent,Y,SBrkr,1142,878,0,2020,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,466,Typical,Typical,Paved,0,155,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,239500,-93.682176,42.030678 -Two_Story_1945_and_Older,Residential_Low_Density,120,26400,Pave,No_Alley_Access,Regular,Bnk,AllPub,FR2,Gtl,Sawyer_West,Feedr,Norm,OneFam,Two_Story,Average,Good,1880,2007,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1288,728,0,2016,0,0,1,0,4,1,Typical,7,Mod,1,Typical,Attchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,131000,-93.679789,42.03252 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,410,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,118964,-93.679784,42.031622 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,SFoyer,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,Stone,275,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1086,GasA,Typical,Y,SBrkr,1224,0,0,1224,2,0,0,2,2,2,Typical,6,Typ,2,Typical,Detchd,Unf,2,528,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,153337,-93.6791403,42.031834 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1114,1114,0,2228,0,0,2,0,6,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,73,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,147983,-93.679109,42.031623 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Alloca,118858,-93.679784,42.031545 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,118858,-93.6795273,42.0314669 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1120,1120,0,2240,0,0,2,0,6,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,154,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,142953,-93.6790969,42.0314606 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7040,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,SFoyer,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,BrkFace,216,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1094,GasA,Typical,Y,SBrkr,1229,0,0,1229,2,0,0,2,2,2,Good,6,Typ,2,Typical,Detchd,Unf,2,672,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,148325,-93.6791456,42.0312822 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7007,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1513,0,0,1513,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,113722,-93.679781,42.03057 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,11855,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,Two_Story,Good,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,23,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,348,1168,GasA,Excellent,Y,SBrkr,1168,1619,0,2787,2,0,4,2,6,2,Typical,8,Typ,2,Typical,BuiltIn,Fin,4,820,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,269500,-93.679856,42.030531 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7939,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Good,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,23,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,348,1168,GasA,Excellent,Y,SBrkr,1168,1619,0,2787,2,0,4,2,6,2,Typical,8,Typ,2,Typical,BuiltIn,Fin,4,820,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,269500,-93.6791251,42.0310946 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7976,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Good,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,23,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,348,1168,GasA,Excellent,Y,SBrkr,1168,1619,0,2787,2,0,4,2,6,2,Typical,8,Typ,2,Typical,BuiltIn,Fin,4,820,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,269500,-93.679181,42.0309301 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,10933,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2009,Hip,CompShg,VinylSd,VinylSd,Stone,242,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,534,1555,GasA,Excellent,Y,SBrkr,1680,0,0,1680,1,0,1,1,1,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,1138,Typical,Typical,Paved,185,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,323262,-93.687067,42.026626 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10637,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,336,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,417,1705,GasA,Excellent,Y,SBrkr,1718,0,0,1718,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,826,Typical,Typical,Paved,208,44,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,297000,-93.688859,42.026482 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,10226,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,270,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1622,1622,GasA,Excellent,Y,SBrkr,1630,0,0,1630,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,860,Typical,Typical,Paved,172,42,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,295493,-93.688993,42.026414 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,10816,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,364,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,616,1720,GasA,Excellent,Y,SBrkr,1720,0,0,1720,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,846,Typical,Typical,Paved,208,104,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,332000,-93.689091,42.026372 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9178,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1468,1468,GasA,Excellent,Y,SBrkr,1468,0,0,1468,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,904,Typical,Typical,Paved,192,142,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,239900,-93.690266,42.025707 -Two_Story_1946_and_Newer,Residential_Low_Density,68,10769,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,846,866,GasA,Excellent,Y,SBrkr,866,902,0,1768,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,144,105,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,212000,-93.690971,42.025293 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,11422,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,352,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,479,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,RFn,2,524,Typical,Typical,Paved,154,222,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,272500,-93.690364,42.025577 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,122,11923,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1800,1800,GasA,Excellent,Y,SBrkr,1800,0,0,1800,0,0,2,0,2,1,Excellent,7,Typ,0,No_Fireplace,Attchd,Fin,2,702,Typical,Typical,Paved,288,136,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,239000,-93.689072,42.024628 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8814,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,180,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,270,1604,GasA,Excellent,Y,SBrkr,1604,0,0,1604,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,660,Typical,Typical,Paved,123,110,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Abnorml,220000,-93.689071,42.024596 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,380,1282,GasA,Excellent,Y,SBrkr,1290,0,0,1290,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,662,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,200000,-93.691433,42.024613 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,10324,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,140,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,382,1254,GasA,Excellent,Y,SBrkr,1254,0,0,1254,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,3,810,Typical,Typical,Paved,168,92,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,221800,-93.69157,42.024765 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7314,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,82,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,508,1232,GasA,Excellent,Y,SBrkr,1232,0,0,1232,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,194500,-93.690111,42.024327 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11645,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,76,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1474,1498,GasA,Excellent,Y,SBrkr,1498,0,0,1498,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,844,Typical,Typical,Paved,144,98,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,237000,-93.689066,42.024399 -Two_Story_1946_and_Newer,Residential_Low_Density,79,11646,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,704,704,GasA,Excellent,Y,SBrkr,704,718,0,1422,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,36,28,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,173000,-93.689081,42.02322 -Split_or_Multilevel,Residential_Low_Density,0,12800,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,SLvl,Good,Average,1989,1989,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,145,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1518,GasA,Good,Y,SBrkr,1644,0,0,1644,1,1,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,569,Typical,Typical,Paved,80,0,0,0,396,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,275000,-93.685125,42.029772 -Two_Story_1946_and_Newer,Residential_Low_Density,0,16698,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Mod,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1992,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,649,1449,GasA,Good,Y,SBrkr,944,815,0,1759,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,525,Typical,Typical,Paved,150,193,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,233500,-93.679297,42.02792 -Two_Story_1946_and_Newer,Residential_Low_Density,0,28698,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,Two_Story,Average,Average,1967,1967,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,PConc,Typical,Good,Gd,LwQ,4,ALQ,764,0,1013,GasA,Typical,Y,SBrkr,1160,966,0,2126,0,1,2,1,3,1,Typical,7,Min2,0,No_Fireplace,Attchd,Fin,2,538,Typical,Typical,Paved,486,0,0,0,225,0,No_Pool,No_Fence,None,0,6,2009,WD ,Abnorml,185000,-93.6825646,42.0255464 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,12464,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Corner,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,308,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,Good_Privacy,None,0,11,2009,WD ,Normal,152000,-93.691883,42.021171 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9757,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,ALQ,1,Unf,0,235,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,440,Typical,Typical,Paved,66,0,0,0,92,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,143000,-93.692135,42.021176 -Two_Story_1946_and_Newer,Residential_Low_Density,65,15426,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,107,928,GasA,Excellent,Y,SBrkr,928,836,0,1764,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,470,Typical,Typical,Paved,276,99,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,231500,-93.688372,42.021207 -Two_Story_1946_and_Newer,Residential_Low_Density,43,10667,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Good,Above_Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,ALQ,344,70,799,GasA,Excellent,Y,SBrkr,827,834,0,1661,1,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,158,61,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,ConLw,Normal,212000,-93.690401,42.020817 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,14753,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,513,1463,GasA,Excellent,Y,SBrkr,1463,0,0,1463,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,539,Typical,Typical,Paved,0,81,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2009,WD ,Normal,207000,-93.691355,42.019784 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,112,10859,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1097,1097,GasA,Excellent,Y,SBrkr,1097,0,0,1097,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,672,Typical,Typical,Paved,392,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,145000,-93.691585,42.018996 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Good,No,ALQ,1,Unf,0,244,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,138000,-93.6928668,42.0197228 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,866,866,GasA,Excellent,Y,SBrkr,866,913,0,1779,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,546,Typical,Typical,Paved,198,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,197900,-93.690548,42.018597 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10335,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,183,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,811,1490,GasA,Excellent,Y,SBrkr,1501,0,0,1501,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,577,Typical,Typical,Paved,144,29,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,204000,-93.690324,42.017569 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9017,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,871,1431,GasA,Excellent,Y,SBrkr,1431,0,0,1431,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,666,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,192000,-93.691667,42.016903 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,322,928,GasA,Excellent,Y,SBrkr,928,844,0,1772,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,492,Typical,Typical,Paved,150,96,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,200000,-93.688383,42.018667 -Two_Story_1946_and_Newer,Residential_Low_Density,68,8935,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,831,831,GasA,Excellent,Y,SBrkr,831,829,0,1660,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,493,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,195000,-93.689431,42.016862 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,9808,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,110,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,785,1573,GasA,Excellent,Y,SBrkr,1573,0,0,1573,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,544,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,227000,-93.691175,42.016399 -Two_Story_1946_and_Newer,Residential_Low_Density,79,12420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,278,944,GasA,Excellent,Y,SBrkr,944,896,0,1840,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,622,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,230000,-93.689438,42.01571 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,16285,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1413,1413,GasA,Excellent,Y,SBrkr,1430,0,0,1430,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,605,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,187100,-93.689942,42.016551 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,10739,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,172,1431,GasA,Excellent,Y,SBrkr,1444,0,0,1444,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,577,Typical,Typical,Paved,144,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,203000,-93.691672,42.01613 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11166,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1468,1492,GasA,Excellent,Y,SBrkr,1492,0,0,1492,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,0,114,0,0,168,0,No_Pool,No_Fence,None,0,7,2009,WD ,Family,201000,-93.688628,42.015713 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8430,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,136,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,424,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,124000,-93.686916,42.021384 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16269,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1978,1978,Gable,CompShg,MetalSd,MetalSd,BrkFace,76,Typical,Typical,BrkTil,Good,Typical,Av,GLQ,3,Unf,0,282,907,GasA,Typical,Y,SBrkr,907,0,0,907,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,343,Typical,Typical,Paved,72,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,140000,-93.687212,42.020087 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,6950,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1979,1979,Gable,CompShg,HdBoard,HdBoard,BrkFace,40,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,72,132,914,GasA,Typical,Y,SBrkr,914,0,0,914,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,444,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,134900,-93.687219,42.019184 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,8800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1977,2008,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Mn,LwQ,4,Rec,144,364,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,288,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,150500,-93.685068,42.019697 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1978,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,90,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,218,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,136500,-93.684887,42.019548 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9286,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1977,1989,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Good,Good,Av,ALQ,1,Unf,0,1072,1268,GasA,Typical,Y,SBrkr,1268,0,0,1268,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,252,Typical,Typical,Paved,173,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,143500,-93.684954,42.019691 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,36,15523,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,404,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Attchd,Unf,1,338,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,133500,-93.683157,42.020666 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,117,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,600,Typical,Typical,Paved,215,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,123000,-93.683159,42.020991 -Split_Foyer,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Very_Good,1972,2003,Gable,CompShg,WdShing,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,108,768,GasA,Good,Y,SBrkr,768,0,0,768,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Fin,1,396,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,133900,-93.68317,42.021788 -Split_Foyer,Residential_Low_Density,70,8445,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Good,1972,2007,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,112,768,GasA,Typical,Y,SBrkr,768,0,0,768,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,396,Typical,Typical,Paved,58,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,133000,-93.68351,42.020843 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11664,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,335,1569,GasA,Excellent,Y,SBrkr,1611,0,0,1611,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,1231,Typical,Typical,Paved,262,93,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,250000,-93.687819,42.017109 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12334,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,198,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1068,1068,GasA,Excellent,Y,SBrkr,1068,1116,0,2184,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,RFn,2,570,Typical,Typical,Paved,192,132,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,254750,-93.686218,42.018722 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11838,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2001,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1753,1753,GasA,Excellent,Y,SBrkr,1788,0,0,1788,0,0,2,0,3,1,Excellent,7,Typ,1,Typical,Attchd,RFn,2,522,Typical,Typical,Paved,202,151,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,236500,-93.68269,42.018847 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11885,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,309,1299,GasA,Excellent,Y,SBrkr,1299,573,0,1872,1,0,2,1,3,1,Excellent,7,Typ,1,Typical,BuiltIn,RFn,2,531,Typical,Typical,Paved,160,122,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,261500,-93.682623,42.01877 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Excellent,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,204,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,536,1440,GasA,Excellent,Y,SBrkr,1476,677,0,2153,1,0,2,1,3,1,Excellent,8,Typ,2,Excellent,Attchd,Fin,3,736,Typical,Typical,Paved,253,142,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,313000,-93.682983,42.018703 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8749,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,885,0,1725,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,550,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,198500,-93.686744,42.016973 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,944,944,GasA,Excellent,Y,SBrkr,944,926,0,1870,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,608,Typical,Typical,Paved,256,43,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,211000,-93.685673,42.016978 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,15750,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,College_Creek,Feedr,Norm,OneFam,One_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,260,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,216,1462,GasA,Excellent,Y,SBrkr,1513,0,0,1513,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,521,Typical,Typical,Paved,135,34,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,220000,-93.67915,42.0181479 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,11883,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1996,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,814,1504,GasA,Excellent,Y,SBrkr,1504,0,0,1504,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,478,Typical,Typical,Paved,115,66,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,203000,-93.680706,42.018875 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12782,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,HdBoard,HdBoard,BrkFace,164,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,462,1822,GasA,Excellent,Y,SBrkr,1828,0,0,1828,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,523,Typical,Typical,Paved,194,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,279500,-93.681109,42.018866 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,209,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,306,1417,GasA,Excellent,Y,SBrkr,1417,0,0,1417,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,511,Typical,Typical,Paved,60,0,0,0,117,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,210000,-93.681343,42.018006 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,573,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,318,1057,GasA,Excellent,Y,SBrkr,1057,977,0,2034,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,645,Typical,Typical,Paved,576,36,0,0,0,0,No_Pool,Good_Privacy,None,0,2,2009,WD ,Normal,219500,-93.681411,42.017856 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,880,880,GasA,Excellent,Y,SBrkr,909,807,0,1716,0,0,2,1,2,1,Good,7,Typ,1,Typical,Attchd,RFn,2,512,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,191000,-93.680782,42.017856 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1212,1212,GasA,Excellent,Y,SBrkr,1212,0,0,1212,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,573,Typical,Typical,Paved,356,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,178000,-93.68218,42.016819 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,171,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1630,1630,GasA,Excellent,Y,SBrkr,1630,0,0,1630,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,451,Typical,Typical,Paved,74,234,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,213000,-93.68167,42.016797 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,163,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,144000,-93.681297,42.016274 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,163,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,140000,-93.681439,42.016271 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,100,1578,GasA,Excellent,Y,SBrkr,1602,0,0,1602,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,810,Typical,Typical,Paved,0,48,0,0,195,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,293200,-93.684115,42.016468 -Two_Story_1946_and_Newer,Residential_Low_Density,65,9313,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,864,864,GasA,Excellent,Y,SBrkr,864,864,0,1728,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,572,Typical,Typical,Paved,187,56,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,190000,-93.685333,42.013957 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,8487,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1480,1500,GasA,Excellent,Y,SBrkr,1500,0,0,1500,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,570,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,190000,-93.686051,42.014054 -Two_Story_1946_and_Newer,Residential_Low_Density,64,8633,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,545,738,GasA,Excellent,Y,SBrkr,738,738,0,1476,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,540,Typical,Typical,Paved,100,35,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,173500,-93.684536,42.013512 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11069,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,608,608,GasA,Excellent,Y,SBrkr,608,788,0,1396,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,440,Typical,Typical,Paved,100,36,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,170000,-93.6843,42.013392 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1949,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1126,0,0,1126,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Fin,2,520,Typical,Typical,Dirt_Gravel,0,96,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,108000,-93.678305,42.020123 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9937,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1964,1999,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Typical,Typical,No,BLQ,2,Unf,0,849,1486,GasA,Excellent,Y,SBrkr,1486,0,0,1486,1,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Fin,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,167000,-93.677407,42.021246 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8877,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1938,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,126,816,GasA,Excellent,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,3,Typ,1,Good,Detchd,Unf,1,288,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,100000,-93.676372,42.021219 -Split_Foyer,Residential_Low_Density,64,7301,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Good,Average,2003,2003,Gable,CompShg,HdBoard,HdBoard,BrkFace,500,Good,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,495,1427,0,1922,0,0,3,0,4,1,Good,7,Typ,1,Excellent,BuiltIn,RFn,2,672,Typical,Typical,Paved,0,0,177,0,0,0,No_Pool,No_Fence,None,0,7,2009,ConLD,Normal,198500,-93.672234,42.018988 -Two_Story_1946_and_Newer,Residential_Low_Density,75,7950,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1977,1977,Hip,CompShg,HdBoard,Plywood,BrkFace,140,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,155,690,GasA,Typical,Y,SBrkr,698,728,0,1426,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,252,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,159500,-93.6755239,42.0186549 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10682,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1960,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,615,1014,GasA,Typical,Y,SBrkr,1149,0,0,1149,1,0,1,0,3,1,Typical,7,Min1,0,No_Fireplace,More_Than_Two_Types,Fin,1,544,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,127000,-93.6739723,42.0196717 -Split_or_Multilevel,Residential_Low_Density,72,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1976,1976,Hip,CompShg,MetalSd,MetalSd,BrkFace,255,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,410,1041,GasA,Excellent,Y,SBrkr,1125,0,0,1125,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,1,352,Typical,Typical,Paved,296,0,0,0,0,0,No_Pool,Good_Wood,None,0,10,2009,WD ,Abnorml,125000,-93.675248,42.018928 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,13286,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,340,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,464,1698,GasA,Excellent,Y,SBrkr,1698,0,0,1698,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,768,Typical,Typical,Paved,327,64,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,320000,-93.676608,42.017026 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Typical,Typical,Wood,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,145500,-93.669952,42.01896 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,9405,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Excellent,1947,2008,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Excellent,Y,SBrkr,698,0,0,698,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,200,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,118000,-93.664779,42.022478 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6410,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1959,1959,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,243,301,876,GasA,Typical,Y,FuseA,876,0,0,876,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,320,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,85000,-93.666315,42.019423 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,Rec,6,Unf,0,0,1078,GasA,Typical,Y,FuseA,1368,0,0,1368,1,0,1,0,3,1,Typical,6,Min1,0,No_Fireplace,Attchd,RFn,1,195,Typical,Typical,Paved,0,41,211,0,0,0,No_Pool,No_Fence,Shed,900,6,2009,WD ,Normal,120000,-93.664685,42.020321 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,8405,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Fair,1945,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Typical,N,FuseF,1088,441,0,1529,0,0,2,0,4,1,Typical,9,Mod,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,92,0,185,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,98000,-93.6644556,42.0217997 -One_Story_1945_and_Older,Residential_Low_Density,56,4060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,864,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,99900,-93.659191,42.021496 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,10206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Fair,1952,1952,Flat,Tar&Grv,BrkComm,Brk Cmn,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasW,Fair,N,FuseF,944,0,0,944,0,0,1,0,2,1,Fair,4,Min1,0,No_Fireplace,Detchd,Unf,2,528,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,82000,-93.664812,42.019004 -Duplex_All_Styles_and_Ages,Residential_Low_Density,65,10926,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1959,1959,Hip,CompShg,VinylSd,VinylSd,BrkFace,74,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1678,1678,GasA,Typical,Y,SBrkr,1678,0,0,1678,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,119900,-93.6644024,42.01822 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,HdBoard,HdBoard,BrkFace,259,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,604,1150,GasA,Excellent,Y,SBrkr,1560,0,0,1560,0,0,1,0,2,1,Typical,7,Min1,1,Typical,Attchd,Unf,1,313,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,103500,-93.664336,42.019381 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8926,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1956,1956,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,FuseA,960,0,0,960,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,1,288,Typical,Typical,Paved,64,0,0,0,160,0,No_Pool,Minimum_Privacy,None,0,10,2009,COD,Abnorml,112000,-93.663302,42.018592 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,67,12354,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1920,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Unf,7,Unf,0,684,684,GasA,Good,Y,SBrkr,684,512,0,1196,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,46,0,0,0,0,No_Pool,Good_Privacy,Shed,800,8,2009,ConLI,Normal,110000,-93.657963,42.022092 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,8212,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,94,720,GasA,Excellent,Y,SBrkr,854,444,0,1298,0,0,1,0,3,1,Typical,6,Typ,2,Good,Detchd,Unf,1,256,Typical,Typical,Paved,84,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,122000,-93.656664,42.022088 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10998,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1941,1960,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,420,156,984,GasA,Excellent,Y,SBrkr,984,620,0,1604,0,0,2,0,3,1,Typical,6,Min2,0,No_Fireplace,Detchd,Unf,2,660,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,117000,-93.655674,42.022091 -One_and_Half_Story_Finished_All_Ages,Residential_High_Density,70,6300,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1938,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,88,Typical,Typical,PConc,Typical,Fair,No,LwQ,4,Unf,0,0,832,GasA,Typical,Y,SBrkr,832,436,0,1268,0,0,1,1,3,1,Typical,7,Typ,2,Good,Basment,Unf,1,250,Typical,Typical,Paved,0,0,55,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Abnorml,160000,-93.651825,42.020123 -One_and_Half_Story_Finished_All_Ages,Residential_High_Density,55,4500,Pave,Paved,Moderately_Irregular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1932,2000,Gable,CompShg,VinylSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,611,793,GasA,Excellent,Y,SBrkr,848,672,0,1520,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,281,Typical,Typical,Paved,0,0,56,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Abnorml,159434,-93.652027,42.019713 -One_Story_1945_and_Older,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Poor,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,0,290,GasA,Typical,N,FuseF,438,0,0,438,0,0,1,0,1,1,Fair,3,Typ,0,No_Fireplace,Detchd,Unf,1,246,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,60000,-93.651786,42.017564 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,778,778,GasA,Typical,Y,SBrkr,944,545,0,1489,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,335,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,139500,-93.64857,42.016921 -Two_and_Half_Story_All_Ages,Residential_Low_Density,102,15863,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_and_Half_Fin,Good,Fair,1920,1970,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Typical,Typical,No,GLQ,3,Unf,0,301,824,GasA,Excellent,Y,SBrkr,1687,998,397,3082,1,0,2,1,5,1,Typical,12,Typ,2,Typical,Basment,Fin,2,672,Typical,Typical,Paved,136,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,197000,-93.651669,42.016182 -Two_Story_1945_and_Older,Residential_Low_Density,43,5707,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Above_Average,Above_Average,1935,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,583,583,GasA,Good,Y,FuseF,647,595,0,1242,0,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,180,Fair,Typical,Paved,329,96,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,105000,-93.646555,42.019094 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,65,7804,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Fair,1928,1950,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,500,1122,GasA,Typical,Y,SBrkr,1328,653,0,1981,1,0,2,0,4,1,Good,7,Min2,2,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,431,44,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2009,WD ,Normal,135000,-93.648414,42.018931 -One_Story_1945_and_Older,Residential_Low_Density,64,8574,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1916,2000,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,1232,0,0,1232,0,0,1,0,3,1,Good,6,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,180,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,147500,-93.646655,42.017889 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,6171,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1925,1990,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,712,976,GasA,Excellent,Y,SBrkr,1160,448,0,1608,0,0,2,1,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,216,Fair,Typical,Paved,147,16,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,137450,-93.648429,42.017835 -Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Good,Excellent,1936,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,PConc,Good,Good,No,ALQ,1,BLQ,210,0,560,GasA,Excellent,Y,SBrkr,575,560,0,1135,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,RFn,2,576,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,155000,-93.6459433,42.0189256 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,57,7558,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1928,1950,Gable,CompShg,BrkFace,Stone,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,896,896,GasA,Good,Y,SBrkr,1172,741,0,1913,0,0,1,1,3,1,Typical,9,Typ,1,Typical,Detchd,Unf,2,342,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,177000,-93.646881,42.016943 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,52,6292,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1928,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,861,861,GasA,Good,Y,SBrkr,877,600,0,1477,0,1,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,145000,-93.648419,42.016835 -Two_Story_1945_and_Older,Residential_Low_Density,53,7155,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1918,1990,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,628,600,0,1228,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,215,Fair,Typical,Paved,0,113,0,0,195,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,137000,-93.644704,42.016957 -Two_Story_1945_and_Older,Residential_Low_Density,53,10918,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Excellent,1926,2004,Gambrel,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,1276,1276,GasA,Excellent,Y,SBrkr,1276,804,0,2080,0,0,1,1,3,1,Good,9,Typ,2,Good,Detchd,Unf,1,282,Typical,Typical,Paved,0,0,0,0,145,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,234000,-93.644636,42.016067 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,13680,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,112,840,GasA,Excellent,Y,SBrkr,840,727,0,1567,1,0,1,1,2,1,Typical,6,Min2,2,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,21,150,0,0,0,No_Pool,Good_Privacy,None,0,2,2009,WD ,Normal,205000,-93.6428055,42.0191252 -Two_Story_1945_and_Older,Residential_Low_Density,0,7500,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1942,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,224,771,GasA,Fair,Y,SBrkr,753,741,0,1494,0,0,1,0,3,1,Good,7,Typ,2,Good,Attchd,Unf,1,213,Typical,Typical,Partial_Pavement,0,0,0,0,224,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,177500,-93.6397,42.019181 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,130,9600,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,BrkTil,Typical,Fair,No,Rec,6,Unf,0,300,728,GasA,Excellent,Y,SBrkr,976,332,0,1308,1,0,1,1,2,1,Typical,7,Min2,2,Typical,Detchd,Unf,1,256,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,160000,-93.642371,42.018707 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,14680,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Below_Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,480,1273,GasA,Excellent,Y,SBrkr,1273,0,0,1273,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,307,Typical,Typical,Paved,483,0,0,0,115,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,155000,-93.64012,42.017583 -One_Story_1945_and_Older,Residential_Low_Density,80,13360,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Good,1921,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Good,Typical,No,ALQ,1,Unf,0,163,876,GasA,Excellent,Y,SBrkr,964,0,0,964,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,432,Typical,Typical,Paved,0,0,44,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,163500,-93.640592,42.017574 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,54,7681,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1921,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,731,731,GasA,Excellent,Y,SBrkr,820,523,0,1343,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,186,Fair,Typical,Paved,192,0,102,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,154900,-93.644295,42.016684 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,8145,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Crawford,Norm,Norm,Duplex,Two_and_Half_Unf,Good,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,674,920,GasA,Excellent,Y,SBrkr,1240,1240,0,2480,0,0,2,1,5,2,Typical,13,Typ,2,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,57,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,205000,-93.6439975,42.0158534 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Good,BrkTil,Typical,Typical,Mn,Rec,6,ALQ,694,264,1112,GasA,Excellent,Y,SBrkr,1112,0,0,1112,1,0,1,0,2,1,Typical,6,Typ,1,Good,Attchd,Unf,1,390,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Family,144800,-93.639431,42.016152 -Two_Story_1945_and_Older,Residential_Low_Density,75,12000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Good,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,429,704,GasA,Excellent,Y,SBrkr,860,704,0,1564,0,0,1,1,3,1,Fair,7,Typ,1,Good,Attchd,Unf,1,234,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,174500,-93.642945,42.014688 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13339,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1960,Gable,CompShg,HdBoard,Plywood,Stone,132,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,875,621,1561,GasA,Typical,Y,SBrkr,1561,0,0,1561,1,0,2,0,3,1,Typical,6,Typ,1,Good,Attchd,Fin,2,463,Typical,Typical,Paved,0,148,0,0,120,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,193000,-93.642885,42.013169 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,15600,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1949,2005,Gable,CompShg,BrkComm,Brk Cmn,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Rec,507,248,1067,GasW,Fair,N,SBrkr,986,537,0,1523,1,0,2,0,3,1,Fair,7,Maj2,1,Typical,Attchd,Unf,1,295,Typical,Typical,Paved,0,0,81,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,158000,-93.640762,42.014943 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,14000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1950,2004,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1092,1092,GasA,Excellent,Y,SBrkr,1152,0,0,1152,0,1,1,0,3,1,Good,6,Typ,1,Good,Attchd,Unf,1,300,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2009,WD ,Family,158500,-93.641356,42.013753 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,17500,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Mod,Crawford,PosA,Norm,OneFam,One_Story,Above_Average,Average,1954,1954,Gable,CompShg,HdBoard,HdBoard,Stone,420,Typical,Typical,PConc,Typical,Typical,Av,LwQ,4,BLQ,435,91,1310,GasA,Excellent,Y,SBrkr,1906,0,0,1906,1,0,1,1,3,1,Typical,6,Typ,2,Good,Basment,Unf,2,576,Typical,Typical,Paved,0,201,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,224000,-93.639407,42.0125 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1733,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,516,516,GasA,Typical,Y,SBrkr,516,516,0,1032,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,452,Typical,Typical,Paved,279,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2009,WD ,Normal,115000,-93.645743,42.009489 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1488,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1980,1992,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,90,561,GasA,Typical,Y,SBrkr,561,668,0,1229,1,0,1,1,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,176,0,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2009,WD ,Normal,137000,-93.645743,42.009489 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1612,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,561,561,GasA,Typical,Y,SBrkr,561,668,0,1229,0,0,1,1,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,154,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,121000,-93.645743,42.009489 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,276,561,GasA,Typical,Y,SBrkr,561,668,0,1229,0,0,1,1,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,150,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2009,WD ,Normal,124000,-93.645743,42.009489 -Split_or_Multilevel,Residential_Low_Density,0,13607,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1986,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,242,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,118,572,GasA,Good,Y,SBrkr,1182,800,0,1982,1,0,2,1,3,1,Typical,6,Typ,1,Typical,BuiltIn,Fin,2,501,Typical,Typical,Paved,400,112,0,0,0,0,No_Pool,No_Fence,Shed,1500,4,2009,WD ,Normal,208000,-93.644864,42.010636 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17597,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Good,Excellent,1971,2009,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Good,CBlock,Good,Typical,No,GLQ,3,ALQ,419,581,1803,GasA,Typical,Y,SBrkr,2365,0,0,2365,1,0,2,0,3,1,Excellent,7,Min1,2,Good,Attchd,Fin,2,551,Typical,Typical,Paved,200,144,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2009,WD ,Normal,315000,-93.643539,42.01141 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,21695,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Excellent,1988,2007,Hip,CompShg,Wd Sdng,Plywood,BrkFace,260,Good,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,72,880,GasA,Excellent,Y,SBrkr,1680,0,0,1680,1,0,2,0,3,1,Good,5,Typ,1,Good,Attchd,Fin,2,540,Typical,Typical,Paved,292,44,0,182,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,222000,-93.643947,42.009134 -Two_Story_1945_and_Older,Residential_Medium_Density,59,10690,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Good,1920,1997,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Fair,No,Rec,6,Unf,0,216,672,GasA,Good,Y,FuseA,672,672,0,1344,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,468,Typical,Fair,Paved,0,128,218,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,147000,-93.62529,42.022721 -Two_Story_1945_and_Older,Residential_Medium_Density,50,8660,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1900,1993,Gambrel,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,760,760,GasA,Excellent,N,SBrkr,928,928,312,2168,0,0,2,0,5,1,Good,11,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,467,160,78,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,123000,-93.626839,42.021425 -Two_Story_1945_and_Older,Residential_Medium_Density,60,6402,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Unf,7,Unf,0,596,596,GasA,Typical,N,SBrkr,596,596,0,1192,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,189,Fair,Fair,Dirt_Gravel,0,0,137,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,78000,-93.625299,42.021596 -One_and_Half_Story_Finished_All_Ages,C_all,105,8470,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Feedr,OneFam,One_and_Half_Fin,Fair,Poor,1915,1982,Hip,CompShg,Plywood,Plywood,None,0,Fair,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,1013,1013,GasA,Typical,N,SBrkr,1013,0,513,1526,0,0,1,0,2,1,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,156,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,ConLD,Abnorml,85000,-93.616895,42.020184 -One_Story_1945_and_Older,C_all,60,10200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,572,572,GasA,Fair,N,FuseP,572,0,0,572,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Typical,Dirt_Gravel,0,0,72,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,75000,-93.615395,42.020002 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3843,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,174,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1596,GasA,Excellent,Y,SBrkr,1648,0,0,1648,1,1,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,162,53,0,153,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,230000,-93.6165709,42.008683 -One_Story_1945_and_Older,I_all,109,21780,Grvl,No_Alley_Access,Regular,Lvl,NoSewr,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Poor,Below_Average,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseA,810,0,0,810,0,0,1,0,1,1,Typical,4,Min1,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Dirt_Gravel,119,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,ConLD,Normal,57625,-93.588227,42.0183 -Two_Story_1946_and_Newer,Residential_Low_Density,75,10125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2001,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,BLQ,250,412,1107,GasA,Excellent,Y,SBrkr,1040,1012,0,2052,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,3,642,Typical,Typical,Paved,210,91,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,251000,-93.606944,41.996888 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,48,12822,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,83,1434,GasA,Excellent,Y,SBrkr,1518,631,0,2149,1,0,1,1,1,1,Good,6,Typ,1,Excellent,Attchd,RFn,2,670,Typical,Typical,Paved,168,43,0,0,198,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,239686,-93.607252,41.996672 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,12118,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,Stone,108,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,1710,1710,GasA,Excellent,Y,SBrkr,1710,0,0,1710,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,100,48,0,0,180,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,240000,-93.607055,41.997119 -Two_Story_1946_and_Newer,Residential_Low_Density,71,12209,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Typical,No,ALQ,1,Unf,0,114,804,GasA,Excellent,Y,SBrkr,804,1157,0,1961,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,560,Typical,Typical,Paved,125,192,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,215000,-93.603717,41.99539 -Split_Foyer,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,135,902,GasA,Excellent,Y,SBrkr,926,0,0,926,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Unf,1,351,Typical,Typical,Paved,319,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,156450,-93.604716,41.997067 -Split_Foyer,Residential_Low_Density,72,9360,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Good,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,LwQ,116,0,957,GasA,Typical,Y,SBrkr,1287,0,0,1287,1,0,1,1,2,1,Typical,5,Typ,2,Fair,Attchd,RFn,2,541,Typical,Typical,Paved,302,39,0,0,120,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Normal,173000,-93.604491,41.997069 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,10125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_and_Half_Fin,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1302,432,0,1734,0,0,2,0,4,2,Good,8,Typ,0,No_Fireplace,Attchd,Unf,2,539,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,COD,Normal,126000,-93.604519,41.996919 -Split_or_Multilevel,Residential_Low_Density,0,9947,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Good,Average,1990,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,577,1188,GasA,Excellent,Y,SBrkr,1217,0,0,1217,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,497,Typical,Typical,Paved,168,27,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,173000,-93.602356,41.995905 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,11070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,1527,1582,GasA,Typical,Y,SBrkr,1595,0,0,1595,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,COD,Abnorml,152000,-93.602424,41.996066 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14375,Pave,No_Alley_Access,Slightly_Irregular,Lvl,NoSeWa,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1958,1958,Gable,CompShg,HdBoard,HdBoard,BrkFace,541,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,354,354,819,GasA,Good,Y,FuseA,1344,0,0,1344,0,1,1,0,3,1,Good,7,Typ,1,Good,Basment,RFn,2,525,Typical,Typical,Paved,0,118,0,0,233,0,No_Pool,No_Fence,None,0,1,2009,COD,Abnorml,137500,-93.64978,42.001246 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,150,215245,Pave,No_Alley_Access,Irregular,Low,AllPub,Inside,Sev,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,1965,1965,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Rec,820,80,2136,GasW,Typical,Y,SBrkr,2036,0,0,2036,2,0,2,0,3,1,Typical,8,Typ,2,Good,Attchd,RFn,2,513,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,375000,-93.652119,42.00138 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,12898,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,70,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,598,1620,GasA,Excellent,Y,SBrkr,1620,0,0,1620,1,0,2,0,2,1,Excellent,6,Typ,1,Excellent,Attchd,Fin,3,912,Typical,Typical,Paved,228,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,315500,-93.65315,41.995027 -Two_Story_1946_and_Newer,Residential_Low_Density,83,13159,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,846,846,GasA,Good,Y,SBrkr,846,846,0,1692,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,650,Typical,Typical,Paved,208,114,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,224500,-93.651832,41.995058 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,113,13438,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,CemntBd,CmentBd,Stone,246,Excellent,Typical,CBlock,Excellent,Typical,Gd,GLQ,3,Unf,0,432,2190,GasA,Excellent,Y,SBrkr,2036,0,0,2036,1,0,2,0,3,1,Excellent,9,Typ,1,Excellent,Attchd,Fin,3,780,Typical,Typical,Paved,90,154,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,410000,-93.651867,41.993759 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,14463,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,CemntBd,CmentBd,BrkFace,406,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,526,1641,GasA,Excellent,Y,SBrkr,1641,0,0,1641,1,0,2,0,3,1,Excellent,7,Typ,0,No_Fireplace,Attchd,Fin,3,885,Typical,Typical,Paved,0,95,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,316500,-93.653039,41.994727 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8925,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1450,1466,GasA,Excellent,Y,SBrkr,1466,0,0,1466,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,610,Typical,Typical,Paved,100,18,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,201000,-93.650453,41.994695 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,130,11457,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Mn,GLQ,3,Unf,0,387,1392,GasA,Typical,Y,SBrkr,1412,0,0,1412,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,576,Typical,Typical,Paved,0,0,169,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,175000,-93.646455,41.997684 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9839,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1980,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Good,Typical,No,ALQ,1,Unf,0,250,712,GasA,Excellent,Y,SBrkr,1375,862,0,2237,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,440,Typical,Typical,Paved,305,24,0,0,0,0,No_Pool,No_Fence,Shed,2500,2,2009,WD ,Normal,204000,-93.644884,41.998952 -Split_or_Multilevel,Residential_Low_Density,125,14419,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Good,Average,1987,1989,Hip,CompShg,Plywood,Plywood,BrkFace,310,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,ALQ,624,117,1645,GasA,Excellent,Y,SBrkr,1479,0,0,1479,2,0,2,1,3,1,Good,7,Min1,1,Fair,Attchd,Fin,2,578,Typical,Typical,Paved,224,238,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,213500,-93.6446004,41.9981656 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6853,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,136,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,262,1267,GasA,Excellent,Y,SBrkr,1296,0,0,1296,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,471,Typical,Typical,Paved,192,25,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,220000,-93.646793,41.996327 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9157,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,912,912,GasA,Excellent,Y,SBrkr,1072,942,0,2014,0,0,2,1,3,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,486,Typical,Typical,Paved,124,114,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Abnorml,170000,-93.646638,41.996093 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14601,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,584,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,578,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,1,0,2,0,2,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,765,Typical,Typical,Paved,270,68,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,315000,-93.646816,41.995359 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,12633,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,Stone,290,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,338,1978,GasA,Excellent,Y,SBrkr,1978,0,0,1978,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,920,Typical,Typical,Paved,308,52,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,425000,-93.651703,41.992964 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,12518,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,182,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,476,1008,GasA,Good,Y,SBrkr,1008,0,0,1008,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,384,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,139500,-93.6042638,41.9935397 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,21750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Below_Average,1960,2006,Hip,CompShg,HdBoard,HdBoard,BrkFace,75,Typical,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1771,0,0,1771,0,0,1,0,3,1,Typical,9,Min1,1,Typical,Attchd,Unf,2,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,11,2009,WD ,Normal,115000,-93.608359,41.992193 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,320,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,338,1204,GasA,Excellent,Y,SBrkr,1204,0,0,1204,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,412,Typical,Typical,Paved,0,247,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,162000,-93.608196,41.993128 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1999,1999,Hip,CompShg,VinylSd,VinylSd,BrkFace,425,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,341,1224,GasA,Excellent,Y,SBrkr,1224,0,0,1224,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,402,Typical,Typical,Paved,0,304,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,165000,-93.60821,41.992122 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1596,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SLvl,Below_Average,Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,462,GasA,Typical,Y,SBrkr,526,462,0,988,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,BuiltIn,Unf,1,297,Typical,Typical,Paved,120,101,0,0,0,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Normal,91000,-93.604252,41.991786 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,9858,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,354,864,GasA,Typical,Y,SBrkr,874,0,0,874,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,33,0,0,0,0,0,No_Pool,Good_Wood,Shed,600,11,2009,WD ,Normal,130000,-93.604218,41.992919 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1526,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Very_Good,1970,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,115,630,GasA,Typical,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,86000,-93.603479,41.992185 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,13383,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1969,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,594,1188,GasA,Excellent,Y,SBrkr,1404,0,0,1404,0,0,2,0,3,1,Typical,7,Typ,1,Poor,Attchd,Unf,2,504,Typical,Typical,Paved,0,16,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,160000,-93.600636,41.9928219 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1477,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,SFoyer,Below_Average,Below_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,121,630,GasA,Typical,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,80000,-93.603111,41.99217 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Above_Average,1970,2008,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Typical,Y,SBrkr,798,546,0,1344,0,0,1,1,3,1,Typical,6,Typ,1,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,97000,-93.601975,41.992539 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2217,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Below_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,273,0,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,238,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,88000,-93.60176,41.991786 -Split_Foyer,Residential_Low_Density,50,7689,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Very_Good,1972,1972,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,BLQ,76,0,796,GasA,Good,Y,SBrkr,796,0,0,796,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,138,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,131900,-93.601127,41.991365 -Split_or_Multilevel,Residential_Low_Density,62,7706,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Average,1993,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,Av,Rec,6,GLQ,270,0,384,GasA,Excellent,Y,SBrkr,1091,0,0,1091,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,429,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,No_Fence,Shed,700,8,2009,WD ,Normal,131250,-93.606688,41.987535 -Split_Foyer,Residential_Low_Density,0,9101,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,104,Typical,Good,PConc,Good,Typical,Av,GLQ,3,Unf,0,0,1097,GasA,Excellent,Y,SBrkr,1110,0,0,1110,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,602,Typical,Typical,Paved,303,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,165500,-93.604928,41.988776 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8780,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1985,1985,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,208,833,GasA,Excellent,Y,SBrkr,833,0,0,833,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,112000,-93.603867,41.988222 -Split_Foyer,Residential_Low_Density,70,7669,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1992,1993,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,LwQ,110,0,828,GasA,Typical,Y,SBrkr,883,0,0,883,1,0,1,0,2,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,698,Typical,Typical,Paved,100,32,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,149000,-93.602777,41.987659 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,85,14115,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1993,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,Wood,Good,Typical,No,GLQ,3,Unf,0,64,796,GasA,Excellent,Y,SBrkr,796,566,0,1362,1,0,1,1,1,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,40,30,0,320,0,0,No_Pool,Minimum_Privacy,Shed,700,10,2009,WD ,Normal,143000,-93.609453,41.986502 -Two_Story_1946_and_Newer,Residential_Low_Density,62,10429,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Average,1992,1992,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,294,624,GasA,Typical,Y,SBrkr,624,663,0,1287,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,150,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,130000,-93.606925,41.986509 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9819,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,ImStucc,None,0,Typical,Typical,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,0,1567,GasA,Typical,Y,SBrkr,1567,0,0,1567,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,RFn,2,714,Typical,Typical,Paved,264,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,196000,-93.599866,41.991493 -Two_Story_1946_and_Newer,Residential_Low_Density,70,10457,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Good,1969,1969,Gable,CompShg,VinylSd,VinylSd,BrkFace,178,Good,Excellent,CBlock,Typical,Typical,Gd,BLQ,2,LwQ,288,0,784,GasA,Excellent,Y,SBrkr,784,848,0,1632,0,0,1,1,4,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,898,Typical,Typical,Paved,0,173,368,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,173000,-93.600191,41.99095 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,11029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1958,2002,Hip,CompShg,MetalSd,MetalSd,None,0,Excellent,Typical,CBlock,Good,Typical,No,ALQ,1,BLQ,411,245,1184,GasA,Excellent,Y,SBrkr,1414,0,0,1414,1,0,1,0,3,1,Typical,6,Min1,1,Typical,Attchd,Unf,2,601,Typical,Typical,Paved,0,51,0,0,190,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,176500,-93.6188286,42.0525525 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12925,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1970,1970,Gable,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,340,1205,GasA,Excellent,Y,SBrkr,2117,0,0,2117,0,0,2,1,4,1,Typical,7,Typ,2,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,237500,-93.6163289,42.051446 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11075,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,ALQ,1,LwQ,276,176,952,GasA,Typical,Y,SBrkr,1092,1020,0,2112,0,0,2,1,4,1,Typical,9,Typ,2,Good,Attchd,Unf,2,576,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,206900,-93.617424,42.049587 -Two_Story_1946_and_Newer,Residential_Low_Density,72,8702,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,220,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,470,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,187500,-93.639062,42.05996 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8139,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,119,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,204,680,GasA,Good,Y,SBrkr,680,790,0,1470,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,420,Typical,Typical,Paved,192,49,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,165000,-93.637796,42.061231 -Two_Story_1946_and_Newer,Residential_Low_Density,59,9535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,75,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,472,Typical,Typical,Paved,100,82,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,195500,-93.638775,42.060771 -Two_Story_1946_and_Newer,Residential_Low_Density,59,9042,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,115,943,GasA,Good,Y,SBrkr,943,695,0,1638,1,0,2,1,3,1,Typical,7,Typ,2,Typical,Attchd,Fin,2,472,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,192000,-93.638778,42.060822 -Two_Story_1946_and_Newer,Residential_Low_Density,0,15038,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1996,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,778,916,GasA,Good,Y,SBrkr,916,720,0,1636,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,386,Typical,Typical,Paved,168,84,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,173000,-93.637482,42.060369 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,14137,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Average,Average,1996,1997,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,898,1348,GasA,Good,Y,SBrkr,1384,0,0,1384,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,404,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,177900,-93.636718,42.060639 -Two_Story_1946_and_Newer,Residential_Low_Density,57,21872,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,FR2,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,125,729,GasA,Excellent,Y,SBrkr,729,717,0,1446,0,1,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,406,Typical,Typical,Paved,264,22,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,175000,-93.636948,42.0623 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,4923,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,440,1593,GasA,Excellent,Y,SBrkr,1593,0,0,1593,1,0,1,1,0,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,682,Typical,Typical,Paved,0,120,0,0,224,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,286000,-93.633876,42.062976 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,45,6264,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,1997,1997,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,1008,1664,GasA,Excellent,Y,SBrkr,1682,0,0,1682,1,0,1,1,1,1,Good,6,Min1,1,Typical,Attchd,Fin,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,247900,-93.633781,42.061374 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,5395,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,604,1337,GasA,Good,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,462,Typical,Typical,Paved,96,0,70,168,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,180000,-93.633905,42.060865 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,39,5070,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1280,1280,GasA,Excellent,Y,SBrkr,1280,0,0,1280,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,506,Typical,Typical,Paved,0,82,0,0,144,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,180000,-93.633797,42.06076 -Two_Story_1946_and_Newer,Residential_Low_Density,100,10839,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,926,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,470,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,181000,-93.639151,42.059408 -Two_Story_1946_and_Newer,Residential_Low_Density,73,11184,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,932,932,GasA,Good,Y,SBrkr,932,701,0,1633,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,460,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Family,183000,-93.639007,42.059364 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,554,773,GasA,Good,Y,SBrkr,773,885,0,1658,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,431,Typical,Typical,Paved,224,84,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,188000,-93.637763,42.059411 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10832,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,712,712,GasA,Excellent,Y,SBrkr,1086,809,0,1895,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,409,Typical,Typical,Paved,143,46,0,0,0,0,No_Pool,No_Fence,Shed,500,10,2008,WD ,Normal,194500,-93.636798,42.058445 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14067,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,194,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,332,836,GasA,Good,Y,SBrkr,851,858,0,1709,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,416,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2008,WD ,Normal,185000,-93.637279,42.05701 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,4671,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1988,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,461,1228,GasA,Good,Y,SBrkr,1228,0,0,1228,1,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,472,Typical,Typical,Paved,168,120,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,189000,-93.632013,42.059518 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,65,5950,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1018,1337,GasA,Good,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,462,Typical,Typical,Paved,0,73,154,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,188500,-93.631581,42.059636 -Two_Story_1946_and_Newer,Residential_Low_Density,101,13543,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,130,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1152,1168,GasA,Excellent,Y,SBrkr,1168,1332,0,2500,0,0,3,1,5,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,683,Typical,Typical,Paved,192,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,355000,-93.628831,42.061394 -Two_Story_1946_and_Newer,Residential_Low_Density,77,11198,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,245,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1122,1122,GasA,Excellent,Y,SBrkr,1134,1370,0,2504,0,0,2,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,656,Typical,Typical,Paved,144,39,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,325000,-93.628901,42.06056 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,15401,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,296,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,547,1884,GasA,Excellent,Y,SBrkr,1884,0,0,1884,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,670,Typical,Typical,Paved,214,76,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,387000,-93.627911,42.060241 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,31220,Pave,No_Alley_Access,Slightly_Irregular,Bnk,NoSewr,FR2,Gtl,Gilbert,Feedr,Norm,OneFam,One_Story,Above_Average,Poor,1952,1952,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1632,1632,GasA,Typical,Y,FuseA,1474,0,0,1474,0,0,1,0,3,1,Typical,7,Min2,2,Good,Attchd,Unf,2,495,Typical,Typical,Paved,0,0,144,0,0,0,No_Pool,No_Fence,Shed,750,5,2008,WD ,Normal,115000,-93.622299,42.059792 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,60,8118,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Stone_Brook,PosN,PosN,TwnhsE,One_Story,Excellent,Average,2007,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,178,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,676,1710,GasA,Excellent,Y,SBrkr,1710,0,0,1710,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,557,Typical,Typical,Paved,156,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,334000,-93.629808,42.058896 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,47280,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,1950,1950,Hip,CompShg,AsbShng,AsbShng,BrkFace,44,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1488,1488,GasA,Good,Y,SBrkr,1488,0,0,1488,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,738,Typical,Typical,Paved,0,0,180,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Family,133000,-93.6225667,42.0586718 -Two_Story_1946_and_Newer,Residential_Low_Density,46,20544,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1986,1991,Gable,CompShg,Plywood,Plywood,BrkFace,123,Typical,Good,CBlock,Good,Typical,No,Unf,7,Unf,0,791,791,GasA,Good,Y,SBrkr,1236,857,0,2093,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,542,Typical,Typical,Paved,364,63,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2008,WD ,Normal,215000,-93.6391494,42.056015 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,12680,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1988,1988,Gable,CompShg,Plywood,Wd Sdng,BrkFace,102,Good,Typical,CBlock,Good,Good,Mn,GLQ,3,Unf,0,692,1675,GasA,Excellent,Y,SBrkr,1688,0,0,1688,1,0,2,0,2,1,Excellent,6,Typ,1,Excellent,Attchd,RFn,2,528,Typical,Typical,Paved,0,48,0,0,141,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,226500,-93.639715,42.054514 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,12090,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1981,1981,Gable,CompShg,MetalSd,MetalSd,BrkFace,210,Typical,Good,CBlock,Good,Typical,No,GLQ,3,LwQ,228,606,1422,GasA,Typical,Y,SBrkr,1422,0,0,1422,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,576,Typical,Typical,Paved,276,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,175500,-93.637825,42.054562 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10825,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1983,1983,Gable,CompShg,WdShing,Plywood,BrkFace,174,Good,Typical,CBlock,Good,Typical,Mn,GLQ,3,Unf,0,513,1260,GasA,Typical,Y,SBrkr,1260,0,0,1260,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,598,Typical,Typical,Paved,152,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,181900,-93.637808,42.054439 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,56,18559,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1978,1978,Hip,CompShg,Plywood,Plywood,BrkFace,383,Good,Good,CBlock,Good,Typical,No,GLQ,3,Rec,186,656,2048,GasA,Typical,Y,SBrkr,2064,0,0,2064,1,0,2,0,3,1,Good,7,Typ,2,Fair,Attchd,Fin,2,550,Typical,Typical,Paved,200,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,235000,-93.636511,42.055358 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1979,1979,Hip,CompShg,Plywood,Plywood,BrkFace,194,Good,Typical,CBlock,Good,Fair,No,ALQ,1,LwQ,449,469,1782,GasA,Typical,Y,SBrkr,1782,0,0,1782,0,1,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,551,Typical,Typical,Paved,467,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,220000,-93.636505,42.054503 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12227,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Good,1977,1995,Gable,CompShg,HdBoard,HdBoard,BrkFace,424,Typical,Good,CBlock,Good,Good,No,ALQ,1,Unf,0,434,1330,GasA,Typical,Y,SBrkr,1542,1330,0,2872,1,0,2,1,4,1,Typical,11,Typ,1,Typical,Attchd,Fin,2,619,Typical,Typical,Paved,550,282,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,272000,-93.635111,42.054455 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13068,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1976,1976,Hip,CompShg,HdBoard,HdBoard,BrkFace,621,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Rec,48,273,1211,GasA,Typical,Y,SBrkr,1211,0,0,1211,1,0,2,0,3,1,Good,6,Typ,1,Poor,Attchd,Fin,2,461,Typical,Typical,Paved,0,0,0,174,0,0,No_Pool,Minimum_Privacy,None,0,11,2008,WD ,Normal,161000,-93.630299,42.054211 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15611,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1977,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,ALQ,1,LwQ,93,266,1126,GasA,Typical,Y,SBrkr,1126,0,0,1126,0,1,2,0,3,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,540,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Abnorml,175000,-93.633168,42.055432 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1980,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,280,Typical,Typical,CBlock,Good,Typical,Mn,Unf,7,Unf,0,738,738,GasA,Typical,Y,SBrkr,1277,767,0,2044,0,0,2,1,3,1,Typical,7,Min1,1,Typical,Attchd,Unf,2,489,Typical,Typical,Paved,28,73,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,185000,-93.635153,42.052544 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9743,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1969,1969,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,440,720,GasA,Good,Y,SBrkr,720,588,0,1308,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,146900,-93.637105,42.050326 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12511,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1978,1978,Mansard,WdShake,Plywood,Plywood,BrkFace,168,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,432,1420,GasA,Excellent,Y,SBrkr,1420,1420,0,2840,0,1,2,1,4,1,Good,8,Min2,2,Good,Attchd,Fin,4,1314,Typical,Good,Paved,0,16,0,0,208,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Normal,292500,-93.63719,42.051473 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,104,11361,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,160,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,549,1193,GasA,Typical,Y,SBrkr,1523,0,0,1523,0,1,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,478,Typical,Typical,Paved,0,0,0,0,189,0,No_Pool,Minimum_Privacy,None,0,5,2008,COD,Abnorml,180000,-93.633905,42.050679 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1444,1444,GasA,Typical,Y,SBrkr,1444,0,0,1444,0,0,2,0,2,1,Typical,5,Typ,1,Good,Attchd,Unf,2,473,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,Good_Wood,None,0,4,2008,WD ,Normal,159900,-93.635216,42.049449 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1976,1976,Hip,CompShg,Plywood,Plywood,BrkFace,660,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,400,803,GasA,Typical,Y,SBrkr,1098,866,0,1964,0,0,2,1,4,1,Typical,8,Typ,1,Good,Attchd,RFn,2,483,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,205000,-93.6341769,42.04986 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1973,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,92,1176,GasA,Good,Y,SBrkr,1178,0,0,1178,0,1,1,1,3,1,Good,5,Typ,1,Fair,Attchd,Unf,2,439,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2008,WD ,Normal,157000,-93.631129,42.049609 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14311,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,402,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,213,1236,GasA,Excellent,Y,SBrkr,1236,1104,0,2340,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,RFn,3,787,Typical,Typical,Paved,192,180,218,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,306000,-93.630231,42.051074 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,Duplex,Two_Story,Average,Average,1974,1974,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,Unf,7,Unf,0,896,896,GasA,Typical,Y,SBrkr,896,896,0,1792,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,133000,-93.626544,42.056306 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,438,14,1054,GasA,Good,Y,SBrkr,1054,0,0,1054,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,460,Typical,Typical,Paved,180,0,0,0,80,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,151000,-93.627875,42.05539 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,10295,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,72,Typical,Typical,CBlock,Good,Typical,Mn,Rec,6,Unf,0,684,936,GasA,Typical,Y,SBrkr,936,0,0,936,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,16,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,COD,Normal,111900,-93.626511,42.055304 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1971,1971,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,Rec,6,ALQ,613,132,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,123000,-93.630208,42.054328 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12735,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,264,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,216,0,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2008,COD,Normal,111250,-93.624265,42.0564348 -One_Story_PUD_1946_and_Newer,Residential_High_Density,34,4060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1998,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1139,1405,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,511,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,COD,Abnorml,181000,-93.624873,42.054693 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,359,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,25,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,1,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,52,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,103400,-93.628971,42.052794 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,158,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,153,483,GasA,Typical,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,100000,-93.627629,42.052755 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,422,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,483,GasA,Good,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,411,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,100500,-93.627114,42.051479 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1869,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,127,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,162,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,106000,-93.629462,42.052332 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,96,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,COD,Abnorml,85400,-93.62765,42.051678 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Above_Average,Fair,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,604,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,125,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,89500,-93.627309,42.051687 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,356,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,280,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,111750,-93.629425,42.051659 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4043,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,510,1069,GasA,Typical,Y,SBrkr,1069,0,0,1069,0,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,0,55,0,0,200,0,No_Pool,No_Fence,None,0,10,2008,COD,Abnorml,140000,-93.627234,42.050001 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,294,275,855,GasA,Good,Y,SBrkr,855,601,0,1456,0,0,2,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,460,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,143000,-93.625986,42.05068 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2529,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Above_Average,1977,1977,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,677,1055,GasA,Fair,Y,SBrkr,1055,0,0,1055,0,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,148500,-93.625848,42.050257 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9477,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,65,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,524,864,GasA,Typical,Y,SBrkr,892,0,0,892,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,10,2008,WD ,Normal,110000,-93.62649,42.04845 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,96,12444,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,426,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,596,1932,GasA,Excellent,Y,SBrkr,1932,0,0,1932,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,774,Typical,Typical,Paved,0,66,0,304,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,394617,-93.658873,42.062046 -Two_Story_1946_and_Newer,Residential_Low_Density,96,12474,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,272,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,402,1682,GasA,Excellent,Y,SBrkr,1742,590,0,2332,1,0,2,1,3,1,Excellent,9,Typ,1,Excellent,BuiltIn,Fin,3,846,Typical,Typical,Paved,196,134,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,New,Partial,426000,-93.6573126,42.0632692 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,114,14803,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,PosN,OneFam,One_Story,Very_Excellent,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,BrkFace,816,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,442,2078,GasA,Excellent,Y,SBrkr,2084,0,0,2084,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1220,Typical,Typical,Paved,188,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,New,Partial,385000,-93.652941,42.063129 -Two_Story_1946_and_Newer,Residential_Low_Density,67,14948,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,268,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,122,1452,GasA,Excellent,Y,SBrkr,1476,1237,0,2713,1,0,2,1,3,1,Excellent,11,Typ,1,Good,Attchd,Fin,3,858,Typical,Typical,Paved,126,66,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,446261,-93.652744,42.062925 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,12704,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,302,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,570,1582,GasA,Excellent,Y,SBrkr,1582,0,0,1582,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,905,Typical,Typical,Paved,209,95,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,New,Partial,317500,-93.654642,42.062259 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,436,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,310,1710,GasA,Excellent,Y,SBrkr,1710,0,0,1710,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,866,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,New,Partial,372402,-93.6566551,42.0624143 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,13693,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,554,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,BLQ,495,195,2418,GasA,Excellent,Y,SBrkr,2464,0,0,2464,1,0,2,1,4,1,Excellent,9,Typ,1,Excellent,Attchd,Fin,3,650,Typical,Typical,Paved,358,78,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,417500,-93.657832,42.062432 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,104,14418,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,480,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,575,1950,GasA,Excellent,Y,SBrkr,1950,0,0,1950,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,706,Typical,Typical,Paved,156,207,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,383000,-93.657889,42.061133 -Two_Story_1946_and_Newer,Residential_Low_Density,108,13418,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,270,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,430,1850,GasA,Excellent,Y,SBrkr,1850,898,0,2748,1,0,2,1,4,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,850,Typical,Typical,Paved,212,182,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Abnorml,390000,-93.657188,42.06027 -Two_Story_1946_and_Newer,Residential_Low_Density,96,12539,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,Norm,OneFam,Two_Story,Very_Excellent,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,468,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,538,1620,GasA,Excellent,Y,SBrkr,1632,1158,0,2790,1,0,2,1,4,1,Excellent,10,Typ,1,Excellent,BuiltIn,Fin,4,1150,Typical,Typical,Paved,30,200,0,0,192,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,460000,-93.656801,42.060427 -Two_Story_1946_and_Newer,Residential_Low_Density,102,12151,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2005,Gable,CompShg,CemntBd,CmentBd,BrkFace,368,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,165,1414,GasA,Excellent,Y,SBrkr,1414,917,0,2331,1,0,2,1,3,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,1003,Typical,Typical,Paved,192,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,379000,-93.65369,42.060766 -Two_Story_1946_and_Newer,Residential_Low_Density,74,8899,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,108,Excellent,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,908,948,GasA,Excellent,Y,SBrkr,948,1140,0,2088,0,0,2,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,656,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,250000,-93.654734,42.060425 -Two_Story_1946_and_Newer,Residential_Low_Density,85,10574,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,126,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,292,1148,GasA,Excellent,Y,SBrkr,1170,1162,0,2332,1,0,2,1,4,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,756,Typical,Typical,Paved,224,142,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,316000,-93.654443,42.0599011 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,106,12720,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2003,2003,Hip,CompShg,MetalSd,MetalSd,Stone,680,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,278,2535,GasA,Excellent,Y,SBrkr,2470,0,0,2470,2,0,1,1,1,1,Excellent,7,Typ,2,Good,Attchd,Fin,3,789,Typical,Typical,Paved,154,65,0,0,216,144,Excellent,No_Fence,None,0,2,2008,WD ,Normal,615000,-93.656958,42.058484 -Two_Story_1946_and_Newer,Residential_Low_Density,110,13688,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,664,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,556,1572,GasA,Excellent,Y,SBrkr,1572,1096,0,2668,1,0,2,1,3,1,Excellent,10,Typ,2,Good,BuiltIn,Fin,3,726,Typical,Typical,Paved,400,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,412500,-93.65572,42.058683 -Two_Story_1946_and_Newer,Residential_Low_Density,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,292,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,132,998,GasA,Excellent,Y,SBrkr,1006,1040,0,2046,1,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,RFn,3,871,Typical,Typical,Paved,320,62,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,284000,-93.652549,42.058573 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,92,10845,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,504,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,454,1603,GasA,Excellent,Y,SBrkr,1575,0,0,1575,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,732,Typical,Typical,Paved,216,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,284000,-93.651216,42.058649 -Two_Story_1946_and_Newer,Residential_Low_Density,130,16900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,1110,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,404,1479,GasA,Excellent,Y,SBrkr,1515,1134,0,2649,1,0,2,1,4,1,Good,10,Typ,1,Typical,Attchd,Fin,3,746,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,421250,-93.655587,42.057028 -Two_Story_1946_and_Newer,Residential_Low_Density,112,16451,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,221,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1765,1765,GasA,Excellent,Y,SBrkr,1804,886,0,2690,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,795,Typical,Typical,Paved,268,58,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,370000,-93.654914,42.058694 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,58,10110,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,492,Excellent,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1486,1858,GasA,Excellent,Y,SBrkr,1866,0,0,1866,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,870,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,336860,-93.654187,42.057164 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,65,8769,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,766,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,162,1702,GasA,Excellent,Y,SBrkr,1702,0,0,1702,1,0,1,1,1,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1052,Typical,Typical,Paved,0,72,0,0,224,0,No_Pool,No_Fence,None,0,10,2008,New,Partial,367294,-93.6513669,42.0579589 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,135,12304,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,144,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1347,1367,GasA,Excellent,Y,SBrkr,1367,0,0,1367,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,192000,-93.649744,42.0582 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,62,12677,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2003,2004,Hip,CompShg,MetalSd,MetalSd,BrkFace,472,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,300,1518,GasA,Excellent,Y,SBrkr,1518,0,0,1518,0,0,1,1,1,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,588,Typical,Typical,Paved,185,140,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,274000,-93.653868,42.056766 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,63,8849,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,616,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1656,1684,GasA,Excellent,Y,SBrkr,1684,0,0,1684,0,0,2,0,2,1,Excellent,6,Typ,1,Excellent,Attchd,RFn,2,564,Typical,Typical,Paved,495,72,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,266000,-93.652769,42.0570189 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,89,8232,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2007,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,714,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,596,1800,GasA,Excellent,Y,SBrkr,1800,0,0,1800,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,3,944,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,New,Partial,370967,-93.651845,42.057446 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,496,1342,GasA,Excellent,Y,SBrkr,1342,0,0,1342,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,234250,-93.651163,42.057179 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,196,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1318,1342,GasA,Excellent,Y,SBrkr,1342,0,0,1342,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,550,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,219500,-93.6514243,42.0571198 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2448,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,764,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,0,0,2,1,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,155000,-93.649997,42.057468 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2448,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,191,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,1,0,2,1,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,154000,-93.650607,42.057094 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,59,8198,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,146,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,638,1358,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,207000,-93.65089,42.056616 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3940,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,143,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,342,1415,GasA,Excellent,Y,SBrkr,1455,0,0,1455,1,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,3,644,Typical,Typical,Paved,156,20,0,0,144,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,195000,-93.640176,42.063336 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,3940,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,24,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,306,1393,GasA,Excellent,Y,SBrkr,1576,0,0,1576,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,RFn,3,668,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,219990,-93.640958,42.062299 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,1330,1346,GasA,Excellent,Y,SBrkr,1504,0,0,1504,0,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,437,Typical,Typical,Paved,156,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,191000,-93.6424399,42.062285 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1266,1266,GasA,Excellent,Y,SBrkr,1266,0,0,1266,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,388,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,159895,-93.643039,42.062004 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,3710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,WdShing,Wd Shng,BrkFace,20,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1146,1146,GasA,Excellent,Y,SBrkr,1246,0,0,1246,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,428,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,New,Partial,187687,-93.642326,42.063301 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9024,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,789,789,GasA,Excellent,Y,SBrkr,813,702,0,1515,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,179000,-93.642641,42.061316 -Two_Story_1946_and_Newer,Residential_Low_Density,62,7415,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,80,839,GasA,Excellent,Y,SBrkr,864,729,0,1593,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,398,Typical,Typical,Paved,100,75,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,193000,-93.643522,42.059635 -Split_or_Multilevel,Residential_Low_Density,59,9587,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,182,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,201,856,GasA,Excellent,Y,SBrkr,1166,0,0,1166,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,212,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,190000,-93.643964,42.061544 -Two_Story_1946_and_Newer,Residential_Low_Density,51,8029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,176000,-93.641771,42.0609759 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8010,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,880,0,1720,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,138,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,188000,-93.641416,42.06132 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8396,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1139,0,1986,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,120,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,213000,-93.640051,42.061312 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,55,7301,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,474,1350,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,26,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,217300,-93.649587,42.058377 -Two_Story_1946_and_Newer,Residential_Low_Density,93,10261,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,318,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,936,936,GasA,Excellent,Y,SBrkr,962,830,0,1792,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,451,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,186500,-93.641331,42.057986 -Two_Story_1946_and_Newer,Residential_Low_Density,71,8220,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,647,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,438,982,GasA,Excellent,Y,SBrkr,1008,884,0,1892,0,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,431,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,226750,-93.642397,42.058595 -Two_Story_1946_and_Newer,Residential_Low_Density,60,15384,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,64,788,GasA,Excellent,Y,SBrkr,788,702,0,1490,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,388,Typical,Typical,Paved,100,75,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,184000,-93.6442698,42.0600066 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,707,707,GasA,Excellent,Y,SBrkr,707,707,0,1414,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,403,Typical,Typical,Paved,100,35,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,176000,-93.643632,42.059507 -Two_Story_1946_and_Newer,Residential_Low_Density,41,12460,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,167,1037,GasA,Excellent,Y,SBrkr,1037,1285,0,2322,0,0,2,1,4,1,Typical,8,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,144,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,225000,-93.641342,42.05695 -Two_Story_1946_and_Newer,Residential_Low_Density,77,8390,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,831,831,GasA,Excellent,Y,SBrkr,873,778,0,1651,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,450,Typical,Typical,Paved,0,103,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,185900,-93.640959,42.058581 -Two_and_Half_Story_All_Ages,Residential_Low_Density,84,9660,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_and_Half_Unf,Very_Good,Average,1997,1997,Hip,CompShg,HdBoard,HdBoard,BrkFace,1290,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1173,1173,GasA,Excellent,Y,SBrkr,1182,1017,0,2199,0,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Fin,2,516,Typical,Typical,Paved,0,131,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,284500,-93.654594,42.053879 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,473,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,484,1470,GasA,Good,Y,SBrkr,1470,1160,0,2630,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,3,696,Typical,Typical,Paved,0,66,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,315000,-93.65412,42.053666 -Two_Story_1946_and_Newer,Residential_Low_Density,84,14260,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,350,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,490,1145,GasA,Excellent,Y,SBrkr,1145,1053,0,2198,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,836,Typical,Typical,Paved,192,84,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,250000,-93.653324,42.055748 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,295,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,1519,1519,GasA,Excellent,Y,SBrkr,1533,639,0,2172,0,0,2,1,4,1,Excellent,8,Typ,1,Typical,BuiltIn,RFn,3,687,Typical,Typical,Paved,162,153,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,291000,-93.650932,42.055777 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,136,11675,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,495,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,322,1982,GasA,Excellent,Y,SBrkr,2006,0,0,2006,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,3,938,Typical,Typical,Paved,144,33,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,350000,-93.651562,42.0522319 -Two_Story_1946_and_Newer,Residential_Low_Density,97,10990,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,213,1064,GasA,Excellent,Y,SBrkr,1064,1061,0,2125,1,0,2,1,4,1,Good,12,Typ,2,Typical,Attchd,RFn,2,576,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,279500,-93.655279,42.052272 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11929,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,466,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1242,1242,GasA,Excellent,Y,SBrkr,1251,1250,0,2501,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,751,Typical,Typical,Paved,192,87,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,290000,-93.656036,42.050383 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,10437,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1995,1995,Hip,CompShg,MetalSd,MetalSd,BrkFace,660,Good,Good,PConc,Good,Typical,Gd,GLQ,3,Unf,0,413,2109,GasA,Excellent,Y,SBrkr,2113,0,0,2113,1,0,2,1,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,839,Typical,Typical,Paved,236,46,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,350000,-93.653929,42.05162 -Two_Story_1946_and_Newer,Residential_Low_Density,96,10542,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Hip,CompShg,Wd Sdng,ImStucc,BrkFace,651,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,138,1311,GasA,Excellent,Y,SBrkr,1325,1093,0,2418,1,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,RFn,3,983,Typical,Typical,Paved,250,154,216,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,341000,-93.65579,42.049501 -Two_Story_1946_and_Newer,Residential_Low_Density,91,10010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Hip,WdShake,VinylSd,VinylSd,BrkFace,320,Good,Typical,PConc,Good,Typical,Av,BLQ,2,GLQ,852,0,1080,GasA,Excellent,Y,SBrkr,1108,1089,0,2197,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Fin,3,783,Typical,Typical,Paved,385,99,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,287500,-93.653908,42.049696 -Two_Story_1946_and_Newer,Residential_Low_Density,81,10944,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,BrkFace,448,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,223,1223,GasA,Excellent,Y,SBrkr,1223,904,0,2127,1,0,2,1,3,1,Good,5,Typ,2,Typical,Attchd,RFn,2,525,Typical,Typical,Paved,171,132,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,271000,-93.651801,42.050692 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,14303,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1994,Hip,CompShg,HdBoard,HdBoard,BrkFace,554,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,672,1986,GasA,Excellent,Y,SBrkr,1987,0,0,1987,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,691,Typical,Typical,Paved,262,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,301500,-93.650996,42.049484 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,92,11932,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,186,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1580,1580,GasA,Excellent,Y,SBrkr,1580,0,0,1580,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,830,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,ConLD,Partial,235128,-93.644107,42.054236 -Two_Story_1946_and_Newer,Residential_Low_Density,86,14598,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,Feedr,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,74,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,894,894,GasA,Excellent,Y,SBrkr,894,1039,0,1933,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,668,Typical,Typical,Paved,100,18,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,214000,-93.644145,42.055157 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11957,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,53,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1550,1574,GasA,Excellent,Y,SBrkr,1574,0,0,1574,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,824,Typical,Typical,Paved,144,104,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,232000,-93.642334,42.055102 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,13253,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,128,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,482,1578,GasA,Good,Y,SBrkr,1578,0,0,1578,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Unf,3,642,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,240000,-93.642108,42.054415 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,14587,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,284,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1498,1498,GasA,Excellent,Y,SBrkr,1506,0,0,1506,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,592,Typical,Typical,Paved,0,174,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,New,Partial,264132,-93.642092,42.054366 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,468,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1530,1563,GasA,Excellent,Y,SBrkr,1563,0,0,1563,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,758,Typical,Typical,Paved,144,99,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,245000,-93.642376,42.054306 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,12274,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1417,1417,GasA,Excellent,Y,SBrkr,1428,0,0,1428,0,0,2,0,3,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,554,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,New,Partial,227680,-93.643976,42.054319 -Two_Story_1946_and_Newer,Residential_Low_Density,73,9801,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,156,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1341,1341,GasA,Excellent,Y,SBrkr,1341,520,0,1861,0,0,3,0,3,1,Good,7,Typ,1,Good,BuiltIn,RFn,3,851,Typical,Typical,Paved,144,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,257000,-93.64403,42.055517 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9428,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,310,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,226,955,GasA,Excellent,Y,SBrkr,955,919,0,1874,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,880,Typical,Typical,Paved,168,108,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,New,Partial,297900,-93.644024,42.054227 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10037,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,794,1460,GasA,Excellent,Y,SBrkr,1460,0,0,1460,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,480,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,247000,-93.643788,42.053183 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1339,1363,GasA,Excellent,Y,SBrkr,1372,0,0,1372,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,192,113,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,New,Partial,212700,-93.650397,42.051646 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1372,1372,GasA,Excellent,Y,SBrkr,1372,0,0,1372,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,529,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,New,Partial,250580,-93.650358,42.05165 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,95,11639,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1428,1428,GasA,Excellent,Y,SBrkr,1428,0,0,1428,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,480,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,New,Partial,182000,-93.648255,42.050307 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9803,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,466,866,GasA,Good,Y,SBrkr,866,902,0,1768,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,603,Typical,Typical,Paved,0,108,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,226700,-93.643836,42.052278 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,768,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1053,1053,GasA,Excellent,Y,SBrkr,1053,939,0,1992,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,RFn,2,648,Typical,Typical,Paved,140,45,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,250000,-93.64119,42.052456 -Two_Story_1946_and_Newer,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1026,1026,GasA,Excellent,Y,SBrkr,1026,932,0,1958,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,936,Typical,Typical,Paved,154,210,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,250000,-93.640924,42.052275 -Two_Story_1946_and_Newer,Floating_Village_Residential,85,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,100,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,245,1034,GasA,Excellent,Y,SBrkr,1050,1028,0,2078,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,836,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,New,Partial,339750,-93.6430423,42.0520487 -Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Above_Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,813,813,GasA,Excellent,Y,SBrkr,822,843,0,1665,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,562,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,205950,-93.643692,42.05138 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,292,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1660,1660,GasA,Excellent,Y,SBrkr,1660,0,0,1660,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,660,Typical,Typical,Paved,133,120,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,256000,-93.641651,42.052173 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,62,7500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,CemntBd,CmentBd,Stone,210,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,316,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,462,Typical,Typical,Paved,168,168,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,New,Partial,230348,-93.644018,42.051224 -Two_Story_1946_and_Newer,Floating_Village_Residential,64,8791,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,361,864,GasA,Excellent,Y,SBrkr,864,864,0,1728,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,673,Typical,Typical,Paved,216,56,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,207500,-93.639662,42.050899 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,400,1080,GasA,Excellent,Y,SBrkr,1080,0,0,1080,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,141000,-93.692497,42.037836 -Two_Story_1946_and_Newer,Residential_Low_Density,68,10110,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,ALQ,555,200,835,GasA,Excellent,Y,SBrkr,835,861,0,1696,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,542,Typical,Typical,Paved,143,66,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,188000,-93.692431,42.035271 -Two_Story_1946_and_Newer,Residential_Low_Density,67,12774,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,835,835,GasA,Excellent,Y,SBrkr,835,828,0,1663,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,478,Typical,Typical,Paved,168,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,192500,-93.691695,42.035132 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,126,Typical,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,1095,1175,GasA,Excellent,Y,SBrkr,1175,0,0,1175,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,146000,-93.691836,42.037918 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,13695,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,468,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,159000,-93.690761,42.037806 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,13695,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,300,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,155000,-93.690364,42.03805 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8366,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,798,798,GasA,Excellent,Y,SBrkr,798,842,0,1640,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,138,45,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,173000,-93.691246,42.036525 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,9260,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1162,1162,GasA,Excellent,Y,SBrkr,1162,0,0,1162,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,483,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,170000,-93.69124,42.037258 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8453,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,38,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,392,754,GasA,Excellent,Y,SBrkr,754,855,0,1609,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,525,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,182000,-93.689769,42.035875 -Two_Story_1946_and_Newer,Residential_Low_Density,50,8480,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,284,886,GasA,Excellent,Y,SBrkr,886,794,0,1680,0,1,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,474,Typical,Typical,Paved,144,96,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,163000,-93.689024,42.035878 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1994,1998,Gable,CompShg,HdBoard,HdBoard,BrkFace,258,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,270,1408,GasA,Excellent,Y,SBrkr,1679,0,0,1679,1,0,2,0,3,1,Good,7,Typ,1,Fair,Attchd,RFn,2,575,Typical,Typical,Paved,224,42,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,193500,-93.688981,42.035878 -Two_Story_1946_and_Newer,Residential_Low_Density,43,14565,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,145,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,295,832,GasA,Excellent,Y,SBrkr,832,825,0,1657,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,483,Typical,Typical,Paved,144,74,0,0,0,0,No_Pool,No_Fence,Shed,2000,11,2008,WD ,Normal,189000,-93.689801,42.034988 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,355,827,GasA,Excellent,Y,SBrkr,827,850,0,1677,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,627,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,190500,-93.688803,42.036089 -Two_Story_1946_and_Newer,Residential_Low_Density,75,8285,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,439,836,GasA,Good,Y,SBrkr,844,893,0,1737,0,1,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,506,Typical,Typical,Paved,192,85,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,175000,-93.687819,42.034535 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7153,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,88,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,78,1278,GasA,Good,Y,SBrkr,1294,0,0,1294,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,496,Typical,Typical,Paved,112,51,0,0,0,0,No_Pool,Good_Wood,None,0,6,2008,WD ,Normal,179200,-93.687831,42.035666 -Two_Story_1946_and_Newer,Residential_Low_Density,76,9291,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,RRNe,Norm,OneFam,Two_Story,Above_Average,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,406,832,GasA,Excellent,Y,SBrkr,832,878,0,1710,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,506,Typical,Typical,Paved,144,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,187000,-93.687847,42.036839 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1993,1994,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,841,598,1604,GasA,Excellent,Y,SBrkr,1617,0,0,1617,1,0,2,0,2,1,Good,5,Typ,1,Fair,Attchd,RFn,2,533,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,193000,-93.686428,42.036501 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,55,7892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1993,1993,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,899,1199,GasA,Excellent,Y,SBrkr,1199,0,0,1199,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,530,Typical,Typical,Paved,0,63,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,153900,-93.686278,42.036466 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Average,1963,1963,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Good,No,BLQ,2,ALQ,799,132,984,GasA,Typical,Y,SBrkr,984,0,0,984,1,0,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,RFn,1,384,Typical,Typical,Paved,145,56,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,8,2008,WD ,Normal,128000,-93.674093,42.036039 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12968,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1962,1962,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,175,912,GasA,Typical,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,140,0,0,0,176,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,144000,-93.675743,42.035179 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1961,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,100,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Good,5,Typ,1,Typical,Detchd,Unf,1,420,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,135000,-93.673965,42.034614 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,TwoFmCon,One_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Fair,Mn,ALQ,1,Unf,0,0,890,GasA,Good,N,SBrkr,890,0,0,890,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,115000,-93.670997,42.03569 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1963,1963,Hip,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,CBlock,Good,Good,No,ALQ,1,Unf,0,375,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,276,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,650,1,2008,COD,Abnorml,119916,-93.672503,42.035703 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6768,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Above_Average,Very_Good,1961,1996,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,80,912,GasA,Good,Y,SBrkr,912,0,0,912,1,1,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,142000,-93.670551,42.03459 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,19508,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Above_Average,Average,1974,1974,Gable,CompShg,HdBoard,ImStucc,BrkFace,144,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,630,1430,GasA,Typical,Y,SBrkr,1430,0,0,1430,0,1,2,0,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,117,108,165,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,192000,-93.66203,42.0375 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,10759,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,No,LwQ,4,ALQ,811,0,1001,GasA,Typical,Y,SBrkr,1001,640,0,1641,0,0,2,0,4,1,Typical,5,Typ,1,Good,Detchd,Unf,2,490,Typical,Typical,Paved,0,0,92,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,WD ,Normal,140000,-93.660522,42.034532 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9205,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1990,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,304,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,226,930,GasA,Excellent,Y,SBrkr,1364,1319,0,2683,1,0,2,1,4,1,Good,9,Typ,2,Good,Attchd,RFn,2,473,Typical,Typical,Paved,237,251,0,0,196,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,290000,-93.653482,42.047666 -Two_Story_1946_and_Newer,Residential_Low_Density,105,11025,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_and_Half_Fin,Excellent,Average,1993,1994,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,568,Good,Typical,PConc,Good,Typical,Gd,BLQ,2,Unf,0,1328,1848,GasA,Excellent,Y,SBrkr,1827,959,0,2786,1,0,2,1,4,1,Good,10,Typ,1,Excellent,Attchd,Fin,2,636,Typical,Typical,Paved,294,49,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,372500,-93.653232,42.048607 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,59,4282,Pave,Paved,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1375,1391,GasA,Excellent,Y,SBrkr,1391,0,0,1391,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,530,Typical,Typical,Paved,156,158,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,196000,-93.647488,42.047467 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4017,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2006,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,625,625,GasA,Excellent,Y,SBrkr,625,625,0,1250,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Fin,2,625,Typical,Typical,Paved,0,54,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,171900,-93.646063,42.048089 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,37,3435,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1211,1235,GasA,Excellent,Y,SBrkr,1245,0,0,1245,0,0,2,0,1,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,495,Typical,Typical,Paved,0,100,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,178000,-93.646113,42.048191 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,210,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,1,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,165000,-93.64489,42.047806 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,689,689,GasA,Excellent,Y,SBrkr,703,689,0,1392,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,540,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Abnorml,146000,-93.644891,42.047571 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3604,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,689,689,GasA,Excellent,Y,SBrkr,703,689,0,1392,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,540,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,146000,-93.644891,42.047548 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2280,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,360,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,195,744,GasA,Good,Y,SBrkr,757,744,0,1501,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,179400,-93.644172,42.047102 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2280,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,Stone,216,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,194,744,GasA,Good,Y,SBrkr,757,792,0,1549,1,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,172900,-93.644118,42.047103 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4765,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2000,2000,Hip,CompShg,MetalSd,MetalSd,BrkFace,260,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,587,1614,GasA,Excellent,Y,SBrkr,1638,0,0,1638,1,0,2,0,2,1,Excellent,5,Typ,1,Typical,Attchd,Fin,2,495,Typical,Typical,Paved,230,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,300000,-93.646984,42.046381 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4538,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,179,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,306,1310,GasA,Excellent,Y,SBrkr,1310,0,0,1310,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,RFn,2,545,Typical,Typical,Paved,277,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,285000,-93.646986,42.046377 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,42,4385,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,455,1419,GasA,Excellent,Y,SBrkr,1419,0,0,1419,1,0,1,1,2,1,Excellent,5,Typ,1,Typical,Attchd,Fin,2,588,Typical,Typical,Paved,155,58,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,290000,-93.646989,42.046372 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4109,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,260,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,416,1557,GasA,Excellent,Y,SBrkr,1557,0,0,1557,1,0,2,0,2,1,Excellent,5,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,124,113,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,305000,-93.647047,42.046088 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,50,5119,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,60,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,460,1698,GasA,Excellent,Y,SBrkr,1709,0,0,1709,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,97,65,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,CWD,Abnorml,328900,-93.647044,42.046085 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2160,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,212,Good,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,90,600,GasA,Excellent,Y,SBrkr,624,628,0,1252,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,462,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,160000,-93.643746,42.046961 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2160,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,SLvl,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,72,672,GasA,Excellent,Y,SBrkr,684,720,0,1404,1,0,2,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,462,Typical,Typical,Paved,20,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,170000,-93.643729,42.046962 -Two_Story_1946_and_Newer,Floating_Village_Residential,79,10646,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,513,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,177,858,GasA,Excellent,Y,SBrkr,872,917,0,1789,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,546,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,223000,-93.643378,42.046647 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,466,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,348,960,GasA,Excellent,Y,SBrkr,962,624,0,1586,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,169,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,170000,-93.641785,42.047171 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,147,960,GasA,Excellent,Y,SBrkr,962,645,0,1607,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,169,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,ConLD,Normal,200000,-93.64177,42.047171 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,36,3951,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Very_Excellent,Average,1998,1999,Gable,CompShg,BrkFace,MetalSd,None,0,Excellent,Typical,PConc,Good,Typical,Mn,BLQ,2,GLQ,842,0,970,GasA,Excellent,Y,SBrkr,1469,924,0,2393,1,0,2,1,2,1,Excellent,7,Typ,1,Typical,Attchd,Fin,2,846,Typical,Typical,Paved,0,90,0,0,94,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,345000,-93.641742,42.047171 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,38,14963,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1996,1996,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,474,1260,GasA,Excellent,Y,SBrkr,1288,0,0,1288,1,0,1,1,1,1,Excellent,4,Typ,2,Good,Attchd,Fin,2,500,Typical,Typical,Paved,120,30,0,0,224,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,245500,-93.647655,42.045012 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,22,11064,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1995,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,Gd,LwQ,4,GLQ,670,0,1230,GasA,Excellent,Y,SBrkr,1239,0,0,1239,1,0,1,1,1,1,Good,4,Typ,1,Fair,Attchd,Fin,2,477,Typical,Typical,Paved,172,24,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,217500,-93.648705,42.044419 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,32,10846,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1993,1993,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,100,1719,GasA,Excellent,Y,SBrkr,1719,0,0,1719,2,0,1,1,1,1,Good,6,Typ,2,Good,Attchd,Fin,2,473,Typical,Typical,Paved,122,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,Con,Normal,324000,-93.648954,42.044642 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11120,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1984,1984,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,572,1232,GasA,Typical,Y,SBrkr,1232,0,0,1232,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,516,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,162500,-93.645713,42.044693 -Two_Story_1946_and_Newer,Residential_Low_Density,0,24572,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,OneFam,Two_Story,Excellent,Fair,1977,1977,Mansard,CompShg,Wd Sdng,Wd Sdng,BrkFace,1050,Good,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,584,994,GasA,Typical,Y,SBrkr,1599,1345,0,2944,0,0,2,2,3,1,Good,9,Typ,1,Good,Attchd,RFn,3,864,Typical,Typical,Paved,140,70,16,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Family,150000,-93.655582,42.036729 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,103,16280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Excellent,1976,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Excellent,Excellent,CBlock,Good,Excellent,Mn,GLQ,3,Rec,382,0,1426,GasA,Excellent,Y,SBrkr,1671,0,0,1671,1,0,3,0,3,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,550,Typical,Typical,Paved,280,90,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,290000,-93.658315,42.037578 -Two_Story_1946_and_Newer,Floating_Village_Residential,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,952,952,GasA,Excellent,Y,SBrkr,952,860,0,1812,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,469,Typical,Typical,Paved,144,112,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,205000,-93.6392251,42.048726 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1970,1970,Hip,CompShg,Plywood,Plywood,BrkFace,288,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1304,1304,GasA,Good,Y,SBrkr,1682,0,0,1682,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,530,Typical,Typical,Paved,98,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,174000,-93.635095,42.047928 -Split_or_Multilevel,Residential_Low_Density,0,11104,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1969,1969,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,599,1427,GasA,Good,Y,SBrkr,1427,0,0,1427,0,1,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,516,Typical,Typical,Paved,0,0,0,0,216,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,183000,-93.6343272,42.0492538 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,1439,1740,GasA,Fair,Y,SBrkr,1740,0,0,1740,0,0,1,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,512,Typical,Typical,Paved,25,0,0,0,192,0,No_Pool,Good_Wood,None,0,10,2008,WD ,Family,150000,-93.634976,42.049141 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11040,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1971,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,602,1258,GasA,Excellent,Y,SBrkr,1258,0,0,1258,0,1,2,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,55,0,0,216,0,0,No_Pool,No_Fence,None,0,10,2008,COD,Abnorml,145000,-93.631526,42.048762 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15387,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,294,723,1620,GasA,Excellent,Y,SBrkr,1620,0,0,1620,0,0,2,0,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,578,Typical,Typical,Paved,0,62,192,0,0,0,No_Pool,No_Fence,Shed,450,8,2008,WD ,Normal,215000,-93.631779,42.046399 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,Duplex,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1625,1625,GasA,Excellent,Y,SBrkr,1625,0,0,1625,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,ConLD,Normal,140500,-93.6324549,42.042943 -Two_Story_1946_and_Newer,Residential_Low_Density,90,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1974,1997,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,182,384,1522,GasA,Typical,Y,SBrkr,1548,1066,0,2614,0,0,2,1,4,1,Typical,9,Typ,1,Typical,Attchd,RFn,2,624,Typical,Typical,Paved,38,243,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,240000,-93.631245,42.04378 -Two_Story_1946_and_Newer,Residential_Low_Density,73,8814,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,Two_Story,Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,732,GasA,Excellent,Y,SBrkr,732,732,0,1464,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,2,470,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,141000,-93.6264356,42.0458538 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,8872,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1965,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,300,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,317,912,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,147000,-93.6261431,42.0471033 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1965,2005,Hip,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,456,196,912,GasA,Excellent,Y,SBrkr,925,0,0,925,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,233,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,135000,-93.624762,42.046584 -Duplex_All_Styles_and_Ages,Residential_Low_Density,72,11072,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1728,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,145000,-93.62462,42.047541 -Duplex_All_Styles_and_Ages,Residential_Low_Density,74,13101,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,BrkFace,108,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,1497,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,142600,-93.624632,42.048185 -Duplex_All_Styles_and_Ages,Residential_Low_Density,87,9246,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,Duplex,One_Story,Average,Average,1973,1973,Gable,CompShg,Plywood,Plywood,BrkFace,564,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1656,1656,GasA,Typical,Y,SBrkr,1656,0,0,1656,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,211,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,135000,-93.6304229,42.0453551 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13355,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Good,No,ALQ,1,Unf,0,242,825,GasA,Typical,Y,SBrkr,845,825,0,1670,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,464,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,170000,-93.629079,42.044262 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8963,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1976,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,289,Excellent,Good,CBlock,Typical,Good,No,GLQ,3,ALQ,80,487,1142,GasA,Excellent,Y,SBrkr,1175,1540,0,2715,0,1,3,1,4,1,Good,11,Typ,2,Typical,BuiltIn,Fin,2,831,Typical,Typical,Paved,0,204,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,299800,-93.630152,42.044058 -Two_Story_1946_and_Newer,Residential_Low_Density,76,9120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1974,1974,Hip,CompShg,HdBoard,HdBoard,BrkFace,270,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,459,901,GasA,Typical,Y,SBrkr,943,933,0,1876,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,540,Good,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,185000,-93.630732,42.044851 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,Two_Story,Above_Average,Very_Good,1966,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,252,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,64,336,800,GasA,Good,Y,SBrkr,800,832,0,1632,0,1,1,1,4,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,173000,-93.625925,42.043142 -Split_Foyer,Residential_Low_Density,0,12122,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Good,Excellent,1961,2007,Gable,CompShg,CemntBd,CmentBd,Stone,210,Excellent,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,77,944,GasA,Good,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,144,76,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,178400,-93.624499,42.043845 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,Two_Story,Good,Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,BrkFace,342,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,280,832,GasA,Good,Y,SBrkr,1098,880,0,1978,0,0,2,1,4,1,Typical,9,Typ,1,Good,Attchd,RFn,2,486,Typical,Typical,Paved,0,43,0,0,0,0,No_Pool,Good_Privacy,None,0,4,2008,WD ,Normal,176000,-93.625799,42.044101 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7785,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1014,0,0,1014,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,267,Typical,Typical,Paved,0,0,40,0,200,0,No_Pool,Good_Wood,None,0,3,2008,WD ,Normal,98000,-93.621336,42.042398 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,8593,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1957,1957,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,619,907,GasA,Excellent,Y,SBrkr,907,0,0,907,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,109008,-93.622673,42.044773 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8475,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,724,952,GasA,Excellent,Y,FuseA,952,0,0,952,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,283,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,135750,-93.622662,42.044773 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,148,Typical,Good,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,344,1120,GasA,Good,Y,SBrkr,1128,0,0,1128,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,525,Typical,Typical,Paved,192,20,123,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Normal,155000,-93.629259,42.041229 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,10175,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,Plywood,BrkFace,272,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,935,1425,GasA,Good,Y,SBrkr,1425,0,0,1425,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,0,0,0,407,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,180500,-93.629555,42.04066 -Split_or_Multilevel,Residential_Low_Density,82,9020,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Good,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,183,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,ALQ,539,276,1127,GasA,Typical,Y,SBrkr,1165,0,0,1165,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,490,Good,Good,Paved,0,129,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,174900,-93.6277409,42.039235 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,244,1136,GasA,Typical,Y,SBrkr,1136,0,0,1136,1,0,1,0,3,1,Typical,5,Typ,1,Good,Attchd,RFn,1,384,Typical,Typical,Paved,426,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,140000,-93.629884,42.040112 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1964,1964,Hip,CompShg,HdBoard,HdBoard,Stone,260,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,305,1092,GasA,Excellent,Y,SBrkr,1092,0,0,1092,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Good,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,145000,-93.6277098,42.0417754 -Split_or_Multilevel,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Very_Good,1965,1999,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Good,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,586,1219,GasA,Good,Y,SBrkr,1265,0,0,1265,0,1,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,502,Typical,Typical,Paved,0,92,0,96,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,179900,-93.6267969,42.0417764 -Split_or_Multilevel,Residential_Low_Density,82,9020,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,SLvl,Above_Average,Average,1964,1964,Gable,WdShngl,Plywood,Wd Sdng,BrkFace,259,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Rec,336,288,1248,GasA,Typical,Y,SBrkr,1350,0,0,1350,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,176,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,168500,-93.626171,42.041811 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1114,1114,GasA,Typical,Y,SBrkr,1114,0,0,1114,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,451,Typical,Typical,Paved,0,0,0,0,164,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,140000,-93.626762,42.038323 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10007,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,2006,Gable,CompShg,HdBoard,HdBoard,BrkFace,54,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,247,1053,GasA,Excellent,Y,SBrkr,1053,0,0,1053,1,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,145500,-93.623267,42.04102 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10721,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,HdBoard,HdBoard,Stone,243,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1252,1252,GasA,Excellent,Y,SBrkr,1252,0,0,1252,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,142000,-93.6219226,42.0418699 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12493,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1960,1960,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Rec,306,375,1100,GasA,Typical,Y,SBrkr,1100,0,0,1100,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,1,312,Typical,Typical,Paved,355,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2008,WD ,Normal,141000,-93.621566,42.041172 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11332,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,590,1118,GasA,Excellent,Y,SBrkr,1118,0,0,1118,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,290,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,153000,-93.622076,42.040296 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6627,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Fair,Above_Average,1949,1950,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Floor,Typical,N,SBrkr,720,0,0,720,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,287,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,72500,-93.628205,42.035582 -One_Story_1945_and_Older,Residential_Low_Density,56,4130,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Fair,Above_Average,1935,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,270,270,GasA,Good,Y,SBrkr,729,0,0,729,0,0,1,0,2,1,Typical,5,Maj2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,52000,-93.62936,42.035571 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,50,7420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Artery,Artery,TwoFmCon,One_and_Half_Unf,Average,Above_Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,GLQ,3,Unf,0,140,991,GasA,Excellent,Y,SBrkr,1077,0,0,1077,1,0,1,0,2,2,Typical,5,Typ,2,Typical,Attchd,RFn,1,205,Good,Typical,Paved,0,4,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,118000,-93.627578,42.034563 -Two_Story_1945_and_Older,Residential_Low_Density,50,4882,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Brookside,RRAn,Feedr,OneFam,Two_Story,Below_Average,Good,1937,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,228,348,GasA,Typical,Y,SBrkr,453,453,0,906,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,1,231,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,87000,-93.628236,42.035741 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkFace,203,Fair,Fair,CBlock,Typical,Typical,No,Rec,6,Unf,0,638,1296,GasA,Typical,Y,SBrkr,1496,0,0,1496,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,450,Typical,Typical,Paved,0,22,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,141500,-93.6285065,42.0380577 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1950,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,752,1032,GasA,Typical,Y,FuseA,1032,220,0,1252,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Abnorml,119000,-93.622644,42.036178 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1950,2006,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Rec,308,232,572,GasA,Good,Y,SBrkr,1337,0,0,1337,1,0,1,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,1,264,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,141500,-93.622644,42.036102 -Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1955,1996,Hip,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,161,992,GasA,Good,Y,SBrkr,1661,0,0,1661,1,0,1,0,3,1,Good,8,Typ,1,Typical,BuiltIn,RFn,1,377,Typical,Typical,Paved,0,28,0,0,178,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,165500,-93.623556,42.03713 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7584,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,88,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,505,1036,GasA,Excellent,Y,SBrkr,1036,0,0,1036,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,120,24,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,112900,-93.620487,42.034679 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1965,1988,Gable,CompShg,VinylSd,VinylSd,Stone,288,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,410,864,GasA,Typical,Y,SBrkr,902,918,0,1820,0,0,1,2,4,1,Good,8,Typ,2,Good,Attchd,Unf,2,492,Typical,Typical,Paved,60,84,0,0,273,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,201800,-93.6167,42.048952 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10197,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1961,1961,Gable,CompShg,WdShing,Wd Shng,BrkCmn,491,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,374,700,1362,GasA,Typical,Y,SBrkr,1362,0,0,1362,1,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,504,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,COD,Normal,163000,-93.616552,42.047782 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkFace,136,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,572,1144,GasA,Good,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,456,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2008,WD ,Normal,139950,-93.6178602,42.0470858 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Excellent,1962,2005,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,237,Good,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1319,1319,GasA,Typical,Y,SBrkr,1537,0,0,1537,1,0,1,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,462,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,COD,Abnorml,174000,-93.6169916,42.0470799 -Split_or_Multilevel,Residential_Low_Density,101,9150,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1962,1962,Gable,Tar&Grv,Plywood,Plywood,BrkFace,305,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,728,1099,GasA,Good,Y,SBrkr,1431,0,0,1431,0,1,1,0,3,1,Typical,6,Typ,1,Good,Basment,RFn,1,296,Typical,Typical,Paved,64,110,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,165000,-93.615685,42.046935 -Two_Story_1946_and_Newer,Residential_Low_Density,90,14670,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1966,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,410,Good,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,529,1104,GasA,Excellent,Y,SBrkr,1104,884,0,1988,0,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,RFn,2,480,Typical,Typical,Paved,0,230,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,227000,-93.615614,42.046572 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7390,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,151,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,196,1098,GasA,Typical,Y,SBrkr,1098,0,0,1098,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,260,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,135000,-93.617179,42.042373 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9204,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,872,247,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,COD,Normal,124000,-93.6193303,42.0423032 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7763,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1962,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,BLQ,108,319,931,GasA,Typical,Y,SBrkr,1283,0,0,1283,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,140000,-93.619022,42.044736 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8856,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Below_Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,143,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,52,503,1176,GasA,Typical,Y,SBrkr,1176,0,0,1176,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,RFn,1,292,Typical,Typical,Paved,0,88,0,0,95,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,136500,-93.618634,42.043988 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,9840,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1998,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,195,1248,GasA,Typical,Y,SBrkr,1440,0,0,1440,1,0,2,0,2,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,1,480,Typical,Typical,Paved,150,0,0,0,256,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,185000,-93.617544,42.044791 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,599,1261,GasA,Excellent,Y,SBrkr,1261,0,0,1261,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,433,Typical,Typical,Paved,0,0,0,0,288,0,No_Pool,No_Fence,Shed,1400,11,2008,WD ,Normal,163000,-93.617543,42.044853 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13200,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,187,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,437,1395,GasA,Excellent,Y,SBrkr,1570,0,0,1570,1,0,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,441,Typical,Typical,Paved,490,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,166800,-93.613222,42.044304 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,74,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Unf,0,580,1196,GasA,Good,Y,FuseA,1196,0,0,1196,1,0,1,0,2,1,Typical,6,Typ,1,Good,Attchd,RFn,1,297,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,139000,-93.614856,42.042187 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11900,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1957,1957,Gable,CompShg,HdBoard,HdBoard,BrkFace,387,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,352,1392,GasA,Typical,Y,FuseA,1392,0,0,1392,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,458,Typical,Typical,Paved,0,0,0,0,192,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,166000,-93.613077,42.0438199 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9464,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1958,1958,Hip,CompShg,MetalSd,MetalSd,BrkFace,135,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,510,1080,GasA,Good,Y,SBrkr,1080,0,0,1080,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,130,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,136000,-93.612394,42.042023 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10425,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,330,1104,GasA,Good,Y,SBrkr,1104,0,0,1104,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,133000,-93.613383,42.042304 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11556,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1952,1952,Gable,CompShg,MetalSd,MetalSd,Stone,52,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,572,720,GasA,Excellent,Y,FuseA,882,0,0,882,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,116000,-93.617238,42.040479 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,9373,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,BrkFace,84,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,196,456,1152,GasA,Typical,Y,SBrkr,1152,0,0,1152,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,636,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,137500,-93.617084,42.040301 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12774,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Sev,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,LwQ,128,232,984,GasW,Typical,N,SBrkr,950,0,0,950,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,Good_Wood,None,0,7,2008,WD ,Normal,130000,-93.617086,42.040391 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,14250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1957,1957,Gable,CompShg,Plywood,Plywood,BrkFace,360,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,998,998,GasA,Typical,Y,SBrkr,1790,0,0,1790,0,0,2,0,3,1,Typical,6,Typ,2,Good,Attchd,Fin,2,540,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,Shed,1500,9,2008,WD ,Normal,180000,-93.6194043,42.0404436 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,1951,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,100,952,GasA,Typical,Y,SBrkr,952,0,0,952,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,840,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,COD,Abnorml,139000,-93.615621,42.039531 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,7677,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,203,773,GasA,Good,Y,SBrkr,773,0,0,773,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Abnorml,110000,-93.61707,42.038516 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,8838,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1957,1982,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1764,0,0,1764,0,0,2,1,4,1,Typical,7,Maj2,1,Typical,Attchd,Fin,1,301,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,100000,-93.612264,42.039445 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,8480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1947,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,390,832,GasA,Typical,Y,SBrkr,832,384,0,1216,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,158,0,102,0,0,0,No_Pool,No_Fence,None,0,10,2008,COD,Abnorml,118500,-93.6165173,42.0372039 -Duplex_All_Styles_and_Ages,Residential_Low_Density,76,12436,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1824,1824,GasA,Fair,Y,FuseA,1824,0,0,1824,0,0,2,0,5,2,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,146000,-93.618457,42.03471 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10122,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1948,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,869,0,0,869,0,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,390,Fair,Typical,Dirt_Gravel,0,0,66,0,0,0,No_Pool,Good_Privacy,None,0,8,2008,WD ,Normal,89900,-93.615484,42.037302 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,7506,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1925,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,747,747,GasA,Typical,Y,SBrkr,747,412,0,1159,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,288,Fair,Typical,Dirt_Gravel,84,0,96,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,114000,-93.615484,42.037459 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10930,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,333,913,GasA,Typical,Y,FuseA,1048,510,0,1558,1,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,140000,-93.61076,42.035893 -One_Story_1945_and_Older,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1940,2005,Gambrel,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Good,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,88,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,86900,-93.612269,42.035723 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10836,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,892,892,GasA,Excellent,Y,SBrkr,1254,182,0,1436,0,1,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,4,1488,Fair,Typical,Dirt_Gravel,0,0,100,0,0,0,No_Pool,Good_Wood,None,0,6,2008,WD ,Normal,139000,-93.612265,42.035938 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Good,1900,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseA,694,600,0,1294,0,0,2,0,3,2,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,220,114,210,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,106250,-93.612253,42.035222 -Two_Story_1946_and_Newer,Residential_Low_Density,70,9247,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,BrkFace,318,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,539,858,GasA,Excellent,Y,SBrkr,858,858,0,1716,0,0,1,1,4,1,Typical,8,Typ,1,Good,Attchd,Fin,2,490,Typical,Typical,Paved,0,84,0,0,120,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,171000,-93.607422,42.040021 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10180,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,168,912,GasA,Typical,Y,SBrkr,1044,0,0,1044,0,1,1,1,3,1,Typical,5,Typ,1,Fair,Attchd,Fin,2,372,Typical,Typical,Paved,200,48,0,0,0,0,No_Pool,Good_Wood,Shed,450,6,2008,WD ,Normal,139000,-93.610663,42.040142 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,11355,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1958,2001,Gable,Tar&Grv,HdBoard,HdBoard,BrkFace,125,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,675,1312,GasA,Excellent,Y,SBrkr,1312,0,0,1312,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,304,144,0,0,0,No_Pool,Minimum_Privacy,Othr,6500,4,2008,WD ,Normal,186000,-93.605189,42.041173 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10920,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Hip,CompShg,MetalSd,MetalSd,BrkFace,212,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,520,1253,GasA,Typical,Y,SBrkr,1253,0,0,1253,1,0,1,1,2,1,Typical,5,Typ,1,Fair,Attchd,RFn,1,352,Typical,Typical,Paved,0,213,176,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,157000,-93.605659,42.038686 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12929,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1993,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,276,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,384,1081,GasA,Typical,Y,SBrkr,1081,0,0,1081,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,CarPort,Unf,1,401,Typical,Typical,Paved,36,82,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,148000,-93.607174,42.039987 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,313,27650,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,Inside,Mod,North_Ames,PosA,Norm,OneFam,One_Story,Good,Good,1960,2007,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,160,585,GasA,Excellent,Y,SBrkr,2069,0,0,2069,1,0,2,0,4,1,Good,9,Typ,1,Good,Attchd,RFn,2,505,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,242000,-93.604106,42.039568 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1953,1996,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Typical,Stone,Typical,Typical,Av,Unf,7,Unf,0,105,105,GasA,Good,Y,SBrkr,910,0,0,910,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,414,Typical,Typical,Paved,196,0,150,0,0,0,No_Pool,Good_Wood,None,0,10,2008,WD ,Normal,116000,-93.608219,42.03807 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,1951,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,LwQ,4,Unf,0,444,876,GasA,Typical,Y,SBrkr,876,0,0,876,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,94000,-93.607957,42.037125 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1950,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1048,0,0,1048,0,0,1,0,3,1,Typical,7,Min1,0,No_Fireplace,Detchd,Unf,2,420,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,98300,-93.608909,42.037104 -Split_or_Multilevel,Residential_Low_Density,70,7910,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1960,1960,Hip,CompShg,BrkFace,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,409,1075,GasA,Good,Y,SBrkr,1507,0,0,1507,0,0,2,0,4,1,Typical,7,Maj1,0,No_Fireplace,Basment,Unf,1,404,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,8,2008,WD ,Normal,127000,-93.60688,42.035813 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Rec,488,292,1256,GasA,Good,Y,FuseA,1256,0,0,1256,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,311,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,159000,-93.605971,42.037162 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Gable,CompShg,BrkFace,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,319,188,1027,GasA,Typical,Y,SBrkr,1027,0,0,1027,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,299,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,125900,-93.60597,42.037006 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1960,1960,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,532,363,1269,GasA,Typical,Y,FuseA,1269,0,0,1269,0,0,1,1,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,155000,-93.606788,42.037291 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,453,768,GasA,Excellent,Y,SBrkr,819,501,0,1320,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2008,WD ,Normal,138000,-93.610604,42.034811 -One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,64,6390,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_and_Half_Unf,Above_Average,Good,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,936,936,GasA,Typical,Y,FuseA,984,0,0,984,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2008,WD ,Normal,112500,-93.607948,42.034564 -One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,59,7227,Pave,No_Alley_Access,Regular,HLS,AllPub,Corner,Mod,North_Ames,Artery,Norm,OneFam,One_and_Half_Unf,Above_Average,Above_Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Good,Y,SBrkr,832,0,0,832,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,105500,-93.608765,42.034789 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,181,854,GasA,Fair,Y,FuseA,854,424,0,1278,0,0,1,0,4,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2008,WD ,Normal,127500,-93.608903,42.034686 -Duplex_All_Styles_and_Ages,Residential_Low_Density,113,8513,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,Duplex,One_Story,Average,Average,1961,1961,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,1800,1800,GasA,Typical,N,SBrkr,1800,0,0,1800,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,130000,-93.6071073,42.0347301 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1955,1967,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,398,768,GasA,Good,Y,SBrkr,1024,564,0,1588,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,150000,-93.607799,42.034636 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,825,825,GasA,Typical,Y,FuseA,825,0,0,825,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,350,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,109500,-93.607801,42.034801 -Two_Story_1946_and_Newer,Residential_Low_Density,71,7056,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,BrkFace,415,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,380,780,GasA,Typical,Y,SBrkr,983,813,0,1796,1,0,1,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,483,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,140000,-93.604986,42.036973 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,360,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,LwQ,106,0,780,GasA,Typical,Y,SBrkr,798,813,0,1611,1,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,442,Typical,Typical,Paved,328,128,0,0,189,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,167900,-93.604837,42.037025 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,7590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Average,Average,1963,1963,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1117,1117,GasA,Excellent,Y,SBrkr,1117,0,0,1117,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Normal,136870,-93.603702,42.035724 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,56,9836,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,96,192,GasA,Good,N,SBrkr,1133,0,0,1133,1,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,175,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Abnorml,143000,-93.6195726,42.0342174 -One_Story_1945_and_Older,Residential_Medium_Density,30,5232,Pave,Gravel,Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Average,1925,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,680,680,GasA,Good,N,FuseP,764,0,0,764,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,504,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,73000,-93.619415,42.034128 -Two_Story_1945_and_Older,Residential_Medium_Density,60,10440,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1904,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,650,650,GasA,Good,Y,SBrkr,958,581,0,1539,0,0,2,0,3,1,Good,8,Typ,1,Poor,Detchd,Unf,2,686,Good,Typical,Partial_Pavement,70,78,68,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,157500,-93.615582,42.033379 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,57,9184,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1948,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Excellent,Y,SBrkr,948,375,0,1323,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,122600,-93.62005,42.032611 -Two_Story_1945_and_Older,Residential_Medium_Density,80,4800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Average,1910,2003,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,680,680,GasA,Fair,N,SBrkr,680,680,0,1360,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,330,Fair,Typical,Paved,192,50,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,111000,-93.61648,42.0330566 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10440,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Good,Good,BrkTil,Good,Typical,No,LwQ,4,Unf,0,1017,1510,GasW,Excellent,Y,SBrkr,1584,1208,0,2792,0,0,2,0,5,1,Typical,8,Mod,2,Typical,Detchd,Unf,2,520,Fair,Typical,Paved,0,547,0,0,480,0,No_Pool,Minimum_Privacy,Shed,1150,6,2008,WD ,Normal,256000,-93.615596,42.032392 -One_Story_1945_and_Older,Residential_Medium_Density,60,4800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Below_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,672,672,GasA,Typical,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,256,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,64000,-93.616991,42.032419 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Good,1915,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,728,728,GasA,Good,Y,SBrkr,728,728,0,1456,0,0,1,1,4,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Fair,Fair,Dirt_Gravel,0,0,248,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,139500,-93.617106,42.03136 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,63,11426,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Above_Average,1910,1996,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Stone,Typical,Typical,No,Unf,7,Unf,0,828,828,GasA,Good,Y,FuseA,828,658,108,1594,0,0,2,0,3,2,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,172,109,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,118000,-93.6189373,42.030427 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,11426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1362,1362,GasA,Excellent,Y,SBrkr,1362,720,0,2082,0,0,2,1,3,1,Good,6,Mod,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Dirt_Gravel,280,238,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,200000,-93.6195822,42.0304142 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,7628,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1940,1985,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,801,801,GasA,Good,Y,FuseA,1095,561,0,1656,0,0,2,0,2,1,Typical,8,Mod,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,187,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,119164,-93.6198478,42.030418 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,81,7308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Average,1920,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Fair,Fair,BrkTil,Typical,Typical,No,Rec,6,Unf,0,576,936,GasA,Good,N,FuseA,960,780,0,1740,0,0,1,0,2,1,Excellent,6,Typ,1,Good,Detchd,Unf,1,225,Fair,Fair,Dirt_Gravel,0,0,236,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,122250,-93.620219,42.030482 -One_Story_1945_and_Older,Residential_Medium_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Good,Above_Average,1920,2006,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,931,931,GasA,Typical,Y,SBrkr,1027,0,0,1027,0,1,1,0,2,1,Good,5,Typ,1,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,28,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,120000,-93.613969,42.032161 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,294,884,GasA,Typical,Y,SBrkr,884,552,0,1436,0,0,2,0,3,2,Typical,8,Typ,2,Good,Detchd,Unf,2,828,Typical,Typical,Paved,0,0,126,0,0,0,No_Pool,No_Fence,None,0,5,2008,Con,Normal,155000,-93.615447,42.032272 -One_Story_1945_and_Older,Residential_Medium_Density,60,6756,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1910,1950,Mansard,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Good,No,Unf,7,Unf,0,481,481,GasA,Typical,N,FuseA,899,0,0,899,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Partial_Pavement,0,0,96,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,78000,-93.615031,42.030406 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,44,5914,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Excellent,1890,1996,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,684,684,GasA,Good,Y,SBrkr,684,396,0,1080,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,165,30,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,95000,-93.6112887,42.0303597 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Average,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,169,516,789,GasA,Excellent,Y,SBrkr,789,0,0,789,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Partial,115000,-93.608867,42.033435 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,100,12000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1948,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,ALQ,608,172,924,GasA,Excellent,Y,SBrkr,1122,0,0,1122,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,147000,-93.60887,42.033571 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1946,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,459,904,GasA,Excellent,Y,FuseA,904,595,0,1499,0,0,1,0,3,1,Typical,5,Typ,1,Poor,Detchd,Unf,3,869,Typical,Good,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,140000,-93.607771,42.033249 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,62,7311,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Poor,Average,1946,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,407,407,GasA,Typical,N,FuseA,407,0,0,407,0,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,297,Fair,Typical,Paved,76,0,120,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Abnorml,46500,-93.606764,42.032008 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1957,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,620,928,GasA,Good,Y,FuseA,928,0,0,928,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,112500,-93.607713,42.032094 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1954,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,901,901,GasA,Excellent,Y,SBrkr,901,0,0,901,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,281,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,107900,-93.607724,42.032381 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,103,12205,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Very_Poor,1949,1992,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Fair,No,Unf,7,Unf,0,448,448,GasA,Good,Y,SBrkr,1588,0,0,1588,0,0,2,0,5,1,Typical,6,Maj2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Abnorml,65000,-93.606802,42.0311 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,87,18386,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Fin,Good,Excellent,1880,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1470,1470,GasA,Excellent,Y,SBrkr,1675,1818,0,3493,0,0,3,0,3,1,Good,10,Typ,1,Excellent,Attchd,Unf,3,870,Typical,Typical,Paved,302,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,295000,-93.6077128,42.0304823 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,69,9142,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Average,1900,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Fair,Typical,No,Unf,7,Unf,0,797,797,GasA,Typical,N,FuseA,830,797,0,1627,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,420,Fair,Poor,Dirt_Gravel,192,0,60,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,139500,-93.61844,42.029028 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,100,12665,Pave,Gravel,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,876,876,GasA,Good,Y,SBrkr,876,540,0,1416,0,0,1,1,4,1,Typical,7,Typ,1,Good,Detchd,Unf,3,720,Typical,Typical,Paved,418,0,194,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,153900,-93.620179,42.029058 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,53,5350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Good,Very_Good,1920,1965,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,508,624,GasA,Excellent,Y,SBrkr,730,720,0,1450,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2008,WD ,Normal,132000,-93.620183,42.02928 -Two_Story_1945_and_Older,Residential_Medium_Density,34,4571,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Average,1916,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Fair,N,SBrkr,624,720,0,1344,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,3,513,Fair,Fair,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,98000,-93.618296,42.029253 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,69,9143,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1900,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,346,346,GasA,Excellent,Y,SBrkr,709,308,0,1017,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,139,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,114000,-93.618297,42.029281 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Average,1920,1960,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,739,973,GasA,Typical,Y,FuseP,1377,973,0,2350,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,2,393,Typical,Typical,Paved,0,0,219,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,130000,-93.617386,42.028287 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Average,1917,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Rec,6,Unf,0,416,735,OthW,Fair,N,SBrkr,1134,924,0,2058,0,0,1,1,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,396,Fair,Fair,Partial_Pavement,0,0,259,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,129500,-93.616885,42.02793 -Two_Story_1945_and_Older,Residential_Medium_Density,60,6000,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Excellent,1905,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,572,572,GasA,Excellent,Y,SBrkr,884,656,0,1540,0,0,1,1,3,1,Good,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,240,77,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,129400,-93.616998,42.027073 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Excellent,1928,2005,Gambrel,CompShg,MetalSd,MetalSd,None,0,Typical,Excellent,BrkTil,Typical,Typical,No,Rec,6,Unf,0,548,689,GasA,Excellent,Y,SBrkr,689,689,0,1378,0,0,2,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Dirt_Gravel,0,0,116,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,161000,-93.616996,42.027032 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10800,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Good,1905,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,482,482,GasA,Excellent,N,SBrkr,1221,691,0,1912,0,0,2,0,3,2,Typical,7,Typ,1,Typical,Detchd,Unf,2,672,Good,Typical,Paved,0,25,212,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,163000,-93.612239,42.029084 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,35,6300,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Above_Average,1914,2001,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,742,742,GasA,Excellent,Y,SBrkr,742,742,0,1484,0,0,2,0,3,1,Typical,9,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,291,134,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,128000,-93.613845,42.02813 -Two_Story_1945_and_Older,Residential_Medium_Density,50,5250,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1872,1987,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Fair,No,LwQ,4,Unf,0,425,684,OthW,Fair,N,SBrkr,938,1215,205,2358,0,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,54,20,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,122000,-93.6096888,42.0288284 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,5700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,572,572,GasA,Typical,Y,SBrkr,572,539,0,1111,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,176,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,116900,-93.6096482,42.0287814 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,75,13500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,PosA,OneFam,Two_and_Half_Unf,Very_Excellent,Excellent,1893,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Excellent,Excellent,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1237,1237,GasA,Good,Y,SBrkr,1521,1254,0,2775,0,0,3,1,3,1,Good,9,Typ,1,Good,Detchd,Unf,2,880,Good,Typical,Paved,105,502,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,325000,-93.610008,42.028397 -Two_Story_1945_and_Older,Residential_Medium_Density,60,11340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Poor,Very_Poor,1920,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fair,Fair,BrkTil,Fair,Fair,No,Unf,7,Unf,0,723,723,GasA,Typical,N,SBrkr,723,363,0,1086,0,0,1,0,2,1,Typical,5,Maj1,0,No_Fireplace,Detchd,Unf,2,400,Fair,Poor,Dirt_Gravel,0,24,144,0,0,0,No_Pool,No_Fence,None,0,11,2008,ConLD,Normal,55000,-93.608752,42.029209 -Two_Story_1945_and_Older,Residential_Medium_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1890,1999,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,1313,1313,GasW,Good,Y,SBrkr,1313,1182,0,2495,0,0,2,0,5,1,Typical,10,Typ,1,Good,Detchd,Unf,2,342,Typical,Fair,Paved,0,299,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,184000,-93.6075521,42.0273991 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,65,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,MetalSd,MetalSd,BrkFace,164,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,784,984,GasA,Good,Y,SBrkr,984,0,0,984,1,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,110000,-93.6059697,42.0272517 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,81,12150,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,BrkFace,335,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1050,1050,GasA,Excellent,N,FuseF,1050,745,0,1795,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,352,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,131500,-93.606196,42.02774 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,70,12702,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,FuseA,882,0,0,882,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,97000,-93.604486,42.026691 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,52,8516,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1958,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,869,869,GasA,Typical,Y,SBrkr,1093,0,0,1093,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,115500,-93.6043548,42.0272664 -One_Story_1945_and_Older,Residential_Low_Density,55,7111,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1928,1983,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Good,BrkTil,Typical,Typical,No,LwQ,4,BLQ,273,329,1008,GasA,Typical,Y,SBrkr,1143,0,0,1143,0,0,1,0,2,1,Typical,5,Typ,1,Poor,Detchd,Unf,1,288,Typical,Typical,Paved,265,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,WD ,Normal,138000,-93.628806,42.03361 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,7425,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Brookside,RRAn,Artery,OneFam,One_and_Half_Fin,Good,Good,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Fair,No,Unf,7,Unf,0,672,672,GasA,Good,Y,SBrkr,1195,473,0,1668,0,0,1,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Unf,1,252,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Abnorml,108000,-93.6267116,42.0343116 -One_Story_with_Finished_Attic_All_Ages,Residential_Medium_Density,50,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Feedr,OneFam,One_Story,Average,Above_Average,1930,1960,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,742,742,GasA,Typical,Y,FuseA,779,0,156,935,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,Shed,600,8,2008,WD ,Normal,79500,-93.625728,42.03338 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,7010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1935,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,849,1024,GasA,Typical,Y,SBrkr,1144,594,0,1738,0,0,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,153000,-93.625926,42.03047 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,6130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Average,Above_Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,0,784,GasA,Good,Y,SBrkr,784,0,0,784,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,116,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,109500,-93.624715,42.033456 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Good,1941,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,72,672,GasA,Excellent,Y,SBrkr,832,378,0,1210,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,124000,-93.625584,42.033568 -Two_Story_1945_and_Older,Residential_Medium_Density,59,5870,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Feedr,OneFam,Two_Story,Above_Average,Excellent,1900,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,554,554,GasA,Excellent,Y,SBrkr,736,554,0,1290,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Paved,38,112,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,4,2008,WD ,Normal,106900,-93.625589,42.033699 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,460,981,GasA,Excellent,Y,SBrkr,1014,658,0,1672,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,11,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,WD ,Normal,164900,-93.623666,42.03337 -One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1924,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,949,949,GasA,Excellent,Y,SBrkr,949,0,0,949,0,0,1,0,2,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,370,Typical,Typical,Paved,0,0,48,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,119000,-93.624566,42.033511 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1929,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,862,862,GasA,Typical,Y,SBrkr,950,208,0,1158,0,0,1,0,3,1,Typical,5,Typ,1,Good,BuiltIn,RFn,1,208,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,120000,-93.623514,42.033321 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1937,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,LwQ,162,462,825,GasA,Excellent,Y,SBrkr,825,672,0,1497,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,672,Typical,Typical,Paved,272,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,157000,-93.621532,42.033473 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,475,739,GasA,Excellent,Y,SBrkr,874,468,0,1342,0,0,2,0,2,2,Typical,7,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,105000,-93.621532,42.033366 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Good,1926,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,992,992,GasA,Excellent,Y,SBrkr,1013,0,0,1013,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,160,Fair,Typical,Paved,0,0,101,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,113000,-93.622102,42.033493 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5520,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Good,1920,1997,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Good,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,497,565,GasA,Typical,Y,SBrkr,565,651,0,1216,1,0,1,0,3,1,Typical,6,Typ,1,Good,BuiltIn,RFn,1,355,Fair,Typical,Paved,0,0,180,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,115000,-93.624688,42.031354 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,99,5940,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Below_Average,Good,1946,1950,Gable,CompShg,MetalSd,CBlock,None,0,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,0,No_Basement,0,0,0,GasA,Typical,Y,FuseA,896,0,0,896,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,ConLD,Abnorml,79000,-93.624687,42.031293 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1929,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,624,704,GasA,Excellent,Y,SBrkr,624,512,0,1136,0,1,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,0,365,80,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,123000,-93.623632,42.031493 -One_Story_1945_and_Older,Residential_Medium_Density,0,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Above_Average,1945,1995,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,N,SBrkr,808,0,0,808,0,0,1,0,1,1,Typical,6,Min2,0,No_Fireplace,Attchd,Unf,1,164,Typical,Typical,Partial_Pavement,0,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,81300,-93.622566,42.032476 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1992,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1078,1078,GasA,Typical,Y,SBrkr,1128,445,0,1573,0,0,2,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,162900,-93.622552,42.031461 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Rec,6,LwQ,240,449,989,GasA,Typical,Y,SBrkr,1245,764,0,2009,0,0,2,0,4,1,Typical,7,Min2,1,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,150000,-93.621485,42.031496 -Two_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Average,Average,1923,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,897,1100,GasA,Typical,Y,SBrkr,1226,676,0,1902,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,139,55,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,123500,-93.620392,42.031467 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Good,Average,1931,1950,Gable,CompShg,BrkFace,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,952,952,GasA,Good,Y,FuseF,1022,752,0,1774,0,0,2,0,2,2,Typical,8,Min1,2,Typical,Detchd,Unf,2,468,Fair,Typical,Paved,90,0,205,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Abnorml,129900,-93.620391,42.031432 -Two_Story_1945_and_Older,Residential_Medium_Density,60,6155,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Brookside,RRNn,Feedr,OneFam,Two_Story,Above_Average,Very_Good,1920,1999,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Fair,Mn,Unf,7,Unf,0,611,611,GasA,Excellent,Y,SBrkr,751,611,0,1362,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,2,502,Typical,Fair,Paved,0,0,84,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,128000,-93.624591,42.031049 -One_Story_1945_and_Older,Residential_Medium_Density,60,6324,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,RRNn,OneFam,One_Story,Below_Average,Above_Average,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,520,520,GasA,Fair,N,SBrkr,520,0,0,520,0,0,1,0,1,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Paved,49,0,87,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,68500,-93.624471,42.030388 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,8635,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1948,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,GLQ,41,295,672,GasA,Typical,Y,SBrkr,1072,213,0,1285,1,0,1,0,2,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,127000,-93.6218858,42.031057 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,64,13053,Pave,Paved,Regular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1923,2000,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,833,833,GasA,Good,Y,SBrkr,1053,795,0,1848,0,0,1,1,4,1,Good,8,Typ,1,Good,Detchd,Unf,2,370,Typical,Typical,Dirt_Gravel,0,0,0,0,220,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,207000,-93.627,42.029399 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9120,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Good,Above_Average,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Typical,No,Rec,6,Unf,0,697,1026,GasA,Excellent,Y,SBrkr,1133,687,0,1820,1,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,100,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,184000,-93.625556,42.028426 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9144,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Below_Average,1915,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,810,810,GasA,Excellent,Y,SBrkr,1170,546,0,1716,0,0,2,0,4,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,195,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,162500,-93.625539,42.028274 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,9144,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1940,1982,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,484,883,GasA,Good,Y,SBrkr,988,517,0,1505,1,0,1,0,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,145000,-93.625434,42.027065 -One_Story_1945_and_Older,Residential_Low_Density,55,10267,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_Story,Above_Average,Good,1918,2000,Gable,CompShg,Stucco,Wd Shng,None,0,Typical,Good,BrkTil,Typical,Good,Mn,Rec,6,ALQ,606,0,816,GasA,Excellent,Y,SBrkr,838,0,0,838,1,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Fin,1,275,Typical,Typical,Dirt_Gravel,0,0,112,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,5,2008,WD ,Normal,130000,-93.625623,42.029556 -Two_Story_1946_and_Newer,Residential_Medium_Density,57,8094,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_and_Half_Unf,Above_Average,Very_Good,1910,1983,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Rec,6,Unf,0,1046,1242,GasA,Good,Y,SBrkr,1242,742,0,1984,0,0,2,0,5,1,Typical,8,Typ,0,No_Fireplace,Detchd,No_Garage,1,360,No_Garage,No_Garage,Paved,64,0,180,0,0,0,No_Pool,Minimum_Privacy,Shed,1000,9,2008,WD ,Normal,160000,-93.621676,42.02903 -Two_Story_1945_and_Older,Residential_Medium_Density,0,5100,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1925,1996,Hip,CompShg,Stucco,Wd Shng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,588,588,GasA,Fair,Y,SBrkr,833,833,0,1666,0,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,228,Typical,Typical,Paved,192,63,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,161000,-93.621065,42.029038 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,4347,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1910,1950,Gambrel,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Good,Good,No,Unf,7,Unf,0,796,796,GasA,Excellent,Y,SBrkr,825,784,0,1609,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,228,Fair,Fair,Dirt_Gravel,0,182,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,127500,-93.6208294,42.0286305 -One_Story_1945_and_Older,Residential_Medium_Density,0,6291,Grvl,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,RRNe,Norm,OneFam,One_Story,Above_Average,Above_Average,1930,1950,Gable,CompShg,Stucco,Wd Shng,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,768,768,GasA,Typical,Y,SBrkr,768,0,0,768,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Dirt_Gravel,0,0,84,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,93850,-93.6259506,42.0255246 -Two_Story_1945_and_Older,Residential_Medium_Density,60,10266,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1952,1952,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,396,768,GasA,Typical,Y,FuseA,768,768,0,1536,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,216,80,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,136000,-93.628447,42.024111 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6876,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1938,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1272,1272,GasA,Typical,Y,SBrkr,1272,0,697,1969,0,0,2,0,4,1,Typical,9,Min1,1,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,COD,Normal,141000,-93.627729,42.024265 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1915,1978,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,880,880,GasA,Good,Y,SBrkr,880,428,0,1308,0,0,2,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Fair,Fair,Paved,0,0,117,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,120000,-93.6261621,42.0249938 -One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Good,1925,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1040,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,320,Typical,Typical,Paved,0,132,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,127500,-93.6265884,42.0242588 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,62,7006,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1925,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,647,768,GasA,Typical,Y,SBrkr,788,448,0,1236,1,0,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Family,127000,-93.6295,42.022574 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,7920,Pave,Gravel,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,319,319,GasA,Typical,Y,FuseA,1035,371,0,1406,0,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,144,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2008,WD ,Normal,89500,-93.625289,42.02295 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,49,8235,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Old_Town,Feedr,RRNn,OneFam,One_Story,Average,Good,1955,1995,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,Rec,645,0,825,GasA,Typical,Y,SBrkr,825,0,0,825,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,RFn,2,720,Typical,Typical,Paved,140,50,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,125000,-93.623738,42.026478 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5586,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,901,901,GasA,Good,Y,SBrkr,1088,110,0,1198,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,98,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2008,ConLD,Abnorml,79900,-93.621921,42.0262558 -One_Story_1945_and_Older,Residential_Medium_Density,60,10320,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,RRNe,Norm,OneFam,One_Story,Average,Very_Good,1912,1991,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,451,451,GasA,Typical,Y,SBrkr,759,0,0,759,0,0,1,0,1,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Dirt_Gravel,40,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,ConLD,Family,85000,-93.624819,42.024885 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,Two_Story,Fair,Fair,1915,1950,Gable,CompShg,AsphShn,AsphShn,None,0,Fair,Fair,PConc,Typical,Fair,No,Unf,7,Unf,0,536,536,GasA,Excellent,N,FuseF,808,536,0,1344,0,0,2,0,3,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,42,0,204,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,82375,-93.6246096,42.0242331 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1906,1995,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,756,756,GasA,Excellent,Y,SBrkr,756,713,0,1469,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,57,0,239,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,135000,-93.62458,42.023716 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,9488,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1947,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1046,1046,GasA,Good,N,SBrkr,1054,0,0,1054,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,60,122,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Abnorml,124000,-93.658159,42.034391 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,55,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,492,210,952,GasA,Excellent,Y,SBrkr,1211,0,0,1211,0,0,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,Unf,1,322,Typical,Typical,Paved,0,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,134000,-93.656979,42.034409 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,115,21286,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,720,720,GasA,Typical,Y,SBrkr,720,551,0,1271,0,0,2,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,1,312,Typical,Typical,Paved,0,0,108,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,135000,-93.657958,42.033423 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,17120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1959,1959,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1120,468,0,1588,0,0,2,0,4,1,Typical,7,Min2,1,Good,Detchd,Fin,2,680,Typical,Typical,Dirt_Gravel,0,59,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,134432,-93.657031,42.031281 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,62,10106,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Good,1940,1999,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,ALQ,1,Rec,181,112,644,GasA,Good,Y,SBrkr,808,547,0,1355,1,0,2,0,4,2,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,127500,-93.658395,42.02393 -Split_Foyer,Residential_Low_Density,0,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,SFoyer,Average,Above_Average,1963,1963,Gable,CompShg,HdBoard,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,197,981,GasA,Typical,Y,SBrkr,1075,0,0,1075,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,64,0,0,0,64,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Abnorml,148000,-93.675551,42.033604 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,11200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,134,1040,GasA,Excellent,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,129500,-93.669806,42.033225 -Split_or_Multilevel,Residential_Low_Density,80,13014,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Average,1978,1978,Gable,CompShg,HdBoard,Plywood,BrkFace,39,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,480,1008,GasA,Typical,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,Unf,2,484,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,157500,-93.672241,42.032365 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7162,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkCmn,41,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,876,876,GasA,Typical,Y,SBrkr,904,0,0,904,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,408,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Abnorml,109900,-93.676873,42.031571 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,10265,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1967,2005,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Fair,CBlock,Typical,Typical,No,ALQ,1,Unf,0,234,992,GasA,Excellent,Y,SBrkr,992,0,0,992,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,204,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,600,7,2008,WD ,Normal,145000,-93.677717,42.031248 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,9764,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Good,1967,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,192,894,GasA,Excellent,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,450,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,130000,-93.677945,42.031123 -Split_Foyer,Residential_Low_Density,0,7703,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Above_Average,Very_Good,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkCmn,40,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,0,450,GasA,Excellent,Y,SBrkr,1034,0,0,1034,0,1,1,0,3,1,Typical,6,Typ,1,Poor,Basment,Fin,2,504,Typical,Typical,Paved,311,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,153000,-93.672718,42.031348 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9981,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,340,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,852,1073,GasA,Good,Y,SBrkr,1073,0,0,1073,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,270,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2008,WD ,Normal,120000,-93.674179,42.03156 -Split_Foyer,Residential_Low_Density,0,7400,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Average,1984,1984,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,LwQ,4,ALQ,956,0,1060,GasA,Typical,Y,SBrkr,1126,0,0,1126,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,506,Typical,Typical,Paved,178,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,152000,-93.671905,42.030552 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,12900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,Duplex,SFoyer,Below_Average,Below_Average,1969,1969,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1198,GasA,Typical,Y,SBrkr,1258,0,0,1258,2,0,0,2,0,2,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,400,Fair,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Alloca,108959,-93.666954,42.034428 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,12900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,TwoFmCon,One_Story,Average,Below_Average,1920,1950,Gable,CompShg,BrkFace,Stucco,None,0,Typical,Typical,PConc,Typical,Fair,No,BLQ,2,Unf,0,0,1300,GasA,Fair,Y,SBrkr,1140,0,0,1140,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,400,Typical,Typical,Paved,0,0,190,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Alloca,95541,-93.666912,42.034428 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Fair,1959,1959,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,718,1006,GasA,Typical,Y,SBrkr,1006,0,0,1006,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,80000,-93.665521,42.033454 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8544,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,2006,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1228,1228,GasA,Good,Y,SBrkr,1228,0,0,1228,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,271,Typical,Typical,Paved,0,65,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,149350,-93.669465,42.033834 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,9239,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1963,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,326,960,GasA,Excellent,Y,SBrkr,960,0,0,960,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,1,300,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,144900,-93.663727,42.034414 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,14175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,PosA,Norm,OneFam,One_Story,Above_Average,Very_Good,1956,1956,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Good,Gd,GLQ,3,Unf,0,212,988,GasA,Typical,Y,FuseA,1188,0,0,1188,1,0,1,0,1,1,Typical,4,Typ,1,Typical,Attchd,Unf,2,621,Typical,Typical,Paved,102,89,231,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,185000,-93.665355,42.032495 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13284,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,PosN,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,319,1383,GasA,Typical,Y,SBrkr,1383,0,0,1383,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,354,Typical,Typical,Paved,511,116,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,165000,-93.665356,42.032566 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Good,1960,1975,Flat,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,BLQ,2,ALQ,1080,93,1602,GasA,Good,Y,SBrkr,1252,0,0,1252,1,0,1,0,1,1,Typical,4,Typ,1,Good,Attchd,RFn,2,564,Typical,Typical,Paved,409,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,235000,-93.666979,42.032155 -Split_or_Multilevel,Residential_Low_Density,85,13825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Above_Average,1958,1987,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,533,533,GasA,Typical,Y,SBrkr,1021,580,0,1601,0,1,1,0,3,1,Typical,6,Min2,0,No_Fireplace,BuiltIn,RFn,1,300,Typical,Typical,Paved,280,34,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,140000,-93.662134,42.03392 -One_Story_1945_and_Older,Residential_Low_Density,60,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Very_Good,1940,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,168,0,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2008,WD ,Normal,108000,-93.660673,42.033692 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,0,10532,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Fair,1960,1960,Flat,Tar&Grv,Plywood,Plywood,Stone,275,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,0,988,GasA,Good,Y,SBrkr,1721,0,0,1721,1,0,2,0,3,1,Typical,7,Mod,2,Typical,Basment,Unf,2,626,Typical,Typical,Paved,50,84,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Abnorml,145000,-93.660812,42.032653 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,63,8375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1941,1973,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,240,576,GasA,Good,Y,SBrkr,864,486,0,1350,1,0,1,1,2,1,Good,6,Min1,0,No_Fireplace,More_Than_Two_Types,Unf,3,627,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,152400,-93.660521,42.033559 -Split_or_Multilevel,Residential_Low_Density,0,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Very_Good,1970,1970,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Good,PConc,Good,Typical,Av,ALQ,1,Unf,0,160,864,GasA,Excellent,Y,SBrkr,904,0,0,904,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,912,Typical,Typical,Paved,143,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,144000,-93.678125,42.029453 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,2887,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1996,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,288,1291,GasA,Excellent,Y,SBrkr,1291,0,0,1291,1,0,1,0,2,1,Good,6,Typ,1,Good,Attchd,Unf,2,431,Typical,Typical,Paved,307,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,173000,-93.676219,42.024662 -Two_Story_1946_and_Newer,Residential_Low_Density,83,10005,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,299,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,768,1160,GasA,Excellent,Y,SBrkr,1156,866,0,2022,0,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,505,Typical,Typical,Paved,288,117,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,192000,-93.6739889,42.0243033 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,20270,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,925,1524,GasA,Typical,Y,SBrkr,1524,0,0,1524,1,0,2,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,478,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,245000,-93.67358,42.02521 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,39104,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Good,1954,2005,Flat,Membran,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,LwQ,4,GLQ,1063,96,1385,GasA,Excellent,Y,SBrkr,1363,0,0,1363,1,0,1,0,2,1,Typical,5,Mod,2,Typical,Attchd,Unf,2,439,Typical,Typical,Paved,81,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,241500,-93.666066,42.028568 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,53227,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1954,1994,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,BLQ,2,Unf,0,248,1364,GasA,Excellent,Y,SBrkr,1663,0,0,1663,1,0,1,0,2,1,Good,6,Min1,2,Good,Attchd,Fin,2,529,Typical,Typical,Paved,224,137,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,256000,-93.666216,42.028595 -Split_or_Multilevel,Residential_Low_Density,124,11512,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Good,1959,2006,Gable,CompShg,Plywood,Plywood,BrkFace,84,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,300,1019,GasA,Good,Y,SBrkr,1357,0,0,1357,1,0,1,0,2,1,Excellent,5,Typ,1,Good,Basment,RFn,1,312,Typical,Typical,Paved,0,0,0,0,163,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,177000,-93.666147,42.027332 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,5190,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1948,1950,Gable,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,570,570,GasA,Typical,Y,SBrkr,617,462,0,1079,0,0,1,0,2,1,Typical,5,Typ,1,Good,Attchd,Unf,1,249,Typical,Typical,Paved,135,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,125600,-93.66515,42.027507 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10452,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,216,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,594,1094,GasA,Excellent,Y,SBrkr,1094,0,0,1094,0,0,1,0,3,1,Typical,5,Typ,2,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,0,0,0,287,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,155000,-93.665571,42.027555 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,45600,Pave,No_Alley_Access,Moderately_Irregular,Bnk,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1908,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,907,907,GasA,Typical,Y,SBrkr,1307,1051,0,2358,0,0,3,0,5,1,Typical,10,Typ,1,Good,Detchd,Unf,2,360,Fair,Typical,Paved,486,40,0,0,175,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,240000,-93.663054,42.028381 -One_Story_1945_and_Older,Residential_Low_Density,85,19550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1940,2007,Flat,Tar&Grv,PreCast,PreCast,None,0,Typical,Typical,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,545,1580,GasA,Excellent,Y,SBrkr,1518,0,0,1518,1,0,1,0,2,1,Fair,5,Typ,2,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,260000,-93.663123,42.028233 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,66,21780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1918,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Unf,7,Unf,0,1163,1163,GasA,Excellent,Y,SBrkr,1163,511,0,1674,0,0,2,0,4,1,Typical,8,Typ,1,Good,Detchd,Fin,2,396,Typical,Typical,Dirt_Gravel,72,36,0,0,144,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,185000,-93.660595,42.028188 -Two_Story_1945_and_Older,Residential_Low_Density,120,13728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1935,1986,Hip,CompShg,Stucco,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,501,1127,GasA,Excellent,Y,SBrkr,1236,872,0,2108,0,0,2,0,4,1,Good,7,Typ,2,Typical,Basment,Unf,2,540,Typical,Typical,Paved,0,0,0,0,90,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,235000,-93.6608625,42.0280297 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,639,1509,GasA,Typical,Y,FuseA,1509,0,0,1509,1,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,1,322,Typical,Typical,Paved,158,0,0,0,576,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,159900,-93.667534,42.024025 -Duplex_All_Styles_and_Ages,Residential_Low_Density,50,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Average,Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,0,864,GasA,Fair,N,SBrkr,864,0,0,864,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,99600,-93.6650659,42.0257161 -Two_Story_1946_and_Newer,Residential_Low_Density,50,9360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1962,2001,Gable,CompShg,VinylSd,VinylSd,BrkCmn,216,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,297,621,GasA,Typical,Y,SBrkr,621,648,0,1269,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,236,0,0,0,0,No_Pool,Good_Wood,None,0,11,2008,WD ,Normal,134500,-93.66526,42.025036 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,8405,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Good,No,Rec,6,BLQ,391,229,861,GasA,Excellent,Y,SBrkr,961,406,0,1367,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,130,112,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,119000,-93.6644616,42.0236702 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,Two_Story,Average,Good,1910,1991,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,148,1117,GasA,Typical,Y,SBrkr,820,527,0,1347,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,85,0,148,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,107500,-93.6618779,42.0244599 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,68,10880,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_Story,Average,Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,124,1164,GasW,Typical,N,SBrkr,1164,0,0,1164,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,ConLD,Normal,125000,-93.662924,42.022902 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,572,572,Grav,Fair,N,FuseF,572,524,0,1096,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,8,128,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,79000,-93.658528,42.022769 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,Mn,LwQ,4,Rec,380,0,616,GasA,Typical,N,SBrkr,616,495,0,1111,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Fair,Paved,48,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,ConLw,Normal,95000,-93.659949,42.02283 -Two_Story_1946_and_Newer,Residential_Low_Density,86,11839,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1990,Hip,CompShg,HdBoard,HdBoard,BrkFace,99,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,390,1475,GasA,Excellent,Y,SBrkr,1532,797,0,2329,1,0,2,1,4,1,Good,10,Typ,1,Excellent,Attchd,Unf,2,514,Typical,Typical,Paved,192,121,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,262280,-93.683223,42.031766 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9771,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,2002,Gable,CompShg,HdBoard,HdBoard,BrkFace,190,Good,Typical,PConc,Good,Typical,No,LwQ,4,Unf,0,298,1077,GasA,Excellent,Y,SBrkr,1093,1721,0,2814,0,1,2,1,4,1,Good,9,Typ,1,Typical,BuiltIn,Fin,2,614,Typical,Typical,Paved,48,32,0,0,216,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,260000,-93.683302,42.033912 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9938,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,251,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,331,1602,GasA,Excellent,Y,SBrkr,1626,0,0,1626,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,534,Typical,Typical,Paved,424,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,226500,-93.682029,42.03085 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14171,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,457,812,GasA,Excellent,Y,SBrkr,1101,1099,0,2200,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,2,453,Typical,Typical,Paved,168,98,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,225000,-93.682032,42.031077 -Split_or_Multilevel,Residential_Low_Density,85,10541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,SLvl,Good,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,1302,735,0,2037,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,472,Typical,Typical,Paved,100,33,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,229000,-93.682175,42.030621 -Two_Story_1946_and_Newer,Residential_Low_Density,65,10616,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,628,628,GasA,Excellent,Y,SBrkr,628,728,0,1356,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,484,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,New,Partial,177439,-93.691284,42.025411 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9345,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,156,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1615,1615,GasA,Excellent,Y,SBrkr,1615,0,0,1615,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,864,Typical,Typical,Paved,168,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,248500,-93.691095,42.025275 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,554,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,186,2271,GasA,Excellent,Y,SBrkr,2276,0,0,2276,1,0,2,0,3,1,Excellent,7,Typ,2,Good,Attchd,RFn,3,1348,Good,Typical,Paved,0,0,70,0,255,0,No_Pool,No_Fence,None,0,6,2008,WD ,Abnorml,475000,-93.68698,42.027368 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,402,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,598,1751,GasA,Excellent,Y,SBrkr,1766,0,0,1766,1,0,2,1,3,1,Excellent,8,Typ,2,Good,Attchd,Fin,3,874,Typical,Typical,Paved,216,36,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,New,Partial,395039,-93.686789,42.027193 -Two_Story_1946_and_Newer,Residential_Low_Density,59,11228,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,BLQ,2,GLQ,531,499,1080,GasA,Excellent,Y,SBrkr,1080,1017,0,2097,0,1,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Unf,3,678,Typical,Typical,Paved,196,187,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,228000,-93.681199,42.029437 -Split_or_Multilevel,Residential_Low_Density,0,11454,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,SLvl,Very_Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,302,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,631,1401,GasA,Excellent,Y,SBrkr,1511,0,0,1511,1,0,2,0,3,1,Good,6,Typ,1,Fair,Attchd,Fin,3,811,Typical,Typical,Paved,168,42,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,225000,-93.681136,42.030276 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11500,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,CulDSac,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1966,1966,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Good,Gd,LwQ,4,ALQ,723,197,1182,GasA,Excellent,Y,SBrkr,1643,0,0,1643,1,0,2,0,2,1,Typical,6,Typ,1,Good,Attchd,Unf,2,438,Typical,Typical,Paved,339,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,195000,-93.6826513,42.0249247 -Two_Story_1946_and_Newer,Residential_Low_Density,79,12798,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Clear_Creek,Feedr,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,154,616,GasA,Good,Y,SBrkr,616,1072,0,1688,1,0,2,1,4,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,603,Typical,Typical,Paved,403,114,185,0,0,0,No_Pool,No_Fence,Shed,400,5,2008,WD ,Normal,200000,-93.679198,42.025781 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9750,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,268,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,155000,-93.692009,42.021173 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1995,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1099,1099,GasA,Excellent,Y,SBrkr,1099,0,0,1099,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,352,Typical,Typical,Paved,278,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,144000,-93.691213,42.01915 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,9245,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,304,990,GasA,Excellent,Y,SBrkr,990,0,0,990,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,145000,-93.691612,42.019153 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8696,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,110,1418,GasA,Excellent,Y,SBrkr,1418,0,0,1418,1,0,2,0,3,1,Good,5,Typ,1,Typical,Attchd,RFn,2,558,Typical,Typical,Paved,208,110,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,226001,-93.690719,42.018501 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13142,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,128,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,176,864,GasA,Excellent,Y,SBrkr,872,899,0,1771,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,600,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,215700,-93.689772,42.018786 -Two_Story_1946_and_Newer,Residential_Low_Density,68,8998,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,255,782,GasA,Excellent,Y,SBrkr,782,870,0,1652,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,532,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,207500,-93.690276,42.017423 -Two_Story_1946_and_Newer,Residential_Low_Density,68,9179,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,158,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,240,873,GasA,Excellent,Y,SBrkr,882,908,0,1790,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Abnorml,193000,-93.690113,42.017517 -Two_Story_1946_and_Newer,Residential_Low_Density,57,8924,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,880,880,GasA,Excellent,Y,SBrkr,880,844,0,1724,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,527,Typical,Typical,Paved,120,155,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,188000,-93.688995,42.017914 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,9382,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,125,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1468,1468,GasA,Excellent,Y,SBrkr,1479,0,0,1479,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,577,Typical,Typical,Paved,120,25,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,191000,-93.689019,42.017766 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,12803,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,99,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,572,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,530,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,221000,-93.691706,42.016276 -Two_Story_1946_and_Newer,Residential_Low_Density,80,12435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,172,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,602,963,GasA,Excellent,Y,SBrkr,963,829,0,1792,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,96,0,245,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,231500,-93.689101,42.015974 -Two_Story_1946_and_Newer,Residential_Low_Density,75,12192,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,265,928,GasA,Excellent,Y,SBrkr,928,895,0,1823,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,626,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,Shed,4500,5,2008,WD ,Normal,235000,-93.688958,42.01626 -Two_Story_1946_and_Newer,Residential_Low_Density,68,11250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,162,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,434,920,GasA,Excellent,Y,SBrkr,920,866,0,1786,1,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,223500,-93.68895,42.015949 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9200,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1980,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Av,LwQ,4,BLQ,491,167,938,GasA,Typical,Y,SBrkr,938,0,0,938,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,145,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,130250,-93.685742,42.022136 -Split_or_Multilevel,Residential_Low_Density,65,8385,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Very_Good,1977,1977,Gable,CompShg,HdBoard,HdBoard,BrkFace,220,Good,Typical,CBlock,Good,Good,Av,GLQ,3,Unf,0,390,985,GasA,Typical,Y,SBrkr,985,0,0,985,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,328,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,149900,-93.686888,42.021234 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,180,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,83,864,GasA,Excellent,Y,SBrkr,1174,0,0,1174,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,211,0,280,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,158000,-93.687518,42.018839 -Split_or_Multilevel,Residential_Low_Density,0,10970,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,Plywood,HdBoard,None,0,Typical,Typical,CBlock,Good,Good,Gd,GLQ,3,LwQ,435,0,940,GasA,Typical,Y,SBrkr,1026,0,0,1026,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Fair,Paved,0,0,34,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,147000,-93.685158,42.019769 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9216,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,782,1076,GasA,Typical,Y,SBrkr,1076,0,0,1076,0,0,1,1,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2008,WD ,Abnorml,143195,-93.685865,42.020694 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,40,14330,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,2001,Gable,CompShg,Plywood,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,596,180,864,GasA,Typical,Y,SBrkr,1558,0,0,1558,1,0,2,0,2,1,Typical,5,Min2,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,140,0,239,0,227,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,163000,-93.6844,42.02107 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,7990,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,924,924,GasA,Typical,Y,SBrkr,924,0,0,924,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,110000,-93.684289,42.021275 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7180,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,474,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,127000,-93.683158,42.020796 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,148,780,GasA,Excellent,Y,SBrkr,780,0,0,780,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,196,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,124900,-93.683309,42.020991 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9742,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,281,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1777,1777,GasA,Excellent,Y,SBrkr,1795,0,0,1795,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,534,Typical,Typical,Paved,171,159,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,230000,-93.68786,42.018735 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9473,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,324,1128,GasA,Excellent,Y,SBrkr,1128,903,0,2031,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,577,Typical,Typical,Paved,0,211,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,237000,-93.687704,42.018412 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10316,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,257,992,GasA,Excellent,Y,SBrkr,992,873,0,1865,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,839,Typical,Typical,Paved,0,184,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,235000,-93.686189,42.018354 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,227,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1257,1257,GasA,Excellent,Y,SBrkr,1290,871,0,2161,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,570,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,230500,-93.684227,42.018709 -Two_Story_1946_and_Newer,Residential_Low_Density,73,9066,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,320,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,336,1004,GasA,Excellent,Y,SBrkr,1004,848,0,1852,0,0,2,1,3,1,Good,7,Typ,2,Typical,Attchd,Fin,3,660,Typical,Typical,Paved,224,106,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2008,WD ,Normal,230000,-93.684396,42.017598 -Two_Story_1946_and_Newer,Residential_Low_Density,75,11404,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,202,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,901,1153,GasA,Excellent,Y,SBrkr,1153,878,0,2031,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,541,Typical,Typical,Paved,192,84,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,222500,-93.682718,42.018006 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,396,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1055,1055,GasA,Excellent,Y,SBrkr,1055,1208,0,2263,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,905,Typical,Typical,Paved,0,45,0,0,189,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,287000,-93.685572,42.01727 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9720,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Excellent,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,134,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,163,1357,GasA,Excellent,Y,SBrkr,1366,581,0,1947,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,3,725,Typical,Typical,Paved,168,116,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,274000,-93.685569,42.017484 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14860,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,240,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,240,1778,GasA,Excellent,Y,SBrkr,1786,0,0,1786,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,715,Typical,Typical,Paved,182,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,300000,-93.68522,42.018186 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1128,1128,GasA,Excellent,Y,SBrkr,1149,1141,0,2290,0,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Unf,2,779,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,255900,-93.686644,42.016975 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8158,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,252,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,334,884,GasA,Excellent,Y,SBrkr,884,884,0,1768,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,543,Typical,Typical,Paved,0,63,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,224900,-93.686982,42.016207 -Two_Story_1946_and_Newer,Residential_Low_Density,41,10905,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1129,1129,GasA,Excellent,Y,SBrkr,1129,1198,0,2327,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,RFn,2,596,Typical,Typical,Paved,0,57,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,240000,-93.688193,42.016341 -Two_Story_1946_and_Newer,Residential_Low_Density,72,7226,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,798,798,GasA,Excellent,Y,SBrkr,798,842,0,1640,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,595,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,183000,-93.687674,42.014435 -Two_Story_1946_and_Newer,Residential_Low_Density,96,11690,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,192,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,850,850,GasA,Excellent,Y,SBrkr,886,878,0,1764,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,560,Typical,Typical,Paved,120,29,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,207000,-93.683856,42.016709 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,273,915,GasA,Excellent,Y,SBrkr,933,975,0,1908,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Unf,2,493,Typical,Typical,Paved,144,133,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,210000,-93.67994,42.017866 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,37,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,162,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,136500,-93.681121,42.016277 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,205,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,RFn,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,145000,-93.68369,42.016254 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,13125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,215,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,484,1478,GasA,Excellent,Y,SBrkr,1493,0,0,1493,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,508,Typical,Typical,Paved,140,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,208900,-93.679505,42.017854 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,83,10126,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,Gd,GLQ,3,LwQ,162,83,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,3,721,Typical,Typical,Paved,160,67,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Abnorml,185000,-93.68031,42.016262 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9135,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,113,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,726,1536,GasA,Excellent,Y,SBrkr,1536,0,0,1536,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,532,Typical,Typical,Paved,192,74,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,214000,-93.687825,42.014549 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,150,856,GasA,Excellent,Y,SBrkr,856,854,0,1710,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,548,Typical,Typical,Paved,0,61,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,208500,-93.686931,42.013952 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1421,1445,GasA,Excellent,Y,SBrkr,1445,0,0,1445,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,470,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,186500,-93.685207,42.013912 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,266,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,490,1436,GasA,Excellent,Y,SBrkr,1436,0,0,1436,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,139,98,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,210000,-93.684451,42.013641 -Two_Story_1946_and_Newer,Residential_Low_Density,64,8320,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,280,770,GasA,Excellent,Y,SBrkr,770,812,0,1582,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,185900,-93.684397,42.013622 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11049,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1234,1234,GasA,Excellent,Y,SBrkr,1234,0,0,1234,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,179900,-93.684343,42.013603 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11058,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,212,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1508,1564,GasA,Excellent,Y,SBrkr,1564,0,0,1564,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,814,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,218836,-93.683435,42.015975 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,9627,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1327,1351,GasA,Excellent,Y,SBrkr,1361,0,0,1361,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,610,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,193000,-93.683435,42.015368 -Two_Story_1946_and_Newer,Residential_Low_Density,63,8199,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,80,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,410,Typical,Typical,Paved,36,18,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,189000,-93.6838447,42.0138316 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,147,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,151,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Typical,Attchd,RFn,2,420,Typical,Typical,Paved,149,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,149300,-93.683127,42.015927 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,147,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,151,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Typical,Attchd,RFn,2,420,Typical,Typical,Paved,149,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,141000,-93.682991,42.015927 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4438,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,205,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Good,Attchd,RFn,2,420,Typical,Typical,Paved,149,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,149000,-93.681436,42.016121 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4438,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,1,Good,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,ConLD,Normal,144500,-93.681401,42.016122 -One_Story_1945_and_Older,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Average,Good,1910,1950,Gable,CompShg,MetalSd,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,796,796,GasA,Good,Y,FuseA,796,0,0,796,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Partial_Pavement,328,0,164,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,85000,-93.678303,42.01975 -Duplex_All_Styles_and_Ages,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,Duplex,One_and_Half_Fin,Average,Good,1900,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,188,1272,GasA,Good,Y,SBrkr,1272,928,0,2200,2,0,2,2,4,2,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,70,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2008,WD ,Normal,145900,-93.678304,42.019937 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,483,1092,GasA,Typical,Y,SBrkr,1092,0,0,1092,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,259,0,0,0,161,0,No_Pool,Minimum_Privacy,None,0,4,2008,COD,Abnorml,123000,-93.677551,42.020905 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9937,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,290,136,1256,GasA,Good,Y,SBrkr,1256,0,0,1256,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,276,Typical,Typical,Paved,736,68,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,147500,-93.677407,42.021244 -Split_Foyer,Residential_Low_Density,64,12102,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Average,Average,1976,1976,Gable,CompShg,HdBoard,Plywood,BrkFace,222,Typical,Typical,CBlock,Good,Good,Gd,ALQ,1,Unf,0,0,456,GasA,Excellent,Y,SBrkr,1033,0,0,1033,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,504,Fair,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Family,165000,-93.673447,42.019997 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1976,1976,Hip,CompShg,HdBoard,Plywood,BrkFace,84,Typical,Typical,CBlock,Typical,Excellent,No,BLQ,2,Unf,0,94,1127,GasA,Typical,Y,SBrkr,1127,0,0,1127,0,1,1,1,3,1,Typical,6,Typ,1,Poor,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,138,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,135000,-93.673571,42.019996 -One_Story_1945_and_Older,Residential_Low_Density,63,13907,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1940,1969,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,706,996,GasA,Excellent,Y,SBrkr,996,0,0,996,1,0,1,0,3,1,Typical,6,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,108000,-93.673137,42.02099 -Split_Foyer,Residential_Low_Density,90,10012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Below_Average,Average,1972,1972,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,Rec,180,38,1138,GasA,Typical,Y,SBrkr,1181,0,0,1181,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,588,Typical,Typical,Paved,0,0,180,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,137500,-93.671443,42.019861 -Two_Story_1946_and_Newer,Residential_Low_Density,60,6931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,Stone,92,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,746,746,GasA,Excellent,Y,SBrkr,760,896,0,1656,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,397,Typical,Typical,Paved,178,128,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,165400,-93.672408,42.018991 -Split_or_Multilevel,Residential_Low_Density,0,9638,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1977,1977,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Rec,120,541,1029,GasA,Typical,Y,SBrkr,1117,0,0,1117,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,542,Typical,Typical,Paved,292,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,147000,-93.675294,42.018852 -Two_Story_1946_and_Newer,Residential_Low_Density,72,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1976,2001,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,396,684,GasA,Typical,Y,SBrkr,684,714,0,1398,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,160500,-93.675173,42.018912 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7024,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,108,1132,GasA,Excellent,Y,SBrkr,1132,0,0,1132,1,0,1,1,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,451,Typical,Typical,Paved,252,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,176000,-93.673251,42.01883 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,123,47007,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1959,1996,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,3820,0,0,3820,0,0,3,1,5,1,Excellent,11,Typ,2,Good,Attchd,Unf,2,624,Typical,Typical,Paved,0,372,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,284700,-93.675582,42.017386 -Two_Story_1946_and_Newer,Residential_Low_Density,313,63887,Pave,No_Alley_Access,Irregular,Bnk,AllPub,Corner,Gtl,Edwards,Feedr,Norm,OneFam,Two_Story,Very_Excellent,Average,2008,2008,Hip,ClyTile,Stucco,Stucco,Stone,796,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,466,6110,GasA,Excellent,Y,SBrkr,4692,950,0,5642,2,0,2,1,3,1,Excellent,12,Typ,3,Good,Attchd,Fin,2,1418,Typical,Typical,Paved,214,292,0,0,0,480,Good,No_Fence,None,0,1,2008,New,Partial,160000,-93.674898,42.016804 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SLvl,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,88,547,GasA,Excellent,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,RFn,2,525,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,148000,-93.670627,42.018898 -Duplex_All_Styles_and_Ages,Residential_Low_Density,65,6040,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,AsbShng,Plywood,None,0,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseP,1152,0,0,1152,0,0,2,0,2,2,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,AdjLand,82000,-93.665043,42.020483 -Duplex_All_Styles_and_Ages,Residential_Low_Density,65,6012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,AsbShng,Plywood,None,0,Typical,Fair,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1152,0,0,1152,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,AdjLand,82000,-93.6649923,42.0202945 -Duplex_All_Styles_and_Ages,Residential_Low_Density,92,12108,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Below_Average,1955,1955,Gable,CompShg,VinylSd,VinylSd,BrkFace,270,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,1307,1440,GasA,Typical,N,FuseF,1440,0,0,1440,0,0,2,0,4,2,Fair,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,118000,-93.6653819,42.020394 -Duplex_All_Styles_and_Ages,Residential_Low_Density,74,6845,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,WdShing,Wd Shng,BrkCmn,58,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseF,1152,0,0,1152,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,82500,-93.665562,42.019544 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,56,6931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_Story,Below_Average,Average,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,0,784,GasA,Typical,N,FuseP,784,0,0,784,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,112,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,91900,-93.661895,42.020112 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,12180,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1938,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Good,Y,FuseF,585,468,0,1053,0,0,1,1,2,1,Excellent,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,42,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Family,120000,-93.660115,42.021582 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,57,8050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1947,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,929,208,0,1137,0,0,1,1,4,1,Typical,8,Min1,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,96000,-93.664925,42.01797 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9520,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,Stone,115,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,144,911,GasA,Typical,Y,SBrkr,930,0,0,930,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,134,0,0,0,0,0,No_Pool,Minimum_Privacy,Gar2,3000,5,2008,WD ,Normal,99000,-93.665829,42.017962 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1946,1995,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,416,768,GasA,Excellent,Y,SBrkr,768,432,0,1200,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,130500,-93.664506,42.0198028 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1954,1972,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,941,941,GasA,Excellent,Y,SBrkr,997,473,0,1470,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,548,Typical,Typical,Paved,0,0,0,0,156,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,135000,-93.663479,42.019959 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,59,16466,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,Y,SBrkr,872,521,0,1393,0,0,1,1,3,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,1,300,Typical,Typical,Paved,121,0,0,0,265,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,135500,-93.66345,42.018541 -Split_or_Multilevel,Residential_Low_Density,62,7692,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,SLvl,Below_Average,Above_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Excellent,Typical,Av,Unf,7,Unf,0,416,416,GasA,Good,Y,FuseA,1204,0,0,1204,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Basment,Unf,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,100000,-93.661763,42.017874 -One_Story_1945_and_Older,Residential_Low_Density,67,5142,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1923,2008,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,725,949,GasA,Typical,Y,SBrkr,949,343,0,1292,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,205,Typical,Typical,Dirt_Gravel,0,0,183,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,108000,-93.6558,42.02224 -One_Story_1945_and_Older,Residential_Low_Density,67,5604,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1925,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,396,864,GasA,Typical,N,FuseA,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,98000,-93.656681,42.022238 -One_Story_1945_and_Older,Residential_Low_Density,45,8248,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Fair,1914,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,823,864,GasA,Typical,N,FuseF,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,100,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,67000,-93.656735,42.022089 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,55,5687,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,Two_Story,Average,Above_Average,1912,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Fair,PConc,Typical,Fair,No,Rec,6,Unf,0,570,780,GasA,Excellent,N,SBrkr,936,780,0,1716,1,0,2,0,6,1,Fair,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,184,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,135900,-93.655851,42.0214496 -Two_Story_1945_and_Older,Residential_High_Density,0,12155,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,516,672,GasA,Typical,N,SBrkr,810,672,0,1482,0,0,2,0,4,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,1,400,Typical,Typical,Partial_Pavement,0,0,254,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,140000,-93.64856,42.019986 -Two_and_Half_Story_All_Ages,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_and_Half_Fin,Average,Very_Good,1939,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,342,720,GasA,Excellent,Y,SBrkr,1052,720,420,2192,0,0,2,1,4,1,Good,8,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,262,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,179500,-93.65181,42.018715 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,758,1181,GasA,Fair,Y,SBrkr,1390,304,0,1694,0,0,2,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,342,0,128,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,136500,-93.651785,42.017413 -One_Story_1945_and_Older,Residential_Low_Density,60,7290,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Good,Very_Good,1921,1950,Gable,CompShg,WdShing,Wd Shng,BrkFace,174,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1228,1228,GasA,Excellent,Y,SBrkr,1424,0,0,1424,0,0,2,0,2,1,Typical,7,Typ,1,Good,Attchd,Unf,1,312,Typical,Typical,Paved,0,0,90,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,168000,-93.651635,42.017083 -Two_Story_1945_and_Older,Residential_High_Density,55,8525,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1911,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,Av,Unf,7,Unf,0,940,940,GasA,Typical,N,FuseA,1024,940,0,1964,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,192,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,130000,-93.647024,42.019272 -Two_Story_1945_and_Older,Residential_Low_Density,51,9842,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Average,Above_Average,1921,1998,Gable,CompShg,MetalSd,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,612,612,GasA,Excellent,Y,SBrkr,990,1611,0,2601,0,0,3,1,4,1,Typical,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,621,Typical,Typical,Paved,183,0,301,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,189000,-93.646748,42.019092 -Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7804,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,Duplex,Two_and_Half_Unf,Above_Average,Good,1930,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Rec,679,0,960,GasA,Excellent,Y,SBrkr,960,960,0,1920,2,0,2,2,4,2,Typical,10,Typ,2,Good,Detchd,Unf,2,480,Typical,Typical,Paved,248,0,121,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Abnorml,161900,-93.648411,42.019005 -Two_Story_1945_and_Older,Residential_Low_Density,66,8969,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1926,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,265,644,GasA,Excellent,Y,SBrkr,672,644,0,1316,1,0,1,0,2,1,Typical,6,Typ,1,Good,Detchd,Unf,1,369,Typical,Typical,Partial_Pavement,0,0,0,0,192,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,145000,-93.646438,42.01791 -Two_Story_1945_and_Older,Residential_Low_Density,79,11526,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_and_Half_Fin,Above_Average,Good,1922,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Excellent,Typical,No,Unf,7,Unf,0,588,588,GasA,Fair,Y,SBrkr,1423,748,384,2555,0,0,2,0,3,1,Typical,11,Min1,1,Good,Detchd,Fin,2,672,Typical,Typical,Paved,431,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,191000,-93.646438,42.017834 -Two_Story_1945_and_Older,Residential_Low_Density,63,15576,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1915,1976,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,840,0,1680,0,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,0,160,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,177000,-93.6447083,42.0176853 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,63,15564,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1914,1995,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,676,676,GasA,Excellent,Y,SBrkr,676,588,0,1264,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,424,0,0,0,0,0,No_Pool,No_Fence,Shed,400,1,2008,WD ,Normal,147500,-93.646288,42.01789 -One_Story_1945_and_Older,Residential_Low_Density,52,6292,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Above_Average,Average,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Rec,6,Unf,0,384,768,GasA,Typical,N,SBrkr,790,0,0,790,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,160,Fair,Typical,Paved,0,141,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,91000,-93.64842,42.016887 -Two_Story_1945_and_Older,Residential_Low_Density,54,7609,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1925,1997,Gable,CompShg,Stucco,Stucco,None,0,Good,Good,PConc,Fair,Typical,No,ALQ,1,Unf,0,392,798,GasA,Excellent,Y,SBrkr,798,714,0,1512,1,0,2,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,180,Typical,Typical,Partial_Pavement,85,16,41,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,231000,-93.6447134,42.0168678 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,10480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1064,1064,GasA,Excellent,Y,FuseA,1166,0,473,1639,0,0,1,0,3,1,Typical,6,Maj2,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,115000,-93.646735,42.016072 -Two_Story_1945_and_Older,Residential_Low_Density,0,9650,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Fair,1923,1950,Hip,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,784,784,GasA,Typical,Y,SBrkr,819,784,0,1603,0,0,1,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,2,599,Typical,Typical,Paved,0,217,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,138000,-93.642886,42.019833 -Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1931,2000,Gable,CompShg,Stucco,Wd Shng,None,0,Typical,Fair,BrkTil,Good,Typical,No,Unf,7,Unf,0,776,776,GasA,Typical,Y,SBrkr,851,651,0,1502,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,1,270,Typical,Typical,Partial_Pavement,0,0,112,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,149000,-93.642821,42.019617 -Two_Story_1945_and_Older,Residential_Low_Density,74,11988,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1934,1995,Hip,CompShg,Stucco,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,389,715,GasA,Fair,Y,FuseA,849,811,0,1660,0,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,188700,-93.641405,42.018443 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,11700,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1937,1995,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,336,942,GasA,Excellent,Y,SBrkr,1265,673,0,1938,0,0,2,0,4,1,Good,7,Min2,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,198000,-93.63962,42.018888 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,9260,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1938,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Typical,Y,FuseF,932,442,0,1374,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,225,Typical,Typical,Paved,64,0,0,0,100,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,110000,-93.639497,42.018766 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,7801,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1951,1951,Hip,CompShg,WdShing,Plywood,BrkFace,88,Typical,Fair,PConc,Typical,Typical,No,Rec,6,Unf,0,591,1091,GasA,Fair,N,FuseA,1091,0,0,1091,0,1,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,1,344,Typical,Typical,Paved,66,105,0,0,221,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,104000,-93.639467,42.017528 -Two_Story_1945_and_Older,Residential_Low_Density,100,9670,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Above_Average,1935,1950,Gable,CompShg,BrkFace,Stucco,Stone,40,Typical,Typical,PConc,Typical,Fair,No,LwQ,4,Unf,0,398,608,GasA,Typical,Y,SBrkr,983,890,0,1873,0,0,1,1,4,1,Typical,9,Typ,2,Good,Detchd,Fin,2,786,Fair,Typical,Paved,0,0,207,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Alloca,257076,-93.641084,42.016769 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,14100,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Excellent,1935,1997,Gable,CompShg,Stucco,Stucco,BrkFace,632,Typical,Good,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,536,728,GasA,Excellent,Y,SBrkr,1968,1479,0,3447,0,0,3,1,4,1,Good,11,Typ,2,Good,BuiltIn,Unf,3,1014,Typical,Typical,Paved,314,12,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,381000,-93.642597,42.01601 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,15660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Excellent,1939,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,312,Good,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,457,798,GasA,Excellent,Y,SBrkr,1137,817,0,1954,0,1,1,1,3,1,Good,8,Typ,2,Typical,Attchd,Unf,2,431,Typical,Typical,Paved,0,119,150,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,311500,-93.643378,42.015667 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,12392,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Excellent,1950,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Good,No,GLQ,3,Unf,0,397,832,GasA,Excellent,Y,SBrkr,1218,943,0,2161,1,0,2,1,3,1,Good,8,Typ,2,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,263400,-93.64293,42.014567 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,56,26073,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,BrkFace,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,782,1898,GasA,Excellent,Y,FuseA,1898,0,0,1898,0,0,2,1,3,1,Typical,7,Typ,2,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,0,51,224,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,236500,-93.640311,42.012638 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14778,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Gtl,Crawford,PosN,Norm,OneFam,One_Story,Above_Average,Good,1954,2006,Hip,CompShg,HdBoard,HdBoard,BrkFace,72,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,568,1296,GasA,Excellent,Y,SBrkr,1640,0,0,1640,1,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,2,924,Typical,Typical,Paved,108,0,0,216,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,224000,-93.640293,42.012488 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1879,Pave,No_Alley_Access,Regular,Lvl,AllPub,CulDSac,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,150,516,GasA,Typical,Y,SBrkr,516,516,0,1032,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,462,Typical,Typical,Paved,213,0,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2008,WD ,Normal,116500,-93.645743,42.009489 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Good,Typical,No,LwQ,4,GLQ,612,23,716,GasA,Typical,Y,SBrkr,716,840,0,1556,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,452,Typical,Typical,Paved,161,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,COD,Normal,151000,-93.645743,42.009489 -One_Story_1945_and_Older,Residential_Medium_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1926,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,GLQ,40,555,894,GasA,Typical,Y,SBrkr,919,0,0,919,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,195,Typical,Typical,Partial_Pavement,0,0,116,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,126000,-93.628409,42.022607 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1940,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,720,720,GasA,Good,Y,SBrkr,760,330,0,1090,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,120000,-93.627459,42.021806 -One_Story_1945_and_Older,Residential_Medium_Density,40,3636,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1922,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,796,796,GasA,Fair,N,SBrkr,796,0,0,796,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,100,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,55000,-93.625291,42.022797 -One_Story_1945_and_Older,Residential_Medium_Density,0,5890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1930,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,Av,ALQ,1,Unf,0,278,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,432,Typical,Typical,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,120500,-93.625288,42.022516 -One_Story_1945_and_Older,Residential_Medium_Density,71,6900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Above_Average,1940,1955,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,125,212,740,GasA,Excellent,Y,SBrkr,778,0,0,778,0,0,1,0,2,1,Typical,4,Typ,1,Good,Detchd,Fin,1,924,Excellent,Excellent,Paved,0,25,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,120500,-93.6291062,42.0213831 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,58,8155,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,780,780,GasA,Good,Y,FuseA,780,420,0,1200,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,119000,-93.628385,42.021276 -Split_or_Multilevel,Residential_Medium_Density,75,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,SLvl,Average,Average,1967,1967,Hip,CompShg,HdBoard,Plywood,None,0,Fair,Typical,CBlock,Good,Typical,Mn,Rec,6,Unf,0,367,624,GasA,Excellent,Y,SBrkr,1092,564,0,1656,0,0,1,1,3,1,Typical,7,Mod,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,0,180,0,0,100,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,126000,-93.6258429,42.0214037 -One_Story_1945_and_Older,Residential_Medium_Density,60,7392,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Good,1930,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,520,520,GasA,Typical,Y,FuseA,912,0,0,912,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,360,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,99500,-93.626835,42.020379 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1958,1958,Gable,CompShg,WdShing,Wd Shng,BrkFace,162,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,821,1151,GasA,Good,Y,FuseA,1151,804,0,1955,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,356,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,165500,-93.625143,42.021389 -One_Story_1946_and_Newer_All_Styles,A_agr,80,14584,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Very_Poor,Average,1952,1952,Gable,CompShg,AsbShng,VinylSd,None,0,Fair,Poor,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Poor,N,FuseA,733,0,0,733,0,0,1,0,2,1,Fair,4,Sal,0,No_Fireplace,Attchd,Unf,2,487,Fair,Poor,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,13100,-93.625217,42.018806 -Two_Story_1945_and_Older,C_all,60,5280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,Two_Story,Below_Average,Good,1895,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Stone,Poor,Fair,No,Unf,7,Unf,0,173,173,GasA,Excellent,N,SBrkr,825,536,0,1361,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,185,Fair,Typical,Paved,0,123,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,89000,-93.615426,42.021162 -Two_Story_1945_and_Older,C_all,50,8500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,Two_Story,Below_Average,Below_Average,1920,1950,Gambrel,CompShg,BrkFace,BrkFace,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,649,649,GasA,Typical,N,SBrkr,649,668,0,1317,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Fair,Dirt_Gravel,0,54,172,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,40000,-93.616848,42.021506 -One_and_Half_Story_Finished_All_Ages,C_all,52,5150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1910,2000,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Poor,Typical,No,Unf,7,Unf,0,356,356,GasA,Typical,N,FuseA,671,378,0,1049,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,195,Poor,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,80900,-93.616849,42.021594 -One_Story_1945_and_Older,C_all,120,18000,Grvl,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Fair,Below_Average,1935,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Fair,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,894,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,3,1248,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,Shed,560,8,2008,ConLD,Normal,81000,-93.604195,42.022458 -Two_Story_1945_and_Older,C_all,60,9000,Grvl,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Below_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Stone,Fair,Fair,Mn,Unf,7,Unf,0,592,592,GasA,Excellent,Y,SBrkr,432,432,0,864,0,0,1,1,3,1,Fair,5,Min2,0,No_Fireplace,Detchd,Unf,1,216,Fair,Fair,Dirt_Gravel,0,30,160,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,65000,-93.604193,42.022626 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,64,5587,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2008,2008,Hip,CompShg,CemntBd,CmentBd,Stone,186,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1600,GasA,Excellent,Y,SBrkr,1652,0,0,1652,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,162,53,0,153,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,392500,-93.616242,42.009326 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3843,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,186,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1596,GasA,Excellent,Y,SBrkr,1648,0,0,1648,1,1,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,525,Typical,Typical,Paved,162,53,0,153,0,0,No_Pool,No_Fence,None,0,8,2008,New,Partial,294464,-93.616584,42.008803 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3811,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,174,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,250000,-93.615985,42.008806 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3842,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,174,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,221,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,275000,-93.615921,42.0091899 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,23730,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,1996,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,668,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,1140,1840,GasA,Excellent,Y,SBrkr,2032,0,0,2032,1,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,786,Typical,Typical,Paved,0,46,192,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,300000,-93.607342,41.99507 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13265,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,CemntBd,CmentBd,BrkFace,148,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,350,1568,GasA,Excellent,Y,SBrkr,1689,0,0,1689,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,RFn,3,857,Typical,Typical,Paved,150,59,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,271000,-93.607121,41.997124 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,910,910,GasA,Excellent,Y,SBrkr,910,910,0,1820,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Unf,3,816,Typical,Typical,Paved,318,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,213000,-93.60489,41.997062 -Split_or_Multilevel,Residential_Low_Density,76,9880,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,574,1096,GasA,Typical,Y,SBrkr,1118,0,0,1118,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,Fin,1,358,Typical,Typical,Paved,203,0,0,0,0,576,Good,Good_Privacy,None,0,7,2008,WD ,Normal,171000,-93.604549,41.997069 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,279,276,1196,GasA,Typical,Y,SBrkr,1279,0,0,1279,0,1,2,0,3,1,Typical,6,Typ,2,Fair,Detchd,Unf,2,473,Typical,Typical,Paved,238,83,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,171500,-93.604548,41.996919 -Duplex_All_Styles_and_Ages,Residential_Low_Density,76,10260,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,Two_Story,Average,Below_Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,936,936,0,1872,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,100000,-93.602912,41.996903 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,9990,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,297,1680,GasA,Good,Y,SBrkr,1689,0,0,1689,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,432,Typical,Typical,Paved,428,120,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,228500,-93.602429,41.995991 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,0,164660,Grvl,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Sev,Timberland,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Above_Average,1965,1965,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,BLQ,147,103,1499,GasA,Excellent,Y,SBrkr,1619,167,0,1786,2,0,2,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,529,Typical,Typical,Paved,670,0,0,0,0,0,No_Pool,No_Fence,Shed,700,8,2008,WD ,Normal,228950,-93.658875,41.998543 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,42,4084,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,1986,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,340,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,384,1277,GasA,Good,Y,SBrkr,1501,0,0,1501,1,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,2,512,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,241500,-93.639793,42.00514 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15498,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1976,1976,Hip,WdShake,Stone,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,400,0,1565,GasA,Typical,Y,SBrkr,2898,0,0,2898,1,0,2,0,2,1,Good,10,Typ,1,Good,Attchd,Fin,2,665,Typical,Typical,Paved,0,72,174,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,287000,-93.650229,41.998999 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,11563,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,258,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,482,1518,GasA,Excellent,Y,SBrkr,1537,0,0,1537,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,3,788,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,294000,-93.653122,41.995141 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9520,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,338,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,125,1638,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,800,Typical,Typical,Paved,192,44,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,New,Partial,293077,-93.653025,41.994841 -Two_Story_1946_and_Newer,Residential_Low_Density,107,12852,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,150,920,GasA,Excellent,Y,SBrkr,920,860,0,1780,1,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,612,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,New,Partial,264966,-93.651718,41.99373 -Split_or_Multilevel,Residential_Low_Density,73,9802,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,352,352,GasA,Good,Y,SBrkr,712,730,0,1442,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,172500,-93.649198,41.995142 -Split_or_Multilevel,Residential_Low_Density,73,9735,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,Unknown,754,640,0,1394,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,167500,-93.649475,41.993751 -Two_Story_1946_and_Newer,Residential_Low_Density,81,12018,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,60,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,796,796,GasA,Excellent,Y,SBrkr,796,816,0,1612,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,3,666,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,New,Partial,218689,-93.650431,41.995175 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,12890,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1989,1989,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,128,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1495,1495,GasA,Excellent,Y,SBrkr,1495,0,0,1495,0,0,2,0,3,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,438,Typical,Typical,Paved,252,0,192,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,150000,-93.644679,41.999812 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,47,12416,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1986,1986,Gable,CompShg,VinylSd,Plywood,Stone,132,Typical,Typical,CBlock,Good,Fair,No,ALQ,1,LwQ,208,0,1606,GasA,Typical,Y,SBrkr,1651,0,0,1651,1,0,2,0,3,1,Typical,7,Min2,1,Typical,Attchd,Fin,2,616,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,184000,-93.646054,41.999575 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,18265,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1986,1986,Gable,CompShg,Plywood,HdBoard,BrkFace,228,Good,Good,CBlock,Good,Good,Av,GLQ,3,Rec,60,276,1256,GasA,Excellent,Y,SBrkr,1256,0,0,1256,0,0,2,0,3,1,Good,6,Typ,1,Fair,Attchd,Unf,2,578,Typical,Typical,Paved,282,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,180000,-93.6470597,41.9976055 -Split_or_Multilevel,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Above_Average,Average,1988,1989,Gable,CompShg,HdBoard,HdBoard,BrkFace,219,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,678,1461,GasA,Excellent,Y,SBrkr,1509,0,0,1509,1,0,2,0,3,1,Good,5,Typ,1,Fair,Attchd,RFn,2,600,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Abnorml,175000,-93.64427,41.998554 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,11202,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,206,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,403,1432,GasA,Excellent,Y,SBrkr,1440,0,0,1440,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,467,Typical,Typical,Paved,185,95,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,232500,-93.646726,41.995512 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7915,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1999,2000,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,443,1666,GasA,Excellent,Y,SBrkr,1675,0,0,1675,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,435,Typical,Typical,Paved,165,52,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,195000,-93.646645,41.99639 -Two_Story_1946_and_Newer,Residential_Low_Density,85,12244,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,Stone,226,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,611,1482,GasA,Excellent,Y,SBrkr,1482,780,0,2262,1,0,2,1,4,1,Good,10,Typ,2,Good,Attchd,Fin,3,749,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,305000,-93.647834,41.99423 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,11449,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,873,1884,GasA,Excellent,Y,SBrkr,1728,0,0,1728,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,520,Typical,Typical,Paved,0,276,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,298751,-93.653218,41.99312 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11447,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,674,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,393,1964,GasA,Excellent,Y,SBrkr,1964,0,0,1964,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,892,Typical,Typical,Paved,0,265,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,370000,-93.647214,41.993611 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,97,8940,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,Typical,Good,PConc,Good,Good,Gd,GLQ,3,Unf,0,35,1344,GasA,Excellent,Y,SBrkr,1344,0,0,1344,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,4,784,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Alloca,209200,-93.608267,41.99338 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,78,7060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,Typical,Good,PConc,Good,Good,Gd,GLQ,3,Unf,0,35,1344,GasA,Excellent,Y,SBrkr,1344,0,0,1344,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,4,784,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Alloca,206300,-93.608343,41.993335 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,9278,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Feedr,Artery,OneFam,One_Story,Average,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1092,1092,GasA,Excellent,Y,SBrkr,1092,0,0,1092,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,52,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,146000,-93.610145,41.990054 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1997,1997,Hip,CompShg,VinylSd,VinylSd,BrkFace,197,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,325,1189,GasA,Excellent,Y,SBrkr,1189,0,0,1189,1,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,2,392,Typical,Typical,Paved,0,122,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,160500,-93.608197,41.993059 -Split_Foyer,Residential_Low_Density,150,14137,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Below_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,98,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,247,88,1200,GasA,Good,Y,SBrkr,1200,0,0,1200,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,More_Than_Two_Types,Fin,3,850,Typical,Typical,Paved,0,119,0,0,171,0,No_Pool,No_Fence,None,0,11,2008,ConLD,Normal,173000,-93.606374,41.993185 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4224,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,271,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,499,Typical,Typical,Paved,0,100,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,124000,-93.605351,41.99261 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,264,264,GasA,Typical,Y,SBrkr,616,688,0,1304,0,0,1,1,3,1,Typical,4,Typ,1,Good,BuiltIn,Fin,1,336,Typical,Typical,Paved,141,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,115000,-93.604649,41.991372 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Good,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,232,550,GasA,Typical,Y,SBrkr,925,550,0,1475,0,0,2,0,4,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,336,Typical,Typical,Paved,92,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,129500,-93.604184,41.990899 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1974,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Good,1973,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,25,526,GasA,Good,Y,SBrkr,526,462,0,988,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,BuiltIn,RFn,1,297,Typical,Typical,Paved,120,101,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,105000,-93.603601,41.991883 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1596,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SLvl,Below_Average,Above_Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,25,462,GasA,Typical,Y,SBrkr,526,462,0,988,1,0,1,0,1,1,Typical,4,Typ,1,Poor,BuiltIn,RFn,1,297,Typical,Typical,Paved,0,101,0,120,0,0,No_Pool,Good_Wood,None,0,7,2008,WD ,Normal,94900,-93.604269,41.991779 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17979,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,328,1113,GasA,Excellent,Y,SBrkr,1160,0,0,1160,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,257,Typical,Typical,Paved,81,0,0,0,0,0,No_Pool,Good_Wood,Shed,500,2,2008,WD ,Normal,152500,-93.601444,41.993249 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1477,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Above_Average,Excellent,1970,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,188,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,187,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,98000,-93.603178,41.992133 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Good,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Good,Typical,Av,ALQ,1,Unf,0,135,630,GasA,Good,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,88,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,81000,-93.603458,41.991933 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,21750,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Mitchell,Artery,Norm,OneFam,One_Story,Average,Average,1954,1954,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,988,988,GasA,Excellent,Y,FuseA,988,0,0,988,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,113000,-93.610109,41.988675 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,6490,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1983,1983,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,282,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,315,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2008,WD ,Normal,128500,-93.6051072,41.987109 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,37,6951,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1984,1985,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,218,876,GasA,Typical,Y,SBrkr,923,0,0,923,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,362,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,119500,-93.604761,41.988934 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Excellent,1982,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,175,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,1,1,0,3,1,Good,5,Typ,1,Excellent,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,130500,-93.603917,41.98811 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Very_Good,1982,2003,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,PConc,Typical,Good,No,GLQ,3,Unf,0,0,816,GasA,Excellent,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Fin,2,816,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,138000,-93.603916,41.988076 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1982,2005,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,207,845,GasA,Good,Y,SBrkr,845,0,0,845,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,134500,-93.603886,41.987289 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,12508,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1985,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,323,983,GasA,Excellent,Y,SBrkr,983,767,0,1750,1,0,2,0,4,1,Typical,7,Mod,0,No_Fireplace,Attchd,Unf,1,423,Typical,Typical,Paved,245,0,156,0,0,0,No_Pool,No_Fence,Shed,1300,7,2008,WD ,Normal,160000,-93.608291,41.98658 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,12395,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1984,1984,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,217,864,GasA,Typical,Y,SBrkr,889,0,0,889,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,137500,-93.603722,41.986498 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11075,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Below_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,LwQ,193,29,784,GasA,Excellent,Y,SBrkr,1168,800,0,1968,0,1,2,1,4,1,Typical,7,Min2,1,Poor,Attchd,RFn,2,530,Typical,Typical,Paved,305,189,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,9,2008,WD ,Normal,172000,-93.600066,41.991885 -Two_Story_1945_and_Older,I_all,0,56600,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_and_Half_Unf,Average,Very_Poor,1900,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,686,686,GasA,Excellent,Y,SBrkr,1150,686,0,1836,0,0,2,0,4,1,Typical,7,Maj1,0,No_Fireplace,Detchd,Unf,1,288,Typical,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,103000,-93.577427,42.022745 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10667,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1971,1971,Gable,CompShg,MetalSd,MetalSd,BrkFace,302,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,749,1587,GasA,Typical,Y,SBrkr,1587,0,0,1587,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,167300,-93.618504,42.051297 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10628,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1970,1970,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Good,Gd,GLQ,3,Unf,0,499,1277,GasA,Typical,Y,SBrkr,1277,0,0,1277,1,0,1,0,2,1,Typical,5,Typ,1,Poor,Attchd,Unf,2,526,Typical,Typical,Paved,0,0,0,0,176,0,No_Pool,Good_Wood,None,0,4,2007,WD ,Normal,167000,-93.618503,42.051357 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,13651,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1973,1973,Gable,CompShg,Plywood,Plywood,BrkFace,1115,Typical,Good,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,343,2223,GasA,Excellent,Y,SBrkr,2223,0,0,2223,1,0,2,0,3,1,Typical,8,Typ,2,Good,Attchd,Fin,2,516,Typical,Typical,Paved,300,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,244000,-93.617273,42.049671 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,15865,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1970,1970,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Good,Good,PConc,Typical,Good,Gd,ALQ,1,Rec,823,1043,2217,GasA,Excellent,Y,SBrkr,2217,0,0,2217,1,0,2,0,4,1,Good,8,Typ,1,Typical,Attchd,Unf,2,621,Typical,Typical,Paved,81,207,0,0,224,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,268000,-93.61728,42.050443 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12394,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,886,0,1733,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,433,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Family,225000,-93.63988,42.061275 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10364,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,806,806,GasA,Good,Y,SBrkr,806,766,0,1572,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,373,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,168000,-93.63736,42.060466 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13869,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,612,794,GasA,Good,Y,SBrkr,794,676,0,1470,0,1,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,388,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,177000,-93.636146,42.061664 -Two_Story_1946_and_Newer,Residential_Low_Density,57,8773,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,916,916,GasA,Good,Y,SBrkr,916,684,0,1600,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,460,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,169000,-93.636972,42.061326 -Split_or_Multilevel,Residential_Low_Density,56,8872,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,754,630,0,1384,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,390,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,161500,-93.637189,42.061242 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,5330,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2000,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,298,1494,GasA,Excellent,Y,SBrkr,1652,0,0,1652,1,0,2,0,2,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,499,Typical,Typical,Paved,96,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,251000,-93.634341,42.063067 -Split_or_Multilevel,Residential_Low_Density,0,10147,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Above_Average,Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,32,Typical,Typical,PConc,Good,Typical,No,LwQ,4,Unf,0,206,392,GasA,Good,Y,SBrkr,924,770,0,1694,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,398,Typical,Typical,Paved,256,64,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,160000,-93.636345,42.059592 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8637,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,52,923,GasA,Good,Y,SBrkr,947,767,0,1714,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,451,Typical,Typical,Paved,256,48,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Abnorml,180000,-93.640613,42.058953 -Two_Story_1946_and_Newer,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,277,691,GasA,Good,Y,SBrkr,691,862,0,1553,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,178750,-93.637865,42.059562 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,38,Typical,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1237,1237,GasA,Good,Y,SBrkr,1253,0,0,1253,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,402,Typical,Typical,Paved,220,21,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,176000,-93.637883,42.059517 -Two_Story_1946_and_Newer,Residential_Low_Density,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,938,938,GasA,Excellent,Y,SBrkr,957,1342,0,2299,0,0,3,1,5,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,482,Typical,Typical,Paved,188,30,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,204000,-93.639068,42.059242 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9556,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1992,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,52,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1168,1168,GasA,Good,Y,SBrkr,1187,0,0,1187,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,420,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,160000,-93.637848,42.059293 -Split_or_Multilevel,Residential_Low_Density,0,10784,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1991,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,76,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,802,670,0,1472,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,402,Typical,Typical,Paved,164,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,160000,-93.637299,42.057006 -Split_or_Multilevel,Residential_Low_Density,0,9125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,170,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,812,670,0,1482,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,392,Typical,Typical,Paved,100,25,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,163900,-93.637917,42.05817 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7655,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,903,903,GasA,Good,Y,SBrkr,910,732,0,1642,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,392,Typical,Typical,Paved,290,84,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,168000,-93.635833,42.057533 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,160,18160,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkCmn,138,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,752,1302,GasA,Fair,Y,SBrkr,1128,0,0,1128,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,480,Typical,Typical,Partial_Pavement,0,108,246,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Alloca,154204,-93.633329,42.057022 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3696,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1986,1986,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1074,1074,GasA,Excellent,Y,SBrkr,1088,0,0,1088,0,0,1,1,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,461,Typical,Typical,Paved,0,74,137,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,170000,-93.634513,42.059375 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,0,5062,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,TwnhsE,Two_Story,Good,Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Mn,GLQ,3,LwQ,182,180,1190,GasA,Good,Y,SBrkr,1190,900,0,2090,1,0,2,0,3,1,Good,6,Min1,1,Typical,Attchd,Fin,2,577,Typical,Typical,Paved,219,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,207500,-93.632917,42.059239 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,38,4740,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1988,1988,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,918,1166,GasA,Good,Y,SBrkr,1179,0,0,1179,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,480,Typical,Typical,Paved,0,108,0,0,135,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,182000,-93.631622,42.059309 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,35,5118,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1990,1990,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,386,1312,GasA,Good,Y,SBrkr,1321,0,0,1321,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,0,64,140,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,212000,-93.631633,42.059304 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,14157,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,Stone,200,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,673,1922,GasA,Excellent,Y,SBrkr,1922,0,0,1922,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,676,Typical,Typical,Paved,178,51,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,377426,-93.629016,42.060431 -Two_Story_1946_and_Newer,Residential_Low_Density,98,12328,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,Stone,146,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,163,1149,GasA,Excellent,Y,SBrkr,1164,1377,0,2541,1,0,3,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,729,Typical,Typical,Paved,120,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,349265,-93.629075,42.060144 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,52,51974,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,PosN,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,710,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1559,2660,GasA,Excellent,Y,SBrkr,2338,0,0,2338,1,0,2,1,4,1,Good,8,Typ,2,Good,Attchd,Fin,3,1110,Good,Typical,Paved,0,135,0,0,322,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,591587,-93.626366,42.061202 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,195,41600,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,Norm,Norm,TwoFmCon,One_Story,Average,Average,1969,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,53,1100,GasW,Typical,Y,SBrkr,1424,0,0,1424,1,0,1,1,3,1,Typical,7,Mod,0,No_Fireplace,More_Than_Two_Types,Unf,3,828,Typical,Typical,Dirt_Gravel,144,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,155000,-93.622874,42.060096 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,8035,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2006,2006,Gable,CompShg,MetalSd,MetalSd,BrkFace,165,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,815,1612,GasA,Excellent,Y,SBrkr,1612,0,0,1612,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,556,Typical,Typical,Paved,0,164,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,319900,-93.629538,42.059743 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8089,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,2007,2007,Gable,CompShg,MetalSd,MetalSd,BrkFace,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,474,1419,GasA,Excellent,Y,SBrkr,1419,0,0,1419,1,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,RFn,2,567,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,392000,-93.630074,42.058893 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14082,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,945,Good,Typical,PConc,Excellent,Good,Gd,GLQ,3,Unf,0,662,2220,GasA,Excellent,Y,SBrkr,2234,0,0,2234,1,0,1,1,1,1,Good,7,Typ,1,Good,Attchd,RFn,2,724,Typical,Typical,Paved,390,80,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,441929,-93.628754,42.058998 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,13870,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,PosA,PosA,OneFam,One_Story,Very_Excellent,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,250,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,590,1742,GasA,Excellent,Y,SBrkr,2042,0,0,2042,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,724,Typical,Typical,Paved,240,52,0,0,174,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,455000,-93.628723,42.058996 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12546,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1981,1981,Gable,CompShg,MetalSd,MetalSd,BrkFace,310,Good,Good,CBlock,Good,Typical,No,BLQ,2,Unf,0,762,1440,GasA,Excellent,Y,SBrkr,1440,0,0,1440,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,467,Typical,Typical,Paved,0,0,99,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,182900,-93.637875,42.054368 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10960,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1984,1984,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,1028,1284,GasA,Typical,Y,SBrkr,1284,0,0,1284,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,4,2007,COD,Abnorml,174000,-93.638853,42.053916 -Split_or_Multilevel,Residential_Low_Density,78,12090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Good,1984,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,74,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,585,585,GasA,Excellent,Y,SBrkr,1140,728,0,1868,0,0,3,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,477,Typical,Typical,Paved,268,112,0,0,147,0,No_Pool,No_Fence,None,0,1,2007,WD ,Abnorml,178000,-93.637876,42.055545 -Two_Story_1946_and_Newer,Residential_Low_Density,78,12090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1981,2003,Gable,CompShg,MetalSd,MetalSd,BrkFace,306,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,404,725,GasA,Excellent,Y,SBrkr,725,754,0,1479,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,167,72,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,176000,-93.637816,42.0545 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,12299,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1978,1985,Gable,CompShg,Plywood,Plywood,Stone,67,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,201,1529,GasA,Typical,Y,SBrkr,1664,0,0,1664,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,663,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,213000,-93.637158,42.054395 -Two_Story_1946_and_Newer,Residential_Low_Density,61,11339,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Average,1979,1979,Hip,WdShake,HdBoard,Plywood,BrkFace,549,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,22,780,GasA,Typical,Y,SBrkr,1085,845,0,1930,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,2,481,Typical,Typical,Paved,192,72,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,192000,-93.634665,42.054941 -Two_Story_1946_and_Newer,Residential_Low_Density,92,11952,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Above_Average,1977,1977,Mansard,WdShake,WdShing,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,808,808,GasA,Typical,Y,SBrkr,1161,808,0,1969,0,0,2,1,3,1,Typical,8,Typ,1,Good,Attchd,RFn,2,534,Typical,Typical,Paved,0,0,0,0,276,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,190000,-93.633973,42.055217 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12046,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,298,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,692,848,GasA,Typical,Y,SBrkr,1118,912,0,2030,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,2,551,Typical,Typical,Paved,0,224,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,195000,-93.631528,42.054427 -Split_or_Multilevel,Residential_Low_Density,0,10395,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,233,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,427,1032,GasA,Typical,Y,SBrkr,1032,0,0,1032,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,564,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,500,7,2007,WD ,Normal,148000,-93.632669,42.05622 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,11850,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1984,1984,Gable,CompShg,Plywood,Plywood,BrkFace,98,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,372,1153,GasA,Typical,Y,SBrkr,1177,0,0,1177,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,495,Typical,Typical,Paved,204,103,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,151500,-93.631795,42.056211 -Two_Story_1946_and_Newer,Residential_Low_Density,80,11584,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Good,Above_Average,1979,1979,Hip,CompShg,HdBoard,HdBoard,BrkFace,96,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,110,114,539,GasA,Typical,Y,SBrkr,1040,685,0,1725,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,0,88,216,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,197000,-93.638554,42.052353 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1979,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,253,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,356,1259,GasA,Excellent,Y,SBrkr,1353,0,0,1353,1,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,240,141,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,192350,-93.635411,42.053271 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10793,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Average,Average,1969,1969,Mansard,CompShg,WdShing,HdBoard,BrkFace,263,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,287,0,780,GasA,Excellent,Y,SBrkr,780,840,0,1620,0,0,2,1,4,1,Typical,7,Min1,0,No_Fireplace,Attchd,Fin,2,462,Typical,Typical,Paved,208,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2007,WD ,Normal,152000,-93.63724,42.05026 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,128,13001,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Above_Average,Average,1971,1971,Hip,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,BLQ,121,1012,1625,GasA,Typical,Y,SBrkr,1220,0,0,1220,0,1,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,944,Typical,Typical,Paved,0,0,249,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,170000,-93.634714,42.049832 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,12243,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1971,1971,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,486,1484,GasA,Good,Y,SBrkr,1484,0,0,1484,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,487,Typical,Typical,Paved,224,0,0,0,180,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,175000,-93.634581,42.050075 -Two_Story_1946_and_Newer,Residential_Low_Density,90,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Mansard,CompShg,HdBoard,AsphShn,BrkFace,365,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,175,143,702,GasA,Good,Y,SBrkr,1041,702,0,1743,0,1,1,2,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,539,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,175000,-93.636497,42.050372 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12384,Pave,No_Alley_Access,Regular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,233,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,793,793,GasA,Typical,Y,SBrkr,1142,793,0,1935,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,0,113,252,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,197900,-93.633898,42.049956 -Split_or_Multilevel,Residential_Low_Density,64,8991,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,SLvl,Good,Above_Average,1976,1976,Gable,CompShg,Plywood,Plywood,Stone,130,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Rec,604,0,1228,GasA,Typical,Y,SBrkr,1324,0,0,1324,0,1,2,0,3,1,Good,5,Typ,1,Fair,Attchd,Fin,2,585,Typical,Typical,Paved,407,36,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,174000,-93.633757,42.051039 -Two_Story_1946_and_Newer,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Above_Average,1974,1974,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,153,0,1084,GasA,Typical,Y,SBrkr,1084,793,0,1877,1,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,488,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,178900,-93.632191,42.048765 -Split_or_Multilevel,Residential_Low_Density,70,10500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1971,2005,Gambrel,CompShg,MetalSd,AsphShn,BrkFace,82,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,23,372,GasA,Typical,Y,SBrkr,576,533,0,1109,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,BuiltIn,Unf,1,288,Typical,Typical,Paved,35,0,0,0,0,0,No_Pool,Good_Wood,None,0,12,2007,WD ,Normal,139000,-93.627736,42.055392 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10530,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1971,1971,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,35,664,981,GasA,Typical,Y,SBrkr,981,0,0,981,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,312,40,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,143250,-93.626331,42.055264 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7472,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Excellent,1972,2004,Gable,CompShg,HdBoard,HdBoard,BrkFace,138,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,99,725,GasA,Good,Y,SBrkr,725,754,0,1479,1,0,1,1,4,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,484,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,184000,-93.626565,42.054603 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,9457,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1970,1990,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,359,925,GasA,Typical,Y,SBrkr,1422,0,0,1422,1,0,1,0,3,1,Typical,7,Min2,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,252,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2007,WD ,Normal,155000,-93.629118,42.053407 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,7920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1970,2002,Gable,CompShg,HdBoard,HdBoard,BrkFace,32,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,GLQ,619,214,914,GasA,Excellent,Y,SBrkr,914,0,0,914,0,0,1,0,3,1,Excellent,5,Typ,0,No_Fireplace,Attchd,RFn,1,368,Typical,Good,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,133000,-93.626722,42.054612 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,9758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,287,251,950,GasA,Typical,Y,SBrkr,950,0,0,950,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,119500,-93.627112,42.053547 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8294,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1971,1971,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,858,858,GasA,Typical,Y,SBrkr,872,0,0,872,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,4,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2007,WD ,Normal,123000,-93.628047,42.05346 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7340,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,536,858,GasA,Typical,Y,SBrkr,858,0,0,858,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,684,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,110000,-93.62805,42.053503 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17199,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1961,1961,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,600,914,GasA,Excellent,Y,SBrkr,914,0,0,914,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Basment,Unf,1,270,Fair,Typical,Paved,140,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Normal,128000,-93.6247461,42.0562952 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,248,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2007,WD ,Normal,120500,-93.621646,42.056395 -One_Story_PUD_1946_and_Newer,Residential_High_Density,34,4058,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwnhsE,SFoyer,Good,Average,1998,1998,Gable,CompShg,MetalSd,MetalSd,BrkFace,182,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,LwQ,139,0,723,GasA,Excellent,Y,SBrkr,767,0,0,767,1,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,367,Typical,Typical,Paved,120,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,133000,-93.624798,42.05467 -One_Story_PUD_1946_and_Newer,Residential_High_Density,33,4113,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2001,2001,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1337,1337,GasA,Excellent,Y,SBrkr,1337,0,0,1337,0,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,511,Typical,Typical,Paved,136,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,155000,-93.624798,42.054789 -One_Story_PUD_1946_and_Newer,Residential_High_Density,26,10943,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1997,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,475,1405,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,522,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,177000,-93.624431,42.055319 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2205,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,567,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,213,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,113500,-93.629747,42.052669 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,285,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,316,672,GasA,Typical,Y,SBrkr,672,546,0,1218,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,144,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,113000,-93.628656,42.052698 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2058,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,265,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,672,546,0,1218,0,0,1,1,4,1,Excellent,7,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,113700,-93.627855,42.052696 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2016,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,304,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,106000,-93.629777,42.052294 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,376,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,294,765,GasA,Excellent,Y,SBrkr,765,600,0,1365,1,0,1,1,2,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,240,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,122500,-93.627242,42.051833 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,4928,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,958,1078,GasA,Typical,Y,SBrkr,958,0,0,958,0,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,205,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,128000,-93.627238,42.050397 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,248,804,GasA,Typical,Y,SBrkr,804,744,0,1548,1,0,2,1,3,1,Good,7,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,48,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,155000,-93.624575,42.05057 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2304,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Good,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,6,423,1061,GasA,Typical,Y,SBrkr,1055,0,0,1055,0,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,319,Typical,Typical,Paved,108,32,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,142500,-93.625672,42.049996 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,1966,Hip,CompShg,HdBoard,HdBoard,BrkFace,52,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,263,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,360,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,129250,-93.6257971,42.0488205 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,96,12469,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,378,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,639,1790,GasA,Excellent,Y,SBrkr,1816,0,0,1816,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,730,Typical,Typical,Paved,186,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,356000,-93.6588809,42.062574 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11694,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,452,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1774,1822,GasA,Excellent,Y,SBrkr,1828,0,0,1828,0,0,2,0,3,1,Good,9,Typ,1,Good,Attchd,Unf,3,774,Typical,Typical,Paved,0,108,0,0,260,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,314813,-93.658015,42.063296 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,12030,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,254,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1694,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,818,Typical,Typical,Paved,168,228,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,318000,-93.657679,42.063298 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,CemntBd,CmentBd,Stone,302,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,1694,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,856,Typical,Typical,Paved,0,112,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,322400,-93.6569765,42.0632689 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,12085,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,328,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,730,1734,GasA,Excellent,Y,SBrkr,1734,0,0,1734,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,RFn,3,928,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,318000,-93.6566403,42.0632722 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14333,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,590,2108,GasA,Excellent,Y,SBrkr,2122,0,0,2122,1,0,2,1,2,1,Good,7,Typ,1,Excellent,Attchd,Fin,3,938,Typical,Typical,Paved,130,142,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,345474,-93.654243,42.063141 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,14450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Gable,CompShg,CemntBd,CmentBd,BrkFace,315,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,2121,2121,GasA,Excellent,Y,SBrkr,2121,0,0,2121,0,0,2,1,3,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,732,Typical,Typical,Paved,124,98,0,0,142,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,415298,-93.6570383,42.0619544 -Two_Story_1946_and_Newer,Residential_Low_Density,107,13641,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,456,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,630,1934,GasA,Excellent,Y,SBrkr,1943,713,0,2656,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,1040,Typical,Typical,Paved,268,58,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,492000,-93.6566568,42.061976 -Two_Story_1946_and_Newer,Residential_Low_Density,110,13440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,190,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1108,1108,GasA,Excellent,Y,SBrkr,1148,1402,0,2550,0,0,2,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,670,Typical,Typical,Paved,120,39,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,New,Partial,338931,-93.6562702,42.0619709 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,15431,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,400,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,564,1994,GasA,Excellent,Y,SBrkr,2046,0,0,2046,1,0,2,1,2,1,Excellent,7,Typ,2,Good,Attchd,Fin,3,878,Typical,Typical,Paved,188,65,0,0,175,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,450000,-93.657967,42.061284 -Two_Story_1946_and_Newer,Residential_Low_Density,109,14154,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,350,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1063,1063,GasA,Excellent,Y,SBrkr,1071,1101,0,2172,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,RFn,3,947,Typical,Typical,Paved,192,62,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,280000,-93.654774,42.062259 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,456,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,740,2552,GasA,Excellent,Y,SBrkr,2552,0,0,2552,1,0,2,0,3,1,Excellent,8,Typ,2,Excellent,Attchd,Fin,3,932,Typical,Typical,Paved,130,28,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,479069,-93.6570542,42.0628153 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14226,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,375,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1935,1935,GasA,Good,Y,SBrkr,1973,0,0,1973,0,0,2,0,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,895,Typical,Typical,Paved,315,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,395000,-93.6562861,42.0624082 -Two_Story_1946_and_Newer,Residential_Low_Density,105,13693,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,772,Excellent,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,2153,2153,GasA,Excellent,Y,SBrkr,2069,574,0,2643,0,0,2,1,3,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,694,Typical,Typical,Paved,414,84,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,380000,-93.657444,42.062434 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,11428,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,248,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1626,1626,GasA,Excellent,Y,SBrkr,1634,0,0,1634,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,866,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,250000,-93.65331,42.061504 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14977,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,304,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,626,1976,GasA,Excellent,Y,SBrkr,1976,0,0,1976,1,0,2,0,2,1,Good,7,Typ,1,Excellent,Attchd,RFn,3,908,Typical,Typical,Paved,250,63,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,440000,-93.657524,42.061131 -Two_Story_1946_and_Newer,Residential_Low_Density,118,13654,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Gable,CompShg,MetalSd,MetalSd,BrkFace,365,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1704,1704,GasA,Excellent,Y,SBrkr,1722,1036,0,2758,0,0,2,1,4,1,Excellent,9,Typ,1,Excellent,BuiltIn,Fin,3,814,Typical,Typical,Paved,282,55,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,418000,-93.657211,42.06027 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,17169,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,970,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,636,2320,GasA,Excellent,Y,SBrkr,2290,0,0,2290,2,0,2,1,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1174,Typical,Typical,Paved,192,30,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,500067,-93.657235,42.060269 -Two_Story_1946_and_Newer,Residential_Low_Density,134,16659,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1582,1582,GasA,Excellent,Y,SBrkr,1582,570,0,2152,0,0,2,1,3,1,Good,7,Typ,1,Good,Detchd,Unf,2,728,Typical,Typical,Paved,0,368,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,260116,-93.6554248,42.061133 -Two_Story_1946_and_Newer,Residential_Low_Density,76,9591,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,344,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1143,1143,GasA,Excellent,Y,SBrkr,1143,1330,0,2473,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,RFn,3,852,Typical,Typical,Paved,192,151,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,317000,-93.654689,42.060576 -Two_Story_1946_and_Newer,Residential_Low_Density,82,9709,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,120,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,140,918,GasA,Excellent,Y,SBrkr,958,1142,0,2100,1,0,2,1,3,1,Excellent,8,Typ,2,Good,BuiltIn,Fin,3,786,Typical,Typical,Paved,172,104,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,319500,-93.654617,42.060427 -Two_Story_1946_and_Newer,Residential_Low_Density,86,10562,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,300,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,294,1582,GasA,Excellent,Y,SBrkr,1610,551,0,2161,1,0,1,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,789,Typical,Typical,Paved,178,120,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,325624,-93.650917,42.059561 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,13615,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,Stone,510,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1802,1802,GasA,Excellent,Y,SBrkr,1802,0,0,1802,0,0,2,1,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,843,Typical,Typical,Paved,158,105,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,372000,-93.657492,42.06113 -Two_Story_1946_and_Newer,Residential_Low_Density,99,13069,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,502,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1706,1706,GasA,Excellent,Y,SBrkr,1718,1238,0,2956,0,0,2,1,5,1,Excellent,11,Typ,1,Excellent,BuiltIn,RFn,3,916,Typical,Typical,Paved,194,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,342000,-93.656249,42.059761 -Two_Story_1946_and_Newer,Residential_Low_Density,99,12099,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,388,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,166,1136,GasA,Excellent,Y,SBrkr,1136,1332,0,2468,1,0,2,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,872,Typical,Typical,Paved,184,154,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,354000,-93.6565389,42.059677 -Two_Story_1946_and_Newer,Residential_Low_Density,110,14277,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,280,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,379,1317,GasA,Excellent,Y,SBrkr,1217,1168,0,2385,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,818,Typical,Typical,Paved,192,228,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,350000,-93.65699,42.058634 -Two_Story_1946_and_Newer,Residential_Low_Density,93,11999,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,340,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1181,1181,GasA,Excellent,Y,SBrkr,1234,1140,0,2374,0,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,656,Typical,Typical,Paved,104,100,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,285000,-93.6545189,42.058619 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12568,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,246,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,226,895,GasA,Excellent,Y,SBrkr,895,923,0,1818,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,774,Typical,Typical,Paved,196,104,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,281500,-93.650597,42.059211 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9926,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,436,1614,GasA,Excellent,Y,SBrkr,1614,0,0,1614,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,878,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,245700,-93.650499,42.059202 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9254,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1602,1721,GasA,Excellent,Y,SBrkr,1721,0,0,1721,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,554,Typical,Typical,Paved,168,27,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,232698,-93.65045,42.059197 -Two_Story_1946_and_Newer,Residential_Low_Density,92,10732,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1298,1298,GasA,Excellent,Y,SBrkr,1298,530,0,1828,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,RFn,3,876,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,250000,-93.650406,42.059194 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,7577,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,1342,1362,GasA,Excellent,Y,SBrkr,1362,0,0,1362,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,460,Typical,Typical,Paved,192,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,194700,-93.649838,42.059175 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,3901,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,182,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,436,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,631,Typical,Typical,Paved,110,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,204000,-93.649828,42.059175 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,3903,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,182,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,272,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,631,Typical,Typical,Paved,110,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,200000,-93.649808,42.059176 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,6289,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,600,1362,GasA,Excellent,Y,SBrkr,1362,0,0,1362,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,460,Typical,Typical,Paved,192,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,207000,-93.649798,42.059176 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,4590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,108,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,1530,1554,GasA,Excellent,Y,SBrkr,1554,0,0,1554,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,627,Typical,Typical,Paved,156,73,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,209500,-93.649397,42.05826 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,4590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,108,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,1530,1554,GasA,Excellent,Y,SBrkr,1554,0,0,1554,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,627,Typical,Typical,Paved,156,73,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,CWD,Normal,209500,-93.649319,42.058276 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,44,6442,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,178,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,1346,1370,GasA,Excellent,Y,SBrkr,1370,0,0,1370,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,120,49,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,202500,-93.649291,42.058284 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,7841,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,394,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,729,1577,GasA,Excellent,Y,SBrkr,1577,0,0,1577,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,564,Typical,Typical,Paved,203,39,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,280000,-93.653414,42.056827 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,60,7313,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,246,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,408,1561,GasA,Excellent,Y,SBrkr,1561,0,0,1561,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,556,Typical,Typical,Paved,203,47,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,277500,-93.652565,42.057124 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,68,7820,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2007,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,362,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1869,1869,GasA,Excellent,Y,SBrkr,1869,0,0,1869,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,617,Typical,Typical,Paved,210,54,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,318061,-93.652054,42.05738 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1300,1324,GasA,Excellent,Y,SBrkr,1324,0,0,1324,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,221370,-93.6514105,42.0568529 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,3242,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,235,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,405,1405,GasA,Excellent,Y,SBrkr,1405,0,0,1405,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,478,Typical,Typical,Paved,136,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,200000,-93.650608,42.057209 -Two_Story_1946_and_Newer,Residential_Low_Density,59,15810,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,768,728,0,1496,0,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,572,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,181755,-93.646937,42.063388 -Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,768,768,0,1536,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,176000,-93.646429,42.063381 -Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2005,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,783,783,GasA,Excellent,Y,SBrkr,783,701,0,1484,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,178900,-93.645875,42.062483 -Two_Story_1946_and_Newer,Residential_Low_Density,58,13204,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,44,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,608,608,GasA,Excellent,Y,SBrkr,608,850,0,1458,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,BuiltIn,Fin,2,454,Typical,Typical,Paved,100,33,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,168165,-93.64573,42.062279 -Two_Story_1946_and_Newer,Residential_Low_Density,62,8857,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,757,0,1495,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,440,Typical,Typical,Paved,100,30,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,New,Partial,171925,-93.644788,42.0626729 -Two_Story_1946_and_Newer,Residential_Low_Density,63,9729,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,698,698,GasA,Excellent,Y,SBrkr,698,1048,0,1746,1,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Unf,3,350,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,198444,-93.644415,42.06208 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,12216,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,408,1326,GasA,Excellent,Y,SBrkr,1326,0,0,1326,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,388,Typical,Typical,Paved,120,23,0,0,0,0,No_Pool,No_Fence,Shed,2000,6,2007,WD ,Normal,203000,-93.6443,42.062067 -Two_Story_1946_and_Newer,Residential_Low_Density,65,9018,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,179540,-93.645732,42.062533 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8993,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1302,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,436,Typical,Typical,Paved,0,22,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,176485,-93.644377,42.062887 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8899,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1316,1340,GasA,Excellent,Y,SBrkr,1340,0,0,1340,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,396,Typical,Typical,Paved,100,30,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,181134,-93.644376,42.062931 -Two_Story_1946_and_Newer,Residential_Low_Density,73,8499,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,616,616,GasA,Excellent,Y,SBrkr,616,796,0,1412,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,432,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,New,Partial,156932,-93.644375,42.062974 -Two_Story_1946_and_Newer,Residential_Low_Density,72,8229,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,22,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,752,752,GasA,Excellent,Y,SBrkr,752,752,0,1504,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,100,32,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,166000,-93.644374,42.063019 -Two_Story_1946_and_Newer,Residential_Low_Density,64,7713,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,16,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,177594,-93.644447,42.063106 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7697,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1246,1246,GasA,Excellent,Y,SBrkr,1258,0,0,1258,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,173500,-93.644366,42.063342 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3922,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,WdShing,Wd Shng,BrkFace,72,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1258,1258,GasA,Excellent,Y,SBrkr,1258,0,0,1258,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,144,16,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,172500,-93.641795,42.062584 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3922,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,72,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1258,1258,GasA,Excellent,Y,SBrkr,1402,0,0,1402,0,2,0,2,2,1,Good,7,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,120,16,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,194201,-93.641716,42.062581 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3621,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,72,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,322,1406,GasA,Excellent,Y,SBrkr,1589,0,0,1589,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,630,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,226500,-93.640513,42.062946 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,3710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1266,1266,GasA,Excellent,Y,SBrkr,1266,0,0,1266,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,388,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,185485,-93.642177,42.063301 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,182,14572,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,230,1530,GasA,Excellent,Y,SBrkr,1530,0,0,1530,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,630,Typical,Typical,Paved,144,36,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Family,259000,-93.644256,42.061713 -Split_or_Multilevel,Residential_Low_Density,65,16219,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,56,835,GasA,Excellent,Y,SBrkr,1119,0,0,1119,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,437,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,188500,-93.644064,42.061429 -Split_or_Multilevel,Residential_Low_Density,87,11084,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,192,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,1,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Family,175000,-93.643062,42.061483 -Two_Story_1946_and_Newer,Residential_Low_Density,59,11796,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1112,0,1959,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,215000,-93.643961,42.061541 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,10936,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1510,1510,GasA,Excellent,Y,SBrkr,1525,0,0,1525,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,534,Typical,Typical,Paved,100,18,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,206580,-93.643954,42.061531 -Two_Story_1946_and_Newer,Residential_Low_Density,62,8244,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,880,0,1720,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,183500,-93.641781,42.061318 -Split_or_Multilevel,Residential_Low_Density,0,11950,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,754,640,0,1394,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,165500,-93.639977,42.061392 -Two_Story_1946_and_Newer,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1101,0,1948,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,211000,-93.64021,42.060021 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8063,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,924,924,GasA,Excellent,Y,SBrkr,948,742,0,1690,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,463,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Abnorml,181000,-93.642288,42.058858 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8740,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,280,854,GasA,Excellent,Y,SBrkr,864,1131,0,1995,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,435,Typical,Typical,Paved,264,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,219500,-93.64334,42.059181 -Two_Story_1946_and_Newer,Residential_Low_Density,62,7917,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,953,953,GasA,Excellent,Y,SBrkr,953,694,0,1647,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,175000,-93.641279,42.057981 -Split_or_Multilevel,Residential_Low_Density,76,9967,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,8,Typ,1,Typical,BuiltIn,RFn,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,170000,-93.642836,42.058401 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,660,660,GasA,Excellent,Y,SBrkr,660,660,0,1320,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,162000,-93.643641,42.059526 -Two_Story_1946_and_Newer,Residential_Low_Density,58,9487,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,395,915,GasA,Excellent,Y,SBrkr,940,750,0,1690,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,442,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,187000,-93.641323,42.057836 -Two_Story_1946_and_Newer,Residential_Low_Density,59,9649,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,941,941,GasA,Excellent,Y,SBrkr,961,683,0,1644,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,460,42,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,185000,-93.640658,42.058897 -Two_Story_1946_and_Newer,Residential_Low_Density,160,15623,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,1996,1996,Hip,CompShg,Wd Sdng,ImStucc,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,300,2396,GasA,Excellent,Y,SBrkr,2411,2065,0,4476,1,0,3,1,4,1,Excellent,10,Typ,2,Typical,Attchd,Fin,3,813,Typical,Typical,Paved,171,78,0,0,0,555,Excellent,Minimum_Privacy,None,0,7,2007,WD ,Abnorml,745000,-93.6575919,42.0533209 -Two_Story_1946_and_Newer,Residential_Low_Density,100,12191,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1997,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,515,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,598,1779,GasA,Excellent,Y,SBrkr,1779,772,0,2551,1,0,2,1,4,1,Good,8,Typ,2,Typical,Attchd,Fin,3,925,Typical,Typical,Paved,76,61,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,384500,-93.655711,42.053364 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,72,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,969,969,GasA,Excellent,Y,SBrkr,997,1288,0,2285,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,3,648,Typical,Typical,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,248000,-93.652894,42.055751 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2001,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,705,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1351,2633,GasA,Excellent,Y,SBrkr,2633,0,0,2633,1,0,2,1,2,1,Excellent,8,Typ,2,Good,Attchd,RFn,3,804,Typical,Typical,Paved,314,140,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,466500,-93.651096,42.055627 -Two_Story_1946_and_Newer,Residential_Low_Density,89,10557,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,1998,1998,Gable,CompShg,MetalSd,MetalSd,BrkFace,422,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,736,1408,GasA,Excellent,Y,SBrkr,1671,1407,0,3078,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,3,806,Typical,Typical,Paved,108,87,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,410000,-93.652641,42.05359 -Two_Story_1946_and_Newer,Residential_Low_Density,74,11002,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,341,1389,GasA,Excellent,Y,SBrkr,1411,1171,0,2582,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,3,758,Typical,Typical,Paved,286,60,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,322500,-93.652777,42.053436 -Two_Story_1946_and_Newer,Residential_Low_Density,83,10790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,275,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1066,1066,GasA,Excellent,Y,SBrkr,1108,1277,0,2385,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,3,600,Typical,Typical,Paved,120,38,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,252000,-93.649901,42.053467 -Two_Story_1946_and_Newer,Residential_Low_Density,104,21535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Excellent,Above_Average,1994,1995,Gable,WdShngl,HdBoard,HdBoard,BrkFace,1170,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,989,2444,GasA,Excellent,Y,SBrkr,2444,1872,0,4316,0,1,3,1,4,1,Excellent,10,Typ,2,Excellent,Attchd,Fin,3,832,Typical,Typical,Paved,382,50,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,755000,-93.657271,42.05198 -Two_Story_1946_and_Newer,Residential_Low_Density,92,9920,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,255,1117,GasA,Excellent,Y,SBrkr,1127,886,0,2013,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,455,Typical,Typical,Paved,180,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,269790,-93.655159,42.052361 -Two_Story_1946_and_Newer,Residential_Low_Density,95,11787,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,594,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,660,1379,GasA,Excellent,Y,SBrkr,1383,1015,0,2398,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,3,834,Typical,Typical,Paved,239,60,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,315750,-93.655905,42.052247 -Two_Story_1946_and_Newer,Residential_Low_Density,82,9950,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,290,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,638,1203,GasA,Excellent,Y,SBrkr,1214,1306,0,2520,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,721,Typical,Typical,Paved,224,114,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Abnorml,290000,-93.653708,42.051813 -Two_Story_1946_and_Newer,Residential_Low_Density,65,12257,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,513,Good,Typical,PConc,Good,Typical,Av,LwQ,4,ALQ,64,1198,1318,GasA,Excellent,Y,SBrkr,1328,1203,0,2531,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,752,Typical,Typical,Paved,222,98,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,290000,-93.653712,42.05048 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1992,1993,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1969,3200,GasA,Excellent,Y,SBrkr,3228,0,0,3228,1,0,3,0,4,1,Good,10,Typ,1,Good,Attchd,RFn,2,546,Typical,Typical,Paved,264,75,291,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,430000,-93.65334,42.049518 -Two_Story_1946_and_Newer,Residential_Low_Density,88,11762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,309,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,770,1105,GasA,Excellent,Y,SBrkr,1105,1097,0,2202,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,2,517,Typical,Typical,Paved,0,65,0,0,144,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,263000,-93.652946,42.049509 -Two_Story_1946_and_Newer,Residential_Low_Density,82,9044,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,526,Good,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,100,1325,GasA,Excellent,Y,SBrkr,1335,1203,0,2538,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,933,Typical,Typical,Paved,198,92,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,330000,-93.653864,42.052416 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9910,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1369,1369,GasA,Excellent,Y,SBrkr,1369,0,0,1369,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,605,Typical,Typical,Paved,0,203,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,213133,-93.644118,42.054408 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11830,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,164,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,322,1542,GasA,Excellent,Y,SBrkr,1542,0,0,1542,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Unf,3,852,Typical,Typical,Paved,168,110,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,260261,-93.643769,42.055757 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,11670,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,RRNn,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,ImStucc,Stone,302,Excellent,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1905,1905,GasA,Excellent,Y,SBrkr,1905,0,0,1905,0,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,788,Typical,Typical,Paved,0,191,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,320000,-93.642069,42.054318 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,49,15256,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,84,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,556,1485,GasA,Excellent,Y,SBrkr,1464,0,0,1464,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,3,754,Typical,Typical,Paved,168,160,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,282922,-93.64176,42.054177 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,10612,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,248,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1496,1524,GasA,Good,Y,SBrkr,1534,0,0,1534,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,484,Typical,Typical,Paved,168,46,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Family,215000,-93.642261,42.054417 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,12291,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,754,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,394,1966,GasA,Excellent,Y,SBrkr,1966,0,0,1966,1,0,2,0,1,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,1092,Typical,Typical,Paved,76,52,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,419005,-93.642243,42.054354 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,116,13501,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,208,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1560,1623,GasA,Excellent,Y,SBrkr,1636,0,0,1636,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,865,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,255000,-93.642234,42.054309 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9986,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,428,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1795,1795,GasA,Excellent,Y,SBrkr,1795,0,0,1795,0,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,RFn,3,895,Typical,Typical,Paved,0,49,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,147000,-93.64396,42.054514 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,9965,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1528,1528,GasA,Excellent,Y,SBrkr,1528,0,0,1528,0,0,3,2,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,480,Typical,Typical,Paved,0,228,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,235876,-93.644086,42.054154 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,9416,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,Stone,205,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,600,1726,GasA,Excellent,Y,SBrkr,1726,0,0,1726,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,786,Typical,Typical,Paved,171,138,0,0,266,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,311872,-93.643836,42.054153 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,9849,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1689,1689,GasA,Excellent,Y,SBrkr,1689,0,0,1689,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,954,Typical,Typical,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,248328,-93.64248,42.054153 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,410,Good,Typical,PConc,Good,Good,Av,Unf,7,Unf,0,1588,1588,GasA,Excellent,Y,SBrkr,1588,0,0,1588,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,825,Typical,Typical,Paved,144,45,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,227000,-93.642344,42.054157 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8847,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,148,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,769,1538,GasA,Excellent,Y,SBrkr,1538,0,0,1538,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,146,40,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,260000,-93.643544,42.053185 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9158,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,Stone,140,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1496,1496,GasA,Excellent,Y,SBrkr,1496,0,0,1496,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,474,Typical,Typical,Paved,168,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,225000,-93.643449,42.053186 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8251,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,143,Good,Typical,PConc,Good,Good,Av,GLQ,3,Unf,0,716,1494,GasA,Excellent,Y,SBrkr,1506,0,0,1506,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,672,Typical,Typical,Paved,192,35,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,249000,-93.642197,42.053227 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,Stone,186,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,317,1686,GasA,Excellent,Y,SBrkr,1694,0,0,1694,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,636,Typical,Typical,Paved,255,57,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,307000,-93.641251,42.053108 -Two_Story_1946_and_Newer,Residential_Low_Density,70,9605,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,982,982,GasA,Excellent,Y,SBrkr,982,995,0,1977,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,574,Typical,Typical,Paved,240,53,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Family,250000,-93.643748,42.053032 -Two_Story_1946_and_Newer,Residential_Low_Density,75,8778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1302,1302,GasA,Excellent,Y,SBrkr,1302,528,0,1830,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,RFn,3,859,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,221300,-93.642275,42.053067 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1314,1338,GasA,Excellent,Y,SBrkr,1338,0,0,1338,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,598,Typical,Typical,Paved,0,141,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,208900,-93.650341,42.051802 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1335,1335,GasA,Excellent,Y,SBrkr,1335,0,0,1335,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,575,Typical,Typical,Paved,0,210,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,210400,-93.65042,42.051795 -Two_Story_1946_and_Newer,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,546,896,GasA,Excellent,Y,SBrkr,896,896,0,1792,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,590,Typical,Typical,Paved,184,96,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,255000,-93.650299,42.051655 -Two_Story_1946_and_Newer,Floating_Village_Residential,81,10411,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Good,Typical,No_Basement,Unf,7,Unf,0,725,725,GasA,Excellent,Y,SBrkr,725,863,0,1588,0,0,3,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Unf,2,561,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,212109,-93.650279,42.051657 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1088,1088,GasA,Excellent,Y,SBrkr,1088,871,0,1959,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,1025,Typical,Typical,Paved,208,46,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,270000,-93.647584,42.050206 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1120,1184,GasA,Excellent,Y,SBrkr,1184,1426,0,2610,0,0,2,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,2,550,Typical,Typical,Paved,208,364,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,303477,-93.647933,42.050301 -Two_Story_1946_and_Newer,Floating_Village_Residential,112,12217,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Hip,CompShg,WdShing,Wd Shng,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,210,955,GasA,Excellent,Y,SBrkr,955,925,0,1880,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,880,Typical,Typical,Paved,168,127,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,310013,-93.648742,42.051681 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,84,11096,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1632,1656,GasA,Excellent,Y,SBrkr,1656,0,0,1656,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,826,Typical,Typical,Paved,0,146,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,249700,-93.643974,42.052269 -Two_Story_1946_and_Newer,Floating_Village_Residential,84,10207,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,874,874,GasA,Excellent,Y,SBrkr,874,887,0,1761,0,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,578,Typical,Typical,Paved,144,105,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,227875,-93.64397,42.052119 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,84,10440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Excellent,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1574,1574,GasA,Excellent,Y,SBrkr,1584,0,0,1584,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,594,Typical,Typical,Paved,0,256,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,261329,-93.643792,42.051377 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,100,11824,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,298,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1685,1685,GasA,Excellent,Y,SBrkr,1685,0,0,1685,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,3,658,Typical,Typical,Paved,112,63,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,253000,-93.642046,42.052267 -Two_Story_1946_and_Newer,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,353,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1158,1158,GasA,Excellent,Y,SBrkr,1158,1285,0,2443,0,0,2,1,4,1,Good,9,Min2,1,Good,BuiltIn,RFn,3,744,Typical,Typical,Paved,193,127,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,265000,-93.641233,42.052146 -Two_Story_1946_and_Newer,Floating_Village_Residential,102,11143,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1580,1580,GasA,Excellent,Y,SBrkr,1580,886,0,2466,0,0,3,0,4,1,Good,8,Typ,1,Good,Attchd,RFn,2,610,Typical,Typical,Paved,159,214,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,340000,-93.639574,42.051054 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,SBrkr,1100,0,0,1100,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,136,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,146000,-93.69179,42.037586 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1143,1143,GasA,Excellent,Y,SBrkr,1143,0,0,1143,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,55,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,147000,-93.692245,42.037636 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,12450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,126,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,365,1094,GasA,Excellent,Y,SBrkr,1094,0,0,1094,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,149000,-93.68912,42.037643 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7441,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1461,1461,GasA,Excellent,Y,SBrkr,1486,0,0,1486,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,566,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,181000,-93.691244,42.03722 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8462,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,105,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,114,928,GasA,Excellent,Y,SBrkr,936,785,0,1721,0,1,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,471,Typical,Typical,Paved,300,87,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,201000,-93.689026,42.036028 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11613,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,384,864,GasA,Excellent,Y,SBrkr,920,900,0,1820,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,492,Typical,Typical,Paved,144,85,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,191750,-93.689767,42.035259 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1349,1349,GasA,Excellent,Y,SBrkr,1349,0,0,1349,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,539,Typical,Typical,Paved,120,55,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2007,WD ,Normal,179000,-93.688804,42.036038 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16196,Pave,No_Alley_Access,Irregular,Low,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,39,1482,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Good,5,Typ,1,Fair,Attchd,RFn,2,514,Typical,Typical,Paved,402,25,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,215000,-93.689449,42.035071 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1992,1992,Gable,CompShg,Plywood,ImStucc,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1145,1575,GasA,Good,Y,SBrkr,1575,0,0,1575,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,529,Typical,Typical,Paved,0,0,52,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,179200,-93.686428,42.036464 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Excellent,Y,SBrkr,630,636,0,1266,0,0,1,1,2,1,Typical,5,Typ,2,Typical,Attchd,RFn,1,283,Typical,Typical,Paved,340,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,128000,-93.686413,42.034569 -Split_Foyer,Residential_Low_Density,0,9180,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,SFoyer,Average,Good,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,93,0,840,GasA,Good,Y,SBrkr,884,0,0,884,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Good,Paved,240,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2007,WD ,Normal,144000,-93.685185,42.035678 -Duplex_All_Styles_and_Ages,Residential_High_Density,60,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,One_Story,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,320,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,2020,0,0,2020,0,0,2,0,4,2,Typical,10,Typ,2,Typical,Detchd,Unf,2,630,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,144000,-93.6791142,42.0362103 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6285,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Rec,351,405,894,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,120750,-93.677007,42.034678 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,340,970,GasA,Typical,Y,SBrkr,970,0,0,970,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,24,0,0,192,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,132500,-93.674475,42.035946 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,640,1057,GasA,Typical,Y,SBrkr,1057,0,0,1057,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,132000,-93.673987,42.035871 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1962,2001,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,411,999,GasA,Good,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,132,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,132500,-93.673889,42.035857 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,6970,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,108,1040,GasA,Typical,Y,SBrkr,1120,0,0,1120,1,0,1,1,3,1,Fair,5,Typ,0,No_Fireplace,Attchd,RFn,2,544,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,Shed,400,5,2007,WD ,Normal,129000,-93.669559,42.034532 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,40,13673,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1140,1140,GasA,Typical,Y,SBrkr,1696,0,0,1696,0,0,1,1,3,1,Typical,8,Min2,1,Typical,Attchd,RFn,1,349,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,143900,-93.669552,42.034724 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,7476,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,228,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Fin,2,686,Typical,Typical,Paved,188,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,145000,-93.672318,42.035675 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9945,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,57,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,161,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,572,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,128500,-93.673518,42.034652 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6173,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,HdBoard,Wd Sdng,BrkFace,75,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,277,876,GasA,Typical,Y,SBrkr,902,0,0,902,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,125500,-93.671941,42.034819 -Two_Story_1946_and_Newer,Residential_Low_Density,0,19522,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,272,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,496,1223,GasA,Good,Y,SBrkr,1271,1232,0,2503,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,99,0,0,182,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,300000,-93.661846,42.037652 -Two_Story_1946_and_Newer,Residential_Low_Density,0,17542,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,Two_Story,Good,Good,1974,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,Gd,LwQ,4,ALQ,1031,36,1192,GasA,Typical,Y,SBrkr,1516,651,0,2167,1,0,2,1,3,1,Good,9,Typ,2,Good,Attchd,RFn,2,518,Typical,Typical,Paved,220,47,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,294000,-93.661848,42.037502 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,44,10751,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Average,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,44,Typical,Typical,CBlock,Fair,Typical,Gd,ALQ,1,Unf,0,250,1037,GasA,Typical,Y,SBrkr,1037,0,0,1037,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,431,Typical,Typical,Paved,136,47,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,129000,-93.660522,42.03466 -Split_Foyer,Residential_Low_Density,0,16647,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,SFoyer,Average,Average,1975,1981,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,0,1390,GasA,Typical,Y,SBrkr,1701,0,0,1701,1,0,2,0,3,1,Typical,6,Min2,2,Typical,Basment,Fin,2,611,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,171000,-93.661983,42.034644 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,12712,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Above_Average,Good,1973,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,76,1044,GasA,Typical,Y,SBrkr,1055,0,0,1055,1,0,1,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,542,Typical,Typical,Paved,455,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Abnorml,147000,-93.662134,42.034673 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9572,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1990,1990,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,336,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,971,1453,GasA,Excellent,Y,SBrkr,1453,1357,0,2810,0,0,2,1,4,1,Good,9,Typ,1,Excellent,Attchd,RFn,2,750,Good,Good,Paved,500,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,302000,-93.653082,42.047384 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,85,10678,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Average,1992,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,337,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,983,1683,GasA,Excellent,Y,SBrkr,2129,743,0,2872,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,2,541,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,285000,-93.652658,42.045776 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,45,4379,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,527,1378,GasA,Excellent,Y,SBrkr,1378,0,0,1378,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,540,Typical,Typical,Paved,160,56,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,214000,-93.647974,42.047618 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,80,3523,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,30,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1081,1141,GasA,Excellent,Y,SBrkr,1151,0,0,1151,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,484,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,166000,-93.64733,42.0474249 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,32,3784,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,36,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1451,1511,GasA,Excellent,Y,SBrkr,1565,0,0,1565,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,476,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,193800,-93.647166,42.047406 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,47,4230,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1352,1352,GasA,Excellent,Y,SBrkr,1352,0,0,1352,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,RFn,2,466,Typical,Typical,Paved,0,241,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,208900,-93.646653,42.047711 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,40,3606,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,415,1352,GasA,Excellent,Y,SBrkr,1352,0,0,1352,1,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,RFn,2,466,Typical,Typical,Paved,0,241,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,194000,-93.646637,42.047705 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,5330,Pave,Paved,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1550,1550,GasA,Excellent,Y,SBrkr,1550,0,0,1550,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,528,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,207500,-93.646607,42.047699 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4274,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,135,1241,GasA,Excellent,Y,SBrkr,1241,0,0,1241,1,0,1,1,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,569,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,199900,-93.646576,42.047687 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4251,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2006,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,625,625,GasA,Excellent,Y,SBrkr,625,625,0,1250,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,0,54,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,164700,-93.646478,42.047667 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2280,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,342,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,179,744,GasA,Good,Y,SBrkr,757,744,0,1501,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,176500,-93.644107,42.047104 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3230,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,1129,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,310,729,GasA,Good,Y,SBrkr,729,729,0,1458,0,0,2,1,2,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,176000,-93.644096,42.047104 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,336,756,GasA,Excellent,Y,SBrkr,756,756,0,1512,0,0,2,1,2,1,Good,4,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,168500,-93.645671,42.046144 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,339,756,GasA,Excellent,Y,SBrkr,769,804,0,1573,0,0,2,1,3,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,177000,-93.645639,42.046145 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,320,756,GasA,Excellent,Y,SBrkr,769,756,0,1525,0,0,2,1,3,1,Good,5,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,177000,-93.645595,42.046145 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,5105,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,312,551,GasA,Excellent,Y,SBrkr,551,551,0,1102,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,148800,-93.645482,42.046405 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,776,776,GasA,Excellent,Y,SBrkr,764,677,0,1441,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,492,Typical,Typical,Paved,206,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,174000,-93.641813,42.047171 -Two_Story_1946_and_Newer,Floating_Village_Residential,66,7399,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,1600,Good,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,326,975,GasA,Excellent,Y,SBrkr,975,975,0,1950,0,0,2,1,3,1,Good,7,Typ,1,Typical,Detchd,RFn,2,576,Typical,Typical,Paved,0,10,0,0,198,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,239000,-93.642182,42.046318 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,73,7321,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,1999,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1339,1339,GasA,Excellent,Y,SBrkr,1358,0,0,1358,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,625,Typical,Typical,Paved,176,174,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,COD,Normal,204000,-93.639931,42.046137 -Two_Story_1946_and_Newer,Floating_Village_Residential,0,8010,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Hip,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,90,1054,GasA,Excellent,Y,SBrkr,1072,976,0,2048,1,0,2,1,3,1,Good,8,Typ,2,Good,Detchd,Unf,2,552,Typical,Typical,Paved,0,48,0,0,180,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,301000,-93.639878,42.046135 -Two_Story_1946_and_Newer,Floating_Village_Residential,106,8413,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,319,1220,GasA,Excellent,Y,SBrkr,1220,1142,0,2362,1,0,2,1,3,1,Good,8,Typ,2,Typical,Attchd,RFn,2,1105,Good,Typical,Paved,147,0,36,0,144,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,312500,-93.643456,42.046411 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,9466,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1994,1995,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,Gd,LwQ,4,ALQ,1037,0,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,Fin,2,478,Typical,Typical,Paved,0,30,0,0,217,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,279700,-93.648204,42.044383 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Good,Above_Average,1980,1980,Hip,CompShg,VinylSd,MetalSd,BrkFace,600,Good,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,270,2002,GasA,Excellent,Y,SBrkr,2362,0,0,2362,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,546,Good,Typical,Paved,180,16,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,255000,-93.645798,42.044762 -Split_or_Multilevel,Residential_Low_Density,0,16157,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,OneFam,SLvl,Average,Good,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Gd,ALQ,1,Rec,391,289,1360,GasA,Excellent,Y,SBrkr,1432,0,0,1432,1,0,1,1,2,1,Good,5,Typ,1,Typical,Attchd,Unf,2,588,Typical,Typical,Paved,168,180,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,194000,-93.645589,42.044989 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,10768,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Average,Very_Good,1976,2004,Gable,CompShg,Plywood,Plywood,None,0,Good,Good,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,280,1437,GasA,Typical,Y,SBrkr,1437,0,0,1437,1,0,2,0,3,1,Good,6,Typ,1,Fair,Attchd,RFn,2,528,Typical,Typical,Paved,0,21,0,0,180,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,218000,-93.64577,42.042994 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3840,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Above_Average,1978,1998,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,LwQ,176,0,1057,GasA,Typical,Y,SBrkr,1295,0,0,1295,1,0,1,0,1,1,Good,4,Typ,2,Typical,Attchd,Fin,2,571,Typical,Typical,Paved,133,89,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,213750,-93.648172,42.043754 -Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,OneFam,SLvl,Above_Average,Good,1976,1994,Hip,CompShg,Plywood,Plywood,BrkFace,360,Good,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,528,528,GasA,Excellent,Y,SBrkr,1094,761,0,1855,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,512,Typical,Typical,Paved,113,100,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,187000,-93.645547,42.043048 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,OneFam,One_Story,Above_Average,Very_Good,1976,1976,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,284,1262,GasA,Excellent,Y,SBrkr,1262,0,0,1262,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,298,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,181500,-93.645544,42.042961 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,17778,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Average,1981,1981,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Typical,Gd,ALQ,1,Rec,829,0,2461,GasA,Good,Y,SBrkr,2497,0,0,2497,1,0,2,0,2,1,Good,7,Typ,2,Good,Attchd,RFn,2,676,Typical,Typical,Paved,266,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,373000,-93.658232,42.037103 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,18890,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,RRAe,Duplex,One_and_Half_Fin,Average,Average,1977,1977,Shed,CompShg,Plywood,Plywood,None,1,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Rec,211,652,1361,GasA,Excellent,Y,SBrkr,1361,1259,0,2620,0,0,2,2,4,2,Typical,12,Typ,1,Typical,BuiltIn,RFn,2,600,Typical,Typical,Dirt_Gravel,155,24,145,0,0,0,No_Pool,No_Fence,Gar2,8300,8,2007,WD ,Normal,190000,-93.6574973,42.0349702 -Two_Story_1946_and_Newer,Floating_Village_Residential,0,7050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,319,1057,GasA,Excellent,Y,SBrkr,1057,872,0,1929,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,650,Typical,Typical,Paved,0,235,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,226000,-93.639515,42.048488 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,BrkFace,41,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,912,912,GasA,Excellent,Y,SBrkr,1152,0,0,1152,1,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,412,Typical,Typical,Paved,0,126,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,CWD,Normal,143450,-93.634545,42.046549 -Two_Story_1946_and_Newer,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Above_Average,Above_Average,1970,1970,Gable,CompShg,VinylSd,VinylSd,BrkFace,525,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,93,1008,GasA,Typical,Y,SBrkr,1403,1008,0,2411,1,0,2,1,4,1,Typical,8,Typ,1,Poor,Attchd,RFn,2,570,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,240050,-93.635094,42.047758 -Split_Foyer,Residential_Low_Density,0,8723,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,SFoyer,Above_Average,Above_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,BLQ,2,Unf,0,0,973,GasA,Excellent,Y,SBrkr,1082,0,0,1082,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,160,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,162500,-93.634528,42.048067 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,130,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,196,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,385,1295,GasA,Fair,Y,SBrkr,1295,0,0,1295,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,528,Typical,Typical,Paved,0,194,0,0,200,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,164000,-93.635899,42.04849 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,108,11358,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1972,1987,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,778,1124,GasA,Typical,Y,SBrkr,1610,0,0,1610,0,0,2,0,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,515,Typical,Typical,Paved,202,0,0,0,256,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,185000,-93.631554,42.047615 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9547,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,112,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1594,1594,GasA,Excellent,Y,SBrkr,1594,0,0,1594,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,RFn,2,472,Typical,Typical,Paved,190,80,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,188500,-93.638051,42.043363 -Two_Story_1946_and_Newer,Residential_Low_Density,78,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1993,1993,Gable,CompShg,MetalSd,MetalSd,BrkFace,194,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,329,1148,GasA,Excellent,Y,SBrkr,1091,984,0,2075,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Unf,2,473,Typical,Typical,Paved,235,86,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,255000,-93.63687,42.043005 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Feedr,RRAn,Duplex,One_Story,Average,Above_Average,1976,1976,Gable,CompShg,VinylSd,VinylSd,BrkFace,164,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1680,1680,GasA,Fair,Y,SBrkr,1680,0,0,1680,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,136905,-93.633748,42.045483 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,10738,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1966,1966,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,301,1093,GasA,Good,Y,SBrkr,1093,0,0,1093,1,0,2,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,484,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,11,2007,WD ,Normal,158500,-93.631673,42.04259 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1973,1973,Hip,CompShg,HdBoard,HdBoard,BrkFace,320,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,326,1242,GasA,Fair,Y,SBrkr,1242,0,0,1242,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,175500,-93.631302,42.04389 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,203,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,264,171,1052,GasA,Typical,Y,SBrkr,1052,0,0,1052,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,311,Typical,Typical,Paved,0,133,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,COD,Normal,127000,-93.6299222,42.0421698 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,38,437,949,GasA,Typical,Y,SBrkr,1107,0,0,1107,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,88,64,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,127000,-93.62919,42.047811 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10355,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,196,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,519,1214,GasA,Typical,Y,SBrkr,1214,0,0,1214,0,0,2,0,3,1,Typical,5,Typ,1,Fair,Attchd,RFn,1,318,Typical,Typical,Paved,0,111,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,143000,-93.626518,42.048292 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10289,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1965,1965,Hip,CompShg,MetalSd,MetalSd,BrkFace,168,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,237,1073,GasA,Typical,Y,SBrkr,1073,0,0,1073,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,515,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,156000,-93.625898,42.046504 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9503,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1958,1983,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,374,193,1024,GasA,Typical,Y,SBrkr,1344,0,0,1344,1,0,1,0,2,1,Typical,6,Min1,1,Typical,Detchd,Unf,1,484,Typical,Typical,Paved,316,28,0,0,0,0,No_Pool,Good_Wood,None,0,6,2007,WD ,Normal,144000,-93.624758,42.044758 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,10624,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Below_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,84,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,264,1424,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,1,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,155,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,119000,-93.624592,42.043984 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,10899,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Below_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1224,0,0,1224,0,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,CarPort,Unf,3,530,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,103000,-93.624541,42.043929 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12342,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1978,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,978,978,GasA,Typical,Y,SBrkr,1422,0,0,1422,0,0,1,0,3,1,Typical,6,Min1,1,Typical,Attchd,RFn,1,286,Typical,Typical,Paved,0,0,36,0,0,0,No_Pool,Good_Wood,Shed,600,8,2007,WD ,Normal,139900,-93.624745,42.043121 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12772,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1960,1998,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,460,958,GasA,Typical,Y,SBrkr,958,0,0,958,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,301,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Gar2,15500,4,2007,WD ,Normal,151500,-93.623636,42.042124 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8892,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkCmn,66,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1065,1065,GasA,Good,Y,SBrkr,1065,0,0,1065,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,461,Typical,Typical,Paved,74,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,COD,Normal,126500,-93.624606,42.04227 -Split_or_Multilevel,Residential_Low_Density,65,10482,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Very_Good,1958,1958,Hip,CompShg,VinylSd,VinylSd,BrkFace,63,Typical,Good,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,81,588,GasA,Excellent,Y,SBrkr,1138,0,0,1138,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,6,2007,WD ,Normal,145000,-93.623104,42.044036 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1958,1985,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,279,522,912,GasA,Fair,Y,SBrkr,912,0,0,912,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,297,Typical,Typical,Paved,12,285,0,0,0,0,No_Pool,Minimum_Wood_Wire,Shed,480,6,2007,WD ,Normal,120000,-93.621352,42.042577 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,206,250,894,GasA,Good,Y,SBrkr,1074,0,0,1074,0,0,1,0,2,1,Good,6,Min1,1,Good,Detchd,Unf,2,396,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,Good_Wood,None,0,1,2007,WD ,Normal,124000,-93.621344,42.042485 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8339,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1959,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,882,0,0,882,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,1200,7,2007,WD ,Normal,106500,-93.62232,42.042479 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14357,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,167,386,864,GasA,Typical,Y,SBrkr,1187,0,0,1187,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,128,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,CWD,Normal,140500,-93.622834,42.044921 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,8243,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkFace,56,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,264,964,GasA,Excellent,Y,SBrkr,964,0,0,964,0,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Fin,2,784,Typical,Typical,Paved,170,0,0,0,0,0,No_Pool,Good_Privacy,None,0,2,2007,WD ,Normal,137500,-93.621452,42.043609 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,1960,Hip,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,894,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,117600,-93.62144,42.043627 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1966,1966,Hip,CompShg,HdBoard,Plywood,BrkFace,202,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,520,1174,GasA,Excellent,Y,SBrkr,1200,0,0,1200,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2007,CWD,Normal,163500,-93.626945,42.039141 -Split_or_Multilevel,Residential_Low_Density,80,9200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,BrkFace,98,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,548,1042,GasA,Typical,Y,SBrkr,1042,0,0,1042,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,161000,-93.630023,42.040143 -Split_or_Multilevel,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Good,1964,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,108,Typical,Typical,CBlock,Good,Typical,Gd,LwQ,4,ALQ,580,452,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,0,1,2,0,3,1,Good,7,Min1,0,No_Fireplace,Attchd,RFn,1,309,Typical,Typical,Paved,333,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2007,CWD,Normal,158000,-93.6279875,42.0417369 -Two_Story_1946_and_Newer,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1964,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,306,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,431,845,GasA,Excellent,Y,SBrkr,845,1309,0,2154,0,0,2,1,5,1,Typical,8,Typ,1,Good,Attchd,RFn,2,539,Typical,Typical,Paved,0,0,0,0,161,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,224500,-93.626864,42.039069 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11382,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1964,1964,Gable,CompShg,Wd Sdng,Plywood,BrkFace,212,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,543,533,1130,GasA,Typical,Y,SBrkr,1374,0,0,1374,0,1,1,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,1,286,Typical,Typical,Paved,0,28,84,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,147000,-93.6276375,42.0413937 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,22002,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1959,1991,Gable,CompShg,MetalSd,MetalSd,BrkFace,136,Typical,Good,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,206,1592,GasA,Good,Y,SBrkr,1652,0,0,1652,1,0,1,1,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,510,Typical,Typical,Paved,0,0,0,0,201,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,200000,-93.6249615,42.0417604 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14585,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1987,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,85,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,219,331,1144,GasA,Excellent,Y,SBrkr,1429,0,0,1429,0,1,1,0,3,1,Good,7,Typ,2,Good,Attchd,Unf,2,572,Typical,Typical,Paved,216,110,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,181900,-93.624484,42.04074 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7388,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,658,1063,GasA,Good,Y,SBrkr,1327,0,0,1327,1,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,150750,-93.62326,42.041245 -Duplex_All_Styles_and_Ages,Residential_Low_Density,70,9842,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Below_Average,Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1224,0,0,1224,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,101800,-93.621368,42.041332 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,8280,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1950,1950,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,932,932,GasA,Excellent,Y,FuseA,932,0,0,932,0,0,1,0,2,1,Good,4,Typ,1,Good,Attchd,Unf,1,306,Typical,Typical,Paved,0,0,214,0,0,0,No_Pool,Good_Privacy,None,0,11,2007,WD ,Normal,124000,-93.622804,42.039212 -One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,85,12172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Unf,Average,Good,1940,1996,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Rec,259,433,822,GasA,Typical,Y,SBrkr,908,0,0,908,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,512,Typical,Typical,Paved,284,24,0,0,192,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,138500,-93.621503,42.0392 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5000,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Brookside,Norm,Norm,OneFam,One_Story,Very_Poor,Fair,1946,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Fair,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,FuseF,334,0,0,334,0,0,1,0,1,1,Fair,2,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,39300,-93.629661,42.036575 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Fair,Fair,1946,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Fair,Fair,CBlock,Fair,Fair,No,LwQ,4,Unf,0,367,666,GasA,Fair,N,SBrkr,666,0,0,666,0,1,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,52,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,64500,-93.629545,42.035684 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,3500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Fair,Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,226,370,GasA,Typical,N,FuseA,442,228,0,670,1,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,21,0,0,0,0,No_Pool,Minimum_Privacy,Shed,2000,7,2007,WD ,Normal,64000,-93.628654,42.036023 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Feedr,OneFam,One_Story,Average,Very_Good,1958,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,ALQ,404,254,808,GasA,Excellent,Y,SBrkr,808,0,0,808,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,143,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,COD,Normal,112000,-93.628582,42.036223 -Two_Story_1946_and_Newer,Residential_Low_Density,100,9500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Above_Average,Above_Average,1964,1978,Gable,CompShg,VinylSd,VinylSd,BrkCmn,272,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,374,816,GasA,Typical,Y,SBrkr,1127,850,0,1977,0,1,1,1,4,1,Typical,9,Typ,1,Typical,Attchd,RFn,2,540,Typical,Typical,Paved,0,52,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,165000,-93.625632,42.034615 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1955,Hip,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,710,1078,GasA,Excellent,Y,SBrkr,1150,0,0,1150,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,288,Typical,Typical,Paved,0,0,0,0,175,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,144000,-93.623704,42.0362 -Duplex_All_Styles_and_Ages,Residential_Low_Density,95,11345,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,Duplex,Two_Story,Average,Average,1948,1950,Gable,Roll,AsbShng,AsbShng,Stone,567,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,708,928,GasA,Good,Y,FuseA,928,992,0,1920,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,137000,-93.621485,42.03465 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,79,9492,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Average,1941,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,41,359,768,GasA,Typical,Y,SBrkr,968,408,0,1376,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,105000,-93.620497,42.034906 -Two_Story_1945_and_Older,Residential_Low_Density,79,9480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Average,Good,1942,1995,Gable,CompShg,MetalSd,MetalSd,Stone,224,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,342,728,GasA,Excellent,Y,SBrkr,888,756,0,1644,0,0,1,1,3,1,Good,7,Typ,2,Good,Attchd,Unf,1,312,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,146500,-93.620491,42.034755 -Duplex_All_Styles_and_Ages,Residential_Low_Density,63,8668,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1560,1560,GasA,Typical,Y,SBrkr,1560,0,0,1560,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,3,792,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,126000,-93.619383,42.049216 -Split_Foyer,Residential_Low_Density,0,10050,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,87,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,191,793,GasA,Excellent,Y,SBrkr,1280,0,0,1280,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Basment,Fin,2,432,Typical,Typical,Paved,140,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,175000,-93.61706,42.048934 -Split_or_Multilevel,Residential_Low_Density,100,9600,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1961,1961,Hip,CompShg,WdShing,Wd Shng,BrkFace,291,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,618,1218,GasA,Typical,Y,SBrkr,1254,0,0,1254,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,525,Typical,Typical,Paved,0,0,0,0,168,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,158500,-93.615687,42.046714 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1958,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,1,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,456,912,GasA,Excellent,Y,FuseA,912,0,0,912,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,275,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,COD,Normal,114500,-93.616099,42.045809 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,11344,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,414,874,GasW,Typical,Y,FuseA,874,650,0,1524,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,315,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Normal,144000,-93.618143,42.0459325 -Two_Story_1946_and_Newer,Residential_Low_Density,0,18450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1965,1979,Flat,Tar&Grv,Plywood,Plywood,BrkCmn,113,Typical,Good,CBlock,Good,Typical,No,LwQ,4,Rec,723,111,1021,GasA,Typical,Y,SBrkr,1465,915,0,2380,0,0,2,1,3,1,Typical,7,Sev,1,Poor,CarPort,Unf,2,596,Typical,Typical,Paved,0,265,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Abnorml,129000,-93.615535,42.046882 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1957,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,63,936,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,1,315,Typical,Typical,Paved,0,0,0,219,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,141000,-93.618786,42.044053 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6860,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,54,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,100,1008,GasA,Excellent,Y,SBrkr,1008,0,0,1008,1,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,131000,-93.618404,42.043143 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1963,1963,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,765,1053,GasA,Good,Y,SBrkr,1053,0,0,1053,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,More_Than_Two_Types,RFn,2,692,Typical,Typical,Paved,240,0,0,0,109,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,142100,-93.619352,42.044735 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8176,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,210,1056,GasA,Fair,Y,SBrkr,1056,0,0,1056,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,139000,-93.617521,42.043932 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,BrkFace,243,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,834,1442,GasA,Good,Y,SBrkr,1442,0,0,1442,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,1,301,Typical,Typical,Paved,0,0,275,0,0,0,No_Pool,No_Fence,Shed,500,4,2007,COD,Normal,157900,-93.615281,42.043946 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1954,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,468,108,1056,GasA,Typical,Y,SBrkr,1056,0,0,1056,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,260,Typical,Typical,Paved,390,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,139400,-93.6168658,42.0423095 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11988,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1957,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,467,1244,GasA,Excellent,Y,FuseA,1244,0,0,1244,0,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,336,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,150000,-93.613046,42.044218 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Hip,CompShg,HdBoard,HdBoard,BrkCmn,69,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1144,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,0,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,120000,-93.611697,42.045669 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9736,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1969,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,289,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,138,525,1331,GasA,Good,Y,SBrkr,1721,0,0,1721,0,0,1,0,4,1,Typical,8,Typ,3,Typical,Attchd,Unf,2,464,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,174850,-93.612347,42.043952 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,9770,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,410,922,GasA,Typical,Y,FuseA,922,0,0,922,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,Good_Wood,None,0,4,2007,WD ,Normal,116000,-93.613457,42.043233 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10152,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,MetalSd,MetalSd,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,462,1048,GasA,Typical,Y,SBrkr,1048,0,0,1048,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,20,0,0,192,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,135000,-93.613983,42.0426969 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12155,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Above_Average,Fair,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Unf,0,420,1657,GasA,Good,Y,SBrkr,1657,0,0,1657,0,1,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,147,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,163500,-93.610875,42.042728 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,12198,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1975,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,424,1204,GasA,Typical,Y,SBrkr,1411,0,0,1411,0,0,1,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,1,310,Typical,Typical,Paved,278,82,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,COD,Normal,130000,-93.612301,42.04302 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,928,1216,GasA,Typical,Y,SBrkr,1216,0,0,1216,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,129500,-93.6192677,42.0418697 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11556,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Excellent,1953,2006,Gable,CompShg,VinylSd,MetalSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,BLQ,2,Unf,0,456,864,GasA,Good,Y,SBrkr,1154,0,0,1154,0,0,1,1,3,1,Excellent,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,63,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,142000,-93.617242,42.041243 -Duplex_All_Styles_and_Ages,Residential_Low_Density,70,8078,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,260,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1560,1560,GasA,Typical,Y,SBrkr,1560,0,0,1560,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,128600,-93.61848,42.040773 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,507,948,GasA,Typical,Y,SBrkr,948,0,0,948,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,410,Typical,Typical,Dirt_Gravel,0,48,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,125000,-93.615622,42.040451 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,7942,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,955,0,1040,GasA,Typical,Y,FuseF,1040,0,0,1040,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,293,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,136000,-93.618428,42.03981 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,8923,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1953,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,365,1008,GasA,Good,Y,SBrkr,1008,0,0,1008,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,18,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,134500,-93.617075,42.039369 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,8540,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1956,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,ALQ,691,120,925,GasA,Typical,Y,SBrkr,925,0,0,925,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,252,Typical,Typical,Paved,152,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,127000,-93.612414,42.039482 -Split_or_Multilevel,Residential_Low_Density,60,7134,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,0,384,GasA,Typical,Y,SBrkr,1360,0,0,1360,0,0,1,0,3,1,Typical,6,Min1,1,Typical,Detchd,Unf,2,572,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,130000,-93.612401,42.03846 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,55,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,1040,1190,GasA,Good,Y,SBrkr,1040,500,0,1540,1,0,1,0,4,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,115000,-93.61245,42.03837 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,130,923,GasA,Typical,Y,SBrkr,925,0,0,925,1,0,1,0,3,1,Typical,5,Typ,2,Typical,Attchd,Unf,1,390,Typical,Typical,Paved,81,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2007,WD ,Normal,116900,-93.612075,42.0384781 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,13300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1956,2000,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,551,928,GasA,Typical,Y,SBrkr,928,0,0,928,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,252,Typical,Typical,Paved,261,0,156,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,132000,-93.612252,42.038545 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9532,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1953,1953,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,354,156,1105,GasA,Good,Y,SBrkr,1647,0,0,1647,1,0,1,0,3,1,Typical,6,Min1,1,Fair,Attchd,Fin,1,280,Typical,Typical,Paved,225,0,0,0,0,368,Typical,Good_Privacy,None,0,2,2007,WD ,Normal,153000,-93.618606,42.034789 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15783,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Average,1952,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,632,924,GasA,Typical,Y,SBrkr,924,0,0,924,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,420,Typical,Typical,Paved,0,324,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,6,2007,WD ,Normal,112500,-93.6197518,42.0347206 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,14190,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1890,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,925,925,GasA,Good,Y,SBrkr,1000,544,0,1544,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,231,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,138000,-93.620343,42.034812 -One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Unf,Average,Good,1949,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,625,845,GasA,Typical,Y,SBrkr,893,0,0,893,0,1,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,135000,-93.617191,42.035932 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,12099,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1953,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,1018,1216,GasA,Excellent,Y,SBrkr,1216,0,512,1728,1,0,1,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,1,371,Typical,Typical,Paved,200,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2007,WD ,Normal,156000,-93.618445,42.036077 -Two_Story_1945_and_Older,Residential_Low_Density,113,21281,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Below_Average,1935,2007,Gable,CompShg,Wd Sdng,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,666,666,GasA,Good,Y,SBrkr,1308,1778,0,3086,0,0,3,1,4,1,Good,9,Min1,0,No_Fireplace,BuiltIn,Unf,3,1200,Typical,Typical,Paved,0,208,290,0,156,0,No_Pool,No_Fence,None,0,11,2007,WD ,Family,301600,-93.615616,42.036027 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10134,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,735,735,GasA,Good,Y,FuseA,735,299,0,1034,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,109000,-93.615485,42.03725 -One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,55,5500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Unf,Below_Average,Above_Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,882,882,GasA,Excellent,Y,SBrkr,882,0,0,882,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,103200,-93.6133009,42.0380982 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10284,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1925,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,LwQ,66,55,1151,GasA,Excellent,Y,SBrkr,845,436,0,1281,1,0,2,0,1,1,Typical,6,Mod,0,No_Fireplace,Detchd,Unf,2,580,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Abnorml,128500,-93.615536,42.0357 -One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1927,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,656,656,GasA,Typical,Y,SBrkr,968,0,0,968,0,0,2,0,4,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,64500,-93.612418,42.035779 -Two_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Excellent,1895,1999,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,Av,Unf,7,Unf,0,736,736,GasA,Excellent,Y,SBrkr,751,783,0,1534,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,112,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,148000,-93.6139,42.035854 -One_Story_1945_and_Older,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,691,691,GasA,Excellent,Y,FuseA,691,0,0,691,0,0,1,0,2,1,Excellent,4,Typ,0,No_Fireplace,Detchd,Unf,1,216,Fair,Typical,Dirt_Gravel,0,20,94,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Abnorml,86000,-93.613899,42.034878 -Split_or_Multilevel,Residential_Low_Density,93,10090,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Good,Average,1963,1999,Gable,CompShg,Plywood,Plywood,BrkFace,364,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,ALQ,483,0,725,GasA,Typical,Y,SBrkr,1035,616,0,1651,0,1,2,0,4,1,Typical,6,Typ,2,Typical,BuiltIn,Unf,1,276,Typical,Typical,Paved,460,46,0,0,165,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,172000,-93.607117,42.039338 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1961,1961,Gable,CompShg,HdBoard,HdBoard,BrkFace,53,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,728,920,GasA,Good,Y,SBrkr,888,0,0,888,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,COD,Abnorml,120000,-93.609148,42.039983 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,8300,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,86,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,0,952,GasA,Good,Y,SBrkr,952,0,0,952,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,128500,-93.610561,42.040146 -Two_Story_1946_and_Newer,Residential_Low_Density,70,11606,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Sev,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Average,1969,1969,Gable,CompShg,Plywood,Plywood,BrkFace,192,Typical,Typical,PConc,Good,Typical,Av,Rec,6,Unf,0,390,1040,GasA,Typical,Y,SBrkr,1040,1040,0,2080,0,1,1,2,5,1,Fair,9,Typ,2,Typical,Attchd,Unf,2,504,Typical,Typical,Paved,335,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Family,135000,-93.605388,42.038888 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1949,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,672,252,0,924,0,0,1,0,3,1,Typical,6,Typ,1,Poor,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,2000,7,2007,WD ,Normal,122900,-93.610609,42.035733 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11664,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Above_Average,Average,1948,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,206,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,746,1082,GasA,Typical,Y,SBrkr,1082,0,0,1082,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,130,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,119200,-93.610611,42.035921 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,10496,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1949,1950,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,320,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,844,1040,GasA,Excellent,Y,SBrkr,1168,678,0,1846,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,1,315,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,1,2007,WD ,Normal,143000,-93.610623,42.037329 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,96,246,876,GasA,Typical,Y,SBrkr,988,0,0,988,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,276,Typical,Typical,Paved,0,80,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,119000,-93.607957,42.037203 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1950,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,432,864,GasA,Fair,Y,FuseA,1238,0,0,1238,0,0,1,1,3,1,Typical,6,Min2,1,Typical,Attchd,Unf,1,357,Typical,Typical,Paved,0,171,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,102000,-93.607963,42.035954 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1950,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,149,317,864,GasA,Good,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,720,Typical,Typical,Paved,194,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,129000,-93.607963,42.035876 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,2002,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,45,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,422,1010,GasA,Excellent,Y,SBrkr,1134,0,0,1134,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,254,Typical,Typical,Paved,0,16,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2007,WD ,Family,135000,-93.606932,42.037106 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,2003,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,466,1040,GasA,Excellent,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,0,7,2007,WD ,Normal,152000,-93.605968,42.035862 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7315,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1958,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,545,1170,GasA,Typical,Y,SBrkr,1170,0,0,1170,0,1,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,338,Typical,Typical,Paved,0,0,0,0,225,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,140000,-93.60674,42.036002 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7903,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,503,1242,GasA,Good,Y,FuseA,1242,0,0,1242,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,324,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Family,152000,-93.606782,42.037084 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,154,125,1377,GasA,Typical,Y,SBrkr,1377,0,0,1377,1,0,1,0,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,1,351,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2007,WD ,Normal,156500,-93.605942,42.034685 -Duplex_All_Styles_and_Ages,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,Duplex,One_Story,Average,Below_Average,1961,1961,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1800,1800,GasA,Excellent,N,SBrkr,1800,0,0,1800,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,141000,-93.606746,42.034614 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,925,0,0,925,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,99000,-93.606746,42.034564 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1964,1993,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,340,Typical,Typical,CBlock,Typical,Typical,Gd,BLQ,2,Rec,117,169,822,GasA,Good,Y,SBrkr,1020,831,0,1851,0,0,2,1,3,1,Good,7,Typ,1,Fair,Attchd,RFn,2,440,Typical,Typical,Paved,239,42,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,7,2007,WD ,Normal,187000,-93.604836,42.036949 -Two_Story_1946_and_Newer,Residential_Low_Density,69,7590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,Two_Story,Average,Average,1962,1962,Gable,CompShg,VinylSd,VinylSd,BrkFace,288,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,324,864,GasA,Typical,Y,SBrkr,876,936,0,1812,0,0,2,0,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,1,264,Typical,Typical,Paved,0,168,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,169500,-93.603701,42.035423 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,442,312,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,294,Typical,Typical,Paved,58,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,125500,-93.603703,42.0346 -Split_Foyer,Residential_Low_Density,66,6760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,162,896,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,1,288,Typical,Typical,Paved,24,90,160,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,130000,-93.604562,42.035034 -One_Story_1945_and_Older,Residential_Medium_Density,60,6978,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Good,1926,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,850,850,GasA,Typical,Y,SBrkr,960,0,0,960,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,2,576,Typical,Typical,Paved,0,0,116,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,103000,-93.618969,42.034403 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1910,2002,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Good,BrkTil,Fair,Fair,No,Unf,7,Unf,0,264,264,GasA,Excellent,Y,SBrkr,768,664,0,1432,0,0,2,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,360,Typical,Good,Paved,270,0,112,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Abnorml,132000,-93.616988,42.032254 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,569,845,GasA,Typical,Y,SBrkr,866,430,0,1296,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,175,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,127500,-93.6190545,42.0314733 -One_Story_1945_and_Older,Residential_Medium_Density,56,4480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Average,1922,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,PConc,Fair,Fair,No,LwQ,4,Unf,0,240,1022,GasA,Typical,N,FuseF,1022,0,0,1022,1,0,1,0,2,1,Fair,4,Typ,1,Good,Detchd,Unf,1,184,Typical,Fair,Dirt_Gravel,0,122,20,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,89500,-93.62024,42.03131 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,60,7200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,SFoyer,Average,Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,BrkFace,180,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,936,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,49,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,140000,-93.617135,42.031369 -One_Story_1945_and_Older,Residential_Medium_Density,56,3153,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1920,1990,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,967,967,GasA,Good,Y,SBrkr,967,0,0,967,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,180,Fair,Typical,Dirt_Gravel,0,0,26,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,99900,-93.6176729,42.0311108 -One_Story_1945_and_Older,Residential_Medium_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1885,1995,Mansard,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,641,641,GasA,Good,Y,SBrkr,1047,0,0,1047,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,273,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,Shed,450,8,2007,WD ,Normal,100000,-93.615432,42.033405 -One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Very_Good,1940,1950,Gable,CompShg,VinylSd,VinylSd,Stone,279,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,808,808,GasA,Excellent,Y,SBrkr,1072,0,0,1072,0,0,1,0,2,1,Typical,5,Typ,2,Good,Detchd,Unf,2,379,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,130000,-93.615446,42.032343 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,120,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1900,2006,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Fair,No,BLQ,2,Unf,0,550,680,GasA,Excellent,Y,SBrkr,680,494,0,1174,0,0,1,0,2,1,Good,6,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,116,26,40,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,135000,-93.60861,42.033385 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Good,1948,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,318,649,GasA,Excellent,Y,SBrkr,679,504,0,1183,0,0,1,1,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,176,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,120000,-93.608868,42.033489 -One_Story_1945_and_Older,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1937,2000,Hip,CompShg,Stucco,Stucco,BrkCmn,435,Typical,Typical,BrkTil,Fair,Typical,No,Rec,6,Unf,0,739,907,GasA,Typical,Y,SBrkr,1131,0,0,1131,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Alloca,89471,-93.606857,42.033313 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Rec,448,0,570,GasA,Good,N,SBrkr,761,380,0,1141,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,252,Fair,Fair,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,5,2007,ConLw,Normal,85000,-93.606842,42.032276 -One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1920,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,710,960,GasA,Good,Y,FuseA,960,0,0,960,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,168,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,108500,-93.607723,42.032357 -Two_Story_1945_and_Older,Residential_Medium_Density,57,9639,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Below_Average,Very_Good,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1075,1075,GasA,Excellent,Y,SBrkr,1156,642,0,1798,0,0,2,1,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,342,Typical,Typical,Dirt_Gravel,0,0,160,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,137000,-93.61047,42.030579 -One_Story_1945_and_Older,Residential_Medium_Density,40,3880,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Excellent,1945,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,357,686,GasA,Good,Y,SBrkr,866,0,0,866,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,58,42,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,110500,-93.6082631,42.0318018 -One_Story_1945_and_Older,Residential_Medium_Density,60,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1923,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,968,968,GasA,Typical,Y,SBrkr,968,0,0,968,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Fair,Typical,Dirt_Gravel,0,0,184,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,100000,-93.606789,42.030311 -Two_Story_1945_and_Older,Residential_Medium_Density,0,10337,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1910,1999,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,725,725,GasA,Excellent,N,SBrkr,909,863,0,1772,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,816,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,184900,-93.618662,42.03013 -Two_Story_1945_and_Older,Residential_Medium_Density,53,9863,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1927,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Rec,210,322,728,GasA,Typical,Y,SBrkr,914,728,0,1642,0,1,1,1,4,1,Typical,9,Typ,1,Good,Detchd,Unf,1,374,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Abnorml,145000,-93.6190848,42.0299846 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1925,1994,Gambrel,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Unf,7,Unf,0,702,702,GasA,Good,Y,SBrkr,842,630,0,1472,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Fair,Partial_Pavement,0,0,84,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Normal,125000,-93.617061,42.029218 -Two_Story_1945_and_Older,Residential_Medium_Density,35,4571,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1910,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,448,616,GasA,Excellent,Y,SBrkr,616,616,0,1232,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,480,Fair,Fair,Paved,280,0,143,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,114000,-93.618294,42.029198 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,85,13600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Average,1900,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,662,662,GasA,Typical,N,SBrkr,1422,915,0,2337,0,0,2,0,5,2,Typical,10,Min2,0,No_Fireplace,Detchd,Unf,2,560,Typical,Typical,Paved,0,57,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,90000,-93.616911,42.029223 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,56,8398,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1910,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Good,No,BLQ,2,Unf,0,667,926,GasA,Typical,Y,SBrkr,991,659,0,1650,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,468,Typical,Typical,Dirt_Gravel,128,103,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,144100,-93.6188503,42.0280396 -Two_Story_1945_and_Older,Residential_Medium_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Below_Average,1880,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,1008,1008,GasW,Typical,Y,SBrkr,1178,1032,0,2210,0,0,2,0,5,1,Fair,8,Typ,0,No_Fireplace,Detchd,Unf,1,205,Fair,Typical,Dirt_Gravel,0,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,117500,-93.617037,42.028076 -Two_Story_1945_and_Older,Residential_Medium_Density,60,3600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1930,2005,Gambrel,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Fair,No,Rec,6,Unf,0,371,687,GasA,Good,Y,SBrkr,687,671,0,1358,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,336,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Partial,124500,-93.615578,42.028847 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,75,13500,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Very_Good,1879,1987,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,819,819,GasA,Typical,Y,FuseA,1312,1142,0,2454,0,0,2,0,3,1,Typical,8,Typ,1,Good,Attchd,Unf,2,576,Typical,Typical,Dirt_Gravel,0,148,150,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,185000,-93.612061,42.02819 -Two_Family_conversion_All_Styles_and_Ages,C_all,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_and_Half_Unf,Above_Average,Above_Average,1910,1998,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Rec,6,Unf,0,168,1214,GasW,Excellent,N,SBrkr,1260,1031,0,2291,0,1,2,0,4,2,Typical,9,Typ,1,Good,Detchd,Unf,2,506,Typical,Typical,Paved,0,0,0,0,99,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,133900,-93.612191,42.027154 -One_Story_with_Finished_Attic_All_Ages,Residential_Medium_Density,40,5400,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Good,1926,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,Mn,LwQ,4,Unf,0,779,1149,GasA,Good,Y,FuseA,1149,467,0,1616,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,183,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,152000,-93.608882,42.0282 -One_Story_1945_and_Older,Residential_Medium_Density,90,8100,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1948,1973,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,1221,1559,GasA,Good,Y,SBrkr,1559,0,0,1559,1,0,1,0,2,1,Typical,5,Min2,0,No_Fireplace,Detchd,Unf,2,812,Typical,Typical,Paved,0,116,230,0,0,0,No_Pool,Good_Wood,None,0,6,2007,COD,Normal,153500,-93.6094532,42.0277574 -Two_Story_1945_and_Older,Residential_Medium_Density,60,10800,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1890,1998,Gable,CompShg,Wd Sdng,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,718,718,GasA,Excellent,Y,SBrkr,1576,978,0,2554,0,0,1,1,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,704,Typical,Typical,Partial_Pavement,0,48,143,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,159500,-93.608678,42.026089 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,9439,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,588,912,GasA,Good,Y,FuseA,912,336,0,1248,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,160,Fair,Fair,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,87000,-93.6047622,42.0268089 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,52,8626,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,1,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,968,0,0,968,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,331,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,104500,-93.6041765,42.0272605 -Split_or_Multilevel,Residential_Medium_Density,76,11800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,SLvl,Below_Average,Good,1949,2002,Gable,CompShg,Stucco,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1382,0,0,1382,0,0,2,0,1,1,Typical,6,Mod,1,Typical,Attchd,RFn,1,384,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,110000,-93.6051157,42.0272703 -One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,55,6854,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1925,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Typical,Typical,No,LwQ,4,Rec,227,212,756,GasA,Typical,N,FuseA,916,144,0,1060,1,0,1,0,1,1,Typical,6,Mod,1,Good,Detchd,Unf,1,308,Fair,Typical,Paved,0,65,0,0,150,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,136500,-93.6281873,42.033311 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,55,8674,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Brookside,RRNn,Artery,OneFam,One_and_Half_Fin,Average,Above_Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Rec,6,Unf,0,0,910,GasA,Typical,Y,SBrkr,910,525,0,1435,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,128250,-93.6262716,42.034339 -One_Story_1945_and_Older,Residential_Low_Density,98,8731,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Average,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Fair,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,270,915,GasA,Typical,Y,SBrkr,1167,0,0,1167,0,0,1,0,3,1,Typical,6,Maj1,1,Good,Detchd,Unf,2,495,Typical,Typical,Paved,0,0,216,0,126,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,144000,-93.627605,42.031846 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,8737,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1923,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Rec,6,Unf,0,765,1065,GasA,Excellent,Y,FuseA,915,720,0,1635,0,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,0,38,0,144,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,210000,-93.627487,42.030823 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1939,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,422,728,GasA,Excellent,Y,SBrkr,728,546,0,1274,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,224,Fair,Typical,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,3,2007,CWD,Normal,135000,-93.624715,42.033482 -Two_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1939,1950,Gable,CompShg,MetalSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,324,600,GasA,Excellent,Y,SBrkr,608,624,0,1232,0,0,1,1,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,1,217,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,2,2007,WD ,Normal,128000,-93.623521,42.033612 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Good,No,ALQ,1,Unf,0,360,735,GasA,Excellent,Y,SBrkr,869,349,0,1218,0,1,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,131000,-93.621501,42.033336 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Average,Good,1939,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,884,0,0,884,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,136,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,113000,-93.623643,42.032399 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1938,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,809,861,GasA,Good,Y,SBrkr,861,548,0,1409,1,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,225,0,84,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,126000,-93.623631,42.031433 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1939,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,Unf,0,624,672,GasA,Excellent,Y,SBrkr,899,423,0,1322,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,132000,-93.624539,42.031438 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,966,966,GasA,Excellent,Y,SBrkr,1014,412,0,1426,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,230,Fair,Typical,Paved,174,0,96,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,131750,-93.622551,42.03142 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,405,756,GasA,Good,Y,FuseA,903,378,0,1281,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,379,Typical,Typical,Paved,25,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,132500,-93.621504,42.032505 -Two_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1920,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,939,939,GasA,Excellent,Y,SBrkr,939,574,0,1513,0,0,1,1,4,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,24,0,150,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,137500,-93.621502,42.032386 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Good,Very_Good,1929,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Excellent,Y,FuseA,854,0,0,854,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,48,112,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,132000,-93.6224,42.031321 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1931,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,LwQ,4,Unf,0,459,884,GasA,Typical,Y,FuseA,959,408,0,1367,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,560,Typical,Typical,Paved,0,0,0,0,120,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,127500,-93.622402,42.031483 -One_Story_1945_and_Older,Residential_Medium_Density,60,6180,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Above_Average,Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Typical,N,SBrkr,986,0,0,986,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,180,Typical,Typical,Paved,0,128,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,102000,-93.6212119,42.0314873 -Two_Story_1945_and_Older,Residential_Medium_Density,47,7755,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1918,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,FuseA,1100,1164,0,2264,0,0,2,1,4,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,408,Typical,Typical,Paved,0,152,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,200000,-93.6209132,42.0304477 -Two_Story_1945_and_Older,Residential_Low_Density,60,13515,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1919,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,764,764,GasA,Excellent,Y,FuseA,1060,764,0,1824,0,0,1,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,520,Typical,Typical,Dirt_Gravel,0,0,126,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,180500,-93.62576,42.029451 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8850,Pave,Paved,Regular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,752,624,0,1376,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,54,144,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,165000,-93.625435,42.026912 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_and_Half_Fin,Average,Average,1926,1950,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,750,750,GasA,Typical,Y,SBrkr,960,356,0,1316,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,ConLw,Family,90000,-93.625285,42.026998 -Two_Story_1945_and_Older,Residential_Low_Density,60,8730,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,Two_Story,Above_Average,Good,1915,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Excellent,Y,FuseA,698,698,0,1396,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,0,0,0,259,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,153575,-93.625284,42.027088 -Two_Story_1945_and_Older,Residential_Medium_Density,0,5700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Old_Town,Feedr,Norm,OneFam,Two_Story,Good,Above_Average,1929,1990,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,336,672,GasA,Good,N,FuseA,672,672,0,1344,1,0,1,1,3,1,Good,6,Typ,1,Good,Attchd,Unf,2,456,Typical,Typical,Paved,0,0,70,0,0,0,No_Pool,Good_Privacy,None,0,9,2007,WD ,Normal,140000,-93.6241649,42.0299664 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,8520,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1916,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,LwQ,546,0,714,GasW,Typical,N,SBrkr,1664,862,0,2526,0,0,2,0,5,1,Good,10,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,88,15,0,0,0,0,No_Pool,Good_Wood,None,0,8,2007,CWD,Family,136000,-93.62033,42.02911 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,40,5680,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1901,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,592,592,GasA,Typical,N,FuseA,933,240,0,1173,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Fair,Paved,0,25,77,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,WD ,AdjLand,113000,-93.622147,42.02752 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,40,5680,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Fair,1901,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,969,969,GasA,Typical,N,FuseA,969,245,0,1214,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,0,0,77,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,WD ,AdjLand,117000,-93.6220032,42.0275775 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,7758,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Good,Below_Average,1931,1950,Gable,CompShg,Stucco,Stucco,BrkFace,600,Typical,Fair,PConc,Typical,Typical,No,LwQ,4,Unf,0,816,1040,GasA,Excellent,Y,FuseF,1226,592,0,1818,0,0,1,1,4,1,Typical,7,Typ,2,Typical,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,184,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,169500,-93.628447,42.025229 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,57,7449,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Unf,Good,Good,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,637,637,GasA,Excellent,Y,FuseF,1108,0,0,1108,0,0,1,0,3,1,Good,6,Typ,1,Good,Attchd,Unf,1,280,Typical,Typical,Dirt_Gravel,0,0,205,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,139400,-93.628301,42.02543 -Split_or_Multilevel,Residential_Medium_Density,120,13200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,234,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,366,741,GasA,Fair,Y,SBrkr,1497,797,0,2294,0,0,3,0,5,1,Typical,9,Typ,1,Good,Attchd,Unf,2,658,Typical,Typical,Paved,0,110,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,202500,-93.629496,42.021576 -Two_Story_1945_and_Older,Residential_Medium_Density,60,6882,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1914,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,684,684,GasA,Typical,Y,SBrkr,773,582,0,1355,0,0,1,1,3,1,Good,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,136,0,115,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,127000,-93.6277573,42.0249982 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,9000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1937,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Typical,Y,SBrkr,780,595,0,1375,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,544,Typical,Typical,Partial_Pavement,0,162,0,0,126,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,141000,-93.6275137,42.0242321 -Two_Story_1946_and_Newer,Residential_Medium_Density,60,9780,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Good,Excellent,1950,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,LwQ,4,Rec,398,224,976,GasA,Excellent,Y,SBrkr,976,976,0,1952,0,0,1,1,4,1,Good,8,Typ,2,Typical,Detchd,Fin,1,299,Typical,Typical,Paved,285,0,0,0,216,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,212300,-93.629501,42.0227 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,763,1138,GasA,Good,Y,SBrkr,1138,1042,0,2180,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,720,Typical,Typical,Dirt_Gravel,0,0,170,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,161000,-93.6274918,42.0238856 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,52,4330,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Above_Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Rec,6,ALQ,127,0,808,GasA,Typical,Y,SBrkr,838,477,0,1315,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,2,436,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,COD,Abnorml,99500,-93.628263,42.022963 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_and_Half_Unf,Average,Good,1920,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,530,530,GasA,Typical,N,SBrkr,581,530,0,1111,0,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Dirt_Gravel,0,0,144,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,101000,-93.6263765,42.0238391 -One_Story_1945_and_Older,Residential_Medium_Density,40,4800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Good,1916,1990,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,999,1196,GasA,Excellent,Y,FuseA,1196,0,0,1196,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,109900,-93.625297,42.023518 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,12358,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,360,720,GasA,Typical,Y,SBrkr,854,0,528,1382,0,0,1,1,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,660,Typical,Typical,Paved,237,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,128500,-93.622793,42.026723 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10120,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,TwoFmCon,Two_and_Half_Unf,Good,Below_Average,1910,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,925,925,GasA,Typical,N,FuseF,964,925,0,1889,0,0,1,1,4,2,Typical,9,Typ,1,Good,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,264,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,122000,-93.622025,42.025893 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,55,4388,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Unf,Average,Good,1930,1950,Gable,CompShg,WdShing,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,556,672,GasA,Excellent,Y,SBrkr,840,0,0,840,0,0,1,0,3,1,Typical,5,Typ,1,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,87000,-93.62188,42.0261614 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Very_Poor,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,771,771,GasA,Fair,Y,SBrkr,866,504,114,1484,0,0,2,0,3,1,Typical,6,Sal,0,No_Fireplace,Detchd,Unf,1,264,Typical,Fair,Dirt_Gravel,14,211,0,0,84,0,No_Pool,No_Fence,None,0,9,2007,COD,Abnorml,50000,-93.623814,42.022951 -Two_Story_1945_and_Older,Residential_Low_Density,107,12888,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,Two_Story,Good,Very_Good,1937,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,717,1005,GasA,Typical,Y,SBrkr,1262,1005,0,2267,1,0,1,1,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,498,Typical,Typical,Paved,521,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,219000,-93.655699,42.033524 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,138,18030,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1946,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,469,977,1598,GasA,Typical,Y,SBrkr,1636,971,479,3086,0,0,3,0,3,1,Excellent,12,Maj1,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,122,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,200500,-93.656124,42.033285 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,444,756,GasA,Fair,N,FuseF,756,378,0,1134,1,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,AdjLand,126000,-93.657089,42.028048 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,59,4484,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Above_Average,1942,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,187,672,GasA,Typical,N,SBrkr,778,504,0,1282,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,108500,-93.655924,42.028004 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,75,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,TwoFmCon,SFoyer,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,0,925,GasA,Typical,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,40,176,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,130000,-93.675549,42.033272 -Split_Foyer,Residential_Low_Density,75,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Average,1964,1980,Gable,CompShg,HdBoard,HdBoard,BrkFace,30,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,Unf,0,635,1420,GasA,Good,Y,SBrkr,1452,0,0,1452,1,0,1,0,2,1,Typical,6,Min2,1,Typical,Detchd,Unf,2,572,Typical,Typical,Paved,92,0,88,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,158450,-93.675559,42.032426 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,14299,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Fair,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,144,348,1005,GasA,Typical,Y,SBrkr,1005,0,0,1005,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,115400,-93.672207,42.034453 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7943,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkCmn,192,Typical,Fair,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,126,1029,GasA,Good,Y,SBrkr,1029,0,0,1029,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,261,Typical,Typical,Paved,64,0,39,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,118500,-93.669741,42.034429 -Split_or_Multilevel,Residential_Low_Density,65,14149,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Very_Good,1964,2001,Hip,CompShg,HdBoard,HdBoard,BrkFace,50,Good,Good,CBlock,Typical,Typical,Gd,LwQ,4,BLQ,722,190,980,GasA,Typical,Y,SBrkr,1020,0,0,1020,0,1,2,0,3,1,Typical,5,Typ,1,Poor,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,165000,-93.674417,42.033193 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11677,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Below_Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,442,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,BLQ,761,30,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,123000,-93.67369,42.032385 -Split_Foyer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Above_Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,LwQ,4,Rec,627,0,814,GasA,Good,Y,SBrkr,913,0,0,913,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,252,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,129000,-93.677504,42.032098 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,99,7094,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,374,340,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,125000,-93.676953,42.031533 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8978,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,948,948,GasA,Typical,Y,SBrkr,948,0,0,948,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,300,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Family,108000,-93.677778,42.031203 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,8425,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Sawyer,Feedr,Norm,TwoFmCon,One_Story,Average,Above_Average,1971,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,20,768,GasA,Good,Y,SBrkr,868,0,0,868,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,138,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,119900,-93.678149,42.029562 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,8665,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,89,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,BLQ,288,420,876,GasA,Typical,Y,SBrkr,897,0,0,897,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,115000,-93.675714,42.031215 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8398,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,323,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,BLQ,529,300,943,GasA,Typical,Y,SBrkr,943,0,0,943,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,134500,-93.677997,42.029679 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,7742,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1966,1966,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,192,955,GasA,Excellent,Y,SBrkr,955,0,0,955,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,386,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,127000,-93.677601,42.031139 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,109,8724,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,402,894,GasA,Good,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,1,Poor,Attchd,Fin,2,450,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,129000,-93.676681,42.029786 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,8197,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,148,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,660,660,GasA,Excellent,Y,SBrkr,1285,0,0,1285,0,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,138,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,143500,-93.672119,42.030591 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8169,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Good,1966,1966,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,ALQ,435,261,912,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,315,Typical,Typical,Paved,204,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,129000,-93.668544,42.034422 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,14175,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Sawyer,PosA,Norm,OneFam,One_Story,Average,Average,1956,1998,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,522,332,1240,GasA,Good,Y,SBrkr,1375,0,0,1375,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,323,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,187000,-93.664037,42.032528 -Two_Story_1946_and_Newer,Residential_Low_Density,99,16779,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,Two_Story,Average,Below_Average,1920,1996,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,356,Typical,Fair,CBlock,Good,Typical,No,BLQ,2,Unf,0,404,671,GasA,Fair,Y,SBrkr,1567,1087,0,2654,0,0,3,0,4,1,Typical,11,Mod,1,Good,Attchd,Unf,2,638,Typical,Typical,Paved,128,570,0,0,0,0,No_Pool,No_Fence,Shed,500,5,2007,WD ,Normal,158000,-93.662239,42.034421 -One_Story_1945_and_Older,Residential_Low_Density,0,25339,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1918,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,1416,0,0,1416,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Dirt_Gravel,0,0,112,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,112000,-93.660671,42.033557 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,6960,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Good,Very_Good,1940,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,422,680,GasA,Excellent,Y,FuseA,798,504,0,1302,0,0,1,1,2,1,Good,6,Typ,2,Good,Attchd,Unf,1,224,Typical,Typical,Paved,0,0,0,0,126,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,165250,-93.660691,42.032638 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Above_Average,Average,1954,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,231,967,GasA,Typical,Y,SBrkr,1299,0,0,1299,0,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,494,Typical,Typical,Paved,81,0,280,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,150000,-93.661983,42.033726 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,13770,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1958,1998,Gable,CompShg,Plywood,Plywood,BrkFace,340,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,BLQ,873,95,1158,GasA,Typical,Y,SBrkr,1176,0,0,1176,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,303,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,VWD,Normal,137000,-93.661985,42.034018 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Good,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseA,998,0,0,998,0,0,1,0,3,1,Typical,5,Min2,0,No_Fireplace,More_Than_Two_Types,Unf,2,460,Fair,Typical,Paved,0,0,140,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,89500,-93.659355,42.032699 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,115149,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1971,2002,Gable,CompShg,Plywood,Plywood,Stone,351,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,424,1643,GasA,Typical,Y,SBrkr,1824,0,0,1824,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,Unf,2,739,Typical,Typical,Paved,380,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,302000,-93.676272,42.02868 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11075,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1984,1984,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,136,Typical,Typical,PConc,Good,Typical,No,BLQ,2,LwQ,891,0,1190,GasA,Excellent,Y,SBrkr,1522,0,0,1522,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,552,Typical,Typical,Paved,0,77,0,0,168,0,No_Pool,Good_Privacy,None,0,2,2007,WD ,Normal,182000,-93.673958,42.02384 -Two_Story_1946_and_Newer,Residential_Low_Density,97,10029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1988,1989,Gable,CompShg,Plywood,Plywood,BrkFace,268,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,320,1151,GasA,Typical,Y,SBrkr,1164,896,0,2060,0,1,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,521,Typical,Typical,Paved,0,228,0,0,192,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,211000,-93.674582,42.024087 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1948,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Good,Mn,BLQ,2,Unf,0,109,409,GasA,Excellent,Y,SBrkr,1325,0,0,1325,0,0,2,0,3,1,Good,6,Typ,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,216000,-93.669606,42.026185 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,22692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,486,1073,GasA,Typical,Y,SBrkr,1630,0,0,1630,0,0,2,0,3,1,Typical,6,Mod,1,Typical,Detchd,Unf,2,649,Typical,Typical,Partial_Pavement,0,64,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,COD,Normal,130000,-93.671221,42.023229 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,17808,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1946,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,484,484,GasA,Typical,N,SBrkr,1242,0,0,1242,0,0,1,0,2,1,Typical,4,Mod,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,109900,-93.671511,42.023021 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,12671,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Good,1954,1994,Hip,CompShg,MetalSd,MetalSd,Stone,300,Typical,Good,CBlock,Good,Fair,No,LwQ,4,Unf,0,935,1288,GasA,Excellent,Y,SBrkr,2422,0,0,2422,0,0,3,0,4,1,Good,6,Min2,2,Good,Attchd,Fin,2,527,Typical,Typical,Paved,0,63,0,0,144,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,255000,-93.66504,42.028424 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,12615,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Good,1950,2001,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Good,Av,ALQ,1,Unf,0,725,1202,GasA,Typical,Y,SBrkr,2158,0,0,2158,1,0,2,0,4,1,Good,7,Typ,1,Good,Attchd,Unf,2,576,Typical,Typical,Paved,0,29,39,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,243000,-93.663415,42.028236 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,10512,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,497,988,GasA,Excellent,Y,SBrkr,988,638,0,1626,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,332,Typical,Typical,Paved,366,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,154000,-93.66712,42.024949 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,411,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,399,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,118000,-93.66061,42.025818 -One_Story_1945_and_Older,Residential_Low_Density,0,11515,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1958,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,943,0,0,943,0,0,1,0,3,1,Good,5,Min2,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,60,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,80000,-93.663517,42.025001 -Duplex_All_Styles_and_Ages,Residential_Low_Density,42,7711,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Fair,1977,1977,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1440,GasA,Typical,Y,SBrkr,1440,0,0,1440,2,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,321,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,Oth,Abnorml,150000,-93.6644275,42.0205201 -One_Story_1945_and_Older,Residential_Low_Density,58,9098,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Good,1920,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,ALQ,1,Unf,0,180,528,GasA,Excellent,Y,SBrkr,605,0,0,605,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,144,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,86000,-93.66468,42.020186 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,39,3869,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,TwnhsE,One_Story,Average,Above_Average,1984,1984,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,149,Good,Excellent,CBlock,Typical,Typical,No,LwQ,4,GLQ,755,0,1038,GasA,Good,Y,SBrkr,1038,0,0,1038,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,105,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,130000,-93.6643138,42.0246868 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,58,9280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,ALQ,1,Unf,0,785,1342,GasA,Excellent,Y,SBrkr,1342,0,0,1342,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,256,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,125000,-93.658545,42.023874 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,11100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1951,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,LwQ,4,Unf,0,0,1080,GasA,Typical,N,SBrkr,1080,400,0,1480,1,0,1,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,1,253,Typical,Typical,Paved,0,0,68,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,136500,-93.66057,42.023945 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1920,1950,Gambrel,CompShg,MetalSd,MetalSd,None,0,Fair,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,951,951,GasW,Fair,N,SBrkr,986,376,0,1362,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Fair,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,96000,-93.659659,42.023451 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,One_Story,Average,Above_Average,1957,2006,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,98,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1340,0,0,1340,0,0,1,0,3,1,Typical,7,Typ,1,Good,Attchd,RFn,1,252,Typical,Typical,Paved,116,0,0,180,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,120000,-93.658537,42.022824 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,104,23920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1984,1984,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1105,1105,GasA,Excellent,Y,SBrkr,1105,717,0,1822,0,0,2,0,4,1,Good,7,Min2,1,Poor,Attchd,Unf,2,515,Typical,Typical,Partial_Pavement,0,195,1012,0,0,444,Typical,No_Fence,None,0,4,2007,WD ,Normal,228500,-93.693153,42.034453 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,114,10357,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_Story,Good,Average,1990,1991,Hip,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,172,910,GasA,Good,Y,SBrkr,1442,0,0,1442,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,719,Typical,Typical,Paved,0,244,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,179900,-93.684102,42.033662 -Two_Story_1946_and_Newer,Residential_Low_Density,116,13474,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,Two_Story,Good,Average,1990,1991,Gable,CompShg,HdBoard,Plywood,BrkFace,246,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,0,700,GasA,Good,Y,SBrkr,1122,1121,0,2243,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,746,Typical,Typical,Paved,127,44,224,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,225000,-93.684102,42.033576 -Two_Story_1946_and_Newer,Residential_Low_Density,86,10380,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1986,1987,Gable,CompShg,Plywood,Plywood,BrkFace,172,Good,Typical,CBlock,Typical,Typical,Gd,LwQ,4,ALQ,1474,0,1502,GasA,Excellent,Y,SBrkr,1553,1177,0,2730,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,2,576,Typical,Typical,Paved,201,96,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,301000,-93.68347,42.031546 -Two_Story_1946_and_Newer,Residential_Low_Density,103,13125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Excellent,Typical,Mn,BLQ,2,GLQ,634,422,1104,GasA,Excellent,Y,SBrkr,912,1215,0,2127,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,833,Typical,Typical,Paved,72,192,224,0,0,0,No_Pool,Good_Privacy,None,0,11,2007,WD ,Normal,238000,-93.683309,42.032467 -Two_Story_1946_and_Newer,Residential_Low_Density,82,11287,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1989,1989,Gable,CompShg,Plywood,Plywood,BrkFace,340,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,386,807,GasA,Good,Y,SBrkr,1175,807,0,1982,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,575,Typical,Typical,Paved,0,84,0,196,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,228500,-93.683226,42.031566 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9317,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1994,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,282,779,GasA,Excellent,Y,SBrkr,1029,929,0,1958,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,499,Typical,Typical,Paved,202,93,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,CWD,Normal,220000,-93.681061,42.030801 -Two_Story_1946_and_Newer,Residential_Low_Density,77,9965,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,220,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,280,1264,GasA,Excellent,Y,SBrkr,1282,1414,0,2696,1,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,792,Typical,Typical,Paved,120,184,0,0,168,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,383970,-93.686828,42.02667 -Two_Story_1946_and_Newer,Residential_Low_Density,77,9965,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,340,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,316,1466,GasA,Excellent,Y,SBrkr,1466,1362,0,2828,1,0,3,0,4,1,Good,11,Typ,1,Typical,BuiltIn,RFn,3,1052,Typical,Typical,Paved,125,144,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,424870,-93.686941,42.026642 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9178,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,306,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1643,1643,GasA,Excellent,Y,SBrkr,1651,0,0,1651,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,870,Typical,Typical,Paved,204,64,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,250000,-93.690342,42.025666 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,10481,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,2140,2140,GasA,Excellent,Y,SBrkr,2140,0,0,2140,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,3,894,Typical,Typical,Paved,136,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,360000,-93.69067,42.025707 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,10652,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,306,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1494,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,840,Typical,Typical,Paved,160,33,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,279500,-93.691248,42.025594 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,103,11175,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1316,1316,GasA,Excellent,Y,SBrkr,1316,0,0,1316,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,200141,-93.691213,42.025264 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,10235,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,306,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1643,1643,GasA,Excellent,Y,SBrkr,1651,0,0,1651,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,870,Typical,Typical,Paved,192,64,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,246500,-93.691015,42.025281 -Two_Story_1946_and_Newer,Residential_Low_Density,69,9588,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,Stone,270,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1482,1482,GasA,Excellent,Y,SBrkr,1482,1092,0,2574,0,0,2,1,3,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,868,Typical,Typical,Paved,0,148,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,276000,-93.690234,42.025558 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8814,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,349,1274,GasA,Excellent,Y,SBrkr,1274,0,0,1274,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,508,Typical,Typical,Paved,264,98,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,New,Partial,203000,-93.689071,42.024598 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11103,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,155835,-93.689071,42.024594 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8556,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1240,1240,GasA,Excellent,Y,SBrkr,1240,0,0,1240,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,3,826,Typical,Typical,Paved,140,93,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,194000,-93.69003,42.024633 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,544,1208,GasA,Excellent,Y,SBrkr,1208,0,0,1208,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,628,Typical,Typical,Paved,105,54,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,195400,-93.69135,42.024614 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,204,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1526,1546,GasA,Excellent,Y,SBrkr,1546,0,0,1546,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,796,Typical,Typical,Paved,144,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,217000,-93.688941,42.025726 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,132,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1453,1489,GasA,Excellent,Y,SBrkr,1500,0,0,1500,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,674,Typical,Typical,Paved,144,38,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,212999,-93.688923,42.025156 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7242,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1270,1270,GasA,Excellent,Y,SBrkr,1270,0,0,1270,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,524,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,175900,-93.689884,42.024635 -Two_Story_1946_and_Newer,Residential_Low_Density,78,9316,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,879,879,GasA,Excellent,Y,SBrkr,879,916,0,1795,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,164,111,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,191000,-93.688917,42.024485 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9317,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1290,1314,GasA,Good,Y,SBrkr,1314,0,0,1314,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,440,Typical,Typical,Paved,0,22,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,176432,-93.688931,42.023226 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14364,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1988,1989,Gable,CompShg,Plywood,Plywood,BrkFace,128,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,92,1157,GasA,Excellent,Y,SBrkr,1180,882,0,2062,1,0,2,1,3,1,Typical,7,Typ,1,Good,Attchd,Fin,2,454,Typical,Typical,Paved,60,55,0,0,154,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,277000,-93.685142,42.029694 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8883,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,360,Good,Typical,PConc,Good,Typical,No,GLQ,3,LwQ,321,0,929,GasA,Excellent,Y,SBrkr,946,927,0,1873,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,619,Typical,Typical,Paved,108,48,0,0,144,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,207000,-93.684944,42.029803 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,159000,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1958,2006,Gable,CompShg,Wd Sdng,HdBoard,BrkCmn,472,Good,Typical,CBlock,Good,Typical,Gd,Rec,6,Unf,0,747,1444,GasA,Good,Y,SBrkr,1444,700,0,2144,0,1,2,0,4,1,Good,7,Typ,2,Typical,Attchd,Fin,2,389,Typical,Typical,Paved,0,98,0,0,0,0,No_Pool,No_Fence,Shed,500,6,2007,WD ,Normal,277000,-93.682439,42.027956 -Two_Story_1946_and_Newer,Residential_Low_Density,0,53107,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Corner,Mod,Clear_Creek,Feedr,Norm,OneFam,Two_Story,Above_Average,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,595,1580,GasA,Excellent,Y,SBrkr,1079,874,0,1953,1,0,2,1,3,1,Good,9,Typ,2,Fair,Attchd,Fin,2,501,Typical,Typical,Paved,216,231,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,240000,-93.679216,42.027969 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12205,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1966,2007,Gable,CompShg,HdBoard,HdBoard,BrkFace,157,Typical,Typical,CBlock,Typical,Fair,Gd,LwQ,4,Unf,0,264,832,GasA,Good,Y,SBrkr,976,1111,0,2087,0,0,2,1,5,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,444,Typical,Typical,Paved,133,168,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,187500,-93.6825099,42.024734 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,155,20064,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1976,1976,Shed,WdShngl,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Good,Gd,LwQ,4,GLQ,915,0,966,GasA,Excellent,Y,SBrkr,1743,0,0,1743,2,0,0,1,0,1,Good,5,Typ,2,Fair,Attchd,Fin,2,529,Typical,Typical,Paved,646,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,279000,-93.683612,42.024305 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14217,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,472,1022,GasA,Good,Y,SBrkr,1022,0,0,1022,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,747,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,139500,-93.691971,42.022228 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8775,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,495,990,GasA,Good,Y,SBrkr,990,0,0,990,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,299,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,126000,-93.692069,42.021324 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,11249,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,No,ALQ,1,BLQ,544,322,1200,GasA,Excellent,Y,SBrkr,1200,0,0,1200,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,521,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,177500,-93.688862,42.02121 -Two_Story_1946_and_Newer,Residential_Low_Density,57,10021,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1997,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,96,635,GasA,Excellent,Y,SBrkr,646,662,0,1308,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,497,Typical,Typical,Paved,142,54,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,175000,-93.690402,42.020966 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9531,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,88,794,GasA,Excellent,Y,SBrkr,882,914,0,1796,1,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,546,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,211000,-93.690545,42.020912 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8428,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Good,No,GLQ,3,Unf,0,570,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,146000,-93.692286,42.019032 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,16561,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,548,1097,GasA,Excellent,Y,SBrkr,1097,0,0,1097,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,242,Typical,Typical,Paved,306,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,147900,-93.692696,42.019083 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Below_Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,402,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,123600,-93.692415,42.019028 -Two_Story_1946_and_Newer,Residential_Low_Density,69,9337,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,176,824,GasA,Excellent,Y,SBrkr,905,881,0,1786,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,684,Typical,Typical,Paved,0,162,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,204750,-93.690633,42.018378 -Two_Story_1946_and_Newer,Residential_Low_Density,47,10820,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,646,988,GasA,Excellent,Y,SBrkr,988,885,0,1873,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,597,Typical,Typical,Paved,202,123,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,235500,-93.688952,42.017907 -Two_Story_1946_and_Newer,Residential_Low_Density,43,12352,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,290,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,215,853,GasA,Excellent,Y,SBrkr,853,900,0,1753,1,0,2,1,3,1,Typical,7,Typ,1,Fair,Attchd,RFn,2,534,Typical,Typical,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,217000,-93.68892,42.017826 -Two_Story_1946_and_Newer,Residential_Low_Density,68,9543,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,845,845,GasA,Excellent,Y,SBrkr,845,845,0,1690,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,517,Typical,Typical,Paved,0,103,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,190550,-93.689344,42.016896 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8826,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,144,985,GasA,Excellent,Y,SBrkr,985,857,0,1842,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,486,Typical,Typical,Paved,193,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,217500,-93.689518,42.016829 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10665,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,167,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,440,1453,GasA,Excellent,Y,SBrkr,1479,0,0,1479,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,558,Typical,Typical,Paved,144,29,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,226000,-93.691469,42.016297 -Two_Story_1946_and_Newer,Residential_Low_Density,72,11317,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,101,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,828,0,1668,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,500,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,180000,-93.692052,42.016214 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,97,11800,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1974,1974,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,Unf,0,201,864,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Family,131000,-93.684962,42.021992 -Split_or_Multilevel,Residential_Low_Density,59,8660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Average,1976,1976,Gable,CompShg,VinylSd,VinylSd,BrkFace,113,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,513,1015,GasA,Typical,Y,SBrkr,1025,0,0,1025,0,0,2,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,2,370,Typical,Typical,Paved,127,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,153500,-93.686678,42.021386 -Split_or_Multilevel,Residential_Low_Density,72,9720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Good,1977,1977,Gable,CompShg,Plywood,VinylSd,BrkFace,51,Typical,Typical,CBlock,Typical,Excellent,Av,ALQ,1,Unf,0,240,995,GasA,Typical,Y,SBrkr,1009,0,0,1009,0,0,2,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,160000,-93.6874333,42.0218643 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,45,8982,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,501,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,748,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,134900,-93.687734,42.022239 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,39,16300,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Below_Average,1977,1977,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,Rec,6,BLQ,417,399,876,GasA,Typical,Y,SBrkr,907,0,0,907,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,130000,-93.684837,42.01972 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,432,102,864,GasA,Typical,Y,SBrkr,879,0,0,879,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,80,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,Con,Normal,120000,-93.684993,42.021163 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,468,276,882,GasA,Typical,Y,SBrkr,882,0,0,882,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,461,Typical,Typical,Paved,96,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,112500,-93.683235,42.021056 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,10356,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,253,969,GasA,Typical,Y,SBrkr,969,0,0,969,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,122000,-93.684354,42.021025 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1972,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,193,864,GasA,Good,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Excellent,5,Typ,0,No_Fireplace,Detchd,Fin,2,576,Good,Excellent,Paved,155,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,127000,-93.683158,42.020861 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Below_Average,Very_Good,1972,2006,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,0,385,GasA,Good,Y,SBrkr,875,0,0,875,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,728,Typical,Typical,Paved,352,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,117000,-93.683169,42.021737 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10386,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,CemntBd,CmentBd,Stone,246,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,536,2000,GasA,Excellent,Y,SBrkr,2000,0,0,2000,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,3,888,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,305900,-93.687856,42.018518 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,149,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1284,1284,GasA,Excellent,Y,SBrkr,1284,885,0,2169,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,647,Typical,Typical,Paved,192,87,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,228500,-93.686575,42.018424 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11354,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Typical,Typical,Gd,GLQ,3,Unf,0,261,1673,GasA,Excellent,Y,SBrkr,1673,0,0,1673,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,583,Typical,Typical,Paved,306,113,0,0,116,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,270000,-93.682805,42.018849 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8749,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,100,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1459,1459,GasA,Excellent,Y,SBrkr,1459,0,0,1459,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,527,Typical,Typical,Paved,192,39,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,192000,-93.686795,42.016973 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8749,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,325,979,GasA,Excellent,Y,SBrkr,992,940,0,1932,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,610,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,213000,-93.687786,42.016961 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8158,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,214,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,860,860,GasA,Excellent,Y,SBrkr,860,869,0,1729,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,542,Typical,Typical,Paved,386,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,200000,-93.68675,42.016212 -Two_Story_1946_and_Newer,Residential_Low_Density,66,16226,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,747,1028,GasA,Excellent,Y,SBrkr,1210,1242,0,2452,0,0,2,1,4,1,Good,9,Typ,1,Typical,BuiltIn,Fin,2,683,Typical,Typical,Paved,208,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,267000,-93.683813,42.0172 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,11927,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Hip,CompShg,HdBoard,HdBoard,BrkFace,519,Good,Typical,PConc,Good,Typical,Gd,BLQ,2,GLQ,465,683,1556,GasA,Excellent,Y,SBrkr,1592,0,0,1592,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,120,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,228000,-93.679583,42.018564 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,256,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,333,1531,GasA,Excellent,Y,SBrkr,1531,908,0,2439,1,0,2,1,4,1,Good,7,Typ,1,Typical,Attchd,Fin,2,560,Typical,Typical,Paved,184,121,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,275000,-93.681196,42.018864 -Two_Story_1946_and_Newer,Residential_Low_Density,0,15295,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1996,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,254,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,98,860,GasA,Excellent,Y,SBrkr,1212,780,0,1992,1,0,2,1,3,1,Good,7,Min2,2,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,225,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,211000,-93.6800822,42.0185298 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11248,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,Stone,215,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,567,1626,GasA,Excellent,Y,SBrkr,1668,0,0,1668,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,702,Typical,Typical,Paved,257,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,285000,-93.679805,42.018017 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,438,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,141,1220,GasA,Excellent,Y,SBrkr,1220,870,0,2090,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,556,Typical,Typical,Paved,0,65,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,240000,-93.681274,42.017856 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,36,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,189,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,131500,-93.681085,42.016277 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,17227,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,158,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,426,1341,GasA,Excellent,Y,SBrkr,1341,0,0,1341,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,RFn,2,482,Typical,Typical,Paved,240,84,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,246900,-93.679505,42.017753 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1081,0,1928,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,219500,-93.687684,42.015974 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,638,1337,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,531,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,185000,-93.686903,42.014038 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8145,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,738,0,1476,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,552,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,179400,-93.684118,42.014039 -Two_Story_1946_and_Newer,Residential_Low_Density,79,9245,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,939,939,GasA,Excellent,Y,SBrkr,939,858,0,1797,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,639,Typical,Typical,Paved,144,53,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,213500,-93.684137,42.014823 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,8500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,106,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1422,1422,GasA,Excellent,Y,SBrkr,1422,0,0,1422,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,626,Typical,Typical,Paved,192,60,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,179600,-93.684714,42.013576 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8769,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,460,1169,GasA,Excellent,Y,SBrkr,1190,0,0,1190,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,100,41,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,173500,-93.684625,42.013544 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,8334,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1330,1330,GasA,Excellent,Y,SBrkr,1330,0,0,1330,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,437,Typical,Typical,Paved,0,23,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,167840,-93.684285,42.01343 -Two_Story_1946_and_Newer,Residential_Low_Density,64,8333,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Good,Y,SBrkr,738,753,0,1491,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,100,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,168675,-93.684271,42.013467 -Two_Story_1946_and_Newer,Residential_Low_Density,64,9045,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,768,768,0,1536,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,167000,-93.683732,42.01469 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9170,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Average,Good,1965,1965,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,GLQ,96,420,1214,GasA,Excellent,Y,SBrkr,1214,0,0,1214,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,461,Fair,Fair,Paved,0,0,184,0,0,0,No_Pool,Good_Privacy,Shed,400,4,2007,WD ,Normal,140000,-93.6783521,42.0210331 -Split_Foyer,Residential_Low_Density,75,9825,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,162,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,0,936,GasA,Good,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,4,Typ,1,Fair,Attchd,Unf,1,384,Typical,Typical,Paved,405,0,0,0,0,0,No_Pool,No_Fence,Shed,450,8,2007,WD ,Abnorml,118500,-93.677545,42.020703 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1963,1963,Gable,CompShg,VinylSd,VinylSd,Stone,20,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,LwQ,841,115,1088,GasA,Typical,Y,SBrkr,1088,0,0,1088,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,520,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,COD,Normal,110000,-93.676222,42.021221 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,10921,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1965,1965,Hip,CompShg,HdBoard,HdBoard,BrkFace,48,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,440,960,GasA,Typical,Y,FuseF,960,0,0,960,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,432,Typical,Typical,Partial_Pavement,120,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,94750,-93.671455,42.021195 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,74,16287,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1925,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,BLQ,105,666,901,GasA,Typical,Y,SBrkr,901,450,0,1351,1,0,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Dirt_Gravel,0,0,43,0,100,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,122000,-93.671454,42.02103 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1960,Hip,CompShg,HdBoard,HdBoard,Stone,198,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1179,1179,GasA,Good,Y,SBrkr,1179,0,0,1179,0,0,1,0,2,1,Typical,5,Min2,0,No_Fireplace,Attchd,Fin,2,622,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,110000,-93.671453,42.020893 -Duplex_All_Styles_and_Ages,Residential_Low_Density,81,11841,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,104,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,816,GasA,Typical,Y,SBrkr,816,0,0,816,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,118500,-93.673197,42.019926 -Split_or_Multilevel,Residential_Low_Density,65,6285,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1976,1976,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Fair,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,456,960,GasA,Typical,Y,SBrkr,1044,0,0,1044,1,0,1,0,3,1,Typical,7,Typ,1,Fair,Detchd,Unf,2,528,Typical,Fair,Paved,228,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,133500,-93.6747048,42.0196959 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7917,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Good,1976,1976,Hip,CompShg,HdBoard,HdBoard,BrkFace,174,Typical,Good,CBlock,Typical,Good,No,BLQ,2,Unf,0,392,1143,GasA,Typical,Y,SBrkr,1113,0,0,1113,1,0,1,1,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,1,504,Typical,Good,Paved,370,30,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,148000,-93.67525,42.019902 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,9555,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1100,1133,0,2233,0,0,2,1,5,2,Typical,11,Typ,0,No_Fireplace,Attchd,Fin,2,579,Typical,Good,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,141000,-93.674327,42.01917 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8536,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1125,1125,GasA,Good,Y,SBrkr,1125,0,0,1125,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,430,Typical,Typical,Paved,80,64,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,155000,-93.674307,42.018283 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7024,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Good,No,ALQ,1,Unf,0,110,1090,GasA,Good,Y,SBrkr,1090,0,0,1090,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,450,Typical,Typical,Paved,0,49,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,157000,-93.673253,42.018873 -Two_Story_1946_and_Newer,Residential_Low_Density,60,7023,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,123,734,GasA,Good,Y,SBrkr,734,674,0,1408,1,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,BuiltIn,Fin,2,489,Typical,Typical,Paved,0,85,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,171500,-93.67318,42.018922 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,128,39290,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2008,2009,Hip,CompShg,CemntBd,CmentBd,Stone,1224,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1085,5095,GasA,Excellent,Y,SBrkr,5095,0,0,5095,1,1,2,1,2,1,Excellent,15,Typ,2,Good,Attchd,Fin,3,1154,Typical,Typical,Paved,546,484,0,0,0,0,No_Pool,No_Fence,Elev,17000,10,2007,New,Partial,183850,-93.6762195,42.0164532 -Two_Story_1946_and_Newer,Residential_Low_Density,130,40094,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Edwards,PosN,PosN,OneFam,Two_Story,Very_Excellent,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,762,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,878,3138,GasA,Excellent,Y,SBrkr,3138,1538,0,4676,1,0,3,1,3,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,884,Typical,Typical,Paved,208,406,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,184750,-93.676241,42.016642 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,76,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,80,547,GasA,Excellent,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,140000,-93.670587,42.018973 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1959,2000,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,ALQ,831,52,960,GasA,Excellent,Y,SBrkr,960,0,0,960,1,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,392,Typical,Typical,Paved,144,0,35,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,131750,-93.665737,42.018965 -Duplex_All_Styles_and_Ages,Residential_Low_Density,74,6882,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Fair,1955,1955,Gable,CompShg,AsbShng,Plywood,BrkCmn,128,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1152,0,0,1152,0,0,2,0,2,2,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,61500,-93.664768,42.020062 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10215,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,132,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,372,864,GasA,Excellent,Y,SBrkr,948,0,0,948,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,248,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,111000,-93.663509,42.020783 -Duplex_All_Styles_and_Ages,Residential_Low_Density,52,8741,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Above_Average,1946,1950,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1195,1195,GasA,Typical,N,SBrkr,1195,0,0,1195,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,118,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Abnorml,98500,-93.664686,42.020504 -One_Story_1945_and_Older,Residential_High_Density,70,4270,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Fair,Above_Average,1931,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,0,544,GasA,Excellent,Y,SBrkr,774,0,0,774,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,286,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,79000,-93.661542,42.022655 -One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,62,10042,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1920,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,ALQ,278,238,660,GasA,Typical,Y,SBrkr,740,125,0,865,1,0,1,0,2,1,Typical,4,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,84,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,108500,-93.660303,42.021436 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9450,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,342,894,GasA,Excellent,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Abnorml,110000,-93.665875,42.017962 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Below_Average,Good,1910,2000,Gable,CompShg,MetalSd,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,560,560,GasA,Good,N,SBrkr,796,550,0,1346,0,0,1,1,2,1,Typical,6,Min2,0,No_Fireplace,Detchd,Unf,1,384,Fair,Typical,Paved,168,24,0,0,0,0,No_Pool,Good_Wood,None,0,5,2007,WD ,Normal,112000,-93.664821,42.01965 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,224,768,GasA,Typical,Y,SBrkr,768,0,0,768,0,0,1,0,2,1,Typical,4,Typ,1,Fair,Detchd,Unf,1,355,Typical,Typical,Paved,0,0,196,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,125000,-93.66346,42.018943 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,182,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Dirt_Gravel,196,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,COD,Family,79275,-93.663455,42.01873 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8190,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,216,948,GasA,Excellent,Y,SBrkr,948,0,0,948,1,0,1,0,3,1,Typical,5,Typ,1,Typical,Detchd,Unf,1,280,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,119000,-93.663329,42.019937 -Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,60,10896,Pave,Paved,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,TwoFmCon,Two_and_Half_Fin,Above_Average,Good,1914,1995,Hip,CompShg,VinylSd,VinylSd,None,0,Fair,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,1184,1440,GasA,Excellent,Y,FuseA,1440,1440,515,3395,0,0,2,0,8,2,Fair,14,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,110,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Abnorml,200000,-93.650162,42.01902 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,10890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1923,1950,Hip,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Rec,6,Unf,0,925,1296,Grav,Fair,N,FuseA,1296,1296,0,2592,2,0,2,0,6,2,Typical,12,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,742,240,0,0,0,No_Pool,No_Fence,Shed,1512,1,2007,WD ,AdjLand,150000,-93.65181,42.018761 -Two_Story_1945_and_Older,Residential_Low_Density,55,10592,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1923,1996,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Fair,No,Unf,7,Unf,0,602,602,GasA,Typical,Y,SBrkr,900,602,0,1502,0,0,1,1,3,1,Good,7,Typ,2,Typical,Detchd,Unf,1,180,Typical,Typical,Paved,96,0,112,0,53,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,165000,-93.646624,42.01804 -One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,55,10594,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Unf,Average,Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,768,768,Grav,Fair,N,SBrkr,789,0,0,789,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Poor,Poor,Paved,0,0,112,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,96500,-93.646688,42.018039 -One_Story_1945_and_Older,Residential_Low_Density,54,7223,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Average,1926,1950,Hip,CompShg,Stucco,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,Mn,BLQ,2,Unf,0,971,1290,GasA,Typical,Y,SBrkr,1422,0,0,1422,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,64,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,136500,-93.646817,42.017888 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,6821,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1921,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,538,651,GasA,Good,Y,SBrkr,759,539,0,1298,0,0,2,0,2,1,Typical,8,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,216,0,168,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,146500,-93.64672,42.017889 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Good,1937,1950,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,252,Typical,Typical,BrkTil,Good,Typical,No,ALQ,1,Unf,0,162,731,GasA,Excellent,Y,SBrkr,981,787,0,1768,1,0,1,1,3,1,Good,7,Typ,2,Typical,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,264,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,175000,-93.6461654,42.018921 -Two_Story_1945_and_Older,Residential_Low_Density,63,4000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1930,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,GLQ,3,Unf,0,285,531,GasA,Typical,Y,SBrkr,567,531,0,1098,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,148500,-93.644544,42.017103 -Two_Story_1945_and_Older,Residential_Low_Density,53,6720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1921,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Typical,N,SBrkr,851,585,0,1436,0,0,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,228,Typical,Typical,Paved,184,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,CWD,Normal,141500,-93.644669,42.017106 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,55,7642,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Very_Good,1918,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,912,912,GasA,Good,Y,SBrkr,912,514,0,1426,0,0,1,1,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,189950,-93.6447083,42.0162813 -Two_Story_1945_and_Older,Residential_Low_Density,53,7155,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1926,1991,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,686,686,GasA,Typical,Y,SBrkr,686,775,0,1461,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,225,Typical,Typical,Dirt_Gravel,0,0,116,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,135000,-93.644511,42.016216 -Two_and_Half_Story_All_Ages,Residential_Low_Density,53,7128,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,554,918,GasA,Good,Y,SBrkr,918,728,0,1646,0,0,2,0,4,1,Typical,7,Typ,2,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,126,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,164000,-93.644698,42.016217 -One_Story_1945_and_Older,Residential_Low_Density,40,4280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Above_Average,1913,2002,Gable,CompShg,WdShing,Stucco,None,0,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,75,440,GasA,Typical,N,SBrkr,694,0,0,694,0,0,1,0,2,1,Good,4,Typ,1,Good,Detchd,Unf,1,352,Good,Typical,Partial_Pavement,0,0,34,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,90350,-93.64476,42.016217 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,53,5362,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1910,2003,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,661,661,GasA,Excellent,Y,SBrkr,661,589,0,1250,0,0,2,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,552,Typical,Typical,Paved,242,0,81,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,139000,-93.646289,42.016853 -One_Story_1945_and_Older,Residential_Low_Density,50,6305,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Good,1938,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Fair,Fair,No,Unf,7,Unf,0,920,920,GasA,Excellent,Y,SBrkr,954,0,0,954,0,0,1,0,2,1,Fair,5,Typ,1,Good,Basment,Unf,1,240,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,119750,-93.646416,42.016067 -Duplex_All_Styles_and_Ages,Residential_High_Density,82,7136,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,Duplex,Two_Story,Above_Average,Above_Average,1946,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,423,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,495,979,GasA,Typical,N,FuseF,979,979,0,1958,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,492,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,145000,-93.6435257,42.0199537 -Duplex_All_Styles_and_Ages,Residential_High_Density,82,6270,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1949,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,717,1001,GasA,Typical,N,FuseA,1001,1001,0,2002,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,871,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,145000,-93.643215,42.019952 -Two_Story_1945_and_Older,Residential_Low_Density,60,7230,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Good,1927,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,851,851,GasA,Good,Y,SBrkr,867,851,0,1718,0,0,2,1,4,1,Good,8,Typ,1,Typical,Detchd,Unf,2,264,Typical,Typical,Paved,291,0,60,0,153,0,No_Pool,Good_Privacy,None,0,10,2007,WD ,Normal,238000,-93.64281,42.018796 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,126,13108,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Average,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,Y,SBrkr,1226,0,0,1226,0,0,1,1,2,1,Typical,7,Min1,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,174,24,120,0,228,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,153500,-93.641948,42.017572 -Two_Story_1945_and_Older,Residential_Low_Density,86,22420,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Feedr,Norm,OneFam,Two_Story,Above_Average,Above_Average,1918,1950,Hip,CompShg,Wd Sdng,Stucco,None,0,Typical,Typical,BrkTil,Good,Typical,No,BLQ,2,Unf,0,242,1370,GasW,Typical,N,FuseA,1370,1254,0,2624,1,0,2,1,4,1,Typical,10,Typ,1,Good,Detchd,Unf,3,864,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,239000,-93.644426,42.020037 -Two_Story_1945_and_Older,Residential_Low_Density,78,12168,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Above_Average,1934,1998,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,537,965,GasA,Typical,Y,SBrkr,1940,1254,0,3194,0,0,2,1,4,1,Typical,10,Typ,2,Good,Basment,Unf,2,380,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Alloca,359100,-93.641322,42.018504 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,110,7810,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1930,2003,Gable,CompShg,AsbShng,CmentBd,None,0,Typical,Good,BrkTil,Typical,Good,No,GLQ,3,Unf,0,741,930,GasA,Excellent,Y,SBrkr,1230,525,0,1755,0,0,2,0,4,1,Good,7,Typ,1,Typical,Detchd,Unf,1,231,Fair,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,188000,-93.642359,42.018941 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,11275,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1932,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,480,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,LwQ,557,0,854,GasA,Typical,Y,SBrkr,1096,895,0,1991,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,432,Typical,Fair,Paved,0,0,19,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,220000,-93.6428531,42.0168895 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,79,6221,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Average,1941,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,195,728,GasA,Excellent,Y,SBrkr,760,595,0,1355,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,144,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,140000,-93.640497,42.017573 -Two_Story_1945_and_Older,Residential_Low_Density,70,10570,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Very_Good,1932,1994,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Good,No,Rec,6,Unf,0,556,853,GasA,Typical,Y,SBrkr,1549,1178,0,2727,0,0,2,1,3,1,Good,10,Maj1,2,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,315000,-93.641156,42.016961 -Two_Story_1945_and_Older,Residential_Low_Density,61,7259,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1945,2002,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,150,104,1028,GasA,Excellent,Y,SBrkr,1436,884,0,2320,1,0,2,1,3,1,Good,9,Typ,1,Typical,Detchd,Unf,1,180,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,259500,-93.642663,42.01629 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14442,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Good,1957,2004,Hip,CompShg,CemntBd,CmentBd,BrkFace,106,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,291,1477,GasA,Excellent,Y,SBrkr,1839,0,0,1839,1,0,2,0,3,1,Good,7,Typ,2,Typical,Attchd,Fin,2,416,Typical,Typical,Paved,0,87,0,0,200,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,257500,-93.642883,42.013003 -Two_Story_1945_and_Older,Residential_Low_Density,75,13125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1940,1984,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,390,800,GasA,Typical,Y,SBrkr,960,780,0,1740,0,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,CWD,Normal,219500,-93.6440062,42.0145503 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,21000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,184,Typical,Good,CBlock,Good,Typical,Mn,ALQ,1,Rec,869,905,1809,GasA,Typical,Y,SBrkr,2259,0,0,2259,1,0,2,0,3,1,Good,7,Typ,2,Good,Basment,Unf,2,450,Typical,Typical,Paved,166,120,192,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2007,COD,Abnorml,217000,-93.639393,42.01265 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,25485,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Below_Average,1960,1960,Gable,CompShg,Wd Sdng,MetalSd,BrkFace,423,Typical,Fair,CBlock,Typical,Good,Mn,LwQ,4,Rec,1020,0,1560,GasA,Typical,Y,SBrkr,1560,0,0,1560,0,0,1,1,3,1,Typical,6,Typ,3,Typical,Attchd,RFn,2,580,Typical,Typical,Paved,0,75,584,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,201000,-93.640326,42.012488 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,21579,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1968,1968,Hip,CompShg,HdBoard,BrkFace,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,675,1488,GasA,Excellent,Y,SBrkr,1488,0,0,1488,0,1,2,0,3,1,Typical,7,Typ,2,Good,Attchd,RFn,2,552,Typical,Typical,Paved,0,0,216,0,0,0,No_Pool,No_Fence,None,0,9,2007,CWD,Normal,215000,-93.641849,42.011506 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1782,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,186,516,GasA,Good,Y,SBrkr,529,516,0,1045,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,462,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2007,WD ,Normal,123900,-93.645743,42.009489 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17871,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Below_Average,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1680,1680,GasA,Good,Y,SBrkr,1680,0,0,1680,0,0,2,0,4,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,628,Typical,Typical,Paved,152,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,170000,-93.644923,42.011836 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,35,3907,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Blueste,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,427,1004,GasA,Good,Y,SBrkr,1020,0,0,1020,1,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,509,Typical,Typical,Paved,135,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,200000,-93.645785,42.009321 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20693,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Good,Average,1971,1971,Gable,CompShg,Plywood,Plywood,BrkFace,652,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,1262,1696,GasA,Excellent,Y,SBrkr,1696,0,0,1696,0,0,2,0,3,1,Typical,7,Typ,2,Typical,Attchd,Fin,2,625,Typical,Typical,Paved,0,146,0,0,0,0,No_Pool,Good_Wood,None,0,2,2007,WD ,Normal,198000,-93.643548,42.011343 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,32668,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Fair,1957,1975,Hip,CompShg,Wd Sdng,Stone,None,0,Good,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,816,2035,GasA,Typical,Y,SBrkr,2515,0,0,2515,1,0,3,0,4,2,Typical,9,Maj1,2,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,0,0,200,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Alloca,200624,-93.643429,42.010333 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,18044,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Very_Good,Average,1986,1986,Gable,CompShg,WdShing,Plywood,None,0,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,279,279,GasA,Good,Y,SBrkr,2726,0,0,2726,0,0,2,1,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,691,Good,Good,Paved,216,64,169,0,0,228,Excellent,No_Fence,None,0,8,2007,WD ,Normal,315000,-93.640182,42.010076 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,85,8500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1919,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,793,793,GasW,Typical,N,FuseF,997,520,0,1517,0,0,2,0,3,1,Fair,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,144,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,115000,-93.628412,42.022836 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,345,720,GasA,Good,Y,FuseA,720,495,0,1215,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Fin,2,720,Typical,Typical,Paved,0,0,30,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,141000,-93.6295,42.022474 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,RRAe,Norm,OneFam,One_and_Half_Unf,Average,Above_Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,604,720,GasA,Poor,N,FuseF,803,0,0,803,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,244,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,87000,-93.596569,42.022658 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,7288,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1925,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,BrkTil,Typical,Poor,No,Unf,7,Unf,0,936,936,GasA,Excellent,Y,SBrkr,936,665,0,1601,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,176,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,129850,-93.597005,42.022658 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1939,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,732,732,GasA,Good,Y,SBrkr,772,351,0,1123,0,0,1,0,3,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Partial_Pavement,0,0,140,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,100000,-93.626875,42.022566 -Two_Story_1945_and_Older,Residential_Medium_Density,50,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1923,1999,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Good,Typical,No,ALQ,1,Unf,0,311,859,GasA,Excellent,Y,SBrkr,942,886,0,1828,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,No_Garage,0,0,No_Garage,No_Garage,Paved,174,0,212,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Alloca,150909,-93.62826,42.022685 -One_Story_1945_and_Older,Residential_Medium_Density,46,3672,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_Story,Average,Good,1922,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,100,Fair,Fair,Dirt_Gravel,0,0,96,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,75200,-93.625313,42.0228 -One_Story_1945_and_Older,Residential_Medium_Density,59,8263,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Average,1920,1950,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1012,1012,GasA,Typical,Y,FuseA,1012,0,0,1012,0,0,1,0,2,1,Typical,6,Typ,1,Good,Detchd,Unf,1,308,Typical,Typical,Paved,0,22,112,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,118400,-93.629496,42.021547 -One_Story_1945_and_Older,Residential_Medium_Density,60,8967,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Poor,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Fair,Poor,No,Unf,7,Unf,0,961,961,GasA,Good,Y,Mix,1077,0,0,1077,0,0,1,0,2,1,Typical,6,Maj2,0,No_Fireplace,Detchd,Unf,1,338,Poor,Poor,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Abnorml,67000,-93.628236,42.021278 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,13710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,490,910,GasA,Typical,Y,FuseA,910,648,0,1558,0,0,1,1,4,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,282,Typical,Typical,Paved,289,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,152000,-93.626689,42.021497 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,64,11067,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Poor,Below_Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,640,0,205,845,0,0,1,0,1,1,Typical,4,Maj2,0,No_Fireplace,Detchd,Unf,1,256,Typical,Fair,Dirt_Gravel,48,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,68104,-93.628222,42.020161 -Two_Family_conversion_All_Styles_and_Ages,C_all,75,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,TwoFmCon,Two_Story,Average,Above_Average,1895,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,957,957,GasA,Fair,N,SBrkr,1034,957,0,1991,0,0,2,0,4,2,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,133,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,119600,-93.616969,42.020077 -One_Story_1946_and_Newer_All_Styles,C_all,65,6565,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1957,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,106,1073,GasA,Good,Y,FuseA,1073,0,0,1073,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,0,444,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Abnorml,140000,-93.6150637,42.0214218 -One_Story_1945_and_Older,C_all,60,6060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Excellent,1930,2007,Hip,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,100,837,GasA,Excellent,Y,SBrkr,1001,0,0,1001,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Poor,Dirt_Gravel,154,0,42,86,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,124900,-93.613886,42.021555 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,59,5568,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,Stone,473,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1573,GasA,Good,Y,SBrkr,1625,0,0,1625,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,495,Typical,Typical,Paved,123,0,0,0,153,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,375000,-93.615697,42.009401 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,4750,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,481,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1573,GasA,Excellent,Y,SBrkr,1625,0,0,1625,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,538,Typical,Typical,Paved,123,0,0,0,153,0,No_Pool,No_Fence,None,0,12,2007,WD ,Family,235000,-93.615618,42.009401 -Split_Foyer,Residential_Low_Density,0,12150,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1979,1979,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1001,GasA,Typical,Y,SBrkr,1299,0,0,1299,1,0,2,0,2,1,Good,5,Typ,1,Poor,BuiltIn,RFn,2,486,Typical,Typical,Paved,84,0,222,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,183500,-93.606577,41.994357 -Split_Foyer,Residential_Low_Density,0,7540,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1978,1978,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,115,888,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,470,Typical,Typical,Paved,0,0,0,0,192,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,156000,-93.606672,41.994207 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9187,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Average,1983,1983,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,ALQ,1,Unf,0,748,1084,GasA,Typical,Y,SBrkr,1080,0,0,1080,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,120,0,158,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,134000,-93.607597,41.994206 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,166,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,856,1441,GasA,Excellent,Y,SBrkr,1392,0,0,1392,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,3,650,Typical,Typical,Paved,168,49,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,215000,-93.607551,41.995218 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,44,12864,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,17,1409,GasA,Excellent,Y,SBrkr,1409,0,0,1409,1,0,1,1,1,1,Good,4,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,0,144,0,0,145,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,245000,-93.606933,41.995973 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9928,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,1991,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,215,1454,GasA,Typical,Y,SBrkr,1478,0,0,1478,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,506,Typical,Typical,Paved,114,22,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,210000,-93.602505,41.995931 -Split_or_Multilevel,Residential_Low_Density,0,8750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Good,Above_Average,1975,1975,Gable,CompShg,Plywood,Plywood,Stone,50,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,GLQ,530,98,852,GasA,Typical,Y,SBrkr,918,0,0,918,0,1,1,0,3,0,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,360,Typical,Typical,Paved,192,84,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,148000,-93.60104,41.995512 -Split_Foyer,Residential_Low_Density,82,8410,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1974,1974,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,46,970,GasA,Typical,Y,SBrkr,1026,0,0,1026,1,0,1,0,2,1,Typical,5,Typ,1,Poor,Attchd,Unf,2,528,Typical,Typical,Paved,193,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,143000,-93.601779,41.994188 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,46,4054,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,1987,1987,Gable,CompShg,VinylSd,VinylSd,BrkFace,352,Good,Typical,BrkTil,Good,Typical,Av,GLQ,3,Unf,0,552,1501,GasA,Excellent,Y,SBrkr,1501,0,0,1501,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,Fin,2,512,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,244000,-93.63966,42.005692 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,9763,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Green_Hills,Norm,Norm,TwnhsE,One_Story,Good,Average,1998,1998,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,239,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,72,328,1502,GasA,Excellent,Y,SBrkr,1502,0,0,1502,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,Fin,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,330000,-93.647403,42.001694 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,115,16905,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,Gd,BLQ,2,Unf,0,383,1350,GasA,Good,Y,SBrkr,1328,0,0,1328,0,1,1,1,2,1,Typical,5,Typ,2,Good,Attchd,RFn,1,308,Typical,Typical,Partial_Pavement,0,104,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,170000,-93.649757,41.99854 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,149,19958,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1958,1995,Hip,CompShg,HdBoard,HdBoard,BrkFace,1224,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Good,Y,SBrkr,2279,0,0,2279,0,0,2,1,4,1,Good,7,Typ,1,Good,Attchd,RFn,2,461,Typical,Typical,Paved,274,0,0,0,138,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,257000,-93.6494462,42.0004101 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8368,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1689,1689,GasA,Excellent,Y,SBrkr,1689,0,0,1689,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,433,Typical,Typical,Paved,100,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,231713,-93.650588,41.994241 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8298,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,963,1546,GasA,Excellent,Y,SBrkr,1564,0,0,1564,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,502,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,267300,-93.650569,41.994137 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,9037,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,32,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1048,1476,GasA,Excellent,Y,SBrkr,1484,0,0,1484,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,472,Typical,Typical,Paved,120,33,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,265900,-93.650561,41.9941 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,10991,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1571,1571,GasA,Excellent,Y,SBrkr,1571,0,0,1571,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,722,Typical,Typical,Paved,100,36,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,239000,-93.651732,41.994824 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,10656,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,274,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1638,1638,GasA,Excellent,Y,SBrkr,1646,0,0,1646,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,870,Typical,Typical,Paved,192,80,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,248900,-93.650417,41.995088 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,42,10331,Pave,No_Alley_Access,Regular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Good,1985,1985,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,BLQ,80,970,1265,GasA,Good,Y,SBrkr,1240,0,0,1240,0,1,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,232,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,174000,-93.646617,41.998023 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,44,13758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1990,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,117,Good,Good,CBlock,Good,Typical,Mn,LwQ,4,Unf,0,254,1156,GasA,Excellent,Y,SBrkr,1187,530,0,1717,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,400,Typical,Typical,Paved,168,36,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,187500,-93.644366,41.997992 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9303,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1996,1997,Hip,CompShg,VinylSd,VinylSd,BrkFace,42,Good,Typical,PConc,Excellent,Typical,No,ALQ,1,Unf,0,130,872,GasA,Excellent,Y,SBrkr,888,868,0,1756,1,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,422,Typical,Typical,Paved,144,122,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,204000,-93.646782,41.997364 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6718,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,86,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,1017,1267,GasA,Excellent,Y,SBrkr,1312,0,0,1312,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,471,Typical,Typical,Paved,256,28,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,180500,-93.646791,41.997126 -Split_or_Multilevel,Residential_Low_Density,73,9590,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,442,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,82,868,GasA,Excellent,Y,SBrkr,1146,0,0,1146,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,438,Typical,Typical,Paved,160,22,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,187500,-93.646788,41.996089 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11305,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,886,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,593,1922,GasA,Excellent,Y,SBrkr,1922,0,0,1922,1,0,2,0,2,1,Good,6,Typ,1,Excellent,Attchd,Fin,3,692,Typical,Typical,Paved,201,64,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,282000,-93.646894,41.995508 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7777,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,203,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1491,1491,GasA,Excellent,Y,SBrkr,1491,0,0,1491,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,571,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,178000,-93.646634,41.997299 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14536,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,236,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,316,1616,GasA,Excellent,Y,SBrkr,1629,0,0,1629,1,0,2,0,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,808,Typical,Typical,Paved,0,252,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,270000,-93.647585,41.995341 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,94,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,356,1122,GasA,Excellent,Y,SBrkr,1146,1340,0,2486,1,0,3,1,5,1,Good,10,Typ,1,Good,BuiltIn,Fin,2,452,Typical,Typical,Paved,143,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,279900,-93.647199,41.993986 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,35133,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Timberland,Norm,Norm,OneFam,One_Story,Average,Below_Average,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,226,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,Unf,0,413,1572,GasA,Good,Y,SBrkr,1572,0,0,1572,1,0,1,1,3,1,Typical,5,Typ,2,Typical,More_Than_Two_Types,RFn,3,995,Typical,Typical,Paved,0,263,0,0,263,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,186700,-93.662162,41.997352 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,15306,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,100,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1652,1732,GasA,Excellent,Y,SBrkr,1776,0,0,1776,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,712,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,283463,-93.653109,41.993288 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,12633,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,PosA,PosA,OneFam,One_Story,Very_Excellent,Average,2006,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,242,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1824,1824,GasA,Excellent,Y,SBrkr,1824,0,0,1824,0,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,932,Typical,Typical,Paved,160,36,0,0,108,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,392000,-93.652429,41.992975 -Two_Story_1946_and_Newer,Residential_Low_Density,88,12665,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,245,Good,Typical,PConc,Good,Good,Gd,Unf,7,Unf,0,1094,1094,GasA,Excellent,Y,SBrkr,1133,1349,0,2482,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,642,Typical,Typical,Paved,144,39,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,281213,-93.647569,41.993261 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8402,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Feedr,Norm,OneFam,One_Story,Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,914,1120,GasA,Excellent,Y,SBrkr,1120,0,0,1120,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,147000,-93.610102,41.990054 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,200,43500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Artery,Norm,OneFam,One_Story,Fair,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,2034,0,0,2034,0,0,1,0,2,1,Typical,9,Min1,0,No_Fireplace,More_Than_Two_Types,RFn,4,1041,Typical,Typical,Dirt_Gravel,483,266,0,0,0,561,Typical,Good_Privacy,None,0,6,2007,WD ,Normal,130000,-93.610118,41.992318 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,62,6710,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Mitchell,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,134,Typical,Typical,PConc,Excellent,Typical,Av,Rec,6,GLQ,904,0,920,GasA,Excellent,Y,SBrkr,936,0,0,936,2,0,0,1,0,1,Typical,3,Typ,0,No_Fireplace,Attchd,Fin,2,460,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,140000,-93.608192,41.993356 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,25286,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Average,1963,1963,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,431,1064,GasA,Good,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,648,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,132250,-93.605507,41.993107 -Duplex_All_Styles_and_Ages,Residential_Low_Density,100,25000,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Average,Below_Average,1967,1967,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,1632,1632,GasA,Typical,Y,SBrkr,1632,0,0,1632,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,143000,-93.6072813,41.9927922 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,0,32463,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Mitchell,Norm,Norm,TwoFmCon,One_Story,Below_Average,Below_Average,1961,1975,Gable,CompShg,MetalSd,MetalSd,Stone,149,Typical,Good,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,90,1249,GasA,Excellent,Y,SBrkr,1622,0,0,1622,1,0,1,0,3,1,Typical,7,Typ,1,Typical,More_Than_Two_Types,Fin,4,1356,Typical,Typical,Paved,439,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,168000,-93.607395,41.991211 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,4224,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,One_Story,Average,Good,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,200,1040,GasA,Typical,Y,SBrkr,1060,0,0,1060,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,572,Typical,Typical,Paved,100,110,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,129900,-93.605355,41.992917 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,4224,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,One_Story,Average,Average,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,268,1142,GasA,Typical,Y,SBrkr,1142,0,0,1142,1,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,Fin,2,528,Typical,Typical,Paved,536,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,134000,-93.605354,41.99284 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1504,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Below_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,294,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,253,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,85500,-93.603546,41.992145 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,0,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SLvl,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,27,530,GasA,Typical,Y,SBrkr,530,462,0,992,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,BuiltIn,Fin,1,297,Typical,Typical,Paved,112,97,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,106500,-93.601862,41.992516 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1495,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Above_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,BrkFace,189,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,162,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,93900,-93.601827,41.992479 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2001,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,BrkFace,80,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Fair,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,75000,-93.601507,41.991709 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Above_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,GLQ,499,0,630,GasA,Good,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2007,WD ,Normal,84500,-93.602094,41.991641 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Fair,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2007,COD,Normal,75190,-93.603422,41.991868 -Split_or_Multilevel,Residential_Low_Density,0,11333,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,490,1029,GasA,Typical,Y,SBrkr,1062,0,0,1062,1,0,1,0,3,1,Typical,5,Typ,2,Typical,Attchd,RFn,2,539,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,146800,-93.604923,41.991612 -Split_Foyer,Residential_Low_Density,72,9129,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,144,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,0,923,GasA,Typical,Y,SBrkr,1008,0,0,1008,1,0,1,0,1,1,Good,4,Typ,1,Fair,Attchd,Fin,2,678,Typical,Typical,Paved,201,66,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,153500,-93.604644,41.991098 -Split_or_Multilevel,Residential_Low_Density,0,15957,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Corner,Mod,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1977,1977,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,96,1244,GasA,Typical,Y,SBrkr,1356,0,0,1356,2,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,528,Typical,Typical,Paved,1424,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,WD ,Normal,188000,-93.604521,41.989581 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,33983,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1994,Gable,CompShg,Plywood,Plywood,None,0,Typical,Fair,PConc,Typical,Typical,Mn,ALQ,1,Unf,0,48,1160,GasA,Typical,Y,SBrkr,1676,0,0,1676,1,0,1,1,3,1,Good,6,Mod,2,Typical,Attchd,RFn,2,672,Typical,Typical,Partial_Pavement,690,90,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,196000,-93.60678,41.990264 -Two_Story_1946_and_Newer,Residential_Low_Density,68,8286,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Good,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,185,716,GasA,Excellent,Y,SBrkr,716,716,0,1432,1,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,Fin,2,531,Typical,Typical,Paved,0,136,0,0,240,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,157000,-93.604524,41.989488 -Split_Foyer,Residential_Low_Density,50,6723,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Good,1971,1971,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,796,GasA,Typical,Y,SBrkr,796,0,0,796,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,129,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2007,WD ,Normal,138000,-93.602559,41.991144 -Split_Foyer,Residential_Low_Density,54,7244,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Good,1970,1970,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Good,Typical,Av,ALQ,1,Unf,0,149,768,GasA,Excellent,Y,SBrkr,768,0,0,768,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,104,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,129500,-93.6015199,41.991371 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,124,27697,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1961,1961,Shed,CompShg,Plywood,Plywood,CBlock,198,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,585,1396,GasA,Typical,N,SBrkr,1608,0,0,1608,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,444,Typical,Fair,Paved,152,38,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,COD,Abnorml,80000,-93.607114,41.989905 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,7599,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1982,2006,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,280,845,GasA,Typical,Y,SBrkr,845,0,0,845,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,129500,-93.604858,41.987606 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1976,2003,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,LwQ,4,Unf,0,0,1090,GasA,Typical,Y,SBrkr,1178,0,0,1178,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,502,Typical,Typical,Paved,0,44,0,0,88,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,135000,-93.605119,41.989768 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,8314,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1982,1982,Gable,CompShg,HdBoard,ImStucc,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,270,816,GasA,Typical,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,124500,-93.604146,41.98822 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,11625,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,220,816,GasA,Typical,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,264,Typical,Typical,Paved,330,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,139000,-93.6030815,41.9881729 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,10712,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1991,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,762,974,GasA,Typical,Y,SBrkr,974,0,0,974,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,28,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,Oth,Abnorml,93500,-93.606641,41.98651 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,10447,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1984,1984,Gable,CompShg,Plywood,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,348,864,GasA,Typical,Y,SBrkr,887,0,0,887,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,124500,-93.604858,41.986588 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11027,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,28,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,539,171,1178,GasA,Good,Y,SBrkr,1293,0,0,1293,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,452,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,149900,-93.618462,42.053406 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10533,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1956,1956,Hip,CompShg,VinylSd,VinylSd,BrkFace,244,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,235,1008,GasA,Typical,Y,SBrkr,1024,0,0,1024,1,0,1,0,2,1,Good,5,Typ,2,Typical,Attchd,Unf,1,313,Typical,Typical,Paved,0,0,0,0,280,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,157500,-93.618182,42.053327 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11765,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,Stone,302,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,490,1617,GasA,Fair,Y,SBrkr,1797,0,0,1797,0,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,3,963,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,169000,-93.6192621,42.0531078 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,39384,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Sev,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,Stone,902,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,595,1705,GasA,Excellent,Y,SBrkr,1390,0,0,1390,1,0,1,1,1,1,Excellent,4,Min1,2,Good,Attchd,Unf,2,550,Typical,Typical,Paved,0,189,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,252000,-93.616439,42.052519 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11727,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,434,Typical,Good,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1851,1851,GasA,Good,Y,SBrkr,1851,0,0,1851,0,0,2,0,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,0,146,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,192100,-93.617432,42.050507 -Two_Story_1946_and_Newer,Residential_Low_Density,60,8238,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,113,813,GasA,Excellent,Y,SBrkr,813,712,0,1525,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,400,Typical,Typical,Paved,421,72,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,183500,-93.639067,42.059909 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13006,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1997,Gable,CompShg,HdBoard,HdBoard,BrkFace,285,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,964,964,GasA,Good,Y,SBrkr,993,1243,0,2236,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,2,642,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,222000,-93.637871,42.061407 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13041,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,781,781,GasA,Good,Y,SBrkr,781,890,0,1671,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,423,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,170000,-93.637482,42.060425 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13031,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,99,691,GasA,Good,Y,SBrkr,691,807,0,1498,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,409,Typical,Typical,Paved,315,44,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,187500,-93.637566,42.06048 -Two_Story_1946_and_Newer,Residential_Low_Density,54,9783,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1996,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,821,821,GasA,Good,Y,SBrkr,821,955,0,1776,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,443,Typical,Typical,Paved,286,116,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,202000,-93.636378,42.060736 -Two_Story_1946_and_Newer,Residential_Low_Density,70,11207,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,FR2,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,88,802,GasA,Good,Y,SBrkr,802,709,0,1511,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,413,Typical,Typical,Paved,95,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,185000,-93.636373,42.061879 -Two_Story_1946_and_Newer,Residential_Low_Density,134,19378,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,456,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1335,1392,GasA,Excellent,Y,SBrkr,1392,1070,0,2462,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,239,132,0,168,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,320000,-93.636052,42.062828 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,14859,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,27,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1670,1670,GasA,Excellent,Y,SBrkr,1670,0,0,1670,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,690,Typical,Typical,Paved,144,60,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,240000,-93.635967,42.06281 -Two_Story_1946_and_Newer,Residential_Low_Density,50,13128,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1074,1074,GasA,Excellent,Y,SBrkr,1074,990,0,2064,0,0,2,1,4,1,Good,7,Typ,1,Good,Attchd,Fin,2,527,Typical,Typical,Paved,0,119,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,250000,-93.635935,42.062802 -Two_Story_1946_and_Newer,Residential_Low_Density,72,10463,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,893,893,GasA,Excellent,Y,SBrkr,901,900,0,1801,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,800,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,239900,-93.635837,42.062798 -Two_Story_1946_and_Newer,Residential_Low_Density,42,13751,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,248,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1640,1700,GasA,Excellent,Y,SBrkr,1700,512,0,2212,1,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,773,Typical,Typical,Paved,237,38,0,0,115,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,309000,-93.635798,42.062891 -Two_Story_1946_and_Newer,Residential_Low_Density,168,23257,Pave,No_Alley_Access,Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,868,868,GasA,Excellent,Y,SBrkr,887,1134,0,2021,0,0,2,1,3,1,Good,9,Typ,1,Good,BuiltIn,RFn,2,422,Typical,Typical,Paved,0,100,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,274725,-93.635932,42.062956 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,68,13108,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1994,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,2062,2062,GasA,Excellent,Y,SBrkr,2079,608,0,2687,0,0,2,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,618,Typical,Typical,Paved,168,12,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,270000,-93.631604,42.060625 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8076,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,112,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,455,1160,GasA,Good,Y,SBrkr,1169,0,0,1169,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,402,Typical,Typical,Paved,0,26,0,0,144,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,183500,-93.635716,42.0591 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7685,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,112,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,179,697,GasA,Good,Y,SBrkr,697,804,0,1501,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,420,Typical,Typical,Paved,190,63,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,165600,-93.636949,42.058421 -Two_Story_1946_and_Newer,Residential_Low_Density,63,9084,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,935,935,GasA,Good,Y,SBrkr,955,677,0,1632,0,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,176500,-93.639293,42.058201 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,30,3701,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1987,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1191,1191,GasA,Typical,Y,SBrkr,1204,0,0,1204,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,461,Typical,Typical,Paved,120,70,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,130000,-93.634231,42.059599 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,44,5306,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,Two_Story,Good,Good,1987,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Rec,215,354,1064,GasA,Good,Y,SBrkr,1064,703,0,1767,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,504,Good,Typical,Paved,441,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,239000,-93.631572,42.059332 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,6563,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Mod,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,1985,1985,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,594,1742,GasA,Typical,Y,SBrkr,1742,0,0,1742,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,114,28,234,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,275000,-93.632571,42.058136 -Two_Story_1946_and_Newer,Residential_Low_Density,59,16023,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,600,Good,Excellent,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,180,1398,GasA,Excellent,Y,SBrkr,1414,1384,0,2798,1,0,3,1,3,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,670,Typical,Typical,Paved,182,37,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,457347,-93.629601,42.060459 -Two_Story_1946_and_Newer,Residential_Low_Density,60,18062,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2006,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,662,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1528,1528,GasA,Excellent,Y,SBrkr,1528,1862,0,3390,0,0,3,1,5,1,Excellent,10,Typ,1,Excellent,BuiltIn,Fin,3,758,Typical,Typical,Paved,204,34,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,545224,-93.629337,42.060527 -Two_Story_1946_and_Newer,Residential_Low_Density,63,12292,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,184,Good,Typical,PConc,Excellent,Good,Gd,GLQ,3,Unf,0,889,1094,GasA,Excellent,Y,SBrkr,1102,1371,0,2473,0,0,2,1,4,1,Good,11,Typ,1,Good,BuiltIn,Fin,3,675,Typical,Typical,Paved,246,39,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,356383,-93.629174,42.060516 -Two_Story_1946_and_Newer,Residential_Low_Density,85,16056,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,Stone,208,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1752,1992,GasA,Excellent,Y,SBrkr,1992,876,0,2868,0,0,3,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,716,Typical,Typical,Paved,214,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,556581,-93.628607,42.061478 -Two_Story_1946_and_Newer,Residential_Low_Density,82,12438,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,466,Excellent,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1234,1234,GasA,Excellent,Y,SBrkr,1264,1312,0,2576,0,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,666,Typical,Typical,Paved,324,100,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,361919,-93.62877,42.061008 -Two_Story_1946_and_Newer,Residential_Low_Density,82,16052,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,734,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,644,1850,GasA,Excellent,Y,SBrkr,1850,848,0,2698,1,0,2,1,4,1,Excellent,11,Typ,1,Good,Attchd,RFn,3,736,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,535000,-93.627767,42.060111 -Two_Story_1946_and_Newer,Residential_Low_Density,92,15922,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,550,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1390,1390,GasA,Excellent,Y,SBrkr,1390,1405,0,2795,0,0,3,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,660,Typical,Typical,Paved,272,102,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,New,Partial,401179,-93.626337,42.059957 -Two_Story_1946_and_Newer,Residential_Low_Density,66,13682,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,1031,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1410,1410,GasA,Excellent,Y,SBrkr,1426,1519,0,2945,0,0,3,1,3,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,641,Typical,Typical,Paved,192,0,37,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,438780,-93.626477,42.061096 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14762,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Feedr,Norm,OneFam,Two_Story,Average,Above_Average,1948,1950,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1547,720,53,2320,0,0,2,0,2,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,672,Typical,Typical,Partial_Pavement,120,144,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,169000,-93.6220148,42.0593867 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,100,34650,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Gilbert,Norm,Norm,TwoFmCon,One_Story,Average,Average,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,0,1056,GasA,Typical,N,SBrkr,1056,0,0,1056,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,572,Typical,Typical,Paved,264,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,145000,-93.625169,42.056702 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,60,8147,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,230,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,523,1714,GasA,Excellent,Y,SBrkr,1714,0,0,1714,1,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,517,Typical,Typical,Paved,156,55,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,318000,-93.63007,42.058563 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,11302,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,Other,BrkFace,238,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,392,1814,GasA,Excellent,Y,SBrkr,1826,0,0,1826,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,758,Typical,Typical,Paved,180,75,0,0,120,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,319000,-93.624062,42.059947 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,18261,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,420,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,494,1910,GasA,Excellent,Y,SBrkr,2000,0,0,2000,1,0,2,1,3,1,Excellent,8,Typ,2,Good,Attchd,Unf,3,722,Typical,Typical,Paved,351,102,0,0,123,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,470000,-93.628269,42.058816 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,14145,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1984,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,995,1208,GasA,Excellent,Y,SBrkr,1621,0,0,1621,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,108,45,0,0,0,0,No_Pool,No_Fence,Shed,400,5,2006,WD ,Normal,202500,-93.638026,42.055607 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,13837,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,178,Good,Good,PConc,Good,Good,No,GLQ,3,LwQ,202,0,1204,GasA,Good,Y,SBrkr,1377,806,0,2183,0,0,2,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,Unf,3,786,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,229000,-93.637874,42.054367 -Two_Story_1946_and_Newer,Residential_Low_Density,0,16659,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Good,1977,1994,Gable,CompShg,Plywood,Plywood,BrkFace,34,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,0,795,GasA,Fair,Y,SBrkr,1468,795,0,2263,1,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Fin,2,539,Typical,Typical,Paved,0,250,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,235000,-93.6326645,42.0543376 -Two_Story_1946_and_Newer,Residential_Low_Density,0,18800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,84,796,GasA,Typical,Y,SBrkr,790,784,0,1574,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,566,Typical,Typical,Paved,306,111,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,190000,-93.631515,42.054956 -Split_Foyer,Residential_Low_Density,0,10464,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1980,1980,Gable,CompShg,HdBoard,HdBoard,BrkFace,130,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,138,988,GasA,Typical,Y,SBrkr,1102,0,0,1102,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,582,Typical,Typical,Paved,140,22,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,169000,-93.633002,42.0562619 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1988,1988,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,102,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,916,1845,GasA,Good,Y,SBrkr,1872,0,0,1872,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,604,Typical,Typical,Paved,197,39,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,241500,-93.635486,42.05327 -Two_Story_1946_and_Newer,Residential_Low_Density,81,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,68,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,945,945,GasA,Typical,Y,SBrkr,945,912,0,1857,0,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,482,Typical,Typical,Paved,400,105,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,188900,-93.6353259,42.0524029 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,Plywood,Plywood,BrkFace,157,Typical,Good,CBlock,Good,Typical,No,BLQ,2,LwQ,1061,0,1686,GasA,Typical,Y,SBrkr,1686,0,0,1686,1,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,612,Typical,Typical,Paved,384,131,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,207500,-93.636044,42.053418 -Two_Story_1946_and_Newer,Residential_Low_Density,80,16692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Good,Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,184,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,LwQ,469,133,1392,GasA,Typical,Y,SBrkr,1392,1392,0,2784,1,0,3,1,5,1,Good,12,Typ,2,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,112,0,0,440,519,Fair,Minimum_Privacy,TenC,2000,7,2006,WD ,Normal,250000,-93.637387,42.050514 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Above_Average,Above_Average,1975,1975,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,163,713,GasA,Typical,Y,SBrkr,811,741,0,1552,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,434,Typical,Typical,Paved,209,208,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2006,WD ,Normal,179900,-93.637191,42.050176 -Split_Foyer,Residential_Low_Density,0,9927,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Good,Average,1976,1976,Gable,CompShg,VinylSd,Wd Shng,Stone,252,Good,Typical,CBlock,Excellent,Typical,Gd,GLQ,3,Unf,0,42,1047,GasA,Typical,Y,SBrkr,1083,0,0,1083,1,0,1,0,2,1,Typical,5,Typ,1,Fair,Attchd,RFn,2,596,Typical,Typical,Paved,444,0,40,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,172000,-93.635367,42.049771 -Two_Story_1946_and_Newer,Floating_Village_Residential,75,9512,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,172,960,GasA,Excellent,Y,SBrkr,960,1358,0,2318,1,0,2,1,3,1,Good,8,Typ,1,Excellent,BuiltIn,Fin,2,541,Typical,Typical,Paved,0,246,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,294323,-93.639516,42.051077 -Split_or_Multilevel,Residential_Low_Density,81,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1975,2000,Gable,CompShg,Plywood,Plywood,BrkFace,248,Typical,Typical,CBlock,Typical,Fair,No,ALQ,1,Unf,0,127,675,GasA,Typical,Y,SBrkr,1109,766,0,1875,0,0,3,0,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,485,Typical,Typical,Paved,48,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,184500,-93.632369,42.052362 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,268,1056,GasA,Excellent,Y,SBrkr,1074,0,0,1074,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,495,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,153500,-93.630348,42.052522 -Two_Story_1946_and_Newer,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,638,832,GasA,Typical,Y,SBrkr,832,832,0,1664,0,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,28,0,0,259,0,No_Pool,Good_Wood,None,0,3,2006,WD ,Normal,162900,-93.632355,42.052359 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,1999,Hip,CompShg,HdBoard,HdBoard,BrkFace,99,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,377,0,1040,GasA,Fair,Y,SBrkr,1309,0,0,1309,1,0,1,1,3,1,Good,5,Typ,1,Fair,Attchd,RFn,2,484,Typical,Typical,Paved,265,0,0,0,0,648,Fair,Good_Privacy,None,0,1,2006,WD ,Normal,181000,-93.630289,42.04977 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,1974,Gable,CompShg,HdBoard,Plywood,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,348,1103,GasA,Typical,Y,SBrkr,1103,0,0,1103,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,462,Typical,Typical,Paved,295,84,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,155000,-93.631162,42.049608 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15870,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1969,1969,Gable,CompShg,VinylSd,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,791,230,1096,GasA,Excellent,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,299,Typical,Typical,Paved,240,32,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Abnorml,138800,-93.62626,42.055131 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9353,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1970,1970,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,864,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,0,7,2006,Oth,Abnorml,116050,-93.625373,42.053459 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,244,858,GasA,Typical,Y,SBrkr,858,0,0,858,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,88000,-93.627981,42.05446 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,620,224,864,GasA,Typical,Y,SBrkr,874,0,0,874,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,63,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,97500,-93.627849,42.054385 -One_Story_PUD_1946_and_Newer,Residential_High_Density,26,8773,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2002,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,536,1487,GasA,Excellent,Y,SBrkr,1419,0,0,1419,1,0,2,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,543,Typical,Typical,Paved,196,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,190000,-93.624411,42.055319 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,514,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,525,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,105500,-93.629906,42.052655 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,363,765,GasA,Good,Y,SBrkr,765,600,0,1365,0,0,1,1,3,1,Good,7,Min1,1,Fair,Attchd,Unf,2,440,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,125500,-93.6292319,42.0524739 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1953,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,408,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,174,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,72,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,83000,-93.627932,42.0527019 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Good,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,380,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,212,494,GasA,Excellent,Y,SBrkr,494,536,0,1030,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,116000,-93.627103,42.051798 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,CemntBd,CmentBd,BrkFace,236,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Typical,Y,SBrkr,672,546,0,1218,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,201,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,91500,-93.627328,42.051839 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Good,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,281,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,355,672,GasA,Good,Y,SBrkr,672,546,0,1218,0,1,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,118000,-93.627366,42.051691 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,207,483,GasA,Typical,Y,SBrkr,483,465,0,948,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,89000,-93.629877,42.051672 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,143,525,GasA,Good,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,352,0,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2006,WD ,Normal,108000,-93.629846,42.051673 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,297,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,483,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,94500,-93.629516,42.051664 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4043,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1975,1975,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,BLQ,156,186,1069,GasA,Good,Y,SBrkr,1069,0,0,1069,0,1,2,0,2,1,Typical,4,Typ,1,Poor,Attchd,RFn,2,440,Typical,Typical,Paved,0,55,0,0,225,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,146300,-93.627169,42.050595 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7514,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1967,1975,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Rec,108,462,943,GasA,Typical,Y,SBrkr,1387,0,0,1387,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,300,Typical,Typical,Paved,0,0,240,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,145000,-93.628857,42.048947 -Split_Foyer,Residential_Low_Density,68,7838,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,95,864,GasA,Typical,Y,SBrkr,900,0,0,900,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,1,288,Typical,Typical,Paved,175,144,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,12,2006,WD ,Normal,123000,-93.62895,42.048946 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Good,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,492,443,1055,GasA,Typical,Y,SBrkr,1055,0,0,1055,1,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,319,Typical,Typical,Paved,0,29,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,137500,-93.624767,42.050705 -Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2179,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,785,855,GasA,Good,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,144000,-93.625709,42.050441 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,16737,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,66,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,533,1980,GasA,Excellent,Y,SBrkr,1980,0,0,1980,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,770,Typical,Typical,Paved,194,45,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,315000,-93.658864,42.061472 -Two_Story_1946_and_Newer,Residential_Low_Density,72,16387,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,215,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,369,1738,GasA,Good,Y,SBrkr,1738,851,0,2589,1,0,2,1,4,1,Excellent,11,Typ,1,Good,Attchd,RFn,3,831,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,412083,-93.658413,42.063307 -Two_Story_1946_and_Newer,Residential_Low_Density,111,16259,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,370,Typical,Typical,PConc,Excellent,Good,Av,Unf,7,Unf,0,1249,1249,GasA,Excellent,Y,SBrkr,1249,1347,0,2596,0,0,3,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,RFn,3,840,Typical,Typical,Paved,240,154,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,342643,-93.658226,42.061901 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,16163,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,232,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1618,1618,GasA,Excellent,Y,SBrkr,1618,0,0,1618,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,3,880,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,252000,-93.657991,42.061284 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Excellent,Typical,PConc,Excellent,Good,Gd,GLQ,3,Unf,0,690,2076,GasA,Excellent,Y,SBrkr,2076,0,0,2076,1,0,2,1,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,850,Typical,Typical,Paved,216,229,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,465000,-93.6566537,42.0628074 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,108,12228,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,206,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1721,1721,GasA,Excellent,Y,SBrkr,1740,0,0,1740,0,0,2,0,4,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,3,874,Typical,Typical,Paved,0,43,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,293000,-93.654774,42.062109 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,14780,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,568,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,363,1868,GasA,Excellent,Y,SBrkr,1868,0,0,1868,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1085,Typical,Typical,Paved,354,56,0,0,156,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,415000,-93.657984,42.061134 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,16033,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,378,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,572,1833,GasA,Excellent,Y,SBrkr,1850,0,0,1850,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,772,Typical,Typical,Paved,519,112,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,326000,-93.658788,42.060342 -Two_Story_1946_and_Newer,Residential_Low_Density,101,14215,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,380,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1158,1158,GasA,Excellent,Y,SBrkr,1158,1218,0,2376,0,0,3,1,4,1,Good,9,Typ,1,Good,BuiltIn,RFn,3,853,Typical,Typical,Paved,240,154,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,325300,-93.657118,42.060272 -Two_Story_1946_and_Newer,Residential_Low_Density,108,13418,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,Stone,132,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1117,1117,GasA,Excellent,Y,SBrkr,1132,1320,0,2452,0,0,3,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,691,Typical,Typical,Paved,113,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,309000,-93.657149,42.060271 -Two_Story_1946_and_Newer,Residential_Low_Density,120,13975,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,525,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1090,1090,GasA,Excellent,Y,SBrkr,1117,1089,0,2206,0,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,670,Typical,Typical,Paved,148,95,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,300000,-93.656864,42.060425 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,178,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1030,1030,GasA,Good,Y,SBrkr,1038,1060,0,2098,0,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,878,Typical,Typical,Paved,192,52,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,275500,-93.653016,42.060835 -Two_Story_1946_and_Newer,Residential_Low_Density,82,9942,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Gable,CompShg,MetalSd,MetalSd,BrkFace,385,Excellent,Typical,PConc,Excellent,Good,Av,GLQ,3,Unf,0,316,1606,GasA,Excellent,Y,SBrkr,1625,466,0,2091,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,2,521,Typical,Typical,Paved,194,84,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,338500,-93.654642,42.060577 -Two_Story_1946_and_Newer,Residential_Low_Density,85,11924,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,WdShing,Wd Shng,Stone,286,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,177,1175,GasA,Excellent,Y,SBrkr,1182,1142,0,2324,1,0,3,0,4,1,Excellent,11,Typ,2,Good,BuiltIn,Fin,3,736,Typical,Typical,Paved,147,21,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,345000,-93.652334,42.060866 -Two_Story_1946_and_Newer,Residential_Low_Density,103,12867,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1209,1209,GasA,Excellent,Y,SBrkr,1209,1044,0,2253,0,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,2,575,Typical,Typical,Paved,243,142,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,344133,-93.654831,42.060424 -Two_Story_1946_and_Newer,Residential_Low_Density,82,10672,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1054,1054,GasA,Good,Y,SBrkr,1054,1335,0,2389,0,0,2,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,672,Typical,Typical,Paved,176,64,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,298236,-93.653452,42.060357 -Two_Story_1946_and_Newer,Residential_Low_Density,82,11643,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,142,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,644,1524,GasA,Excellent,Y,SBrkr,1544,814,0,2358,1,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,784,Typical,Typical,Paved,120,34,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,329900,-93.6531488,42.060377 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,12378,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,622,1896,GasA,Excellent,Y,SBrkr,1944,0,0,1944,1,0,2,0,3,1,Excellent,8,Typ,3,Excellent,Attchd,Fin,3,708,Typical,Typical,Paved,208,175,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,360000,-93.643412,42.061544 -Two_Story_1946_and_Newer,Residential_Low_Density,86,11065,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,788,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1085,1085,GasA,Excellent,Y,SBrkr,1120,850,0,1970,0,0,2,1,3,1,Excellent,8,Typ,1,Good,BuiltIn,Fin,3,753,Typical,Typical,Paved,177,74,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,281000,-93.643862,42.061377 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,121,13758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,430,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,560,1792,GasA,Excellent,Y,SBrkr,1792,0,0,1792,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,925,Typical,Typical,Paved,204,49,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,372397,-93.654997,42.059636 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,131,14828,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,674,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,397,1780,GasA,Excellent,Y,SBrkr,1780,0,0,1780,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,816,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,378000,-93.655461,42.059825 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,11846,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,562,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,225,1792,GasA,Excellent,Y,SBrkr,1792,0,0,1792,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,874,Typical,Typical,Paved,206,49,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,374000,-93.657883,42.058544 -Two_Story_1946_and_Newer,Residential_Low_Density,56,20431,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,870,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,438,1848,GasA,Excellent,Y,SBrkr,1848,880,0,2728,1,0,2,1,4,1,Excellent,10,Typ,2,Excellent,Attchd,Fin,3,706,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,New,Partial,437154,-93.653084,42.05869 -Two_Story_1946_and_Newer,Residential_Low_Density,74,10927,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,280,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,512,1058,GasA,Excellent,Y,SBrkr,1058,846,0,1904,1,0,2,1,3,1,Excellent,8,Typ,1,Good,BuiltIn,Fin,2,736,Typical,Typical,Paved,179,60,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,275000,-93.650626,42.059216 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13215,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,112,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,426,1420,GasA,Excellent,Y,SBrkr,1426,488,0,1914,1,0,2,1,3,1,Good,9,Typ,1,Typical,BuiltIn,RFn,3,746,Typical,Typical,Paved,168,127,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,279000,-93.6534099,42.059017 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,7052,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,240,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,705,1364,GasA,Excellent,Y,SBrkr,1364,0,0,1364,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,185850,-93.649714,42.059184 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,5911,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,278,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1088,1560,GasA,Excellent,Y,SBrkr,1565,0,0,1565,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,556,Typical,Typical,Paved,196,56,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,282500,-93.654138,42.057164 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6955,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,94,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1368,1368,GasA,Excellent,Y,SBrkr,1368,0,0,1368,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,474,Typical,Typical,Paved,132,35,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,202500,-93.649553,42.058231 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,6792,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,94,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1368,1368,GasA,Excellent,Y,SBrkr,1368,0,0,1368,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,474,Typical,Typical,Paved,132,35,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,202665,-93.649525,42.058238 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7740,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,518,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,663,1686,GasA,Excellent,Y,SBrkr,1686,0,0,1686,1,0,2,0,1,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,899,Typical,Typical,Paved,266,100,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,332200,-93.654124,42.057164 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6373,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,572,Excellent,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,1251,1666,GasA,Excellent,Y,SBrkr,1666,0,0,1666,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,575,Typical,Typical,Paved,228,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,310090,-93.65414,42.057164 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3136,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,163,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1405,1405,GasA,Excellent,Y,SBrkr,1405,0,0,1405,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,478,Typical,Typical,Paved,148,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,171750,-93.650617,42.05758 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1288,1316,GasA,Excellent,Y,SBrkr,1316,0,0,1316,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,397,Typical,Typical,Paved,100,0,0,23,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,169990,-93.646459,42.063304 -Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,New,Partial,170440,-93.645841,42.062442 -Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,754,0,1492,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,169985,-93.645774,42.062361 -Two_Story_1946_and_Newer,Residential_Low_Density,58,17104,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,GLQ,3,Unf,0,100,654,GasA,Excellent,Y,SBrkr,664,832,0,1496,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,426,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,179665,-93.644411,42.061915 -Two_Story_1946_and_Newer,Residential_Low_Density,58,14054,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,879,879,GasA,Excellent,Y,SBrkr,879,984,0,1863,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,660,Typical,Typical,Paved,100,17,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,219210,-93.644366,42.061928 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,11660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1326,1326,GasA,Excellent,Y,SBrkr,1326,0,0,1326,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,427,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,174190,-93.644627,42.063031 -Two_Story_1946_and_Newer,Residential_Low_Density,105,15578,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Good,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Typical,8,Typ,0,No_Fireplace,Attchd,RFn,2,429,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,172785,-93.645755,42.062576 -Two_Story_1946_and_Newer,Residential_Low_Density,96,11631,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,236,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1052,1052,GasA,Excellent,Y,SBrkr,1052,1321,0,2373,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,632,Typical,Typical,Paved,120,46,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,258000,-93.644213,42.061984 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9073,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,754,0,1492,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,100,32,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,185101,-93.644306,42.061987 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3087,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,767,1220,GasA,Excellent,Y,SBrkr,1364,0,0,1364,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,437,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,210250,-93.641662,42.062574 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3196,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,18,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1374,1374,GasA,Excellent,Y,SBrkr,1557,0,0,1557,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,420,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,234000,-93.641946,42.063298 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3196,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1273,1273,GasA,Excellent,Y,SBrkr,1456,0,0,1456,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,400,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,215000,-93.640287,42.063329 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,2938,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,330,1368,GasA,Excellent,Y,SBrkr,1511,0,0,1511,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,398,Typical,Typical,Paved,130,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,246990,-93.640227,42.063185 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,3072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,18,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1375,1375,GasA,Excellent,Y,SBrkr,1414,0,0,1414,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,2,398,Typical,Typical,Paved,144,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,178740,-93.641321,42.062374 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,3072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,18,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,306,1365,GasA,Excellent,Y,SBrkr,1548,0,0,1548,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,388,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,225000,-93.6419,42.0623 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1232,1248,GasA,Excellent,Y,SBrkr,1248,0,0,1248,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,438,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,167240,-93.642261,42.063301 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1126,1142,GasA,Excellent,Y,SBrkr,1142,0,0,1142,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,90,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,156820,-93.642241,42.063301 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3013,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,145,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1346,1362,GasA,Excellent,Y,SBrkr,1506,0,0,1506,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,142,20,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,213490,-93.642116,42.063301 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3982,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,GLQ,3,Unf,0,366,1520,GasA,Excellent,Y,SBrkr,1567,0,0,1567,1,0,2,0,1,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,264561,-93.641518,42.062831 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4045,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,45,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,286,1356,GasA,Excellent,Y,SBrkr,1500,0,0,1500,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,161,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,246578,-93.641522,42.062873 -Two_Story_1946_and_Newer,Residential_Low_Density,60,21930,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,Unf,7,Unf,0,732,732,GasA,Excellent,Y,SBrkr,734,1104,0,1838,0,0,2,1,4,1,Typical,7,Typ,1,Good,BuiltIn,Fin,2,372,Typical,Typical,Paved,100,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,192140,-93.64458,42.060239 -Two_Story_1946_and_Newer,Residential_Low_Density,0,7861,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,326,783,GasA,Excellent,Y,SBrkr,807,702,0,1509,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,100,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,183200,-93.642588,42.060293 -Two_Story_1946_and_Newer,Residential_Low_Density,74,9056,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,Av,Unf,7,Unf,0,707,707,GasA,Excellent,Y,SBrkr,707,707,0,1414,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,403,Typical,Typical,Paved,100,35,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,178000,-93.6436049,42.059803 -Two_Story_1946_and_Newer,Residential_Low_Density,59,9171,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,848,848,GasA,Excellent,Y,SBrkr,848,750,0,1598,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,433,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,201490,-93.644094,42.061727 -Two_Story_1946_and_Newer,Residential_Low_Density,106,11194,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1406,1406,GasA,Excellent,Y,SBrkr,1454,482,0,1936,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,504,Typical,Typical,Paved,188,124,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,230500,-93.641467,42.060836 -Two_Story_1946_and_Newer,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,783,783,GasA,Excellent,Y,SBrkr,807,702,0,1509,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,180000,-93.640211,42.060074 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8121,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,953,953,GasA,Excellent,Y,SBrkr,953,711,0,1664,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,100,40,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,172400,-93.642713,42.05865 -Two_Story_1946_and_Newer,Residential_Low_Density,0,8658,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,250,982,GasA,Excellent,Y,SBrkr,1008,881,0,1889,0,0,2,1,3,1,Typical,9,Typ,1,Typical,Attchd,Fin,2,431,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,215000,-93.642913,42.05853 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11214,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,930,930,GasA,Good,Y,SBrkr,956,930,0,1886,0,0,2,1,4,1,Good,10,Typ,1,Typical,Attchd,Fin,2,431,Typical,Typical,Paved,89,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,199900,-93.641281,42.057905 -Two_Story_1946_and_Newer,Residential_Low_Density,58,10852,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,173,959,GasA,Excellent,Y,SBrkr,959,712,0,1671,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,472,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,173000,-93.64136,42.057838 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12104,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1035,1035,GasA,Good,Y,SBrkr,1082,1240,0,2322,0,0,3,1,4,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,3,617,Typical,Typical,Paved,400,45,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,287602,-93.641283,42.05696 -Two_Story_1946_and_Newer,Residential_Low_Density,66,8738,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,302,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,975,975,GasA,Excellent,Y,SBrkr,1005,1286,0,2291,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,2,429,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,230000,-93.641053,42.058461 -Two_Story_1946_and_Newer,Residential_Low_Density,82,9452,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,423,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,322,1396,GasA,Excellent,Y,SBrkr,1407,985,0,2392,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,870,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,348000,-93.655739,42.05443 -Two_Story_1946_and_Newer,Residential_Low_Density,84,9660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,242,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,253,1044,GasA,Excellent,Y,SBrkr,1079,897,0,1976,1,0,2,1,3,1,Good,7,Typ,1,Excellent,Attchd,Fin,3,885,Typical,Typical,Paved,210,31,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,286000,-93.654519,42.05384 -Two_Story_1946_and_Newer,Residential_Low_Density,83,9545,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,322,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,655,1160,GasA,Excellent,Y,SBrkr,1205,1029,0,2234,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,RFn,3,768,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,300000,-93.654444,42.053801 -Two_Story_1946_and_Newer,Residential_Low_Density,118,35760,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,1995,1996,Hip,CompShg,HdBoard,HdBoard,BrkFace,1378,Good,Good,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,543,1930,GasA,Excellent,Y,SBrkr,1831,1796,0,3627,1,0,3,1,4,1,Good,10,Typ,1,Typical,Attchd,Fin,3,807,Typical,Typical,Paved,361,76,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,625000,-93.657851,42.053314 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9233,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,877,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,358,1540,GasA,Excellent,Y,SBrkr,1540,1315,0,2855,1,0,2,1,4,1,Excellent,9,Typ,1,Typical,Attchd,RFn,3,774,Typical,Typical,Paved,247,55,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,405000,-93.6532,42.055601 -Two_Story_1946_and_Newer,Residential_Low_Density,78,12011,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,530,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,130,1086,GasA,Excellent,Y,SBrkr,1086,838,0,1924,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,592,Typical,Typical,Paved,208,75,0,0,374,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,280000,-93.65227,42.053154 -Two_Story_1946_and_Newer,Residential_Low_Density,93,12090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,650,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1141,1141,GasA,Good,Y,SBrkr,1165,1098,0,2263,0,0,2,1,4,1,Good,10,Typ,1,Typical,BuiltIn,Fin,2,420,Typical,Typical,Paved,144,123,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,258000,-93.650059,42.053466 -Two_Story_1946_and_Newer,Residential_Low_Density,83,10019,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1995,Hip,CompShg,VinylSd,VinylSd,BrkFace,397,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,815,1342,GasA,Excellent,Y,SBrkr,1358,1368,0,2726,0,0,2,1,4,1,Good,9,Typ,1,Excellent,Attchd,RFn,3,725,Typical,Typical,Paved,307,169,168,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,350000,-93.653915,42.05178 -Two_Story_1946_and_Newer,Residential_Low_Density,114,17242,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,1993,1994,Hip,CompShg,MetalSd,MetalSd,BrkFace,738,Good,Good,PConc,Excellent,Typical,Gd,Rec,6,GLQ,1393,48,1733,GasA,Excellent,Y,SBrkr,1933,1567,0,3500,1,0,3,1,4,1,Excellent,11,Typ,1,Typical,Attchd,RFn,3,959,Typical,Typical,Paved,870,86,0,0,210,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,584500,-93.655997,42.049423 -Two_Story_1946_and_Newer,Residential_Low_Density,0,10236,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,501,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,BLQ,168,742,1290,GasA,Excellent,Y,SBrkr,1305,1189,0,2494,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,3,803,Typical,Typical,Paved,200,95,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,321000,-93.655704,42.051073 -Two_Story_1946_and_Newer,Residential_Low_Density,92,10120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1994,Hip,CompShg,VinylSd,VinylSd,BrkFace,391,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,425,1165,GasA,Excellent,Y,SBrkr,1203,1323,0,2526,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,844,Typical,Typical,Paved,309,78,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,290000,-93.653608,42.049687 -Two_Story_1946_and_Newer,Residential_Low_Density,0,12585,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1993,Gable,CompShg,HdBoard,ImStucc,BrkFace,420,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,1039,0,1286,GasA,Excellent,Y,SBrkr,1565,1234,0,2799,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,3,704,Typical,Typical,Paved,432,136,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,315000,-93.653479,42.049528 -Two_Story_1946_and_Newer,Residential_Low_Density,75,12447,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,CemntBd,CmentBd,Stone,192,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,SBrkr,1116,848,0,1964,0,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,760,Typical,Typical,Paved,200,70,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,New,Partial,252000,-93.642317,42.055056 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,49,15218,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,108,1670,GasA,Excellent,Y,SBrkr,1670,0,0,1670,1,0,2,1,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,928,Typical,Typical,Paved,0,240,200,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,336820,-93.642082,42.054318 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,49,20896,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,356,2077,GasA,Excellent,Y,SBrkr,2097,0,0,2097,1,0,1,1,1,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,1134,Typical,Typical,Paved,192,267,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,New,Partial,423000,-93.64178,42.054176 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10182,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRNn,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,420,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,440,1660,GasA,Excellent,Y,SBrkr,1660,0,0,1660,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,500,Typical,Typical,Paved,322,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,290000,-93.641793,42.054175 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8688,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,228,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1616,1616,GasA,Excellent,Y,SBrkr,1616,0,0,1616,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,834,Typical,Typical,Paved,208,59,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,232000,-93.643635,42.053183 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,60,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1504,1504,GasA,Excellent,Y,SBrkr,1504,0,0,1504,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,510,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,199000,-93.643811,42.053033 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1254,1278,GasA,Excellent,Y,SBrkr,1278,0,0,1278,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,584,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,198600,-93.650361,42.0518 -Two_Story_1946_and_Newer,Floating_Village_Residential,100,13162,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Feedr,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,200,2036,GasA,Excellent,Y,SBrkr,2036,604,0,2640,1,0,3,1,3,1,Excellent,11,Typ,1,Good,Attchd,RFn,3,792,Typical,Typical,Paved,0,265,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,405749,-93.64407,42.052265 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,81,11216,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1489,1489,GasA,Excellent,Y,SBrkr,1489,0,0,1489,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,776,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,232600,-93.643898,42.052272 -Two_Story_1946_and_Newer,Floating_Village_Residential,84,10728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1095,1095,GasA,Good,Y,SBrkr,1095,844,0,1939,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,1053,Typical,Typical,Paved,192,51,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,266000,-93.642862,42.052346 -Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,756,756,GasA,Excellent,Y,SBrkr,756,797,0,1553,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,615,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,186500,-93.642309,42.051355 -Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,858,858,GasA,Excellent,Y,SBrkr,858,858,0,1716,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,615,Typical,Typical,Paved,0,53,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,200825,-93.642374,42.051426 -Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,770,770,GasA,Excellent,Y,SBrkr,778,798,0,1576,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,614,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,197000,-93.64357,42.051382 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,60,7200,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,68,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,357,1262,GasA,Good,Y,SBrkr,1262,0,0,1262,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,572,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,185000,-93.643173,42.051245 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,62,7500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1257,1257,GasA,Excellent,Y,SBrkr,1266,0,0,1266,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,453,Typical,Typical,Paved,38,144,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,176000,-93.643859,42.051225 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,60,7200,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1293,1293,GasA,Excellent,Y,SBrkr,1301,0,0,1301,1,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,RFn,2,572,Typical,Typical,Paved,216,121,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,187750,-93.643977,42.051224 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7733,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Mn,GLQ,3,Unf,0,1118,1142,GasA,Excellent,Y,SBrkr,1142,0,0,1142,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,4,50,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,139500,-93.692575,42.037843 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11024,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,118,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1400,1400,GasA,Excellent,Y,SBrkr,1400,0,0,1400,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,612,Typical,Typical,Paved,144,55,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,186800,-93.691403,42.036149 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1131,1131,GasA,Excellent,Y,SBrkr,1131,0,0,1131,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,132000,-93.691787,42.037915 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1141,1141,GasA,Excellent,Y,SBrkr,1141,0,0,1141,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Abnorml,142500,-93.691754,42.037911 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,217,1158,GasA,Excellent,Y,SBrkr,1158,0,0,1158,1,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,158000,-93.690959,42.037827 -Two_Story_1946_and_Newer,Residential_Low_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,172,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,891,891,GasA,Excellent,Y,SBrkr,891,795,0,1686,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,462,Typical,Typical,Paved,144,101,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,184000,-93.691246,42.036605 -Two_Story_1946_and_Newer,Residential_Low_Density,74,7632,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,96,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,784,784,GasA,Excellent,Y,SBrkr,831,754,0,1585,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,449,Typical,Typical,Paved,100,77,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,184900,-93.691247,42.03718 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8304,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,941,941,GasA,Excellent,Y,SBrkr,941,896,0,1837,0,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,688,Typical,Typical,Paved,150,165,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,187000,-93.689021,42.036849 -Two_Story_1946_and_Newer,Residential_Low_Density,65,7153,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Typical,No,ALQ,1,Unf,0,374,761,GasA,Excellent,Y,SBrkr,810,793,0,1603,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,0,124,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,175900,-93.68782,42.034595 -Two_Story_1946_and_Newer,Residential_Low_Density,70,9370,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,78,836,GasA,Excellent,Y,SBrkr,844,887,0,1731,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,462,Typical,Typical,Paved,307,85,0,0,224,0,No_Pool,No_Fence,Othr,3000,10,2006,WD ,Family,248500,-93.687695,42.03674 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1990,1991,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,494,1398,GasA,Good,Y,SBrkr,1398,0,0,1398,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,542,Typical,Typical,Paved,0,46,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,172000,-93.686267,42.035555 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1991,1991,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,939,1217,GasA,Good,Y,SBrkr,1217,0,0,1217,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,151000,-93.686267,42.035629 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9019,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,Two_Story,Above_Average,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,362,636,GasA,Excellent,Y,SBrkr,636,684,0,1320,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,472,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,150000,-93.68665,42.03748 -One_Story_1946_and_Newer_All_Styles,Residential_High_Density,0,8900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,0,1056,GasA,Typical,Y,SBrkr,1056,0,0,1056,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Normal,107000,-93.681595,42.035795 -One_Story_1946_and_Newer_All_Styles,Residential_High_Density,60,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,187,828,GasA,Good,Y,SBrkr,965,0,0,965,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,300,Typical,Typical,Paved,421,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Abnorml,119900,-93.681407,42.035235 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Below_Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,51,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,952,988,GasA,Excellent,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,129500,-93.674167,42.036051 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,One_Story,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,336,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,396,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,1,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,ConLI,Abnorml,125000,-93.675963,42.036087 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,68,8927,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,One_and_Half_Fin,Above_Average,Above_Average,1977,1977,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1286,368,0,1654,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,119500,-93.677123,42.036206 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9240,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1962,2002,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,252,864,GasA,Good,Y,SBrkr,1211,0,0,1211,0,0,1,0,2,1,Typical,6,Min1,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,161,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,144000,-93.675723,42.035302 -Split_or_Multilevel,Residential_Low_Density,88,8471,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Good,1977,1995,Gable,CompShg,HdBoard,Plywood,BrkFace,46,Typical,Typical,CBlock,Good,Good,Av,ALQ,1,Unf,0,0,506,GasA,Typical,Y,SBrkr,1212,0,0,1212,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,492,Typical,Typical,Paved,292,12,0,0,0,0,No_Pool,Good_Wood,None,0,7,2006,WD ,Normal,151000,-93.677012,42.035094 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9308,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRNe,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,430,984,GasA,Typical,Y,SBrkr,984,0,0,984,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,310,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,126000,-93.669553,42.034692 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,Plywood,Plywood,BrkFace,90,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,270,450,882,GasA,Typical,Y,SBrkr,909,0,0,909,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,294,Typical,Typical,Paved,0,155,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,COD,Normal,116000,-93.671037,42.035696 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8638,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,744,925,GasA,Good,Y,SBrkr,925,0,0,925,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,203,74,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,133500,-93.672411,42.035841 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13052,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,312,1024,GasA,Typical,Y,SBrkr,1024,0,0,1024,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2006,WD ,Normal,120875,-93.670919,42.035325 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13526,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Hip,CompShg,HdBoard,Plywood,BrkFace,114,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,375,0,935,GasA,Typical,Y,SBrkr,935,0,0,935,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Normal,137000,-93.670998,42.035536 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8020,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,268,912,GasA,Typical,N,SBrkr,912,0,0,912,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2006,WD ,Normal,124000,-93.669708,42.03457 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6993,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Good,1961,1994,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,447,912,GasA,Typical,Y,SBrkr,1236,0,0,1236,0,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,135000,-93.669757,42.03458 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8789,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,253,912,GasA,Typical,Y,SBrkr,941,0,0,941,0,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,64,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,129200,-93.671926,42.034663 -Split_or_Multilevel,Residential_Low_Density,100,14330,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Corner,Gtl,Veenker,Norm,Norm,OneFam,SLvl,Good,Below_Average,1974,1974,Gable,CompShg,WdShing,Wd Sdng,BrkFace,145,Good,Fair,CBlock,Good,Typical,Gd,ALQ,1,BLQ,497,228,1748,GasA,Good,Y,SBrkr,2151,495,0,2646,1,2,2,0,3,1,Good,9,Mod,4,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,641,100,0,0,0,800,Good,Good_Privacy,None,0,1,2006,WD ,Normal,260000,-93.660643,42.037065 -Two_Story_1946_and_Newer,Residential_Low_Density,121,16059,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1991,1992,Hip,CompShg,HdBoard,HdBoard,BrkFace,284,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1288,1288,GasA,Excellent,Y,SBrkr,1301,1116,0,2417,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,462,Typical,Typical,Paved,127,82,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,260000,-93.653876,42.045763 -Two_Story_1946_and_Newer,Residential_Low_Density,105,11025,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1993,Gable,CompShg,HdBoard,ImStucc,BrkFace,692,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,216,1334,GasA,Excellent,Y,SBrkr,1520,1306,0,2826,1,0,2,1,3,1,Good,9,Typ,3,Typical,Attchd,RFn,3,888,Typical,Typical,Paved,177,208,186,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,334000,-93.653512,42.048608 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1993,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,326,1338,GasA,Excellent,Y,SBrkr,1352,1168,0,2520,1,0,2,1,5,1,Good,10,Typ,1,Typical,Attchd,RFn,3,796,Typical,Typical,Paved,209,55,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Abnorml,310000,-93.652348,42.048371 -Two_Story_1946_and_Newer,Residential_Low_Density,0,13346,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1992,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,367,1095,GasA,Excellent,Y,SBrkr,1166,1129,0,2295,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,2,590,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,268000,-93.650474,42.047997 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,12461,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Gable,CompShg,ImStucc,ImStucc,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,168,1624,GasA,Excellent,Y,SBrkr,1624,0,0,1624,1,0,2,0,2,1,Good,5,Typ,1,Fair,Attchd,RFn,3,757,Typical,Typical,Paved,0,114,192,0,0,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,262000,-93.650057,42.048058 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3628,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1143,1143,GasA,Excellent,Y,SBrkr,1143,0,0,1143,0,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,588,Typical,Typical,Paved,0,191,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,176400,-93.647832,42.047592 -One_Story_PUD_1946_and_Newer,Floating_Village_Residential,37,3316,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,208,1247,GasA,Excellent,Y,SBrkr,1247,0,0,1247,1,0,1,1,1,1,Good,4,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,197000,-93.646526,42.04768 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,600,80,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,151000,-93.644891,42.047665 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,623,80,1223,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,147400,-93.644891,42.047642 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,623,80,1223,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,149900,-93.644891,42.047618 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,600,80,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,144152,-93.644891,42.047595 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2998,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,403,756,GasA,Excellent,Y,SBrkr,768,756,0,1524,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,166000,-93.645617,42.046145 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,3735,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,1999,1999,Hip,CompShg,MetalSd,MetalSd,BrkFace,218,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,241,691,GasA,Excellent,Y,SBrkr,713,739,0,1452,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,183900,-93.645482,42.046431 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2651,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,32,673,GasA,Excellent,Y,SBrkr,673,709,0,1382,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,490,Typical,Typical,Paved,153,50,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,165000,-93.645479,42.046127 -Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4447,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,530,530,GasA,Excellent,Y,SBrkr,530,550,0,1080,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,496,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,139000,-93.645478,42.046107 -Two_Story_1946_and_Newer,Floating_Village_Residential,114,8314,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,569,569,GasA,Excellent,Y,SBrkr,854,840,0,1694,0,0,2,1,3,1,Good,6,Typ,1,Typical,BuiltIn,Unf,1,434,Typical,Typical,Paved,0,382,0,0,110,0,No_Pool,Good_Privacy,None,0,11,2006,WD ,Normal,200000,-93.643373,42.046429 -One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,60,7180,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1568,1568,GasA,Excellent,Y,SBrkr,1568,0,0,1568,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,266,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,221000,-93.64114,42.046416 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,30,9549,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1996,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Good,PConc,Good,Good,Av,LwQ,4,GLQ,1057,0,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,1,1,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,481,Typical,Typical,Paved,0,30,0,0,216,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,270000,-93.648931,42.044609 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,441,1208,GasA,Typical,Y,SBrkr,1208,0,0,1208,1,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,546,Typical,Typical,Paved,198,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,165000,-93.646065,42.043779 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3760,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,LwQ,182,44,1235,GasA,Good,Y,SBrkr,1235,0,0,1235,1,0,1,0,1,1,Typical,4,Typ,3,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,133,78,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,214000,-93.649329,42.042369 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,36,3640,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,176,1036,GasA,Typical,Y,SBrkr,1036,0,0,1036,1,0,1,0,1,1,Good,4,Typ,1,Typical,Detchd,Fin,2,484,Typical,Typical,Paved,133,108,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,188000,-93.649439,42.042481 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3874,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,419,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,1,Fair,Attchd,Fin,2,484,Typical,Typical,Paved,133,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,190000,-93.649352,42.042826 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3876,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Average,1978,1978,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Rec,526,48,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,133,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,170000,-93.649329,42.043351 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,50271,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Excellent,Average,1981,1987,Gable,WdShngl,WdShing,Wd Shng,None,0,Good,Typical,CBlock,Excellent,Typical,Gd,GLQ,3,Unf,0,32,1842,GasA,Good,Y,SBrkr,1842,0,0,1842,2,0,0,1,0,1,Good,5,Typ,1,Good,Attchd,Fin,3,894,Typical,Typical,Paved,857,72,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,385000,-93.658237,42.037409 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,79,13110,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,RRAn,Feedr,TwoFmCon,One_Story,Average,Above_Average,1972,1972,Gable,CompShg,Plywood,Plywood,BrkFace,144,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,191,1153,GasA,Excellent,Y,SBrkr,1193,0,0,1193,1,0,2,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,501,Typical,Typical,Paved,140,153,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,146500,-93.633752,42.045632 -Split_or_Multilevel,Residential_Low_Density,102,10192,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Good,Above_Average,1968,1992,Gable,CompShg,MetalSd,MetalSd,BrkFace,143,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,570,570,GasA,Good,Y,SBrkr,1222,698,0,1920,0,0,3,0,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,487,Typical,Typical,Paved,0,98,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2006,WD ,Normal,170000,-93.6346,42.048199 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20781,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Good,Good,1968,2003,Hip,CompShg,BrkFace,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,68,1203,1568,GasA,Typical,Y,SBrkr,2156,0,0,2156,0,0,2,0,3,1,Typical,9,Typ,1,Good,Attchd,RFn,2,508,Good,Typical,Paved,0,80,0,290,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,262500,-93.634738,42.048997 -Two_Story_1946_and_Newer,Residential_Low_Density,90,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,420,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,304,708,GasA,Good,Y,SBrkr,708,708,0,1416,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,776,Typical,Typical,Paved,0,169,0,0,119,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,179900,-93.635873,42.048602 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Above_Average,Good,1968,1984,Gable,CompShg,HdBoard,HdBoard,BrkFace,220,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,435,1054,GasA,Typical,Y,SBrkr,1512,1142,0,2654,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Unf,2,619,Typical,Typical,Paved,0,65,0,0,222,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,250000,-93.633466,42.046472 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,288,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,485,732,GasA,Good,Y,SBrkr,1012,778,0,1790,1,0,1,2,4,1,Typical,8,Min2,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,148,0,0,0,147,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,165150,-93.630258,42.04806 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Good,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,264,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,68,713,1334,GasA,Good,Y,SBrkr,1334,0,0,1334,1,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,477,Typical,Typical,Paved,0,20,35,0,264,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,164500,-93.632472,42.043548 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1969,1969,Hip,CompShg,HdBoard,HdBoard,BrkFace,168,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1164,1164,GasA,Typical,Y,SBrkr,1164,0,0,1164,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,COD,Normal,140000,-93.633098,42.044669 -Split_or_Multilevel,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1967,1967,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Av,ALQ,1,Rec,480,100,980,GasA,Good,Y,SBrkr,980,0,0,980,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,384,Typical,Typical,Paved,68,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,135500,-93.627935,42.046496 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,293,1051,GasA,Good,Y,SBrkr,1051,0,0,1051,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,142000,-93.625976,42.045706 -Two_Story_1946_and_Newer,Residential_Low_Density,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,Two_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,300,Typical,Typical,CBlock,Good,Fair,Mn,ALQ,1,Rec,483,56,900,GasA,Excellent,Y,SBrkr,884,886,0,1770,1,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,530,Typical,Typical,Paved,0,60,0,0,270,0,No_Pool,No_Fence,Shed,455,6,2006,WD ,Normal,155000,-93.626097,42.045728 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Good,1968,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,203,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,216,976,GasA,Typical,Y,SBrkr,976,0,0,976,1,0,1,0,2,1,Good,4,Typ,1,Fair,Attchd,RFn,2,504,Typical,Typical,Paved,94,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Abnorml,157500,-93.626161,42.045728 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,BrkFace,168,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,36,1052,GasA,Good,Y,SBrkr,1052,0,0,1052,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,356,0,0,0,0,0,No_Pool,Good_Wood,None,0,11,2006,WD ,Normal,138500,-93.627517,42.045885 -Split_Foyer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,SFoyer,Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,89,833,GasA,Good,Y,SBrkr,898,0,0,898,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,326,Typical,Typical,Paved,143,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,140000,-93.628069,42.045878 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9759,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,252,1051,GasA,Typical,Y,SBrkr,1051,0,0,1051,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,182,88,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,124400,-93.6269,42.04795 -Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,140,Typical,Typical,PConc,Typical,Typical,Av,ALQ,1,Rec,402,137,1141,GasA,Good,Y,SBrkr,1141,0,0,1141,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,568,Typical,Typical,Paved,0,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,158000,-93.626345,42.04657 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9230,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1965,1998,Hip,CompShg,MetalSd,MetalSd,BrkFace,166,Typical,Typical,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,203,864,GasA,Good,Y,SBrkr,1200,0,0,1200,1,0,1,1,1,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,884,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2006,WD ,Normal,146000,-93.624612,42.045685 -Two_Story_1946_and_Newer,Residential_Low_Density,0,14803,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,252,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,409,825,GasA,Good,Y,SBrkr,1097,896,0,1993,0,0,2,1,4,1,Typical,8,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,66,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,190000,-93.629155,42.044308 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10659,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,135,1050,GasA,Typical,Y,SBrkr,1050,0,0,1050,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,368,Typical,Typical,Paved,0,319,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,COD,Normal,136500,-93.623414,42.043214 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1958,1958,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,174,169,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,129900,-93.623564,42.044978 -Duplex_All_Styles_and_Ages,Residential_Low_Density,70,8393,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1959,2005,Gable,CompShg,MetalSd,MetalSd,BrkFace,122,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,1098,1626,GasA,Excellent,Y,SBrkr,1712,0,0,1712,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,2,588,Typical,Typical,Paved,272,54,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Family,145000,-93.622312,42.042114 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,RRAn,Norm,TwoFmCon,One_Story,Above_Average,Good,1965,2000,Hip,CompShg,BrkFace,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,Mn,ALQ,1,BLQ,252,34,1187,GasA,Excellent,Y,SBrkr,1565,0,0,1565,1,0,2,0,3,1,Good,7,Min1,2,Typical,Attchd,RFn,1,299,Typical,Typical,Paved,200,25,211,0,0,0,No_Pool,Minimum_Privacy,Shed,460,6,2006,WD ,Abnorml,185900,-93.630156,42.040072 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Hip,CompShg,HdBoard,HdBoard,BrkFace,425,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,698,1251,GasA,Typical,Y,SBrkr,1251,0,0,1251,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,RFn,1,461,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,Minimum_Privacy,Shed,700,3,2006,WD ,Normal,160000,-93.626765,42.038434 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10368,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Hip,CompShg,HdBoard,HdBoard,BrkFace,112,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,748,0,1008,GasA,Excellent,Y,SBrkr,1488,0,0,1488,1,0,1,1,3,1,Typical,7,Typ,1,Good,Attchd,Fin,2,430,Typical,Typical,Paved,154,60,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,167000,-93.627065,42.04102 -Two_Story_1946_and_Newer,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,360,720,GasA,Good,Y,SBrkr,720,720,0,1440,0,0,1,1,4,1,Typical,7,Typ,1,Poor,Attchd,Fin,2,480,Typical,Typical,Paved,0,32,240,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,157500,-93.6282949,42.0414084 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,75,10382,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwoFmCon,SLvl,Above_Average,Average,1958,1958,Hip,CompShg,HdBoard,HdBoard,BrkFace,105,Typical,Fair,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,75,588,GasA,Typical,Y,SBrkr,1095,0,0,1095,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,ConLD,Normal,140000,-93.621621,42.041314 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,8973,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1958,1991,Gable,CompShg,Plywood,Plywood,BrkFace,85,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,28,413,1008,GasA,Typical,Y,FuseA,1053,0,0,1053,0,1,1,1,3,1,Excellent,6,Typ,0,No_Fireplace,More_Than_Two_Types,RFn,2,750,Typical,Typical,Paved,0,80,0,180,0,0,No_Pool,Minimum_Wood_Wire,None,0,7,2006,WD ,Abnorml,150000,-93.621362,42.040273 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,88,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,616,1248,GasA,Excellent,Y,SBrkr,1248,0,0,1248,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,151500,-93.622017,42.040143 -One_Story_1945_and_Older,Residential_Low_Density,60,8550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1934,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,242,816,GasA,Excellent,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,4,Typ,1,Fair,Attchd,Unf,1,240,Typical,Typical,Paved,228,0,40,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,129800,-93.62357,42.039195 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11425,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,522,1008,GasA,Good,Y,SBrkr,1008,0,0,1008,0,0,1,0,2,1,Typical,4,Typ,1,Good,Attchd,RFn,1,275,Typical,Typical,Paved,0,0,120,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,137000,-93.622649,42.038351 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9724,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1947,1950,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,938,938,GasA,Excellent,Y,SBrkr,1043,0,0,1043,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,273,Typical,Typical,Paved,125,48,0,0,0,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,137000,-93.621399,42.039202 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,4712,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1946,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,363,747,GasA,Typical,Y,SBrkr,774,456,0,1230,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,305,Typical,Typical,Paved,0,57,0,0,63,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Abnorml,121600,-93.628568,42.035836 -One_Story_1945_and_Older,Residential_Low_Density,51,5900,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Below_Average,Good,1923,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,440,440,GasA,Typical,Y,FuseA,869,0,0,869,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,85500,-93.629185,42.03537 -One_Story_1945_and_Older,Residential_Low_Density,52,5825,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Below_Average,Average,1926,1953,Gable,CompShg,MetalSd,MetalSd,BrkFace,108,Typical,Good,PConc,Fair,Typical,Mn,Unf,7,Unf,0,600,600,GasA,Good,Y,SBrkr,747,0,0,747,0,0,1,0,1,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,32,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,79900,-93.629256,42.035379 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,92,7438,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Feedr,OneFam,One_and_Half_Fin,Average,Very_Good,1908,1991,Gable,CompShg,AsbShng,Plywood,None,0,Typical,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,504,504,GasA,Good,Y,SBrkr,936,316,0,1252,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Paved,104,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,127000,-93.628176,42.035666 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1990,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,336,1251,GasA,Typical,Y,SBrkr,1433,0,0,1433,1,0,1,0,3,1,Typical,7,Min1,1,Good,Attchd,Unf,2,441,Typical,Typical,Paved,144,0,205,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,161000,-93.6270261,42.0380632 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,10858,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1952,1952,Gable,CompShg,Wd Sdng,Plywood,Stone,150,Typical,Good,CBlock,Typical,Typical,Mn,LwQ,4,Unf,0,1404,1444,GasA,Excellent,Y,SBrkr,1624,0,0,1624,1,0,1,0,2,1,Typical,6,Min1,1,Good,Attchd,RFn,1,240,Typical,Typical,Paved,0,40,324,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Partial,146500,-93.624738,42.034612 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1951,1951,Gable,CompShg,HdBoard,HdBoard,Stone,144,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,60,1056,GasA,Excellent,Y,FuseA,1216,0,0,1216,1,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,135000,-93.623704,42.036024 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,10680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1951,1951,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,44,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,1380,2136,GasA,Typical,N,FuseA,2136,0,0,2136,0,0,2,0,4,1,Typical,7,Mod,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2006,WD ,Normal,137900,-93.622645,42.037463 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,79,9490,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,165,238,806,GasA,Typical,Y,FuseA,958,620,0,1578,1,0,1,0,3,1,Fair,5,Typ,2,Typical,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,32,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Normal,133000,-93.620494,42.034831 -Two_Story_1946_and_Newer,Residential_Low_Density,79,9462,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1949,1973,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,704,704,GasA,Good,Y,FuseA,1024,704,0,1728,0,0,1,1,3,1,Typical,7,Min1,1,Good,Attchd,Unf,1,234,Typical,Typical,Paved,245,60,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,137000,-93.621426,42.035987 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,9888,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1975,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,450,936,GasA,Typical,Y,FuseA,936,0,0,936,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,160,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,121000,-93.62143,42.037247 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,8917,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1584,1584,GasA,Typical,Y,SBrkr,1584,0,0,1584,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,119000,-93.619673,42.0493 -Split_or_Multilevel,Residential_Low_Density,0,12700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,307,1246,GasA,Typical,Y,SBrkr,1246,0,0,1246,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,441,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,172000,-93.616852,42.049031 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,8500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1961,1961,Hip,CompShg,HdBoard,HdBoard,BrkCmn,203,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,635,1235,GasA,Typical,Y,SBrkr,1235,0,0,1235,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,12,2006,WD ,Abnorml,98600,-93.616012,42.04665 -Two_Story_1946_and_Newer,Residential_Low_Density,88,14200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1966,1966,Gable,CompShg,MetalSd,MetalSd,BrkFace,309,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,479,924,GasA,Excellent,Y,SBrkr,1216,941,0,2157,0,0,2,1,4,1,Good,8,Typ,2,Good,Attchd,Fin,2,487,Typical,Typical,Paved,105,66,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2006,WD ,Normal,226000,-93.615629,42.048528 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,124,936,GasA,Typical,Y,SBrkr,1128,0,0,1128,0,0,1,0,2,1,Typical,5,Min1,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Family,118000,-93.6181153,42.0422973 -Two_and_Half_Story_All_Ages,Residential_Low_Density,174,25419,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Very_Good,Below_Average,1918,1990,Gable,CompShg,Stucco,Stucco,None,0,Good,Good,PConc,Typical,Typical,No,GLQ,3,LwQ,184,140,1360,GasA,Good,Y,SBrkr,1360,1360,392,3112,1,1,2,0,4,1,Good,8,Typ,1,Excellent,Detchd,Unf,2,795,Typical,Typical,Paved,0,16,552,0,0,512,Excellent,Good_Privacy,None,0,3,2006,WD ,Abnorml,235000,-93.620355,42.042156 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,109,9723,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,332,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1008,1008,GasA,Typical,Y,SBrkr,1008,0,0,1008,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,430,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,135000,-93.618389,42.043129 -Duplex_All_Styles_and_Ages,Residential_Low_Density,70,7728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,SLvl,Average,Above_Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,303,1106,GasA,Typical,Y,SBrkr,1190,0,0,1190,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,540,Typical,Typical,Paved,0,18,0,0,0,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,132500,-93.619334,42.044735 -Split_or_Multilevel,Residential_Low_Density,70,8163,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Above_Average,1959,1959,Gable,CompShg,HdBoard,HdBoard,BrkFace,128,Typical,Good,CBlock,Typical,Typical,Av,ALQ,1,BLQ,294,102,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,796,Typical,Typical,Paved,86,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,143000,-93.617499,42.043897 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,BLQ,2,Unf,0,556,1179,GasA,Good,Y,SBrkr,1364,0,0,1364,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,1,331,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,Good_Privacy,None,0,3,2006,WD ,Normal,132000,-93.618625,42.04498 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,175,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,818,1383,GasA,Typical,Y,SBrkr,1383,0,0,1383,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,1,292,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,145250,-93.612886,42.044975 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9610,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,632,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,918,1121,GasA,Excellent,Y,FuseA,1336,0,0,1336,0,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,488,Typical,Typical,Paved,80,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,162000,-93.612566,42.044254 -Split_or_Multilevel,Residential_Low_Density,125,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Above_Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,272,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Rec,281,99,1058,GasA,Excellent,Y,SBrkr,1370,0,0,1370,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Basment,RFn,1,300,Typical,Typical,Paved,191,0,0,0,120,0,No_Pool,Minimum_Privacy,None,0,2,2006,WD ,Normal,148000,-93.615882,42.042105 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,14850,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,197,1092,GasA,Typical,Y,FuseA,1092,0,0,1092,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,Fin,1,299,Typical,Typical,Paved,268,0,0,0,122,0,No_Pool,Minimum_Wood_Wire,None,0,5,2006,WD ,Normal,141000,-93.613296,42.043626 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10152,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1956,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,210,1124,GasA,Excellent,Y,SBrkr,1124,0,0,1124,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,1,353,Typical,Typical,Paved,0,211,180,0,142,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,153000,-93.614251,42.0427 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10011,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,64,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,710,1070,GasA,Typical,Y,SBrkr,1236,0,0,1236,0,1,1,0,2,1,Good,6,Min1,1,Fair,Attchd,Unf,1,447,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,134450,-93.612462,42.042905 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7032,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,SFoyer,Average,Average,1979,1979,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,943,GasA,Typical,Y,SBrkr,943,0,0,943,1,0,1,0,2,1,Typical,4,Typ,2,Typical,Detchd,Unf,2,600,Typical,Typical,Paved,42,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,135960,-93.618319,42.041821 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8092,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1954,2000,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,176,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,226,1050,GasA,Excellent,Y,SBrkr,1050,0,0,1050,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Abnorml,156000,-93.618481,42.040745 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11310,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1954,1954,Hip,CompShg,Wd Sdng,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1367,1367,GasA,Excellent,Y,SBrkr,1375,0,0,1375,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Unf,2,451,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,140000,-93.618593,42.040252 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,12778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1952,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,350,1008,GasA,Excellent,Y,FuseA,1008,0,0,1008,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,1,280,Typical,Typical,Paved,0,154,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2006,WD ,Normal,139500,-93.617225,42.039447 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10170,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1951,1951,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,522,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,216,216,GasA,Typical,Y,SBrkr,1575,0,0,1575,0,0,1,1,2,1,Good,5,Typ,1,Good,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,155000,-93.617069,42.038357 -Split_or_Multilevel,Residential_Low_Density,55,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,30,301,GasA,Excellent,Y,FuseA,1145,0,0,1145,0,0,1,0,3,1,Typical,6,Min2,0,No_Fireplace,Detchd,Unf,2,684,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2006,WD ,Normal,127000,-93.612464,42.039387 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,517,1005,GasA,Excellent,Y,SBrkr,1005,0,0,1005,0,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,319,Typical,Typical,Paved,0,0,0,0,288,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,133500,-93.613491,42.038602 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,13600,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1955,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,912,0,1056,GasA,Good,Y,SBrkr,1056,0,0,1056,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,650,11,2006,WD ,Normal,125000,-93.6136767,42.0383475 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,15428,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1951,1991,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,143,884,GasA,Excellent,Y,SBrkr,884,0,0,884,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,270,Typical,Typical,Paved,0,0,0,0,195,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,142000,-93.615474,42.038836 -One_Story_1945_and_Older,Residential_Low_Density,118,21299,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1941,1963,Hip,WdShake,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,929,929,GasA,Excellent,Y,SBrkr,2039,0,0,2039,1,0,1,1,3,1,Typical,7,Min1,3,Good,More_Than_Two_Types,Unf,3,791,Typical,Typical,Paved,0,0,90,0,0,0,No_Pool,No_Fence,None,0,12,2006,COD,Abnorml,167000,-93.61547,42.040276 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,13300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,2001,Hip,CompShg,Wd Sdng,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,521,1015,GasA,Good,Y,SBrkr,1384,0,0,1384,1,0,1,0,2,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,2,896,Typical,Typical,Paved,75,0,0,323,0,0,No_Pool,No_Fence,Shed,400,6,2006,WD ,Normal,159000,-93.612263,42.039351 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,94,22136,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,TwoFmCon,One_and_Half_Fin,Average,Average,1925,1975,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,1153,2171,GasA,Typical,Y,SBrkr,1392,1248,0,2640,2,0,2,1,5,1,Typical,10,Maj1,1,Good,Attchd,RFn,3,1008,Typical,Typical,Dirt_Gravel,631,48,148,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,180000,-93.620354,42.034934 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1947,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,784,784,GasA,Excellent,Y,FuseA,900,412,0,1312,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,649,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,127000,-93.617182,42.034617 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,0,617,GasA,Good,Y,SBrkr,865,445,0,1310,0,0,2,0,2,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,1,398,Typical,Typical,Paved,0,0,126,0,0,0,No_Pool,No_Fence,None,0,5,2006,COD,Normal,112000,-93.6168701,42.0347269 -One_Story_1945_and_Older,Residential_Low_Density,60,10410,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Very_Good,1930,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,713,713,GasA,Excellent,Y,SBrkr,713,0,0,713,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,371,Fair,Fair,Dirt_Gravel,0,75,161,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,99800,-93.6143498,42.0378602 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1954,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,418,833,GasA,Excellent,Y,SBrkr,833,0,0,833,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,1,326,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Normal,117000,-93.6133726,42.0371796 -One_Story_1945_and_Older,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Sev,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1921,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1073,1073,GasA,Excellent,Y,SBrkr,1073,0,0,1073,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,326,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,108480,-93.613965,42.037001 -One_Story_1945_and_Older,Residential_Low_Density,60,10914,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Fair,Fair,1929,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,715,715,GasA,Fair,N,FuseP,715,0,0,715,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,660,Fair,Typical,Dirt_Gravel,0,0,75,0,112,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,68000,-93.610772,42.037097 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7008,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Very_Good,1900,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Fair,Fair,No,Unf,7,Unf,0,448,448,GasA,Excellent,Y,SBrkr,448,272,0,720,0,0,1,0,1,1,Fair,5,Typ,0,No_Fireplace,Attchd,Unf,1,280,Fair,Typical,Paved,0,0,70,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,86900,-93.612239,42.037041 -One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1920,1950,Hip,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,299,1120,GasA,Excellent,Y,SBrkr,1130,0,0,1130,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,2,720,Typical,Typical,Paved,229,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,120000,-93.6139,42.0358 -Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,Rec,6,Unf,0,325,663,GasA,Excellent,Y,SBrkr,774,821,0,1595,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,49,0,231,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,155500,-93.6116427,42.0367836 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10818,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,1077,1077,GasA,Typical,Y,FuseA,981,779,0,1760,0,0,1,1,4,1,Typical,7,Typ,1,Typical,Detchd,Unf,2,648,Fair,Typical,Paved,120,0,96,0,0,0,No_Pool,No_Fence,None,0,2,2006,COD,Abnorml,80000,-93.612266,42.035857 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10410,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Fair,Below_Average,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Typical,Y,SBrkr,694,520,0,1214,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,3,936,Typical,Typical,Paved,216,0,160,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2006,WD ,Family,105000,-93.614049,42.034574 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Below_Average,1900,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Fair,No,BLQ,2,Unf,0,290,954,GasA,Typical,N,FuseA,1766,648,0,2414,0,0,2,0,3,2,Typical,10,Mod,1,Good,Attchd,Unf,2,520,Typical,Fair,Dirt_Gravel,142,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,ConLD,Normal,160000,-93.612404,42.035277 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1936,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,ALQ,1,Unf,0,170,796,GasA,Good,Y,SBrkr,1096,370,0,1466,0,1,2,0,3,1,Good,7,Min1,1,Typical,Attchd,Unf,2,566,Typical,Typical,Paved,436,21,0,0,0,0,No_Pool,No_Fence,Shed,500,4,2006,WD ,Normal,170000,-93.613899,42.034761 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,8658,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1965,1965,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,101,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,445,1088,GasA,Excellent,Y,SBrkr,1324,0,0,1324,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,138,0,0,0,0,No_Pool,Good_Wood,None,0,12,2006,WD ,Abnorml,160000,-93.608909,42.040944 -Split_or_Multilevel,Residential_Low_Density,85,13400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,1047,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,BLQ,128,380,1024,GasA,Typical,Y,SBrkr,1086,0,0,1086,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,159950,-93.608984,42.040811 -Split_or_Multilevel,Residential_Low_Density,83,10184,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,379,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,503,1083,GasA,Typical,Y,SBrkr,1146,0,0,1146,0,1,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,294,Typical,Typical,Paved,345,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,165000,-93.607582,42.040105 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,77,9786,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1962,1981,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,312,912,GasA,Typical,Y,SBrkr,1085,649,0,1734,0,0,1,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,440,Typical,Typical,Paved,0,0,0,0,128,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,159000,-93.60942,42.040081 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,9510,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1962,1985,Gable,CompShg,HdBoard,HdBoard,BrkCmn,161,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,434,1135,GasA,Excellent,Y,SBrkr,1207,0,0,1207,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,147000,-93.610278,42.040297 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,7800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,600,0,912,GasA,Typical,Y,SBrkr,912,0,0,912,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Abnorml,115000,-93.610501,42.040148 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,8910,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,0,655,GasA,Excellent,Y,SBrkr,1194,0,0,1194,0,1,1,0,3,1,Typical,6,Typ,1,Fair,BuiltIn,Fin,2,539,Typical,Typical,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,159500,-93.607257,42.0385 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7332,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Gable,CompShg,WdShing,Wd Shng,BrkFace,207,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,450,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,120000,-93.608422,42.038497 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,7100,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1957,1957,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,108,816,GasA,Typical,Y,FuseA,816,0,0,816,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,129900,-93.609073,42.038297 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Above_Average,Above_Average,1961,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,104,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,400,1313,GasA,Typical,Y,SBrkr,1773,0,0,1773,1,0,2,0,3,1,Typical,6,Min2,2,Typical,Attchd,RFn,2,418,Typical,Typical,Paved,355,98,0,0,144,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,183000,-93.606508,42.041874 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,11650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Good,Average,1959,1959,Hip,CompShg,Plywood,Plywood,BrkCmn,58,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,FuseA,1472,0,0,1472,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Unf,2,484,Typical,Typical,Paved,0,68,0,0,227,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,157500,-93.605384,42.0416529 -Two_Story_1946_and_Newer,Residential_Low_Density,0,18275,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1962,1998,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,802,1438,GasA,Typical,Y,SBrkr,1900,548,0,2448,1,0,3,0,3,1,Typical,9,Typ,2,Good,Attchd,RFn,2,441,Typical,Typical,Paved,520,102,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,277500,-93.60674,42.040768 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,47,16321,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,BLQ,2,Unf,0,207,1484,GasA,Typical,Y,SBrkr,1600,0,0,1600,1,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,RFn,1,319,Typical,Typical,Paved,288,258,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,207500,-93.605727,42.03902 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,12144,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1950,1950,Gable,CompShg,BrkComm,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,455,910,GasA,Good,Y,SBrkr,910,611,0,1521,0,0,1,1,3,1,Good,6,Min2,0,No_Fireplace,Detchd,Unf,1,597,Fair,Typical,Paved,199,0,168,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,147500,-93.609054,42.036098 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1949,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,720,720,GasA,Typical,N,FuseA,720,472,0,1192,0,0,1,1,4,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,135000,-93.610503,42.035782 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1950,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseF,1040,0,0,1040,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,625,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,109500,-93.608903,42.035996 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1950,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1040,0,0,1040,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,420,Typical,Typical,Paved,0,29,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,109900,-93.608904,42.036099 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,671,1040,GasA,Typical,Y,FuseA,1040,0,0,1040,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,140,0,252,0,0,0,No_Pool,Good_Wood,None,0,7,2006,WD ,Normal,133700,-93.606743,42.036053 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1950,1950,Gable,CompShg,BrkFace,Stone,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,81400,-93.609053,42.034805 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1949,1950,Gable,CompShg,Stucco,Stucco,BrkFace,340,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseA,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,87500,-93.609053,42.03471 -Duplex_All_Styles_and_Ages,Residential_Low_Density,60,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1950,1950,Gable,CompShg,Stucco,Stone,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,93500,-93.609053,42.034662 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8512,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1960,1960,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Fair,No,Unf,7,Unf,0,1556,1556,GasA,Typical,Y,SBrkr,1556,0,0,1556,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,119000,-93.606892,42.034829 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7945,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,506,465,1150,GasA,Excellent,Y,FuseA,1150,0,0,1150,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,141000,-93.606893,42.034757 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Below_Average,1961,1961,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,369,1150,GasA,Typical,Y,SBrkr,1150,0,0,1150,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,162,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,146000,-93.606744,42.034708 -Two_Story_1946_and_Newer,Residential_Low_Density,69,7590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,266,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,148,660,GasA,Typical,Y,SBrkr,660,688,0,1348,0,0,1,1,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,453,Typical,Typical,Paved,188,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,155000,-93.603704,42.036792 -Split_Foyer,Residential_Low_Density,69,10205,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Average,1962,1962,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,141,925,GasA,Typical,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,300,Typical,Typical,Paved,150,72,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,134500,-93.605839,42.03548 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Good,Above_Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,236,1045,GasA,Good,Y,SBrkr,1045,0,0,1045,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,120000,-93.604687,42.034618 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Average,Above_Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,396,864,GasA,Good,Y,SBrkr,864,0,0,864,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,2,2006,WD ,Normal,105000,-93.604568,42.034753 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,TwoFmCon,SFoyer,Average,Average,1962,1962,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,72,1025,GasA,Typical,Y,SBrkr,1025,0,0,1025,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,96,80,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,124000,-93.6050674,42.0345614 -Two_Story_1945_and_Older,Residential_Medium_Density,62,9856,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1900,2005,Hip,CompShg,CemntBd,CmentBd,None,0,Good,Good,PConc,Fair,Typical,No,Unf,7,Unf,0,716,716,GasA,Excellent,Y,FuseA,1007,1007,0,2014,0,0,2,0,5,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,72,167,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,169000,-93.618539,42.033267 -Two_Story_1945_and_Older,Residential_Medium_Density,57,9906,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Below_Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,686,686,GasA,Fair,N,SBrkr,810,518,0,1328,0,0,1,0,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,210,Typical,Typical,Paved,0,172,60,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Family,107000,-93.6195519,42.0334994 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1948,1950,Gable,CompShg,MetalSd,MetalSd,Stone,264,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,936,1212,GasA,Good,Y,FuseA,1226,442,0,1668,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,140,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,135000,-93.617164,42.033385 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1920,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Fair,No,LwQ,4,Unf,0,863,1147,GasA,Typical,N,SBrkr,1147,510,0,1657,0,0,1,0,4,1,Fair,9,Typ,1,Typical,Detchd,Unf,1,162,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,111500,-93.61852,42.032144 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1900,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,399,780,GasA,Excellent,Y,SBrkr,940,476,0,1416,0,1,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,156500,-93.617139,42.032346 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1925,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Good,No,LwQ,4,Unf,0,712,728,GasA,Excellent,Y,SBrkr,832,809,0,1641,0,1,1,1,3,1,Excellent,6,Typ,1,Good,Detchd,Unf,2,546,Fair,Typical,Paved,0,0,234,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,178000,-93.618489,42.03139 -Two_Story_1945_and_Older,Residential_Medium_Density,58,6451,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1900,1970,Gable,CompShg,AsbShng,Wd Sdng,None,0,Typical,Typical,Stone,Typical,Typical,No,Rec,6,Unf,0,504,712,GasA,Good,Y,SBrkr,848,580,0,1428,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Fin,2,576,Typical,Typical,Paved,264,0,84,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,139900,-93.619513,42.031506 -Two_Story_1945_and_Older,Residential_Medium_Density,66,3960,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,502,502,GasA,Typical,N,SBrkr,502,502,0,1004,0,0,1,0,2,1,Good,5,Typ,1,Poor,Detchd,Unf,1,200,Fair,Typical,Dirt_Gravel,280,0,68,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,105000,-93.615681,42.031347 -One_Story_1945_and_Older,Residential_Medium_Density,70,5684,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1930,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,813,813,GasA,Excellent,Y,FuseA,813,0,0,813,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,270,Fair,Fair,Dirt_Gravel,0,113,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,110000,-93.618799,42.03121 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,56,7745,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Above_Average,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,938,938,GasA,Good,N,SBrkr,1084,867,0,1951,0,0,2,0,4,2,Fair,9,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,6,28,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,111500,-93.618335,42.030396 -One_Story_1945_and_Older,Residential_Medium_Density,56,7741,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,BLQ,2,Rec,72,817,1032,GasA,Good,N,FuseA,1032,0,0,1032,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,COD,Abnorml,108000,-93.618334,42.030463 -One_Story_1945_and_Older,Residential_Medium_Density,50,5633,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1925,1950,Gable,CompShg,MetalSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,844,844,GasA,Typical,Y,SBrkr,844,0,0,844,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,50,81,123,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,111500,-93.615638,42.031196 -Two_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Average,Above_Average,1880,1991,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Fair,Fair,No,Unf,7,Unf,0,636,636,GasA,Typical,Y,FuseA,1089,661,0,1750,0,0,1,0,3,1,Excellent,8,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Poor,Dirt_Gravel,0,0,293,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Abnorml,124000,-93.614011,42.033623 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,60,7200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,576,576,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,528,Typical,Typical,Paved,0,0,0,0,115,0,No_Pool,No_Fence,None,0,8,2006,COD,Normal,105000,-93.6145858,42.0330459 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,40,4400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,648,648,GasA,Typical,Y,FuseA,734,384,0,1118,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,116000,-93.614615,42.032305 -Two_Story_1945_and_Older,Residential_Medium_Density,42,7614,Pave,Gravel,Regular,Lvl,AllPub,Inside,Mod,Old_Town,Norm,Norm,OneFam,Two_Story,Fair,Average,1905,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Unf,7,Unf,0,738,738,GasA,Good,Y,FuseA,714,662,0,1376,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,0,0,104,0,225,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,96900,-93.613905,42.030267 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_Story,Average,Good,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,384,960,GasA,Typical,Y,FuseA,960,0,0,960,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,135500,-93.607862,42.032064 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1924,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,145,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,816,750,0,1566,0,0,1,1,5,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,450,Typical,Typical,Paved,24,0,296,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Normal,139000,-93.608833,42.032267 -One_Story_1945_and_Older,Residential_Medium_Density,52,7830,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Average,1921,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,76,492,GasA,Typical,Y,SBrkr,492,0,0,492,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Dirt_Gravel,0,0,78,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,61500,-93.610354,42.031964 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,56,9576,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1945,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Rec,6,Unf,0,460,770,GasA,Typical,Y,SBrkr,885,297,0,1182,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,378,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,120000,-93.608938,42.030368 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,49,5820,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Very_Good,1955,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,906,1162,GasA,Excellent,Y,SBrkr,1163,0,0,1163,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,220,Fair,Typical,Paved,142,98,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,126175,-93.607744,42.03146 -One_Story_1945_and_Older,Residential_Medium_Density,48,5747,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Below_Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,798,798,GasA,Good,Y,SBrkr,840,0,0,840,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Fair,Dirt_Gravel,112,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,64000,-93.606803,42.031138 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,69,9142,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Above_Average,Very_Good,1910,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,Stone,Fair,Typical,No,Unf,7,Unf,0,1020,1020,GasA,Good,N,FuseP,908,1020,0,1928,0,0,2,0,4,2,Fair,9,Typ,0,No_Fireplace,Detchd,Unf,1,440,Poor,Poor,Paved,0,60,112,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,137000,-93.618293,42.029142 -Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Poor,1900,1950,Gable,CompShg,AsbShng,Stucco,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1095,1095,GasW,Fair,N,SBrkr,1095,679,0,1774,1,0,2,0,4,2,Typical,8,Min2,0,No_Fireplace,More_Than_Two_Types,Unf,3,779,Fair,Fair,Dirt_Gravel,0,0,90,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,87000,-93.616886,42.027993 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Good,1914,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Rec,6,Unf,0,490,880,GasW,Fair,N,SBrkr,880,888,0,1768,0,0,1,1,2,1,Typical,6,Typ,2,Typical,Detchd,Unf,2,320,Typical,Typical,Dirt_Gravel,0,341,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,163000,-93.613889,42.029136 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,70,6300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Above_Average,1910,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1226,1226,GasA,Excellent,Y,SBrkr,1226,878,0,2104,0,0,2,0,5,1,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,2,432,Fair,Typical,Partial_Pavement,0,341,88,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,155000,-93.615361,42.028951 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1900,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,540,540,GasA,Good,N,FuseA,889,551,0,1440,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,352,Fair,Typical,Paved,0,0,77,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,79000,-93.615365,42.029281 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,65,8850,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Above_Average,1916,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,815,815,GasA,Excellent,Y,SBrkr,815,875,0,1690,0,0,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,225,Typical,Typical,Paved,0,0,330,0,0,0,No_Pool,No_Fence,None,0,7,2006,ConLw,Normal,144000,-93.615337,42.027953 -Two_Story_1945_and_Older,Residential_Medium_Density,60,3600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1910,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,684,684,GasA,Excellent,N,FuseA,684,684,0,1368,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Fair,Dirt_Gravel,0,158,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,114504,-93.6127958,42.0281148 -Two_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1920,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,596,596,GasA,Excellent,Y,SBrkr,998,764,0,1762,1,0,1,1,4,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Dirt_Gravel,36,0,221,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,157000,-93.613763,42.027953 -One_Story_1945_and_Older,Residential_Medium_Density,84,11340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Average,1923,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1200,1200,GasA,Typical,Y,FuseA,1200,0,0,1200,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,312,Fair,Fair,Paved,0,0,228,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Family,125000,-93.608901,42.028963 -Two_and_Half_Story_All_Ages,Residential_Medium_Density,90,22950,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_and_Half_Fin,Very_Excellent,Excellent,1892,1993,Gable,WdShngl,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,1107,1107,GasA,Excellent,Y,SBrkr,1518,1518,572,3608,0,0,2,1,4,1,Excellent,12,Typ,2,Typical,Detchd,Unf,3,840,Excellent,Typical,Paved,0,260,0,0,410,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,475000,-93.610438,42.029025 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,33,5976,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,Two_Story,Average,Good,1920,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Good,N,FuseA,624,624,0,1248,0,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,130,256,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,93500,-93.609665,42.027992 -One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,65,9750,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Average,1958,1958,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Excellent,Y,SBrkr,960,0,0,960,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Gar2,4500,7,2006,WD ,Normal,125000,-93.606195,42.0272703 -One_and_Half_Story_Finished_All_Ages,C_all,63,4761,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Unf,Fair,Fair,1918,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1020,1020,GasA,Fair,N,FuseP,1020,0,0,1020,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,105,0,0,0,No_Pool,No_Fence,None,0,10,2006,ConLD,Normal,64500,-93.606869,42.02297 -One_Story_1945_and_Older,Residential_Low_Density,0,7446,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1941,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,522,788,GasA,Typical,Y,FuseA,788,0,0,788,0,0,1,0,2,1,Typical,4,Typ,2,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2006,WD ,Abnorml,100000,-93.628478,42.03405 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,65,6435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,972,972,GasA,Good,Y,SBrkr,972,605,0,1577,0,0,1,0,3,1,Fair,6,Typ,1,Good,Detchd,Unf,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,140200,-93.626022,42.03179 -Two_Story_1945_and_Older,Residential_Low_Density,69,11737,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1924,1996,Gambrel,CompShg,BrkComm,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,848,848,GasW,Typical,N,SBrkr,1017,810,0,1827,0,0,1,0,2,1,Typical,9,Typ,1,Good,Detchd,Unf,1,240,Fair,Typical,Paved,27,36,42,0,0,0,No_Pool,Good_Privacy,None,0,5,2006,WD ,Normal,202500,-93.627385,42.030786 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1947,1950,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,605,1004,GasA,Excellent,Y,SBrkr,1004,660,0,1664,0,0,2,0,3,1,Typical,7,Typ,2,Good,Detchd,Unf,2,420,Typical,Typical,Paved,0,24,36,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,159000,-93.625576,42.033306 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,TwoFmCon,One_and_Half_Fin,Average,Good,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,Rec,6,Unf,0,286,793,GasA,Typical,Y,SBrkr,793,325,0,1118,1,0,1,0,3,1,Typical,5,Typ,1,Good,Detchd,Unf,2,410,Typical,Typical,Paved,0,0,0,0,271,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,119900,-93.620433,42.033369 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1934,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,Y,SBrkr,816,0,360,1176,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,112,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,9,2006,WD ,Normal,114500,-93.623642,42.032299 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,896,896,GasA,Good,Y,FuseA,896,448,0,1344,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Paved,200,114,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,115000,-93.62454,42.031499 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Average,1930,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,381,728,GasA,Excellent,Y,SBrkr,728,434,0,1162,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,258,Fair,Poor,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,ConLI,Abnorml,75000,-93.624553,42.032396 -One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,279,768,GasA,Typical,N,SBrkr,1015,0,0,1015,0,0,1,0,3,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,1,450,Typical,Typical,Paved,0,0,112,0,120,0,No_Pool,Minimum_Privacy,Shed,620,7,2006,WD ,Abnorml,88000,-93.624553,42.032478 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1947,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,N,SBrkr,672,240,0,912,0,0,1,0,2,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,80500,-93.623481,42.031415 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1928,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1042,1042,GasA,Excellent,Y,SBrkr,1042,534,0,1576,0,0,1,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,1,225,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Family,149000,-93.621484,42.031436 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1984,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,741,741,GasA,Good,Y,SBrkr,741,583,0,1324,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Typical,Paved,0,0,55,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,110000,-93.622415,42.032418 -One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Above_Average,1925,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,706,1103,GasA,Good,Y,SBrkr,1103,0,0,1103,0,0,1,0,2,1,Good,5,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,166,120,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,110500,-93.620411,42.032417 -One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1921,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,616,GasA,Good,Y,SBrkr,616,0,0,616,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,205,Typical,Typical,Paved,0,0,129,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,89000,-93.6243778,42.0310818 -One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,58,6380,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Average,Above_Average,1922,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,993,993,GasA,Typical,Y,FuseA,1048,0,0,1048,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,116,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,113000,-93.622386,42.030389 -One_Story_1945_and_Older,Residential_Low_Density,50,11672,Pave,Paved,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,Y,FuseA,816,0,0,816,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,210,Fair,Fair,Dirt_Gravel,168,0,112,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,109000,-93.62577,42.029526 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,90,33120,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,RRAn,Feedr,OneFam,One_and_Half_Fin,Above_Average,Average,1962,1962,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1595,1595,GasA,Typical,Y,SBrkr,1611,875,0,2486,0,0,2,0,5,1,Typical,8,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,220000,-93.622773,42.02686 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,9873,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,TwoFmCon,One_Story,Below_Average,Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,160,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,171,960,GasW,Typical,N,SBrkr,960,0,0,960,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,288,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,129000,-93.625436,42.028544 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,59,5310,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1910,2003,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Fair,No,Unf,7,Unf,0,485,485,GasA,Good,Y,SBrkr,1001,634,0,1635,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,1,255,Fair,Typical,Paved,394,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,117000,-93.621562,42.026886 -One_Story_1945_and_Older,Residential_Medium_Density,153,4118,Pave,Gravel,Slightly_Irregular,Bnk,AllPub,Corner,Mod,Old_Town,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,693,693,Grav,Fair,N,FuseA,693,0,0,693,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,20,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,52500,-93.622624,42.02689 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,596,596,GasA,Poor,Y,FuseF,834,596,0,1430,0,0,2,0,3,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,2,370,Fair,Fair,Paved,218,0,0,0,210,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,107000,-93.626858,42.025258 -Two_Story_1945_and_Older,Residential_Medium_Density,60,7518,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1910,2004,Gable,CompShg,AsbShng,Plywood,None,0,Fair,Good,BrkTil,Fair,Fair,No,Unf,7,Unf,0,396,396,GasA,Good,Y,SBrkr,665,665,0,1330,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,390,Typical,Typical,Dirt_Gravel,0,72,45,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,116500,-93.626631,42.025459 -One_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Below_Average,1919,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,610,610,GasA,Excellent,N,FuseA,819,0,0,819,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Abnorml,72000,-93.6275675,42.0249549 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,8600,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1937,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Typical,Y,SBrkr,780,596,0,1376,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,198,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,119500,-93.6273219,42.0242386 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1938,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,803,803,GasA,Excellent,Y,SBrkr,803,557,0,1360,0,0,1,1,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,297,Typical,Typical,Paved,0,65,190,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,125500,-93.6255067,42.0242578 -One_Story_1945_and_Older,Residential_Medium_Density,60,9786,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Fair,Below_Average,1922,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1007,1007,GasA,Fair,N,SBrkr,1077,0,0,1077,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,210,Typical,Fair,Partial_Pavement,0,100,48,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,91000,-93.628416,42.023117 -Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,6780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Very_Good,1935,1982,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,30,520,GasA,Good,N,SBrkr,520,0,234,754,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,53,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,84500,-93.629501,42.022825 -One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1930,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,641,984,GasA,Typical,Y,FuseF,984,0,0,984,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,164,0,0,0,No_Pool,No_Fence,None,0,3,2006,ConLI,Family,90000,-93.6265888,42.0238089 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,82,12375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Average,1951,1951,Gable,CompShg,HdBoard,HdBoard,Stone,41,Typical,Fair,CBlock,Typical,Typical,No,BLQ,2,Unf,0,477,806,GasA,Typical,Y,SBrkr,1081,341,0,1422,1,0,1,0,3,1,Typical,7,Typ,1,Typical,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,137500,-93.657017,42.034411 -Duplex_All_Styles_and_Ages,Residential_Low_Density,120,11136,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Feedr,Duplex,One_Story,Above_Average,Average,1964,1964,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1921,1921,GasA,Typical,Y,SBrkr,1921,0,0,1921,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,180,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,150000,-93.65565,42.034576 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,21370,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1950,1950,Gable,CompShg,Wd Sdng,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1640,0,0,1640,0,0,1,0,3,1,Typical,7,Min1,1,Good,Attchd,RFn,2,394,Typical,Typical,Paved,0,0,225,0,0,0,No_Pool,No_Fence,Shed,600,6,2006,WD ,Normal,131000,-93.65815,42.033296 -Split_or_Multilevel,Residential_Low_Density,92,6930,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,SLvl,Average,Below_Average,1958,1958,Hip,CompShg,Wd Sdng,ImStucc,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Rec,294,468,1062,GasA,Excellent,Y,FuseF,1352,0,0,1352,0,1,1,0,3,1,Good,6,Min2,0,No_Fireplace,BuiltIn,Unf,1,288,Typical,Typical,Paved,168,0,294,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,130000,-93.6578344,42.0331453 -One_Story_1945_and_Older,Residential_Low_Density,55,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1935,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1032,0,0,1032,0,0,1,0,2,1,Typical,6,Typ,1,Typical,Detchd,Unf,1,260,Typical,Typical,Paved,0,0,121,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,125000,-93.6600396,42.0277962 -One_Story_1945_and_Older,Residential_Low_Density,50,5220,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Average,Fair,1936,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,830,830,GasA,Good,Y,SBrkr,879,0,0,879,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,180,Typical,Typical,Partial_Pavement,0,108,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,80000,-93.655885,42.028002 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Shed,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Good,Mn,GLQ,3,LwQ,373,190,1073,GasA,Excellent,Y,SBrkr,1073,0,0,1073,1,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,246,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,157000,-93.655786,42.027997 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Average,1963,1979,Gable,CompShg,HdBoard,HdBoard,BrkFace,51,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,504,1051,GasA,Good,Y,SBrkr,1382,0,0,1382,0,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,Unf,2,459,Typical,Typical,Paved,0,82,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,148000,-93.67555,42.033521 -Duplex_All_Styles_and_Ages,Residential_Low_Density,72,10791,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Below_Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1296,0,0,1296,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,516,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,500,10,2006,WD ,Normal,90000,-93.671213,42.033388 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8414,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1963,2003,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,396,1059,GasA,Typical,Y,SBrkr,1068,0,0,1068,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2006,WD ,Normal,154500,-93.675399,42.0333 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11327,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,305,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,285,1064,GasA,Typical,Y,SBrkr,1064,0,0,1064,0,1,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,528,Typical,Typical,Paved,314,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,153600,-93.674456,42.03224 -Split_or_Multilevel,Residential_Low_Density,96,11777,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Above_Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,97,Typical,Typical,CBlock,Typical,Typical,Av,LwQ,4,ALQ,551,285,1164,GasA,Excellent,Y,SBrkr,1320,0,0,1320,1,0,1,0,3,1,Typical,6,Typ,2,Fair,Attchd,RFn,2,564,Typical,Typical,Paved,160,68,240,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Abnorml,164500,-93.674517,42.032304 -Split_or_Multilevel,Residential_Low_Density,80,10366,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Good,1964,1964,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,456,912,GasA,Typical,Y,SBrkr,934,0,0,934,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,77,0,0,0,0,0,No_Pool,Good_Privacy,Shed,500,7,2006,WD ,Normal,132000,-93.675408,42.032383 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,1966,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,286,1059,GasA,Good,Y,SBrkr,1059,0,0,1059,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,286,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Abnorml,142500,-93.675606,42.031356 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11553,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,Plywood,Plywood,BrkFace,188,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,378,1051,GasA,Typical,Y,SBrkr,1159,0,0,1159,0,0,1,1,3,1,Typical,7,Typ,1,Fair,Attchd,Unf,1,336,Typical,Typical,Paved,466,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,158000,-93.676455,42.030611 -Two_Story_1946_and_Newer,Residential_Low_Density,74,7844,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkFace,203,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,463,672,GasA,Typical,Y,SBrkr,672,728,0,1400,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,149500,-93.672278,42.032216 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,450,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,982,0,1176,GasA,Typical,Y,SBrkr,1458,0,0,1458,1,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,512,Typical,Typical,Paved,284,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,165000,-93.674385,42.030702 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10335,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1993,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,891,1461,GasA,Good,Y,SBrkr,1721,0,0,1721,0,0,2,1,3,1,Typical,7,Min1,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,96,180,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,180000,-93.673601,42.031362 -Split_or_Multilevel,Residential_Low_Density,0,7176,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,CBlock,Typical,Good,Gd,GLQ,3,Unf,0,166,960,GasA,Fair,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,2,616,Typical,Typical,Paved,131,0,0,0,180,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,160500,-93.672645,42.031347 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,9662,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Average,Below_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1967,1967,GasA,Typical,Y,SBrkr,1967,0,0,1967,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Attchd,Fin,2,580,Typical,Typical,Paved,170,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,156500,-93.670204,42.030231 -Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Average,Below_Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,99,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,1466,1949,GasA,Typical,Y,SBrkr,1949,0,0,1949,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Attchd,RFn,2,586,Typical,Typical,Paved,32,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,157000,-93.670514,42.030232 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,13650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,CBlock,Typical,Typical,No,ALQ,1,BLQ,441,554,1052,GasA,Excellent,Y,SBrkr,1252,668,0,1920,1,0,2,0,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,451,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,172500,-93.665569,42.033285 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,13125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Below_Average,1957,2000,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,67,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,682,284,1134,GasA,Excellent,Y,SBrkr,1803,0,0,1803,1,0,2,0,3,1,Typical,8,Maj1,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,1,2006,WD ,Normal,155000,-93.665505,42.03247 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,17920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Below_Average,1955,1974,Hip,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Rec,1085,372,1763,GasA,Typical,Y,SBrkr,1779,0,0,1779,1,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,Unf,2,454,Typical,Typical,Paved,0,418,0,0,312,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,170000,-93.66563,42.032291 -One_Story_1945_and_Older,Residential_Low_Density,0,17529,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1924,1950,Gable,CompShg,BrkFace,Wd Sdng,Stone,65,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,872,872,GasA,Fair,N,FuseF,872,0,0,872,0,0,1,0,2,1,Fair,5,Mod,1,Good,Detchd,Unf,1,322,Fair,Fair,Dirt_Gravel,0,0,116,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,105000,-93.667081,42.032307 -Split_or_Multilevel,Residential_Low_Density,0,10246,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Below_Average,Excellent,1965,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Good,Av,GLQ,3,Unf,0,0,648,GasA,Excellent,Y,SBrkr,960,0,0,960,1,1,0,0,0,1,Typical,3,Typ,0,No_Fireplace,Attchd,Unf,1,364,Typical,Typical,Paved,88,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,145000,-93.669544,42.033686 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14175,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Mod,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1987,Gable,CompShg,CemntBd,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,200,1188,GasA,Good,Y,SBrkr,1437,0,0,1437,1,0,1,1,3,1,Typical,6,Min2,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,304,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,168000,-93.66412,42.032478 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20355,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,1967,1967,Gable,Tar&Grv,Plywood,Plywood,BrkFace,123,Typical,Typical,CBlock,Good,Typical,Av,Rec,6,ALQ,826,229,1865,GasA,Typical,Y,SBrkr,1830,0,0,1830,1,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,Unf,2,521,Typical,Typical,Paved,0,115,168,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,278000,-93.668392,42.031635 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,13050,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Flat,Tar&Grv,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Good,Typical,Av,Rec,6,ALQ,850,46,1000,GasA,Excellent,Y,SBrkr,1000,0,0,1000,1,0,1,0,1,1,Typical,4,Typ,2,Typical,Attchd,Unf,2,575,Typical,Typical,Paved,238,0,148,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,164000,-93.667082,42.032157 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,81,15593,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Good,Below_Average,1953,1953,Gable,CompShg,BrkFace,AsbShng,None,0,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,701,1304,GasW,Typical,Y,SBrkr,1304,983,0,2287,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,667,Typical,Typical,Paved,0,21,114,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,225000,-93.662039,42.032575 -Split_Foyer,Residential_Low_Density,72,10820,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,SFoyer,Average,Good,1971,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,153,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Rec,159,88,782,GasA,Excellent,Y,SBrkr,810,0,0,810,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,130000,-93.678165,42.029453 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,13350,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1974,1974,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,102,864,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Attchd,Unf,2,440,Typical,Typical,Paved,241,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,142500,-93.676352,42.028836 -One_and_Half_Story_PUD_All_Ages,Residential_Low_Density,0,1700,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,Twnhs,One_and_Half_Fin,Good,Average,1980,1981,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,33,430,GasA,Typical,Y,SBrkr,880,680,140,1700,1,0,2,1,2,1,Good,7,Typ,0,No_Fireplace,Basment,Fin,1,450,Good,Typical,Paved,188,36,0,0,200,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,148400,-93.674402,42.023651 -One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,5271,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1986,1986,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,371,1453,GasA,Good,Y,SBrkr,1453,0,0,1453,1,0,1,1,2,1,Good,6,Typ,1,Typical,Attchd,RFn,2,445,Typical,Typical,Paved,0,80,0,0,184,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,190000,-93.676681,42.024705 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,168,967,GasA,Excellent,Y,SBrkr,1350,0,0,1350,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,504,Typical,Typical,Paved,237,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,146000,-93.666035,42.02653 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,62,6488,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1942,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,569,799,GasA,Excellent,N,FuseA,799,351,0,1150,0,0,1,0,3,1,Typical,6,Mod,2,Typical,BuiltIn,Unf,1,215,Typical,Typical,Paved,264,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Family,128000,-93.665153,42.027664 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,7800,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1948,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Good,No,GLQ,3,Unf,0,293,896,GasA,Excellent,Y,SBrkr,1112,896,0,2008,1,0,3,0,3,1,Excellent,8,Typ,0,No_Fireplace,Attchd,Unf,1,230,Typical,Typical,Paved,103,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,225000,-93.662765,42.026667 -Split_or_Multilevel,Residential_Low_Density,0,19690,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Good,1966,1966,Flat,Tar&Grv,Plywood,Plywood,None,0,Good,Good,CBlock,Good,Typical,Av,Unf,7,Unf,0,697,697,GasA,Typical,Y,SBrkr,1575,626,0,2201,0,0,2,0,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,432,Good,Good,Paved,586,236,0,0,0,738,Good,Good_Privacy,None,0,8,2006,WD ,Alloca,274970,-93.663475,42.026614 -Two_Story_1945_and_Older,Residential_Low_Density,114,19950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1928,1950,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,1337,672,0,2009,0,0,2,0,4,1,Typical,8,Typ,2,Good,More_Than_Two_Types,Unf,3,795,Typical,Typical,Partial_Pavement,0,42,0,0,180,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,235000,-93.661562,42.02821 -Two_and_Half_Story_All_Ages,Residential_Low_Density,60,19800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Very_Good,1935,1990,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,1411,1836,GasA,Good,Y,SBrkr,1836,1836,0,3672,0,0,3,1,5,1,Good,7,Typ,2,Good,Detchd,Unf,2,836,Typical,Typical,Paved,684,80,32,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,415000,-93.660664,42.028191 -Split_or_Multilevel,Residential_Low_Density,78,11679,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Average,Average,1962,1962,Gable,CompShg,Plywood,Plywood,Stone,96,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Rec,1164,0,1776,GasA,Excellent,Y,SBrkr,1560,0,0,1560,0,1,2,0,3,1,Typical,6,Min2,1,Fair,Attchd,Fin,2,528,Typical,Typical,Paved,453,253,144,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,182000,-93.660227,42.026458 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12048,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1952,2002,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,232,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1488,0,0,1488,0,0,1,0,3,1,Typical,7,Typ,1,Excellent,Attchd,RFn,2,569,Typical,Typical,Paved,0,189,36,0,348,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,135000,-93.666255,42.025846 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10519,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Very_Good,1955,1999,Hip,CompShg,MetalSd,MetalSd,Stone,164,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1057,1057,GasA,Good,Y,SBrkr,1057,0,0,1057,0,1,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,137000,-93.667188,42.025524 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Below_Average,1954,1972,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,550,994,GasA,Good,Y,SBrkr,1216,639,0,1855,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,325,Typical,Typical,Paved,182,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,136900,-93.666095,42.025102 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,532,1000,GasA,Typical,Y,SBrkr,1068,541,0,1609,0,0,1,1,5,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,305,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,149900,-93.666096,42.025197 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,6420,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1952,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Excellent,Good,Mn,LwQ,4,ALQ,551,219,980,GasA,Fair,Y,FuseA,1148,0,0,1148,0,1,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,123500,-93.660187,42.026308 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,8335,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1124,0,0,1124,0,0,1,0,3,1,Typical,5,Min2,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,36,190,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,93000,-93.6627359,42.0247523 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7585,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Average,Fair,1948,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Fair,Mn,Unf,7,Unf,0,810,810,GasA,Fair,Y,FuseA,1002,454,0,1456,1,1,1,0,4,1,Typical,7,Typ,1,Typical,Detchd,Unf,1,280,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,91500,-93.658506,42.022824 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1985,1985,Gable,CompShg,Wd Sdng,Wd Shng,BrkFace,85,Good,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,40,1298,GasA,Typical,Y,SBrkr,1298,0,0,1298,1,0,2,0,3,1,Good,5,Typ,1,Typical,Attchd,Unf,2,403,Typical,Typical,Paved,165,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,180000,-93.685537,42.032082 -Two_Story_1946_and_Newer,Residential_Low_Density,88,12128,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Below_Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,319,868,GasA,Excellent,Y,SBrkr,1313,1246,0,2559,0,0,2,1,4,1,Excellent,9,Typ,1,Typical,Attchd,RFn,2,506,Typical,Typical,Paved,0,245,0,0,168,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Abnorml,209000,-93.683235,42.032493 -Two_Story_1946_and_Newer,Residential_Low_Density,80,9554,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,BrkFace,125,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,397,777,GasA,Excellent,Y,SBrkr,1065,846,0,1911,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,471,Typical,Typical,Paved,182,81,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,215000,-93.68218,42.030933 -Duplex_All_Styles_and_Ages,Residential_Low_Density,73,9069,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,SFoyer,Above_Average,Very_Good,1993,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Good,PConc,Good,Typical,Av,LwQ,4,GLQ,1083,0,1344,GasA,Good,Y,SBrkr,1440,0,0,1440,2,0,2,0,2,2,Good,8,Typ,0,No_Fireplace,Attchd,Unf,4,920,Typical,Typical,Paved,288,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,224500,-93.6801515,42.0315156 -Two_Story_1946_and_Newer,Residential_Low_Density,133,11003,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1308,1308,GasA,Excellent,Y,SBrkr,1308,568,0,1876,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,RFn,3,848,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,229800,-93.689071,42.024601 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7488,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,815,1208,GasA,Excellent,Y,SBrkr,1208,0,0,1208,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,105,58,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Abnorml,183600,-93.689816,42.024635 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7406,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,84,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,515,1199,GasA,Excellent,Y,SBrkr,1220,0,0,1220,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,105,54,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,194000,-93.690366,42.024555 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,Unf,7,Unf,0,1286,1286,GasA,Excellent,Y,SBrkr,1294,0,0,1294,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,662,Typical,Typical,Paved,168,55,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,193879,-93.691234,42.024615 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,11210,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,240,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1594,1614,GasA,Excellent,Y,SBrkr,1614,0,0,1614,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,865,Typical,Typical,Paved,144,59,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,221500,-93.688937,42.025687 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8990,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1498,1498,GasA,Excellent,Y,SBrkr,1498,0,0,1498,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,675,Typical,Typical,Paved,351,33,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,New,Partial,204900,-93.688925,42.025218 -Two_Story_1946_and_Newer,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,927,1391,GasA,Excellent,Y,SBrkr,1391,571,0,1962,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,868,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,239799,-93.688924,42.025194 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13377,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,260,1836,GasA,Good,Y,SBrkr,1846,0,0,1846,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,233555,-93.689485,42.02437 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11645,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,434,1734,GasA,Excellent,Y,SBrkr,1734,0,0,1734,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,660,Typical,Typical,Paved,160,24,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,260000,-93.689067,42.024481 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11645,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,198,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,448,1570,GasA,Excellent,Y,SBrkr,1590,0,0,1590,1,0,2,1,2,1,Excellent,6,Typ,0,No_Fireplace,Attchd,Fin,3,754,Typical,Typical,Paved,176,80,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,294900,-93.689084,42.023306 -Two_Story_1946_and_Newer,Residential_Low_Density,91,10984,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,945,945,GasA,Excellent,Y,SBrkr,945,864,0,1809,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,638,Typical,Typical,Paved,144,54,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,209700,-93.688786,42.024376 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9316,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,140,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1558,1614,GasA,Excellent,Y,SBrkr,1614,0,0,1614,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,576,Typical,Typical,Paved,100,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,225000,-93.688934,42.023308 -Two_Story_1946_and_Newer,Residential_Low_Density,78,9316,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,532,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,784,784,GasA,Excellent,Y,SBrkr,784,812,0,1596,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,610,Typical,Typical,Paved,144,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,181000,-93.688929,42.023138 -Two_Story_1946_and_Newer,Residential_Low_Density,80,10041,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,119,908,GasA,Excellent,Y,SBrkr,927,988,0,1915,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,120,150,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Abnorml,220000,-93.68216,42.029884 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,36500,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,621,Typical,Good,CBlock,Typical,Typical,Av,Rec,6,Unf,0,812,1624,GasA,Fair,Y,SBrkr,1582,0,0,1582,0,1,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,2,390,Typical,Typical,Dirt_Gravel,168,198,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,190000,-93.681349,42.028168 -Split_or_Multilevel,Residential_Low_Density,0,21453,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Average,1969,1969,Flat,Metal,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,0,938,GasA,Excellent,Y,SBrkr,988,0,0,988,1,0,1,0,1,1,Typical,4,Typ,2,Typical,Attchd,Unf,2,540,Typical,Typical,Paved,0,130,0,130,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,180000,-93.682095,42.025573 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,70761,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1975,1975,Gable,WdShngl,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,878,1533,GasA,Typical,Y,SBrkr,1533,0,0,1533,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,Unf,2,576,Typical,Typical,Paved,200,54,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,280000,-93.683877,42.02438 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,Typical,Fair,CBlock,Good,Fair,No,LwQ,4,Unf,0,535,1388,GasA,Good,Y,SBrkr,1388,0,0,1388,1,0,2,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,2,522,Typical,Typical,Paved,0,58,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,COD,Abnorml,158900,-93.680401,42.024592 -Split_Foyer,Residential_Low_Density,57,8846,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,572,870,GasA,Excellent,Y,SBrkr,914,0,0,914,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,148000,-93.691896,42.022227 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,13015,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,SBrkr,1100,0,0,1100,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,145000,-93.691873,42.022077 -Two_Story_1946_and_Newer,Residential_Low_Density,65,12438,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,781,781,GasA,Excellent,Y,SBrkr,795,704,0,1499,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,473,Typical,Typical,Paved,413,91,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,187000,-93.688302,42.021209 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8685,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,579,1425,GasA,Excellent,Y,SBrkr,1425,0,0,1425,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,591,Typical,Typical,Paved,0,130,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,193000,-93.690363,42.0209 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,13568,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,274,990,GasA,Excellent,Y,SBrkr,990,0,0,990,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,156000,-93.692005,42.019047 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,244,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,125,1525,GasA,Excellent,Y,SBrkr,1525,0,0,1525,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,541,Typical,Typical,Paved,219,36,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,235000,-93.692606,42.018043 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,9236,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,Gd,GLQ,3,Unf,0,279,1479,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,576,Typical,Typical,Paved,168,27,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,217000,-93.692221,42.018123 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10264,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,183,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,426,1588,GasA,Excellent,Y,SBrkr,1588,0,0,1588,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,472,Typical,Typical,Paved,158,29,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,214000,-93.690215,42.017631 -Two_Story_1946_and_Newer,Residential_Low_Density,68,9272,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,342,842,GasA,Excellent,Y,SBrkr,856,893,0,1749,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,515,Typical,Typical,Paved,140,85,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,196000,-93.691931,42.0168729 -Two_Story_1946_and_Newer,Residential_Low_Density,72,13426,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,57,951,GasA,Excellent,Y,SBrkr,951,828,0,1779,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,586,Typical,Typical,Paved,208,107,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,217000,-93.689083,42.017929 -Two_Story_1946_and_Newer,Residential_Low_Density,95,13450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,216,916,GasA,Excellent,Y,SBrkr,920,941,0,1861,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,492,Typical,Typical,Paved,146,91,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,200000,-93.68923,42.01686 -Two_Story_1946_and_Newer,Residential_Low_Density,65,14006,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Good,Typical,PConc,Good,Typical,No_Basement,Unf,7,Unf,0,936,936,GasA,Excellent,Y,SBrkr,936,840,0,1776,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,474,Typical,Typical,Paved,144,96,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,192500,-93.691243,42.016217 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,118,13704,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1541,1541,GasA,Excellent,Y,SBrkr,1541,0,0,1541,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,3,843,Typical,Typical,Paved,468,81,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,205000,-93.688577,42.016913 -Split_or_Multilevel,Residential_Low_Density,55,10780,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Average,1976,1976,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,428,911,GasA,Good,Y,SBrkr,954,0,0,954,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,132500,-93.686629,42.021388 -Two_Story_1946_and_Newer,Residential_Low_Density,50,8340,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,HdBoard,Plywood,BrkFace,62,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,166,675,GasA,Typical,Y,SBrkr,686,702,0,1388,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,317,Typical,Typical,Paved,406,36,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,157500,-93.686825,42.020145 -Split_or_Multilevel,Residential_Low_Density,42,10385,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,123,Typical,Typical,CBlock,Typical,Good,Av,ALQ,1,LwQ,400,0,995,GasA,Typical,Y,SBrkr,1282,0,0,1282,0,1,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,3,672,Fair,Typical,Paved,386,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,174000,-93.687158,42.02007 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,9920,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1969,1969,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,448,971,GasA,Typical,Y,SBrkr,971,0,0,971,0,0,1,1,3,1,Typical,5,Typ,1,Poor,Attchd,Unf,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,128500,-93.684982,42.019697 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,9560,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,504,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,128500,-93.683468,42.021811 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,0,894,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,552,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2006,WD ,Abnorml,149900,-93.683246,42.021889 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,427,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,297,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,119900,-93.682979,42.020998 -Two_Story_PUD_1946_and_Newer,Residential_High_Density,0,3612,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Landmark,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,292,630,GasA,Excellent,Y,SBrkr,630,690,0,1320,0,0,2,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,137000,-93.681378,42.021785 -Two_Story_1946_and_Newer,Residential_Low_Density,90,11367,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,133,1065,GasA,Excellent,Y,SBrkr,1091,898,0,1989,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,586,Typical,Typical,Paved,199,60,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,255000,-93.687703,42.01834 -Two_Story_1946_and_Newer,Residential_Low_Density,0,9930,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,199,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,370,826,GasA,Excellent,Y,SBrkr,878,884,0,1762,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,591,Typical,Typical,Paved,320,54,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,222000,-93.686288,42.018664 -Two_Story_1946_and_Newer,Residential_Low_Density,45,9468,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,148,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,201,840,GasA,Excellent,Y,SBrkr,840,915,0,1755,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,530,Typical,Typical,Paved,176,73,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,214000,-93.684421,42.017854 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11088,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,Stucco,Stucco,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,476,1348,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,Unf,2,418,Typical,Typical,Paved,68,166,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,220000,-93.685504,42.017055 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14781,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,178,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1753,1753,GasA,Excellent,Y,SBrkr,1787,0,0,1787,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,748,Typical,Typical,Paved,198,150,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,275000,-93.685697,42.018188 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8726,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,872,872,GasA,Excellent,Y,SBrkr,872,1037,0,1909,0,0,2,1,4,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,529,Typical,Typical,Paved,0,108,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,197000,-93.687675,42.014494 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,Stone,295,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,668,1654,GasA,Excellent,Y,SBrkr,1654,0,0,1654,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,3,900,Typical,Typical,Paved,0,136,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,230000,-93.685571,42.016979 -Two_Story_1946_and_Newer,Residential_Low_Density,67,10566,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,261,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,170,1090,GasA,Excellent,Y,SBrkr,1090,1124,0,2214,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,3,646,Typical,Typical,Paved,197,80,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,268500,-93.683718,42.016904 -Two_Story_1946_and_Newer,Residential_Low_Density,0,21533,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Feedr,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1065,1065,GasA,Excellent,Y,SBrkr,1065,984,0,2049,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,467,Typical,Typical,Paved,120,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,210900,-93.679147,42.01869 -Two_Story_1946_and_Newer,Residential_Low_Density,90,11250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,227,Typical,Typical,PConc,Good,Typical,Mn,ALQ,1,Unf,0,258,1054,GasA,Excellent,Y,SBrkr,1070,869,0,1939,0,1,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,3,555,Typical,Typical,Paved,128,84,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,227000,-93.681479,42.017856 -Two_Story_1946_and_Newer,Residential_Low_Density,90,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,Av,GLQ,3,Unf,0,245,930,GasA,Excellent,Y,SBrkr,950,1045,0,1995,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,610,Typical,Typical,Paved,275,170,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,237000,-93.679803,42.017867 -Two_Story_1946_and_Newer,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,133,1108,GasA,Excellent,Y,SBrkr,1108,989,0,2097,1,0,2,1,3,1,Good,8,Typ,1,Typical,Detchd,RFn,2,583,Typical,Typical,Paved,253,170,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,274300,-93.682686,42.017858 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1836,1860,GasA,Excellent,Y,SBrkr,1836,0,0,1836,0,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,484,Typical,Typical,Paved,120,33,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,216837,-93.681895,42.017186 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,37,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,131,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,133000,-93.681191,42.016275 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,37,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Good,Attchd,RFn,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,155900,-93.683728,42.016252 -Two_Story_1946_and_Newer,Residential_Low_Density,120,15611,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1079,1079,GasA,Excellent,Y,SBrkr,1079,840,0,1919,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,685,Good,Typical,Paved,0,51,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,233230,-93.683777,42.016618 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8810,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,390,1390,GasA,Excellent,Y,SBrkr,1390,0,0,1390,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,545,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,207000,-93.687684,42.015914 -Two_Story_1946_and_Newer,Residential_Low_Density,41,12393,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1101,0,1948,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,195000,-93.687685,42.016092 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9135,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1342,1682,GasA,Excellent,Y,SBrkr,1700,0,0,1700,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,544,Typical,Typical,Paved,192,23,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,200000,-93.687826,42.014636 -Two_Story_1946_and_Newer,Residential_Low_Density,74,8581,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Mn,Unf,7,Unf,0,851,851,GasA,Excellent,Y,SBrkr,851,886,0,1737,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,0,105,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,203160,-93.684089,42.014109 -Two_Story_1946_and_Newer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,784,784,GasA,Excellent,Y,SBrkr,784,827,0,1611,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,572,Typical,Typical,Paved,144,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,195800,-93.684116,42.015793 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,10084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1528,1552,GasA,Excellent,Y,SBrkr,1552,0,0,1552,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,782,Typical,Typical,Paved,144,20,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,212900,-93.684116,42.016004 -Two_Story_1946_and_Newer,Residential_Low_Density,89,11645,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,860,860,GasA,Excellent,Y,SBrkr,860,860,0,1720,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,565,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,196500,-93.685369,42.01397 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8772,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,340,1336,GasA,Excellent,Y,SBrkr,1336,0,0,1336,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,502,Typical,Typical,Paved,136,43,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,198000,-93.685872,42.013991 -Two_Story_1946_and_Newer,Residential_Low_Density,68,8846,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,750,750,GasA,Excellent,Y,SBrkr,750,750,0,1500,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,564,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,173900,-93.683435,42.015765 -Two_Story_1946_and_Newer,Residential_Low_Density,65,8461,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,163990,-93.684332,42.013519 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8767,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1286,1310,GasA,Excellent,Y,SBrkr,1310,0,0,1310,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,164990,-93.683758,42.014593 -Two_Story_1945_and_Older,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,Two_Story,Below_Average,Above_Average,1910,2000,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Good,Typical,No,Rec,6,BLQ,337,166,676,GasA,Good,Y,SBrkr,760,676,0,1436,1,0,2,0,3,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,Shed,420,10,2006,WD ,Normal,98000,-93.678267,42.0195855 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,134,17755,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Below_Average,1959,1959,Gable,CompShg,HdBoard,Plywood,BrkFace,132,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,1290,1466,GasA,Typical,Y,SBrkr,1466,0,0,1466,0,0,1,1,3,1,Fair,6,Typ,2,Good,Attchd,Fin,2,528,Typical,Typical,Paved,0,140,0,0,100,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,149900,-93.676369,42.020995 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8877,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Fair,No,LwQ,4,Unf,0,0,836,GasA,Typical,Y,FuseF,1220,0,0,1220,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,396,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,COD,Normal,102000,-93.676361,42.020342 -Duplex_All_Styles_and_Ages,Residential_Low_Density,38,7840,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1975,1975,Flat,Tar&Grv,Plywood,Wd Shng,BrkFace,355,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,976,GasA,Typical,Y,SBrkr,1012,0,0,1012,0,2,2,0,4,0,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,AdjLand,127500,-93.675297,42.020592 -Duplex_All_Styles_and_Ages,Residential_Low_Density,35,9400,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1975,1975,Flat,Tar&Grv,WdShing,Plywood,BrkFace,250,Typical,Typical,CBlock,Good,Good,Gd,GLQ,3,Unf,0,0,945,GasA,Typical,Y,SBrkr,980,0,0,980,0,2,2,0,4,0,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,AdjLand,127500,-93.675548,42.020547 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,16133,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Below_Average,1969,1969,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,329,1176,GasA,Typical,Y,SBrkr,1176,0,0,1176,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,360,Typical,Typical,Paved,0,92,0,0,112,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,119900,-93.671454,42.021113 -Two_Story_1946_and_Newer,Residential_Low_Density,62,7162,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Hip,CompShg,HdBoard,Stucco,BrkFace,190,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,796,796,GasA,Excellent,Y,SBrkr,806,918,0,1724,0,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,616,Typical,Typical,Paved,168,57,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,196500,-93.672379,42.01899 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,ALQ,297,142,914,GasA,Excellent,Y,SBrkr,914,0,0,914,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,32,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,117250,-93.671304,42.021168 -Two_Story_1946_and_Newer,Residential_Low_Density,90,11060,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1150,1150,GasA,Excellent,Y,SBrkr,1164,1150,0,2314,0,0,2,1,3,1,Good,9,Typ,1,Excellent,BuiltIn,Fin,2,502,Typical,Typical,Paved,0,274,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,ConLD,Normal,229000,-93.671612,42.018648 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SLvl,Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,145000,-93.670025,42.018961 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,82,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,140000,-93.669977,42.01896 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,82,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,142500,-93.669927,42.018959 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,134000,-93.670063,42.018813 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,130000,-93.670039,42.018812 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2004,2006,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,130000,-93.669985,42.01881 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,137500,-93.66996,42.01881 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,3363,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,117,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,976,976,GasA,Excellent,Y,SBrkr,976,732,0,1708,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,130000,-93.669924,42.018809 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,56,6956,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Fair,Typical,Mn,Unf,7,Unf,0,624,624,GasA,Excellent,Y,SBrkr,624,312,0,936,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,265,Typical,Poor,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,97900,-93.661951,42.02011 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,72,7822,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Fair,1915,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,BrkTil,Fair,Fair,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,FuseF,846,492,0,1338,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Dirt_Gravel,0,0,208,0,0,0,No_Pool,Good_Privacy,None,0,5,2006,WD ,AdjLand,92000,-93.659182,42.022676 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,62,8707,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Edwards,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1093,1093,GasA,Typical,N,FuseF,1093,576,0,1669,0,0,1,1,4,1,Typical,9,Min2,0,No_Fireplace,Attchd,Unf,1,288,Fair,Typical,Paved,0,0,56,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,AdjLand,107000,-93.659222,42.022659 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,58,8410,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Edwards,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Fair,1910,1996,Gambrel,CompShg,Wd Sdng,VinylSd,None,0,Typical,Fair,PConc,Typical,Typical,No,Unf,7,Unf,0,658,658,GasA,Typical,Y,SBrkr,658,526,0,1184,0,0,1,0,5,1,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,151,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,AdjLand,81000,-93.659222,42.022641 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9738,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1924,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,392,784,GasA,Good,Y,SBrkr,949,272,0,1221,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,392,Typical,Typical,Paved,0,0,236,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,104900,-93.660119,42.021432 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1955,1990,Hip,CompShg,WdShing,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,697,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,572,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,115000,-93.663458,42.018848 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,16012,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1954,1968,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,60,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,263,954,GasA,Excellent,Y,SBrkr,1482,0,0,1482,0,1,2,0,3,1,Typical,6,Min1,1,Good,More_Than_Two_Types,Unf,2,609,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2006,WD ,Abnorml,125000,-93.661995,42.017727 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,8248,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1918,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,686,686,GasW,Good,Y,SBrkr,686,564,0,1250,0,1,1,1,3,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Partial_Pavement,207,0,96,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,116000,-93.656753,42.022089 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,8248,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,864,864,GasA,Typical,N,SBrkr,964,0,450,1414,0,0,1,0,3,1,Typical,8,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,112,0,0,0,No_Pool,No_Fence,None,0,9,2006,COD,Abnorml,83000,-93.6567,42.022088 -Two_and_Half_Story_All_Ages,Residential_Low_Density,60,6204,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_and_Half_Fin,Below_Average,Average,1912,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Typical,Fair,No,Unf,7,Unf,0,795,795,GasA,Typical,N,SBrkr,954,795,481,2230,1,0,1,0,5,1,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,1,440,Typical,Good,Paved,0,188,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,118500,-93.65564,42.02197 -One_Story_1945_and_Older,Residential_Low_Density,60,8088,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Poor,Fair,1922,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,498,498,GasA,Typical,N,FuseF,498,0,0,498,0,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,216,Fair,Fair,Dirt_Gravel,0,0,100,0,0,0,No_Pool,No_Fence,None,0,2,2006,ConLD,Normal,35000,-93.659043,42.021559 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,76,11388,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1910,1993,Gable,CompShg,VinylSd,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,616,616,GasA,Typical,N,SBrkr,1055,218,0,1273,0,0,1,0,3,1,Good,5,Min2,0,No_Fireplace,Detchd,Unf,1,275,Typical,Fair,Dirt_Gravel,212,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,121000,-93.655631,42.020871 -Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,0,7082,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,Two_Story,Average,Very_Good,1916,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Unf,7,Unf,0,686,686,GasA,Good,Y,SBrkr,948,980,0,1928,0,0,2,0,5,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,228,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,160000,-93.648562,42.020286 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1940,2000,Gable,CompShg,Wd Sdng,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,552,940,GasA,Excellent,Y,SBrkr,1192,403,0,1595,0,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,108,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,140000,-93.651811,42.018837 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1938,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,128,1058,GasA,Typical,Y,SBrkr,1058,493,0,1551,1,0,2,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,137000,-93.651285,42.018282 -Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,58,6430,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Above_Average,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,0,780,GasA,Typical,N,FuseF,816,524,0,1340,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,128000,-93.646942,42.01924 -Two_Story_1945_and_Older,Residential_Low_Density,43,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Good,Very_Good,1926,1997,Gable,CompShg,Wd Sdng,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,200,624,GasA,Excellent,Y,SBrkr,743,736,0,1479,1,0,1,0,3,1,Good,6,Typ,2,Good,Detchd,Unf,1,312,Typical,Typical,Paved,530,0,56,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,160000,-93.646619,42.019093 -Two_Story_1945_and_Older,Residential_Low_Density,96,13132,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Average,Average,1914,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Unf,7,Unf,0,747,747,GasA,Good,Y,FuseF,892,892,0,1784,0,0,1,1,4,1,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,203,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,138887,-93.646438,42.01895 -Two_Story_1945_and_Older,Residential_Low_Density,69,4899,Pave,No_Alley_Access,Regular,HLS,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,450,755,GasA,Excellent,Y,SBrkr,755,755,0,1510,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,164,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,149000,-93.648417,42.018808 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1008,1008,GasA,Excellent,Y,SBrkr,1008,0,514,1522,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Partial_Pavement,0,0,138,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,102000,-93.648416,42.018857 -Two_Story_1945_and_Older,Residential_Low_Density,54,9399,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Very_Good,1919,1950,Gable,CompShg,MetalSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,818,818,GasA,Typical,Y,SBrkr,818,818,0,1636,0,0,1,1,4,1,Good,7,Typ,1,Good,Detchd,Unf,1,288,Fair,Typical,Dirt_Gravel,0,0,212,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Abnorml,167000,-93.64644,42.016941 -Two_Story_1945_and_Older,Residential_Low_Density,54,7588,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,441,793,GasA,Good,Y,SBrkr,901,901,0,1802,0,0,1,1,4,1,Typical,9,Typ,1,Good,Detchd,Unf,1,216,Fair,Typical,Paved,0,0,40,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,200100,-93.6447118,42.0165794 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,84,10164,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1939,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,Av,LwQ,4,Unf,0,346,992,GasA,Fair,Y,SBrkr,992,473,0,1465,0,0,2,0,3,1,Typical,6,Typ,2,Typical,Detchd,Unf,1,240,Typical,Typical,Paved,0,126,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,131000,-93.648415,42.016086 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,6191,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Fair,No,LwQ,4,Unf,0,440,824,GasA,Typical,N,SBrkr,824,464,0,1288,0,0,1,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,112000,-93.647027,42.016072 -Two_Story_1945_and_Older,Residential_Low_Density,60,9550,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Average,1915,1970,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Good,No,ALQ,1,Unf,0,540,756,GasA,Good,Y,SBrkr,961,756,0,1717,1,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,3,642,Typical,Typical,Paved,0,35,272,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Abnorml,140000,-93.644307,42.017609 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,66,21780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Fair,No,Unf,7,Unf,0,817,817,GasA,Good,Y,FuseF,940,610,0,1550,0,0,1,1,3,1,Typical,7,Min2,1,Typical,Detchd,Unf,1,318,Typical,Typical,Partial_Pavement,0,0,429,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2006,WD ,Normal,195000,-93.644258,42.01874 -Two_Story_1945_and_Older,Residential_Low_Density,0,11435,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1929,1950,Gable,CompShg,BrkFace,Stucco,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,792,792,GasA,Fair,Y,SBrkr,792,725,0,1517,0,0,1,0,3,1,Good,7,Typ,2,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,230000,-93.642431,42.019059 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,12400,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Mn,BLQ,2,Unf,0,299,901,GasA,Typical,Y,SBrkr,1125,592,0,1717,0,0,1,1,2,1,Typical,7,Typ,1,Good,Attchd,Unf,1,410,Typical,Typical,Paved,0,0,0,0,113,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,194000,-93.64048,42.019759 -One_Story_1945_and_Older,Residential_Low_Density,80,11600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Below_Average,Average,1922,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,700,700,GasA,Excellent,Y,SBrkr,1180,0,0,1180,0,0,1,0,2,1,Fair,5,Typ,1,Good,Detchd,Unf,1,252,Typical,Fair,Paved,0,0,67,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,137500,-93.643443,42.017131 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,86,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1936,1987,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,794,1017,GasA,Good,Y,SBrkr,1020,1037,0,2057,0,0,1,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,1,180,Fair,Typical,Paved,0,0,0,0,322,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,250000,-93.641155,42.016827 -One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,81,8170,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1929,1950,Gable,CompShg,Stucco,Wd Sdng,BrkFace,270,Good,Good,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,496,1022,GasA,Excellent,Y,FuseA,1122,549,0,1671,0,0,2,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,2,451,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,218000,-93.642319,42.016385 -Two_Story_1945_and_Older,Residential_Low_Density,80,16560,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1932,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Rec,6,Unf,0,449,952,GasA,Typical,Y,SBrkr,1170,1175,0,2345,0,0,2,1,4,1,Typical,9,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,239000,-93.641002,42.017589 -Two_Story_1945_and_Older,Residential_Low_Density,70,12320,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Good,1932,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,637,637,GasA,Excellent,Y,SBrkr,959,650,0,1609,0,0,1,1,3,1,Good,8,Typ,2,Good,More_Than_Two_Types,Unf,3,579,Typical,Typical,Paved,0,0,0,0,104,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,199500,-93.6416718,42.0157866 -Two_Story_1945_and_Older,Residential_Low_Density,70,14210,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1930,1959,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,697,697,GasA,Excellent,Y,SBrkr,1104,697,0,1801,0,0,1,1,3,1,Typical,8,Typ,1,Good,Attchd,Unf,2,365,Fair,Typical,Paved,0,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Normal,210000,-93.640612,42.016124 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,14115,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Good,1956,2004,Gable,CompShg,Stone,Stone,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,GLQ,547,230,1073,GasA,Excellent,Y,SBrkr,1811,0,0,1811,0,0,1,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,470,Typical,Typical,Paved,0,0,280,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,230000,-93.642884,42.013296 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12984,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,459,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,LwQ,147,0,1430,GasA,Excellent,Y,SBrkr,1647,0,0,1647,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,621,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,217500,-93.644203,42.013495 -Two_Story_1946_and_Newer,Residential_Low_Density,78,15600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Average,Good,1950,1963,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,405,Typical,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,408,1168,GasA,Good,Y,SBrkr,1278,1037,0,2315,1,0,2,0,4,1,Typical,9,Typ,3,Good,Attchd,Fin,1,342,Typical,Typical,Paved,0,0,0,0,192,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,257000,-93.639622,42.014936 -Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,90,15750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,TwoFmCon,One_Story,Average,Average,1953,1953,Hip,CompShg,MetalSd,MetalSd,BrkFace,56,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,324,1165,GasA,Typical,Y,SBrkr,1336,0,0,1336,1,0,1,0,2,1,Typical,5,Typ,2,Good,Attchd,Unf,1,375,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,157000,-93.639428,42.01359 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16381,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Average,1969,1969,Gable,CompShg,Plywood,Plywood,BrkFace,312,Good,Good,CBlock,Typical,Typical,Av,Rec,6,Unf,0,734,1844,GasA,Good,Y,SBrkr,1844,0,0,1844,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,540,Typical,Typical,Paved,0,73,216,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,223000,-93.643489,42.011793 -One_Story_1945_and_Older,Residential_Medium_Density,50,7288,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_Story,Average,Above_Average,1942,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,671,976,GasA,Typical,N,SBrkr,976,0,0,976,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,215,Typical,Typical,Dirt_Gravel,160,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,AdjLand,102000,-93.628467,42.022786 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1926,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,487,861,GasA,Excellent,Y,SBrkr,861,424,0,1285,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,2,506,Typical,Typical,Paved,96,0,132,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,145400,-93.62841,42.022709 -One_Story_1945_and_Older,Residential_Medium_Density,61,8534,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,432,432,GasA,Typical,N,FuseA,672,0,0,672,0,0,1,0,2,1,Typical,4,Min1,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,112,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,72000,-93.628298,42.020441 -One_Story_1945_and_Older,Residential_Medium_Density,50,7030,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,641,641,GasA,Good,Y,SBrkr,641,0,0,641,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,272,Typical,Typical,Dirt_Gravel,184,0,70,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,85000,-93.629496,42.020855 -One_Story_1945_and_Older,Residential_Medium_Density,50,8765,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1936,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,666,951,GasA,Excellent,N,SBrkr,951,0,0,951,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,327,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,106500,-93.626683,42.020207 -One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1957,1957,Gable,CompShg,MetalSd,MetalSd,BrkFace,327,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,967,967,GasA,Good,Y,SBrkr,967,671,0,1638,0,0,2,0,4,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,139000,-93.625147,42.02153 -One_and_Half_Story_Finished_All_Ages,C_all,60,11040,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,0,637,GasA,Good,Y,SBrkr,897,439,0,1336,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,CarPort,Unf,1,570,Typical,Typical,Paved,0,47,120,0,0,0,No_Pool,No_Fence,None,0,9,2006,COD,Normal,108000,-93.615307,42.019025 -One_Story_1945_and_Older,C_all,69,12366,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,One_Story,Fair,Average,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,729,0,0,729,0,0,1,0,2,1,Typical,5,Mod,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,23,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,51689,-93.61385,42.019349 -One_Story_1946_and_Newer_All_Styles,C_all,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Poor,Fair,1949,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,430,480,GasA,Typical,N,FuseA,480,0,0,480,1,0,0,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,35311,-93.615012,42.019099 -Two_Family_conversion_All_Styles_and_Ages,C_all,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Above_Average,1951,1951,Gable,CompShg,WdShing,Wd Shng,None,0,Fair,Fair,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,660,660,GasA,Typical,N,SBrkr,1060,336,0,1396,0,0,2,0,4,2,Typical,8,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,115000,-93.615024,42.019099 -One_and_Half_Story_Finished_All_Ages,C_all,60,8520,Grvl,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Average,1916,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Fair,Fair,No,Unf,7,Unf,0,216,216,GasA,Fair,N,SBrkr,576,360,0,936,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,78000,-93.604344,42.022603 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,5748,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,Stone,473,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,0,1573,GasA,Excellent,Y,SBrkr,1778,0,0,1778,2,0,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,495,Typical,Typical,Paved,123,53,0,0,153,0,No_Pool,No_Fence,None,0,2,2006,New,Partial,375000,-93.616026,42.009398 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3842,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,186,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,30,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,525,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,300000,-93.616242,42.008375 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,23580,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,849,1625,GasA,Typical,Y,SBrkr,1625,0,0,1625,0,1,2,0,3,1,Fair,6,Typ,1,Typical,Attchd,Fin,2,576,Typical,Typical,Paved,136,28,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,242500,-93.599969,41.996849 -Duplex_All_Styles_and_Ages,Residential_Low_Density,65,8385,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1664,1664,GasA,Typical,Y,SBrkr,1664,0,0,1664,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,2,616,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,145000,-93.606736,41.994357 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9116,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2001,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1491,1491,GasA,Excellent,Y,SBrkr,1491,0,0,1491,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,490,Typical,Typical,Paved,120,100,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,209000,-93.603671,41.995987 -Duplex_All_Styles_and_Ages,Residential_Low_Density,78,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,ImStucc,BrkFace,90,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,975,GasA,Typical,Y,SBrkr,1004,0,0,1004,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,135000,-93.603173,41.996907 -Split_or_Multilevel,Residential_Low_Density,140,11080,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Plywood,BrkFace,257,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,552,1128,GasA,Typical,Y,SBrkr,1210,0,0,1210,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,148000,-93.601191,41.995702 -Duplex_All_Styles_and_Ages,Residential_Low_Density,82,11070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Good,Average,1988,1989,Gable,CompShg,VinylSd,VinylSd,BrkFace,70,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1907,1907,GasA,Good,Y,SBrkr,1959,0,0,1959,0,0,3,0,5,2,Typical,9,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,766,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Family,171000,-93.603854,41.996063 -Two_Story_1946_and_Newer,Residential_Low_Density,42,26178,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,1989,1990,Hip,CompShg,MetalSd,MetalSd,BrkFace,293,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,245,1210,GasA,Excellent,Y,SBrkr,1238,1281,0,2519,1,0,2,1,4,1,Good,9,Typ,2,Good,Attchd,RFn,2,628,Typical,Typical,Paved,320,27,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,335000,-93.644527,42.004509 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,8239,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Green_Hills,Norm,Norm,TwnhsE,One_Story,Good,Average,1986,1986,Gable,CompShg,BrkFace,Wd Sdng,None,0,Good,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1295,0,0,1295,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,230000,-93.645586,42.000966 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,50102,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1958,1958,Gable,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,BLQ,2,Unf,0,723,1632,GasA,Typical,Y,SBrkr,1650,0,0,1650,1,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,Unf,2,518,Typical,Typical,Paved,0,0,0,0,138,0,No_Pool,No_Fence,None,0,3,2006,WD ,Alloca,250764,-93.651077,41.99984 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16669,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1981,1981,Hip,WdShake,Plywood,Plywood,BrkFace,653,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1686,1686,GasA,Typical,Y,SBrkr,1707,0,0,1707,0,0,2,1,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,511,Typical,Typical,Paved,574,64,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,228000,-93.649773,41.997906 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,54,13811,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1987,1987,Gable,CompShg,HdBoard,HdBoard,BrkFace,72,Typical,Typical,CBlock,Good,Good,No,GLQ,3,LwQ,40,92,1112,GasA,Good,Y,SBrkr,1137,0,0,1137,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Unf,2,551,Typical,Typical,Paved,125,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,176000,-93.646099,41.999553 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,8049,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,1990,1990,Hip,CompShg,HdBoard,HdBoard,BrkFace,54,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,256,1309,GasA,Typical,Y,SBrkr,1339,0,0,1339,1,0,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,484,Good,Good,Paved,0,58,0,0,90,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,180000,-93.646575,41.998023 -Two_Story_1946_and_Newer,Residential_Low_Density,0,11170,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1991,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Wood,Good,Typical,No,LwQ,4,Unf,0,0,1216,GasA,Excellent,Y,SBrkr,1298,1216,0,2514,0,0,2,1,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,2,693,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,4,2006,WD ,Normal,250000,-93.646172,41.999342 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8098,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,Wood,Good,Typical,Av,GLQ,3,BLQ,116,129,1381,GasA,Excellent,Y,SBrkr,1403,0,0,1403,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,470,Typical,Typical,Paved,0,173,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,202000,-93.646643,41.996331 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14331,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,630,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,526,1800,GasA,Excellent,Y,SBrkr,1800,0,0,1800,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,765,Typical,Typical,Paved,270,78,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,312500,-93.646758,41.994347 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,13618,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,198,Good,Typical,PConc,Excellent,Good,Av,GLQ,3,Unf,0,378,1728,GasA,Excellent,Y,SBrkr,1960,0,0,1960,1,0,2,0,3,1,Good,8,Typ,2,Good,Attchd,Fin,3,714,Typical,Typical,Paved,172,38,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,320000,-93.64789,41.994229 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11443,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,208,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,408,1868,GasA,Excellent,Y,SBrkr,2028,0,0,2028,1,0,2,0,2,1,Good,7,Typ,2,Good,Attchd,RFn,3,880,Typical,Typical,Paved,326,66,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,369900,-93.64898,41.993258 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11577,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,382,Excellent,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,383,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,1,0,2,0,3,1,Excellent,9,Typ,1,Good,Attchd,Fin,3,682,Typical,Typical,Paved,161,225,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,359900,-93.647474,41.993047 -One_Story_1946_and_Newer_All_Styles,A_agr,125,31250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Artery,Norm,OneFam,One_Story,Very_Poor,Fair,1951,1951,Gable,CompShg,CBlock,VinylSd,None,0,Typical,Fair,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1600,0,0,1600,0,0,1,1,3,1,Typical,6,Mod,0,No_Fireplace,Attchd,Unf,1,270,Fair,Typical,Dirt_Gravel,0,0,135,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,81500,-93.610268,41.99222 -Duplex_All_Styles_and_Ages,Residential_Medium_Density,78,7020,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Good,Average,1997,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,200,Typical,Good,PConc,Good,Typical,Gd,GLQ,3,Unf,0,45,1288,GasA,Good,Y,SBrkr,1368,0,0,1368,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,4,784,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,215000,-93.608343,41.99329 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,Twnhs,One_Story,Above_Average,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,116,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,319,1216,GasA,Excellent,Y,SBrkr,1216,0,0,1216,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,402,Typical,Typical,Paved,0,125,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,164000,-93.608195,41.993173 -One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,443,Typical,Good,PConc,Excellent,Good,No,GLQ,3,Unf,0,36,1237,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,405,Typical,Typical,Paved,0,199,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,153500,-93.608195,41.99315 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,17217,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1140,1140,GasA,Excellent,Y,SBrkr,1140,0,0,1140,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,36,56,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,84500,-93.607142,41.990054 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,264,264,GasA,Typical,Y,SBrkr,616,688,0,1304,0,0,1,1,3,1,Typical,5,Typ,1,Typical,BuiltIn,RFn,1,336,Typical,Typical,Paved,141,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,104500,-93.604391,41.990993 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Rec,173,36,757,GasA,Excellent,Y,SBrkr,925,550,0,1475,0,0,2,0,4,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,336,Typical,Typical,Paved,104,26,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,127000,-93.603922,41.990523 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,42,3964,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Above_Average,Below_Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,105,942,GasA,Good,Y,SBrkr,1291,1230,0,2521,1,0,2,1,5,1,Typical,10,Maj1,1,Good,Attchd,Fin,2,576,Typical,Typical,Paved,728,20,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,151400,-93.603518,41.991731 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,58,10172,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1968,2003,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,423,864,GasA,Excellent,Y,SBrkr,874,0,0,874,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,126500,-93.604279,41.993062 -Duplex_All_Styles_and_Ages,Residential_Low_Density,0,11836,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Average,Average,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,1503,1652,GasA,Typical,Y,SBrkr,1652,0,0,1652,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,928,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,146500,-93.601089,41.991574 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1470,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Above_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,108,630,GasA,Typical,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,73000,-93.603181,41.992303 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1484,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Below_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,294,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,253,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,79400,-93.603524,41.992159 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,13384,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1969,1979,Gable,CompShg,Plywood,Plywood,BrkFace,194,Typical,Typical,PConc,Typical,Typical,Av,Rec,6,BLQ,344,641,1104,GasA,Fair,Y,SBrkr,1360,0,0,1360,1,0,1,0,3,1,Typical,8,Typ,1,Typical,Attchd,RFn,1,336,Typical,Typical,Paved,160,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,140000,-93.600439,41.991708 -PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Average,Good,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,77,630,GasA,Excellent,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Excellent,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Abnorml,92000,-93.602043,41.992533 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,138,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,CarPort,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,87550,-93.6019,41.992522 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1526,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,34,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,79500,-93.601844,41.992497 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Good,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Good,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,90500,-93.601615,41.99171 -Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1894,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,294,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,1,286,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,71000,-93.602345,41.991532 -Duplex_All_Styles_and_Ages,Residential_Low_Density,55,12640,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,LwQ,396,396,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,574,Typical,Typical,Paved,40,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,150900,-93.6044746,41.9900428 -Duplex_All_Styles_and_Ages,Residential_Low_Density,63,9297,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,122,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,2,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,560,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Family,188000,-93.603534,41.990134 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,17400,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1977,1977,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,190,1126,GasA,Fair,Y,SBrkr,1126,0,0,1126,1,0,2,0,3,1,Typical,5,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Partial_Pavement,295,41,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,160000,-93.6086884,41.9887374 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,160,20000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1960,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,0,1224,GasA,Excellent,Y,SBrkr,1224,0,0,1224,1,0,1,0,4,1,Typical,7,Typ,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,474,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Abnorml,131000,-93.606842,41.987686 -Split_or_Multilevel,Residential_Low_Density,37,7937,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,184,1003,GasA,Typical,Y,SBrkr,1003,0,0,1003,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,588,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,Good_Privacy,None,0,3,2006,WD ,Normal,142500,-93.604776,41.988964 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8885,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,ALQ,324,239,864,GasA,Typical,Y,SBrkr,902,0,0,902,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,164,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,131000,-93.60268,41.988314 -Split_Foyer,Residential_Low_Density,62,10441,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Average,1992,1992,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,575,912,GasA,Typical,Y,SBrkr,970,0,0,970,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,80,32,0,0,0,0,No_Pool,Minimum_Privacy,Shed,700,7,2006,WD ,Normal,132000,-93.606847,41.98651 -One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,10010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1974,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,123,195,1389,GasA,Good,Y,SBrkr,1389,0,0,1389,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,418,Typical,Typical,Paved,240,38,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,170000,-93.60019,41.990921 -Two_Story_1946_and_Newer,Residential_Low_Density,74,9627,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,94,Typical,Typical,PConc,Good,Typical,Av,LwQ,4,Unf,0,238,996,GasA,Excellent,Y,SBrkr,996,1004,0,2000,0,0,2,1,3,1,Typical,9,Typ,1,Typical,Attchd,Fin,3,650,Typical,Typical,Paved,190,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,188000,-93.599996,41.989265 +MS_SubClass,MS_Zoning,Lot_Frontage,Lot_Area,Street,Alley,Lot_Shape,Land_Contour,Utilities,Lot_Config,Land_Slope,Neighborhood,Condition_1,Condition_2,Bldg_Type,House_Style,Overall_Qual,Overall_Cond,Year_Built,Year_Remod_Add,Roof_Style,Roof_Matl,Exterior_1st,Exterior_2nd,Mas_Vnr_Type,Mas_Vnr_Area,Exter_Qual,Exter_Cond,Foundation,Bsmt_Qual,Bsmt_Cond,Bsmt_Exposure,BsmtFin_Type_1,BsmtFin_SF_1,BsmtFin_Type_2,BsmtFin_SF_2,Bsmt_Unf_SF,Total_Bsmt_SF,Heating,Heating_QC,Central_Air,Electrical,First_Flr_SF,Second_Flr_SF,Low_Qual_Fin_SF,Gr_Liv_Area,Bsmt_Full_Bath,Bsmt_Half_Bath,Full_Bath,Half_Bath,Bedroom_AbvGr,Kitchen_AbvGr,Kitchen_Qual,TotRms_AbvGrd,Functional,Fireplaces,Fireplace_Qu,Garage_Type,Garage_Finish,Garage_Cars,Garage_Area,Garage_Qual,Garage_Cond,Paved_Drive,Wood_Deck_SF,Open_Porch_SF,Enclosed_Porch,Three_season_porch,Screen_Porch,Pool_Area,Pool_QC,Fence,Misc_Feature,Misc_Val,Mo_Sold,Year_Sold,Sale_Type,Sale_Condition,Sale_Price,Longitude,Latitude +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,141,31770,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Hip,CompShg,BrkFace,Plywood,Stone,112,Typical,Typical,CBlock,Typical,Good,Gd,BLQ,2,Unf,0,441,1080,GasA,Fair,Y,SBrkr,1656,0,0,1656,1,0,1,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,528,Typical,Typical,Partial_Pavement,210,62,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,215000,-93.619754,42.054035 +One_Story_1946_and_Newer_All_Styles,Residential_High_Density,80,11622,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,144,270,882,GasA,Typical,Y,SBrkr,896,0,0,896,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,730,Typical,Typical,Paved,140,0,0,0,120,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,105000,-93.619756,42.053014 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,14267,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,108,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,406,1329,GasA,Typical,Y,SBrkr,1329,0,0,1329,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,312,Typical,Typical,Paved,393,36,0,0,0,0,No_Pool,No_Fence,Gar2,12500,6,2010,WD ,Normal,172000,-93.6193873,42.052659 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,11160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1968,1968,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,1045,2110,GasA,Excellent,Y,SBrkr,2110,0,0,2110,1,0,2,1,3,1,Excellent,8,Typ,2,Typical,Attchd,Fin,2,522,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,244000,-93.61732,42.051245 +Two_Story_1946_and_Newer,Residential_Low_Density,74,13830,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,137,928,GasA,Good,Y,SBrkr,928,701,0,1629,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,482,Typical,Typical,Paved,212,34,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,189900,-93.638933,42.060899 +Two_Story_1946_and_Newer,Residential_Low_Density,78,9978,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,20,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,324,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,470,Typical,Typical,Paved,360,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,195500,-93.638925,42.060779 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,4920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2001,2001,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,722,1338,GasA,Excellent,Y,SBrkr,1338,0,0,1338,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,582,Typical,Typical,Paved,0,0,170,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,213500,-93.633792,42.062978 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,5005,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,1017,1280,GasA,Excellent,Y,SBrkr,1280,0,0,1280,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,506,Typical,Typical,Paved,0,82,0,0,144,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,191500,-93.633826,42.060728 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,39,5389,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1996,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,415,1595,GasA,Excellent,Y,SBrkr,1616,0,0,1616,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,237,152,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,236500,-93.632852,42.06112 +Two_Story_1946_and_Newer,Residential_Low_Density,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,994,994,GasA,Good,Y,SBrkr,1028,776,0,1804,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,442,Typical,Typical,Paved,140,60,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,189000,-93.639068,42.059193 +Two_Story_1946_and_Newer,Residential_Low_Density,75,10000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,763,763,GasA,Good,Y,SBrkr,763,892,0,1655,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,157,84,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,175900,-93.636947,42.05848 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7980,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Good,1992,2007,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,PConc,Good,Typical,No,ALQ,1,Unf,0,233,1168,GasA,Excellent,Y,SBrkr,1187,0,0,1187,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,483,21,0,0,0,0,No_Pool,Good_Privacy,Shed,500,3,2010,WD ,Normal,185000,-93.635951,42.057419 +Two_Story_1946_and_Newer,Residential_Low_Density,63,8402,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,789,789,GasA,Good,Y,SBrkr,789,676,0,1465,0,0,2,1,3,1,Typical,7,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,180400,-93.638647,42.058151 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10176,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,663,1300,GasA,Good,Y,SBrkr,1341,0,0,1341,1,0,1,1,2,1,Good,5,Typ,1,Poor,Attchd,Unf,2,506,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,171500,-93.634626,42.057268 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,6820,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1985,1985,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,BLQ,1120,0,1488,GasA,Typical,Y,SBrkr,1502,0,0,1502,1,0,1,1,1,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,0,54,0,0,140,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,212000,-93.632913,42.059169 +Two_Story_1946_and_Newer,Residential_Low_Density,47,53504,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,CulDSac,Mod,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Hip,CompShg,CemntBd,Wd Shng,BrkFace,603,Excellent,Typical,PConc,Good,Typical,Gd,ALQ,1,Unf,0,234,1650,GasA,Excellent,Y,SBrkr,1690,1589,0,3279,1,0,3,1,4,1,Excellent,12,Mod,1,Good,BuiltIn,Fin,3,841,Typical,Typical,Paved,503,36,0,0,210,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,538000,-93.62655,42.061239 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,152,12134,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Gilbert,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Good,1988,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,Wood,Good,Typical,Av,GLQ,3,Unf,0,132,559,GasA,Good,Y,SBrkr,1080,672,0,1752,0,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Basment,RFn,2,492,Typical,Typical,Paved,325,12,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,164000,-93.6235954,42.0603514 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11394,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Poor,2010,2010,Hip,CompShg,VinylSd,VinylSd,Stone,350,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,411,1856,GasA,Excellent,Y,SBrkr,1856,0,0,1856,1,0,1,1,1,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,834,Typical,Typical,Paved,113,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,394432,-93.628804,42.05885 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,140,19138,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Below_Average,Average,1951,1951,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,744,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,141000,-93.622971,42.056673 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,13175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1978,1988,Gable,CompShg,Plywood,Plywood,Stone,119,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Rec,163,589,1542,GasA,Typical,Y,SBrkr,2073,0,0,2073,1,0,2,0,3,1,Typical,7,Min1,2,Typical,Attchd,Unf,2,500,Typical,Typical,Paved,349,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,210000,-93.636655,42.054453 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,11751,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,480,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,1139,1844,GasA,Excellent,Y,SBrkr,1844,0,0,1844,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,546,Typical,Typical,Paved,0,122,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2010,COD,Normal,190000,-93.633962,42.050346 +Split_Foyer,Residential_Low_Density,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Good,Above_Average,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,81,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,LwQ,168,0,1053,GasA,Typical,Y,SBrkr,1173,0,0,1173,1,0,2,0,3,1,Good,6,Typ,2,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2010,WD ,Family,170000,-93.636372,42.05027 +Two_Story_1946_and_Newer,Floating_Village_Residential,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,281,814,GasA,Excellent,Y,SBrkr,814,860,0,1674,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,663,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,216000,-93.639366,42.049297 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11241,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1970,1970,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,426,1004,GasA,Excellent,Y,SBrkr,1004,0,0,1004,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,700,3,2010,WD ,Normal,149000,-93.626231,42.055147 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12537,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1971,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,344,1078,GasA,Excellent,Y,SBrkr,1078,0,0,1078,1,0,1,1,3,1,Typical,6,Typ,1,Fair,Attchd,Fin,2,500,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,149900,-93.626537,42.054592 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,281,1056,GasA,Excellent,Y,SBrkr,1056,0,0,1056,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,Unf,1,304,Typical,Typical,Paved,0,85,184,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,142000,-93.628806,42.055227 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,78,0,882,GasA,Typical,Y,SBrkr,882,0,0,882,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,2,525,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,126000,-93.627112,42.053395 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,432,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,1,Poor,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLI,Normal,115000,-93.622769,42.056375 +One_Story_PUD_1946_and_Newer,Residential_High_Density,26,5858,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Good,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,BLQ,0,354,1405,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Fair,Attchd,Fin,2,511,Typical,Typical,Paved,203,68,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,184000,-93.624373,42.055318 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,327,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,275,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,COD,Normal,96000,-93.627271,42.051835 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,492,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,225,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,320,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,105500,-93.627536,42.051684 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,ImStucc,BrkFace,381,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,525,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Family,88000,-93.62728,42.051685 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4043,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,709,1069,GasA,Typical,Y,SBrkr,1069,0,0,1069,0,0,2,0,2,1,Typical,4,Typ,1,Fair,Attchd,RFn,2,440,Typical,Typical,Paved,0,55,0,0,165,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,127500,-93.627232,42.049672 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,341,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,173,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,149900,-93.625924,42.050683 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Above_Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,836,836,GasA,Excellent,Y,SBrkr,836,0,0,836,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,120000,-93.625966,42.050681 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,544,855,GasA,Fair,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,26,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,146000,-93.625848,42.049811 +Two_Story_1946_and_Newer,Residential_Low_Density,102,12858,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,162,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1590,1590,GasA,Excellent,Y,SBrkr,1627,707,0,2334,0,0,2,1,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,751,Typical,Typical,Paved,144,133,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,New,Partial,376162,-93.653201,42.062352 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,11478,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,200,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,486,1704,GasA,Excellent,Y,SBrkr,1704,0,0,1704,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,772,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,306000,-93.654645,42.062109 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,10159,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2010,Hip,CompShg,VinylSd,VinylSd,Stone,450,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,284,1930,GasA,Excellent,Y,SBrkr,1940,0,0,1940,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,606,Typical,Typical,Paved,168,95,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,New,Partial,395192,-93.6537329,42.0611331 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,12883,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1544,1544,GasA,Excellent,Y,SBrkr,1544,0,0,1544,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,868,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,290941,-93.6528306,42.0612885 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,12182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,226,Good,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,340,1541,GasA,Excellent,Y,SBrkr,1541,0,0,1541,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,532,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,New,Partial,220000,-93.652713,42.060872 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,Norm,OneFam,One_Story,Excellent,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,615,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1588,1698,GasA,Excellent,Y,SBrkr,1698,0,0,1698,0,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,730,Typical,Typical,Paved,192,74,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,275000,-93.65332,42.0608079 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,14122,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,240,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1794,1822,GasA,Excellent,Y,SBrkr,1822,0,0,1822,0,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,678,Typical,Typical,Paved,0,119,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,259000,-93.652336,42.060879 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10171,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,168,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1515,1517,GasA,Excellent,Y,SBrkr,1535,0,0,1535,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,532,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,214000,-93.652307,42.060298 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,12919,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2010,Hip,CompShg,VinylSd,VinylSd,Stone,760,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,142,2330,GasA,Excellent,Y,SBrkr,2364,0,0,2364,1,0,2,1,2,1,Excellent,11,Typ,2,Good,Attchd,Fin,3,820,Typical,Typical,Paved,0,67,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,New,Partial,611657,-93.655051,42.059617 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,44,6371,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,128,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,625,1358,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,224000,-93.650436,42.058388 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14300,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,1095,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1473,2846,GasA,Excellent,Y,SBrkr,2696,0,0,2696,1,0,2,1,3,1,Excellent,10,Typ,2,Good,Attchd,Fin,3,958,Typical,Typical,Paved,220,150,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,500000,-93.656067,42.058599 +Two_Story_1946_and_Newer,Residential_Low_Density,105,13650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,232,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1093,1671,GasA,Excellent,Y,SBrkr,1687,563,0,2250,1,0,2,1,3,1,Good,7,Typ,1,Excellent,Attchd,Fin,3,756,Typical,Typical,Paved,238,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,320000,-93.654853,42.057102 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,7658,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,412,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1296,1752,GasA,Excellent,Y,SBrkr,1752,0,0,1752,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,196,82,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,319900,-93.654121,42.057164 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,7132,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,178,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1346,1370,GasA,Excellent,Y,SBrkr,1370,0,0,1370,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,120,49,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,205000,-93.649447,42.058252 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2628,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,764,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,0,0,2,1,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,175500,-93.651013,42.057181 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,18494,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1324,1324,GasA,Excellent,Y,SBrkr,1324,0,0,1324,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,430,Typical,Typical,Paved,36,23,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,199500,-93.644356,42.061928 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3203,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1129,1145,GasA,Excellent,Y,SBrkr,1145,0,0,1145,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,437,Typical,Typical,Paved,100,116,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,160000,-93.641635,42.06257 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1232,1256,GasA,Excellent,Y,SBrkr,1269,0,0,1269,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,2,430,Typical,Typical,Paved,146,20,0,0,144,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,192000,-93.642252,42.062303 +Split_or_Multilevel,Residential_Low_Density,67,13300,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,58,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,1,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,184500,-93.644076,42.061443 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7851,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,235,860,GasA,Excellent,Y,SBrkr,860,1100,0,1960,1,0,2,1,4,1,Good,8,Typ,2,Typical,BuiltIn,Fin,2,440,Typical,Typical,Paved,288,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,216500,-93.641487,42.060977 +Two_Story_1946_and_Newer,Residential_Low_Density,63,8577,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,886,0,1733,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,433,Typical,Typical,Paved,144,48,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,185088,-93.642447,42.061193 +Split_or_Multilevel,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,134,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,180000,-93.641325,42.060936 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9505,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,884,1151,0,2035,0,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,144,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,222500,-93.641337,42.057103 +Two_Story_1946_and_Newer,Residential_Low_Density,108,14774,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,165,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1393,1393,GasA,Excellent,Y,SBrkr,1422,1177,0,2599,0,0,2,1,4,1,Good,10,Typ,1,Typical,BuiltIn,Fin,3,779,Typical,Typical,Paved,668,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,333168,-93.651381,42.054386 +Two_Story_1946_and_Newer,Residential_Low_Density,60,17433,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,114,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1629,1629,GasA,Excellent,Y,SBrkr,1645,830,0,2475,0,0,2,1,4,1,Good,7,Typ,1,Typical,Attchd,Fin,3,962,Typical,Typical,Paved,23,172,0,0,256,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,355000,-93.652334,42.053243 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,10593,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Good,Average,1996,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,338,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,801,1720,GasA,Excellent,Y,SBrkr,1720,0,0,1720,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,527,Typical,Typical,Paved,240,56,154,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,260400,-93.654206,42.051721 +Two_Story_1946_and_Newer,Residential_Low_Density,98,12256,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1995,Gable,CompShg,HdBoard,HdBoard,BrkFace,362,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,431,1463,GasA,Excellent,Y,SBrkr,1500,1122,0,2622,1,0,2,1,3,1,Good,9,Typ,2,Typical,Attchd,RFn,2,712,Typical,Typical,Paved,186,32,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,325000,-93.653616,42.051674 +Two_Story_1946_and_Newer,Residential_Low_Density,92,11764,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1999,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,348,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,628,1152,GasA,Excellent,Y,SBrkr,1164,1106,0,2270,0,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Fin,3,671,Typical,Typical,Paved,132,57,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,290000,-93.650356,42.052457 +Two_Story_1946_and_Newer,Residential_Low_Density,58,16770,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,30,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1195,1195,GasA,Good,Y,SBrkr,1195,644,0,1839,0,0,2,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,486,Typical,Typical,Paved,0,81,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,221000,-93.650578,42.052437 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,56,14720,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Average,1995,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,579,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1217,2033,GasA,Excellent,Y,SBrkr,2053,1185,0,3238,1,0,2,1,4,1,Good,9,Typ,1,Excellent,Attchd,Fin,3,666,Typical,Typical,Paved,283,86,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,410000,-93.652972,42.050909 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8987,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,226,Good,Typical,PConc,Good,Typical,No_Basement,Unf,7,Unf,0,1595,1595,GasA,Excellent,Y,SBrkr,1595,0,0,1595,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,880,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,221500,-93.642368,42.05306 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,92,9215,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2009,2010,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1218,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,0,0,2,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,676,Typical,Typical,Paved,0,136,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,New,Partial,204500,-93.649976,42.051827 +Two_Story_1946_and_Newer,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,732,756,GasA,Excellent,Y,SBrkr,764,783,0,1547,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,614,Typical,Typical,Paved,169,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,Con,Normal,215200,-93.650318,42.051653 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,36,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,488,1566,GasA,Excellent,Y,SBrkr,1566,0,0,1566,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,750,Typical,Typical,Paved,144,168,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,262500,-93.647105,42.050084 +Two_Story_1946_and_Newer,Floating_Village_Residential,100,12552,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,769,991,GasA,Excellent,Y,SBrkr,991,956,0,1947,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,678,Typical,Typical,Paved,0,136,0,0,0,0,No_Pool,Good_Wood,None,0,5,2010,WD ,Normal,254900,-93.642773,42.052347 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,84,10440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,54,1468,GasA,Excellent,Y,SBrkr,1468,0,0,1468,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,528,Typical,Typical,Paved,0,102,0,0,216,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,271500,-93.642224,42.051429 +Two_Story_1946_and_Newer,Residential_Low_Density,76,10142,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,300,956,GasA,Excellent,Y,SBrkr,956,1128,0,2084,1,0,2,1,4,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,618,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,233000,-93.692309,42.036255 +Two_Story_1946_and_Newer,Residential_Low_Density,70,11920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,122,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,831,831,GasA,Excellent,Y,SBrkr,831,828,0,1659,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,181000,-93.692412,42.036113 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8880,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1994,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,253,948,GasA,Excellent,Y,SBrkr,1222,888,0,2110,1,0,2,1,3,1,Good,8,Typ,2,Fair,Attchd,RFn,2,463,Typical,Typical,Paved,0,130,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,205000,-93.68981,42.036026 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,BLQ,119,261,923,GasA,Typical,Y,SBrkr,923,0,0,923,0,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,1,264,Typical,Typical,Paved,80,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,143000,-93.686417,42.03548 +Two_Story_1946_and_Newer,Residential_Low_Density,70,11218,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1055,1055,GasA,Excellent,Y,SBrkr,1055,790,0,1845,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,462,Typical,Typical,Paved,635,104,0,0,0,0,No_Pool,Good_Privacy,Shed,400,5,2010,WD ,Normal,189000,-93.687694,42.036659 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,55,7892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,918,918,GasA,Typical,Y,SBrkr,918,0,0,918,0,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,Unf,1,264,Typical,Typical,Paved,28,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,99500,-93.686263,42.034589 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1984,1984,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,121,0,744,GasA,Typical,Y,SBrkr,752,0,0,752,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,353,0,0,0,90,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,125000,-93.686264,42.0347 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9453,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,RRNe,Norm,OneFam,Two_Story,Good,Good,1993,2003,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,594,996,GasA,Excellent,Y,SBrkr,1014,730,0,1744,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,457,Typical,Typical,Paved,370,70,0,238,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,194500,-93.68628,42.036633 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,9672,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1984,1985,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,702,1040,GasA,Typical,Y,SBrkr,1097,0,0,1097,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2010,WD ,Normal,152000,-93.685028,42.034555 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1980,1981,Gable,CompShg,HdBoard,HdBoard,BrkFace,130,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,650,650,GasA,Typical,Y,SBrkr,888,676,0,1564,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,476,Typical,Typical,Paved,0,50,0,0,204,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,171000,-93.685029,42.034678 +One_Story_1945_and_Older,Residential_High_Density,70,9800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_Story,Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,N,FuseA,1012,0,0,1012,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,429,Typical,Typical,Paved,121,0,80,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,67500,-93.6790694,42.0360754 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,68,8930,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,One_and_Half_Fin,Above_Average,Average,1978,1978,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1318,584,0,1902,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,539,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,112000,-93.677205,42.036535 +Split_Foyer,Residential_Low_Density,88,11782,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Good,1961,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,210,1109,GasA,Typical,Y,SBrkr,1155,0,0,1155,1,0,1,0,3,1,Good,6,Min2,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,6,2010,WD ,Normal,148000,-93.673669,42.034793 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Very_Good,1965,2009,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,BLQ,117,224,894,GasA,Excellent,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,1,Good,Detchd,Unf,1,336,Typical,Typical,Paved,416,144,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,138500,-93.670956,42.035685 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9819,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,31,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,432,882,GasA,Typical,Y,SBrkr,900,0,0,900,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,122000,-93.672096,42.034856 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,216,1040,GasA,Fair,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,308,Typical,Typical,Paved,0,0,220,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,133000,-93.672067,42.03457 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6897,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Very_Good,1962,2010,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,381,1040,GasA,Excellent,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,260,Typical,Typical,Paved,0,104,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,127000,-93.669688,42.035333 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,39,15410,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRNe,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,2002,Hip,CompShg,Plywood,Plywood,BrkCmn,250,Typical,Good,CBlock,Typical,Typical,Gd,BLQ,2,GLQ,859,223,1208,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Typical,7,Typ,2,Fair,Attchd,Fin,2,461,Typical,Typical,Paved,296,0,186,0,0,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Abnorml,169000,-93.660672,42.034603 +Two_Story_1946_and_Newer,Residential_Low_Density,107,10186,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,76,750,GasA,Excellent,Y,SBrkr,1061,862,0,1923,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,240,39,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,190000,-93.646749,42.048253 +Two_Story_1946_and_Newer,Residential_Low_Density,85,13143,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1993,Gable,CompShg,HdBoard,ImStucc,BrkFace,504,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,981,0,1231,GasA,Excellent,Y,SBrkr,1251,1098,0,2349,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,762,Typical,Typical,Paved,32,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,362500,-93.651773,42.046825 +Two_Story_1946_and_Newer,Residential_Low_Density,88,11134,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,180,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,261,1390,GasA,Excellent,Y,SBrkr,1402,823,0,2225,1,0,2,1,4,1,Good,7,Typ,1,Typical,Attchd,RFn,3,713,Typical,Typical,Paved,198,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,285000,-93.652773,42.045773 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,25,4835,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,190,1488,GasA,Excellent,Y,SBrkr,1488,0,0,1488,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,506,Typical,Typical,Paved,168,50,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,260000,-93.647988,42.047452 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,39,3515,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,840,0,1680,0,0,2,1,2,1,Good,3,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,0,111,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,190000,-93.64614,42.048086 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3215,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,BrkFace,120,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,320,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLD,Normal,155000,-93.645599,42.048566 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3182,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,151000,-93.644889,42.047899 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,42,190,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,149500,-93.644889,42.047876 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,224,600,GasA,Excellent,Y,SBrkr,600,636,0,1236,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,152000,-93.64489,42.047782 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,0,4403,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2009,2009,Gable,CompShg,MetalSd,MetalSd,Stone,432,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,892,1470,GasA,Excellent,Y,SBrkr,1478,0,0,1478,1,0,2,1,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,484,Typical,Typical,Paved,0,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,New,Partial,222000,-93.6468879,42.0471748 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,378,756,GasA,Excellent,Y,SBrkr,769,804,0,1573,0,0,2,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,177500,-93.645606,42.046145 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2980,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,1159,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,290,756,GasA,Excellent,Y,SBrkr,756,756,0,1512,1,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,177000,-93.645585,42.046145 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2572,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,1999,1999,Hip,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,92,696,GasA,Excellent,Y,SBrkr,696,720,0,1416,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,155000,-93.645482,42.046424 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2403,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,286,530,GasA,Excellent,Y,SBrkr,530,550,0,1080,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,496,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,147110,-93.645478,42.046114 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,57,12853,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2010,2010,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Poor,No,GLQ,3,Unf,0,610,1642,GasA,Excellent,Y,SBrkr,1418,0,0,1418,1,0,1,1,1,1,Good,6,Typ,1,Good,Attchd,RFn,3,852,Typical,Typical,Paved,160,192,0,224,0,0,No_Pool,No_Fence,None,0,4,2010,New,Partial,267916,-93.641253,42.0470316 +Two_Story_1946_and_Newer,Floating_Village_Residential,68,7379,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,491,975,GasA,Excellent,Y,SBrkr,975,873,0,1848,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,592,Typical,Typical,Paved,280,184,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,254000,-93.643556,42.04638 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,30,4420,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,728,GasA,Typical,Y,SBrkr,788,0,0,788,1,0,1,0,1,1,Good,3,Typ,1,Typical,Detchd,Fin,2,484,Typical,Typical,Paved,133,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,155000,-93.649361,42.042565 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3880,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Above_Average,1978,1978,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,174,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,223,78,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,206000,-93.648143,42.043704 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13517,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,Two_Story,Above_Average,Very_Good,1976,2005,Gable,CompShg,HdBoard,Plywood,BrkFace,289,Good,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,192,725,GasA,Excellent,Y,SBrkr,725,754,0,1479,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,475,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,130500,-93.65921,42.034561 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,659,1492,GasA,Excellent,Y,SBrkr,1492,0,0,1492,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,596,Typical,Typical,Paved,277,137,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,230000,-93.639516,42.048606 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10456,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,1323,1829,GasA,Good,Y,SBrkr,1829,0,0,1829,1,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,RFn,2,535,Typical,Typical,Paved,0,76,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,218500,-93.633351,42.046322 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10791,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,143,1280,GasA,Excellent,Y,SBrkr,1280,1215,0,2495,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,660,Typical,Typical,Paved,224,32,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,243500,-93.636879,42.043038 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10603,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1977,2001,Gable,CompShg,Plywood,Plywood,BrkFace,28,Typical,Typical,PConc,Typical,Typical,Mn,ALQ,1,Unf,0,410,1610,GasA,Good,Y,SBrkr,1610,0,0,1610,1,0,2,0,3,1,Good,6,Typ,2,Typical,Attchd,RFn,2,480,Typical,Typical,Paved,168,68,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,205000,-93.637463,42.043306 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,18837,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1978,1978,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,ALQ,1,LwQ,46,491,1224,GasA,Typical,Y,SBrkr,1287,604,0,1891,0,1,3,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,678,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,212500,-93.637531,42.043306 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10421,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,42,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,586,980,GasA,Typical,Y,SBrkr,980,734,0,1714,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,496,Typical,Typical,Paved,228,66,156,0,0,0,No_Pool,Minimum_Privacy,Shed,500,3,2010,WD ,Normal,196500,-93.637788,42.043329 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1972,2006,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,179,1161,GasA,Typical,Y,SBrkr,1381,0,0,1381,1,0,1,1,3,1,Good,5,Typ,1,Typical,Attchd,RFn,2,676,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,197500,-93.631435,42.043822 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1971,1971,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,386,715,GasA,Typical,Y,SBrkr,930,715,0,1645,0,0,1,2,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,441,Typical,Typical,Paved,0,78,0,0,0,0,No_Pool,Good_Wood,None,0,6,2010,WD ,Normal,171000,-93.632554,42.043821 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Hip,CompShg,VinylSd,VinylSd,BrkFace,172,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,534,1232,GasA,Typical,Y,SBrkr,1232,0,0,1232,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,490,Typical,Typical,Paved,0,224,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,142250,-93.626137,42.046574 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,BrkFace,451,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,81,678,1328,GasA,Typical,Y,SBrkr,1328,0,0,1328,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,528,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,143000,-93.625926,42.042205 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,9320,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1959,1959,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,381,950,GasA,Fair,Y,SBrkr,1225,0,0,1225,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,352,0,0,0,0,0,No_Pool,No_Fence,Shed,400,1,2010,WD ,Normal,128950,-93.623383,42.043363 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,150,1209,GasA,Good,Y,SBrkr,1209,0,0,1209,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Typical,Paved,0,0,120,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLD,Normal,159000,-93.626939,42.039085 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,9680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,Wd Sdng,Plywood,BrkFace,268,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,500,1510,GasA,Excellent,Y,SBrkr,1510,0,0,1510,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,517,Typical,Typical,Paved,0,40,0,0,204,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Normal,178900,-93.627536,42.038971 +Split_or_Multilevel,Residential_Low_Density,0,10600,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,533,533,GasA,Typical,Y,SBrkr,1131,644,0,1775,0,0,2,0,3,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,172,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,COD,Family,136300,-93.626785,42.040326 +Split_or_Multilevel,Residential_Low_Density,0,14112,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1964,1964,Hip,CompShg,Wd Sdng,HdBoard,BrkFace,86,Typical,Typical,PConc,Typical,Typical,Av,GLQ,3,Unf,0,138,1152,GasA,Typical,Y,SBrkr,1152,0,0,1152,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,227,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,180500,-93.627809,42.041146 +Split_Foyer,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,SFoyer,Above_Average,Good,1963,1963,Gable,CompShg,MetalSd,MetalSd,BrkFace,156,Typical,Good,PConc,Typical,Typical,Gd,GLQ,3,Unf,0,173,936,GasA,Excellent,Y,SBrkr,1054,0,0,1054,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,480,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Abnorml,137500,-93.625728,42.040391 +Duplex_All_Styles_and_Ages,Residential_Low_Density,98,13260,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Above_Average,1962,2001,Hip,CompShg,HdBoard,HdBoard,BrkFace,144,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,228,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,2,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,Oth,Abnorml,84900,-93.622904,42.0418387 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9717,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1950,1996,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,GLQ,3,Rec,1029,0,1078,GasA,Good,Y,FuseA,1078,0,0,1078,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,366,0,112,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,142125,-93.622864,42.039211 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,68,9724,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1952,2002,Gable,CompShg,MetalSd,MetalSd,BrkFace,265,Good,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,470,1140,GasA,Good,Y,SBrkr,1929,532,0,2461,0,0,2,0,3,1,Typical,7,Min2,2,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2010,WD ,Normal,197600,-93.620651,42.039211 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,120,17360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1949,1950,Gable,CompShg,MetalSd,MetalSd,Stone,340,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,482,782,GasA,Typical,Y,SBrkr,1019,537,0,1556,0,0,2,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,2,470,Typical,Typical,Paved,0,0,150,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,172500,-93.620504,42.038538 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,7207,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1958,2008,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,Gd,BLQ,2,Unf,0,162,858,GasA,Good,Y,SBrkr,858,0,0,858,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,117,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,116500,-93.629287,42.035256 +One_Story_1945_and_Older,Residential_Low_Density,55,5350,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Fair,Poor,1940,1966,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Poor,CBlock,Typical,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,1306,0,0,1306,0,0,1,0,3,1,Fair,6,Mod,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,263,0,0,0,0,0,No_Pool,Good_Wood,Shed,450,5,2010,WD ,Normal,76500,-93.629313,42.035482 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,HdBoard,HdBoard,Stone,110,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,290,412,1056,GasA,Typical,Y,SBrkr,1063,0,0,1063,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,280,Typical,Typical,Paved,0,0,164,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,128000,-93.62475,42.035943 +Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1955,1972,Gable,CompShg,AsbShng,AsbShng,BrkFace,164,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,LwQ,132,350,1156,GasA,Excellent,Y,SBrkr,1520,0,0,1520,1,0,1,0,3,1,Typical,7,Typ,2,Good,Basment,RFn,1,364,Typical,Typical,Paved,0,0,189,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,153000,-93.624601,42.036095 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,728,1268,GasA,Good,Y,SBrkr,1268,0,0,1268,0,0,1,0,2,1,Typical,7,Typ,1,Good,Attchd,Fin,1,244,Typical,Typical,Paved,0,98,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,132000,-93.621431,42.037424 +Split_Foyer,Residential_Low_Density,75,11380,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Above_Average,Very_Good,1966,2008,Gable,CompShg,HdBoard,HdBoard,BrkFace,216,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,136,1080,GasA,Good,Y,SBrkr,1128,0,0,1128,1,0,1,0,2,1,Good,5,Typ,1,Good,Attchd,Unf,1,315,Typical,Typical,Paved,238,0,0,0,0,0,No_Pool,No_Fence,Shed,1500,1,2010,WD ,Normal,178000,-93.618221,42.048494 +Duplex_All_Styles_and_Ages,Residential_Low_Density,80,11600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,Duplex,Two_Story,Average,Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkFace,361,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,662,1105,GasA,Typical,Y,FuseA,1105,1169,0,2274,0,0,2,0,5,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,154300,-93.6184183,42.0459375 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,19900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Good,Average,1970,1989,Gable,CompShg,Plywood,Plywood,BrkFace,287,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,1035,1947,GasA,Typical,Y,SBrkr,2207,0,0,2207,1,0,2,0,3,1,Typical,7,Min1,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,301,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,180000,-93.614307,42.049514 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,137,16492,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Above_Average,Above_Average,1966,2002,Gable,CompShg,BrkFace,Plywood,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,713,557,1517,GasA,Excellent,Y,SBrkr,1888,0,0,1888,0,0,2,1,2,1,Good,6,Mod,1,Good,Attchd,Fin,2,578,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,190000,-93.614244,42.047447 +Duplex_All_Styles_and_Ages,Residential_Low_Density,70,8267,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,Duplex,One_Story,Average,Average,1958,1958,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1604,1604,GasA,Typical,Y,SBrkr,1604,0,0,1604,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Paved,42,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,135000,-93.617693,42.044895 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8197,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,2003,2009,Gable,CompShg,VinylSd,VinylSd,BrkFace,506,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,292,1480,GasA,Excellent,Y,SBrkr,1480,0,0,1480,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,620,Typical,Typical,Paved,252,73,0,0,0,0,No_Pool,Minimum_Privacy,Shed,300,2,2010,WD ,Normal,214000,-93.617553,42.043984 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8050,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,150,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,162,125,1143,GasA,Typical,Y,SBrkr,1143,0,0,1143,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2010,WD ,Normal,136000,-93.617117,42.042908 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10552,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,380,1398,GasA,Good,Y,SBrkr,1700,0,0,1700,0,1,1,1,4,1,Good,6,Typ,1,Good,Attchd,RFn,2,447,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,165500,-93.6177041,42.0430839 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1957,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,392,1314,GasA,Typical,Y,SBrkr,1314,0,0,1314,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,145000,-93.618626,42.044847 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,220,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1194,1194,GasA,Typical,Y,SBrkr,1194,0,0,1194,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,0,120,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,148000,-93.616582,42.043969 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12160,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,Plywood,Plywood,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,188,1188,GasA,Fair,Y,SBrkr,1188,0,0,1188,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,531,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,COD,Abnorml,142000,-93.614882,42.044052 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,9300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1960,1960,Gable,CompShg,MetalSd,HdBoard,BrkFace,324,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,571,1268,GasA,Typical,Y,SBrkr,1264,0,0,1264,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,461,Typical,Typical,Paved,0,0,0,0,143,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,167500,-93.613301,42.044176 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10725,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,91,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,270,1206,GasA,Fair,Y,SBrkr,1206,0,0,1206,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,108538,-93.61513,42.044883 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10032,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1959,1959,Gable,CompShg,Wd Sdng,Wd Sdng,Stone,432,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,510,1244,GasA,Excellent,Y,SBrkr,1580,0,0,1580,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,440,Typical,Typical,Paved,0,28,0,0,160,0,No_Pool,Good_Wood,None,0,6,2010,WD ,Normal,159500,-93.614241,42.0435919 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8382,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,FuseA,832,505,0,1337,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,263,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,108000,-93.613624,42.042308 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1952,1952,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,525,864,GasA,Typical,Y,SBrkr,1064,0,0,1064,0,1,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,318,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,135000,-93.615622,42.040396 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,119,10895,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,324,972,GasA,Typical,Y,SBrkr,972,0,0,972,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,305,Typical,Typical,Paved,0,0,205,0,0,0,No_Pool,Good_Wood,None,0,6,2010,WD ,Normal,122500,-93.617092,42.04137 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,13587,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwoFmCon,One_Story,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,456,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Abnorml,119000,-93.61722,42.038515 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,7922,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1953,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,326,1057,GasA,Typical,Y,SBrkr,1057,0,0,1057,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,246,Typical,Typical,Paved,0,52,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Abnorml,109000,-93.618428,42.039841 +One_Story_1945_and_Older,Residential_Low_Density,65,7898,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1920,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,576,576,GasA,Good,Y,SBrkr,985,0,0,985,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,676,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,105000,-93.615475,42.038443 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1955,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,827,0,0,827,0,0,1,0,2,1,Typical,5,Mod,1,Poor,Detchd,Unf,1,392,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,107500,-93.611642,42.038521 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1954,2003,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,104,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,362,404,1086,GasA,Good,Y,SBrkr,1086,0,0,1086,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,490,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2010,WD ,Normal,144900,-93.618788,42.038249 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,5868,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1956,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,240,448,936,GasA,Excellent,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,308,Typical,Typical,Paved,0,0,80,0,160,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,129000,-93.6192014,42.0381282 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,17503,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,912,912,GasA,Typical,Y,SBrkr,912,546,0,1458,0,1,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,330,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Abnorml,97500,-93.620341,42.035869 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,6979,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,0,1056,GasA,Good,Y,SBrkr,1056,0,0,1056,2,0,0,0,0,2,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,264,56,0,0,0,0,No_Pool,Good_Privacy,Shed,600,6,2010,WD ,Normal,144000,-93.6116762,42.0372097 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9830,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Rec,258,733,1063,GasA,Excellent,Y,SBrkr,1287,0,0,1287,1,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Fin,2,576,Typical,Typical,Paved,364,17,0,0,182,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,162000,-93.607275,42.040085 +Split_or_Multilevel,Residential_Low_Density,96,11275,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,PosN,Norm,OneFam,SLvl,Good,Good,1967,2007,Mansard,WdShake,Wd Sdng,Wd Sdng,BrkFace,300,Good,Good,CBlock,Good,Typical,No,Unf,7,Unf,0,710,710,GasA,Excellent,Y,SBrkr,1898,1080,0,2978,0,0,2,1,5,1,Good,11,Typ,1,Good,BuiltIn,Fin,2,564,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,242000,-93.606905,42.041119 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1948,2004,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,174,161,816,GasA,Typical,Y,SBrkr,816,408,0,1224,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,414,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2010,WD ,Normal,132000,-93.61061,42.035867 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1994,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,906,0,1246,GasA,Excellent,Y,SBrkr,1246,0,0,1246,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,305,Typical,Typical,Paved,218,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2010,WD ,Normal,154000,-93.605969,42.03594 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,71,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1952,1952,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,No,Rec,6,Unf,0,403,910,GasA,Fair,Y,SBrkr,910,475,0,1385,0,0,2,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,166000,-93.609218,42.0347571 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,486,180,900,GasA,Typical,Y,SBrkr,900,0,0,900,0,1,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,222,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,134800,-93.607801,42.034848 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,261,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1116,1116,GasA,Typical,Y,SBrkr,1116,0,0,1116,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,385,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,160000,-93.605943,42.034748 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,7635,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1960,1960,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,350,237,1175,GasA,Excellent,Y,SBrkr,1175,0,0,1175,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,148000,-93.606742,42.034851 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1963,1984,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,218,Typical,Typical,CBlock,Typical,Typical,Gd,BLQ,2,LwQ,263,415,1395,GasA,Typical,Y,SBrkr,1395,0,0,1395,1,0,1,0,2,1,Typical,7,Min1,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,657,0,113,0,240,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,192000,-93.604835,42.036873 +Two_Story_1946_and_Newer,Residential_Low_Density,70,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1966,1966,Gable,CompShg,MetalSd,MetalSd,BrkFace,351,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,756,756,GasA,Typical,Y,SBrkr,1051,788,0,1839,0,0,1,1,4,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,442,Typical,Typical,Paved,0,124,216,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,155000,-93.603705,42.037093 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,4800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1900,1954,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,771,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,661,709,GasA,Typical,Y,SBrkr,1157,687,0,1844,1,0,1,0,3,1,Typical,9,Min2,2,Good,Basment,Unf,1,240,Typical,Typical,Paved,84,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,COD,Abnorml,80400,-93.617014,42.033313 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,55,8800,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1910,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,576,576,GasA,Good,Y,SBrkr,792,348,0,1140,0,0,1,0,3,1,Typical,7,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,160,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,96500,-93.617135,42.032134 +One_Story_1945_and_Older,Residential_Medium_Density,56,4485,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Good,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,357,936,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,5,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Partial_Pavement,51,0,135,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,109500,-93.62024,42.031363 +One_Story_1945_and_Older,Residential_Medium_Density,56,8960,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Above_Average,1927,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1008,1008,GasA,Good,Y,FuseA,1028,0,0,1028,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,130,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,115000,-93.620241,42.031381 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,69,5805,Pave,Gravel,Regular,Bnk,AllPub,Inside,Mod,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1957,1957,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,1073,0,1347,GasA,Good,Y,SBrkr,1347,0,0,1347,1,1,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,551,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,143000,-93.6179112,42.0303783 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Fair,Above_Average,1915,1950,Gambrel,CompShg,VinylSd,VinylSd,None,0,Good,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,840,840,GasA,Good,N,SBrkr,840,765,0,1605,0,0,2,0,3,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,379,Typical,Typical,Paved,0,0,202,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,107400,-93.614002,42.033279 +One_Story_1945_and_Older,Residential_Medium_Density,47,4608,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Below_Average,Above_Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,747,747,GasA,Typical,Y,SBrkr,747,0,0,747,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,220,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,80000,-93.615431,42.033644 +One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1940,1992,Gable,CompShg,MetalSd,MetalSd,Stone,294,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,278,788,GasA,Typical,Y,SBrkr,804,0,0,804,1,0,1,0,2,1,Good,4,Typ,2,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,154,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Abnorml,119000,-93.615446,42.032391 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1929,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,ALQ,692,0,926,GasA,Typical,Y,SBrkr,926,0,390,1316,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,130000,-93.608954,42.03128 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,6900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1938,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,827,827,GasA,Good,Y,SBrkr,827,424,0,1251,0,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,119000,-93.6090761,42.0303113 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,69,11851,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1948,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,247,1027,GasA,Excellent,Y,SBrkr,1027,606,0,1633,0,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,100,126,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,129000,-93.610466,42.030383 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,8239,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1920,1962,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,832,1008,GasA,Typical,Y,SBrkr,1060,185,0,1245,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,315,Typical,Typical,Paved,0,0,334,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,100000,-93.610468,42.030467 +One_Story_1945_and_Older,Residential_Medium_Density,68,9656,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Poor,Poor,1923,1970,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,BrkTil,Fair,Fair,No,Unf,7,Unf,0,678,678,GasA,Typical,N,SBrkr,832,0,0,832,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,2,780,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Abnorml,12789,-93.606789,42.030388 +One_Story_1945_and_Older,Residential_Medium_Density,60,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1928,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Good,Y,SBrkr,720,0,0,720,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,106,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,105900,-93.606789,42.030331 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1900,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,930,930,GasW,Typical,N,SBrkr,930,636,0,1566,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,54,228,246,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Abnorml,150000,-93.616887,42.028099 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Fair,Typical,No,LwQ,4,Unf,0,0,686,GasA,Typical,Y,SBrkr,966,686,0,1652,1,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,416,Typical,Typical,Paved,0,0,196,0,0,0,No_Pool,No_Fence,Shed,1200,6,2010,WD ,Normal,139000,-93.610588,42.029146 +Two_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1890,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,Stone,Fair,Fair,No,Unf,7,Unf,0,346,346,GasA,Excellent,Y,SBrkr,1157,1111,0,2268,0,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Dirt_Gravel,0,108,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,240000,-93.61221,42.028145 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,100,9045,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Fair,1910,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,840,840,Grav,Fair,N,FuseF,1128,1128,0,2256,0,0,2,0,4,2,Fair,12,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,18,18,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Abnorml,76500,-93.608878,42.028083 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,90,7407,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Good,1957,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,312,912,GasA,Typical,Y,FuseA,1236,0,0,1236,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,923,Typical,Typical,Paved,0,158,158,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,149700,-93.608246,42.03005 +Two_Story_1945_and_Older,Residential_Medium_Density,60,7740,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Good,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,622,622,GasA,Good,Y,SBrkr,741,622,0,1363,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,168,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,125500,-93.607238,42.028164 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,11340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Above_Average,1885,1950,Gable,CompShg,VinylSd,AsbShng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,777,777,GasA,Good,Y,SBrkr,1246,1044,0,2290,0,0,2,0,4,2,Typical,11,Typ,0,No_Fireplace,Detchd,Unf,2,560,Typical,Typical,Dirt_Gravel,0,0,114,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,122500,-93.608752,42.02916 +Two_Story_1945_and_Older,Residential_Medium_Density,60,10560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1922,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,Rec,6,Unf,0,455,738,GasA,Excellent,Y,SBrkr,868,602,0,1470,0,0,1,1,2,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,140750,-93.607088,42.028165 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,53,5830,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Feedr,OneFam,One_and_Half_Fin,Average,Above_Average,1950,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,200,988,GasA,Excellent,Y,SBrkr,1030,582,0,1612,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,363,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,128500,-93.625737,42.033642 +Two_and_Half_Story_All_Ages,Residential_Low_Density,0,7793,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Good,1922,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,BLQ,2,Unf,0,634,1108,GasA,Typical,N,FuseA,1160,908,0,2068,0,0,1,1,3,1,Good,8,Typ,1,Good,Detchd,Unf,1,315,Typical,Typical,Paved,0,0,60,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,209500,-93.625825,42.030187 +One_Story_1945_and_Older,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Below_Average,Good,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,577,765,GasA,Typical,N,FuseF,765,0,0,765,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Partial_Pavement,135,0,41,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,87000,-93.625586,42.033621 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Good,No,BLQ,2,LwQ,12,144,608,GasA,Typical,Y,SBrkr,608,524,0,1132,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,128,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Abnorml,134000,-93.624566,42.033563 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,Unf,0,308,572,GasA,Excellent,Y,FuseA,848,348,0,1196,0,1,1,1,3,1,Typical,6,Typ,2,Good,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,128000,-93.622597,42.0336 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,53,6360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1942,1950,Gable,CompShg,MetalSd,MetalSd,Stone,300,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,159,316,835,GasA,Typical,Y,FuseA,955,498,0,1453,0,0,1,1,3,1,Good,7,Min2,2,Fair,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,35,0,148,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,132000,-93.623523,42.033665 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,480,780,GasA,Typical,Y,SBrkr,780,636,0,1416,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,312,Typical,Typical,Partial_Pavement,221,0,48,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,139900,-93.622448,42.033631 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1936,1980,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,252,528,GasA,Good,Y,SBrkr,548,492,0,1040,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Fin,2,624,Typical,Typical,Partial_Pavement,306,0,32,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,123900,-93.623483,42.031556 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1930,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,480,928,GasA,Typical,Y,FuseF,928,608,0,1536,0,0,2,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,2,480,Typical,Typical,Paved,0,10,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,138400,-93.621482,42.031337 +One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Average,1923,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Fair,No,ALQ,1,Unf,0,164,1124,GasA,Typical,Y,SBrkr,1068,0,0,1068,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,128,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,109500,-93.620409,42.032302 +Two_Story_1945_and_Older,Residential_Medium_Density,50,10300,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,RRAn,Feedr,OneFam,Two_Story,Good,Above_Average,1921,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,576,576,GasA,Good,Y,SBrkr,902,808,0,1710,0,0,2,0,3,1,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,12,11,64,0,0,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Normal,140000,-93.62338,42.027986 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,57,8094,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,888,888,GasA,Excellent,Y,SBrkr,888,1074,0,1962,0,0,1,1,4,1,Typical,9,Typ,1,Typical,Detchd,Unf,2,572,Typical,Typical,Paved,160,0,364,0,0,0,No_Pool,Good_Privacy,None,0,6,2010,WD ,Normal,149500,-93.621674,42.028888 +Two_Story_1945_and_Older,Residential_Medium_Density,60,12900,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1912,2009,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Excellent,Y,SBrkr,780,780,0,1560,0,0,1,1,3,1,Good,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,344,0,0,0,168,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,159900,-93.621835,42.028875 +Two_Story_1945_and_Older,Residential_Medium_Density,52,3068,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1920,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,662,662,GasA,Excellent,Y,SBrkr,662,662,0,1324,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,180,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,Good_Privacy,None,0,2,2010,WD ,Normal,122000,-93.621632,42.026914 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,110,8472,Grvl,No_Alley_Access,Moderately_Irregular,Bnk,AllPub,Corner,Mod,Iowa_DOT_and_Rail_Road,RRNn,Norm,Duplex,One_Story,Average,Average,1963,1963,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,CBlock,Good,Typical,Gd,LwQ,4,GLQ,712,0,816,GasA,Typical,N,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,CarPort,Unf,2,516,Typical,Typical,Paved,106,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,110000,-93.629495,42.020693 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,70,5600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Average,1930,1950,Hip,CompShg,VinylSd,Wd Shng,None,0,Fair,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,SBrkr,372,720,0,1092,0,0,2,0,3,2,Fair,7,Mod,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,Othr,3500,7,2010,WD ,Normal,55000,-93.626876,42.024141 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,76,7630,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,TwoFmCon,Two_Story,Average,Excellent,1900,1996,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Good,Typical,No,Unf,7,Unf,0,360,360,GasA,Good,Y,SBrkr,1032,780,0,1812,0,0,2,0,4,2,Good,8,Typ,1,Poor,Detchd,Unf,2,672,Typical,Typical,Dirt_Gravel,344,0,40,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,140000,-93.620386,42.026741 +Two_Story_1945_and_Older,Residential_Low_Density,0,24090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Good,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1032,1032,GasA,Excellent,Y,SBrkr,1207,1196,0,2403,0,0,2,0,4,1,Typical,10,Typ,2,Typical,Attchd,Unf,1,349,Typical,Typical,Paved,56,0,318,0,0,0,No_Pool,No_Fence,None,0,6,2010,COD,Normal,244400,-93.656231,42.033454 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,15263,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Feedr,Norm,OneFam,One_Story,Average,Average,1959,1959,Gable,CompShg,HdBoard,HdBoard,BrkFace,90,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,656,1422,GasA,Good,Y,SBrkr,1675,0,0,1675,0,0,2,0,3,1,Typical,8,Typ,2,Good,Attchd,Unf,1,365,Typical,Typical,Paved,0,132,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,173000,-93.655538,42.032432 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,72,10632,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Average,Fair,1917,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Fair,No,Unf,7,Unf,0,689,689,GasA,Good,N,SBrkr,725,499,0,1224,0,0,1,1,3,1,Poor,6,Mod,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,0,0,248,0,0,0,No_Pool,No_Fence,None,0,1,2010,COD,Normal,107500,-93.658265,42.028256 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Below_Average,1915,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Fair,Fair,BrkTil,Typical,Typical,No,Rec,6,Unf,0,186,1212,GasA,Typical,N,SBrkr,1212,180,0,1392,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,168,0,0,0,No_Pool,No_Fence,None,0,2,2010,ConLD,Normal,100000,-93.656924,42.026752 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,65,6001,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,LwQ,4,Unf,0,232,600,GasA,Excellent,N,SBrkr,600,319,0,919,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,231,Typical,Typical,Paved,0,0,45,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,95000,-93.658081,42.02616 +Two_Story_1945_and_Older,C_all,0,6449,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Below_Average,Very_Poor,1907,1950,Gambrel,CompShg,Wd Sdng,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,634,707,GasW,Typical,N,SBrkr,942,942,0,1884,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,239,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Abnorml,93369,-93.657573,42.025255 +Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,60,6048,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Artery,Norm,TwoFmCon,One_and_Half_Fin,Average,Good,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,Mn,LwQ,4,Unf,0,120,856,GasA,Good,Y,SBrkr,936,744,0,1680,1,0,2,0,2,2,Typical,7,Typ,1,Good,Detchd,Unf,2,450,Typical,Fair,Partial_Pavement,56,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,COD,Normal,114900,-93.655849,42.022825 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,54,6342,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1875,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Good,N,SBrkr,780,240,0,1020,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,176,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,94000,-93.672036,42.034451 +Duplex_All_Styles_and_Ages,Residential_Low_Density,72,10773,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Below_Average,Fair,1967,1967,Gable,Tar&Grv,Plywood,Plywood,BrkFace,72,Fair,Fair,CBlock,Typical,Typical,No,ALQ,1,Unf,0,1128,1832,GasA,Typical,N,SBrkr,1832,0,0,1832,2,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,58,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,136000,-93.671021,42.033386 +Duplex_All_Styles_and_Ages,Residential_Low_Density,72,10778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Below_Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1768,1768,GasA,Typical,N,SBrkr,1768,0,0,1768,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,136500,-93.671076,42.033387 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,75,11625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,TwoFmCon,One_Story,Average,Below_Average,1965,1965,Hip,CompShg,Plywood,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,Mn,BLQ,2,Unf,0,198,1039,GasA,Excellent,Y,SBrkr,1039,0,0,1039,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,131500,-93.673401,42.033008 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11341,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1996,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,180,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,90,1392,GasA,Typical,Y,SBrkr,1392,0,0,1392,1,0,1,1,3,1,Typical,5,Mod,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,95,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,121500,-93.672381,42.03237 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8521,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,70,912,GasA,Typical,Y,SBrkr,912,0,0,912,0,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,125000,-93.678141,42.029867 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8246,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Very_Good,1968,2001,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Rec,6,ALQ,668,204,1060,GasA,Excellent,Y,SBrkr,1060,0,0,1060,1,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,1,270,Typical,Typical,Paved,406,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,154000,-93.676659,42.03069 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1966,2008,Hip,CompShg,HdBoard,HdBoard,BrkFace,47,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,474,150,864,GasA,Excellent,Y,SBrkr,892,0,0,892,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,416,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,137900,-93.677992,42.029924 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9364,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1969,1969,Gable,CompShg,HdBoard,HdBoard,Stone,143,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,292,663,GasA,Typical,Y,SBrkr,663,689,0,1352,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,299,Typical,Typical,Paved,379,36,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,158000,-93.676654,42.029731 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7832,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,89,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,226,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,137250,-93.676674,42.030519 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7424,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,SFoyer,Average,Average,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1319,GasA,Typical,Y,SBrkr,1373,0,0,1373,1,0,1,0,3,1,Typical,5,Typ,2,Typical,Attchd,Fin,2,591,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,160250,-93.671511,42.031187 +Two_Story_1946_and_Newer,Residential_Low_Density,86,11227,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Good,No,Rec,6,ALQ,453,0,720,GasA,Excellent,Y,SBrkr,720,720,0,1440,0,0,1,1,4,1,Typical,7,Typ,2,Typical,Attchd,Unf,2,480,Typical,Typical,Paved,192,38,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Normal,163000,-93.673565,42.03136 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11616,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkCmn,328,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,234,672,GasA,Typical,Y,SBrkr,672,714,0,1386,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,335,0,0,0,0,0,No_Pool,Good_Privacy,None,0,4,2010,WD ,Abnorml,158900,-93.672523,42.03128 +Two_Story_1946_and_Newer,Residential_Low_Density,80,14000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,105,1306,GasA,Excellent,Y,SBrkr,1306,954,0,2260,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,533,Typical,Typical,Paved,296,44,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,328000,-93.670503,42.030082 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20062,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Good,1977,2001,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,328,1420,GasA,Good,Y,SBrkr,1483,0,0,1483,1,0,1,1,1,1,Typical,4,Typ,2,Typical,Attchd,RFn,2,690,Typical,Typical,Paved,496,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,270000,-93.670114,42.03009 +One_Story_1945_and_Older,Residential_Low_Density,94,9259,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1927,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,660,660,GasA,Typical,N,SBrkr,756,0,0,756,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Dirt_Gravel,80,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,85000,-93.663619,42.03387 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Good,1950,1995,Gable,CompShg,VinylSd,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,625,1067,GasA,Typical,Y,SBrkr,1067,0,0,1067,0,0,2,0,2,1,Good,4,Min2,0,No_Fireplace,Attchd,Unf,2,436,Typical,Typical,Paved,290,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,128000,-93.668378,42.032164 +One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,0,23595,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,1979,1979,Shed,WdShake,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,74,1332,GasA,Typical,Y,SBrkr,1332,192,0,1524,2,0,0,1,0,1,Good,4,Typ,1,Typical,Attchd,Fin,2,586,Typical,Typical,Paved,268,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,260000,-93.667904,42.026547 +Two_Story_1946_and_Newer,Residential_Low_Density,0,17082,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Mod,Clear_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1978,1992,Gable,CompShg,VinylSd,VinylSd,BrkFace,288,Typical,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,153,1117,GasA,Excellent,Y,SBrkr,1117,864,0,1981,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,2,522,Typical,Typical,Paved,336,104,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,230000,-93.675038,42.024673 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,124,18600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Below_Average,1938,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,LwQ,684,0,972,GasA,Typical,Y,FuseA,1052,558,0,1610,0,1,2,0,4,1,Fair,8,Typ,1,Good,Attchd,RFn,1,480,Typical,Typical,Paved,0,0,60,0,0,0,No_Pool,No_Fence,Shed,450,6,2010,WD ,Normal,124000,-93.671178,42.023079 +One_Story_1945_and_Older,Residential_Low_Density,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,350,458,GasA,Fair,N,SBrkr,835,0,0,835,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,1,366,Fair,Typical,Paved,0,0,77,0,0,0,No_Pool,No_Fence,Shed,400,5,2010,COD,Abnorml,83000,-93.662762,42.028226 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,11479,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Good,1950,1987,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,387,172,663,GasA,Excellent,Y,SBrkr,1074,0,0,1074,1,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,1,467,Typical,Typical,Paved,0,52,52,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,144500,-93.66357,42.026533 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,405,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,596,Typical,Typical,Paved,44,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,129000,-93.667708,42.024208 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,9350,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1947,1979,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,564,756,GasA,Excellent,Y,SBrkr,1169,0,362,1531,0,0,1,0,3,1,Typical,8,Typ,1,Typical,Detchd,Unf,1,209,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,127000,-93.66526,42.025173 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1954,1998,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,218,1172,GasA,Typical,Y,SBrkr,1172,0,0,1172,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,366,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,128000,-93.666097,42.02526 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,10420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1176,1212,GasA,Excellent,Y,SBrkr,1212,0,0,1212,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,460,Typical,Typical,Paved,100,22,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,186000,-93.6658876,42.0247497 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,44,17485,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,96,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,162,1508,GasA,Excellent,Y,SBrkr,1508,0,0,1508,1,0,1,0,1,1,Good,5,Typ,2,Typical,Attchd,RFn,2,572,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,Con,Partial,308030,-93.665799,42.024126 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11200,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Fair,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1250,1250,GasA,Excellent,Y,SBrkr,1298,0,0,1298,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,504,Typical,Fair,Dirt_Gravel,0,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,COD,Normal,114000,-93.663367,42.025019 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11100,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Good,1946,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,930,0,0,930,0,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Abnorml,84900,-93.659808,42.024162 +Two_Story_1946_and_Newer,Residential_Low_Density,77,9206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1985,1985,Gable,CompShg,HdBoard,HdBoard,BrkFace,336,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,741,741,GasA,Typical,Y,SBrkr,977,755,0,1732,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,476,Typical,Typical,Paved,192,46,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,178000,-93.685001,42.03374 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,11980,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,1987,1987,Gable,CompShg,Plywood,Plywood,BrkFace,177,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,1433,GasA,Excellent,Y,SBrkr,1433,0,0,1433,1,0,1,1,1,1,Good,4,Typ,2,Typical,Attchd,RFn,2,528,Good,Good,Paved,0,278,0,0,266,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,270000,-93.685988,42.032076 +Two_Story_1946_and_Newer,Residential_Low_Density,87,12361,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,85,Good,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,86,946,GasA,Excellent,Y,SBrkr,964,838,0,1802,0,1,2,1,3,1,Good,8,Typ,1,Good,More_Than_Two_Types,RFn,4,1017,Typical,Typical,Paved,450,92,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,218000,-93.683167,42.033783 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9938,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,MetalSd,MetalSd,BrkFace,246,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,300,1050,GasA,Excellent,Y,SBrkr,1062,887,0,1949,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,574,Typical,Typical,Paved,156,90,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2010,WD ,Normal,236000,-93.682026,42.03068 +Split_Foyer,Residential_Low_Density,73,9069,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,189,936,GasA,Excellent,Y,SBrkr,996,0,0,996,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,564,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,147000,-93.6801568,42.0311198 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,10475,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,72,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1694,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,776,Typical,Typical,Paved,160,33,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,245350,-93.69019,42.025747 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,24,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,501,1187,GasA,Excellent,Y,SBrkr,1208,0,0,1208,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,105,61,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,206000,-93.691269,42.024614 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,10402,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1226,1226,GasA,Excellent,Y,SBrkr,1226,0,0,1226,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,740,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,198900,-93.69245,42.024604 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2010,2010,Gable,CompShg,VinylSd,VinylSd,Stone,80,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1198,1222,GasA,Excellent,Y,SBrkr,1222,0,0,1222,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,615,Typical,Typical,Paved,0,54,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,187000,-93.692521,42.024604 +Two_Story_1946_and_Newer,Residential_Low_Density,90,12376,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1990,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,203,1673,GasA,Good,Y,SBrkr,1699,1523,0,3222,1,0,3,0,5,1,Good,11,Typ,2,Typical,Attchd,Unf,3,594,Typical,Typical,Paved,367,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,320000,-93.68329,42.030601 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,82,14235,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1900,1993,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,PConc,Fair,Good,No,Unf,7,Unf,0,676,676,GasA,Typical,Y,SBrkr,831,614,0,1445,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Dirt_Gravel,0,59,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,138500,-93.6801805,42.0298532 +Split_or_Multilevel,Residential_Low_Density,80,8816,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Good,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,80,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,506,1010,GasA,Good,Y,SBrkr,1052,0,0,1052,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,155000,-93.682138,42.024318 +Split_Foyer,Residential_Low_Density,82,11105,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Fair,Av,GLQ,3,Unf,0,0,870,GasA,Good,Y,SBrkr,965,0,0,965,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,580,Good,Typical,Paved,71,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2010,WD ,Normal,159000,-93.69188,42.021321 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9337,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,525,878,GasA,Excellent,Y,SBrkr,892,800,0,1692,0,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,513,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,191000,-93.690542,42.018411 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,116,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,299,804,GasA,Excellent,Y,SBrkr,804,878,0,1682,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,523,Typical,Typical,Paved,0,77,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,200500,-93.688258,42.01867 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,38,15240,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1977,2004,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Good,CBlock,Good,Typical,No,GLQ,3,Rec,688,140,1026,GasA,Excellent,Y,SBrkr,1026,0,0,1026,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,316,85,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,150000,-93.68773,42.022186 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10900,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1977,1977,Gable,CompShg,HdBoard,HdBoard,BrkFace,153,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,311,689,GasA,Excellent,Y,SBrkr,689,703,0,1392,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,299,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Minimum_Privacy,Shed,450,3,2010,WD ,Normal,161750,-93.687807,42.022045 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Good,Av,LwQ,4,ALQ,712,0,894,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,365,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2010,WD ,Normal,128200,-93.687745,42.021395 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,7480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1972,1972,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,396,876,GasA,Typical,Y,SBrkr,876,0,0,876,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,127000,-93.683727,42.021133 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10389,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,CemntBd,CmentBd,BrkFace,320,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,296,1978,GasA,Excellent,Y,SBrkr,1978,0,0,1978,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,850,Typical,Typical,Paved,188,25,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,318000,-93.687859,42.018662 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11423,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,479,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,223,1581,GasA,Excellent,Y,SBrkr,1601,0,0,1601,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,670,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,2000,5,2010,WD ,Normal,272000,-93.685523,42.019069 +Two_Story_1946_and_Newer,Residential_Low_Density,44,9548,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Above_Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,223,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,458,941,GasA,Excellent,Y,SBrkr,941,888,0,1829,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,613,Typical,Typical,Paved,192,39,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Normal,237000,-93.687764,42.016121 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1040,1040,GasA,Excellent,Y,SBrkr,1044,1054,0,2098,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,2,621,Typical,Typical,Paved,331,38,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,240000,-93.680864,42.018721 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,48,12137,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,442,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1649,1649,GasA,Excellent,Y,SBrkr,1661,0,0,1661,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,598,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,224900,-93.682686,42.017749 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,176,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,143750,-93.681227,42.016275 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,RFn,2,420,Typical,Typical,Paved,160,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,143000,-93.6837,42.016253 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,48,10635,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,171,Good,Typical,PConc,Good,Typical,Av,BLQ,2,GLQ,972,315,1657,GasA,Excellent,Y,SBrkr,1668,0,0,1668,1,0,2,0,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,502,Typical,Typical,Paved,0,262,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,232000,-93.687922,42.01404 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,109,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,761,1473,GasA,Excellent,Y,SBrkr,1484,0,0,1484,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,606,Typical,Typical,Paved,0,35,0,144,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,213000,-93.684015,42.014092 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8773,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,98,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1390,1414,GasA,Excellent,Y,SBrkr,1414,0,0,1414,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,494,Typical,Typical,Paved,132,105,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,185500,-93.685961,42.014023 +One_Story_1945_and_Older,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Feedr,Norm,OneFam,One_Story,Fair,Above_Average,1945,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,640,0,0,640,0,0,1,0,2,1,Typical,5,Min1,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLD,Normal,84900,-93.6783313,42.0203585 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8842,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,381,381,GasA,Excellent,Y,SBrkr,992,0,0,992,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,319,Typical,Typical,Paved,60,0,56,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2010,Oth,Abnorml,155891,-93.677482,42.021245 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,9825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1965,1965,Hip,CompShg,AsphShn,AsphShn,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1664,0,0,1664,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,100000,-93.677553,42.021041 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10044,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,126,1196,GasA,Typical,Y,SBrkr,1196,0,0,1196,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,336,Typical,Typical,Paved,257,0,168,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2010,WD ,Normal,144000,-93.677545,42.020635 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,89,11792,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,744,744,GasA,Excellent,N,FuseF,792,328,0,1120,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Fair,Partial_Pavement,0,0,0,0,160,0,No_Pool,No_Fence,None,0,6,2010,WD ,Abnorml,64000,-93.676372,42.021163 +Split_or_Multilevel,Residential_Low_Density,65,6305,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,480,1008,GasA,Typical,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,125200,-93.675268,42.020306 +Split_or_Multilevel,Residential_Low_Density,94,7819,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,BLQ,127,480,1029,GasA,Typical,Y,SBrkr,1117,0,0,1117,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Detchd,Unf,2,672,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2010,WD ,Abnorml,107000,-93.675175,42.019849 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6410,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1958,1958,Hip,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Excellent,Y,SBrkr,960,0,0,960,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,90000,-93.6663375,42.019629 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,80,8546,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1121,1121,GasA,Excellent,Y,SBrkr,1121,0,0,1121,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,132,64,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,140000,-93.66751,42.020196 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,52,8741,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Below_Average,1945,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,641,735,GasA,Typical,Y,FuseA,798,689,0,1487,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,220,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,113000,-93.664841,42.021793 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,12180,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Below_Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Good,Typical,No,BLQ,2,Unf,0,324,672,Grav,Fair,N,FuseA,672,252,0,924,1,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2010,WD ,Normal,80000,-93.660168,42.021583 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8562,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,2002,Hip,CompShg,HdBoard,HdBoard,Stone,145,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,833,1216,GasA,Excellent,Y,FuseA,1526,0,0,1526,0,0,1,0,4,1,Typical,7,Min2,1,Good,Basment,Unf,1,364,Typical,Typical,Paved,116,78,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,144500,-93.661944,42.017726 +One_Story_1945_and_Older,Residential_Low_Density,67,4853,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Artery,Norm,OneFam,One_Story,Average,Above_Average,1924,1999,Gable,CompShg,MetalSd,VinylSd,BrkFace,203,Typical,Typical,BrkTil,Typical,Typical,Mn,Rec,6,Unf,0,974,1107,GasA,Fair,N,FuseA,1296,0,0,1296,0,0,2,0,2,1,Fair,5,Typ,1,Good,Detchd,Unf,1,260,Typical,Typical,Paved,0,0,36,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,104000,-93.655762,42.022674 +Two_Story_1945_and_Older,Residential_Low_Density,66,6858,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Above_Average,Below_Average,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,806,806,GasA,Typical,N,FuseF,841,806,0,1647,1,0,1,1,4,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,0,66,136,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,128000,-93.655653,42.022702 +One_Story_1945_and_Older,Residential_Low_Density,45,8212,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Fair,1914,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Fair,BrkTil,Typical,Fair,No,Rec,6,Unf,0,661,864,GasA,Typical,N,FuseF,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Fair,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,58500,-93.656718,42.022089 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,5000,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1924,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,808,1026,GasA,Typical,Y,SBrkr,1026,665,0,1691,0,0,2,0,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,242,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,127000,-93.650321,42.017737 +One_Story_1945_and_Older,Residential_Low_Density,0,7890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,618,856,GasA,Typical,Y,SBrkr,856,0,0,856,1,0,1,0,2,1,Typical,4,Typ,1,Good,Detchd,Unf,2,399,Typical,Typical,Paved,0,0,0,0,166,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,126000,-93.6514413,42.0177987 +Duplex_All_Styles_and_Ages,Residential_High_Density,60,7200,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,Duplex,Two_Story,Below_Average,Above_Average,1967,1967,Flat,Tar&Grv,Plywood,CBlock,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,862,1788,0,2650,0,0,3,0,6,2,Typical,10,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,500,2,2010,WD ,Normal,160000,-93.6507568,42.0163944 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,9839,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Poor,1931,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Fair,No,Unf,7,Unf,0,894,894,GasA,Excellent,Y,SBrkr,894,772,0,1666,1,0,1,0,3,1,Typical,7,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,156,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,100000,-93.64678,42.019091 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,9638,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1919,1990,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,804,804,GasA,Excellent,Y,SBrkr,1699,748,0,2447,0,0,2,0,4,1,Good,10,Min2,1,Good,Detchd,Unf,1,336,Typical,Typical,Paved,272,0,42,0,116,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,169000,-93.646683,42.019093 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,10452,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Above_Average,1941,1985,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,371,Good,Good,BrkTil,Good,Typical,No,ALQ,1,BLQ,252,850,1528,GasA,Excellent,Y,SBrkr,1225,908,0,2133,1,0,1,1,4,1,Typical,8,Typ,2,Typical,Attchd,Unf,1,312,Typical,Typical,Paved,0,0,86,0,0,0,No_Pool,No_Fence,None,0,7,2010,WD ,Normal,257500,-93.640041,42.016226 +Duplex_All_Styles_and_Ages,Residential_Low_Density,78,15600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1950,1991,Gable,CompShg,VinylSd,VinylSd,BrkFace,430,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,657,1032,GasA,Excellent,Y,SBrkr,1102,1075,0,2177,0,0,2,1,5,2,Typical,11,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,215000,-93.639781,42.014937 +Two_Story_1945_and_Older,Residential_Low_Density,66,9042,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Excellent,1941,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Excellent,Good,Stone,Typical,Good,No,GLQ,3,Unf,0,877,1152,GasA,Excellent,Y,SBrkr,1188,1152,0,2340,0,0,2,0,4,1,Good,9,Typ,2,Good,Attchd,RFn,1,252,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,Good_Privacy,Shed,2500,5,2010,WD ,Normal,266500,-93.642793,42.014682 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,17500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,PosA,Norm,OneFam,One_Story,Good,Very_Good,1959,2002,Gable,CompShg,BrkFace,HdBoard,None,0,Good,Good,PConc,Good,Typical,Av,GLQ,3,Unf,0,496,1902,GasA,Typical,Y,SBrkr,1902,0,0,1902,1,0,2,0,3,1,Excellent,7,Typ,2,Typical,Attchd,Fin,2,567,Typical,Typical,Paved,0,207,162,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,335000,-93.639678,42.011005 +Split_or_Multilevel,Residential_Low_Density,85,19645,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Crawford,Norm,Norm,OneFam,SLvl,Good,Above_Average,1994,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,44,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,80,423,GasA,Excellent,Y,SBrkr,896,756,0,1652,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,473,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,203135,-93.645014,42.010606 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,35,3907,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Blueste,Norm,Norm,TwnhsE,One_Story,Very_Good,Above_Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,235,982,GasA,Good,Y,SBrkr,1034,0,0,1034,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,598,Typical,Typical,Paved,141,36,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,185000,-93.645701,42.009345 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,35,3907,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Blueste,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,1115,1191,GasA,Good,Y,SBrkr,1191,0,0,1191,0,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Unf,2,531,Typical,Typical,Paved,112,81,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,162500,-93.645729,42.009337 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,15602,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Good,Very_Good,1959,1997,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,254,1501,GasA,Typical,Y,SBrkr,1801,0,0,1801,1,0,2,0,1,1,Typical,6,Typ,2,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,0,54,0,0,161,0,No_Pool,Good_Wood,None,0,3,2010,WD ,Normal,289000,-93.641677,42.010797 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,40,5436,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Very_Good,1922,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,61,796,GasA,Good,Y,SBrkr,796,358,0,1154,1,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,96,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2010,WD ,Normal,125500,-93.625291,42.022772 +One_Story_1945_and_Older,Residential_Medium_Density,58,8154,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Poor,Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,0,480,GasA,Typical,Y,SBrkr,540,0,0,540,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,ConLw,Normal,82000,-93.629496,42.021414 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,9140,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1921,1975,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,BLQ,2,Unf,0,321,629,GasA,Fair,Y,SBrkr,727,380,0,1107,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,625,Typical,Typical,Paved,0,56,0,0,200,0,No_Pool,Minimum_Privacy,None,0,4,2010,COD,Normal,110000,-93.626689,42.021474 +One_and_Half_Story_Finished_All_Ages,C_all,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Typical,Y,FuseA,698,430,0,1128,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,30,0,164,0,0,0,No_Pool,No_Fence,None,0,4,2010,COD,Abnorml,68400,-93.615272,42.021456 +One_and_Half_Story_Finished_All_Ages,C_all,66,8712,Pave,Paved,Regular,HLS,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Stone,Typical,Typical,Mn,Unf,7,Unf,0,859,859,GasA,Good,Y,SBrkr,859,319,0,1178,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,RFn,1,384,Typical,Typical,Dirt_Gravel,68,0,98,0,0,0,No_Pool,No_Fence,None,0,1,2010,WD ,Abnorml,102776,-93.606593,42.022653 +One_Story_1946_and_Newer_All_Styles,C_all,66,8712,Grvl,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Average,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,540,540,GasA,Typical,N,FuseA,1044,0,0,1044,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Basment,Unf,2,504,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,Shed,54,6,2010,WD ,Alloca,55993,-93.608271,42.021327 +One_Story_1945_and_Older,C_all,66,8712,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Good,1896,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,756,756,GasA,Good,Y,SBrkr,952,0,0,952,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,330,Typical,Typical,Dirt_Gravel,0,0,265,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Alloca,50138,-93.60775,42.02152 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3811,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Crawford,Artery,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,186,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,221,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,525,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,246000,-93.615993,42.008687 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,434,1049,GasA,Excellent,Y,SBrkr,1036,880,0,1916,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Unf,3,741,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,254900,-93.604947,41.99706 +Split_or_Multilevel,Residential_Low_Density,74,9620,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Good,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,564,1243,GasA,Typical,Y,SBrkr,1285,0,0,1285,0,1,2,0,3,1,Good,6,Typ,1,Fair,Attchd,Unf,2,473,Typical,Typical,Paved,375,26,0,0,0,0,No_Pool,Good_Privacy,Shed,80,5,2010,WD ,Normal,190000,-93.602823,41.997052 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,9196,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1560,1560,GasA,Excellent,Y,SBrkr,1560,0,0,1560,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,573,Typical,Typical,Paved,100,150,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,201000,-93.603972,41.996065 +Split_or_Multilevel,Residential_Low_Density,0,12328,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,335,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,473,1012,GasA,Typical,Y,SBrkr,1034,0,0,1034,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,3,888,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,169900,-93.600797,41.994784 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,12760,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1958,1958,GasA,Typical,Y,SBrkr,2048,0,0,2048,0,0,3,0,5,2,Typical,9,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,2,776,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,ConLD,Normal,170000,-93.602002,41.994013 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,57200,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Sev,Timberland,Norm,Norm,OneFam,One_Story,Average,Average,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Rec,334,60,747,GasA,Typical,Y,SBrkr,1687,0,0,1687,1,0,1,0,3,1,Typical,7,Min1,2,Typical,Detchd,Unf,2,572,Typical,Typical,Dirt_Gravel,0,0,50,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,160000,-93.657894,41.997741 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11896,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,60,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1258,1336,GasA,Excellent,Y,SBrkr,1346,0,0,1346,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,3,660,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,220000,-93.649203,41.995346 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,9803,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1214,1214,GasA,Excellent,Y,SBrkr,1214,0,0,1214,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,0,25,0,0,0,0,No_Pool,No_Fence,None,0,1,2010,New,Partial,179781,-93.649201,41.995278 +Two_Story_1946_and_Newer,Residential_Low_Density,73,9802,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,744,700,0,1444,0,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,174000,-93.649096,41.994284 +Two_Story_1946_and_Newer,Residential_Low_Density,92,12003,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2009,2010,Gable,CompShg,VinylSd,VinylSd,BrkFace,84,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,774,774,GasA,Excellent,Y,SBrkr,774,1194,0,1968,0,0,2,1,4,1,Excellent,8,Typ,1,Good,BuiltIn,Fin,3,680,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,New,Partial,269500,-93.649471,41.99353 +Two_Story_1946_and_Newer,Residential_Low_Density,80,11316,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,44,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,193,817,GasA,Excellent,Y,SBrkr,824,1070,0,1894,1,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,510,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,2,2010,WD ,Normal,214900,-93.647645,41.995489 +Two_Story_1946_and_Newer,Residential_Low_Density,85,14191,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,967,967,GasA,Excellent,Y,SBrkr,993,915,0,1908,0,0,2,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,431,Typical,Typical,Paved,135,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,202900,-93.646814,41.99435 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,13214,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2009,Hip,CompShg,Stucco,CmentBd,None,0,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,2002,2002,GasA,Excellent,Y,SBrkr,2018,0,0,2018,0,0,2,0,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,746,Typical,Typical,Paved,144,76,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,378500,-93.652495,41.992989 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,15300,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1965,1977,Hip,CompShg,Plywood,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,1026,1068,GasA,Typical,Y,SBrkr,1264,0,0,1264,1,0,1,0,2,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,169000,-93.60743,41.99321 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,10114,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1430,1430,GasA,Excellent,Y,SBrkr,1430,0,0,1430,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,173500,-93.608372,41.990934 +Duplex_All_Styles_and_Ages,Residential_Low_Density,94,9400,Pave,No_Alley_Access,Regular,Low,AllPub,Corner,Gtl,Mitchell,Norm,Norm,Duplex,Two_Story,Above_Average,Average,1971,1971,Mansard,CompShg,MetalSd,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,912,912,GasA,Typical,Y,SBrkr,912,912,0,1824,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,128,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,139000,-93.607622,41.993253 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1344,1344,GasA,Excellent,Y,SBrkr,1344,0,0,1344,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,686,Typical,Typical,Paved,328,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,166500,-93.608223,41.991004 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,1974,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,212,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,120,96,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,83500,-93.60359,41.991861 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,31,2394,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Meadow_Village,Norm,Norm,Twnhs,One_Story,Average,Above_Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,30,945,GasA,Excellent,Y,SBrkr,945,0,0,945,1,1,1,0,2,1,Typical,5,Typ,1,Poor,Attchd,RFn,1,253,Typical,Typical,Paved,174,0,56,0,108,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,119500,-93.604318,41.991758 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,36,2592,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Fair,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,No,Rec,6,BLQ,232,175,536,GasA,Typical,Y,SBrkr,536,576,0,1112,0,0,1,1,3,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,182,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,85000,-93.604425,41.991876 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1476,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Good,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,370,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,200,26,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,76000,-93.603435,41.992212 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1491,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,SFoyer,Below_Average,Above_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,LwQ,4,GLQ,480,0,630,GasA,Excellent,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,96,24,0,0,0,0,No_Pool,No_Fence,None,0,5,2010,WD ,Normal,75500,-93.60359,41.992119 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Below_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,88250,-93.601806,41.9917 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Above_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,252,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,64,0,0,0,No_Pool,No_Fence,None,0,6,2010,WD ,Normal,85500,-93.603398,41.991824 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,6953,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1971,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,395,864,GasA,Excellent,Y,SBrkr,874,0,0,874,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2010,ConLD,Normal,130000,-93.601449,41.991558 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,26142,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1962,1962,Gable,CompShg,HdBoard,HdBoard,BrkFace,189,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,595,1188,GasA,Typical,Y,SBrkr,1188,0,0,1188,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,312,Typical,Typical,Partial_Pavement,261,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,157900,-93.606856,41.988729 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,12887,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1984,1984,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,Rec,6,GLQ,590,36,833,GasA,Typical,Y,SBrkr,833,0,0,833,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,495,Typical,Typical,Paved,431,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2010,WD ,Normal,149900,-93.6035354,41.9889358 +Duplex_All_Styles_and_Ages,Residential_Low_Density,70,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,Two_Story,Average,Poor,1985,1986,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Poor,PConc,Typical,Typical,No,Unf,7,Unf,0,1216,1216,GasA,Good,Y,SBrkr,1216,1216,0,2432,0,0,4,2,4,2,Typical,10,Typ,0,No_Fireplace,Attchd,Unf,2,616,Typical,Fair,Paved,200,0,0,0,0,0,No_Pool,No_Fence,Shed,600,2,2010,WD ,Normal,159000,-93.6030339,41.98766 +Two_Story_1946_and_Newer,Residential_Low_Density,63,10475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,166,624,GasA,Good,Y,SBrkr,624,650,0,1274,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,22,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2010,WD ,Normal,136000,-93.600582,41.986647 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,68,10544,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,388,864,GasA,Typical,Y,SBrkr,864,615,0,1479,0,0,2,0,5,1,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,1,275,Typical,Typical,Paved,287,0,280,0,0,0,No_Pool,No_Fence,None,0,4,2010,WD ,Normal,161000,-93.599791,41.9913 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,Gd,GLQ,3,LwQ,284,54,1679,GasA,Excellent,Y,SBrkr,1803,0,0,1803,1,1,2,1,3,1,Good,6,Typ,2,Typical,Attchd,Unf,2,482,Typical,Typical,Paved,129,64,222,0,0,0,No_Pool,Good_Wood,None,0,2,2010,WD ,Normal,285000,-93.600006,41.989848 +Two_Story_1946_and_Newer,Residential_Low_Density,74,12961,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Typical,Mn,GLQ,3,Unf,0,208,1152,GasA,Excellent,Y,SBrkr,1152,645,0,1797,1,0,2,1,3,1,Good,7,Typ,1,Fair,Attchd,Fin,2,616,Typical,Typical,Paved,162,312,0,0,0,0,No_Pool,No_Fence,None,0,3,2010,WD ,Normal,231000,-93.600147,41.989185 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,13008,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Fair,No,Rec,6,Unf,0,318,882,GasA,Typical,Y,SBrkr,882,0,0,882,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,502,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,124500,-93.6188955,42.0530363 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1974,1974,Hip,CompShg,Plywood,Plywood,BrkFace,440,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,590,1434,GasA,Typical,Y,SBrkr,1434,0,0,1434,1,0,2,0,4,1,Typical,7,Typ,1,Good,Attchd,RFn,2,528,Typical,Typical,Paved,80,21,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,157000,-93.619562,42.05139 +Split_or_Multilevel,Residential_Low_Density,75,13860,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Very_Good,Good,1972,1995,Gable,CompShg,Plywood,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,542,1952,GasA,Good,Y,SBrkr,2000,704,0,2704,1,0,2,1,4,1,Excellent,9,Typ,3,Typical,Attchd,Fin,2,538,Typical,Typical,Paved,269,111,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,345000,-93.615524,42.049322 +Two_Story_1946_and_Newer,Residential_Low_Density,88,10179,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,98,945,GasA,Excellent,Y,SBrkr,945,663,0,1608,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,470,Typical,Typical,Paved,252,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,189500,-93.639388,42.059956 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11792,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,188,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,158,1008,GasA,Excellent,Y,SBrkr,1008,1275,0,2283,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,632,Typical,Typical,Paved,120,46,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,270000,-93.639915,42.063294 +Split_or_Multilevel,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1996,1997,Gable,CompShg,HdBoard,HdBoard,BrkFace,32,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,100,384,GasA,Good,Y,SBrkr,958,670,0,1628,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,390,Typical,Typical,Paved,48,72,0,0,0,0,No_Pool,No_Fence,Shed,490,6,2009,WD ,Normal,189000,-93.6364,42.060896 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,42,14892,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,160,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,426,1746,GasA,Excellent,Y,SBrkr,1746,0,0,1746,1,0,2,0,3,1,Excellent,7,Typ,2,Good,Attchd,Fin,3,758,Typical,Typical,Paved,201,39,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,377500,-93.635838,42.062956 +Split_or_Multilevel,Residential_Low_Density,0,8530,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,22,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,804,670,0,1474,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,120,72,0,0,0,0,No_Pool,No_Fence,Shed,700,5,2009,WD ,Normal,168500,-93.637442,42.060598 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,28,7296,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,243,2208,GasA,Excellent,Y,SBrkr,2522,0,0,2522,1,0,2,0,1,1,Good,8,Typ,1,Good,Attchd,Fin,2,564,Typical,Typical,Paved,182,57,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,375000,-93.634265,42.062903 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,5664,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2000,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,343,1501,GasA,Excellent,Y,SBrkr,1659,0,0,1659,1,0,2,0,2,1,Excellent,5,Typ,1,Excellent,Attchd,Fin,2,499,Typical,Typical,Paved,212,59,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,278000,-93.633739,42.061981 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,7380,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,1998,1998,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1077,1418,GasA,Excellent,Y,SBrkr,1478,0,0,1478,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,495,Typical,Typical,Paved,168,43,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,240000,-93.633547,42.061633 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,57,8013,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1996,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,846,1587,GasA,Excellent,Y,SBrkr,1734,0,0,1734,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,52,50,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,239500,-93.633024,42.061178 +Split_or_Multilevel,Residential_Low_Density,57,8923,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,195,384,GasA,Good,Y,SBrkr,751,631,0,1382,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,396,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,177500,-93.63921,42.059283 +Two_Story_1946_and_Newer,Residential_Low_Density,74,10141,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,832,832,GasA,Good,Y,SBrkr,885,833,0,1718,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,427,Typical,Typical,Paved,0,94,0,0,291,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,185000,-93.639069,42.059129 +Two_Story_1946_and_Newer,Residential_Low_Density,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,476,952,GasA,Good,Y,SBrkr,952,684,0,1636,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,191000,-93.639069,42.059161 +Two_Story_1946_and_Newer,Residential_Low_Density,59,7837,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,799,799,GasA,Good,Y,SBrkr,799,772,0,1571,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,178000,-93.636955,42.058274 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9765,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Excellent,Good,PConc,Good,Good,No,ALQ,1,Unf,0,370,680,GasA,Good,Y,SBrkr,680,790,0,1470,0,0,2,1,3,1,Typical,6,Typ,1,Typical,BuiltIn,Fin,2,420,Typical,Typical,Paved,232,63,0,0,0,0,No_Pool,No_Fence,Shed,480,4,2009,WD ,Normal,185000,-93.637046,42.058059 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8803,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1994,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,107,707,GasA,Good,Y,SBrkr,707,809,0,1516,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,409,Typical,Typical,Paved,0,46,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,181316,-93.637671,42.059645 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,58,7250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,45,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1181,1181,GasA,Excellent,Y,SBrkr,1190,0,0,1190,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,430,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,166000,-93.635824,42.058194 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9636,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,808,808,GasA,Good,Y,SBrkr,808,785,0,1593,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,RFn,2,389,Typical,Typical,Paved,342,40,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2009,WD ,Normal,178000,-93.635753,42.05793 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1994,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,702,702,GasA,Good,Y,SBrkr,702,779,0,1481,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,343,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,174000,-93.636822,42.058173 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9248,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,106,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,598,1158,GasA,Good,Y,SBrkr,1167,0,0,1167,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,400,Typical,Typical,Paved,120,26,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,173000,-93.636047,42.057854 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10762,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,344,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,284,978,GasA,Excellent,Y,SBrkr,1005,978,0,1983,0,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Fin,2,490,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,225000,-93.639325,42.058201 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,99,11851,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1424,1424,GasA,Excellent,Y,SBrkr,1442,0,0,1442,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,500,Typical,Typical,Paved,0,34,0,508,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,180500,-93.634644,42.058747 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,5814,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,184,1220,GasA,Good,Y,SBrkr,1360,0,0,1360,1,0,1,0,1,1,Good,4,Typ,1,Excellent,Attchd,RFn,2,565,Typical,Typical,Paved,63,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,COD,Abnorml,187500,-93.633245,42.059242 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,17423,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,748,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,312,2216,GasA,Excellent,Y,SBrkr,2234,0,0,2234,1,0,2,0,1,1,Excellent,9,Typ,1,Good,Attchd,Fin,3,1166,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,501837,-93.627751,42.060265 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11844,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,464,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,2046,2046,GasA,Excellent,Y,SBrkr,2046,0,0,2046,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,834,Typical,Typical,Paved,322,82,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,372500,-93.628639,42.059817 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,124,16158,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Stone_Brook,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,Stone,16,Good,Typical,PConc,Excellent,Typical,Av,ALQ,1,Unf,0,256,1530,GasA,Excellent,Y,SBrkr,1530,0,0,1530,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,430,Typical,Typical,Paved,168,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,260000,-93.629357,42.058439 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1977,1977,Gable,CompShg,HdBoard,Wd Sdng,BrkFace,157,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,722,1122,GasA,Excellent,Y,SBrkr,946,988,0,1934,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,567,Typical,Typical,Partial_Pavement,0,176,0,0,200,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,185000,-93.623975,42.056796 +Two_Story_1946_and_Newer,Residential_Low_Density,94,13005,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1980,1980,Gable,CompShg,CemntBd,CmentBd,BrkFace,278,Good,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,153,845,GasA,Typical,Y,SBrkr,1153,1200,0,2353,1,0,2,1,4,1,Excellent,10,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,288,195,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2009,WD ,Normal,260000,-93.637343,42.053532 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,48,17043,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1979,1998,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Good,Fair,No,Unf,7,Unf,0,1362,1362,GasA,Typical,Y,SBrkr,1586,0,0,1586,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,435,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,181000,-93.636512,42.055408 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,209,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,564,1386,GasA,Typical,Y,SBrkr,1411,0,0,1411,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,544,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Family,82500,-93.635056,42.055081 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16635,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1977,2000,Gable,CompShg,CemntBd,CmentBd,Stone,126,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,356,1602,GasA,Good,Y,SBrkr,1602,0,0,1602,0,1,2,0,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,529,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,215000,-93.632417,42.055572 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRNn,Norm,OneFam,Two_Story,Good,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,256,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,SBrkr,1154,896,0,2050,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,529,Typical,Typical,Paved,192,192,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Abnorml,154000,-93.637774,42.05184 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10928,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1978,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,101,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,1064,1427,GasA,Typical,Y,SBrkr,1671,0,0,1671,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,252,55,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,200000,-93.63745,42.052507 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12388,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1980,1991,Gable,CompShg,Plywood,Plywood,BrkFace,229,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,441,1043,GasA,Typical,Y,SBrkr,1539,1134,0,2673,0,0,2,1,4,1,Good,9,Typ,1,Typical,BuiltIn,RFn,2,441,Typical,Typical,Paved,178,84,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,249000,-93.638366,42.052102 +Split_or_Multilevel,Residential_Low_Density,0,14115,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Good,Average,1980,1980,Gable,CompShg,Plywood,Plywood,BrkFace,225,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,336,1372,GasA,Typical,Y,SBrkr,1472,0,0,1472,1,0,2,0,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,2,588,Typical,Typical,Paved,233,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,187500,-93.634212,42.052719 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11088,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1978,1998,Gable,CompShg,HdBoard,HdBoard,BrkFace,144,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,308,1140,GasA,Good,Y,SBrkr,1707,0,0,1707,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,479,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,184000,-93.637043,42.05288 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Very_Good,Average,1981,1981,Hip,WdShngl,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,1420,2524,GasA,Typical,Y,SBrkr,2524,0,0,2524,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Fin,2,542,Typical,Typical,Paved,474,120,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,278000,-93.633796,42.052915 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,11880,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Good,Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,206,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,567,1271,GasA,Typical,Y,SBrkr,1601,0,0,1601,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,COD,Abnorml,157000,-93.637289,42.050345 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,189,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1090,1090,GasA,Typical,Y,SBrkr,1370,0,0,1370,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,479,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Family,152000,-93.634694,42.0496539 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10304,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Average,Good,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,44,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,399,780,GasA,Excellent,Y,SBrkr,1088,780,0,1868,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,448,96,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,197500,-93.635919,42.049394 +Two_Story_1946_and_Newer,Floating_Village_Residential,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,304,926,GasA,Excellent,Y,SBrkr,1016,868,0,1884,1,0,2,1,3,1,Excellent,7,Typ,1,Excellent,Attchd,RFn,2,581,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,240900,-93.639516,42.049106 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,8004,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,Two_Story,Very_Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,110,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,288,832,GasA,Excellent,Y,SBrkr,832,1103,0,1935,1,0,2,1,3,1,Typical,8,Typ,0,No_Fireplace,BuiltIn,Fin,2,552,Typical,Typical,Paved,0,150,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,New,Partial,263435,-93.639511,42.051619 +Two_Story_1946_and_Newer,Floating_Village_Residential,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,RRNn,Norm,OneFam,Two_Story,Very_Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1058,1058,GasA,Excellent,Y,SBrkr,1058,816,0,1874,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,588,Typical,Typical,Paved,0,134,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,220000,-93.6392308,42.0503771 +Two_Story_1946_and_Newer,Floating_Village_Residential,0,8470,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,276,471,972,GasA,Excellent,Y,SBrkr,972,839,0,1811,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,565,Typical,Typical,Paved,225,48,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,235000,-93.639366,42.049406 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9373,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Average,Good,1975,1975,Gable,CompShg,HdBoard,HdBoard,BrkFace,161,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,168,120,1621,GasA,Typical,Y,SBrkr,1621,0,0,1621,1,0,2,0,3,1,Typical,7,Typ,2,Fair,Attchd,RFn,2,478,Typical,Typical,Paved,0,0,0,0,490,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,213000,-93.632915,42.053166 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,1974,Hip,CompShg,Plywood,Plywood,BrkFace,196,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,228,1116,GasA,Excellent,Y,SBrkr,1116,0,0,1116,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2009,WD ,Normal,167900,-93.632178,42.052355 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11050,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1975,1975,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,540,1176,GasA,Fair,Y,SBrkr,1193,0,0,1193,0,0,2,0,3,1,Typical,5,Typ,1,Typical,Attchd,Unf,2,506,Typical,Typical,Paved,40,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,158000,-93.630253,42.052448 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1974,1974,Hip,CompShg,HdBoard,HdBoard,BrkFace,174,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1064,1064,GasA,Typical,Y,SBrkr,1350,0,0,1350,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,165000,-93.631432,42.05267 +Split_or_Multilevel,Residential_Low_Density,0,10448,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,333,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,689,689,GasA,Typical,Y,SBrkr,1378,741,0,2119,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,583,Typical,Typical,Paved,0,104,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2009,COD,Abnorml,158000,-93.631791,42.04891 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,Duplex,Two_Story,Average,Average,1974,1974,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,896,896,GasA,Typical,Y,SBrkr,896,896,0,1792,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,32,45,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,136000,-93.625856,42.056398 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1970,1970,Gable,CompShg,MetalSd,MetalSd,BrkFace,76,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,174,1002,GasA,Typical,Y,SBrkr,1002,0,0,1002,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,902,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,148500,-93.626554,42.054626 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7930,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1969,2005,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,LwQ,472,115,1026,GasA,Good,Y,SBrkr,1026,0,0,1026,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,440,Typical,Typical,Paved,171,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,156000,-93.628707,42.055228 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7830,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1180,1180,GasA,Typical,Y,SBrkr,1180,0,0,1180,0,0,1,1,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,477,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,COD,Normal,128000,-93.626603,42.055184 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8510,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1971,1971,Gable,CompShg,Plywood,Plywood,BrkFace,178,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,543,1043,GasA,Excellent,Y,SBrkr,1050,0,0,1050,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,143000,-93.62655,42.055154 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7038,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1970,1970,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,138,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2009,WD ,Abnorml,76500,-93.627037,42.05337 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6960,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1970,1970,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,239,250,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,660,Typical,Typical,Paved,96,0,0,0,0,0,No_Pool,No_Fence,Shed,500,11,2009,WD ,Normal,120500,-93.625657,42.053306 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1970,1970,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,673,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,463,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,124500,-93.625542,42.053307 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,180,160,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,216,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Abnorml,97000,-93.6234986,42.0564419 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1971,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,624,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,200,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,130000,-93.6223289,42.056461 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,229,483,GasA,Typical,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,111000,-93.627396,42.052758 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2368,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,None,312,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,765,GasA,Typical,Y,SBrkr,765,600,0,1365,0,0,1,1,3,1,Typical,7,Min1,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,125000,-93.628119,42.052338 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,142,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,COD,Abnorml,112000,-93.627565,42.051683 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,425,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,294,79,483,GasA,Typical,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,97000,-93.629456,42.051661 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Very_Good,1972,2007,Gable,CompShg,HdBoard,HdBoard,BrkFace,510,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,321,483,GasA,Good,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,118000,-93.629851,42.051823 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Good,1972,1972,Gable,CompShg,CemntBd,CmentBd,BrkFace,268,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,399,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,185,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,119500,-93.629881,42.051822 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,4928,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Unf,0,0,958,GasA,Typical,Y,SBrkr,958,0,0,958,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,143750,-93.627244,42.050615 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,498,804,GasA,Typical,Y,SBrkr,804,744,0,1548,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Detchd,RFn,2,440,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,146000,-93.624729,42.050705 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,289,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,87,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,148500,-93.624748,42.050705 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,622,0,1057,GasA,Typical,Y,SBrkr,1055,0,0,1055,0,1,2,0,2,1,Typical,4,Typ,1,Fair,Attchd,RFn,2,440,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,123000,-93.625945,42.050682 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1974,1974,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,495,103,855,GasA,Typical,Y,SBrkr,855,467,0,1322,0,1,2,1,3,1,Typical,6,Typ,1,Fair,Attchd,Unf,2,440,Typical,Typical,Paved,260,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,147000,-93.625918,42.050533 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2349,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,466,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,137900,-93.625846,42.050213 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2364,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,484,804,GasA,Typical,Y,SBrkr,804,744,0,1548,0,1,2,1,3,1,Typical,7,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,147000,-93.625688,42.05009 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2289,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,544,855,GasA,Typical,Y,SBrkr,855,586,0,1441,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,440,Typical,Typical,Paved,28,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,148500,-93.62569,42.050134 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2364,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,576,855,GasA,Typical,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,138000,-93.625696,42.050222 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2104,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,300,836,GasA,Typical,Y,SBrkr,836,0,0,836,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,345,Typical,Typical,Paved,150,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,128500,-93.625707,42.050397 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,60,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,460,892,GasA,Typical,Y,SBrkr,892,0,0,892,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,100000,-93.6256036,42.0488934 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1966,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,165,Good,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,220,864,GasA,Excellent,Y,SBrkr,1120,0,0,1120,0,1,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,656,Typical,Typical,Paved,0,162,0,0,0,0,No_Pool,No_Fence,Shed,1200,7,2009,WD ,Normal,148800,-93.626521,42.048486 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,96,12456,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,Stone,230,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,528,1700,GasA,Excellent,Y,SBrkr,1718,0,0,1718,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,786,Typical,Typical,Paved,216,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,337500,-93.658877,42.0623099 +Two_Story_1946_and_Newer,Residential_Low_Density,110,14257,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,Norm,OneFam,Two_Story,Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,726,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,416,1776,GasA,Excellent,Y,SBrkr,1794,978,0,2772,1,0,3,1,4,1,Excellent,10,Typ,3,Good,BuiltIn,Fin,3,754,Typical,Typical,Paved,135,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,462000,-93.6562749,42.063304 +Two_Story_1946_and_Newer,Residential_Low_Density,104,13518,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,860,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1926,1926,GasA,Excellent,Y,SBrkr,1966,1174,0,3140,0,0,3,1,4,1,Excellent,11,Typ,2,Good,BuiltIn,Fin,3,820,Typical,Typical,Paved,144,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,485000,-93.652716,42.063025 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,15431,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,200,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,ALQ,539,788,3094,GasA,Excellent,Y,SBrkr,2402,0,0,2402,1,0,2,0,2,1,Excellent,10,Typ,2,Good,Attchd,Fin,3,672,Typical,Typical,Paved,0,72,0,0,170,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,555000,-93.657828,42.061902 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,108,13173,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,300,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,80,1652,GasA,Excellent,Y,SBrkr,1652,0,0,1652,1,0,2,0,2,1,Excellent,6,Typ,2,Excellent,Attchd,Fin,2,840,Typical,Typical,Paved,404,102,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,325000,-93.657483,42.06128 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14230,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,640,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1566,1566,GasA,Excellent,Y,SBrkr,1600,0,0,1600,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,890,Typical,Typical,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,256300,-93.654241,42.062991 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,12704,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,BrkFace,306,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,2042,2042,GasA,Excellent,Y,SBrkr,2042,0,0,2042,0,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,1390,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,New,Partial,253293,-93.654144,42.062992 +Two_Story_1946_and_Newer,Residential_Low_Density,95,12350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,379,1365,GasA,Excellent,Y,SBrkr,1365,1325,0,2690,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,864,Typical,Typical,Paved,0,197,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,398800,-93.653955,42.062994 +Two_Story_1946_and_Newer,Residential_Low_Density,96,11308,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,154,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,168,1104,GasA,Excellent,Y,SBrkr,1130,1054,0,2184,1,0,2,1,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,836,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,335000,-93.652863,42.062942 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,12350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,450,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,788,2020,GasA,Excellent,Y,SBrkr,2020,0,0,2020,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,RFn,3,896,Typical,Typical,Paved,192,98,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,404000,-93.654439,42.062258 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,12220,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2009,2009,Hip,CompShg,CemntBd,CmentBd,BrkFace,305,Excellent,Typical,CBlock,Excellent,Typical,No,GLQ,3,Unf,0,570,2006,GasA,Excellent,Y,SBrkr,2020,0,0,2020,1,0,2,1,3,1,Excellent,9,Typ,1,Good,Attchd,Fin,3,900,Typical,Typical,Paved,156,54,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,402861,-93.654567,42.062257 +Two_Story_1946_and_Newer,Residential_Low_Density,97,13478,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2008,2008,Gable,CompShg,CemntBd,CmentBd,Stone,420,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,384,1722,GasA,Excellent,Y,SBrkr,1728,568,0,2296,1,0,2,1,3,1,Excellent,10,Typ,1,Good,BuiltIn,RFn,3,842,Typical,Typical,Paved,382,274,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,ConLI,Normal,451950,-93.658224,42.062776 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,13693,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosA,PosA,OneFam,One_Story,Very_Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,472,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,342,2630,GasA,Excellent,Y,SBrkr,2674,0,0,2674,2,0,2,1,2,1,Excellent,8,Typ,2,Good,Attchd,Fin,3,762,Typical,Typical,Paved,360,50,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,610000,-93.657835,42.062789 +Two_Story_1946_and_Newer,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,424,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1734,1734,GasA,Excellent,Y,SBrkr,1734,1088,0,2822,0,0,3,1,4,1,Excellent,12,Typ,1,Good,BuiltIn,RFn,3,1020,Typical,Typical,Paved,52,170,0,0,192,0,No_Pool,No_Fence,None,0,1,2009,New,Partial,582933,-93.657049,42.0623985 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,11578,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,302,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1736,1736,GasA,Excellent,Y,SBrkr,1736,0,0,1736,0,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,RFn,3,834,Typical,Typical,Paved,319,90,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,360000,-93.654437,42.062108 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,16870,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,238,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,251,1782,GasA,Excellent,Y,SBrkr,1782,0,0,1782,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,Fin,3,932,Typical,Typical,Paved,99,82,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,296000,-93.658854,42.060934 +Two_Story_1946_and_Newer,Residential_Low_Density,59,23303,Pave,No_Alley_Access,Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,20,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,278,1508,GasA,Excellent,Y,SBrkr,1508,1012,0,2520,1,0,2,1,5,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,640,Typical,Typical,Paved,192,273,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Family,409900,-93.656737,42.060353 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,11146,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,250,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1709,1709,GasA,Excellent,Y,SBrkr,1717,0,0,1717,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,908,Typical,Typical,Paved,169,39,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,255500,-93.653152,42.061382 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10367,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,284,Excellent,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,724,1739,GasA,Excellent,Y,SBrkr,1743,0,0,1743,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,927,Typical,Typical,Paved,168,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,ConLI,Normal,335000,-93.652884,42.061437 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9591,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,262,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,625,1713,GasA,Excellent,Y,SBrkr,1713,0,0,1713,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,856,Typical,Typical,Paved,0,26,0,0,170,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,274900,-93.654724,42.060576 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,10872,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,122,Good,Typical,PConc,Excellent,Good,Av,GLQ,3,Unf,0,467,1504,GasA,Excellent,Y,SBrkr,1531,0,0,1531,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,700,Typical,Typical,Paved,184,52,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,300000,-93.65476,42.060575 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,13514,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,None,285,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,632,1774,GasA,Excellent,Y,SBrkr,1808,0,0,1808,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,850,Typical,Typical,Paved,200,26,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,324000,-93.652084,42.061678 +Two_Story_1946_and_Newer,Residential_Low_Density,74,8834,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,Stone,216,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,292,1462,GasA,Excellent,Y,SBrkr,1462,762,0,2224,1,0,2,1,4,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,738,Typical,Typical,Paved,184,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,350000,-93.654652,42.060426 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,11362,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,Stone,42,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,797,1836,GasA,Excellent,Y,SBrkr,1836,0,0,1836,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,862,Typical,Typical,Paved,125,185,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,280000,-93.652724,42.060178 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10655,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,296,Good,Typical,PConc,Good,Typical,No,GLQ,3,No_Basement,479,1603,3206,GasA,Excellent,Y,SBrkr,1629,0,0,1629,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,880,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,284000,-93.6541267,42.0599123 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,12878,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,418,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,498,1760,GasA,Excellent,Y,SBrkr,1760,0,0,1760,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,583,Typical,Typical,Paved,165,190,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,269500,-93.6547754,42.0598989 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,9541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,268,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1502,1502,GasA,Excellent,Y,SBrkr,1502,0,0,1502,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,644,Typical,Typical,Paved,0,114,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,233170,-93.643771,42.061404 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,103,13472,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,922,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,2336,2392,GasA,Excellent,Y,SBrkr,2392,0,0,2392,0,0,2,0,3,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,968,Typical,Typical,Paved,248,105,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,386250,-93.65604,42.059241 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,15274,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,724,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,480,2452,GasA,Excellent,Y,SBrkr,2452,0,0,2452,2,0,2,0,3,1,Excellent,10,Typ,1,Good,Attchd,Fin,3,886,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,445000,-93.65486,42.058782 +Two_Story_1946_and_Newer,Residential_Low_Density,96,13262,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,Stone,186,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1082,1082,GasA,Excellent,Y,SBrkr,1105,1295,0,2400,0,0,3,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,730,Typical,Typical,Paved,114,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,290000,-93.6545189,42.058886 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9658,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,383,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1598,1598,GasA,Good,Y,SBrkr,1606,0,0,1606,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,871,Typical,Typical,Paved,230,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,255900,-93.65048,42.0592 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,47,6904,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,240,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,522,1358,GasA,Excellent,Y,SBrkr,1358,0,0,1358,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,213000,-93.649779,42.059177 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5381,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,135,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,406,1306,GasA,Excellent,Y,SBrkr,1306,0,0,1306,1,0,2,0,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,624,Typical,Typical,Paved,170,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,196000,-93.649743,42.059179 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5122,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,135,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,425,1306,GasA,Excellent,Y,SBrkr,1306,0,0,1306,1,0,2,0,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,624,Typical,Typical,Paved,170,63,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,184500,-93.649723,42.059183 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,80,10307,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,474,1350,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,212500,-93.650258,42.058339 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5001,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,166,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,410,1314,GasA,Excellent,Y,SBrkr,1314,0,0,1314,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,626,Typical,Typical,Paved,172,62,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,230000,-93.650244,42.058336 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,14836,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,730,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,346,2492,GasA,Excellent,Y,SBrkr,2492,0,0,2492,1,0,2,1,2,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,949,Typical,Typical,Paved,226,235,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Abnorml,552000,-93.655483,42.057177 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,117,15262,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,470,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,643,2200,GasA,Excellent,Y,SBrkr,2200,0,0,2200,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,685,Typical,Typical,Paved,208,55,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,382500,-93.6552449,42.05851 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,44,7390,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2009,Hip,CompShg,MetalSd,MetalSd,BrkFace,308,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1084,1884,GasA,Excellent,Y,SBrkr,1884,0,0,1884,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,649,Typical,Typical,Paved,231,90,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,New,Partial,320000,-93.654204,42.057164 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6472,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,BrkFace,500,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1451,1451,GasA,Excellent,Y,SBrkr,1456,0,0,1456,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,539,Typical,Typical,Paved,192,42,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,248500,-93.654215,42.057089 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,16770,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,270,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,516,1712,GasA,Excellent,Y,SBrkr,1712,0,0,1712,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,701,Typical,Typical,Paved,218,183,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,286500,-93.654405,42.057019 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,461,1324,GasA,Excellent,Y,SBrkr,1324,0,0,1324,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,192,38,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,254000,-93.6514849,42.056983 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,3480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,Stone,163,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1405,1405,GasA,Excellent,Y,SBrkr,1405,0,0,1405,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,148,36,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,173000,-93.651014,42.057277 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2268,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,197,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,173000,-93.650238,42.057467 +Two_Story_1946_and_Newer,Residential_Low_Density,63,10928,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,184000,-93.645752,42.06232 +Two_Story_1946_and_Newer,Residential_Low_Density,57,8918,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,745,745,GasA,Excellent,Y,SBrkr,745,745,0,1490,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,392,Typical,Typical,Paved,36,20,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,167800,-93.64466,42.063183 +Two_Story_1946_and_Newer,Residential_Low_Density,149,12589,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,742,742,GasA,Excellent,Y,SBrkr,742,742,0,1484,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,36,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,174000,-93.644457,42.062058 +Two_Story_1946_and_Newer,Residential_Low_Density,122,11911,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,684,684,GasA,Excellent,Y,SBrkr,684,876,0,1560,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,174000,-93.645627,42.062404 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3684,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,130,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1373,1373,GasA,Excellent,Y,SBrkr,1555,0,0,1555,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,660,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,174000,-93.641561,42.062562 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,51,3635,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,130,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,398,1386,GasA,Excellent,Y,SBrkr,1569,0,0,1569,0,1,2,0,1,1,Good,7,Typ,1,Typical,Attchd,RFn,3,660,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,175900,-93.641515,42.062557 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1357,1373,GasA,Excellent,Y,SBrkr,1555,0,0,1555,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,430,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,192500,-93.642094,42.062305 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1330,1346,GasA,Excellent,Y,SBrkr,1504,0,0,1504,0,0,2,0,1,1,Good,7,Typ,1,Good,Attchd,Fin,2,457,Typical,Typical,Paved,156,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,181000,-93.642597,42.062266 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1204,1220,GasA,Excellent,Y,SBrkr,1220,0,0,1220,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,397,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,180000,-93.642911,42.062081 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,11,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1129,1145,GasA,Excellent,Y,SBrkr,1145,0,0,1145,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,397,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,160200,-93.642914,42.062086 +Two_Story_1946_and_Newer,Residential_Low_Density,71,7795,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,291,716,GasA,Excellent,Y,SBrkr,716,716,0,1432,1,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,432,Typical,Typical,Paved,100,51,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,188500,-93.642749,42.061562 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8068,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1010,1010,GasA,Excellent,Y,SBrkr,1010,1257,0,2267,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,RFn,2,390,Typical,Typical,Paved,120,46,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,ConLI,Normal,200000,-93.643312,42.059377 +Split_or_Multilevel,Residential_Low_Density,59,9434,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,170000,-93.644106,42.061739 +Two_Story_1946_and_Newer,Residential_Low_Density,62,7984,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,868,868,GasA,Excellent,Y,SBrkr,868,762,0,1630,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,436,Typical,Typical,Paved,120,48,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,189500,-93.641772,42.061149 +Split_or_Multilevel,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,55,408,GasA,Excellent,Y,SBrkr,779,640,0,1419,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,527,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,184100,-93.641488,42.061114 +Two_Story_1946_and_Newer,Residential_Low_Density,61,10125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,846,846,GasA,Excellent,Y,SBrkr,846,748,0,1594,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,434,Typical,Typical,Paved,300,48,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,195500,-93.642626,42.061656 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8965,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,130,782,GasA,Excellent,Y,SBrkr,806,683,0,1489,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,192000,-93.640051,42.061365 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8174,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,204,698,GasA,Excellent,Y,SBrkr,698,644,0,1342,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,393,Typical,Typical,Paved,100,56,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,178000,-93.640065,42.063283 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,5063,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,166,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,410,1314,GasA,Excellent,Y,SBrkr,1314,0,0,1314,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,626,Typical,Typical,Paved,172,62,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,ConLw,Normal,207500,-93.649649,42.058368 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8795,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,652,952,GasA,Excellent,Y,SBrkr,980,1276,0,2256,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,2,554,Typical,Typical,Paved,224,54,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,236000,-93.642813,42.05859 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12891,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,219,870,GasA,Excellent,Y,SBrkr,878,1126,0,2004,1,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,3,644,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,257500,-93.642932,42.059481 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12224,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,297,992,GasA,Excellent,Y,SBrkr,1022,1032,0,2054,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,RFn,2,390,Typical,Typical,Paved,24,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,244000,-93.641919,42.059068 +Split_or_Multilevel,Residential_Low_Density,61,9734,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,SLvl,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Rec,113,30,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,167000,-93.644251,42.059605 +Two_Story_1946_and_Newer,Residential_Low_Density,60,8123,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,982,982,GasA,Excellent,Y,SBrkr,1007,793,0,1800,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,463,Typical,Typical,Paved,100,63,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,179000,-93.641377,42.057839 +Two_Story_1946_and_Newer,Residential_Low_Density,42,8433,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,111,794,GasA,Excellent,Y,SBrkr,819,695,0,1514,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,394,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,190000,-93.641313,42.057834 +Split_or_Multilevel,Residential_Low_Density,62,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,RFn,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,156000,-93.641303,42.057833 +Two_Story_1946_and_Newer,Residential_Low_Density,0,15896,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,RRNn,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,264,1177,GasA,Excellent,Y,SBrkr,1223,1089,0,2312,1,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,3,658,Typical,Typical,Paved,298,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,245000,-93.641367,42.057098 +Two_Story_1946_and_Newer,Residential_Low_Density,0,24682,Pave,No_Alley_Access,Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,841,841,GasA,Excellent,Y,SBrkr,892,783,0,1675,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,502,Typical,Typical,Paved,0,103,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,181000,-93.641397,42.057093 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8755,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,RRNn,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,298,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,220,992,GasA,Excellent,Y,SBrkr,1022,1038,0,2060,1,0,2,1,3,1,Good,8,Typ,1,Typical,BuiltIn,RFn,2,390,Typical,Typical,Paved,0,0,0,168,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,214000,-93.641312,42.056955 +Split_or_Multilevel,Residential_Low_Density,64,7848,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Above_Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,410,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,168000,-93.640879,42.058668 +Two_Story_1946_and_Newer,Residential_Low_Density,82,9430,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,673,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,89,1252,GasA,Excellent,Y,SBrkr,1268,1097,0,2365,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,856,Typical,Typical,Paved,0,128,0,0,180,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,337000,-93.65429,42.053749 +Two_Story_1946_and_Newer,Residential_Low_Density,174,15138,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,506,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,773,1462,GasA,Excellent,Y,SBrkr,1490,1304,0,2794,1,0,2,1,4,1,Excellent,9,Typ,1,Typical,Attchd,Fin,3,810,Typical,Typical,Paved,0,146,202,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,403000,-93.657163,42.053911 +Two_Story_1946_and_Newer,Residential_Low_Density,106,12720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,282,1455,GasA,Excellent,Y,SBrkr,1466,1221,0,2687,1,0,2,1,4,1,Good,10,Typ,2,Typical,BuiltIn,RFn,3,810,Typical,Typical,Paved,252,30,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,327000,-93.650957,42.055626 +Two_Story_1946_and_Newer,Residential_Low_Density,0,16545,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,731,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,503,1284,GasA,Excellent,Y,SBrkr,1310,1140,0,2450,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,1069,Typical,Typical,Paved,0,126,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,340000,-93.652608,42.053441 +Two_Story_1946_and_Newer,Residential_Low_Density,98,12203,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Hip,CompShg,VinylSd,VinylSd,BrkFace,975,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,371,1225,GasA,Excellent,Y,SBrkr,1276,1336,0,2612,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,3,676,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,336000,-93.65238,42.053041 +Two_Story_1946_and_Newer,Residential_Low_Density,79,10208,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,921,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1264,1264,GasA,Excellent,Y,SBrkr,1277,1067,0,2344,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,889,Typical,Typical,Paved,220,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,265000,-93.655555,42.052668 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,634,Good,Typical,PConc,Good,Typical,Av,BLQ,2,GLQ,1526,262,2024,GasA,Excellent,Y,SBrkr,2063,0,0,2063,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,Fin,3,815,Typical,Typical,Paved,182,56,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,315000,-93.655988,42.050411 +Two_Story_1946_and_Newer,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,256,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,360,1347,GasA,Excellent,Y,SBrkr,1372,1274,0,2646,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,656,Typical,Typical,Paved,340,60,144,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,260000,-93.654048,42.049701 +Two_Story_1946_and_Newer,Residential_Low_Density,79,9085,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,286,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,254,1070,GasA,Excellent,Y,SBrkr,1094,967,0,2061,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,647,Typical,Typical,Paved,296,102,209,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,260000,-93.651503,42.05161 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,372,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,549,1173,GasA,Excellent,Y,SBrkr,1215,1017,0,2232,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,623,Typical,Typical,Paved,173,165,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,263550,-93.652117,42.050215 +Two_Story_1946_and_Newer,Residential_Low_Density,52,46589,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1994,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,528,Good,Typical,PConc,Good,Good,No,GLQ,3,Rec,180,88,1629,GasA,Excellent,Y,SBrkr,1686,762,0,2448,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,711,Typical,Typical,Paved,517,76,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,402000,-93.650529,42.049921 +Two_Story_1946_and_Newer,Residential_Low_Density,0,29959,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,378,973,GasA,Excellent,Y,SBrkr,979,871,0,1850,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,467,Typical,Typical,Paved,168,98,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,248000,-93.650822,42.050971 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,9612,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Gable,CompShg,VinylSd,VinylSd,Stone,72,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1468,1468,GasA,Excellent,Y,SBrkr,1468,0,0,1468,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,3,898,Typical,Typical,Paved,210,150,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,New,Partial,244600,-93.644121,42.054282 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,11194,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,PosN,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,240,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1696,1696,GasA,Excellent,Y,SBrkr,1696,0,0,1696,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,972,Typical,Typical,Paved,120,56,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,275000,-93.64247,42.055036 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,PosN,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,294,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1614,1614,GasA,Excellent,Y,SBrkr,1658,0,0,1658,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,726,Typical,Typical,Paved,144,44,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,257500,-93.642553,42.054302 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9262,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Gable,CompShg,CemntBd,CmentBd,Stone,194,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1573,1573,GasA,Excellent,Y,SBrkr,1578,0,0,1578,0,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,840,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,New,Partial,287090,-93.643944,42.054152 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,260,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,408,1702,GasA,Excellent,Y,SBrkr,1702,0,0,1702,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,844,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,275500,-93.642579,42.054151 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9139,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,206,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1043,1422,GasA,Excellent,Y,SBrkr,1432,0,0,1432,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,492,Typical,Typical,Paved,297,50,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,245000,-93.64236,42.053212 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,772,1113,GasA,Excellent,Y,SBrkr,1113,858,0,1971,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,689,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,253000,-93.641251,42.053258 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11128,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,PosN,PosN,OneFam,One_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,Stone,198,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,300,2458,GasA,Excellent,Y,SBrkr,2490,0,0,2490,1,0,2,0,2,1,Excellent,9,Typ,2,Good,Attchd,Fin,3,795,Typical,Typical,Paved,70,226,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2009,WD ,Normal,468000,-93.641296,42.053108 +Two_Story_1946_and_Newer,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,78,900,GasA,Excellent,Y,SBrkr,932,920,0,1852,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,644,Typical,Typical,Paved,168,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,252678,-93.650321,42.051804 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,786,804,0,1590,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,676,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,210000,-93.6504,42.051797 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,7862,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1191,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,0,0,2,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,676,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,208300,-93.65044,42.051793 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,90,7993,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2008,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1436,1436,GasA,Excellent,Y,SBrkr,1436,0,0,1436,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,529,Typical,Typical,Paved,0,121,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,New,Partial,225000,-93.650417,42.051645 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2009,2009,Gable,CompShg,CemntBd,CmentBd,Stone,72,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,364,1300,GasA,Excellent,Y,SBrkr,1314,0,0,1314,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,552,Typical,Typical,Paved,135,112,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,New,Partial,229456,-93.650377,42.051648 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1402,1402,GasA,Excellent,Y,SBrkr,1402,0,0,1402,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,625,Typical,Typical,Paved,205,126,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,229800,-93.650338,42.051652 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,112,12606,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,120,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1530,1530,GasA,Excellent,Y,SBrkr,1530,0,0,1530,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,984,Typical,Typical,Paved,212,136,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,250000,-93.648275,42.050406 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,85,9187,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2009,Gable,CompShg,CemntBd,CmentBd,Stone,162,Excellent,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,645,1766,GasA,Excellent,Y,SBrkr,1766,0,0,1766,1,0,2,1,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,478,Typical,Typical,Paved,195,130,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,New,Partial,370878,-93.648809,42.051717 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,238,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1348,1372,GasA,Excellent,Y,SBrkr,1448,0,0,1448,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,692,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,238500,-93.648778,42.051795 +Two_Story_1946_and_Newer,Floating_Village_Residential,85,11003,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,160,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,252,1017,GasA,Excellent,Y,SBrkr,1026,981,0,2007,1,0,2,1,3,1,Excellent,10,Typ,1,Excellent,Attchd,Fin,3,812,Typical,Typical,Paved,168,52,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,310000,-93.643001,42.052337 +Two_Story_1946_and_Newer,Floating_Village_Residential,84,10603,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,121,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,218,900,GasA,Excellent,Y,SBrkr,909,886,0,1795,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,782,Typical,Typical,Paved,168,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,270000,-93.64287,42.052346 +Two_Story_1946_and_Newer,Floating_Village_Residential,85,10574,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1082,1082,GasA,Excellent,Y,SBrkr,1082,871,0,1953,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,RFn,3,1043,Typical,Typical,Paved,160,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,252000,-93.643842,42.052127 +Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2007,Gable,CompShg,CemntBd,CmentBd,Stone,100,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,280,1092,GasA,Excellent,Y,SBrkr,1112,438,0,1550,1,0,2,0,2,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,438,Typical,Typical,Paved,0,168,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,241000,-93.642474,42.05142 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2009,Hip,CompShg,VinylSd,VinylSd,BrkFace,288,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1836,1836,GasA,Excellent,Y,SBrkr,1836,0,0,1836,0,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,517,Typical,Typical,Paved,0,175,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,New,Partial,264500,-93.643752,42.051378 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,222,1652,GasA,Excellent,Y,SBrkr,1662,0,0,1662,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,711,Typical,Typical,Paved,168,120,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,291000,-93.640847,42.052122 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,912,912,GasA,Excellent,Y,SBrkr,912,1182,0,2094,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,615,Typical,Typical,Paved,182,182,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,263000,-93.641746,42.051414 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,62,7500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,811,1221,GasA,Excellent,Y,SBrkr,1221,0,0,1221,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,400,Typical,Typical,Paved,0,113,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,185000,-93.643131,42.051247 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,68,8736,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,360,422,1553,GasA,Excellent,Y,SBrkr,1553,0,0,1553,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,192,88,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,234500,-93.641501,42.051251 +Two_Story_1946_and_Newer,Floating_Village_Residential,65,8127,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,402,812,GasA,Excellent,Y,SBrkr,812,841,0,1653,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,628,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,209000,-93.639832,42.050895 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9605,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1218,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,RFn,2,576,Typical,Typical,Paved,0,178,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,159000,-93.69153,42.037557 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,1087,1141,GasA,Excellent,Y,SBrkr,1141,0,0,1141,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,RFn,2,484,Typical,Typical,Paved,182,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,152000,-93.692017,42.037611 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1158,1158,GasA,Excellent,Y,SBrkr,1158,0,0,1158,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,143500,-93.692472,42.037661 +Two_Story_1946_and_Newer,Residential_Low_Density,96,10628,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,835,835,GasA,Excellent,Y,SBrkr,871,941,0,1812,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,478,Typical,Typical,Paved,146,91,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,193000,-93.69143,42.036001 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,10141,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2004,Gable,Tar&Grv,VinylSd,VinylSd,BrkFace,264,Good,Typical,PConc,Good,Typical,Gd,BLQ,2,Rec,774,222,1512,GasA,Excellent,Y,SBrkr,1512,0,0,1512,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,845,Typical,Typical,Paved,210,36,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,203000,-93.691506,42.036011 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,10083,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,343,1176,GasA,Excellent,Y,SBrkr,1200,0,0,1200,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,555,Typical,Typical,Paved,0,41,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,184900,-93.691707,42.035282 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1114,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,248,102,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,159000,-93.691868,42.037923 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1114,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,142000,-93.691068,42.038127 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,12450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,278,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,153000,-93.690159,42.037756 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7328,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2008,2009,Gable,CompShg,VinylSd,VinylSd,BrkFace,140,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1450,1450,GasA,Excellent,Y,SBrkr,1450,0,0,1450,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,788,Typical,Typical,Paved,0,93,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,New,Partial,224243,-93.690402,42.037434 +Two_Story_1946_and_Newer,Residential_Low_Density,43,11492,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,132,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,276,913,GasA,Excellent,Y,SBrkr,913,1209,0,2122,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,559,Typical,Typical,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,220000,-93.689136,42.036852 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,10994,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,366,Good,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,868,1844,GasA,Excellent,Y,SBrkr,1844,0,0,1844,1,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,620,Typical,Typical,Paved,165,44,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,COD,Abnorml,257000,-93.69139,42.03607 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8529,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1434,1454,GasA,Excellent,Y,SBrkr,1434,0,0,1434,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,527,Typical,Typical,Paved,290,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,189000,-93.6888,42.036905 +Two_Story_1946_and_Newer,Residential_Low_Density,70,7703,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Rec,364,400,816,GasA,Excellent,Y,SBrkr,833,897,0,1730,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,0,91,0,0,168,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,171500,-93.687843,42.036576 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,75,10762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,626,626,GasA,Typical,Y,SBrkr,626,591,0,1217,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,288,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,120000,-93.686263,42.034533 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1990,1991,Gable,CompShg,Plywood,ImStucc,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1332,1332,GasA,Good,Y,SBrkr,1332,0,0,1332,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,542,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,145000,-93.686267,42.035518 +Two_Story_1946_and_Newer,Residential_Low_Density,70,9109,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,Two_Story,Good,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,596,122,754,GasA,Excellent,Y,SBrkr,754,786,0,1540,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,495,Typical,Typical,Paved,140,32,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,184000,-93.687804,42.037607 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10274,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1986,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,141,Typical,Good,CBlock,Good,Typical,No,Rec,6,Unf,0,345,676,GasA,Typical,Y,SBrkr,698,702,0,1400,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,465,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,162000,-93.685037,42.035968 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,Two_Story,Above_Average,Good,1981,1981,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,964,918,0,1882,0,0,2,0,4,2,Typical,8,Typ,2,Typical,Attchd,Unf,2,612,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,160000,-93.683957,42.035756 +One_Story_1946_and_Newer_All_Styles,Residential_High_Density,60,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1950,1950,Gable,CompShg,Wd Sdng,AsbShng,None,0,Fair,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,721,721,GasA,Good,Y,SBrkr,841,0,0,841,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,CarPort,Unf,1,294,Typical,Typical,Dirt_Gravel,250,0,24,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,82000,-93.6790752,42.0365385 +One_and_Half_Story_Unfinished_All_Ages,Residential_High_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Fair,1928,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Fair,No,Unf,7,Unf,0,784,784,GasA,Typical,N,FuseA,784,0,0,784,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,360,Fair,Fair,Dirt_Gravel,0,0,91,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,76000,-93.680198,42.035287 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,9750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,884,28,980,GasA,Good,Y,SBrkr,980,0,0,980,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,WD ,Normal,110000,-93.676364,42.035806 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,7064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,153,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,420,980,GasA,Typical,Y,SBrkr,980,0,0,980,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,135000,-93.677008,42.034747 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8499,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,204,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,732,Typical,Typical,Paved,0,312,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,141000,-93.674051,42.034615 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9079,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,0,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,440,Typical,Typical,Paved,158,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,122000,-93.675716,42.035289 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9316,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,480,1024,GasA,Good,Y,SBrkr,1020,0,0,1020,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,171,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,Oth,Family,124100,-93.669554,42.03466 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7791,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Very_Good,1963,1995,Gable,CompShg,Plywood,Plywood,None,0,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,288,912,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,10,2009,WD ,Normal,129000,-93.669538,42.035331 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1983,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,270,864,GasA,Excellent,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,264,Typical,Typical,Paved,165,0,0,0,0,0,No_Pool,Good_Wood,Shed,400,3,2009,WD ,Normal,131400,-93.673518,42.034595 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8281,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,311,0,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,1,Poor,Detchd,Unf,1,360,Typical,Typical,Paved,0,0,236,0,0,0,No_Pool,Good_Wood,None,0,12,2009,WD ,Normal,62383,-93.669707,42.034602 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1962,1962,Gable,CompShg,VinylSd,VinylSd,BrkFace,60,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,590,182,912,GasA,Good,Y,SBrkr,912,0,0,912,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,252,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,123000,-93.669845,42.034581 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15676,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Very_Good,1980,1980,Gable,CompShg,VinylSd,VinylSd,BrkFace,115,Good,Good,CBlock,Good,Good,Gd,ALQ,1,Rec,92,189,2014,GasA,Good,Y,SBrkr,2014,0,0,2014,1,0,2,0,2,1,Good,6,Maj1,2,Good,Attchd,RFn,3,864,Typical,Typical,Paved,462,0,0,255,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,275000,-93.660327,42.037236 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11949,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1991,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Good,PConc,Good,Typical,No,GLQ,3,ALQ,216,158,975,GasA,Excellent,Y,SBrkr,975,780,0,1755,0,1,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,524,Typical,Typical,Paved,502,60,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2009,WD ,Normal,235000,-93.650333,42.047669 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,32,2880,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1376,1376,GasA,Excellent,Y,SBrkr,1376,1629,0,3005,0,0,2,1,3,1,Good,9,Mod,1,Typical,BuiltIn,Fin,3,704,Typical,Typical,Paved,0,177,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,280750,-93.647765,42.047627 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,40,3951,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,76,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,612,612,GasA,Excellent,Y,SBrkr,612,612,0,1224,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,0,234,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,164500,-93.646316,42.048416 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3000,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,76,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,318,612,GasA,Excellent,Y,SBrkr,612,612,0,1224,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,0,234,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,173733,-93.646338,42.048488 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,3830,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,280,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1726,1726,GasA,Excellent,Y,SBrkr,1726,0,0,1726,0,0,2,1,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,561,Typical,Typical,Paved,0,254,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,New,Partial,222000,-93.647039,42.047338 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4217,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,252,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,183,1145,GasA,Excellent,Y,SBrkr,1256,0,0,1256,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,Fin,2,641,Typical,Typical,Paved,0,169,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,195000,-93.646916,42.047253 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3230,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,894,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,348,729,GasA,Good,Y,SBrkr,742,729,0,1471,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,172500,-93.644205,42.047101 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,2998,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,249,756,GasA,Excellent,Y,SBrkr,756,756,0,1512,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,180000,-93.64566,42.046144 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,3768,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,1999,1999,Hip,CompShg,MetalSd,MetalSd,BrkFace,218,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,142,691,GasA,Excellent,Y,SBrkr,713,739,0,1452,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,156000,-93.645482,42.046418 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,321,970,GasA,Excellent,Y,SBrkr,983,756,0,1739,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,480,Typical,Typical,Paved,115,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,172500,-93.641799,42.047171 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14694,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Excellent,1977,2008,Gable,CompShg,MetalSd,MetalSd,BrkFace,450,Excellent,Excellent,CBlock,Good,Good,Gd,GLQ,3,ALQ,136,306,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,1,0,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,642,Typical,Typical,Paved,501,120,0,225,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,318750,-93.645923,42.043782 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,3782,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1981,1981,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,266,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,2,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,133,78,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,211500,-93.649358,42.042652 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,15417,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Good,Average,1981,1981,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Typical,Mn,LwQ,4,Unf,0,1619,1740,GasA,Typical,Y,SBrkr,1740,0,0,1740,0,0,1,1,2,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,540,Typical,Typical,Paved,228,20,218,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,241600,-93.648057,42.043553 +Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Low,AllPub,FR2,Mod,Veenker,Feedr,Norm,OneFam,SLvl,Very_Good,Average,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,200,Good,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,392,392,GasA,Excellent,Y,SBrkr,1487,1012,0,2499,0,0,2,1,4,1,Typical,5,Typ,1,Good,Attchd,Unf,2,527,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Abnorml,180500,-93.645546,42.043019 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9991,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1976,1993,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,165,1281,GasA,Excellent,Y,SBrkr,1620,0,0,1620,1,0,2,0,3,1,Typical,8,Min1,1,Typical,Attchd,Unf,2,490,Typical,Typical,Paved,120,78,0,0,0,0,No_Pool,Good_Wood,None,0,6,2009,WD ,Normal,150000,-93.65921,42.034509 +Two_Story_1946_and_Newer,Residential_Low_Density,78,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,429,727,GasA,Excellent,Y,SBrkr,829,727,0,1556,0,0,1,1,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,441,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,154000,-93.635342,42.047708 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11717,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,One_Story,Above_Average,Above_Average,1970,1970,Hip,CompShg,HdBoard,HdBoard,BrkFace,571,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1442,1442,GasA,Typical,Y,SBrkr,1442,0,0,1442,0,0,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,615,Typical,Typical,Paved,371,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,185000,-93.634634,42.048009 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9156,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Above_Average,Good,1968,1968,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1489,1489,GasA,Good,Y,SBrkr,1489,0,0,1489,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,185750,-93.634803,42.04896 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10382,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Good,Above_Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,Stone,240,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,BLQ,32,216,1107,GasA,Excellent,Y,SBrkr,1107,983,0,2090,1,0,2,1,3,1,Typical,7,Typ,2,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,235,204,228,0,0,0,No_Pool,No_Fence,Shed,350,11,2009,WD ,Normal,200000,-93.631971,42.048761 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12732,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Good,Above_Average,1974,1974,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,GLQ,3,LwQ,42,150,752,GasA,Typical,Y,SBrkr,1285,782,0,2067,0,0,2,1,3,1,Good,7,Typ,2,Typical,Attchd,RFn,2,784,Typical,Typical,Paved,297,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,206000,-93.631881,42.04876 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12936,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Good,No,BLQ,2,Unf,0,130,723,GasA,Typical,Y,SBrkr,735,660,0,1395,0,1,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,497,Typical,Typical,Paved,294,116,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,162000,-93.631519,42.047652 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Above_Average,Good,1967,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,256,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,932,932,GasA,Good,Y,SBrkr,1271,1369,0,2640,0,0,2,1,5,1,Good,8,Typ,1,Typical,Attchd,RFn,2,515,Typical,Typical,Paved,0,120,0,0,168,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,256900,-93.633163,42.046476 +Split_or_Multilevel,Residential_Low_Density,0,17871,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1967,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,359,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,1152,1680,GasA,Fair,Y,SBrkr,1724,0,0,1724,1,0,1,1,3,1,Typical,7,Typ,1,Good,Attchd,RFn,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,197900,-93.631851,42.046476 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,128,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Rec,147,588,1288,GasA,Typical,Y,SBrkr,1336,0,0,1336,0,1,2,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,502,Typical,Typical,Paved,312,11,0,0,0,0,No_Pool,No_Fence,Shed,650,8,2009,WD ,Normal,163000,-93.632537,42.045806 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,261,1216,GasA,Typical,Y,SBrkr,1216,0,0,1216,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Abnorml,113000,-93.632823,42.045799 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13774,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1977,1992,Hip,CompShg,HdBoard,HdBoard,BrkFace,283,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,476,908,GasA,Excellent,Y,SBrkr,1316,972,0,2288,0,0,1,2,4,1,Good,8,Typ,2,Typical,Attchd,RFn,2,520,Typical,Typical,Paved,321,72,0,0,156,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,230000,-93.637964,42.043236 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,360,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,664,1350,GasA,Typical,Y,SBrkr,1334,0,0,1334,0,1,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,630,Typical,Typical,Paved,0,16,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,167900,-93.637905,42.043842 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Good,Average,1998,1999,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,LwQ,4,GLQ,1127,379,1650,GasA,Excellent,Y,SBrkr,1680,0,0,1680,1,0,2,0,3,1,Good,7,Maj1,1,Typical,Attchd,Fin,2,583,Typical,Typical,Paved,78,73,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,213250,-93.632647,42.043673 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Very_Good,Above_Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,167,810,GasA,Excellent,Y,SBrkr,810,855,0,1665,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,528,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,227000,-93.631899,42.044693 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,7130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,216,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,130000,-93.628903,42.048796 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1568,1568,GasA,Typical,Y,SBrkr,1568,0,0,1568,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,160,40,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,COD,Normal,143000,-93.627047,42.0467129 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,466,121,925,GasA,Excellent,Y,SBrkr,925,0,0,925,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,429,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,117500,-93.624612,42.046635 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Average,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,252,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,467,1165,GasA,Good,Y,SBrkr,1165,896,0,2061,0,1,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,498,Typical,Typical,Paved,0,77,0,0,196,0,No_Pool,No_Fence,None,0,5,2009,COD,Abnorml,168500,-93.630714,42.044821 +Split_Foyer,Residential_Low_Density,0,16500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Above_Average,Average,1971,1971,Hip,CompShg,HdBoard,HdBoard,BrkFace,509,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,270,1232,GasA,Fair,Y,SBrkr,1320,0,0,1320,0,1,2,0,3,1,Typical,5,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,172500,-93.627416,42.043935 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1967,1967,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,630,491,1372,GasA,Typical,Y,SBrkr,1342,0,0,1342,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,457,Typical,Typical,Paved,0,0,0,0,197,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,161500,-93.625775,42.043145 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7436,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1960,1960,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,160,894,GasA,Good,Y,SBrkr,894,0,0,894,1,0,1,0,2,1,Typical,5,Typ,1,Poor,Detchd,Unf,2,396,Typical,Typical,Paved,0,0,0,360,0,0,No_Pool,Good_Wood,None,0,8,2009,WD ,Normal,141500,-93.622991,42.043135 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1959,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,461,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,118000,-93.622465,42.042241 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,252,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,300,Excellent,Excellent,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,450,10,2009,WD ,Normal,127500,-93.623485,42.044843 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,160,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,265,1040,GasA,Typical,Y,SBrkr,1362,0,0,1362,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,3,768,Typical,Typical,Paved,0,0,84,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,140000,-93.622635,42.044774 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13495,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,70,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,LwQ,201,222,1048,GasA,Fair,Y,SBrkr,1728,0,0,1728,1,0,2,0,3,1,Typical,7,Min1,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,0,99,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,177625,-93.622476,42.044926 +Duplex_All_Styles_and_Ages,Residential_Low_Density,85,11475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1958,1958,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1584,1584,GasA,Typical,Y,SBrkr,1776,0,0,1776,1,0,2,0,4,2,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,3,888,Typical,Typical,Paved,0,25,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,COD,Abnorml,110000,-93.623411,42.04488 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,10004,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,Plywood,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,345,975,1516,GasA,Typical,Y,SBrkr,1516,0,0,1516,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,472,Typical,Typical,Paved,0,0,0,0,152,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,167000,-93.629415,42.040645 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1995,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,217,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,512,491,1313,GasA,Typical,Y,SBrkr,1313,0,0,1313,1,0,1,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,610,Typical,Typical,Paved,172,28,0,0,121,0,No_Pool,Minimum_Privacy,None,0,2,2009,WD ,Normal,153000,-93.628658,42.038492 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,115,10500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,Stone,144,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,Unf,0,294,1292,GasA,Typical,Y,SBrkr,1292,0,0,1292,1,0,1,1,3,1,Typical,6,Typ,2,Good,Detchd,Unf,2,520,Typical,Typical,Paved,0,32,0,0,92,0,No_Pool,No_Fence,None,0,7,2009,COD,Abnorml,145100,-93.6273127,42.0407264 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,1966,Gable,CompShg,Plywood,Plywood,BrkFace,200,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,230,184,1154,GasA,Excellent,Y,SBrkr,1154,0,0,1154,0,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,2,480,Typical,Typical,Paved,0,58,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,WD ,Normal,154000,-93.627836,42.040011 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8970,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,356,744,GasA,Typical,Y,SBrkr,825,1315,0,2140,0,0,2,1,4,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,549,Typical,Typical,Paved,0,40,264,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,177500,-93.627985,42.039902 +Split_or_Multilevel,Residential_Low_Density,0,12095,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1964,1964,Gable,CompShg,MetalSd,HdBoard,BrkFace,115,Typical,Good,CBlock,Typical,Typical,Gd,Rec,6,Unf,0,563,1127,GasA,Typical,Y,SBrkr,1445,0,0,1445,0,0,1,1,3,1,Typical,7,Typ,1,Fair,Attchd,RFn,2,645,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,158000,-93.626979,42.040888 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,MetalSd,MetalSd,BrkFace,132,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,50,1041,GasA,Excellent,Y,SBrkr,1041,0,0,1041,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,270,Typical,Typical,Paved,224,88,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,124500,-93.624581,42.041104 +Split_or_Multilevel,Residential_Low_Density,85,11475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1961,1961,Hip,CompShg,HdBoard,HdBoard,BrkFace,90,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,640,1208,GasA,Excellent,Y,SBrkr,1576,0,0,1576,1,0,1,0,4,1,Good,7,Typ,1,Poor,BuiltIn,Fin,2,368,Typical,Typical,Paved,85,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,174500,-93.622685,42.039463 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9768,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,247,613,960,GasA,Good,Y,SBrkr,960,0,0,960,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,330,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2009,WD ,Normal,122000,-93.621699,42.038596 +One_Story_1945_and_Older,Residential_Low_Density,50,5330,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Below_Average,Good,1940,1950,Hip,CompShg,VinylSd,VinylSd,None,0,Fair,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,140,420,GasA,Good,Y,SBrkr,708,0,0,708,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,164,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,82500,-93.629499,42.035489 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,7015,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,BrkCmn,161,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,524,709,GasA,Typical,Y,SBrkr,979,224,0,1203,1,0,1,0,3,1,Good,5,Typ,1,Typical,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,248,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,110000,-93.62919,42.035443 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,128,12160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Below_Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,90,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,481,1505,GasA,Excellent,Y,SBrkr,1505,0,0,1505,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,505,Typical,Typical,Paved,0,0,0,162,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,149500,-93.625633,42.03464 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1967,1967,Gable,CompShg,BrkComm,Brk Cmn,None,0,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,507,1680,GasA,Typical,Y,SBrkr,1691,0,0,1691,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,550,Good,Typical,Paved,0,67,260,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,175000,-93.62568,42.036182 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1953,1953,Hip,CompShg,HdBoard,HdBoard,Stone,238,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,131,1416,GasA,Typical,Y,SBrkr,1644,0,0,1644,1,0,1,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,418,Typical,Typical,Paved,110,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,167000,-93.623705,42.034741 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,546,1050,GasA,Good,Y,SBrkr,1050,0,0,1050,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,338,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,128900,-93.624612,42.037452 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1950,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,736,856,GasA,Excellent,Y,SBrkr,1112,556,0,1668,0,0,1,1,3,1,Typical,6,Min2,0,No_Fireplace,Attchd,Unf,1,271,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2009,WD ,Normal,140000,-93.622644,42.035951 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1950,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,506,1113,GasA,Good,Y,SBrkr,1113,0,0,1113,0,0,1,0,3,1,Good,5,Typ,1,Good,Attchd,Unf,1,264,Typical,Typical,Paved,0,80,120,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,147000,-93.622494,42.036178 +Duplex_All_Styles_and_Ages,Residential_Low_Density,65,8944,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1584,1584,GasA,Typical,Y,SBrkr,1584,0,0,1584,0,0,2,0,4,2,Typical,8,Mod,0,No_Fireplace,Detchd,Unf,3,792,Typical,Typical,Paved,0,152,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,124000,-93.619673,42.049306 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,92,10573,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1961,1961,Hip,CompShg,MetalSd,MetalSd,BrkFace,3,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,141,1453,GasA,Excellent,Y,SBrkr,1453,0,0,1453,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,530,Typical,Typical,Paved,0,49,0,0,288,0,No_Pool,Good_Privacy,None,0,4,2009,WD ,Normal,187500,-93.617963,42.048294 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1963,1963,Gable,CompShg,Plywood,Plywood,BrkFace,247,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,785,1394,GasA,Good,Y,SBrkr,1394,0,0,1394,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,514,Typical,Typical,Paved,0,76,0,0,185,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,159000,-93.615686,42.046827 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,14695,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1966,2008,Gable,CompShg,MetalSd,MetalSd,BrkFace,210,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,175,1562,GasA,Good,Y,SBrkr,1567,0,0,1567,1,0,2,0,2,1,Good,5,Typ,2,Good,Attchd,Unf,2,542,Typical,Typical,Paved,0,110,0,0,342,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,256000,-93.615602,42.048608 +Two_Story_1946_and_Newer,Residential_Low_Density,85,13600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1965,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,314,768,GasA,Typical,Y,SBrkr,1186,800,0,1986,0,0,2,1,3,1,Typical,7,Typ,3,Fair,Attchd,Unf,2,486,Typical,Typical,Paved,0,42,0,0,189,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,205000,-93.615558,42.048724 +Two_Story_1946_and_Newer,Residential_Low_Density,100,13000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,VinylSd,VinylSd,BrkFace,576,Typical,Good,CBlock,Good,Typical,No,Rec,6,Unf,0,448,896,GasA,Typical,Y,SBrkr,1182,960,0,2142,0,0,2,1,4,1,Good,8,Typ,1,Good,Attchd,Fin,1,509,Typical,Typical,Paved,0,72,0,0,252,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,193500,-93.614619,42.04806 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,13560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Fair,1968,1968,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,216,Typical,Typical,CBlock,Fair,Fair,No,Unf,7,Unf,0,1392,1392,GasA,Good,Y,SBrkr,1392,0,0,1392,1,0,1,0,2,1,Typical,5,Maj2,2,Typical,Attchd,RFn,2,576,Typical,Typical,Paved,0,0,240,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,110000,-93.614216,42.04579 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,12513,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1920,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Fair,No,Unf,7,Unf,0,715,715,GasA,Good,Y,SBrkr,1281,457,0,1738,0,0,2,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,1,368,Typical,Typical,Paved,55,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,104900,-93.619029,42.044886 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,164,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,313,1169,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,257,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,150000,-93.618784,42.043966 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Fair,1957,1957,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,661,628,1478,GasA,Good,Y,SBrkr,1478,0,0,1478,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,442,Typical,Typical,Paved,114,0,0,0,216,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,156500,-93.61864,42.044125 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12285,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1960,1960,Gable,CompShg,Plywood,Plywood,BrkFace,128,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,785,1329,GasA,Good,Y,SBrkr,1329,0,0,1329,0,0,1,1,3,1,Typical,5,Typ,2,Good,Attchd,Unf,2,441,Typical,Typical,Paved,0,0,203,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,176000,-93.61488,42.043991 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,9240,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1959,1998,Hip,CompShg,MetalSd,MetalSd,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,280,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,297,Typical,Typical,Paved,156,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,149500,-93.616434,42.044083 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1988,Hip,CompShg,Wd Sdng,Wd Sdng,BrkCmn,183,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,620,0,1240,GasA,Good,Y,SBrkr,1632,0,0,1632,1,0,2,0,3,1,Typical,6,Min1,1,Good,Attchd,RFn,1,338,Typical,Typical,Paved,289,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,139000,-93.614751,42.045473 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,12400,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Good,1958,1998,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,176,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,630,1215,GasA,Typical,Y,FuseA,1215,0,0,1215,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,297,Typical,Typical,Paved,0,0,0,0,234,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,155000,-93.614059,42.044914 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,202,565,1202,GasA,Typical,Y,SBrkr,1202,0,0,1202,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,304,Typical,Typical,Paved,0,35,120,0,0,0,No_Pool,Good_Wood,None,0,11,2009,COD,Abnorml,120000,-93.614768,42.04297 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,483,727,1382,GasA,Good,Y,FuseA,1382,0,0,1382,0,1,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,350,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,153000,-93.61588,42.042233 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1956,1956,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,750,295,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,294,Typical,Typical,Paved,0,189,140,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Family,144000,-93.614543,42.043238 +Split_or_Multilevel,Residential_Low_Density,120,19296,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,SLvl,Above_Average,Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,399,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,ALQ,690,0,1362,GasA,Typical,Y,SBrkr,1382,0,0,1382,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,884,Typical,Typical,Paved,0,0,252,0,0,0,No_Pool,Good_Wood,None,0,5,2009,WD ,Normal,176000,-93.620016,42.040931 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8532,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1990,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,650,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,84,1297,GasA,Good,Y,SBrkr,1297,0,0,1297,0,1,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,498,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,153000,-93.618478,42.040857 +Duplex_All_Styles_and_Ages,Residential_Low_Density,76,9482,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Below_Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,657,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1866,1866,GasA,Excellent,Y,SBrkr,1866,0,0,1866,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,495,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,135000,-93.6165473,42.0404571 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,8128,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1954,1954,Hip,CompShg,MetalSd,MetalSd,BrkFace,80,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,572,1062,GasA,Good,Y,SBrkr,1062,0,0,1062,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,297,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,131000,-93.618549,42.039674 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10634,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,LwQ,180,0,608,GasA,Typical,Y,SBrkr,1319,0,0,1319,1,0,1,0,3,1,Typical,5,Min2,0,No_Fireplace,Attchd,Unf,1,270,Typical,Typical,Paved,66,0,0,0,0,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Normal,123000,-93.6190376,42.0385265 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,13070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1951,1951,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,323,631,GasA,Typical,Y,FuseA,1112,0,0,1112,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,2,480,Typical,Typical,Paved,0,0,0,0,255,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,126000,-93.617219,42.038356 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10434,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1005,1005,GasA,Typical,Y,SBrkr,1005,0,0,1005,0,0,1,0,2,1,Fair,5,Typ,1,Typical,Detchd,Unf,2,672,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,115000,-93.6124,42.038398 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,14559,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,2000,Hip,CompShg,Wd Sdng,Wd Sdng,BrkCmn,70,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,180,178,1008,GasA,Excellent,Y,SBrkr,1363,0,0,1363,1,0,1,0,2,1,Typical,6,Min1,2,Typical,CarPort,Unf,1,288,Typical,Typical,Paved,324,42,0,0,168,0,No_Pool,No_Fence,Shed,2000,6,2009,WD ,Normal,164900,-93.617207,42.037122 +One_Story_1945_and_Older,Residential_Low_Density,80,8480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,93,793,GasA,Typical,Y,SBrkr,793,0,0,793,1,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,113000,-93.6167912,42.0372232 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7626,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1952,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,100,1031,GasA,Good,Y,SBrkr,1031,0,0,1031,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,1,230,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,145500,-93.618605,42.034918 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,140,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1210,0,0,1210,0,0,1,1,2,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,616,Typical,Typical,Paved,208,0,100,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,ConLD,Normal,102900,-93.618607,42.034686 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,107,10615,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Mod,Old_Town,Artery,Artery,TwoFmCon,Two_Story,Fair,Average,1900,1970,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,BLQ,2,Unf,0,538,978,GasA,Typical,Y,SBrkr,1014,685,0,1699,1,0,2,0,3,2,Typical,7,Typ,0,No_Fireplace,CarPort,Unf,2,420,Fair,Fair,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,95000,-93.62008,42.034754 +Two_Story_1946_and_Newer,Residential_Low_Density,78,11419,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Good,Good,1948,1999,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,0,699,GasA,Excellent,Y,FuseA,801,726,0,1527,1,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,Unf,2,410,Typical,Typical,Paved,0,0,134,0,0,0,No_Pool,Good_Wood,None,0,12,2009,WD ,Normal,152500,-93.620336,42.036223 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,810,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,246,Typical,Typical,Paved,126,0,0,0,0,0,No_Pool,Good_Wood,None,0,8,2009,WD ,Normal,129900,-93.61719,42.035829 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1948,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,292,972,GasA,Excellent,Y,SBrkr,972,0,0,972,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,132000,-93.617182,42.034746 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,5470,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,792,792,GasA,Good,Y,FuseA,792,0,0,792,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,366,Fair,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,99900,-93.6146206,42.0380659 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10410,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1916,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,660,660,GasA,Excellent,Y,SBrkr,808,704,144,1656,0,0,2,1,3,1,Typical,8,Min2,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,0,0,0,140,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,135000,-93.6144209,42.037679 +Two_Story_1946_and_Newer,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1939,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,676,676,GasA,Excellent,Y,SBrkr,676,676,0,1352,0,1,2,0,4,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,187,0,128,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,149000,-93.61239,42.037144 +Two_Story_1945_and_Older,Residential_Low_Density,80,8146,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Very_Good,1900,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,BrkTil,Fair,Typical,No,Unf,7,Unf,0,405,405,GasA,Good,Y,SBrkr,717,322,0,1039,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,281,Typical,Typical,Dirt_Gravel,0,0,168,0,111,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,114000,-93.612241,42.037328 +One_Story_1945_and_Older,Residential_Low_Density,52,9022,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Very_Good,1924,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,792,0,0,792,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Fair,Dirt_Gravel,316,0,120,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,109500,-93.614049,42.036015 +One_Story_1945_and_Older,Residential_Low_Density,60,10230,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1054,1054,GasA,Excellent,Y,SBrkr,1078,0,0,1078,0,0,1,0,3,1,Excellent,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Dirt_Gravel,0,0,0,0,112,0,No_Pool,Good_Wood,None,0,12,2009,WD ,Normal,125000,-93.615465,42.036002 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10410,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1313,1313,GasA,Typical,Y,SBrkr,1313,0,1064,2377,0,0,2,0,3,1,Good,8,Min2,1,Typical,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,432,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,142900,-93.614049,42.034668 +Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Good,Excellent,1910,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,560,560,GasA,Excellent,Y,SBrkr,930,760,0,1690,0,0,2,0,4,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,156500,-93.613899,42.034574 +One_Story_1945_and_Older,Residential_Low_Density,90,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Poor,Average,1940,1950,Gable,CompShg,Stucco,Stucco,None,0,Fair,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,416,416,GasA,Good,N,FuseA,599,0,0,599,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,81,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,59000,-93.6117397,42.0347572 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,90,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1920,1950,Gable,CompShg,CBlock,CBlock,None,0,Fair,Typical,PConc,Typical,Typical,No,ALQ,1,Rec,105,420,840,GasA,Excellent,Y,SBrkr,840,534,0,1374,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,1,338,Typical,Typical,Paved,0,0,198,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,105000,-93.61203,42.0347398 +One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,720,720,GasA,Typical,N,SBrkr,846,0,0,846,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Fin,2,576,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,106000,-93.612253,42.035257 +One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Average,1890,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Typical,Y,FuseA,725,0,0,725,0,0,1,1,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,320,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,78500,-93.612254,42.03528 +Duplex_All_Styles_and_Ages,Residential_Low_Density,81,9671,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,Duplex,Two_Story,Above_Average,Average,1969,1969,Gable,CompShg,MetalSd,MetalSd,Stone,480,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1248,1248,GasA,Typical,Y,SBrkr,1248,1296,0,2544,0,0,2,2,6,2,Typical,12,Typ,0,No_Fireplace,Attchd,RFn,3,907,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,190000,-93.610355,42.0417879 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,10143,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,295,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,996,1380,GasA,Fair,Y,SBrkr,1380,0,0,1380,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,364,Typical,Typical,Paved,0,0,0,0,216,0,No_Pool,Good_Wood,None,0,6,2009,WD ,Normal,154000,-93.607655,42.04015 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Below_Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Fair,Typical,CBlock,Typical,Fair,No,BLQ,2,Rec,60,108,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,6,Min1,1,Poor,Attchd,RFn,2,480,Typical,Typical,Paved,0,0,156,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,163000,-93.610588,42.040295 +Duplex_All_Styles_and_Ages,Residential_Low_Density,91,11643,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,Duplex,Two_Story,Average,Average,1969,1969,Gable,CompShg,MetalSd,MetalSd,BrkFace,368,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,748,1248,GasA,Typical,Y,SBrkr,1338,1296,0,2634,1,1,2,2,6,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,4,968,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,200000,-93.610649,42.04124 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,8010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Excellent,1958,2007,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,206,951,GasA,Good,Y,SBrkr,951,0,0,951,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,42,0,0,0,No_Pool,No_Fence,Shed,450,9,2009,WD ,Normal,143500,-93.608196,42.038509 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,10454,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1957,Hip,CompShg,Plywood,Plywood,Stone,143,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,559,1105,GasA,Good,Y,FuseA,1105,0,0,1105,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,135000,-93.608927,42.038349 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,8712,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1957,2000,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,132,992,GasA,Typical,Y,SBrkr,1306,0,0,1306,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,756,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,153000,-93.609226,42.039047 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17600,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,30,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,208,1478,GasA,Excellent,Y,FuseA,1478,0,0,1478,1,0,2,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,2,498,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,157500,-93.606573,42.040766 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9000,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Flat,Tar&Grv,Wd Sdng,Wd Sdng,BrkFace,82,Typical,Typical,CBlock,Good,Typical,Gd,Unf,7,Unf,0,160,160,GasA,Fair,Y,SBrkr,1142,0,0,1142,0,0,1,0,2,1,Typical,5,Typ,1,Good,Basment,RFn,1,384,Typical,Typical,Paved,0,28,64,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,113500,-93.60552,42.038811 +One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,60,12144,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1949,1950,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,457,832,GasA,Good,Y,SBrkr,1036,0,232,1268,0,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,288,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,Othr,0,9,2009,WD ,Normal,133000,-93.609059,42.037129 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1950,1950,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseA,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,92900,-93.608977,42.035712 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,174,795,GasA,Good,N,SBrkr,765,368,0,1133,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,900,Typical,Typical,Paved,0,0,0,0,231,0,No_Pool,No_Fence,None,0,12,2009,COD,Abnorml,128500,-93.61061,42.035787 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Below_Average,Average,1949,1950,Gable,CompShg,BrkFace,Stone,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,420,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,90000,-93.608903,42.035841 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1950,1982,Gable,CompShg,VinylSd,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,352,676,1208,GasA,Good,Y,FuseA,1136,768,0,1904,1,0,1,1,3,1,Typical,7,Min1,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,168,0,0,0,No_Pool,Good_Privacy,None,0,5,2009,WD ,Normal,138000,-93.60891,42.037233 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,7350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1958,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1041,1041,GasA,Good,Y,SBrkr,1041,0,0,1041,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2009,WD ,Normal,128000,-93.606891,42.03602 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1965,Hip,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,525,1029,GasA,Typical,Y,SBrkr,1339,0,0,1339,0,0,1,0,3,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,1,294,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,COD,Abnorml,139000,-93.606889,42.035968 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1952,1952,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Good,No,ALQ,1,BLQ,102,0,732,GasA,Typical,Y,SBrkr,732,0,0,732,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,118900,-93.607814,42.03572 +Split_or_Multilevel,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1959,1959,Gable,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Rec,95,0,528,GasA,Typical,Y,SBrkr,1183,0,0,1183,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,138000,-93.605968,42.035706 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Hip,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,0,1148,GasA,Typical,Y,SBrkr,1148,0,0,1148,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,672,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2009,WD ,Normal,132500,-93.606786,42.037214 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1949,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,FuseF,832,629,0,1461,0,0,2,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,204,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,133500,-93.609945,42.0347542 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1948,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,744,864,GasA,Typical,Y,SBrkr,1064,0,431,1495,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,180,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,COD,Abnorml,135000,-93.610605,42.034858 +Two_Story_1946_and_Newer,Residential_Low_Density,76,7570,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,420,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Typical,Y,SBrkr,993,813,0,1806,0,0,1,1,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,483,Typical,Typical,Paved,0,55,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,144750,-93.604987,42.037049 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8604,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,SFoyer,Average,Good,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,124,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,941,GasA,Good,Y,SBrkr,941,0,0,941,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,2,564,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,145000,-93.604047,42.038948 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,7936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,219,1045,GasA,Typical,Y,SBrkr,1045,0,0,1045,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,127000,-93.603879,42.038257 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,894,894,GasA,Good,N,SBrkr,894,0,0,894,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,109500,-93.603701,42.035287 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,3950,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1926,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,350,818,GasA,Typical,Y,SBrkr,818,406,0,1224,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,210,Typical,Typical,Dirt_Gravel,0,0,116,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,115000,-93.6179738,42.0342224 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10440,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1910,1981,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,440,440,GasA,Good,Y,SBrkr,682,548,0,1230,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,74,0,128,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,110000,-93.615583,42.033273 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,68,4080,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1935,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,901,901,GasA,Good,Y,SBrkr,861,517,0,1378,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,162,Fair,Fair,Partial_Pavement,54,0,40,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,128900,-93.619013,42.0320748 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,57,10307,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,Two_Story,Above_Average,Average,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,LwQ,4,Unf,0,339,972,GasA,Good,N,FuseA,972,972,0,1944,1,0,2,0,4,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,2,324,Fair,Typical,Dirt_Gravel,0,28,169,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,103500,-93.6195025,42.0323259 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,5720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,676,676,GasA,Excellent,Y,SBrkr,676,455,0,1131,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Typical,Paved,26,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,66500,-93.62026,42.032396 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,90,15660,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1910,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,240,240,GasA,Typical,Y,SBrkr,810,496,0,1306,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,472,Fair,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,130000,-93.615598,42.032202 +Two_Story_1945_and_Older,Residential_Medium_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1910,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,504,504,GasA,Excellent,Y,SBrkr,764,700,0,1464,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,176,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,129000,-93.615599,42.032108 +Two_Story_1945_and_Older,Residential_Medium_Density,57,6406,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,269,690,GasA,Typical,Y,FuseA,868,690,0,1558,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,36,0,0,182,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,150000,-93.6195361,42.0319053 +Two_Story_1945_and_Older,Residential_Medium_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Below_Average,1892,1965,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Stone,Typical,Typical,No,Unf,7,Unf,0,994,994,GasA,Typical,N,SBrkr,1378,994,0,2372,0,0,2,0,4,2,Typical,11,Min2,0,No_Fireplace,Attchd,RFn,1,432,Typical,Typical,Paved,0,287,0,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,107500,-93.618486,42.031305 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,63,7627,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,TwoFmCon,Two_Story,Below_Average,Above_Average,1920,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fair,Typical,BrkTil,Fair,Poor,No,Unf,7,Unf,0,600,600,GasA,Good,N,SBrkr,1101,600,0,1701,0,0,2,0,4,2,Fair,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,148,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,94550,-93.6200683,42.0304742 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,60,7596,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,Duplex,Two_Story,Average,Average,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,360,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Good,Y,SBrkr,960,1000,0,1960,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,COD,Normal,124500,-93.620216,42.030327 +Two_Story_1946_and_Newer,Residential_Medium_Density,60,3378,Pave,Gravel,Regular,HLS,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1946,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,651,651,GasA,Good,Y,SBrkr,707,682,0,1389,0,0,1,1,3,1,Typical,6,Typ,2,Good,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,0,126,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,135000,-93.6176367,42.0303538 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,56,10134,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1910,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,801,801,GasA,Good,N,SBrkr,801,646,0,1447,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,80,0,244,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,103000,-93.615381,42.030282 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,60,6000,Pave,Paved,Regular,Bnk,AllPub,Inside,Mod,Old_Town,Norm,Norm,TwoFmCon,One_Story,Below_Average,Below_Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Fair,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,936,936,GasA,Typical,N,SBrkr,936,0,0,936,0,0,1,0,2,1,Typical,4,Min2,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,32,112,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,93000,-93.6129105,42.0310603 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1950,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Excellent,CBlock,Typical,Typical,No,BLQ,2,Unf,0,384,768,GasA,Typical,Y,FuseA,768,560,0,1328,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,12,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,129500,-93.608866,42.033381 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Below_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,811,811,GasA,Typical,Y,FuseA,811,576,0,1387,0,0,2,0,3,2,Good,7,Typ,0,No_Fireplace,BuiltIn,Unf,1,256,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,93000,-93.607776,42.033579 +One_Story_1945_and_Older,Residential_Medium_Density,62,7404,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,861,861,GasA,Typical,Y,SBrkr,861,0,0,861,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,288,Typical,Typical,Dirt_Gravel,0,0,128,0,0,0,No_Pool,No_Fence,None,0,11,2009,Oth,Normal,80000,-93.608829,42.032048 +One_Story_1945_and_Older,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Poor,Below_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,FuseA,612,0,0,612,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Fair,Dirt_Gravel,0,0,25,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,45000,-93.606842,42.032301 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Above_Average,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,600,600,Grav,Fair,N,SBrkr,600,368,0,968,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2009,WD ,Abnorml,37900,-93.606841,42.032227 +One_Story_1945_and_Older,Residential_Medium_Density,50,5784,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Very_Good,1938,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,190,190,GasA,Good,Y,FuseA,886,0,0,886,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,273,Typical,Typical,Paved,144,20,80,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,91300,-93.610469,42.030523 +One_Story_1945_and_Older,Residential_Medium_Density,60,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Very_Good,1923,1950,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,624,624,GasA,Typical,Y,SBrkr,792,0,0,792,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,287,Typical,Typical,Paved,0,0,81,0,0,0,No_Pool,Good_Wood,None,0,2,2009,WD ,Normal,99500,-93.606788,42.030196 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,33,4456,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Average,1920,2008,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,736,736,GasA,Good,Y,SBrkr,736,716,0,1452,0,0,2,0,2,3,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,102,0,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,113000,-93.618648,42.0296722 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Below_Average,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,BrkTil,Fair,Typical,No,Unf,7,Unf,0,677,677,GasA,Typical,Y,SBrkr,833,677,0,1510,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Dirt_Gravel,0,0,160,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,87500,-93.617054,42.029007 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,3500,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1947,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,408,720,GasA,Typical,Y,SBrkr,720,564,0,1284,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2009,WD ,Normal,110000,-93.6178679,42.0290352 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,90,8100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Average,Average,1898,1965,Hip,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,849,849,GasA,Typical,N,FuseA,1075,1063,0,2138,0,0,2,0,2,3,Typical,11,Typ,0,No_Fireplace,Detchd,Unf,2,360,Fair,Poor,Dirt_Gravel,40,156,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,WD ,Normal,106000,-93.613887,42.028957 +Two_Story_1945_and_Older,Residential_Medium_Density,65,11700,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1880,2003,Mansard,CompShg,Stucco,Stucco,None,0,Good,Typical,Stone,Typical,Fair,No,Unf,7,Unf,0,1240,1240,GasW,Typical,N,SBrkr,1320,1320,0,2640,0,0,1,1,4,1,Good,8,Typ,1,Good,Detchd,Unf,4,864,Typical,Typical,Dirt_Gravel,181,0,386,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,265979,-93.612245,42.029305 +Two_Story_1945_and_Older,Residential_Medium_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Above_Average,Good,1900,1950,Gable,CompShg,Stucco,BrkFace,None,0,Typical,Typical,BrkTil,Fair,Good,Mn,Unf,7,Unf,0,917,917,GasA,Good,Y,FuseA,1090,917,0,2007,0,0,2,0,3,1,Excellent,8,Typ,0,No_Fireplace,Detchd,Unf,1,357,Typical,Typical,Paved,0,235,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,160000,-93.610588,42.029001 +Two_Story_1945_and_Older,Residential_Medium_Density,60,3600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1917,2007,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Excellent,Y,SBrkr,664,624,0,1288,1,0,1,0,3,1,Excellent,5,Typ,1,Good,Attchd,Unf,1,280,Typical,Typical,Dirt_Gravel,0,103,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,119000,-93.6130761,42.0281106 +Two_Story_1945_and_Older,Residential_Medium_Density,121,17671,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Very_Good,Excellent,1882,1986,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,700,916,GasA,Good,Y,SBrkr,916,826,0,1742,0,0,1,1,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,424,Typical,Typical,Partial_Pavement,0,169,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,168000,-93.610582,42.028104 +One_Story_1945_and_Older,C_all,0,3300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1910,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Excellent,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,33,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,58500,-93.6148612,42.0276972 +Two_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Good,Excellent,1920,1988,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,ALQ,1,Unf,0,26,650,GasA,Excellent,Y,SBrkr,832,650,0,1482,0,1,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,324,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,143000,-93.610044,42.028556 +One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Below_Average,1910,2006,Hip,CompShg,MetalSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Rec,465,310,1022,GasW,Typical,N,SBrkr,1022,0,0,1022,1,0,1,0,2,1,Typical,4,Maj2,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,30,226,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,85000,-93.6076506,42.0282044 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,98,8820,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1890,1996,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,0,1088,GasA,Typical,Y,SBrkr,1188,561,120,1869,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,456,Typical,Typical,Paved,48,0,244,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,9,2009,WD ,Normal,124900,-93.608704,42.027062 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Fair,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,972,972,GasA,Excellent,Y,SBrkr,1044,0,436,1480,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,207,Fair,Typical,Paved,0,0,176,0,0,0,No_Pool,No_Fence,None,0,9,2009,ConLI,Family,119000,-93.608707,42.0272 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9720,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1910,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,741,741,GasA,Excellent,Y,SBrkr,780,741,0,1521,0,0,1,0,4,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,3,640,Typical,Typical,Paved,0,0,238,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2009,WD ,Normal,146500,-93.607058,42.027227 +One_Story_1945_and_Older,C_all,60,7879,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,225,720,GasA,Typical,N,FuseA,720,0,0,720,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,523,115,0,0,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Abnorml,34900,-93.605207,42.023218 +One_Story_1945_and_Older,C_all,72,9392,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Fair,Fair,1900,1950,Mansard,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,CBlock,Fair,Typical,No,Unf,7,Unf,0,245,245,GasA,Typical,N,SBrkr,797,0,0,797,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,36,94,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Abnorml,44000,-93.605421,42.023222 +Two_Story_1945_and_Older,Residential_Low_Density,144,21384,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1923,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,15,1324,GasA,Excellent,Y,SBrkr,1072,504,0,1576,2,0,1,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,312,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,223500,-93.6289369,42.033996 +One_Story_1945_and_Older,Residential_Low_Density,0,6615,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1923,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1022,1022,GasA,Typical,N,FuseA,1432,0,0,1432,0,0,1,0,3,1,Good,6,Typ,1,Good,BuiltIn,Unf,1,216,Fair,Typical,Paved,266,61,0,0,0,0,No_Pool,Good_Wood,None,0,9,2009,WD ,Normal,149000,-93.628848,42.033558 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,55,7264,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1925,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,952,952,GasW,Good,N,SBrkr,952,596,0,1548,0,0,2,1,3,1,Excellent,5,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,74,0,0,0,144,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,205000,-93.6279815,42.0333765 +Two_Story_1945_and_Older,Residential_Low_Density,50,4960,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1982,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,297,297,GasA,Excellent,Y,SBrkr,1001,653,0,1654,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,336,Typical,Typical,Dirt_Gravel,244,60,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,137000,-93.625968,42.031564 +One_Story_1945_and_Older,Residential_Low_Density,0,8854,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Above_Average,1916,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,952,952,Grav,Fair,N,FuseF,952,0,0,952,0,0,1,0,2,1,Fair,4,Typ,1,Good,Detchd,Unf,1,192,Fair,Poor,Partial_Pavement,0,98,0,0,40,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,121000,-93.626116,42.031542 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,0,672,GasA,Typical,Y,SBrkr,757,567,0,1324,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,128000,-93.624564,42.033406 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Excellent,1937,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Typical,Y,SBrkr,786,390,0,1176,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Dirt_Gravel,210,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,134900,-93.624567,42.033616 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1925,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,884,464,0,1348,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,0,0,208,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,117000,-93.622513,42.03329 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Excellent,Y,FuseF,825,587,0,1412,0,0,1,0,4,1,Typical,6,Typ,1,Good,Detchd,Unf,1,280,Typical,Typical,Paved,45,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,132500,-93.623515,42.033347 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1924,1950,Gable,CompShg,Stucco,Stucco,BrkFace,444,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,248,468,GasA,Good,Y,SBrkr,822,320,0,1142,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,320,Typical,Typical,Paved,0,0,98,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,93000,-93.621677,42.033496 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Good,1926,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Good,Y,SBrkr,904,0,0,904,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,180,Typical,Typical,Paved,0,0,105,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,119000,-93.622443,42.033474 +One_Story_1945_and_Older,Residential_Medium_Density,60,6911,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Average,Average,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,LwQ,4,Unf,0,740,1145,GasA,Typical,Y,SBrkr,1301,0,0,1301,0,0,1,0,2,1,Fair,5,Min1,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,100000,-93.624689,42.031395 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1936,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,927,927,GasA,Typical,Y,SBrkr,1067,472,0,1539,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,112,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,141500,-93.623645,42.03252 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1935,1995,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,989,584,0,1573,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,54,0,120,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,133000,-93.623644,42.03246 +One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Poor,Fair,1936,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fair,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,264,264,Grav,Fair,N,FuseA,800,0,0,800,0,0,1,0,1,1,Fair,4,Maj1,1,Poor,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,ConLw,Normal,60000,-93.623643,42.032359 +One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1931,1993,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,506,715,GasA,Typical,Y,FuseA,875,0,0,875,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Typical,Paved,48,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,105000,-93.623629,42.031292 +One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Above_Average,1925,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Good,Y,SBrkr,995,0,0,995,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,15,51,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Abnorml,115000,-93.622565,42.032375 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1938,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,LwQ,4,Unf,0,679,952,GasA,Typical,Y,FuseA,994,588,0,1582,0,0,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,250,Fair,Typical,Paved,189,0,34,150,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,150000,-93.621351,42.03235 +One_Story_1945_and_Older,Residential_Medium_Density,50,8635,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Average,1925,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,938,1072,GasA,Typical,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,5,1184,Fair,Typical,Partial_Pavement,0,0,105,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,126500,-93.6213592,42.0310861 +Two_and_Half_Story_All_Ages,Residential_Low_Density,0,11888,Pave,Paved,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,PosN,Norm,OneFam,Two_and_Half_Unf,Above_Average,Above_Average,1916,1994,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,844,844,GasA,Good,N,FuseA,1445,689,0,2134,0,0,2,0,5,1,Good,10,Typ,0,No_Fireplace,Detchd,Unf,2,441,Typical,Typical,Paved,0,60,268,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,214500,-93.62726,42.030062 +Two_Story_1945_and_Older,Residential_Low_Density,60,11414,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Brookside,RRAn,Feedr,OneFam,Two_Story,Good,Very_Good,1910,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,BrkTil,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Typical,N,SBrkr,1136,883,0,2019,0,0,1,0,3,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,2,532,Typical,Typical,Paved,509,135,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2009,WD ,Normal,167500,-93.625362,42.026759 +Two_Story_1945_and_Older,Residential_Medium_Density,57,8094,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1048,1048,GasA,Good,Y,FuseA,1048,720,0,1768,0,0,2,0,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,576,Fair,Fair,Paved,0,0,150,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,155000,-93.621525,42.028948 +Two_Story_1945_and_Older,Residential_Medium_Density,50,2500,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1915,2005,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,611,910,GasA,Excellent,Y,SBrkr,916,910,0,1826,1,0,1,1,4,1,Excellent,7,Min2,1,Good,Attchd,Unf,1,164,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,155000,-93.621504,42.027854 +Two_Story_1945_and_Older,Residential_Medium_Density,68,9928,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1915,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Fair,Y,SBrkr,1272,672,0,1944,0,0,2,0,3,1,Typical,8,Min2,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,24,28,0,0,0,0,No_Pool,No_Fence,Shed,400,6,2009,WD ,Normal,179900,-93.621506,42.027998 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,92,5520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Fin,Above_Average,Above_Average,1912,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,755,755,GasA,Excellent,Y,SBrkr,929,929,371,2229,0,0,1,0,5,1,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,198,30,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Abnorml,104000,-93.621635,42.027039 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,50,3000,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,Two_Story,Average,Very_Poor,1922,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1040,1040,GasA,Typical,N,SBrkr,1088,1040,0,2128,0,0,2,0,4,2,Typical,11,Sev,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Abnorml,62500,-93.621633,42.026956 +Two_Story_1945_and_Older,Residential_Medium_Density,57,6876,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1927,1950,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,198,720,GasA,Fair,Y,SBrkr,1146,784,0,1930,1,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,1,316,Typical,Typical,Paved,0,0,213,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,149000,-93.628299,42.025306 +One_Story_1945_and_Older,Residential_Medium_Density,52,6240,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,628,780,GasA,Typical,Y,FuseA,848,0,360,1208,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,539,Typical,Typical,Paved,0,23,112,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,103000,-93.626716,42.023085 +Two_Story_1945_and_Older,Residential_Medium_Density,0,5775,Pave,No_Alley_Access,Moderately_Irregular,Bnk,AllPub,Corner,Mod,Old_Town,Feedr,Norm,OneFam,Two_Story,Above_Average,Good,1915,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,483,483,GasA,Excellent,Y,SBrkr,741,686,0,1427,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,1,379,Typical,Typical,Paved,0,24,112,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,123000,-93.621862,42.025796 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,41,5852,Pave,No_Alley_Access,Irregular,Bnk,AllPub,Corner,Gtl,Old_Town,Feedr,Norm,TwoFmCon,Two_and_Half_Unf,Good,Average,1902,2000,Gable,CompShg,MetalSd,MetalSd,Stone,188,Typical,Fair,BrkTil,Typical,Fair,No,Rec,6,Unf,0,851,1020,GasA,Typical,N,FuseF,978,886,0,1864,0,0,2,1,6,1,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,188,102,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,97500,-93.6217097,42.0260296 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1204,1204,GasA,Typical,Y,FuseA,1204,462,0,1666,0,0,1,0,3,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,384,Fair,Typical,Paved,0,0,148,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,135000,-93.6250433,42.0242487 +Split_Foyer,Residential_Medium_Density,86,5160,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,SFoyer,Below_Average,Above_Average,1923,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Good,Fair,Av,BLQ,2,Rec,63,46,858,GasA,Typical,Y,SBrkr,892,0,0,892,1,0,1,0,1,1,Good,5,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,105,0,160,0,No_Pool,No_Fence,None,0,7,2009,COD,Abnorml,70000,-93.625154,42.024143 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,TwoFmCon,Two_Story,Above_Average,Good,1915,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,ALQ,262,160,698,GasA,Excellent,Y,FuseF,754,649,0,1403,1,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,288,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,116000,-93.599575,42.022828 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,4280,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Excellent,1946,2001,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,560,560,GasA,Excellent,Y,FuseA,704,0,0,704,0,1,1,0,2,1,Fair,4,Typ,0,No_Fireplace,CarPort,Unf,1,220,Typical,Typical,Paved,0,0,24,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,88750,-93.659205,42.032681 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1987,1988,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Good,Gd,GLQ,3,Unf,0,0,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,3,0,3,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Alloca,179000,-93.658207,42.028101 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1987,1988,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Good,Good,Gd,GLQ,3,Unf,0,0,1200,GasA,Typical,Y,SBrkr,1200,0,0,1200,3,0,3,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Alloca,179000,-93.658173,42.028098 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,10547,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Average,Average,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Good,Gd,GLQ,3,Unf,0,0,1152,GasA,Typical,Y,SBrkr,1152,0,0,1152,2,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,252,Typical,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,159900,-93.657107,42.028049 +One_Story_1945_and_Older,Residential_Low_Density,0,10020,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Sev,Edwards,Norm,Norm,OneFam,One_Story,Very_Poor,Very_Poor,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,BrkTil,Fair,Poor,Gd,BLQ,2,Unf,0,333,683,GasA,Good,N,FuseA,904,0,0,904,1,0,0,1,1,1,Fair,4,Maj1,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,61000,-93.6601886,42.0269586 +Two_Story_1945_and_Older,Residential_High_Density,54,6629,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,Two_Story,Above_Average,Above_Average,1925,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,121,672,GasA,Typical,N,SBrkr,697,672,0,1369,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,300,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,103600,-93.657241,42.022825 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Fair,1934,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Typical,N,FuseA,687,425,0,1112,1,0,2,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,226,Poor,Poor,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,63000,-93.658381,42.022978 +Split_or_Multilevel,Residential_Low_Density,70,12886,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Above_Average,1963,1999,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,76,520,GasA,Excellent,Y,SBrkr,1464,0,0,1464,0,1,2,0,3,1,Typical,6,Min2,1,Typical,Attchd,RFn,2,480,Typical,Typical,Paved,302,0,0,0,100,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,175000,-93.669839,42.033376 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8816,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,470,1121,GasA,Typical,Y,SBrkr,1121,0,0,1121,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,80,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,139000,-93.675401,42.033633 +Two_Story_1946_and_Newer,Residential_Low_Density,70,11184,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkFace,92,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,500,192,918,GasA,Good,Y,SBrkr,918,765,0,1683,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,243,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,172500,-93.672288,42.032367 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,172,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,525,1052,GasA,Typical,Y,SBrkr,1052,0,0,1052,0,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,Fin,1,668,Typical,Typical,Paved,0,215,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Abnorml,113500,-93.673382,42.032388 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1967,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,89,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,414,864,GasA,Excellent,Y,SBrkr,899,0,0,899,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,1,288,Typical,Typical,Paved,64,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,130000,-93.677991,42.029863 +Split_Foyer,Residential_Low_Density,0,8014,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,HdBoard,BrkFace,23,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,456,GasA,Typical,Y,SBrkr,1034,0,0,1034,0,1,1,0,3,1,Typical,5,Typ,1,Fair,Basment,Fin,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,149900,-93.672651,42.031342 +Split_Foyer,Residential_Low_Density,0,7252,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Average,1982,1982,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,173,858,GasA,Typical,Y,SBrkr,858,0,0,858,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,134900,-93.671849,42.030628 +Split_Foyer,Residential_Low_Density,74,8740,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1982,1982,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,168,840,GasA,Typical,Y,SBrkr,860,0,0,860,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,137000,-93.671369,42.031138 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11616,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,116,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,670,252,1092,GasA,Typical,Y,SBrkr,1092,0,0,1092,0,1,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,0,20,144,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,139000,-93.666432,42.033427 +Split_or_Multilevel,Residential_Low_Density,88,15400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Average,1961,1961,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Good,No,Unf,7,Unf,0,552,552,GasA,Typical,Y,SBrkr,904,611,259,1774,0,0,2,0,4,1,Typical,8,Typ,1,Typical,Attchd,Unf,1,384,Typical,Typical,Paved,290,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,165000,-93.66636,42.033277 +Split_or_Multilevel,Residential_Low_Density,88,15312,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Average,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,54,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,550,1138,GasA,Excellent,Y,SBrkr,1138,0,0,1138,0,1,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,480,Typical,Typical,Paved,0,0,0,0,140,0,No_Pool,Minimum_Privacy,None,0,3,2009,COD,Normal,148000,-93.666813,42.032302 +Split_or_Multilevel,Residential_Low_Density,0,15584,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,SLvl,Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,366,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,825,825,GasA,Excellent,Y,SBrkr,2071,0,0,2071,0,1,1,1,4,1,Typical,9,Typ,1,Typical,Attchd,Unf,1,336,Typical,Typical,Paved,131,28,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,165000,-93.662126,42.032652 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Poor,Poor,1947,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,SBrkr,660,0,0,660,0,0,1,0,2,1,Fair,5,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,100,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,63900,-93.659279,42.032564 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1957,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,1104,1104,GasA,Excellent,Y,FuseA,1104,684,0,1788,1,0,1,0,5,1,Typical,8,Min2,2,Typical,Attchd,Unf,1,304,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,161500,-93.660877,42.032503 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,25095,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1968,2003,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,113,1437,GasA,Excellent,Y,SBrkr,1473,0,0,1473,2,0,1,0,1,1,Excellent,5,Typ,2,Good,Attchd,Unf,1,452,Typical,Typical,Paved,0,48,0,0,60,0,No_Pool,No_Fence,None,0,6,2009,WD ,Partial,143000,-93.66798,42.026548 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,75,1134,GasA,Excellent,Y,FuseA,1229,0,0,1229,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,284,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,135000,-93.671177,42.023229 +One_Story_1945_and_Older,Residential_Low_Density,0,12342,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,599,861,GasA,Excellent,Y,SBrkr,861,0,0,861,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,539,Typical,Typical,Paved,158,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,82500,-93.6721573,42.0240278 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10708,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1955,1993,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,768,470,1617,GasA,Excellent,Y,FuseA,1867,0,0,1867,1,0,1,0,2,1,Typical,7,Typ,3,Good,Attchd,Fin,1,303,Typical,Typical,Paved,476,0,0,0,142,0,No_Pool,Good_Wood,None,0,11,2009,COD,Normal,190000,-93.666242,42.028468 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13680,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Average,1955,1955,Hip,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,FuseA,1733,0,0,1733,0,0,2,0,4,1,Typical,8,Min2,1,Good,Attchd,Unf,2,452,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,139600,-93.665996,42.027656 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15635,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1383,0,0,1383,0,0,1,0,2,1,Typical,6,Mod,0,No_Fireplace,Attchd,Unf,2,498,Fair,Typical,Paved,0,0,90,0,110,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,122000,-93.665994,42.027506 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,9855,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1436,1436,GasA,Fair,Y,SBrkr,1689,0,0,1689,0,0,1,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2009,COD,Normal,127500,-93.667776,42.024483 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,816,1073,GasA,Typical,Y,FuseA,1073,0,0,1073,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,340,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,2,2009,WD ,Normal,121500,-93.668212,42.023982 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Fair,1958,1958,Gable,CompShg,BrkComm,Brk Cmn,None,0,Typical,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,1276,1276,GasA,Typical,Y,FuseA,1276,0,0,1276,0,0,1,0,3,1,Typical,5,Mod,0,No_Fireplace,Attchd,Unf,1,350,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,COD,Abnorml,60000,-93.667209,42.023916 +Two_Story_1946_and_Newer,Residential_Low_Density,50,9350,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1946,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,405,747,GasA,Excellent,Y,SBrkr,892,747,0,1639,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,154400,-93.665271,42.026133 +One_Story_1945_and_Older,Residential_Low_Density,50,9340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1941,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,328,672,GasA,Typical,Y,SBrkr,672,0,0,672,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,234,Typical,Typical,Dirt_Gravel,0,113,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,113000,-93.66527,42.025996 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,7440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,916,1089,GasW,Typical,Y,SBrkr,1089,0,0,1089,1,0,1,0,3,1,Typical,5,Typ,1,Good,Detchd,RFn,1,252,Typical,Typical,Partial_Pavement,328,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2009,WD ,Normal,125000,-93.660147,42.026307 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,N,SBrkr,845,0,0,845,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,290,Typical,Typical,Dirt_Gravel,186,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,84000,-93.663366,42.024993 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,42,4235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,One_Story,Average,Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,BrkFace,149,Good,Typical,CBlock,Good,Typical,Mn,GLQ,3,ALQ,393,104,1049,GasA,Typical,Y,SBrkr,1049,0,0,1049,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,1,266,Typical,Typical,Paved,0,105,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,139500,-93.6641939,42.0246851 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,11409,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1949,2008,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,476,768,GasA,Good,Y,SBrkr,1148,568,0,1716,0,0,1,1,3,1,Typical,8,Min2,1,Good,Attchd,Unf,1,281,Typical,Typical,Paved,0,0,0,0,160,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,131000,-93.66104,42.0247212 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1939,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Rec,6,Unf,0,356,560,GasA,Typical,Y,SBrkr,698,560,0,1258,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,105000,-93.661005,42.023548 +Two_Story_1945_and_Older,Residential_Low_Density,60,9084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,Two_Story,Below_Average,Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,755,755,GasA,Typical,Y,SBrkr,755,755,0,1510,1,0,1,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,1,296,Fair,Poor,Partial_Pavement,120,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,108000,-93.659855,42.022754 +Split_or_Multilevel,Residential_Low_Density,74,10778,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,SLvl,Good,Above_Average,1990,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,286,308,1054,GasA,Good,Y,SBrkr,1061,0,0,1061,1,0,1,1,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,2,462,Typical,Typical,Paved,114,36,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,162000,-93.684708,42.034183 +Split_or_Multilevel,Residential_Low_Density,66,19255,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,SLvl,Above_Average,Average,1983,1983,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,100,Good,Typical,CBlock,Good,Typical,Av,Rec,6,GLQ,450,0,520,GasA,Good,Y,SBrkr,1338,0,0,1338,0,0,1,1,2,1,Good,5,Min2,1,Poor,Attchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,600,9,2009,WD ,Normal,156500,-93.685733,42.033714 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,79,12327,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Very_Good,1983,2009,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,55,1496,GasA,Excellent,Y,SBrkr,1496,636,0,2132,1,0,1,1,1,1,Good,5,Min2,1,Good,BuiltIn,Fin,2,612,Good,Typical,Paved,349,40,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,316600,-93.685986,42.0318449 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,14684,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Good,1990,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,234,Good,Typical,CBlock,Good,Typical,Mn,ALQ,1,BLQ,177,1496,2158,GasA,Good,Y,SBrkr,2196,0,0,2196,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,701,Typical,Typical,Paved,84,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,271900,-93.68341,42.03254 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9317,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,137,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,227,740,GasA,Excellent,Y,SBrkr,1006,769,0,1775,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,425,Typical,Typical,Paved,234,72,192,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,213000,-93.681063,42.030625 +Two_Story_1946_and_Newer,Residential_Low_Density,85,10560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,605,1079,GasA,Excellent,Y,SBrkr,1079,800,0,1879,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,473,Typical,Typical,Paved,400,100,144,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,239900,-93.682025,42.030623 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9828,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,544,1128,GasA,Excellent,Y,SBrkr,1142,878,0,2020,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,466,Typical,Typical,Paved,0,155,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,239500,-93.682176,42.030678 +Two_Story_1945_and_Older,Residential_Low_Density,120,26400,Pave,No_Alley_Access,Regular,Bnk,AllPub,FR2,Gtl,Sawyer_West,Feedr,Norm,OneFam,Two_Story,Average,Good,1880,2007,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1288,728,0,2016,0,0,1,0,4,1,Typical,7,Mod,1,Typical,Attchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,131000,-93.679789,42.03252 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,410,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,118964,-93.679784,42.031622 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,SFoyer,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,Stone,275,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1086,GasA,Typical,Y,SBrkr,1224,0,0,1224,2,0,0,2,2,2,Typical,6,Typ,2,Typical,Detchd,Unf,2,528,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,153337,-93.6791403,42.031834 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1114,1114,0,2228,0,0,2,0,6,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,73,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,147983,-93.679109,42.031623 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Alloca,118858,-93.679784,42.031545 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,118858,-93.6795273,42.0314669 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7018,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1120,1120,0,2240,0,0,2,0,6,2,Typical,12,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,154,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,142953,-93.6790969,42.0314606 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7040,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,SFoyer,Average,Average,1979,1979,Gable,CompShg,Plywood,Plywood,BrkFace,216,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1094,GasA,Typical,Y,SBrkr,1229,0,0,1229,2,0,0,2,2,2,Good,6,Typ,2,Typical,Detchd,Unf,2,672,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,148325,-93.6791456,42.0312822 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7007,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,One_Story,Average,Average,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1513,0,0,1513,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Alloca,113722,-93.679781,42.03057 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,11855,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,Two_Story,Good,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,23,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,348,1168,GasA,Excellent,Y,SBrkr,1168,1619,0,2787,2,0,4,2,6,2,Typical,8,Typ,2,Typical,BuiltIn,Fin,4,820,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,269500,-93.679856,42.030531 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7939,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Good,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,23,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,348,1168,GasA,Excellent,Y,SBrkr,1168,1619,0,2787,2,0,4,2,6,2,Typical,8,Typ,2,Typical,BuiltIn,Fin,4,820,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,269500,-93.6791251,42.0310946 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7976,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,Two_Story,Good,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,23,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,348,1168,GasA,Excellent,Y,SBrkr,1168,1619,0,2787,2,0,4,2,6,2,Typical,8,Typ,2,Typical,BuiltIn,Fin,4,820,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,269500,-93.679181,42.0309301 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,10933,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2009,2009,Hip,CompShg,VinylSd,VinylSd,Stone,242,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,534,1555,GasA,Excellent,Y,SBrkr,1680,0,0,1680,1,0,1,1,1,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,1138,Typical,Typical,Paved,185,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,323262,-93.687067,42.026626 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10637,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,336,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,417,1705,GasA,Excellent,Y,SBrkr,1718,0,0,1718,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,826,Typical,Typical,Paved,208,44,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,297000,-93.688859,42.026482 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,10226,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,270,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1622,1622,GasA,Excellent,Y,SBrkr,1630,0,0,1630,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,860,Typical,Typical,Paved,172,42,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,295493,-93.688993,42.026414 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,10816,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,364,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,616,1720,GasA,Excellent,Y,SBrkr,1720,0,0,1720,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,846,Typical,Typical,Paved,208,104,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,332000,-93.689091,42.026372 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9178,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1468,1468,GasA,Excellent,Y,SBrkr,1468,0,0,1468,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,904,Typical,Typical,Paved,192,142,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,239900,-93.690266,42.025707 +Two_Story_1946_and_Newer,Residential_Low_Density,68,10769,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,846,866,GasA,Excellent,Y,SBrkr,866,902,0,1768,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,144,105,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,212000,-93.690971,42.025293 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,11422,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,352,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,479,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,RFn,2,524,Typical,Typical,Paved,154,222,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,272500,-93.690364,42.025577 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,122,11923,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1800,1800,GasA,Excellent,Y,SBrkr,1800,0,0,1800,0,0,2,0,2,1,Excellent,7,Typ,0,No_Fireplace,Attchd,Fin,2,702,Typical,Typical,Paved,288,136,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,239000,-93.689072,42.024628 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8814,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,180,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,270,1604,GasA,Excellent,Y,SBrkr,1604,0,0,1604,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,660,Typical,Typical,Paved,123,110,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Abnorml,220000,-93.689071,42.024596 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,380,1282,GasA,Excellent,Y,SBrkr,1290,0,0,1290,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,662,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,200000,-93.691433,42.024613 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,10324,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,140,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,382,1254,GasA,Excellent,Y,SBrkr,1254,0,0,1254,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,3,810,Typical,Typical,Paved,168,92,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,221800,-93.69157,42.024765 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7314,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,82,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,508,1232,GasA,Excellent,Y,SBrkr,1232,0,0,1232,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,194500,-93.690111,42.024327 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11645,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,76,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1474,1498,GasA,Excellent,Y,SBrkr,1498,0,0,1498,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,844,Typical,Typical,Paved,144,98,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,237000,-93.689066,42.024399 +Two_Story_1946_and_Newer,Residential_Low_Density,79,11646,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,704,704,GasA,Excellent,Y,SBrkr,704,718,0,1422,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,36,28,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,173000,-93.689081,42.02322 +Split_or_Multilevel,Residential_Low_Density,0,12800,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,SLvl,Good,Average,1989,1989,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,145,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1518,GasA,Good,Y,SBrkr,1644,0,0,1644,1,1,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,569,Typical,Typical,Paved,80,0,0,0,396,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,275000,-93.685125,42.029772 +Two_Story_1946_and_Newer,Residential_Low_Density,0,16698,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Mod,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1992,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,649,1449,GasA,Good,Y,SBrkr,944,815,0,1759,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,525,Typical,Typical,Paved,150,193,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,233500,-93.679297,42.02792 +Two_Story_1946_and_Newer,Residential_Low_Density,0,28698,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,Two_Story,Average,Average,1967,1967,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,PConc,Typical,Good,Gd,LwQ,4,ALQ,764,0,1013,GasA,Typical,Y,SBrkr,1160,966,0,2126,0,1,2,1,3,1,Typical,7,Min2,0,No_Fireplace,Attchd,Fin,2,538,Typical,Typical,Paved,486,0,0,0,225,0,No_Pool,No_Fence,None,0,6,2009,WD ,Abnorml,185000,-93.6825646,42.0255464 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,12464,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Corner,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,308,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,Good_Privacy,None,0,11,2009,WD ,Normal,152000,-93.691883,42.021171 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9757,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,ALQ,1,Unf,0,235,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,440,Typical,Typical,Paved,66,0,0,0,92,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,143000,-93.692135,42.021176 +Two_Story_1946_and_Newer,Residential_Low_Density,65,15426,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,107,928,GasA,Excellent,Y,SBrkr,928,836,0,1764,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,470,Typical,Typical,Paved,276,99,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2009,WD ,Normal,231500,-93.688372,42.021207 +Two_Story_1946_and_Newer,Residential_Low_Density,43,10667,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Good,Above_Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,ALQ,344,70,799,GasA,Excellent,Y,SBrkr,827,834,0,1661,1,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,158,61,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,ConLw,Normal,212000,-93.690401,42.020817 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,14753,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,513,1463,GasA,Excellent,Y,SBrkr,1463,0,0,1463,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,539,Typical,Typical,Paved,0,81,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2009,WD ,Normal,207000,-93.691355,42.019784 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,112,10859,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1097,1097,GasA,Excellent,Y,SBrkr,1097,0,0,1097,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,672,Typical,Typical,Paved,392,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,145000,-93.691585,42.018996 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Good,No,ALQ,1,Unf,0,244,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,138000,-93.6928668,42.0197228 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,866,866,GasA,Excellent,Y,SBrkr,866,913,0,1779,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,546,Typical,Typical,Paved,198,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,197900,-93.690548,42.018597 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10335,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,183,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,811,1490,GasA,Excellent,Y,SBrkr,1501,0,0,1501,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,577,Typical,Typical,Paved,144,29,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,204000,-93.690324,42.017569 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9017,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,871,1431,GasA,Excellent,Y,SBrkr,1431,0,0,1431,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,666,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,192000,-93.691667,42.016903 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,322,928,GasA,Excellent,Y,SBrkr,928,844,0,1772,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,492,Typical,Typical,Paved,150,96,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,200000,-93.688383,42.018667 +Two_Story_1946_and_Newer,Residential_Low_Density,68,8935,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,831,831,GasA,Excellent,Y,SBrkr,831,829,0,1660,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,493,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,195000,-93.689431,42.016862 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,9808,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,110,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,785,1573,GasA,Excellent,Y,SBrkr,1573,0,0,1573,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,544,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,227000,-93.691175,42.016399 +Two_Story_1946_and_Newer,Residential_Low_Density,79,12420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,278,944,GasA,Excellent,Y,SBrkr,944,896,0,1840,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,622,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,230000,-93.689438,42.01571 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,16285,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1413,1413,GasA,Excellent,Y,SBrkr,1430,0,0,1430,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,605,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,187100,-93.689942,42.016551 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,10739,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,172,1431,GasA,Excellent,Y,SBrkr,1444,0,0,1444,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,577,Typical,Typical,Paved,144,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,203000,-93.691672,42.01613 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11166,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1468,1492,GasA,Excellent,Y,SBrkr,1492,0,0,1492,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,0,114,0,0,168,0,No_Pool,No_Fence,None,0,7,2009,WD ,Family,201000,-93.688628,42.015713 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8430,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,136,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,424,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,124000,-93.686916,42.021384 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16269,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1978,1978,Gable,CompShg,MetalSd,MetalSd,BrkFace,76,Typical,Typical,BrkTil,Good,Typical,Av,GLQ,3,Unf,0,282,907,GasA,Typical,Y,SBrkr,907,0,0,907,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,343,Typical,Typical,Paved,72,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,140000,-93.687212,42.020087 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,6950,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1979,1979,Gable,CompShg,HdBoard,HdBoard,BrkFace,40,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,72,132,914,GasA,Typical,Y,SBrkr,914,0,0,914,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,444,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,134900,-93.687219,42.019184 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,8800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1977,2008,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Mn,LwQ,4,Rec,144,364,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,288,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,150500,-93.685068,42.019697 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7000,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1978,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,90,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,218,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,136500,-93.684887,42.019548 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9286,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1977,1989,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Good,Good,Av,ALQ,1,Unf,0,1072,1268,GasA,Typical,Y,SBrkr,1268,0,0,1268,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,252,Typical,Typical,Paved,173,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,143500,-93.684954,42.019691 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,36,15523,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,404,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Attchd,Unf,1,338,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,133500,-93.683157,42.020666 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,117,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,600,Typical,Typical,Paved,215,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,123000,-93.683159,42.020991 +Split_Foyer,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Very_Good,1972,2003,Gable,CompShg,WdShing,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,108,768,GasA,Good,Y,SBrkr,768,0,0,768,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Fin,1,396,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,133900,-93.68317,42.021788 +Split_Foyer,Residential_Low_Density,70,8445,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Good,1972,2007,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,112,768,GasA,Typical,Y,SBrkr,768,0,0,768,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,396,Typical,Typical,Paved,58,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,133000,-93.68351,42.020843 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11664,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,335,1569,GasA,Excellent,Y,SBrkr,1611,0,0,1611,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,1231,Typical,Typical,Paved,262,93,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,250000,-93.687819,42.017109 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12334,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,198,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1068,1068,GasA,Excellent,Y,SBrkr,1068,1116,0,2184,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,RFn,2,570,Typical,Typical,Paved,192,132,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,254750,-93.686218,42.018722 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11838,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2001,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1753,1753,GasA,Excellent,Y,SBrkr,1788,0,0,1788,0,0,2,0,3,1,Excellent,7,Typ,1,Typical,Attchd,RFn,2,522,Typical,Typical,Paved,202,151,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,236500,-93.68269,42.018847 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11885,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,309,1299,GasA,Excellent,Y,SBrkr,1299,573,0,1872,1,0,2,1,3,1,Excellent,7,Typ,1,Typical,BuiltIn,RFn,2,531,Typical,Typical,Paved,160,122,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,261500,-93.682623,42.01877 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Excellent,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,204,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,536,1440,GasA,Excellent,Y,SBrkr,1476,677,0,2153,1,0,2,1,3,1,Excellent,8,Typ,2,Excellent,Attchd,Fin,3,736,Typical,Typical,Paved,253,142,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,313000,-93.682983,42.018703 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8749,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,885,0,1725,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,550,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,198500,-93.686744,42.016973 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,944,944,GasA,Excellent,Y,SBrkr,944,926,0,1870,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,608,Typical,Typical,Paved,256,43,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,211000,-93.685673,42.016978 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,15750,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,College_Creek,Feedr,Norm,OneFam,One_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,260,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,216,1462,GasA,Excellent,Y,SBrkr,1513,0,0,1513,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,521,Typical,Typical,Paved,135,34,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,220000,-93.67915,42.0181479 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,11883,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1996,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,814,1504,GasA,Excellent,Y,SBrkr,1504,0,0,1504,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,478,Typical,Typical,Paved,115,66,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,203000,-93.680706,42.018875 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12782,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,HdBoard,HdBoard,BrkFace,164,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,462,1822,GasA,Excellent,Y,SBrkr,1828,0,0,1828,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,523,Typical,Typical,Paved,194,144,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,279500,-93.681109,42.018866 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,209,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,306,1417,GasA,Excellent,Y,SBrkr,1417,0,0,1417,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,511,Typical,Typical,Paved,60,0,0,0,117,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,210000,-93.681343,42.018006 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,573,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,318,1057,GasA,Excellent,Y,SBrkr,1057,977,0,2034,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,645,Typical,Typical,Paved,576,36,0,0,0,0,No_Pool,Good_Privacy,None,0,2,2009,WD ,Normal,219500,-93.681411,42.017856 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,880,880,GasA,Excellent,Y,SBrkr,909,807,0,1716,0,0,2,1,2,1,Good,7,Typ,1,Typical,Attchd,RFn,2,512,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,191000,-93.680782,42.017856 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1212,1212,GasA,Excellent,Y,SBrkr,1212,0,0,1212,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,573,Typical,Typical,Paved,356,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,178000,-93.68218,42.016819 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,171,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1630,1630,GasA,Excellent,Y,SBrkr,1630,0,0,1630,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,451,Typical,Typical,Paved,74,234,0,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,213000,-93.68167,42.016797 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,163,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,144000,-93.681297,42.016274 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,163,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,140000,-93.681439,42.016271 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,100,1578,GasA,Excellent,Y,SBrkr,1602,0,0,1602,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,810,Typical,Typical,Paved,0,48,0,0,195,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,293200,-93.684115,42.016468 +Two_Story_1946_and_Newer,Residential_Low_Density,65,9313,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,864,864,GasA,Excellent,Y,SBrkr,864,864,0,1728,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,572,Typical,Typical,Paved,187,56,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,190000,-93.685333,42.013957 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,8487,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1480,1500,GasA,Excellent,Y,SBrkr,1500,0,0,1500,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,570,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,190000,-93.686051,42.014054 +Two_Story_1946_and_Newer,Residential_Low_Density,64,8633,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,545,738,GasA,Excellent,Y,SBrkr,738,738,0,1476,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,540,Typical,Typical,Paved,100,35,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,173500,-93.684536,42.013512 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11069,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,608,608,GasA,Excellent,Y,SBrkr,608,788,0,1396,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,440,Typical,Typical,Paved,100,36,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,170000,-93.6843,42.013392 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1949,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1126,0,0,1126,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Fin,2,520,Typical,Typical,Dirt_Gravel,0,96,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,108000,-93.678305,42.020123 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9937,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1964,1999,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Typical,Typical,No,BLQ,2,Unf,0,849,1486,GasA,Excellent,Y,SBrkr,1486,0,0,1486,1,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Fin,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,167000,-93.677407,42.021246 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8877,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1938,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,126,816,GasA,Excellent,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,3,Typ,1,Good,Detchd,Unf,1,288,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,100000,-93.676372,42.021219 +Split_Foyer,Residential_Low_Density,64,7301,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Good,Average,2003,2003,Gable,CompShg,HdBoard,HdBoard,BrkFace,500,Good,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,495,1427,0,1922,0,0,3,0,4,1,Good,7,Typ,1,Excellent,BuiltIn,RFn,2,672,Typical,Typical,Paved,0,0,177,0,0,0,No_Pool,No_Fence,None,0,7,2009,ConLD,Normal,198500,-93.672234,42.018988 +Two_Story_1946_and_Newer,Residential_Low_Density,75,7950,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1977,1977,Hip,CompShg,HdBoard,Plywood,BrkFace,140,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,155,690,GasA,Typical,Y,SBrkr,698,728,0,1426,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,252,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,159500,-93.6755239,42.0186549 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10682,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1960,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,615,1014,GasA,Typical,Y,SBrkr,1149,0,0,1149,1,0,1,0,3,1,Typical,7,Min1,0,No_Fireplace,More_Than_Two_Types,Fin,1,544,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,127000,-93.6739723,42.0196717 +Split_or_Multilevel,Residential_Low_Density,72,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1976,1976,Hip,CompShg,MetalSd,MetalSd,BrkFace,255,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,410,1041,GasA,Excellent,Y,SBrkr,1125,0,0,1125,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,1,352,Typical,Typical,Paved,296,0,0,0,0,0,No_Pool,Good_Wood,None,0,10,2009,WD ,Abnorml,125000,-93.675248,42.018928 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,13286,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,340,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,464,1698,GasA,Excellent,Y,SBrkr,1698,0,0,1698,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,768,Typical,Typical,Paved,327,64,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Normal,320000,-93.676608,42.017026 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Typical,Typical,Wood,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,145500,-93.669952,42.01896 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,9405,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Excellent,1947,2008,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Excellent,Y,SBrkr,698,0,0,698,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,200,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,118000,-93.664779,42.022478 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6410,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1959,1959,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,243,301,876,GasA,Typical,Y,FuseA,876,0,0,876,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,320,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,85000,-93.666315,42.019423 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,Rec,6,Unf,0,0,1078,GasA,Typical,Y,FuseA,1368,0,0,1368,1,0,1,0,3,1,Typical,6,Min1,0,No_Fireplace,Attchd,RFn,1,195,Typical,Typical,Paved,0,41,211,0,0,0,No_Pool,No_Fence,Shed,900,6,2009,WD ,Normal,120000,-93.664685,42.020321 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,8405,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Fair,1945,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Typical,N,FuseF,1088,441,0,1529,0,0,2,0,4,1,Typical,9,Mod,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,92,0,185,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,98000,-93.6644556,42.0217997 +One_Story_1945_and_Older,Residential_Low_Density,56,4060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,864,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,99900,-93.659191,42.021496 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,10206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Fair,1952,1952,Flat,Tar&Grv,BrkComm,Brk Cmn,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasW,Fair,N,FuseF,944,0,0,944,0,0,1,0,2,1,Fair,4,Min1,0,No_Fireplace,Detchd,Unf,2,528,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,82000,-93.664812,42.019004 +Duplex_All_Styles_and_Ages,Residential_Low_Density,65,10926,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Average,1959,1959,Hip,CompShg,VinylSd,VinylSd,BrkFace,74,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1678,1678,GasA,Typical,Y,SBrkr,1678,0,0,1678,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,119900,-93.6644024,42.01822 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,HdBoard,HdBoard,BrkFace,259,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,604,1150,GasA,Excellent,Y,SBrkr,1560,0,0,1560,0,0,1,0,2,1,Typical,7,Min1,1,Typical,Attchd,Unf,1,313,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,103500,-93.664336,42.019381 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8926,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1956,1956,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,FuseA,960,0,0,960,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,1,288,Typical,Typical,Paved,64,0,0,0,160,0,No_Pool,Minimum_Privacy,None,0,10,2009,COD,Abnorml,112000,-93.663302,42.018592 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,67,12354,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1920,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Unf,7,Unf,0,684,684,GasA,Good,Y,SBrkr,684,512,0,1196,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,46,0,0,0,0,No_Pool,Good_Privacy,Shed,800,8,2009,ConLI,Normal,110000,-93.657963,42.022092 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,8212,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,94,720,GasA,Excellent,Y,SBrkr,854,444,0,1298,0,0,1,0,3,1,Typical,6,Typ,2,Good,Detchd,Unf,1,256,Typical,Typical,Paved,84,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,122000,-93.656664,42.022088 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10998,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1941,1960,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,420,156,984,GasA,Excellent,Y,SBrkr,984,620,0,1604,0,0,2,0,3,1,Typical,6,Min2,0,No_Fireplace,Detchd,Unf,2,660,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,117000,-93.655674,42.022091 +One_and_Half_Story_Finished_All_Ages,Residential_High_Density,70,6300,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1938,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,88,Typical,Typical,PConc,Typical,Fair,No,LwQ,4,Unf,0,0,832,GasA,Typical,Y,SBrkr,832,436,0,1268,0,0,1,1,3,1,Typical,7,Typ,2,Good,Basment,Unf,1,250,Typical,Typical,Paved,0,0,55,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Abnorml,160000,-93.651825,42.020123 +One_and_Half_Story_Finished_All_Ages,Residential_High_Density,55,4500,Pave,Paved,Moderately_Irregular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1932,2000,Gable,CompShg,VinylSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,611,793,GasA,Excellent,Y,SBrkr,848,672,0,1520,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,281,Typical,Typical,Paved,0,0,56,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Abnorml,159434,-93.652027,42.019713 +One_Story_1945_and_Older,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Poor,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,0,290,GasA,Typical,N,FuseF,438,0,0,438,0,0,1,0,1,1,Fair,3,Typ,0,No_Fireplace,Detchd,Unf,1,246,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,60000,-93.651786,42.017564 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,778,778,GasA,Typical,Y,SBrkr,944,545,0,1489,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,335,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,139500,-93.64857,42.016921 +Two_and_Half_Story_All_Ages,Residential_Low_Density,102,15863,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_and_Half_Fin,Good,Fair,1920,1970,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Typical,Typical,No,GLQ,3,Unf,0,301,824,GasA,Excellent,Y,SBrkr,1687,998,397,3082,1,0,2,1,5,1,Typical,12,Typ,2,Typical,Basment,Fin,2,672,Typical,Typical,Paved,136,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,197000,-93.651669,42.016182 +Two_Story_1945_and_Older,Residential_Low_Density,43,5707,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Above_Average,Above_Average,1935,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,583,583,GasA,Good,Y,FuseF,647,595,0,1242,0,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,180,Fair,Typical,Paved,329,96,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,105000,-93.646555,42.019094 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,65,7804,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Fair,1928,1950,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,500,1122,GasA,Typical,Y,SBrkr,1328,653,0,1981,1,0,2,0,4,1,Good,7,Min2,2,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,431,44,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2009,WD ,Normal,135000,-93.648414,42.018931 +One_Story_1945_and_Older,Residential_Low_Density,64,8574,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1916,2000,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,1232,0,0,1232,0,0,1,0,3,1,Good,6,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,180,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,147500,-93.646655,42.017889 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,6171,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1925,1990,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,712,976,GasA,Excellent,Y,SBrkr,1160,448,0,1608,0,0,2,1,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,216,Fair,Typical,Paved,147,16,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Normal,137450,-93.648429,42.017835 +Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Good,Excellent,1936,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,PConc,Good,Good,No,ALQ,1,BLQ,210,0,560,GasA,Excellent,Y,SBrkr,575,560,0,1135,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,RFn,2,576,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,155000,-93.6459433,42.0189256 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,57,7558,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1928,1950,Gable,CompShg,BrkFace,Stone,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,896,896,GasA,Good,Y,SBrkr,1172,741,0,1913,0,0,1,1,3,1,Typical,9,Typ,1,Typical,Detchd,Unf,2,342,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,177000,-93.646881,42.016943 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,52,6292,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1928,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,861,861,GasA,Good,Y,SBrkr,877,600,0,1477,0,1,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,145000,-93.648419,42.016835 +Two_Story_1945_and_Older,Residential_Low_Density,53,7155,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1918,1990,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,628,600,0,1228,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,215,Fair,Typical,Paved,0,113,0,0,195,0,No_Pool,Minimum_Privacy,None,0,5,2009,WD ,Normal,137000,-93.644704,42.016957 +Two_Story_1945_and_Older,Residential_Low_Density,53,10918,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Excellent,1926,2004,Gambrel,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,1276,1276,GasA,Excellent,Y,SBrkr,1276,804,0,2080,0,0,1,1,3,1,Good,9,Typ,2,Good,Detchd,Unf,1,282,Typical,Typical,Paved,0,0,0,0,145,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,234000,-93.644636,42.016067 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,13680,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,112,840,GasA,Excellent,Y,SBrkr,840,727,0,1567,1,0,1,1,2,1,Typical,6,Min2,2,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,21,150,0,0,0,No_Pool,Good_Privacy,None,0,2,2009,WD ,Normal,205000,-93.6428055,42.0191252 +Two_Story_1945_and_Older,Residential_Low_Density,0,7500,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1942,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,224,771,GasA,Fair,Y,SBrkr,753,741,0,1494,0,0,1,0,3,1,Good,7,Typ,2,Good,Attchd,Unf,1,213,Typical,Typical,Partial_Pavement,0,0,0,0,224,0,No_Pool,No_Fence,None,0,11,2009,WD ,Normal,177500,-93.6397,42.019181 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,130,9600,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,BrkTil,Typical,Fair,No,Rec,6,Unf,0,300,728,GasA,Excellent,Y,SBrkr,976,332,0,1308,1,0,1,1,2,1,Typical,7,Min2,2,Typical,Detchd,Unf,1,256,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,160000,-93.642371,42.018707 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,14680,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Below_Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,480,1273,GasA,Excellent,Y,SBrkr,1273,0,0,1273,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,307,Typical,Typical,Paved,483,0,0,0,115,0,No_Pool,Minimum_Privacy,None,0,6,2009,WD ,Normal,155000,-93.64012,42.017583 +One_Story_1945_and_Older,Residential_Low_Density,80,13360,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Good,1921,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Good,Typical,No,ALQ,1,Unf,0,163,876,GasA,Excellent,Y,SBrkr,964,0,0,964,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,432,Typical,Typical,Paved,0,0,44,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,163500,-93.640592,42.017574 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,54,7681,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1921,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,731,731,GasA,Excellent,Y,SBrkr,820,523,0,1343,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,186,Fair,Typical,Paved,192,0,102,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,154900,-93.644295,42.016684 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,8145,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Crawford,Norm,Norm,Duplex,Two_and_Half_Unf,Good,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,674,920,GasA,Excellent,Y,SBrkr,1240,1240,0,2480,0,0,2,1,5,2,Typical,13,Typ,2,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,57,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,205000,-93.6439975,42.0158534 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Good,BrkTil,Typical,Typical,Mn,Rec,6,ALQ,694,264,1112,GasA,Excellent,Y,SBrkr,1112,0,0,1112,1,0,1,0,2,1,Typical,6,Typ,1,Good,Attchd,Unf,1,390,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,WD ,Family,144800,-93.639431,42.016152 +Two_Story_1945_and_Older,Residential_Low_Density,75,12000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Good,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,429,704,GasA,Excellent,Y,SBrkr,860,704,0,1564,0,0,1,1,3,1,Fair,7,Typ,1,Good,Attchd,Unf,1,234,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,174500,-93.642945,42.014688 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13339,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1960,Gable,CompShg,HdBoard,Plywood,Stone,132,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,BLQ,875,621,1561,GasA,Typical,Y,SBrkr,1561,0,0,1561,1,0,2,0,3,1,Typical,6,Typ,1,Good,Attchd,Fin,2,463,Typical,Typical,Paved,0,148,0,0,120,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,193000,-93.642885,42.013169 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,15600,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1949,2005,Gable,CompShg,BrkComm,Brk Cmn,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Rec,507,248,1067,GasW,Fair,N,SBrkr,986,537,0,1523,1,0,2,0,3,1,Fair,7,Maj2,1,Typical,Attchd,Unf,1,295,Typical,Typical,Paved,0,0,81,0,0,0,No_Pool,No_Fence,None,0,10,2009,WD ,Normal,158000,-93.640762,42.014943 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,14000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1950,2004,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1092,1092,GasA,Excellent,Y,SBrkr,1152,0,0,1152,0,1,1,0,3,1,Good,6,Typ,1,Good,Attchd,Unf,1,300,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2009,WD ,Family,158500,-93.641356,42.013753 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,17500,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Mod,Crawford,PosA,Norm,OneFam,One_Story,Above_Average,Average,1954,1954,Gable,CompShg,HdBoard,HdBoard,Stone,420,Typical,Typical,PConc,Typical,Typical,Av,LwQ,4,BLQ,435,91,1310,GasA,Excellent,Y,SBrkr,1906,0,0,1906,1,0,1,1,3,1,Typical,6,Typ,2,Good,Basment,Unf,2,576,Typical,Typical,Paved,0,201,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,224000,-93.639407,42.0125 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1733,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,516,516,GasA,Typical,Y,SBrkr,516,516,0,1032,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,452,Typical,Typical,Paved,279,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2009,WD ,Normal,115000,-93.645743,42.009489 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1488,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1980,1992,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,90,561,GasA,Typical,Y,SBrkr,561,668,0,1229,1,0,1,1,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,176,0,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2009,WD ,Normal,137000,-93.645743,42.009489 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1612,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,561,561,GasA,Typical,Y,SBrkr,561,668,0,1229,0,0,1,1,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,154,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,121000,-93.645743,42.009489 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,276,561,GasA,Typical,Y,SBrkr,561,668,0,1229,0,0,1,1,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,150,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2009,WD ,Normal,124000,-93.645743,42.009489 +Split_or_Multilevel,Residential_Low_Density,0,13607,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1986,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,242,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,118,572,GasA,Good,Y,SBrkr,1182,800,0,1982,1,0,2,1,3,1,Typical,6,Typ,1,Typical,BuiltIn,Fin,2,501,Typical,Typical,Paved,400,112,0,0,0,0,No_Pool,No_Fence,Shed,1500,4,2009,WD ,Normal,208000,-93.644864,42.010636 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17597,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Good,Excellent,1971,2009,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Good,CBlock,Good,Typical,No,GLQ,3,ALQ,419,581,1803,GasA,Typical,Y,SBrkr,2365,0,0,2365,1,0,2,0,3,1,Excellent,7,Min1,2,Good,Attchd,Fin,2,551,Typical,Typical,Paved,200,144,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2009,WD ,Normal,315000,-93.643539,42.01141 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,21695,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Excellent,1988,2007,Hip,CompShg,Wd Sdng,Plywood,BrkFace,260,Good,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,72,880,GasA,Excellent,Y,SBrkr,1680,0,0,1680,1,0,2,0,3,1,Good,5,Typ,1,Good,Attchd,Fin,2,540,Typical,Typical,Paved,292,44,0,182,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,222000,-93.643947,42.009134 +Two_Story_1945_and_Older,Residential_Medium_Density,59,10690,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Good,1920,1997,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Fair,No,Rec,6,Unf,0,216,672,GasA,Good,Y,FuseA,672,672,0,1344,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,468,Typical,Fair,Paved,0,128,218,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,147000,-93.62529,42.022721 +Two_Story_1945_and_Older,Residential_Medium_Density,50,8660,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1900,1993,Gambrel,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,760,760,GasA,Excellent,N,SBrkr,928,928,312,2168,0,0,2,0,5,1,Good,11,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,467,160,78,0,0,0,No_Pool,No_Fence,None,0,12,2009,WD ,Normal,123000,-93.626839,42.021425 +Two_Story_1945_and_Older,Residential_Medium_Density,60,6402,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Unf,7,Unf,0,596,596,GasA,Typical,N,SBrkr,596,596,0,1192,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,189,Fair,Fair,Dirt_Gravel,0,0,137,0,0,0,No_Pool,Good_Wood,None,0,7,2009,WD ,Normal,78000,-93.625299,42.021596 +One_and_Half_Story_Finished_All_Ages,C_all,105,8470,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Feedr,OneFam,One_and_Half_Fin,Fair,Poor,1915,1982,Hip,CompShg,Plywood,Plywood,None,0,Fair,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,1013,1013,GasA,Typical,N,SBrkr,1013,0,513,1526,0,0,1,0,2,1,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,156,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2009,ConLD,Abnorml,85000,-93.616895,42.020184 +One_Story_1945_and_Older,C_all,60,10200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,572,572,GasA,Fair,N,FuseP,572,0,0,572,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Typical,Dirt_Gravel,0,0,72,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,75000,-93.615395,42.020002 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3843,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,174,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1596,GasA,Excellent,Y,SBrkr,1648,0,0,1648,1,1,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,162,53,0,153,0,0,No_Pool,No_Fence,None,0,6,2009,New,Partial,230000,-93.6165709,42.008683 +One_Story_1945_and_Older,I_all,109,21780,Grvl,No_Alley_Access,Regular,Lvl,NoSewr,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Poor,Below_Average,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseA,810,0,0,810,0,0,1,0,1,1,Typical,4,Min1,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Dirt_Gravel,119,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,ConLD,Normal,57625,-93.588227,42.0183 +Two_Story_1946_and_Newer,Residential_Low_Density,75,10125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2001,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,BLQ,250,412,1107,GasA,Excellent,Y,SBrkr,1040,1012,0,2052,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,3,642,Typical,Typical,Paved,210,91,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,251000,-93.606944,41.996888 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,48,12822,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,83,1434,GasA,Excellent,Y,SBrkr,1518,631,0,2149,1,0,1,1,1,1,Good,6,Typ,1,Excellent,Attchd,RFn,2,670,Typical,Typical,Paved,168,43,0,0,198,0,No_Pool,No_Fence,None,0,8,2009,WD ,Abnorml,239686,-93.607252,41.996672 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,12118,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,Stone,108,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,1710,1710,GasA,Excellent,Y,SBrkr,1710,0,0,1710,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,100,48,0,0,180,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,240000,-93.607055,41.997119 +Two_Story_1946_and_Newer,Residential_Low_Density,71,12209,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Typical,No,ALQ,1,Unf,0,114,804,GasA,Excellent,Y,SBrkr,804,1157,0,1961,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,560,Typical,Typical,Paved,125,192,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,215000,-93.603717,41.99539 +Split_Foyer,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,135,902,GasA,Excellent,Y,SBrkr,926,0,0,926,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Unf,1,351,Typical,Typical,Paved,319,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,156450,-93.604716,41.997067 +Split_Foyer,Residential_Low_Density,72,9360,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Good,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,LwQ,116,0,957,GasA,Typical,Y,SBrkr,1287,0,0,1287,1,0,1,1,2,1,Typical,5,Typ,2,Fair,Attchd,RFn,2,541,Typical,Typical,Paved,302,39,0,0,120,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Normal,173000,-93.604491,41.997069 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,10125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_and_Half_Fin,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1302,432,0,1734,0,0,2,0,4,2,Good,8,Typ,0,No_Fireplace,Attchd,Unf,2,539,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,COD,Normal,126000,-93.604519,41.996919 +Split_or_Multilevel,Residential_Low_Density,0,9947,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Good,Average,1990,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,577,1188,GasA,Excellent,Y,SBrkr,1217,0,0,1217,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,497,Typical,Typical,Paved,168,27,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,173000,-93.602356,41.995905 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,11070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,1527,1582,GasA,Typical,Y,SBrkr,1595,0,0,1595,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,COD,Abnorml,152000,-93.602424,41.996066 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14375,Pave,No_Alley_Access,Slightly_Irregular,Lvl,NoSeWa,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1958,1958,Gable,CompShg,HdBoard,HdBoard,BrkFace,541,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,354,354,819,GasA,Good,Y,FuseA,1344,0,0,1344,0,1,1,0,3,1,Good,7,Typ,1,Good,Basment,RFn,2,525,Typical,Typical,Paved,0,118,0,0,233,0,No_Pool,No_Fence,None,0,1,2009,COD,Abnorml,137500,-93.64978,42.001246 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,150,215245,Pave,No_Alley_Access,Irregular,Low,AllPub,Inside,Sev,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,1965,1965,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Rec,820,80,2136,GasW,Typical,Y,SBrkr,2036,0,0,2036,2,0,2,0,3,1,Typical,8,Typ,2,Good,Attchd,RFn,2,513,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,375000,-93.652119,42.00138 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,12898,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,70,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,598,1620,GasA,Excellent,Y,SBrkr,1620,0,0,1620,1,0,2,0,2,1,Excellent,6,Typ,1,Excellent,Attchd,Fin,3,912,Typical,Typical,Paved,228,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2009,WD ,Normal,315500,-93.65315,41.995027 +Two_Story_1946_and_Newer,Residential_Low_Density,83,13159,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,846,846,GasA,Good,Y,SBrkr,846,846,0,1692,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,650,Typical,Typical,Paved,208,114,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,New,Partial,224500,-93.651832,41.995058 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,113,13438,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,CemntBd,CmentBd,Stone,246,Excellent,Typical,CBlock,Excellent,Typical,Gd,GLQ,3,Unf,0,432,2190,GasA,Excellent,Y,SBrkr,2036,0,0,2036,1,0,2,0,3,1,Excellent,9,Typ,1,Excellent,Attchd,Fin,3,780,Typical,Typical,Paved,90,154,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,410000,-93.651867,41.993759 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,14463,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,CemntBd,CmentBd,BrkFace,406,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,526,1641,GasA,Excellent,Y,SBrkr,1641,0,0,1641,1,0,2,0,3,1,Excellent,7,Typ,0,No_Fireplace,Attchd,Fin,3,885,Typical,Typical,Paved,0,95,0,0,0,0,No_Pool,No_Fence,None,0,1,2009,WD ,Normal,316500,-93.653039,41.994727 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8925,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1450,1466,GasA,Excellent,Y,SBrkr,1466,0,0,1466,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,610,Typical,Typical,Paved,100,18,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,201000,-93.650453,41.994695 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,130,11457,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Mn,GLQ,3,Unf,0,387,1392,GasA,Typical,Y,SBrkr,1412,0,0,1412,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,576,Typical,Typical,Paved,0,0,169,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,175000,-93.646455,41.997684 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9839,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1980,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Good,Typical,No,ALQ,1,Unf,0,250,712,GasA,Excellent,Y,SBrkr,1375,862,0,2237,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,440,Typical,Typical,Paved,305,24,0,0,0,0,No_Pool,No_Fence,Shed,2500,2,2009,WD ,Normal,204000,-93.644884,41.998952 +Split_or_Multilevel,Residential_Low_Density,125,14419,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Good,Average,1987,1989,Hip,CompShg,Plywood,Plywood,BrkFace,310,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,ALQ,624,117,1645,GasA,Excellent,Y,SBrkr,1479,0,0,1479,2,0,2,1,3,1,Good,7,Min1,1,Fair,Attchd,Fin,2,578,Typical,Typical,Paved,224,238,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,213500,-93.6446004,41.9981656 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6853,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,136,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,262,1267,GasA,Excellent,Y,SBrkr,1296,0,0,1296,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,471,Typical,Typical,Paved,192,25,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,220000,-93.646793,41.996327 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9157,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,912,912,GasA,Excellent,Y,SBrkr,1072,942,0,2014,0,0,2,1,3,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,486,Typical,Typical,Paved,124,114,0,0,0,0,No_Pool,No_Fence,None,0,2,2009,WD ,Abnorml,170000,-93.646638,41.996093 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14601,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,584,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,578,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,1,0,2,0,2,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,765,Typical,Typical,Paved,270,68,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,315000,-93.646816,41.995359 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,12633,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,Stone,290,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,338,1978,GasA,Excellent,Y,SBrkr,1978,0,0,1978,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,920,Typical,Typical,Paved,308,52,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,425000,-93.651703,41.992964 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,12518,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,182,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,476,1008,GasA,Good,Y,SBrkr,1008,0,0,1008,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,384,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,139500,-93.6042638,41.9935397 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,21750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Below_Average,1960,2006,Hip,CompShg,HdBoard,HdBoard,BrkFace,75,Typical,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1771,0,0,1771,0,0,1,0,3,1,Typical,9,Min1,1,Typical,Attchd,Unf,2,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,11,2009,WD ,Normal,115000,-93.608359,41.992193 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,320,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,338,1204,GasA,Excellent,Y,SBrkr,1204,0,0,1204,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,412,Typical,Typical,Paved,0,247,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,162000,-93.608196,41.993128 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1999,1999,Hip,CompShg,VinylSd,VinylSd,BrkFace,425,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,341,1224,GasA,Excellent,Y,SBrkr,1224,0,0,1224,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,402,Typical,Typical,Paved,0,304,0,0,0,0,No_Pool,No_Fence,None,0,6,2009,WD ,Normal,165000,-93.60821,41.992122 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1596,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SLvl,Below_Average,Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,462,GasA,Typical,Y,SBrkr,526,462,0,988,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,BuiltIn,Unf,1,297,Typical,Typical,Paved,120,101,0,0,0,0,No_Pool,Good_Wood,None,0,11,2009,WD ,Normal,91000,-93.604252,41.991786 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,9858,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,354,864,GasA,Typical,Y,SBrkr,874,0,0,874,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,33,0,0,0,0,0,No_Pool,Good_Wood,Shed,600,11,2009,WD ,Normal,130000,-93.604218,41.992919 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1526,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Very_Good,1970,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,115,630,GasA,Typical,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,86000,-93.603479,41.992185 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,13383,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1969,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,594,1188,GasA,Excellent,Y,SBrkr,1404,0,0,1404,0,0,2,0,3,1,Typical,7,Typ,1,Poor,Attchd,Unf,2,504,Typical,Typical,Paved,0,16,0,0,0,0,No_Pool,No_Fence,None,0,3,2009,WD ,Normal,160000,-93.600636,41.9928219 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1477,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,SFoyer,Below_Average,Below_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,121,630,GasA,Typical,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2009,WD ,Normal,80000,-93.603111,41.99217 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Above_Average,1970,2008,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Typical,Y,SBrkr,798,546,0,1344,0,0,1,1,3,1,Typical,6,Typ,1,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,97000,-93.601975,41.992539 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2217,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Below_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,273,0,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,238,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2009,WD ,Normal,88000,-93.60176,41.991786 +Split_Foyer,Residential_Low_Density,50,7689,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Very_Good,1972,1972,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,BLQ,76,0,796,GasA,Good,Y,SBrkr,796,0,0,796,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,138,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2009,WD ,Normal,131900,-93.601127,41.991365 +Split_or_Multilevel,Residential_Low_Density,62,7706,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Average,1993,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,Av,Rec,6,GLQ,270,0,384,GasA,Excellent,Y,SBrkr,1091,0,0,1091,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,429,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,No_Fence,Shed,700,8,2009,WD ,Normal,131250,-93.606688,41.987535 +Split_Foyer,Residential_Low_Density,0,9101,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,104,Typical,Good,PConc,Good,Typical,Av,GLQ,3,Unf,0,0,1097,GasA,Excellent,Y,SBrkr,1110,0,0,1110,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,602,Typical,Typical,Paved,303,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,165500,-93.604928,41.988776 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8780,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1985,1985,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,208,833,GasA,Excellent,Y,SBrkr,833,0,0,833,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2009,WD ,Normal,112000,-93.603867,41.988222 +Split_Foyer,Residential_Low_Density,70,7669,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1992,1993,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,LwQ,110,0,828,GasA,Typical,Y,SBrkr,883,0,0,883,1,0,1,0,2,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,698,Typical,Typical,Paved,100,32,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2009,WD ,Normal,149000,-93.602777,41.987659 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,85,14115,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1993,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,Wood,Good,Typical,No,GLQ,3,Unf,0,64,796,GasA,Excellent,Y,SBrkr,796,566,0,1362,1,0,1,1,1,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,40,30,0,320,0,0,No_Pool,Minimum_Privacy,Shed,700,10,2009,WD ,Normal,143000,-93.609453,41.986502 +Two_Story_1946_and_Newer,Residential_Low_Density,62,10429,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Average,1992,1992,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,294,624,GasA,Typical,Y,SBrkr,624,663,0,1287,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,150,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2009,WD ,Normal,130000,-93.606925,41.986509 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9819,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,ImStucc,None,0,Typical,Typical,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,0,1567,GasA,Typical,Y,SBrkr,1567,0,0,1567,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,RFn,2,714,Typical,Typical,Paved,264,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2009,WD ,Normal,196000,-93.599866,41.991493 +Two_Story_1946_and_Newer,Residential_Low_Density,70,10457,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Good,1969,1969,Gable,CompShg,VinylSd,VinylSd,BrkFace,178,Good,Excellent,CBlock,Typical,Typical,Gd,BLQ,2,LwQ,288,0,784,GasA,Excellent,Y,SBrkr,784,848,0,1632,0,0,1,1,4,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,898,Typical,Typical,Paved,0,173,368,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2009,WD ,Normal,173000,-93.600191,41.99095 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,11029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1958,2002,Hip,CompShg,MetalSd,MetalSd,None,0,Excellent,Typical,CBlock,Good,Typical,No,ALQ,1,BLQ,411,245,1184,GasA,Excellent,Y,SBrkr,1414,0,0,1414,1,0,1,0,3,1,Typical,6,Min1,1,Typical,Attchd,Unf,2,601,Typical,Typical,Paved,0,51,0,0,190,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,176500,-93.6188286,42.0525525 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12925,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1970,1970,Gable,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,340,1205,GasA,Excellent,Y,SBrkr,2117,0,0,2117,0,0,2,1,4,1,Typical,7,Typ,2,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,237500,-93.6163289,42.051446 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11075,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,ALQ,1,LwQ,276,176,952,GasA,Typical,Y,SBrkr,1092,1020,0,2112,0,0,2,1,4,1,Typical,9,Typ,2,Good,Attchd,Unf,2,576,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,206900,-93.617424,42.049587 +Two_Story_1946_and_Newer,Residential_Low_Density,72,8702,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,220,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,470,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,187500,-93.639062,42.05996 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8139,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,119,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,204,680,GasA,Good,Y,SBrkr,680,790,0,1470,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,420,Typical,Typical,Paved,192,49,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,165000,-93.637796,42.061231 +Two_Story_1946_and_Newer,Residential_Low_Density,59,9535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,75,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,472,Typical,Typical,Paved,100,82,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,195500,-93.638775,42.060771 +Two_Story_1946_and_Newer,Residential_Low_Density,59,9042,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,115,943,GasA,Good,Y,SBrkr,943,695,0,1638,1,0,2,1,3,1,Typical,7,Typ,2,Typical,Attchd,Fin,2,472,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,192000,-93.638778,42.060822 +Two_Story_1946_and_Newer,Residential_Low_Density,0,15038,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1996,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,778,916,GasA,Good,Y,SBrkr,916,720,0,1636,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,386,Typical,Typical,Paved,168,84,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,173000,-93.637482,42.060369 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,14137,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Average,Average,1996,1997,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,898,1348,GasA,Good,Y,SBrkr,1384,0,0,1384,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,404,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,177900,-93.636718,42.060639 +Two_Story_1946_and_Newer,Residential_Low_Density,57,21872,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,FR2,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,125,729,GasA,Excellent,Y,SBrkr,729,717,0,1446,0,1,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,406,Typical,Typical,Paved,264,22,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,175000,-93.636948,42.0623 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,4923,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,440,1593,GasA,Excellent,Y,SBrkr,1593,0,0,1593,1,0,1,1,0,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,682,Typical,Typical,Paved,0,120,0,0,224,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,286000,-93.633876,42.062976 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,45,6264,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,1997,1997,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,1008,1664,GasA,Excellent,Y,SBrkr,1682,0,0,1682,1,0,1,1,1,1,Good,6,Min1,1,Typical,Attchd,Fin,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,247900,-93.633781,42.061374 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,5395,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,604,1337,GasA,Good,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,462,Typical,Typical,Paved,96,0,70,168,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,180000,-93.633905,42.060865 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,39,5070,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1280,1280,GasA,Excellent,Y,SBrkr,1280,0,0,1280,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,506,Typical,Typical,Paved,0,82,0,0,144,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,180000,-93.633797,42.06076 +Two_Story_1946_and_Newer,Residential_Low_Density,100,10839,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,926,926,GasA,Excellent,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,470,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,181000,-93.639151,42.059408 +Two_Story_1946_and_Newer,Residential_Low_Density,73,11184,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,932,932,GasA,Good,Y,SBrkr,932,701,0,1633,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,460,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Family,183000,-93.639007,42.059364 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,554,773,GasA,Good,Y,SBrkr,773,885,0,1658,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,431,Typical,Typical,Paved,224,84,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,188000,-93.637763,42.059411 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10832,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,712,712,GasA,Excellent,Y,SBrkr,1086,809,0,1895,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,409,Typical,Typical,Paved,143,46,0,0,0,0,No_Pool,No_Fence,Shed,500,10,2008,WD ,Normal,194500,-93.636798,42.058445 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14067,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,194,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,332,836,GasA,Good,Y,SBrkr,851,858,0,1709,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,416,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2008,WD ,Normal,185000,-93.637279,42.05701 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,4671,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1988,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,461,1228,GasA,Good,Y,SBrkr,1228,0,0,1228,1,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,472,Typical,Typical,Paved,168,120,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,189000,-93.632013,42.059518 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,65,5950,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1018,1337,GasA,Good,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,462,Typical,Typical,Paved,0,73,154,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,188500,-93.631581,42.059636 +Two_Story_1946_and_Newer,Residential_Low_Density,101,13543,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,130,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1152,1168,GasA,Excellent,Y,SBrkr,1168,1332,0,2500,0,0,3,1,5,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,683,Typical,Typical,Paved,192,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,355000,-93.628831,42.061394 +Two_Story_1946_and_Newer,Residential_Low_Density,77,11198,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,245,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1122,1122,GasA,Excellent,Y,SBrkr,1134,1370,0,2504,0,0,2,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,656,Typical,Typical,Paved,144,39,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,325000,-93.628901,42.06056 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,15401,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,296,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,547,1884,GasA,Excellent,Y,SBrkr,1884,0,0,1884,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,670,Typical,Typical,Paved,214,76,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,387000,-93.627911,42.060241 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,31220,Pave,No_Alley_Access,Slightly_Irregular,Bnk,NoSewr,FR2,Gtl,Gilbert,Feedr,Norm,OneFam,One_Story,Above_Average,Poor,1952,1952,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1632,1632,GasA,Typical,Y,FuseA,1474,0,0,1474,0,0,1,0,3,1,Typical,7,Min2,2,Good,Attchd,Unf,2,495,Typical,Typical,Paved,0,0,144,0,0,0,No_Pool,No_Fence,Shed,750,5,2008,WD ,Normal,115000,-93.622299,42.059792 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,60,8118,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Stone_Brook,PosN,PosN,TwnhsE,One_Story,Excellent,Average,2007,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,178,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,676,1710,GasA,Excellent,Y,SBrkr,1710,0,0,1710,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,557,Typical,Typical,Paved,156,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,334000,-93.629808,42.058896 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,47280,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,1950,1950,Hip,CompShg,AsbShng,AsbShng,BrkFace,44,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1488,1488,GasA,Good,Y,SBrkr,1488,0,0,1488,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,738,Typical,Typical,Paved,0,0,180,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Family,133000,-93.6225667,42.0586718 +Two_Story_1946_and_Newer,Residential_Low_Density,46,20544,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1986,1991,Gable,CompShg,Plywood,Plywood,BrkFace,123,Typical,Good,CBlock,Good,Typical,No,Unf,7,Unf,0,791,791,GasA,Good,Y,SBrkr,1236,857,0,2093,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,542,Typical,Typical,Paved,364,63,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2008,WD ,Normal,215000,-93.6391494,42.056015 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,12680,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1988,1988,Gable,CompShg,Plywood,Wd Sdng,BrkFace,102,Good,Typical,CBlock,Good,Good,Mn,GLQ,3,Unf,0,692,1675,GasA,Excellent,Y,SBrkr,1688,0,0,1688,1,0,2,0,2,1,Excellent,6,Typ,1,Excellent,Attchd,RFn,2,528,Typical,Typical,Paved,0,48,0,0,141,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,226500,-93.639715,42.054514 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,12090,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1981,1981,Gable,CompShg,MetalSd,MetalSd,BrkFace,210,Typical,Good,CBlock,Good,Typical,No,GLQ,3,LwQ,228,606,1422,GasA,Typical,Y,SBrkr,1422,0,0,1422,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,576,Typical,Typical,Paved,276,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,175500,-93.637825,42.054562 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10825,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1983,1983,Gable,CompShg,WdShing,Plywood,BrkFace,174,Good,Typical,CBlock,Good,Typical,Mn,GLQ,3,Unf,0,513,1260,GasA,Typical,Y,SBrkr,1260,0,0,1260,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,598,Typical,Typical,Paved,152,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,181900,-93.637808,42.054439 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,56,18559,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1978,1978,Hip,CompShg,Plywood,Plywood,BrkFace,383,Good,Good,CBlock,Good,Typical,No,GLQ,3,Rec,186,656,2048,GasA,Typical,Y,SBrkr,2064,0,0,2064,1,0,2,0,3,1,Good,7,Typ,2,Fair,Attchd,Fin,2,550,Typical,Typical,Paved,200,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,235000,-93.636511,42.055358 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1979,1979,Hip,CompShg,Plywood,Plywood,BrkFace,194,Good,Typical,CBlock,Good,Fair,No,ALQ,1,LwQ,449,469,1782,GasA,Typical,Y,SBrkr,1782,0,0,1782,0,1,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,551,Typical,Typical,Paved,467,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,220000,-93.636505,42.054503 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12227,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Good,1977,1995,Gable,CompShg,HdBoard,HdBoard,BrkFace,424,Typical,Good,CBlock,Good,Good,No,ALQ,1,Unf,0,434,1330,GasA,Typical,Y,SBrkr,1542,1330,0,2872,1,0,2,1,4,1,Typical,11,Typ,1,Typical,Attchd,Fin,2,619,Typical,Typical,Paved,550,282,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,272000,-93.635111,42.054455 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13068,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1976,1976,Hip,CompShg,HdBoard,HdBoard,BrkFace,621,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Rec,48,273,1211,GasA,Typical,Y,SBrkr,1211,0,0,1211,1,0,2,0,3,1,Good,6,Typ,1,Poor,Attchd,Fin,2,461,Typical,Typical,Paved,0,0,0,174,0,0,No_Pool,Minimum_Privacy,None,0,11,2008,WD ,Normal,161000,-93.630299,42.054211 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15611,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1977,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,ALQ,1,LwQ,93,266,1126,GasA,Typical,Y,SBrkr,1126,0,0,1126,0,1,2,0,3,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,540,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Abnorml,175000,-93.633168,42.055432 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1980,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,280,Typical,Typical,CBlock,Good,Typical,Mn,Unf,7,Unf,0,738,738,GasA,Typical,Y,SBrkr,1277,767,0,2044,0,0,2,1,3,1,Typical,7,Min1,1,Typical,Attchd,Unf,2,489,Typical,Typical,Paved,28,73,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,185000,-93.635153,42.052544 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9743,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1969,1969,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,440,720,GasA,Good,Y,SBrkr,720,588,0,1308,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,146900,-93.637105,42.050326 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12511,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1978,1978,Mansard,WdShake,Plywood,Plywood,BrkFace,168,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,432,1420,GasA,Excellent,Y,SBrkr,1420,1420,0,2840,0,1,2,1,4,1,Good,8,Min2,2,Good,Attchd,Fin,4,1314,Typical,Good,Paved,0,16,0,0,208,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Normal,292500,-93.63719,42.051473 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,104,11361,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,160,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,549,1193,GasA,Typical,Y,SBrkr,1523,0,0,1523,0,1,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,478,Typical,Typical,Paved,0,0,0,0,189,0,No_Pool,Minimum_Privacy,None,0,5,2008,COD,Abnorml,180000,-93.633905,42.050679 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1444,1444,GasA,Typical,Y,SBrkr,1444,0,0,1444,0,0,2,0,2,1,Typical,5,Typ,1,Good,Attchd,Unf,2,473,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,Good_Wood,None,0,4,2008,WD ,Normal,159900,-93.635216,42.049449 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1976,1976,Hip,CompShg,Plywood,Plywood,BrkFace,660,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,400,803,GasA,Typical,Y,SBrkr,1098,866,0,1964,0,0,2,1,4,1,Typical,8,Typ,1,Good,Attchd,RFn,2,483,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,205000,-93.6341769,42.04986 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1973,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,92,1176,GasA,Good,Y,SBrkr,1178,0,0,1178,0,1,1,1,3,1,Good,5,Typ,1,Fair,Attchd,Unf,2,439,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2008,WD ,Normal,157000,-93.631129,42.049609 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14311,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,402,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,213,1236,GasA,Excellent,Y,SBrkr,1236,1104,0,2340,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,RFn,3,787,Typical,Typical,Paved,192,180,218,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,306000,-93.630231,42.051074 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,Duplex,Two_Story,Average,Average,1974,1974,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,Unf,7,Unf,0,896,896,GasA,Typical,Y,SBrkr,896,896,0,1792,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,133000,-93.626544,42.056306 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,438,14,1054,GasA,Good,Y,SBrkr,1054,0,0,1054,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,460,Typical,Typical,Paved,180,0,0,0,80,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,151000,-93.627875,42.05539 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,10295,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,72,Typical,Typical,CBlock,Good,Typical,Mn,Rec,6,Unf,0,684,936,GasA,Typical,Y,SBrkr,936,0,0,936,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,16,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,COD,Normal,111900,-93.626511,42.055304 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1971,1971,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,Rec,6,ALQ,613,132,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,123000,-93.630208,42.054328 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12735,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,264,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,216,0,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2008,COD,Normal,111250,-93.624265,42.0564348 +One_Story_PUD_1946_and_Newer,Residential_High_Density,34,4060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1998,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1139,1405,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,511,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,COD,Abnorml,181000,-93.624873,42.054693 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,359,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,25,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,1,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,52,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,103400,-93.628971,42.052794 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,158,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,153,483,GasA,Typical,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,100000,-93.627629,42.052755 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,422,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,483,GasA,Good,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,411,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,100500,-93.627114,42.051479 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1869,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,127,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,162,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,106000,-93.629462,42.052332 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,96,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,COD,Abnorml,85400,-93.62765,42.051678 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Above_Average,Fair,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,604,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,125,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,89500,-93.627309,42.051687 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,356,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,280,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,111750,-93.629425,42.051659 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4043,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,510,1069,GasA,Typical,Y,SBrkr,1069,0,0,1069,0,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,0,55,0,0,200,0,No_Pool,No_Fence,None,0,10,2008,COD,Abnorml,140000,-93.627234,42.050001 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,LwQ,294,275,855,GasA,Good,Y,SBrkr,855,601,0,1456,0,0,2,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,460,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,143000,-93.625986,42.05068 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2529,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Above_Average,1977,1977,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,677,1055,GasA,Fair,Y,SBrkr,1055,0,0,1055,0,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,148500,-93.625848,42.050257 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9477,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,65,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,524,864,GasA,Typical,Y,SBrkr,892,0,0,892,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,10,2008,WD ,Normal,110000,-93.62649,42.04845 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,96,12444,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,426,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,596,1932,GasA,Excellent,Y,SBrkr,1932,0,0,1932,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,774,Typical,Typical,Paved,0,66,0,304,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,394617,-93.658873,42.062046 +Two_Story_1946_and_Newer,Residential_Low_Density,96,12474,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,272,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,402,1682,GasA,Excellent,Y,SBrkr,1742,590,0,2332,1,0,2,1,3,1,Excellent,9,Typ,1,Excellent,BuiltIn,Fin,3,846,Typical,Typical,Paved,196,134,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,New,Partial,426000,-93.6573126,42.0632692 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,114,14803,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,PosN,OneFam,One_Story,Very_Excellent,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,BrkFace,816,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,442,2078,GasA,Excellent,Y,SBrkr,2084,0,0,2084,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1220,Typical,Typical,Paved,188,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,New,Partial,385000,-93.652941,42.063129 +Two_Story_1946_and_Newer,Residential_Low_Density,67,14948,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,268,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,122,1452,GasA,Excellent,Y,SBrkr,1476,1237,0,2713,1,0,2,1,3,1,Excellent,11,Typ,1,Good,Attchd,Fin,3,858,Typical,Typical,Paved,126,66,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,446261,-93.652744,42.062925 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,12704,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,302,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,570,1582,GasA,Excellent,Y,SBrkr,1582,0,0,1582,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,905,Typical,Typical,Paved,209,95,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,New,Partial,317500,-93.654642,42.062259 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,436,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,310,1710,GasA,Excellent,Y,SBrkr,1710,0,0,1710,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,866,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,New,Partial,372402,-93.6566551,42.0624143 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,13693,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,554,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,BLQ,495,195,2418,GasA,Excellent,Y,SBrkr,2464,0,0,2464,1,0,2,1,4,1,Excellent,9,Typ,1,Excellent,Attchd,Fin,3,650,Typical,Typical,Paved,358,78,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,417500,-93.657832,42.062432 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,104,14418,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,480,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,575,1950,GasA,Excellent,Y,SBrkr,1950,0,0,1950,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,706,Typical,Typical,Paved,156,207,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,383000,-93.657889,42.061133 +Two_Story_1946_and_Newer,Residential_Low_Density,108,13418,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,270,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,430,1850,GasA,Excellent,Y,SBrkr,1850,898,0,2748,1,0,2,1,4,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,850,Typical,Typical,Paved,212,182,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Abnorml,390000,-93.657188,42.06027 +Two_Story_1946_and_Newer,Residential_Low_Density,96,12539,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,PosN,Norm,OneFam,Two_Story,Very_Excellent,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,468,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,538,1620,GasA,Excellent,Y,SBrkr,1632,1158,0,2790,1,0,2,1,4,1,Excellent,10,Typ,1,Excellent,BuiltIn,Fin,4,1150,Typical,Typical,Paved,30,200,0,0,192,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,460000,-93.656801,42.060427 +Two_Story_1946_and_Newer,Residential_Low_Density,102,12151,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2005,Gable,CompShg,CemntBd,CmentBd,BrkFace,368,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,165,1414,GasA,Excellent,Y,SBrkr,1414,917,0,2331,1,0,2,1,3,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,1003,Typical,Typical,Paved,192,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,379000,-93.65369,42.060766 +Two_Story_1946_and_Newer,Residential_Low_Density,74,8899,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,108,Excellent,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,908,948,GasA,Excellent,Y,SBrkr,948,1140,0,2088,0,0,2,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,656,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,250000,-93.654734,42.060425 +Two_Story_1946_and_Newer,Residential_Low_Density,85,10574,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,126,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,292,1148,GasA,Excellent,Y,SBrkr,1170,1162,0,2332,1,0,2,1,4,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,756,Typical,Typical,Paved,224,142,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,316000,-93.654443,42.0599011 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,106,12720,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2003,2003,Hip,CompShg,MetalSd,MetalSd,Stone,680,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,278,2535,GasA,Excellent,Y,SBrkr,2470,0,0,2470,2,0,1,1,1,1,Excellent,7,Typ,2,Good,Attchd,Fin,3,789,Typical,Typical,Paved,154,65,0,0,216,144,Excellent,No_Fence,None,0,2,2008,WD ,Normal,615000,-93.656958,42.058484 +Two_Story_1946_and_Newer,Residential_Low_Density,110,13688,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,664,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,556,1572,GasA,Excellent,Y,SBrkr,1572,1096,0,2668,1,0,2,1,3,1,Excellent,10,Typ,2,Good,BuiltIn,Fin,3,726,Typical,Typical,Paved,400,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,412500,-93.65572,42.058683 +Two_Story_1946_and_Newer,Residential_Low_Density,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,292,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,132,998,GasA,Excellent,Y,SBrkr,1006,1040,0,2046,1,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,RFn,3,871,Typical,Typical,Paved,320,62,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,284000,-93.652549,42.058573 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,92,10845,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,504,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,454,1603,GasA,Excellent,Y,SBrkr,1575,0,0,1575,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,732,Typical,Typical,Paved,216,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,284000,-93.651216,42.058649 +Two_Story_1946_and_Newer,Residential_Low_Density,130,16900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,1110,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,404,1479,GasA,Excellent,Y,SBrkr,1515,1134,0,2649,1,0,2,1,4,1,Good,10,Typ,1,Typical,Attchd,Fin,3,746,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,421250,-93.655587,42.057028 +Two_Story_1946_and_Newer,Residential_Low_Density,112,16451,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,221,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1765,1765,GasA,Excellent,Y,SBrkr,1804,886,0,2690,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,795,Typical,Typical,Paved,268,58,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,370000,-93.654914,42.058694 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,58,10110,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,492,Excellent,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1486,1858,GasA,Excellent,Y,SBrkr,1866,0,0,1866,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,870,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,336860,-93.654187,42.057164 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,65,8769,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2008,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,766,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,162,1702,GasA,Excellent,Y,SBrkr,1702,0,0,1702,1,0,1,1,1,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1052,Typical,Typical,Paved,0,72,0,0,224,0,No_Pool,No_Fence,None,0,10,2008,New,Partial,367294,-93.6513669,42.0579589 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,135,12304,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,144,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1347,1367,GasA,Excellent,Y,SBrkr,1367,0,0,1367,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,192000,-93.649744,42.0582 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,62,12677,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2003,2004,Hip,CompShg,MetalSd,MetalSd,BrkFace,472,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,300,1518,GasA,Excellent,Y,SBrkr,1518,0,0,1518,0,0,1,1,1,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,588,Typical,Typical,Paved,185,140,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,274000,-93.653868,42.056766 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,63,8849,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,616,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1656,1684,GasA,Excellent,Y,SBrkr,1684,0,0,1684,0,0,2,0,2,1,Excellent,6,Typ,1,Excellent,Attchd,RFn,2,564,Typical,Typical,Paved,495,72,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,266000,-93.652769,42.0570189 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,89,8232,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2007,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,714,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,596,1800,GasA,Excellent,Y,SBrkr,1800,0,0,1800,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,3,944,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,New,Partial,370967,-93.651845,42.057446 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,496,1342,GasA,Excellent,Y,SBrkr,1342,0,0,1342,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,234250,-93.651163,42.057179 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,196,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1318,1342,GasA,Excellent,Y,SBrkr,1342,0,0,1342,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,550,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,219500,-93.6514243,42.0571198 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2448,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,764,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,0,0,2,1,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,155000,-93.649997,42.057468 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,36,2448,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,191,764,GasA,Excellent,Y,SBrkr,764,862,0,1626,1,0,2,1,2,1,Good,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,474,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,154000,-93.650607,42.057094 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,59,8198,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,146,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,638,1358,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,207000,-93.65089,42.056616 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3940,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,143,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,342,1415,GasA,Excellent,Y,SBrkr,1455,0,0,1455,1,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,3,644,Typical,Typical,Paved,156,20,0,0,144,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,195000,-93.640176,42.063336 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,3940,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,24,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,306,1393,GasA,Excellent,Y,SBrkr,1576,0,0,1576,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,RFn,3,668,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,219990,-93.640958,42.062299 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,1330,1346,GasA,Excellent,Y,SBrkr,1504,0,0,1504,0,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,437,Typical,Typical,Paved,156,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,191000,-93.6424399,42.062285 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,3182,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1266,1266,GasA,Excellent,Y,SBrkr,1266,0,0,1266,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,388,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,159895,-93.643039,42.062004 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,3710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,WdShing,Wd Shng,BrkFace,20,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1146,1146,GasA,Excellent,Y,SBrkr,1246,0,0,1246,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,428,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,New,Partial,187687,-93.642326,42.063301 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9024,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,789,789,GasA,Excellent,Y,SBrkr,813,702,0,1515,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,179000,-93.642641,42.061316 +Two_Story_1946_and_Newer,Residential_Low_Density,62,7415,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,80,839,GasA,Excellent,Y,SBrkr,864,729,0,1593,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,398,Typical,Typical,Paved,100,75,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,193000,-93.643522,42.059635 +Split_or_Multilevel,Residential_Low_Density,59,9587,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,182,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,201,856,GasA,Excellent,Y,SBrkr,1166,0,0,1166,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,212,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,190000,-93.643964,42.061544 +Two_Story_1946_and_Newer,Residential_Low_Density,51,8029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,176000,-93.641771,42.0609759 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8010,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,880,0,1720,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,138,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,188000,-93.641416,42.06132 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8396,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1139,0,1986,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,120,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,213000,-93.640051,42.061312 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,55,7301,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,474,1350,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,26,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,217300,-93.649587,42.058377 +Two_Story_1946_and_Newer,Residential_Low_Density,93,10261,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,318,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,936,936,GasA,Excellent,Y,SBrkr,962,830,0,1792,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,451,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,186500,-93.641331,42.057986 +Two_Story_1946_and_Newer,Residential_Low_Density,71,8220,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,647,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,438,982,GasA,Excellent,Y,SBrkr,1008,884,0,1892,0,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,431,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,226750,-93.642397,42.058595 +Two_Story_1946_and_Newer,Residential_Low_Density,60,15384,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,64,788,GasA,Excellent,Y,SBrkr,788,702,0,1490,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,388,Typical,Typical,Paved,100,75,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,184000,-93.6442698,42.0600066 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,707,707,GasA,Excellent,Y,SBrkr,707,707,0,1414,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,403,Typical,Typical,Paved,100,35,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,176000,-93.643632,42.059507 +Two_Story_1946_and_Newer,Residential_Low_Density,41,12460,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,167,1037,GasA,Excellent,Y,SBrkr,1037,1285,0,2322,0,0,2,1,4,1,Typical,8,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,144,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,225000,-93.641342,42.05695 +Two_Story_1946_and_Newer,Residential_Low_Density,77,8390,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,831,831,GasA,Excellent,Y,SBrkr,873,778,0,1651,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,450,Typical,Typical,Paved,0,103,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,185900,-93.640959,42.058581 +Two_and_Half_Story_All_Ages,Residential_Low_Density,84,9660,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_and_Half_Unf,Very_Good,Average,1997,1997,Hip,CompShg,HdBoard,HdBoard,BrkFace,1290,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1173,1173,GasA,Excellent,Y,SBrkr,1182,1017,0,2199,0,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Fin,2,516,Typical,Typical,Paved,0,131,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,284500,-93.654594,42.053879 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,473,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,484,1470,GasA,Good,Y,SBrkr,1470,1160,0,2630,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,3,696,Typical,Typical,Paved,0,66,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,315000,-93.65412,42.053666 +Two_Story_1946_and_Newer,Residential_Low_Density,84,14260,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,350,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,490,1145,GasA,Excellent,Y,SBrkr,1145,1053,0,2198,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,836,Typical,Typical,Paved,192,84,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,250000,-93.653324,42.055748 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,295,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,1519,1519,GasA,Excellent,Y,SBrkr,1533,639,0,2172,0,0,2,1,4,1,Excellent,8,Typ,1,Typical,BuiltIn,RFn,3,687,Typical,Typical,Paved,162,153,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,291000,-93.650932,42.055777 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,136,11675,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,495,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,322,1982,GasA,Excellent,Y,SBrkr,2006,0,0,2006,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,3,938,Typical,Typical,Paved,144,33,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,350000,-93.651562,42.0522319 +Two_Story_1946_and_Newer,Residential_Low_Density,97,10990,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,213,1064,GasA,Excellent,Y,SBrkr,1064,1061,0,2125,1,0,2,1,4,1,Good,12,Typ,2,Typical,Attchd,RFn,2,576,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,279500,-93.655279,42.052272 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11929,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,466,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1242,1242,GasA,Excellent,Y,SBrkr,1251,1250,0,2501,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,751,Typical,Typical,Paved,192,87,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,290000,-93.656036,42.050383 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,10437,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1995,1995,Hip,CompShg,MetalSd,MetalSd,BrkFace,660,Good,Good,PConc,Good,Typical,Gd,GLQ,3,Unf,0,413,2109,GasA,Excellent,Y,SBrkr,2113,0,0,2113,1,0,2,1,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,839,Typical,Typical,Paved,236,46,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,350000,-93.653929,42.05162 +Two_Story_1946_and_Newer,Residential_Low_Density,96,10542,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Hip,CompShg,Wd Sdng,ImStucc,BrkFace,651,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,138,1311,GasA,Excellent,Y,SBrkr,1325,1093,0,2418,1,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,RFn,3,983,Typical,Typical,Paved,250,154,216,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,341000,-93.65579,42.049501 +Two_Story_1946_and_Newer,Residential_Low_Density,91,10010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Hip,WdShake,VinylSd,VinylSd,BrkFace,320,Good,Typical,PConc,Good,Typical,Av,BLQ,2,GLQ,852,0,1080,GasA,Excellent,Y,SBrkr,1108,1089,0,2197,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Fin,3,783,Typical,Typical,Paved,385,99,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,287500,-93.653908,42.049696 +Two_Story_1946_and_Newer,Residential_Low_Density,81,10944,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,BrkFace,448,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,223,1223,GasA,Excellent,Y,SBrkr,1223,904,0,2127,1,0,2,1,3,1,Good,5,Typ,2,Typical,Attchd,RFn,2,525,Typical,Typical,Paved,171,132,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,271000,-93.651801,42.050692 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,14303,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1994,Hip,CompShg,HdBoard,HdBoard,BrkFace,554,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,672,1986,GasA,Excellent,Y,SBrkr,1987,0,0,1987,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,691,Typical,Typical,Paved,262,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,301500,-93.650996,42.049484 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,92,11932,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,186,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1580,1580,GasA,Excellent,Y,SBrkr,1580,0,0,1580,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,830,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,ConLD,Partial,235128,-93.644107,42.054236 +Two_Story_1946_and_Newer,Residential_Low_Density,86,14598,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,Feedr,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,74,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,894,894,GasA,Excellent,Y,SBrkr,894,1039,0,1933,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,668,Typical,Typical,Paved,100,18,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,214000,-93.644145,42.055157 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11957,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,53,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1550,1574,GasA,Excellent,Y,SBrkr,1574,0,0,1574,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,824,Typical,Typical,Paved,144,104,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,232000,-93.642334,42.055102 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,13253,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,128,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,482,1578,GasA,Good,Y,SBrkr,1578,0,0,1578,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Unf,3,642,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,240000,-93.642108,42.054415 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,14587,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,284,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1498,1498,GasA,Excellent,Y,SBrkr,1506,0,0,1506,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,592,Typical,Typical,Paved,0,174,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,New,Partial,264132,-93.642092,42.054366 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10206,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,468,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1530,1563,GasA,Excellent,Y,SBrkr,1563,0,0,1563,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,758,Typical,Typical,Paved,144,99,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,245000,-93.642376,42.054306 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,12274,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1417,1417,GasA,Excellent,Y,SBrkr,1428,0,0,1428,0,0,2,0,3,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,554,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,New,Partial,227680,-93.643976,42.054319 +Two_Story_1946_and_Newer,Residential_Low_Density,73,9801,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,156,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1341,1341,GasA,Excellent,Y,SBrkr,1341,520,0,1861,0,0,3,0,3,1,Good,7,Typ,1,Good,BuiltIn,RFn,3,851,Typical,Typical,Paved,144,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,257000,-93.64403,42.055517 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9428,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,310,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,226,955,GasA,Excellent,Y,SBrkr,955,919,0,1874,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,880,Typical,Typical,Paved,168,108,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,New,Partial,297900,-93.644024,42.054227 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,10037,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,794,1460,GasA,Excellent,Y,SBrkr,1460,0,0,1460,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,480,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,247000,-93.643788,42.053183 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1339,1363,GasA,Excellent,Y,SBrkr,1372,0,0,1372,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,192,113,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,New,Partial,212700,-93.650397,42.051646 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1372,1372,GasA,Excellent,Y,SBrkr,1372,0,0,1372,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,529,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,New,Partial,250580,-93.650358,42.05165 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,95,11639,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1428,1428,GasA,Excellent,Y,SBrkr,1428,0,0,1428,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,480,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,New,Partial,182000,-93.648255,42.050307 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9803,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,466,866,GasA,Good,Y,SBrkr,866,902,0,1768,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,603,Typical,Typical,Paved,0,108,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,226700,-93.643836,42.052278 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,768,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1053,1053,GasA,Excellent,Y,SBrkr,1053,939,0,1992,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,RFn,2,648,Typical,Typical,Paved,140,45,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,250000,-93.64119,42.052456 +Two_Story_1946_and_Newer,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1026,1026,GasA,Excellent,Y,SBrkr,1026,932,0,1958,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,936,Typical,Typical,Paved,154,210,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,250000,-93.640924,42.052275 +Two_Story_1946_and_Newer,Floating_Village_Residential,85,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,100,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,245,1034,GasA,Excellent,Y,SBrkr,1050,1028,0,2078,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,836,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,New,Partial,339750,-93.6430423,42.0520487 +Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Above_Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,813,813,GasA,Excellent,Y,SBrkr,822,843,0,1665,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,562,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,205950,-93.643692,42.05138 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,292,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1660,1660,GasA,Excellent,Y,SBrkr,1660,0,0,1660,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,660,Typical,Typical,Paved,133,120,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,256000,-93.641651,42.052173 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,62,7500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2008,Gable,CompShg,CemntBd,CmentBd,Stone,210,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,316,1218,GasA,Excellent,Y,SBrkr,1218,0,0,1218,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,462,Typical,Typical,Paved,168,168,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,New,Partial,230348,-93.644018,42.051224 +Two_Story_1946_and_Newer,Floating_Village_Residential,64,8791,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,361,864,GasA,Excellent,Y,SBrkr,864,864,0,1728,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,673,Typical,Typical,Paved,216,56,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,207500,-93.639662,42.050899 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,400,1080,GasA,Excellent,Y,SBrkr,1080,0,0,1080,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,141000,-93.692497,42.037836 +Two_Story_1946_and_Newer,Residential_Low_Density,68,10110,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,ALQ,555,200,835,GasA,Excellent,Y,SBrkr,835,861,0,1696,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,542,Typical,Typical,Paved,143,66,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,188000,-93.692431,42.035271 +Two_Story_1946_and_Newer,Residential_Low_Density,67,12774,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,835,835,GasA,Excellent,Y,SBrkr,835,828,0,1663,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,478,Typical,Typical,Paved,168,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,192500,-93.691695,42.035132 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,126,Typical,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,1095,1175,GasA,Excellent,Y,SBrkr,1175,0,0,1175,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,146000,-93.691836,42.037918 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,13695,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,468,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,159000,-93.690761,42.037806 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,13695,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,300,1114,GasA,Excellent,Y,SBrkr,1114,0,0,1114,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,155000,-93.690364,42.03805 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8366,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,798,798,GasA,Excellent,Y,SBrkr,798,842,0,1640,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,138,45,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,173000,-93.691246,42.036525 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,9260,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1162,1162,GasA,Excellent,Y,SBrkr,1162,0,0,1162,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,483,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,170000,-93.69124,42.037258 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8453,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,38,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,392,754,GasA,Excellent,Y,SBrkr,754,855,0,1609,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,525,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,182000,-93.689769,42.035875 +Two_Story_1946_and_Newer,Residential_Low_Density,50,8480,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,284,886,GasA,Excellent,Y,SBrkr,886,794,0,1680,0,1,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,474,Typical,Typical,Paved,144,96,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,163000,-93.689024,42.035878 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1994,1998,Gable,CompShg,HdBoard,HdBoard,BrkFace,258,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,270,1408,GasA,Excellent,Y,SBrkr,1679,0,0,1679,1,0,2,0,3,1,Good,7,Typ,1,Fair,Attchd,RFn,2,575,Typical,Typical,Paved,224,42,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,193500,-93.688981,42.035878 +Two_Story_1946_and_Newer,Residential_Low_Density,43,14565,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,145,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,295,832,GasA,Excellent,Y,SBrkr,832,825,0,1657,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,483,Typical,Typical,Paved,144,74,0,0,0,0,No_Pool,No_Fence,Shed,2000,11,2008,WD ,Normal,189000,-93.689801,42.034988 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,355,827,GasA,Excellent,Y,SBrkr,827,850,0,1677,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,627,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,190500,-93.688803,42.036089 +Two_Story_1946_and_Newer,Residential_Low_Density,75,8285,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,439,836,GasA,Good,Y,SBrkr,844,893,0,1737,0,1,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,506,Typical,Typical,Paved,192,85,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,175000,-93.687819,42.034535 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7153,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,88,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,78,1278,GasA,Good,Y,SBrkr,1294,0,0,1294,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,496,Typical,Typical,Paved,112,51,0,0,0,0,No_Pool,Good_Wood,None,0,6,2008,WD ,Normal,179200,-93.687831,42.035666 +Two_Story_1946_and_Newer,Residential_Low_Density,76,9291,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,RRNe,Norm,OneFam,Two_Story,Above_Average,Average,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,406,832,GasA,Excellent,Y,SBrkr,832,878,0,1710,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,506,Typical,Typical,Paved,144,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,187000,-93.687847,42.036839 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1993,1994,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,841,598,1604,GasA,Excellent,Y,SBrkr,1617,0,0,1617,1,0,2,0,2,1,Good,5,Typ,1,Fair,Attchd,RFn,2,533,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,193000,-93.686428,42.036501 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,55,7892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1993,1993,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,899,1199,GasA,Excellent,Y,SBrkr,1199,0,0,1199,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,530,Typical,Typical,Paved,0,63,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,153900,-93.686278,42.036466 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Average,1963,1963,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Good,No,BLQ,2,ALQ,799,132,984,GasA,Typical,Y,SBrkr,984,0,0,984,1,0,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,RFn,1,384,Typical,Typical,Paved,145,56,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,8,2008,WD ,Normal,128000,-93.674093,42.036039 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12968,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1962,1962,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,175,912,GasA,Typical,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,140,0,0,0,176,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,144000,-93.675743,42.035179 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1961,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,100,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Good,5,Typ,1,Typical,Detchd,Unf,1,420,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,135000,-93.673965,42.034614 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,TwoFmCon,One_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Fair,Mn,ALQ,1,Unf,0,0,890,GasA,Good,N,SBrkr,890,0,0,890,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,115000,-93.670997,42.03569 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1963,1963,Hip,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,CBlock,Good,Good,No,ALQ,1,Unf,0,375,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,276,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,650,1,2008,COD,Abnorml,119916,-93.672503,42.035703 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6768,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Above_Average,Very_Good,1961,1996,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,80,912,GasA,Good,Y,SBrkr,912,0,0,912,1,1,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,142000,-93.670551,42.03459 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,19508,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Above_Average,Average,1974,1974,Gable,CompShg,HdBoard,ImStucc,BrkFace,144,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,630,1430,GasA,Typical,Y,SBrkr,1430,0,0,1430,0,1,2,0,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,117,108,165,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,192000,-93.66203,42.0375 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,10759,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,No,LwQ,4,ALQ,811,0,1001,GasA,Typical,Y,SBrkr,1001,640,0,1641,0,0,2,0,4,1,Typical,5,Typ,1,Good,Detchd,Unf,2,490,Typical,Typical,Paved,0,0,92,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,WD ,Normal,140000,-93.660522,42.034532 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9205,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1990,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,304,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,226,930,GasA,Excellent,Y,SBrkr,1364,1319,0,2683,1,0,2,1,4,1,Good,9,Typ,2,Good,Attchd,RFn,2,473,Typical,Typical,Paved,237,251,0,0,196,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,290000,-93.653482,42.047666 +Two_Story_1946_and_Newer,Residential_Low_Density,105,11025,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_and_Half_Fin,Excellent,Average,1993,1994,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,568,Good,Typical,PConc,Good,Typical,Gd,BLQ,2,Unf,0,1328,1848,GasA,Excellent,Y,SBrkr,1827,959,0,2786,1,0,2,1,4,1,Good,10,Typ,1,Excellent,Attchd,Fin,2,636,Typical,Typical,Paved,294,49,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,372500,-93.653232,42.048607 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,59,4282,Pave,Paved,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1375,1391,GasA,Excellent,Y,SBrkr,1391,0,0,1391,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,530,Typical,Typical,Paved,156,158,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,196000,-93.647488,42.047467 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4017,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2006,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,625,625,GasA,Excellent,Y,SBrkr,625,625,0,1250,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Fin,2,625,Typical,Typical,Paved,0,54,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,171900,-93.646063,42.048089 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,37,3435,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1211,1235,GasA,Excellent,Y,SBrkr,1245,0,0,1245,0,0,2,0,1,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,495,Typical,Typical,Paved,0,100,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,178000,-93.646113,42.048191 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,210,600,GasA,Excellent,Y,SBrkr,600,600,0,1200,1,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,165000,-93.64489,42.047806 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,689,689,GasA,Excellent,Y,SBrkr,703,689,0,1392,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,540,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Abnorml,146000,-93.644891,42.047571 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3604,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,689,689,GasA,Excellent,Y,SBrkr,703,689,0,1392,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,540,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,146000,-93.644891,42.047548 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2280,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,360,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,195,744,GasA,Good,Y,SBrkr,757,744,0,1501,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,179400,-93.644172,42.047102 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2280,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,Stone,216,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,194,744,GasA,Good,Y,SBrkr,757,792,0,1549,1,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,172900,-93.644118,42.047103 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4765,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2000,2000,Hip,CompShg,MetalSd,MetalSd,BrkFace,260,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,587,1614,GasA,Excellent,Y,SBrkr,1638,0,0,1638,1,0,2,0,2,1,Excellent,5,Typ,1,Typical,Attchd,Fin,2,495,Typical,Typical,Paved,230,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,300000,-93.646984,42.046381 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4538,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,179,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,306,1310,GasA,Excellent,Y,SBrkr,1310,0,0,1310,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,RFn,2,545,Typical,Typical,Paved,277,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,285000,-93.646986,42.046377 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,42,4385,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,455,1419,GasA,Excellent,Y,SBrkr,1419,0,0,1419,1,0,1,1,2,1,Excellent,5,Typ,1,Typical,Attchd,Fin,2,588,Typical,Typical,Paved,155,58,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,290000,-93.646989,42.046372 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4109,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,260,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,416,1557,GasA,Excellent,Y,SBrkr,1557,0,0,1557,1,0,2,0,2,1,Excellent,5,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,124,113,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,305000,-93.647047,42.046088 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,50,5119,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Excellent,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,60,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,460,1698,GasA,Excellent,Y,SBrkr,1709,0,0,1709,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,97,65,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,CWD,Abnorml,328900,-93.647044,42.046085 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2160,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,212,Good,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,90,600,GasA,Excellent,Y,SBrkr,624,628,0,1252,1,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,462,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,160000,-93.643746,42.046961 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2160,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,SLvl,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,72,672,GasA,Excellent,Y,SBrkr,684,720,0,1404,1,0,2,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,462,Typical,Typical,Paved,20,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,170000,-93.643729,42.046962 +Two_Story_1946_and_Newer,Floating_Village_Residential,79,10646,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,513,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,177,858,GasA,Excellent,Y,SBrkr,872,917,0,1789,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,546,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,223000,-93.643378,42.046647 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,466,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,348,960,GasA,Excellent,Y,SBrkr,962,624,0,1586,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,169,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,170000,-93.641785,42.047171 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,147,960,GasA,Excellent,Y,SBrkr,962,645,0,1607,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,169,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,ConLD,Normal,200000,-93.64177,42.047171 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,36,3951,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Very_Excellent,Average,1998,1999,Gable,CompShg,BrkFace,MetalSd,None,0,Excellent,Typical,PConc,Good,Typical,Mn,BLQ,2,GLQ,842,0,970,GasA,Excellent,Y,SBrkr,1469,924,0,2393,1,0,2,1,2,1,Excellent,7,Typ,1,Typical,Attchd,Fin,2,846,Typical,Typical,Paved,0,90,0,0,94,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,345000,-93.641742,42.047171 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,38,14963,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1996,1996,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,474,1260,GasA,Excellent,Y,SBrkr,1288,0,0,1288,1,0,1,1,1,1,Excellent,4,Typ,2,Good,Attchd,Fin,2,500,Typical,Typical,Paved,120,30,0,0,224,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,245500,-93.647655,42.045012 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,22,11064,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1995,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,Gd,LwQ,4,GLQ,670,0,1230,GasA,Excellent,Y,SBrkr,1239,0,0,1239,1,0,1,1,1,1,Good,4,Typ,1,Fair,Attchd,Fin,2,477,Typical,Typical,Paved,172,24,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,217500,-93.648705,42.044419 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,32,10846,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1993,1993,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,100,1719,GasA,Excellent,Y,SBrkr,1719,0,0,1719,2,0,1,1,1,1,Good,6,Typ,2,Good,Attchd,Fin,2,473,Typical,Typical,Paved,122,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,Con,Normal,324000,-93.648954,42.044642 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11120,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1984,1984,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,572,1232,GasA,Typical,Y,SBrkr,1232,0,0,1232,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,516,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,162500,-93.645713,42.044693 +Two_Story_1946_and_Newer,Residential_Low_Density,0,24572,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,OneFam,Two_Story,Excellent,Fair,1977,1977,Mansard,CompShg,Wd Sdng,Wd Sdng,BrkFace,1050,Good,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,584,994,GasA,Typical,Y,SBrkr,1599,1345,0,2944,0,0,2,2,3,1,Good,9,Typ,1,Good,Attchd,RFn,3,864,Typical,Typical,Paved,140,70,16,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Family,150000,-93.655582,42.036729 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,103,16280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Excellent,1976,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Excellent,Excellent,CBlock,Good,Excellent,Mn,GLQ,3,Rec,382,0,1426,GasA,Excellent,Y,SBrkr,1671,0,0,1671,1,0,3,0,3,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,550,Typical,Typical,Paved,280,90,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,290000,-93.658315,42.037578 +Two_Story_1946_and_Newer,Floating_Village_Residential,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,952,952,GasA,Excellent,Y,SBrkr,952,860,0,1812,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,469,Typical,Typical,Paved,144,112,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,205000,-93.6392251,42.048726 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1970,1970,Hip,CompShg,Plywood,Plywood,BrkFace,288,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1304,1304,GasA,Good,Y,SBrkr,1682,0,0,1682,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,530,Typical,Typical,Paved,98,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,174000,-93.635095,42.047928 +Split_or_Multilevel,Residential_Low_Density,0,11104,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1969,1969,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,599,1427,GasA,Good,Y,SBrkr,1427,0,0,1427,0,1,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,516,Typical,Typical,Paved,0,0,0,0,216,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,183000,-93.6343272,42.0492538 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,1439,1740,GasA,Fair,Y,SBrkr,1740,0,0,1740,0,0,1,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,512,Typical,Typical,Paved,25,0,0,0,192,0,No_Pool,Good_Wood,None,0,10,2008,WD ,Family,150000,-93.634976,42.049141 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11040,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1971,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,602,1258,GasA,Excellent,Y,SBrkr,1258,0,0,1258,0,1,2,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,55,0,0,216,0,0,No_Pool,No_Fence,None,0,10,2008,COD,Abnorml,145000,-93.631526,42.048762 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15387,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,294,723,1620,GasA,Excellent,Y,SBrkr,1620,0,0,1620,0,0,2,0,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,578,Typical,Typical,Paved,0,62,192,0,0,0,No_Pool,No_Fence,Shed,450,8,2008,WD ,Normal,215000,-93.631779,42.046399 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,Duplex,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1625,1625,GasA,Excellent,Y,SBrkr,1625,0,0,1625,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,ConLD,Normal,140500,-93.6324549,42.042943 +Two_Story_1946_and_Newer,Residential_Low_Density,90,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1974,1997,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,182,384,1522,GasA,Typical,Y,SBrkr,1548,1066,0,2614,0,0,2,1,4,1,Typical,9,Typ,1,Typical,Attchd,RFn,2,624,Typical,Typical,Paved,38,243,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,240000,-93.631245,42.04378 +Two_Story_1946_and_Newer,Residential_Low_Density,73,8814,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,Two_Story,Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,732,GasA,Excellent,Y,SBrkr,732,732,0,1464,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,2,470,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,141000,-93.6264356,42.0458538 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,8872,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1965,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,300,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,317,912,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,147000,-93.6261431,42.0471033 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1965,2005,Hip,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,456,196,912,GasA,Excellent,Y,SBrkr,925,0,0,925,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,233,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,135000,-93.624762,42.046584 +Duplex_All_Styles_and_Ages,Residential_Low_Density,72,11072,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1728,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,145000,-93.62462,42.047541 +Duplex_All_Styles_and_Ages,Residential_Low_Density,74,13101,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,BrkFace,108,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,1497,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,142600,-93.624632,42.048185 +Duplex_All_Styles_and_Ages,Residential_Low_Density,87,9246,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,Duplex,One_Story,Average,Average,1973,1973,Gable,CompShg,Plywood,Plywood,BrkFace,564,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1656,1656,GasA,Typical,Y,SBrkr,1656,0,0,1656,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,211,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,135000,-93.6304229,42.0453551 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13355,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Good,No,ALQ,1,Unf,0,242,825,GasA,Typical,Y,SBrkr,845,825,0,1670,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,464,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,170000,-93.629079,42.044262 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8963,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1976,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,289,Excellent,Good,CBlock,Typical,Good,No,GLQ,3,ALQ,80,487,1142,GasA,Excellent,Y,SBrkr,1175,1540,0,2715,0,1,3,1,4,1,Good,11,Typ,2,Typical,BuiltIn,Fin,2,831,Typical,Typical,Paved,0,204,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,299800,-93.630152,42.044058 +Two_Story_1946_and_Newer,Residential_Low_Density,76,9120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1974,1974,Hip,CompShg,HdBoard,HdBoard,BrkFace,270,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,459,901,GasA,Typical,Y,SBrkr,943,933,0,1876,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,540,Good,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,185000,-93.630732,42.044851 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,Two_Story,Above_Average,Very_Good,1966,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,252,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,64,336,800,GasA,Good,Y,SBrkr,800,832,0,1632,0,1,1,1,4,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,173000,-93.625925,42.043142 +Split_Foyer,Residential_Low_Density,0,12122,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Good,Excellent,1961,2007,Gable,CompShg,CemntBd,CmentBd,Stone,210,Excellent,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,77,944,GasA,Good,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,144,76,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,178400,-93.624499,42.043845 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Feedr,Norm,OneFam,Two_Story,Good,Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,BrkFace,342,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,280,832,GasA,Good,Y,SBrkr,1098,880,0,1978,0,0,2,1,4,1,Typical,9,Typ,1,Good,Attchd,RFn,2,486,Typical,Typical,Paved,0,43,0,0,0,0,No_Pool,Good_Privacy,None,0,4,2008,WD ,Normal,176000,-93.625799,42.044101 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7785,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1014,0,0,1014,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,267,Typical,Typical,Paved,0,0,40,0,200,0,No_Pool,Good_Wood,None,0,3,2008,WD ,Normal,98000,-93.621336,42.042398 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,8593,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1957,1957,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,619,907,GasA,Excellent,Y,SBrkr,907,0,0,907,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,109008,-93.622673,42.044773 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8475,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,724,952,GasA,Excellent,Y,FuseA,952,0,0,952,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,283,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,135750,-93.622662,42.044773 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,148,Typical,Good,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,344,1120,GasA,Good,Y,SBrkr,1128,0,0,1128,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,525,Typical,Typical,Paved,192,20,123,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Normal,155000,-93.629259,42.041229 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,10175,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,Plywood,BrkFace,272,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,935,1425,GasA,Good,Y,SBrkr,1425,0,0,1425,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,0,0,0,407,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,180500,-93.629555,42.04066 +Split_or_Multilevel,Residential_Low_Density,82,9020,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Good,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,183,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,ALQ,539,276,1127,GasA,Typical,Y,SBrkr,1165,0,0,1165,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,490,Good,Good,Paved,0,129,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,174900,-93.6277409,42.039235 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,244,1136,GasA,Typical,Y,SBrkr,1136,0,0,1136,1,0,1,0,3,1,Typical,5,Typ,1,Good,Attchd,RFn,1,384,Typical,Typical,Paved,426,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,140000,-93.629884,42.040112 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1964,1964,Hip,CompShg,HdBoard,HdBoard,Stone,260,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,305,1092,GasA,Excellent,Y,SBrkr,1092,0,0,1092,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Good,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,145000,-93.6277098,42.0417754 +Split_or_Multilevel,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Very_Good,1965,1999,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Good,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,586,1219,GasA,Good,Y,SBrkr,1265,0,0,1265,0,1,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,502,Typical,Typical,Paved,0,92,0,96,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,179900,-93.6267969,42.0417764 +Split_or_Multilevel,Residential_Low_Density,82,9020,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,SLvl,Above_Average,Average,1964,1964,Gable,WdShngl,Plywood,Wd Sdng,BrkFace,259,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Rec,336,288,1248,GasA,Typical,Y,SBrkr,1350,0,0,1350,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,176,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,168500,-93.626171,42.041811 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1114,1114,GasA,Typical,Y,SBrkr,1114,0,0,1114,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,451,Typical,Typical,Paved,0,0,0,0,164,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,140000,-93.626762,42.038323 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10007,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,2006,Gable,CompShg,HdBoard,HdBoard,BrkFace,54,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,247,1053,GasA,Excellent,Y,SBrkr,1053,0,0,1053,1,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,145500,-93.623267,42.04102 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10721,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,HdBoard,HdBoard,Stone,243,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1252,1252,GasA,Excellent,Y,SBrkr,1252,0,0,1252,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,142000,-93.6219226,42.0418699 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12493,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1960,1960,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Rec,306,375,1100,GasA,Typical,Y,SBrkr,1100,0,0,1100,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,1,312,Typical,Typical,Paved,355,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2008,WD ,Normal,141000,-93.621566,42.041172 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11332,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,590,1118,GasA,Excellent,Y,SBrkr,1118,0,0,1118,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,290,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,153000,-93.622076,42.040296 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6627,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Fair,Above_Average,1949,1950,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Floor,Typical,N,SBrkr,720,0,0,720,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,287,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,72500,-93.628205,42.035582 +One_Story_1945_and_Older,Residential_Low_Density,56,4130,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Fair,Above_Average,1935,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,270,270,GasA,Good,Y,SBrkr,729,0,0,729,0,0,1,0,2,1,Typical,5,Maj2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,52000,-93.62936,42.035571 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,50,7420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Artery,Artery,TwoFmCon,One_and_Half_Unf,Average,Above_Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,GLQ,3,Unf,0,140,991,GasA,Excellent,Y,SBrkr,1077,0,0,1077,1,0,1,0,2,2,Typical,5,Typ,2,Typical,Attchd,RFn,1,205,Good,Typical,Paved,0,4,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,118000,-93.627578,42.034563 +Two_Story_1945_and_Older,Residential_Low_Density,50,4882,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Brookside,RRAn,Feedr,OneFam,Two_Story,Below_Average,Good,1937,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,228,348,GasA,Typical,Y,SBrkr,453,453,0,906,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Unf,1,231,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,87000,-93.628236,42.035741 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkFace,203,Fair,Fair,CBlock,Typical,Typical,No,Rec,6,Unf,0,638,1296,GasA,Typical,Y,SBrkr,1496,0,0,1496,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,450,Typical,Typical,Paved,0,22,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,141500,-93.6285065,42.0380577 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1950,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,752,1032,GasA,Typical,Y,FuseA,1032,220,0,1252,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Abnorml,119000,-93.622644,42.036178 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1950,2006,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Rec,308,232,572,GasA,Good,Y,SBrkr,1337,0,0,1337,1,0,1,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,1,264,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,141500,-93.622644,42.036102 +Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1955,1996,Hip,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,161,992,GasA,Good,Y,SBrkr,1661,0,0,1661,1,0,1,0,3,1,Good,8,Typ,1,Typical,BuiltIn,RFn,1,377,Typical,Typical,Paved,0,28,0,0,178,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,165500,-93.623556,42.03713 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7584,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,88,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,505,1036,GasA,Excellent,Y,SBrkr,1036,0,0,1036,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,120,24,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,112900,-93.620487,42.034679 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1965,1988,Gable,CompShg,VinylSd,VinylSd,Stone,288,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,410,864,GasA,Typical,Y,SBrkr,902,918,0,1820,0,0,1,2,4,1,Good,8,Typ,2,Good,Attchd,Unf,2,492,Typical,Typical,Paved,60,84,0,0,273,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,201800,-93.6167,42.048952 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10197,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1961,1961,Gable,CompShg,WdShing,Wd Shng,BrkCmn,491,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,374,700,1362,GasA,Typical,Y,SBrkr,1362,0,0,1362,1,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,504,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,COD,Normal,163000,-93.616552,42.047782 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkFace,136,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,572,1144,GasA,Good,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,456,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2008,WD ,Normal,139950,-93.6178602,42.0470858 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Excellent,1962,2005,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,237,Good,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1319,1319,GasA,Typical,Y,SBrkr,1537,0,0,1537,1,0,1,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,462,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,COD,Abnorml,174000,-93.6169916,42.0470799 +Split_or_Multilevel,Residential_Low_Density,101,9150,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1962,1962,Gable,Tar&Grv,Plywood,Plywood,BrkFace,305,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,728,1099,GasA,Good,Y,SBrkr,1431,0,0,1431,0,1,1,0,3,1,Typical,6,Typ,1,Good,Basment,RFn,1,296,Typical,Typical,Paved,64,110,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,165000,-93.615685,42.046935 +Two_Story_1946_and_Newer,Residential_Low_Density,90,14670,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1966,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,410,Good,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,529,1104,GasA,Excellent,Y,SBrkr,1104,884,0,1988,0,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,RFn,2,480,Typical,Typical,Paved,0,230,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,227000,-93.615614,42.046572 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7390,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,151,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,196,1098,GasA,Typical,Y,SBrkr,1098,0,0,1098,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,260,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,135000,-93.617179,42.042373 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9204,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,872,247,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,COD,Normal,124000,-93.6193303,42.0423032 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7763,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1962,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,BLQ,108,319,931,GasA,Typical,Y,SBrkr,1283,0,0,1283,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,140000,-93.619022,42.044736 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8856,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Below_Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,143,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,52,503,1176,GasA,Typical,Y,SBrkr,1176,0,0,1176,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,RFn,1,292,Typical,Typical,Paved,0,88,0,0,95,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,136500,-93.618634,42.043988 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,9840,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1998,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,195,1248,GasA,Typical,Y,SBrkr,1440,0,0,1440,1,0,2,0,2,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,1,480,Typical,Typical,Paved,150,0,0,0,256,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,185000,-93.617544,42.044791 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,599,1261,GasA,Excellent,Y,SBrkr,1261,0,0,1261,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,433,Typical,Typical,Paved,0,0,0,0,288,0,No_Pool,No_Fence,Shed,1400,11,2008,WD ,Normal,163000,-93.617543,42.044853 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13200,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,187,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,437,1395,GasA,Excellent,Y,SBrkr,1570,0,0,1570,1,0,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,441,Typical,Typical,Paved,490,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,166800,-93.613222,42.044304 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,74,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Unf,0,580,1196,GasA,Good,Y,FuseA,1196,0,0,1196,1,0,1,0,2,1,Typical,6,Typ,1,Good,Attchd,RFn,1,297,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,139000,-93.614856,42.042187 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11900,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1957,1957,Gable,CompShg,HdBoard,HdBoard,BrkFace,387,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,352,1392,GasA,Typical,Y,FuseA,1392,0,0,1392,1,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,458,Typical,Typical,Paved,0,0,0,0,192,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,166000,-93.613077,42.0438199 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9464,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1958,1958,Hip,CompShg,MetalSd,MetalSd,BrkFace,135,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,510,1080,GasA,Good,Y,SBrkr,1080,0,0,1080,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,130,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,136000,-93.612394,42.042023 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10425,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,330,1104,GasA,Good,Y,SBrkr,1104,0,0,1104,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,133000,-93.613383,42.042304 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11556,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1952,1952,Gable,CompShg,MetalSd,MetalSd,Stone,52,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,572,720,GasA,Excellent,Y,FuseA,882,0,0,882,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,116000,-93.617238,42.040479 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,9373,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,BrkFace,84,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,196,456,1152,GasA,Typical,Y,SBrkr,1152,0,0,1152,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,636,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,137500,-93.617084,42.040301 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12774,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Sev,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,LwQ,128,232,984,GasW,Typical,N,SBrkr,950,0,0,950,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,Good_Wood,None,0,7,2008,WD ,Normal,130000,-93.617086,42.040391 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,14250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1957,1957,Gable,CompShg,Plywood,Plywood,BrkFace,360,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,998,998,GasA,Typical,Y,SBrkr,1790,0,0,1790,0,0,2,0,3,1,Typical,6,Typ,2,Good,Attchd,Fin,2,540,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,Shed,1500,9,2008,WD ,Normal,180000,-93.6194043,42.0404436 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,1951,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,100,952,GasA,Typical,Y,SBrkr,952,0,0,952,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,840,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,COD,Abnorml,139000,-93.615621,42.039531 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,7677,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,203,773,GasA,Good,Y,SBrkr,773,0,0,773,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Abnorml,110000,-93.61707,42.038516 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,8838,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1957,1982,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1764,0,0,1764,0,0,2,1,4,1,Typical,7,Maj2,1,Typical,Attchd,Fin,1,301,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,100000,-93.612264,42.039445 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,8480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1947,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,390,832,GasA,Typical,Y,SBrkr,832,384,0,1216,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,158,0,102,0,0,0,No_Pool,No_Fence,None,0,10,2008,COD,Abnorml,118500,-93.6165173,42.0372039 +Duplex_All_Styles_and_Ages,Residential_Low_Density,76,12436,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1824,1824,GasA,Fair,Y,FuseA,1824,0,0,1824,0,0,2,0,5,2,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,146000,-93.618457,42.03471 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10122,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1948,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,869,0,0,869,0,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,390,Fair,Typical,Dirt_Gravel,0,0,66,0,0,0,No_Pool,Good_Privacy,None,0,8,2008,WD ,Normal,89900,-93.615484,42.037302 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,7506,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1925,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,747,747,GasA,Typical,Y,SBrkr,747,412,0,1159,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,288,Fair,Typical,Dirt_Gravel,84,0,96,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,114000,-93.615484,42.037459 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10930,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,333,913,GasA,Typical,Y,FuseA,1048,510,0,1558,1,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,140000,-93.61076,42.035893 +One_Story_1945_and_Older,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1940,2005,Gambrel,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Good,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,88,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,86900,-93.612269,42.035723 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10836,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,892,892,GasA,Excellent,Y,SBrkr,1254,182,0,1436,0,1,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,4,1488,Fair,Typical,Dirt_Gravel,0,0,100,0,0,0,No_Pool,Good_Wood,None,0,6,2008,WD ,Normal,139000,-93.612265,42.035938 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Below_Average,Good,1900,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseA,694,600,0,1294,0,0,2,0,3,2,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,220,114,210,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,106250,-93.612253,42.035222 +Two_Story_1946_and_Newer,Residential_Low_Density,70,9247,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,BrkFace,318,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,539,858,GasA,Excellent,Y,SBrkr,858,858,0,1716,0,0,1,1,4,1,Typical,8,Typ,1,Good,Attchd,Fin,2,490,Typical,Typical,Paved,0,84,0,0,120,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,171000,-93.607422,42.040021 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10180,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,168,912,GasA,Typical,Y,SBrkr,1044,0,0,1044,0,1,1,1,3,1,Typical,5,Typ,1,Fair,Attchd,Fin,2,372,Typical,Typical,Paved,200,48,0,0,0,0,No_Pool,Good_Wood,Shed,450,6,2008,WD ,Normal,139000,-93.610663,42.040142 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,11355,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1958,2001,Gable,Tar&Grv,HdBoard,HdBoard,BrkFace,125,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,675,1312,GasA,Excellent,Y,SBrkr,1312,0,0,1312,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,304,144,0,0,0,No_Pool,Minimum_Privacy,Othr,6500,4,2008,WD ,Normal,186000,-93.605189,42.041173 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10920,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Hip,CompShg,MetalSd,MetalSd,BrkFace,212,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,520,1253,GasA,Typical,Y,SBrkr,1253,0,0,1253,1,0,1,1,2,1,Typical,5,Typ,1,Fair,Attchd,RFn,1,352,Typical,Typical,Paved,0,213,176,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,157000,-93.605659,42.038686 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12929,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1993,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,276,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,384,1081,GasA,Typical,Y,SBrkr,1081,0,0,1081,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,CarPort,Unf,1,401,Typical,Typical,Paved,36,82,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,148000,-93.607174,42.039987 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,313,27650,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,Inside,Mod,North_Ames,PosA,Norm,OneFam,One_Story,Good,Good,1960,2007,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,160,585,GasA,Excellent,Y,SBrkr,2069,0,0,2069,1,0,2,0,4,1,Good,9,Typ,1,Good,Attchd,RFn,2,505,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,242000,-93.604106,42.039568 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8892,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1953,1996,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Typical,Stone,Typical,Typical,Av,Unf,7,Unf,0,105,105,GasA,Good,Y,SBrkr,910,0,0,910,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,414,Typical,Typical,Paved,196,0,150,0,0,0,No_Pool,Good_Wood,None,0,10,2008,WD ,Normal,116000,-93.608219,42.03807 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1951,1951,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,LwQ,4,Unf,0,444,876,GasA,Typical,Y,SBrkr,876,0,0,876,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,94000,-93.607957,42.037125 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1950,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1048,0,0,1048,0,0,1,0,3,1,Typical,7,Min1,0,No_Fireplace,Detchd,Unf,2,420,Typical,Typical,Paved,0,27,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,98300,-93.608909,42.037104 +Split_or_Multilevel,Residential_Low_Density,70,7910,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1960,1960,Hip,CompShg,BrkFace,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,409,1075,GasA,Good,Y,SBrkr,1507,0,0,1507,0,0,2,0,4,1,Typical,7,Maj1,0,No_Fireplace,Basment,Unf,1,404,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,8,2008,WD ,Normal,127000,-93.60688,42.035813 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Rec,488,292,1256,GasA,Good,Y,FuseA,1256,0,0,1256,1,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,311,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,159000,-93.605971,42.037162 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Gable,CompShg,BrkFace,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,319,188,1027,GasA,Typical,Y,SBrkr,1027,0,0,1027,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,299,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,125900,-93.60597,42.037006 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1960,1960,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,532,363,1269,GasA,Typical,Y,FuseA,1269,0,0,1269,0,0,1,1,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,155000,-93.606788,42.037291 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,453,768,GasA,Excellent,Y,SBrkr,819,501,0,1320,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2008,WD ,Normal,138000,-93.610604,42.034811 +One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,64,6390,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_and_Half_Unf,Above_Average,Good,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,936,936,GasA,Typical,Y,FuseA,984,0,0,984,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2008,WD ,Normal,112500,-93.607948,42.034564 +One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,59,7227,Pave,No_Alley_Access,Regular,HLS,AllPub,Corner,Mod,North_Ames,Artery,Norm,OneFam,One_and_Half_Unf,Above_Average,Above_Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Good,Y,SBrkr,832,0,0,832,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,105500,-93.608765,42.034789 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,181,854,GasA,Fair,Y,FuseA,854,424,0,1278,0,0,1,0,4,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2008,WD ,Normal,127500,-93.608903,42.034686 +Duplex_All_Styles_and_Ages,Residential_Low_Density,113,8513,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,Duplex,One_Story,Average,Average,1961,1961,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,1800,1800,GasA,Typical,N,SBrkr,1800,0,0,1800,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,130000,-93.6071073,42.0347301 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1955,1967,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,398,768,GasA,Good,Y,SBrkr,1024,564,0,1588,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,150000,-93.607799,42.034636 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,825,825,GasA,Typical,Y,FuseA,825,0,0,825,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,350,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,109500,-93.607801,42.034801 +Two_Story_1946_and_Newer,Residential_Low_Density,71,7056,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,BrkFace,415,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,380,780,GasA,Typical,Y,SBrkr,983,813,0,1796,1,0,1,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,483,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,140000,-93.604986,42.036973 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,360,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,LwQ,106,0,780,GasA,Typical,Y,SBrkr,798,813,0,1611,1,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,442,Typical,Typical,Paved,328,128,0,0,189,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,167900,-93.604837,42.037025 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,7590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Average,Average,1963,1963,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1117,1117,GasA,Excellent,Y,SBrkr,1117,0,0,1117,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Normal,136870,-93.603702,42.035724 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,56,9836,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,96,192,GasA,Good,N,SBrkr,1133,0,0,1133,1,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,175,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Abnorml,143000,-93.6195726,42.0342174 +One_Story_1945_and_Older,Residential_Medium_Density,30,5232,Pave,Gravel,Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Average,1925,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,680,680,GasA,Good,N,FuseP,764,0,0,764,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,504,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,73000,-93.619415,42.034128 +Two_Story_1945_and_Older,Residential_Medium_Density,60,10440,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1904,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,650,650,GasA,Good,Y,SBrkr,958,581,0,1539,0,0,2,0,3,1,Good,8,Typ,1,Poor,Detchd,Unf,2,686,Good,Typical,Partial_Pavement,70,78,68,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,157500,-93.615582,42.033379 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,57,9184,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1948,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Excellent,Y,SBrkr,948,375,0,1323,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,122600,-93.62005,42.032611 +Two_Story_1945_and_Older,Residential_Medium_Density,80,4800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Average,1910,2003,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,680,680,GasA,Fair,N,SBrkr,680,680,0,1360,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,330,Fair,Typical,Paved,192,50,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,111000,-93.61648,42.0330566 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10440,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Good,Good,BrkTil,Good,Typical,No,LwQ,4,Unf,0,1017,1510,GasW,Excellent,Y,SBrkr,1584,1208,0,2792,0,0,2,0,5,1,Typical,8,Mod,2,Typical,Detchd,Unf,2,520,Fair,Typical,Paved,0,547,0,0,480,0,No_Pool,Minimum_Privacy,Shed,1150,6,2008,WD ,Normal,256000,-93.615596,42.032392 +One_Story_1945_and_Older,Residential_Medium_Density,60,4800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Below_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,672,672,GasA,Typical,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,256,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Normal,64000,-93.616991,42.032419 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Good,1915,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,728,728,GasA,Good,Y,SBrkr,728,728,0,1456,0,0,1,1,4,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Fair,Fair,Dirt_Gravel,0,0,248,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,139500,-93.617106,42.03136 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,63,11426,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Above_Average,1910,1996,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Stone,Typical,Typical,No,Unf,7,Unf,0,828,828,GasA,Good,Y,FuseA,828,658,108,1594,0,0,2,0,3,2,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,172,109,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,118000,-93.6189373,42.030427 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,11426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1362,1362,GasA,Excellent,Y,SBrkr,1362,720,0,2082,0,0,2,1,3,1,Good,6,Mod,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Dirt_Gravel,280,238,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,200000,-93.6195822,42.0304142 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,7628,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1940,1985,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,801,801,GasA,Good,Y,FuseA,1095,561,0,1656,0,0,2,0,2,1,Typical,8,Mod,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,187,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,119164,-93.6198478,42.030418 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,81,7308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Average,1920,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Fair,Fair,BrkTil,Typical,Typical,No,Rec,6,Unf,0,576,936,GasA,Good,N,FuseA,960,780,0,1740,0,0,1,0,2,1,Excellent,6,Typ,1,Good,Detchd,Unf,1,225,Fair,Fair,Dirt_Gravel,0,0,236,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,122250,-93.620219,42.030482 +One_Story_1945_and_Older,Residential_Medium_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Good,Above_Average,1920,2006,Gable,CompShg,Stucco,Stucco,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,931,931,GasA,Typical,Y,SBrkr,1027,0,0,1027,0,1,1,0,2,1,Good,5,Typ,1,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,28,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,120000,-93.613969,42.032161 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,294,884,GasA,Typical,Y,SBrkr,884,552,0,1436,0,0,2,0,3,2,Typical,8,Typ,2,Good,Detchd,Unf,2,828,Typical,Typical,Paved,0,0,126,0,0,0,No_Pool,No_Fence,None,0,5,2008,Con,Normal,155000,-93.615447,42.032272 +One_Story_1945_and_Older,Residential_Medium_Density,60,6756,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1910,1950,Mansard,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Good,No,Unf,7,Unf,0,481,481,GasA,Typical,N,FuseA,899,0,0,899,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Partial_Pavement,0,0,96,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,78000,-93.615031,42.030406 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,44,5914,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Excellent,1890,1996,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,684,684,GasA,Good,Y,SBrkr,684,396,0,1080,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,165,30,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,95000,-93.6112887,42.0303597 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Average,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,169,516,789,GasA,Excellent,Y,SBrkr,789,0,0,789,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Partial,115000,-93.608867,42.033435 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,100,12000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1948,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,ALQ,608,172,924,GasA,Excellent,Y,SBrkr,1122,0,0,1122,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,147000,-93.60887,42.033571 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1946,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,459,904,GasA,Excellent,Y,FuseA,904,595,0,1499,0,0,1,0,3,1,Typical,5,Typ,1,Poor,Detchd,Unf,3,869,Typical,Good,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,140000,-93.607771,42.033249 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,62,7311,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Poor,Average,1946,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,407,407,GasA,Typical,N,FuseA,407,0,0,407,0,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,297,Fair,Typical,Paved,76,0,120,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Abnorml,46500,-93.606764,42.032008 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1957,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,620,928,GasA,Good,Y,FuseA,928,0,0,928,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,112500,-93.607713,42.032094 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1954,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,901,901,GasA,Excellent,Y,SBrkr,901,0,0,901,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,281,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,107900,-93.607724,42.032381 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,103,12205,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Very_Poor,1949,1992,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Fair,No,Unf,7,Unf,0,448,448,GasA,Good,Y,SBrkr,1588,0,0,1588,0,0,2,0,5,1,Typical,6,Maj2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Abnorml,65000,-93.606802,42.0311 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,87,18386,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Fin,Good,Excellent,1880,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1470,1470,GasA,Excellent,Y,SBrkr,1675,1818,0,3493,0,0,3,0,3,1,Good,10,Typ,1,Excellent,Attchd,Unf,3,870,Typical,Typical,Paved,302,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,295000,-93.6077128,42.0304823 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,69,9142,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Average,1900,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Fair,Typical,No,Unf,7,Unf,0,797,797,GasA,Typical,N,FuseA,830,797,0,1627,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,420,Fair,Poor,Dirt_Gravel,192,0,60,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,139500,-93.61844,42.029028 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,100,12665,Pave,Gravel,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,876,876,GasA,Good,Y,SBrkr,876,540,0,1416,0,0,1,1,4,1,Typical,7,Typ,1,Good,Detchd,Unf,3,720,Typical,Typical,Paved,418,0,194,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,153900,-93.620179,42.029058 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,53,5350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Good,Very_Good,1920,1965,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,508,624,GasA,Excellent,Y,SBrkr,730,720,0,1450,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2008,WD ,Normal,132000,-93.620183,42.02928 +Two_Story_1945_and_Older,Residential_Medium_Density,34,4571,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Average,1916,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Fair,N,SBrkr,624,720,0,1344,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,3,513,Fair,Fair,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,98000,-93.618296,42.029253 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,69,9143,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1900,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,346,346,GasA,Excellent,Y,SBrkr,709,308,0,1017,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,139,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,114000,-93.618297,42.029281 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Average,1920,1960,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,739,973,GasA,Typical,Y,FuseP,1377,973,0,2350,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,2,393,Typical,Typical,Paved,0,0,219,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,130000,-93.617386,42.028287 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Average,1917,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Rec,6,Unf,0,416,735,OthW,Fair,N,SBrkr,1134,924,0,2058,0,0,1,1,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,396,Fair,Fair,Partial_Pavement,0,0,259,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,129500,-93.616885,42.02793 +Two_Story_1945_and_Older,Residential_Medium_Density,60,6000,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Excellent,1905,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,572,572,GasA,Excellent,Y,SBrkr,884,656,0,1540,0,0,1,1,3,1,Good,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,240,77,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,129400,-93.616998,42.027073 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Excellent,1928,2005,Gambrel,CompShg,MetalSd,MetalSd,None,0,Typical,Excellent,BrkTil,Typical,Typical,No,Rec,6,Unf,0,548,689,GasA,Excellent,Y,SBrkr,689,689,0,1378,0,0,2,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Dirt_Gravel,0,0,116,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,161000,-93.616996,42.027032 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10800,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Good,1905,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,482,482,GasA,Excellent,N,SBrkr,1221,691,0,1912,0,0,2,0,3,2,Typical,7,Typ,1,Typical,Detchd,Unf,2,672,Good,Typical,Paved,0,25,212,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,163000,-93.612239,42.029084 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,35,6300,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Above_Average,1914,2001,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,742,742,GasA,Excellent,Y,SBrkr,742,742,0,1484,0,0,2,0,3,1,Typical,9,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,291,134,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,128000,-93.613845,42.02813 +Two_Story_1945_and_Older,Residential_Medium_Density,50,5250,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1872,1987,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Fair,No,LwQ,4,Unf,0,425,684,OthW,Fair,N,SBrkr,938,1215,205,2358,0,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,54,20,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,122000,-93.6096888,42.0288284 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,5700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,572,572,GasA,Typical,Y,SBrkr,572,539,0,1111,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,176,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,116900,-93.6096482,42.0287814 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,75,13500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,PosA,OneFam,Two_and_Half_Unf,Very_Excellent,Excellent,1893,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Excellent,Excellent,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1237,1237,GasA,Good,Y,SBrkr,1521,1254,0,2775,0,0,3,1,3,1,Good,9,Typ,1,Good,Detchd,Unf,2,880,Good,Typical,Paved,105,502,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,325000,-93.610008,42.028397 +Two_Story_1945_and_Older,Residential_Medium_Density,60,11340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Poor,Very_Poor,1920,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fair,Fair,BrkTil,Fair,Fair,No,Unf,7,Unf,0,723,723,GasA,Typical,N,SBrkr,723,363,0,1086,0,0,1,0,2,1,Typical,5,Maj1,0,No_Fireplace,Detchd,Unf,2,400,Fair,Poor,Dirt_Gravel,0,24,144,0,0,0,No_Pool,No_Fence,None,0,11,2008,ConLD,Normal,55000,-93.608752,42.029209 +Two_Story_1945_and_Older,Residential_Medium_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1890,1999,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,1313,1313,GasW,Good,Y,SBrkr,1313,1182,0,2495,0,0,2,0,5,1,Typical,10,Typ,1,Good,Detchd,Unf,2,342,Typical,Fair,Paved,0,299,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,184000,-93.6075521,42.0273991 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,65,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,MetalSd,MetalSd,BrkFace,164,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,784,984,GasA,Good,Y,SBrkr,984,0,0,984,1,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,110000,-93.6059697,42.0272517 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,81,12150,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,BrkFace,335,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1050,1050,GasA,Excellent,N,FuseF,1050,745,0,1795,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,352,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,131500,-93.606196,42.02774 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,70,12702,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,FuseA,882,0,0,882,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,97000,-93.604486,42.026691 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,52,8516,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1958,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,869,869,GasA,Typical,Y,SBrkr,1093,0,0,1093,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,115500,-93.6043548,42.0272664 +One_Story_1945_and_Older,Residential_Low_Density,55,7111,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1928,1983,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Good,BrkTil,Typical,Typical,No,LwQ,4,BLQ,273,329,1008,GasA,Typical,Y,SBrkr,1143,0,0,1143,0,0,1,0,2,1,Typical,5,Typ,1,Poor,Detchd,Unf,1,288,Typical,Typical,Paved,265,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,WD ,Normal,138000,-93.628806,42.03361 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,7425,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Brookside,RRAn,Artery,OneFam,One_and_Half_Fin,Good,Good,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Fair,No,Unf,7,Unf,0,672,672,GasA,Good,Y,SBrkr,1195,473,0,1668,0,0,1,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Unf,1,252,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Abnorml,108000,-93.6267116,42.0343116 +One_Story_with_Finished_Attic_All_Ages,Residential_Medium_Density,50,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Feedr,OneFam,One_Story,Average,Above_Average,1930,1960,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,742,742,GasA,Typical,Y,FuseA,779,0,156,935,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,Shed,600,8,2008,WD ,Normal,79500,-93.625728,42.03338 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,7010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1935,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,849,1024,GasA,Typical,Y,SBrkr,1144,594,0,1738,0,0,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,30,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,153000,-93.625926,42.03047 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,6130,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Average,Above_Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,0,784,GasA,Good,Y,SBrkr,784,0,0,784,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,116,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,109500,-93.624715,42.033456 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Good,1941,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,72,672,GasA,Excellent,Y,SBrkr,832,378,0,1210,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,124000,-93.625584,42.033568 +Two_Story_1945_and_Older,Residential_Medium_Density,59,5870,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Feedr,OneFam,Two_Story,Above_Average,Excellent,1900,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,554,554,GasA,Excellent,Y,SBrkr,736,554,0,1290,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Paved,38,112,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,4,2008,WD ,Normal,106900,-93.625589,42.033699 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,460,981,GasA,Excellent,Y,SBrkr,1014,658,0,1672,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,11,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,WD ,Normal,164900,-93.623666,42.03337 +One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1924,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,949,949,GasA,Excellent,Y,SBrkr,949,0,0,949,0,0,1,0,2,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,370,Typical,Typical,Paved,0,0,48,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,119000,-93.624566,42.033511 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1929,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,862,862,GasA,Typical,Y,SBrkr,950,208,0,1158,0,0,1,0,3,1,Typical,5,Typ,1,Good,BuiltIn,RFn,1,208,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,120000,-93.623514,42.033321 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1937,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,LwQ,162,462,825,GasA,Excellent,Y,SBrkr,825,672,0,1497,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,672,Typical,Typical,Paved,272,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,157000,-93.621532,42.033473 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,475,739,GasA,Excellent,Y,SBrkr,874,468,0,1342,0,0,2,0,2,2,Typical,7,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,105000,-93.621532,42.033366 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Above_Average,Good,1926,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,992,992,GasA,Excellent,Y,SBrkr,1013,0,0,1013,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,160,Fair,Typical,Paved,0,0,101,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,113000,-93.622102,42.033493 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5520,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Good,1920,1997,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Good,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,497,565,GasA,Typical,Y,SBrkr,565,651,0,1216,1,0,1,0,3,1,Typical,6,Typ,1,Good,BuiltIn,RFn,1,355,Fair,Typical,Paved,0,0,180,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,115000,-93.624688,42.031354 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,99,5940,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Below_Average,Good,1946,1950,Gable,CompShg,MetalSd,CBlock,None,0,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,0,No_Basement,0,0,0,GasA,Typical,Y,FuseA,896,0,0,896,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,ConLD,Abnorml,79000,-93.624687,42.031293 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1929,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,624,704,GasA,Excellent,Y,SBrkr,624,512,0,1136,0,1,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,0,365,80,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,123000,-93.623632,42.031493 +One_Story_1945_and_Older,Residential_Medium_Density,0,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Above_Average,1945,1995,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,N,SBrkr,808,0,0,808,0,0,1,0,1,1,Typical,6,Min2,0,No_Fireplace,Attchd,Unf,1,164,Typical,Typical,Partial_Pavement,0,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,81300,-93.622566,42.032476 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1992,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1078,1078,GasA,Typical,Y,SBrkr,1128,445,0,1573,0,0,2,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,162900,-93.622552,42.031461 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1939,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Rec,6,LwQ,240,449,989,GasA,Typical,Y,SBrkr,1245,764,0,2009,0,0,2,0,4,1,Typical,7,Min2,1,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,150000,-93.621485,42.031496 +Two_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Average,Average,1923,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,897,1100,GasA,Typical,Y,SBrkr,1226,676,0,1902,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,139,55,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,123500,-93.620392,42.031467 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Good,Average,1931,1950,Gable,CompShg,BrkFace,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,952,952,GasA,Good,Y,FuseF,1022,752,0,1774,0,0,2,0,2,2,Typical,8,Min1,2,Typical,Detchd,Unf,2,468,Fair,Typical,Paved,90,0,205,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Abnorml,129900,-93.620391,42.031432 +Two_Story_1945_and_Older,Residential_Medium_Density,60,6155,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Brookside,RRNn,Feedr,OneFam,Two_Story,Above_Average,Very_Good,1920,1999,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Fair,Mn,Unf,7,Unf,0,611,611,GasA,Excellent,Y,SBrkr,751,611,0,1362,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,2,502,Typical,Fair,Paved,0,0,84,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,128000,-93.624591,42.031049 +One_Story_1945_and_Older,Residential_Medium_Density,60,6324,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,RRNn,OneFam,One_Story,Below_Average,Above_Average,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,520,520,GasA,Fair,N,SBrkr,520,0,0,520,0,0,1,0,1,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Paved,49,0,87,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,68500,-93.624471,42.030388 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,8635,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1948,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,GLQ,41,295,672,GasA,Typical,Y,SBrkr,1072,213,0,1285,1,0,1,0,2,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,127000,-93.6218858,42.031057 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,64,13053,Pave,Paved,Regular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1923,2000,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,833,833,GasA,Good,Y,SBrkr,1053,795,0,1848,0,0,1,1,4,1,Good,8,Typ,1,Good,Detchd,Unf,2,370,Typical,Typical,Dirt_Gravel,0,0,0,0,220,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,207000,-93.627,42.029399 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9120,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Good,Above_Average,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Typical,No,Rec,6,Unf,0,697,1026,GasA,Excellent,Y,SBrkr,1133,687,0,1820,1,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,100,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,184000,-93.625556,42.028426 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9144,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Below_Average,1915,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,810,810,GasA,Excellent,Y,SBrkr,1170,546,0,1716,0,0,2,0,4,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,195,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,162500,-93.625539,42.028274 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,9144,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1940,1982,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,484,883,GasA,Good,Y,SBrkr,988,517,0,1505,1,0,1,0,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,145000,-93.625434,42.027065 +One_Story_1945_and_Older,Residential_Low_Density,55,10267,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_Story,Above_Average,Good,1918,2000,Gable,CompShg,Stucco,Wd Shng,None,0,Typical,Good,BrkTil,Typical,Good,Mn,Rec,6,ALQ,606,0,816,GasA,Excellent,Y,SBrkr,838,0,0,838,1,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Fin,1,275,Typical,Typical,Dirt_Gravel,0,0,112,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,5,2008,WD ,Normal,130000,-93.625623,42.029556 +Two_Story_1946_and_Newer,Residential_Medium_Density,57,8094,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_and_Half_Unf,Above_Average,Very_Good,1910,1983,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Rec,6,Unf,0,1046,1242,GasA,Good,Y,SBrkr,1242,742,0,1984,0,0,2,0,5,1,Typical,8,Typ,0,No_Fireplace,Detchd,No_Garage,1,360,No_Garage,No_Garage,Paved,64,0,180,0,0,0,No_Pool,Minimum_Privacy,Shed,1000,9,2008,WD ,Normal,160000,-93.621676,42.02903 +Two_Story_1945_and_Older,Residential_Medium_Density,0,5100,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1925,1996,Hip,CompShg,Stucco,Wd Shng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,588,588,GasA,Fair,Y,SBrkr,833,833,0,1666,0,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,228,Typical,Typical,Paved,192,63,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,161000,-93.621065,42.029038 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,4347,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1910,1950,Gambrel,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Good,Good,No,Unf,7,Unf,0,796,796,GasA,Excellent,Y,SBrkr,825,784,0,1609,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,228,Fair,Fair,Dirt_Gravel,0,182,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,127500,-93.6208294,42.0286305 +One_Story_1945_and_Older,Residential_Medium_Density,0,6291,Grvl,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,RRNe,Norm,OneFam,One_Story,Above_Average,Above_Average,1930,1950,Gable,CompShg,Stucco,Wd Shng,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,768,768,GasA,Typical,Y,SBrkr,768,0,0,768,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Dirt_Gravel,0,0,84,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,93850,-93.6259506,42.0255246 +Two_Story_1945_and_Older,Residential_Medium_Density,60,10266,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1952,1952,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,396,768,GasA,Typical,Y,FuseA,768,768,0,1536,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,216,80,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,136000,-93.628447,42.024111 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6876,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1938,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1272,1272,GasA,Typical,Y,SBrkr,1272,0,697,1969,0,0,2,0,4,1,Typical,9,Min1,1,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,COD,Normal,141000,-93.627729,42.024265 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1915,1978,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,880,880,GasA,Good,Y,SBrkr,880,428,0,1308,0,0,2,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Fair,Fair,Paved,0,0,117,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,120000,-93.6261621,42.0249938 +One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Good,1925,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1040,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,320,Typical,Typical,Paved,0,132,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,127500,-93.6265884,42.0242588 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,62,7006,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1925,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,647,768,GasA,Typical,Y,SBrkr,788,448,0,1236,1,0,2,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2008,WD ,Family,127000,-93.6295,42.022574 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,7920,Pave,Gravel,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,No,Unf,7,Unf,0,319,319,GasA,Typical,Y,FuseA,1035,371,0,1406,0,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,144,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2008,WD ,Normal,89500,-93.625289,42.02295 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,49,8235,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Old_Town,Feedr,RRNn,OneFam,One_Story,Average,Good,1955,1995,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,Rec,645,0,825,GasA,Typical,Y,SBrkr,825,0,0,825,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,RFn,2,720,Typical,Typical,Paved,140,50,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,125000,-93.623738,42.026478 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5586,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,901,901,GasA,Good,Y,SBrkr,1088,110,0,1198,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,98,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2008,ConLD,Abnorml,79900,-93.621921,42.0262558 +One_Story_1945_and_Older,Residential_Medium_Density,60,10320,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,RRNe,Norm,OneFam,One_Story,Average,Very_Good,1912,1991,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,451,451,GasA,Typical,Y,SBrkr,759,0,0,759,0,0,1,0,1,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Dirt_Gravel,40,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,ConLD,Family,85000,-93.624819,42.024885 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,Two_Story,Fair,Fair,1915,1950,Gable,CompShg,AsphShn,AsphShn,None,0,Fair,Fair,PConc,Typical,Fair,No,Unf,7,Unf,0,536,536,GasA,Excellent,N,FuseF,808,536,0,1344,0,0,2,0,3,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,42,0,204,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,82375,-93.6246096,42.0242331 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1906,1995,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,756,756,GasA,Excellent,Y,SBrkr,756,713,0,1469,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,57,0,239,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,135000,-93.62458,42.023716 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,9488,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1947,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1046,1046,GasA,Good,N,SBrkr,1054,0,0,1054,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,60,122,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Abnorml,124000,-93.658159,42.034391 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,55,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,492,210,952,GasA,Excellent,Y,SBrkr,1211,0,0,1211,0,0,1,0,3,1,Typical,5,Typ,1,Typical,Attchd,Unf,1,322,Typical,Typical,Paved,0,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,134000,-93.656979,42.034409 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,115,21286,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,720,720,GasA,Typical,Y,SBrkr,720,551,0,1271,0,0,2,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,1,312,Typical,Typical,Paved,0,0,108,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,135000,-93.657958,42.033423 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,17120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1959,1959,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1120,468,0,1588,0,0,2,0,4,1,Typical,7,Min2,1,Good,Detchd,Fin,2,680,Typical,Typical,Dirt_Gravel,0,59,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,134432,-93.657031,42.031281 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,62,10106,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Good,1940,1999,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,ALQ,1,Rec,181,112,644,GasA,Good,Y,SBrkr,808,547,0,1355,1,0,2,0,4,2,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,127500,-93.658395,42.02393 +Split_Foyer,Residential_Low_Density,0,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,SFoyer,Average,Above_Average,1963,1963,Gable,CompShg,HdBoard,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,197,981,GasA,Typical,Y,SBrkr,1075,0,0,1075,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,64,0,0,0,64,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Abnorml,148000,-93.675551,42.033604 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,11200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,134,1040,GasA,Excellent,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,129500,-93.669806,42.033225 +Split_or_Multilevel,Residential_Low_Density,80,13014,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Average,1978,1978,Gable,CompShg,HdBoard,Plywood,BrkFace,39,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,480,1008,GasA,Typical,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,Unf,2,484,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,157500,-93.672241,42.032365 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7162,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkCmn,41,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,876,876,GasA,Typical,Y,SBrkr,904,0,0,904,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,408,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2008,WD ,Abnorml,109900,-93.676873,42.031571 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,10265,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1967,2005,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Fair,CBlock,Typical,Typical,No,ALQ,1,Unf,0,234,992,GasA,Excellent,Y,SBrkr,992,0,0,992,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,204,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,600,7,2008,WD ,Normal,145000,-93.677717,42.031248 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,9764,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Good,1967,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,192,894,GasA,Excellent,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,450,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,130000,-93.677945,42.031123 +Split_Foyer,Residential_Low_Density,0,7703,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Above_Average,Very_Good,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkCmn,40,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,0,450,GasA,Excellent,Y,SBrkr,1034,0,0,1034,0,1,1,0,3,1,Typical,6,Typ,1,Poor,Basment,Fin,2,504,Typical,Typical,Paved,311,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,153000,-93.672718,42.031348 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9981,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,340,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,852,1073,GasA,Good,Y,SBrkr,1073,0,0,1073,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,270,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2008,WD ,Normal,120000,-93.674179,42.03156 +Split_Foyer,Residential_Low_Density,0,7400,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Average,1984,1984,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,LwQ,4,ALQ,956,0,1060,GasA,Typical,Y,SBrkr,1126,0,0,1126,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,506,Typical,Typical,Paved,178,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,152000,-93.671905,42.030552 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,12900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,Duplex,SFoyer,Below_Average,Below_Average,1969,1969,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1198,GasA,Typical,Y,SBrkr,1258,0,0,1258,2,0,0,2,0,2,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,400,Fair,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Alloca,108959,-93.666954,42.034428 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,12900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,TwoFmCon,One_Story,Average,Below_Average,1920,1950,Gable,CompShg,BrkFace,Stucco,None,0,Typical,Typical,PConc,Typical,Fair,No,BLQ,2,Unf,0,0,1300,GasA,Fair,Y,SBrkr,1140,0,0,1140,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,400,Typical,Typical,Paved,0,0,190,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Alloca,95541,-93.666912,42.034428 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Fair,1959,1959,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,718,1006,GasA,Typical,Y,SBrkr,1006,0,0,1006,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,80000,-93.665521,42.033454 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8544,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,2006,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1228,1228,GasA,Good,Y,SBrkr,1228,0,0,1228,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,271,Typical,Typical,Paved,0,65,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,149350,-93.669465,42.033834 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,9239,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1963,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,326,960,GasA,Excellent,Y,SBrkr,960,0,0,960,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,1,300,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,144900,-93.663727,42.034414 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,14175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,PosA,Norm,OneFam,One_Story,Above_Average,Very_Good,1956,1956,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Good,Gd,GLQ,3,Unf,0,212,988,GasA,Typical,Y,FuseA,1188,0,0,1188,1,0,1,0,1,1,Typical,4,Typ,1,Typical,Attchd,Unf,2,621,Typical,Typical,Paved,102,89,231,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,185000,-93.665355,42.032495 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13284,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,PosN,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,319,1383,GasA,Typical,Y,SBrkr,1383,0,0,1383,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,354,Typical,Typical,Paved,511,116,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,165000,-93.665356,42.032566 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Good,1960,1975,Flat,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,BLQ,2,ALQ,1080,93,1602,GasA,Good,Y,SBrkr,1252,0,0,1252,1,0,1,0,1,1,Typical,4,Typ,1,Good,Attchd,RFn,2,564,Typical,Typical,Paved,409,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,235000,-93.666979,42.032155 +Split_or_Multilevel,Residential_Low_Density,85,13825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Above_Average,1958,1987,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,533,533,GasA,Typical,Y,SBrkr,1021,580,0,1601,0,1,1,0,3,1,Typical,6,Min2,0,No_Fireplace,BuiltIn,RFn,1,300,Typical,Typical,Paved,280,34,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,140000,-93.662134,42.03392 +One_Story_1945_and_Older,Residential_Low_Density,60,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Very_Good,1940,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Dirt_Gravel,168,0,0,0,0,0,No_Pool,Good_Privacy,None,0,8,2008,WD ,Normal,108000,-93.660673,42.033692 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,0,10532,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Fair,1960,1960,Flat,Tar&Grv,Plywood,Plywood,Stone,275,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,0,988,GasA,Good,Y,SBrkr,1721,0,0,1721,1,0,2,0,3,1,Typical,7,Mod,2,Typical,Basment,Unf,2,626,Typical,Typical,Paved,50,84,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Abnorml,145000,-93.660812,42.032653 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,63,8375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1941,1973,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,240,576,GasA,Good,Y,SBrkr,864,486,0,1350,1,0,1,1,2,1,Good,6,Min1,0,No_Fireplace,More_Than_Two_Types,Unf,3,627,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,152400,-93.660521,42.033559 +Split_or_Multilevel,Residential_Low_Density,0,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Very_Good,1970,1970,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Good,PConc,Good,Typical,Av,ALQ,1,Unf,0,160,864,GasA,Excellent,Y,SBrkr,904,0,0,904,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,912,Typical,Typical,Paved,143,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2008,WD ,Normal,144000,-93.678125,42.029453 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,2887,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1996,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,288,1291,GasA,Excellent,Y,SBrkr,1291,0,0,1291,1,0,1,0,2,1,Good,6,Typ,1,Good,Attchd,Unf,2,431,Typical,Typical,Paved,307,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,173000,-93.676219,42.024662 +Two_Story_1946_and_Newer,Residential_Low_Density,83,10005,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,299,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,768,1160,GasA,Excellent,Y,SBrkr,1156,866,0,2022,0,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,505,Typical,Typical,Paved,288,117,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,192000,-93.6739889,42.0243033 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,20270,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,925,1524,GasA,Typical,Y,SBrkr,1524,0,0,1524,1,0,2,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,478,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,245000,-93.67358,42.02521 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,39104,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Good,1954,2005,Flat,Membran,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,LwQ,4,GLQ,1063,96,1385,GasA,Excellent,Y,SBrkr,1363,0,0,1363,1,0,1,0,2,1,Typical,5,Mod,2,Typical,Attchd,Unf,2,439,Typical,Typical,Paved,81,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,241500,-93.666066,42.028568 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,53227,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1954,1994,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,BLQ,2,Unf,0,248,1364,GasA,Excellent,Y,SBrkr,1663,0,0,1663,1,0,1,0,2,1,Good,6,Min1,2,Good,Attchd,Fin,2,529,Typical,Typical,Paved,224,137,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,256000,-93.666216,42.028595 +Split_or_Multilevel,Residential_Low_Density,124,11512,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Good,1959,2006,Gable,CompShg,Plywood,Plywood,BrkFace,84,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,300,1019,GasA,Good,Y,SBrkr,1357,0,0,1357,1,0,1,0,2,1,Excellent,5,Typ,1,Good,Basment,RFn,1,312,Typical,Typical,Paved,0,0,0,0,163,0,No_Pool,Good_Privacy,None,0,5,2008,WD ,Normal,177000,-93.666147,42.027332 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,5190,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1948,1950,Gable,CompShg,BrkFace,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,570,570,GasA,Typical,Y,SBrkr,617,462,0,1079,0,0,1,0,2,1,Typical,5,Typ,1,Good,Attchd,Unf,1,249,Typical,Typical,Paved,135,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,125600,-93.66515,42.027507 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10452,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,216,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,594,1094,GasA,Excellent,Y,SBrkr,1094,0,0,1094,0,0,1,0,3,1,Typical,5,Typ,2,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,0,0,0,287,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,155000,-93.665571,42.027555 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,45600,Pave,No_Alley_Access,Moderately_Irregular,Bnk,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1908,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,907,907,GasA,Typical,Y,SBrkr,1307,1051,0,2358,0,0,3,0,5,1,Typical,10,Typ,1,Good,Detchd,Unf,2,360,Fair,Typical,Paved,486,40,0,0,175,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,240000,-93.663054,42.028381 +One_Story_1945_and_Older,Residential_Low_Density,85,19550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1940,2007,Flat,Tar&Grv,PreCast,PreCast,None,0,Typical,Typical,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,545,1580,GasA,Excellent,Y,SBrkr,1518,0,0,1518,1,0,1,0,2,1,Fair,5,Typ,2,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,260000,-93.663123,42.028233 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,66,21780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1918,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Unf,7,Unf,0,1163,1163,GasA,Excellent,Y,SBrkr,1163,511,0,1674,0,0,2,0,4,1,Typical,8,Typ,1,Good,Detchd,Fin,2,396,Typical,Typical,Dirt_Gravel,72,36,0,0,144,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,185000,-93.660595,42.028188 +Two_Story_1945_and_Older,Residential_Low_Density,120,13728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1935,1986,Hip,CompShg,Stucco,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,501,1127,GasA,Excellent,Y,SBrkr,1236,872,0,2108,0,0,2,0,4,1,Good,7,Typ,2,Typical,Basment,Unf,2,540,Typical,Typical,Paved,0,0,0,0,90,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,235000,-93.6608625,42.0280297 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9571,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,639,1509,GasA,Typical,Y,FuseA,1509,0,0,1509,1,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,1,322,Typical,Typical,Paved,158,0,0,0,576,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,159900,-93.667534,42.024025 +Duplex_All_Styles_and_Ages,Residential_Low_Density,50,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Average,Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,0,864,GasA,Fair,N,SBrkr,864,0,0,864,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,99600,-93.6650659,42.0257161 +Two_Story_1946_and_Newer,Residential_Low_Density,50,9360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1962,2001,Gable,CompShg,VinylSd,VinylSd,BrkCmn,216,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,297,621,GasA,Typical,Y,SBrkr,621,648,0,1269,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,236,0,0,0,0,No_Pool,Good_Wood,None,0,11,2008,WD ,Normal,134500,-93.66526,42.025036 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,8405,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Good,No,Rec,6,BLQ,391,229,861,GasA,Excellent,Y,SBrkr,961,406,0,1367,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,130,112,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,119000,-93.6644616,42.0236702 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,Two_Story,Average,Good,1910,1991,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,148,1117,GasA,Typical,Y,SBrkr,820,527,0,1347,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,85,0,148,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,107500,-93.6618779,42.0244599 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,68,10880,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_Story,Average,Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,124,1164,GasW,Typical,N,SBrkr,1164,0,0,1164,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,ConLD,Normal,125000,-93.662924,42.022902 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,572,572,Grav,Fair,N,FuseF,572,524,0,1096,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,8,128,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,79000,-93.658528,42.022769 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,Mn,LwQ,4,Rec,380,0,616,GasA,Typical,N,SBrkr,616,495,0,1111,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Typical,Fair,Paved,48,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,ConLw,Normal,95000,-93.659949,42.02283 +Two_Story_1946_and_Newer,Residential_Low_Density,86,11839,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1990,Hip,CompShg,HdBoard,HdBoard,BrkFace,99,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,390,1475,GasA,Excellent,Y,SBrkr,1532,797,0,2329,1,0,2,1,4,1,Good,10,Typ,1,Excellent,Attchd,Unf,2,514,Typical,Typical,Paved,192,121,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,262280,-93.683223,42.031766 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9771,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,2002,Gable,CompShg,HdBoard,HdBoard,BrkFace,190,Good,Typical,PConc,Good,Typical,No,LwQ,4,Unf,0,298,1077,GasA,Excellent,Y,SBrkr,1093,1721,0,2814,0,1,2,1,4,1,Good,9,Typ,1,Typical,BuiltIn,Fin,2,614,Typical,Typical,Paved,48,32,0,0,216,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,260000,-93.683302,42.033912 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9938,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,251,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,331,1602,GasA,Excellent,Y,SBrkr,1626,0,0,1626,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,534,Typical,Typical,Paved,424,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,226500,-93.682029,42.03085 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14171,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,457,812,GasA,Excellent,Y,SBrkr,1101,1099,0,2200,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,2,453,Typical,Typical,Paved,168,98,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,225000,-93.682032,42.031077 +Split_or_Multilevel,Residential_Low_Density,85,10541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,SLvl,Good,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,1302,735,0,2037,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,472,Typical,Typical,Paved,100,33,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,229000,-93.682175,42.030621 +Two_Story_1946_and_Newer,Residential_Low_Density,65,10616,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,628,628,GasA,Excellent,Y,SBrkr,628,728,0,1356,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,484,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,New,Partial,177439,-93.691284,42.025411 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9345,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,156,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1615,1615,GasA,Excellent,Y,SBrkr,1615,0,0,1615,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,864,Typical,Typical,Paved,168,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,248500,-93.691095,42.025275 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,554,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,186,2271,GasA,Excellent,Y,SBrkr,2276,0,0,2276,1,0,2,0,3,1,Excellent,7,Typ,2,Good,Attchd,RFn,3,1348,Good,Typical,Paved,0,0,70,0,255,0,No_Pool,No_Fence,None,0,6,2008,WD ,Abnorml,475000,-93.68698,42.027368 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,One_Story,Excellent,Average,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,402,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,598,1751,GasA,Excellent,Y,SBrkr,1766,0,0,1766,1,0,2,1,3,1,Excellent,8,Typ,2,Good,Attchd,Fin,3,874,Typical,Typical,Paved,216,36,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,New,Partial,395039,-93.686789,42.027193 +Two_Story_1946_and_Newer,Residential_Low_Density,59,11228,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,BLQ,2,GLQ,531,499,1080,GasA,Excellent,Y,SBrkr,1080,1017,0,2097,0,1,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Unf,3,678,Typical,Typical,Paved,196,187,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,228000,-93.681199,42.029437 +Split_or_Multilevel,Residential_Low_Density,0,11454,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,SLvl,Very_Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,302,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,631,1401,GasA,Excellent,Y,SBrkr,1511,0,0,1511,1,0,2,0,3,1,Good,6,Typ,1,Fair,Attchd,Fin,3,811,Typical,Typical,Paved,168,42,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,225000,-93.681136,42.030276 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11500,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,CulDSac,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1966,1966,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Good,Gd,LwQ,4,ALQ,723,197,1182,GasA,Excellent,Y,SBrkr,1643,0,0,1643,1,0,2,0,2,1,Typical,6,Typ,1,Good,Attchd,Unf,2,438,Typical,Typical,Paved,339,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,195000,-93.6826513,42.0249247 +Two_Story_1946_and_Newer,Residential_Low_Density,79,12798,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Clear_Creek,Feedr,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,154,616,GasA,Good,Y,SBrkr,616,1072,0,1688,1,0,2,1,4,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,603,Typical,Typical,Paved,403,114,185,0,0,0,No_Pool,No_Fence,Shed,400,5,2008,WD ,Normal,200000,-93.679198,42.025781 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,9750,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,268,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,155000,-93.692009,42.021173 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1995,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1099,1099,GasA,Excellent,Y,SBrkr,1099,0,0,1099,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,352,Typical,Typical,Paved,278,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,144000,-93.691213,42.01915 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,9245,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,304,990,GasA,Excellent,Y,SBrkr,990,0,0,990,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,145000,-93.691612,42.019153 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8696,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,110,1418,GasA,Excellent,Y,SBrkr,1418,0,0,1418,1,0,2,0,3,1,Good,5,Typ,1,Typical,Attchd,RFn,2,558,Typical,Typical,Paved,208,110,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,226001,-93.690719,42.018501 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13142,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,128,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,176,864,GasA,Excellent,Y,SBrkr,872,899,0,1771,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,600,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,215700,-93.689772,42.018786 +Two_Story_1946_and_Newer,Residential_Low_Density,68,8998,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,255,782,GasA,Excellent,Y,SBrkr,782,870,0,1652,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,532,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,207500,-93.690276,42.017423 +Two_Story_1946_and_Newer,Residential_Low_Density,68,9179,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,158,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,240,873,GasA,Excellent,Y,SBrkr,882,908,0,1790,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,588,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Abnorml,193000,-93.690113,42.017517 +Two_Story_1946_and_Newer,Residential_Low_Density,57,8924,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,880,880,GasA,Excellent,Y,SBrkr,880,844,0,1724,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,527,Typical,Typical,Paved,120,155,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,188000,-93.688995,42.017914 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,9382,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,125,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1468,1468,GasA,Excellent,Y,SBrkr,1479,0,0,1479,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,577,Typical,Typical,Paved,120,25,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,191000,-93.689019,42.017766 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,12803,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,99,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,572,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,530,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,221000,-93.691706,42.016276 +Two_Story_1946_and_Newer,Residential_Low_Density,80,12435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,172,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,602,963,GasA,Excellent,Y,SBrkr,963,829,0,1792,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,96,0,245,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,231500,-93.689101,42.015974 +Two_Story_1946_and_Newer,Residential_Low_Density,75,12192,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,265,928,GasA,Excellent,Y,SBrkr,928,895,0,1823,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,626,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,Shed,4500,5,2008,WD ,Normal,235000,-93.688958,42.01626 +Two_Story_1946_and_Newer,Residential_Low_Density,68,11250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,162,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,434,920,GasA,Excellent,Y,SBrkr,920,866,0,1786,1,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,223500,-93.68895,42.015949 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9200,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1980,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Av,LwQ,4,BLQ,491,167,938,GasA,Typical,Y,SBrkr,938,0,0,938,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,145,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,130250,-93.685742,42.022136 +Split_or_Multilevel,Residential_Low_Density,65,8385,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Very_Good,1977,1977,Gable,CompShg,HdBoard,HdBoard,BrkFace,220,Good,Typical,CBlock,Good,Good,Av,GLQ,3,Unf,0,390,985,GasA,Typical,Y,SBrkr,985,0,0,985,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,328,Typical,Typical,Paved,210,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,149900,-93.686888,42.021234 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,180,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,83,864,GasA,Excellent,Y,SBrkr,1174,0,0,1174,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,211,0,280,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,158000,-93.687518,42.018839 +Split_or_Multilevel,Residential_Low_Density,0,10970,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,Plywood,HdBoard,None,0,Typical,Typical,CBlock,Good,Good,Gd,GLQ,3,LwQ,435,0,940,GasA,Typical,Y,SBrkr,1026,0,0,1026,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Fair,Paved,0,0,34,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,147000,-93.685158,42.019769 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9216,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,782,1076,GasA,Typical,Y,SBrkr,1076,0,0,1076,0,0,1,1,3,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2008,WD ,Abnorml,143195,-93.685865,42.020694 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,40,14330,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,2001,Gable,CompShg,Plywood,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,596,180,864,GasA,Typical,Y,SBrkr,1558,0,0,1558,1,0,2,0,2,1,Typical,5,Min2,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,140,0,239,0,227,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,163000,-93.6844,42.02107 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,7990,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,924,924,GasA,Typical,Y,SBrkr,924,0,0,924,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,110000,-93.684289,42.021275 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7180,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,474,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,127000,-93.683158,42.020796 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,148,780,GasA,Excellent,Y,SBrkr,780,0,0,780,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,196,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,124900,-93.683309,42.020991 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9742,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,281,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1777,1777,GasA,Excellent,Y,SBrkr,1795,0,0,1795,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,534,Typical,Typical,Paved,171,159,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,230000,-93.68786,42.018735 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9473,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,324,1128,GasA,Excellent,Y,SBrkr,1128,903,0,2031,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,577,Typical,Typical,Paved,0,211,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,237000,-93.687704,42.018412 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10316,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,257,992,GasA,Excellent,Y,SBrkr,992,873,0,1865,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,839,Typical,Typical,Paved,0,184,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,235000,-93.686189,42.018354 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,227,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1257,1257,GasA,Excellent,Y,SBrkr,1290,871,0,2161,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,570,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,230500,-93.684227,42.018709 +Two_Story_1946_and_Newer,Residential_Low_Density,73,9066,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,320,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,336,1004,GasA,Excellent,Y,SBrkr,1004,848,0,1852,0,0,2,1,3,1,Good,7,Typ,2,Typical,Attchd,Fin,3,660,Typical,Typical,Paved,224,106,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2008,WD ,Normal,230000,-93.684396,42.017598 +Two_Story_1946_and_Newer,Residential_Low_Density,75,11404,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,202,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,901,1153,GasA,Excellent,Y,SBrkr,1153,878,0,2031,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,541,Typical,Typical,Paved,192,84,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,222500,-93.682718,42.018006 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,396,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1055,1055,GasA,Excellent,Y,SBrkr,1055,1208,0,2263,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,905,Typical,Typical,Paved,0,45,0,0,189,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,287000,-93.685572,42.01727 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9720,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Excellent,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,134,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,163,1357,GasA,Excellent,Y,SBrkr,1366,581,0,1947,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,3,725,Typical,Typical,Paved,168,116,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,274000,-93.685569,42.017484 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14860,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,240,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,240,1778,GasA,Excellent,Y,SBrkr,1786,0,0,1786,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,715,Typical,Typical,Paved,182,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,300000,-93.68522,42.018186 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1128,1128,GasA,Excellent,Y,SBrkr,1149,1141,0,2290,0,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Unf,2,779,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,255900,-93.686644,42.016975 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8158,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,252,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,334,884,GasA,Excellent,Y,SBrkr,884,884,0,1768,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,543,Typical,Typical,Paved,0,63,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,224900,-93.686982,42.016207 +Two_Story_1946_and_Newer,Residential_Low_Density,41,10905,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1129,1129,GasA,Excellent,Y,SBrkr,1129,1198,0,2327,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,RFn,2,596,Typical,Typical,Paved,0,57,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,240000,-93.688193,42.016341 +Two_Story_1946_and_Newer,Residential_Low_Density,72,7226,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,798,798,GasA,Excellent,Y,SBrkr,798,842,0,1640,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,595,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,183000,-93.687674,42.014435 +Two_Story_1946_and_Newer,Residential_Low_Density,96,11690,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,192,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,850,850,GasA,Excellent,Y,SBrkr,886,878,0,1764,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Unf,2,560,Typical,Typical,Paved,120,29,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,207000,-93.683856,42.016709 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,273,915,GasA,Excellent,Y,SBrkr,933,975,0,1908,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Unf,2,493,Typical,Typical,Paved,144,133,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,210000,-93.67994,42.017866 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,37,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,162,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,136500,-93.681121,42.016277 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,205,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,RFn,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,145000,-93.68369,42.016254 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,13125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,215,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,484,1478,GasA,Excellent,Y,SBrkr,1493,0,0,1493,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,508,Typical,Typical,Paved,140,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,208900,-93.679505,42.017854 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,83,10126,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,Gd,GLQ,3,LwQ,162,83,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,3,721,Typical,Typical,Paved,160,67,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Abnorml,185000,-93.68031,42.016262 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9135,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,113,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,726,1536,GasA,Excellent,Y,SBrkr,1536,0,0,1536,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,532,Typical,Typical,Paved,192,74,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,214000,-93.687825,42.014549 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,150,856,GasA,Excellent,Y,SBrkr,856,854,0,1710,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,548,Typical,Typical,Paved,0,61,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,208500,-93.686931,42.013952 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1421,1445,GasA,Excellent,Y,SBrkr,1445,0,0,1445,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,470,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,186500,-93.685207,42.013912 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,266,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,490,1436,GasA,Excellent,Y,SBrkr,1436,0,0,1436,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,139,98,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,210000,-93.684451,42.013641 +Two_Story_1946_and_Newer,Residential_Low_Density,64,8320,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,280,770,GasA,Excellent,Y,SBrkr,770,812,0,1582,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,185900,-93.684397,42.013622 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11049,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1234,1234,GasA,Excellent,Y,SBrkr,1234,0,0,1234,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,179900,-93.684343,42.013603 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11058,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,212,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1508,1564,GasA,Excellent,Y,SBrkr,1564,0,0,1564,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,3,814,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,218836,-93.683435,42.015975 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,9627,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1327,1351,GasA,Excellent,Y,SBrkr,1361,0,0,1361,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,610,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,193000,-93.683435,42.015368 +Two_Story_1946_and_Newer,Residential_Low_Density,63,8199,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,80,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,410,Typical,Typical,Paved,36,18,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,WD ,Normal,189000,-93.6838447,42.0138316 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,147,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,151,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Typical,Attchd,RFn,2,420,Typical,Typical,Paved,149,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,149300,-93.683127,42.015927 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4426,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,147,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,151,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Typical,Attchd,RFn,2,420,Typical,Typical,Paved,149,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,141000,-93.682991,42.015927 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4438,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,205,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Good,Attchd,RFn,2,420,Typical,Typical,Paved,149,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,149000,-93.681436,42.016121 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4438,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,1,Good,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,ConLD,Normal,144500,-93.681401,42.016122 +One_Story_1945_and_Older,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Average,Good,1910,1950,Gable,CompShg,MetalSd,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,796,796,GasA,Good,Y,FuseA,796,0,0,796,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Partial_Pavement,328,0,164,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,85000,-93.678303,42.01975 +Duplex_All_Styles_and_Ages,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,Duplex,One_and_Half_Fin,Average,Good,1900,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,188,1272,GasA,Good,Y,SBrkr,1272,928,0,2200,2,0,2,2,4,2,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,70,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2008,WD ,Normal,145900,-93.678304,42.019937 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,483,1092,GasA,Typical,Y,SBrkr,1092,0,0,1092,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,259,0,0,0,161,0,No_Pool,Minimum_Privacy,None,0,4,2008,COD,Abnorml,123000,-93.677551,42.020905 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9937,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,290,136,1256,GasA,Good,Y,SBrkr,1256,0,0,1256,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,276,Typical,Typical,Paved,736,68,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,147500,-93.677407,42.021244 +Split_Foyer,Residential_Low_Density,64,12102,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Average,Average,1976,1976,Gable,CompShg,HdBoard,Plywood,BrkFace,222,Typical,Typical,CBlock,Good,Good,Gd,ALQ,1,Unf,0,0,456,GasA,Excellent,Y,SBrkr,1033,0,0,1033,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,BuiltIn,RFn,2,504,Fair,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Family,165000,-93.673447,42.019997 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1976,1976,Hip,CompShg,HdBoard,Plywood,BrkFace,84,Typical,Typical,CBlock,Typical,Excellent,No,BLQ,2,Unf,0,94,1127,GasA,Typical,Y,SBrkr,1127,0,0,1127,0,1,1,1,3,1,Typical,6,Typ,1,Poor,Detchd,Unf,2,480,Typical,Typical,Paved,0,0,138,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,135000,-93.673571,42.019996 +One_Story_1945_and_Older,Residential_Low_Density,63,13907,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1940,1969,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,706,996,GasA,Excellent,Y,SBrkr,996,0,0,996,1,0,1,0,3,1,Typical,6,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,108000,-93.673137,42.02099 +Split_Foyer,Residential_Low_Density,90,10012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Below_Average,Average,1972,1972,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,Rec,180,38,1138,GasA,Typical,Y,SBrkr,1181,0,0,1181,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,2,588,Typical,Typical,Paved,0,0,180,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,137500,-93.671443,42.019861 +Two_Story_1946_and_Newer,Residential_Low_Density,60,6931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,Stone,92,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,746,746,GasA,Excellent,Y,SBrkr,760,896,0,1656,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,397,Typical,Typical,Paved,178,128,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,165400,-93.672408,42.018991 +Split_or_Multilevel,Residential_Low_Density,0,9638,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1977,1977,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Rec,120,541,1029,GasA,Typical,Y,SBrkr,1117,0,0,1117,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,542,Typical,Typical,Paved,292,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,147000,-93.675294,42.018852 +Two_Story_1946_and_Newer,Residential_Low_Density,72,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1976,2001,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,396,684,GasA,Typical,Y,SBrkr,684,714,0,1398,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,160500,-93.675173,42.018912 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7024,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,108,1132,GasA,Excellent,Y,SBrkr,1132,0,0,1132,1,0,1,1,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,451,Typical,Typical,Paved,252,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,176000,-93.673251,42.01883 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,123,47007,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1959,1996,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,3820,0,0,3820,0,0,3,1,5,1,Excellent,11,Typ,2,Good,Attchd,Unf,2,624,Typical,Typical,Paved,0,372,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,284700,-93.675582,42.017386 +Two_Story_1946_and_Newer,Residential_Low_Density,313,63887,Pave,No_Alley_Access,Irregular,Bnk,AllPub,Corner,Gtl,Edwards,Feedr,Norm,OneFam,Two_Story,Very_Excellent,Average,2008,2008,Hip,ClyTile,Stucco,Stucco,Stone,796,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,466,6110,GasA,Excellent,Y,SBrkr,4692,950,0,5642,2,0,2,1,3,1,Excellent,12,Typ,3,Good,Attchd,Fin,2,1418,Typical,Typical,Paved,214,292,0,0,0,480,Good,No_Fence,None,0,1,2008,New,Partial,160000,-93.674898,42.016804 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SLvl,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,88,547,GasA,Excellent,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,RFn,2,525,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,148000,-93.670627,42.018898 +Duplex_All_Styles_and_Ages,Residential_Low_Density,65,6040,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,AsbShng,Plywood,None,0,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseP,1152,0,0,1152,0,0,2,0,2,2,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,AdjLand,82000,-93.665043,42.020483 +Duplex_All_Styles_and_Ages,Residential_Low_Density,65,6012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,AsbShng,Plywood,None,0,Typical,Fair,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1152,0,0,1152,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,AdjLand,82000,-93.6649923,42.0202945 +Duplex_All_Styles_and_Ages,Residential_Low_Density,92,12108,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Below_Average,1955,1955,Gable,CompShg,VinylSd,VinylSd,BrkFace,270,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,1307,1440,GasA,Typical,N,FuseF,1440,0,0,1440,0,0,2,0,4,2,Fair,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,118000,-93.6653819,42.020394 +Duplex_All_Styles_and_Ages,Residential_Low_Density,74,6845,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,WdShing,Wd Shng,BrkCmn,58,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseF,1152,0,0,1152,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,82500,-93.665562,42.019544 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,56,6931,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwoFmCon,One_Story,Below_Average,Average,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,0,784,GasA,Typical,N,FuseP,784,0,0,784,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,112,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,91900,-93.661895,42.020112 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,12180,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1938,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Good,Y,FuseF,585,468,0,1053,0,0,1,1,2,1,Excellent,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,42,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Family,120000,-93.660115,42.021582 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,57,8050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1947,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,929,208,0,1137,0,0,1,1,4,1,Typical,8,Min1,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,96000,-93.664925,42.01797 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9520,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,Stone,115,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,144,911,GasA,Typical,Y,SBrkr,930,0,0,930,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,134,0,0,0,0,0,No_Pool,Minimum_Privacy,Gar2,3000,5,2008,WD ,Normal,99000,-93.665829,42.017962 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1946,1995,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,416,768,GasA,Excellent,Y,SBrkr,768,432,0,1200,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,130500,-93.664506,42.0198028 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1954,1972,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,941,941,GasA,Excellent,Y,SBrkr,997,473,0,1470,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,548,Typical,Typical,Paved,0,0,0,0,156,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,135000,-93.663479,42.019959 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,59,16466,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,Y,SBrkr,872,521,0,1393,0,0,1,1,3,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,1,300,Typical,Typical,Paved,121,0,0,0,265,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,135500,-93.66345,42.018541 +Split_or_Multilevel,Residential_Low_Density,62,7692,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,SLvl,Below_Average,Above_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Excellent,Typical,Av,Unf,7,Unf,0,416,416,GasA,Good,Y,FuseA,1204,0,0,1204,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Basment,Unf,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,100000,-93.661763,42.017874 +One_Story_1945_and_Older,Residential_Low_Density,67,5142,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1923,2008,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,725,949,GasA,Typical,Y,SBrkr,949,343,0,1292,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,205,Typical,Typical,Dirt_Gravel,0,0,183,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,108000,-93.6558,42.02224 +One_Story_1945_and_Older,Residential_Low_Density,67,5604,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1925,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,396,864,GasA,Typical,N,FuseA,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,98000,-93.656681,42.022238 +One_Story_1945_and_Older,Residential_Low_Density,45,8248,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Fair,Fair,1914,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,823,864,GasA,Typical,N,FuseF,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,100,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,67000,-93.656735,42.022089 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,55,5687,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,Two_Story,Average,Above_Average,1912,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Fair,PConc,Typical,Fair,No,Rec,6,Unf,0,570,780,GasA,Excellent,N,SBrkr,936,780,0,1716,1,0,2,0,6,1,Fair,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,184,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,135900,-93.655851,42.0214496 +Two_Story_1945_and_Older,Residential_High_Density,0,12155,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,516,672,GasA,Typical,N,SBrkr,810,672,0,1482,0,0,2,0,4,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,1,400,Typical,Typical,Partial_Pavement,0,0,254,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,140000,-93.64856,42.019986 +Two_and_Half_Story_All_Ages,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_and_Half_Fin,Average,Very_Good,1939,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,342,720,GasA,Excellent,Y,SBrkr,1052,720,420,2192,0,0,2,1,4,1,Good,8,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,262,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,179500,-93.65181,42.018715 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,758,1181,GasA,Fair,Y,SBrkr,1390,304,0,1694,0,0,2,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,342,0,128,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,136500,-93.651785,42.017413 +One_Story_1945_and_Older,Residential_Low_Density,60,7290,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Good,Very_Good,1921,1950,Gable,CompShg,WdShing,Wd Shng,BrkFace,174,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1228,1228,GasA,Excellent,Y,SBrkr,1424,0,0,1424,0,0,2,0,2,1,Typical,7,Typ,1,Good,Attchd,Unf,1,312,Typical,Typical,Paved,0,0,90,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,168000,-93.651635,42.017083 +Two_Story_1945_and_Older,Residential_High_Density,55,8525,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1911,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,Av,Unf,7,Unf,0,940,940,GasA,Typical,N,FuseA,1024,940,0,1964,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,192,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,130000,-93.647024,42.019272 +Two_Story_1945_and_Older,Residential_Low_Density,51,9842,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Average,Above_Average,1921,1998,Gable,CompShg,MetalSd,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,612,612,GasA,Excellent,Y,SBrkr,990,1611,0,2601,0,0,3,1,4,1,Typical,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,621,Typical,Typical,Paved,183,0,301,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,189000,-93.646748,42.019092 +Duplex_All_Styles_and_Ages,Residential_Low_Density,64,7804,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,Duplex,Two_and_Half_Unf,Above_Average,Good,1930,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Rec,679,0,960,GasA,Excellent,Y,SBrkr,960,960,0,1920,2,0,2,2,4,2,Typical,10,Typ,2,Good,Detchd,Unf,2,480,Typical,Typical,Paved,248,0,121,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Abnorml,161900,-93.648411,42.019005 +Two_Story_1945_and_Older,Residential_Low_Density,66,8969,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1926,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,265,644,GasA,Excellent,Y,SBrkr,672,644,0,1316,1,0,1,0,2,1,Typical,6,Typ,1,Good,Detchd,Unf,1,369,Typical,Typical,Partial_Pavement,0,0,0,0,192,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,145000,-93.646438,42.01791 +Two_Story_1945_and_Older,Residential_Low_Density,79,11526,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_and_Half_Fin,Above_Average,Good,1922,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Excellent,Typical,No,Unf,7,Unf,0,588,588,GasA,Fair,Y,SBrkr,1423,748,384,2555,0,0,2,0,3,1,Typical,11,Min1,1,Good,Detchd,Fin,2,672,Typical,Typical,Paved,431,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,191000,-93.646438,42.017834 +Two_Story_1945_and_Older,Residential_Low_Density,63,15576,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1915,1976,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,840,0,1680,0,0,2,0,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,0,160,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,177000,-93.6447083,42.0176853 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,63,15564,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1914,1995,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,676,676,GasA,Excellent,Y,SBrkr,676,588,0,1264,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,424,0,0,0,0,0,No_Pool,No_Fence,Shed,400,1,2008,WD ,Normal,147500,-93.646288,42.01789 +One_Story_1945_and_Older,Residential_Low_Density,52,6292,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Above_Average,Average,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Rec,6,Unf,0,384,768,GasA,Typical,N,SBrkr,790,0,0,790,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,160,Fair,Typical,Paved,0,141,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,91000,-93.64842,42.016887 +Two_Story_1945_and_Older,Residential_Low_Density,54,7609,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1925,1997,Gable,CompShg,Stucco,Stucco,None,0,Good,Good,PConc,Fair,Typical,No,ALQ,1,Unf,0,392,798,GasA,Excellent,Y,SBrkr,798,714,0,1512,1,0,2,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,180,Typical,Typical,Partial_Pavement,85,16,41,0,0,0,No_Pool,Good_Privacy,None,0,6,2008,WD ,Normal,231000,-93.6447134,42.0168678 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,10480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1064,1064,GasA,Excellent,Y,FuseA,1166,0,473,1639,0,0,1,0,3,1,Typical,6,Maj2,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,115000,-93.646735,42.016072 +Two_Story_1945_and_Older,Residential_Low_Density,0,9650,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Fair,1923,1950,Hip,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,784,784,GasA,Typical,Y,SBrkr,819,784,0,1603,0,0,1,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,2,599,Typical,Typical,Paved,0,217,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,138000,-93.642886,42.019833 +Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1931,2000,Gable,CompShg,Stucco,Wd Shng,None,0,Typical,Fair,BrkTil,Good,Typical,No,Unf,7,Unf,0,776,776,GasA,Typical,Y,SBrkr,851,651,0,1502,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,1,270,Typical,Typical,Partial_Pavement,0,0,112,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,149000,-93.642821,42.019617 +Two_Story_1945_and_Older,Residential_Low_Density,74,11988,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1934,1995,Hip,CompShg,Stucco,Stucco,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,389,715,GasA,Fair,Y,FuseA,849,811,0,1660,0,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,188700,-93.641405,42.018443 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,11700,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1937,1995,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,336,942,GasA,Excellent,Y,SBrkr,1265,673,0,1938,0,0,2,0,4,1,Good,7,Min2,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,198000,-93.63962,42.018888 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,9260,Pave,Gravel,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1938,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Typical,Y,FuseF,932,442,0,1374,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,225,Typical,Typical,Paved,64,0,0,0,100,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,110000,-93.639497,42.018766 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,7801,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Feedr,Norm,OneFam,One_Story,Above_Average,Average,1951,1951,Hip,CompShg,WdShing,Plywood,BrkFace,88,Typical,Fair,PConc,Typical,Typical,No,Rec,6,Unf,0,591,1091,GasA,Fair,N,FuseA,1091,0,0,1091,0,1,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Fin,1,344,Typical,Typical,Paved,66,105,0,0,221,0,No_Pool,Minimum_Privacy,None,0,5,2008,WD ,Normal,104000,-93.639467,42.017528 +Two_Story_1945_and_Older,Residential_Low_Density,100,9670,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Above_Average,1935,1950,Gable,CompShg,BrkFace,Stucco,Stone,40,Typical,Typical,PConc,Typical,Fair,No,LwQ,4,Unf,0,398,608,GasA,Typical,Y,SBrkr,983,890,0,1873,0,0,1,1,4,1,Typical,9,Typ,2,Good,Detchd,Fin,2,786,Fair,Typical,Paved,0,0,207,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Alloca,257076,-93.641084,42.016769 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,14100,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Excellent,1935,1997,Gable,CompShg,Stucco,Stucco,BrkFace,632,Typical,Good,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,536,728,GasA,Excellent,Y,SBrkr,1968,1479,0,3447,0,0,3,1,4,1,Good,11,Typ,2,Good,BuiltIn,Unf,3,1014,Typical,Typical,Paved,314,12,0,0,0,0,No_Pool,Good_Wood,None,0,5,2008,WD ,Normal,381000,-93.642597,42.01601 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,15660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Excellent,1939,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,312,Good,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,457,798,GasA,Excellent,Y,SBrkr,1137,817,0,1954,0,1,1,1,3,1,Good,8,Typ,2,Typical,Attchd,Unf,2,431,Typical,Typical,Paved,0,119,150,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,311500,-93.643378,42.015667 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,12392,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Excellent,1950,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Good,No,GLQ,3,Unf,0,397,832,GasA,Excellent,Y,SBrkr,1218,943,0,2161,1,0,2,1,3,1,Good,8,Typ,2,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,263400,-93.64293,42.014567 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,56,26073,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Gable,CompShg,BrkFace,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,782,1898,GasA,Excellent,Y,FuseA,1898,0,0,1898,0,0,2,1,3,1,Typical,7,Typ,2,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,0,51,224,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2008,WD ,Normal,236500,-93.640311,42.012638 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14778,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Gtl,Crawford,PosN,Norm,OneFam,One_Story,Above_Average,Good,1954,2006,Hip,CompShg,HdBoard,HdBoard,BrkFace,72,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,568,1296,GasA,Excellent,Y,SBrkr,1640,0,0,1640,1,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,2,924,Typical,Typical,Paved,108,0,0,216,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,224000,-93.640293,42.012488 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1879,Pave,No_Alley_Access,Regular,Lvl,AllPub,CulDSac,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,150,516,GasA,Typical,Y,SBrkr,516,516,0,1032,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,462,Typical,Typical,Paved,213,0,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2008,WD ,Normal,116500,-93.645743,42.009489 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Good,Typical,No,LwQ,4,GLQ,612,23,716,GasA,Typical,Y,SBrkr,716,840,0,1556,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,452,Typical,Typical,Paved,161,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2008,COD,Normal,151000,-93.645743,42.009489 +One_Story_1945_and_Older,Residential_Medium_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1926,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,GLQ,40,555,894,GasA,Typical,Y,SBrkr,919,0,0,919,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,195,Typical,Typical,Partial_Pavement,0,0,116,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,126000,-93.628409,42.022607 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1940,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,720,720,GasA,Good,Y,SBrkr,760,330,0,1090,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,120000,-93.627459,42.021806 +One_Story_1945_and_Older,Residential_Medium_Density,40,3636,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1922,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,796,796,GasA,Fair,N,SBrkr,796,0,0,796,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,100,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2008,WD ,Normal,55000,-93.625291,42.022797 +One_Story_1945_and_Older,Residential_Medium_Density,0,5890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1930,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,Av,ALQ,1,Unf,0,278,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,432,Typical,Typical,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,120500,-93.625288,42.022516 +One_Story_1945_and_Older,Residential_Medium_Density,71,6900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Above_Average,1940,1955,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,125,212,740,GasA,Excellent,Y,SBrkr,778,0,0,778,0,0,1,0,2,1,Typical,4,Typ,1,Good,Detchd,Fin,1,924,Excellent,Excellent,Paved,0,25,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,120500,-93.6291062,42.0213831 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,58,8155,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,780,780,GasA,Good,Y,FuseA,780,420,0,1200,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,119000,-93.628385,42.021276 +Split_or_Multilevel,Residential_Medium_Density,75,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,SLvl,Average,Average,1967,1967,Hip,CompShg,HdBoard,Plywood,None,0,Fair,Typical,CBlock,Good,Typical,Mn,Rec,6,Unf,0,367,624,GasA,Excellent,Y,SBrkr,1092,564,0,1656,0,0,1,1,3,1,Typical,7,Mod,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,0,180,0,0,100,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,126000,-93.6258429,42.0214037 +One_Story_1945_and_Older,Residential_Medium_Density,60,7392,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Good,1930,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,520,520,GasA,Typical,Y,FuseA,912,0,0,912,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,360,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,99500,-93.626835,42.020379 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1958,1958,Gable,CompShg,WdShing,Wd Shng,BrkFace,162,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,821,1151,GasA,Good,Y,FuseA,1151,804,0,1955,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,356,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,165500,-93.625143,42.021389 +One_Story_1946_and_Newer_All_Styles,A_agr,80,14584,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Very_Poor,Average,1952,1952,Gable,CompShg,AsbShng,VinylSd,None,0,Fair,Poor,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Poor,N,FuseA,733,0,0,733,0,0,1,0,2,1,Fair,4,Sal,0,No_Fireplace,Attchd,Unf,2,487,Fair,Poor,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,13100,-93.625217,42.018806 +Two_Story_1945_and_Older,C_all,60,5280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,Two_Story,Below_Average,Good,1895,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Stone,Poor,Fair,No,Unf,7,Unf,0,173,173,GasA,Excellent,N,SBrkr,825,536,0,1361,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,185,Fair,Typical,Paved,0,123,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,89000,-93.615426,42.021162 +Two_Story_1945_and_Older,C_all,50,8500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,Two_Story,Below_Average,Below_Average,1920,1950,Gambrel,CompShg,BrkFace,BrkFace,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,649,649,GasA,Typical,N,SBrkr,649,668,0,1317,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Fair,Dirt_Gravel,0,54,172,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2008,WD ,Normal,40000,-93.616848,42.021506 +One_and_Half_Story_Finished_All_Ages,C_all,52,5150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1910,2000,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Poor,Typical,No,Unf,7,Unf,0,356,356,GasA,Typical,N,FuseA,671,378,0,1049,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,195,Poor,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,80900,-93.616849,42.021594 +One_Story_1945_and_Older,C_all,120,18000,Grvl,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Fair,Below_Average,1935,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Fair,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,894,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,RFn,3,1248,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,Shed,560,8,2008,ConLD,Normal,81000,-93.604195,42.022458 +Two_Story_1945_and_Older,C_all,60,9000,Grvl,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Below_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Stone,Fair,Fair,Mn,Unf,7,Unf,0,592,592,GasA,Excellent,Y,SBrkr,432,432,0,864,0,0,1,1,3,1,Fair,5,Min2,0,No_Fireplace,Detchd,Unf,1,216,Fair,Fair,Dirt_Gravel,0,30,160,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,65000,-93.604193,42.022626 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,64,5587,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2008,2008,Hip,CompShg,CemntBd,CmentBd,Stone,186,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1600,GasA,Excellent,Y,SBrkr,1652,0,0,1652,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,162,53,0,153,0,0,No_Pool,No_Fence,None,0,11,2008,New,Partial,392500,-93.616242,42.009326 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3843,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,186,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1596,GasA,Excellent,Y,SBrkr,1648,0,0,1648,1,1,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,525,Typical,Typical,Paved,162,53,0,153,0,0,No_Pool,No_Fence,None,0,8,2008,New,Partial,294464,-93.616584,42.008803 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3811,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,174,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,120,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,250000,-93.615985,42.008806 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3842,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,174,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,221,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,482,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,275000,-93.615921,42.0091899 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,23730,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,1996,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,668,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,1140,1840,GasA,Excellent,Y,SBrkr,2032,0,0,2032,1,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,786,Typical,Typical,Paved,0,46,192,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,300000,-93.607342,41.99507 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13265,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,CemntBd,CmentBd,BrkFace,148,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,350,1568,GasA,Excellent,Y,SBrkr,1689,0,0,1689,1,0,2,0,3,1,Good,7,Typ,2,Good,Attchd,RFn,3,857,Typical,Typical,Paved,150,59,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,271000,-93.607121,41.997124 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,910,910,GasA,Excellent,Y,SBrkr,910,910,0,1820,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Unf,3,816,Typical,Typical,Paved,318,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2008,WD ,Normal,213000,-93.60489,41.997062 +Split_or_Multilevel,Residential_Low_Density,76,9880,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,574,1096,GasA,Typical,Y,SBrkr,1118,0,0,1118,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,Fin,1,358,Typical,Typical,Paved,203,0,0,0,0,576,Good,Good_Privacy,None,0,7,2008,WD ,Normal,171000,-93.604549,41.997069 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,279,276,1196,GasA,Typical,Y,SBrkr,1279,0,0,1279,0,1,2,0,3,1,Typical,6,Typ,2,Fair,Detchd,Unf,2,473,Typical,Typical,Paved,238,83,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2008,WD ,Normal,171500,-93.604548,41.996919 +Duplex_All_Styles_and_Ages,Residential_Low_Density,76,10260,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,Two_Story,Average,Below_Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,936,936,0,1872,0,0,2,2,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Abnorml,100000,-93.602912,41.996903 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,9990,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,297,1680,GasA,Good,Y,SBrkr,1689,0,0,1689,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,432,Typical,Typical,Paved,428,120,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,228500,-93.602429,41.995991 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,0,164660,Grvl,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Sev,Timberland,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Above_Average,1965,1965,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,BLQ,147,103,1499,GasA,Excellent,Y,SBrkr,1619,167,0,1786,2,0,2,0,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,529,Typical,Typical,Paved,670,0,0,0,0,0,No_Pool,No_Fence,Shed,700,8,2008,WD ,Normal,228950,-93.658875,41.998543 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,42,4084,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,1986,1986,Gable,CompShg,VinylSd,VinylSd,BrkFace,340,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,384,1277,GasA,Good,Y,SBrkr,1501,0,0,1501,1,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,2,512,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,241500,-93.639793,42.00514 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15498,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1976,1976,Hip,WdShake,Stone,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,400,0,1565,GasA,Typical,Y,SBrkr,2898,0,0,2898,1,0,2,0,2,1,Good,10,Typ,1,Good,Attchd,Fin,2,665,Typical,Typical,Paved,0,72,174,0,0,0,No_Pool,No_Fence,None,0,5,2008,COD,Abnorml,287000,-93.650229,41.998999 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,11563,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,258,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,482,1518,GasA,Excellent,Y,SBrkr,1537,0,0,1537,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,3,788,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,294000,-93.653122,41.995141 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9520,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,338,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,125,1638,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,800,Typical,Typical,Paved,192,44,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,New,Partial,293077,-93.653025,41.994841 +Two_Story_1946_and_Newer,Residential_Low_Density,107,12852,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,150,920,GasA,Excellent,Y,SBrkr,920,860,0,1780,1,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,612,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,New,Partial,264966,-93.651718,41.99373 +Split_or_Multilevel,Residential_Low_Density,73,9802,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,352,352,GasA,Good,Y,SBrkr,712,730,0,1442,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,172500,-93.649198,41.995142 +Split_or_Multilevel,Residential_Low_Density,73,9735,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,Unknown,754,640,0,1394,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,167500,-93.649475,41.993751 +Two_Story_1946_and_Newer,Residential_Low_Density,81,12018,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,60,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,796,796,GasA,Excellent,Y,SBrkr,796,816,0,1612,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,3,666,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,10,2008,New,Partial,218689,-93.650431,41.995175 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,12890,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1989,1989,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,128,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1495,1495,GasA,Excellent,Y,SBrkr,1495,0,0,1495,0,0,2,0,3,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,438,Typical,Typical,Paved,252,0,192,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,150000,-93.644679,41.999812 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,47,12416,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1986,1986,Gable,CompShg,VinylSd,Plywood,Stone,132,Typical,Typical,CBlock,Good,Fair,No,ALQ,1,LwQ,208,0,1606,GasA,Typical,Y,SBrkr,1651,0,0,1651,1,0,2,0,3,1,Typical,7,Min2,1,Typical,Attchd,Fin,2,616,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Normal,184000,-93.646054,41.999575 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,18265,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1986,1986,Gable,CompShg,Plywood,HdBoard,BrkFace,228,Good,Good,CBlock,Good,Good,Av,GLQ,3,Rec,60,276,1256,GasA,Excellent,Y,SBrkr,1256,0,0,1256,0,0,2,0,3,1,Good,6,Typ,1,Fair,Attchd,Unf,2,578,Typical,Typical,Paved,282,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,180000,-93.6470597,41.9976055 +Split_or_Multilevel,Residential_Low_Density,85,10200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Above_Average,Average,1988,1989,Gable,CompShg,HdBoard,HdBoard,BrkFace,219,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,678,1461,GasA,Excellent,Y,SBrkr,1509,0,0,1509,1,0,2,0,3,1,Good,5,Typ,1,Fair,Attchd,RFn,2,600,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Abnorml,175000,-93.64427,41.998554 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,11202,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,206,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,403,1432,GasA,Excellent,Y,SBrkr,1440,0,0,1440,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,467,Typical,Typical,Paved,185,95,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,232500,-93.646726,41.995512 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7915,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1999,2000,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,443,1666,GasA,Excellent,Y,SBrkr,1675,0,0,1675,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,435,Typical,Typical,Paved,165,52,0,0,0,0,No_Pool,No_Fence,None,0,12,2008,WD ,Normal,195000,-93.646645,41.99639 +Two_Story_1946_and_Newer,Residential_Low_Density,85,12244,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,Stone,226,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,611,1482,GasA,Excellent,Y,SBrkr,1482,780,0,2262,1,0,2,1,4,1,Good,10,Typ,2,Good,Attchd,Fin,3,749,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2008,WD ,Normal,305000,-93.647834,41.99423 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,11449,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,873,1884,GasA,Excellent,Y,SBrkr,1728,0,0,1728,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,520,Typical,Typical,Paved,0,276,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,298751,-93.653218,41.99312 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,11447,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,674,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,393,1964,GasA,Excellent,Y,SBrkr,1964,0,0,1964,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,892,Typical,Typical,Paved,0,265,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,370000,-93.647214,41.993611 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,97,8940,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,Typical,Good,PConc,Good,Good,Gd,GLQ,3,Unf,0,35,1344,GasA,Excellent,Y,SBrkr,1344,0,0,1344,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,4,784,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Alloca,209200,-93.608267,41.99338 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,78,7060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,Typical,Good,PConc,Good,Good,Gd,GLQ,3,Unf,0,35,1344,GasA,Excellent,Y,SBrkr,1344,0,0,1344,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,4,784,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2008,WD ,Alloca,206300,-93.608343,41.993335 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,9278,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Feedr,Artery,OneFam,One_Story,Average,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1092,1092,GasA,Excellent,Y,SBrkr,1092,0,0,1092,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,52,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Abnorml,146000,-93.610145,41.990054 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1997,1997,Hip,CompShg,VinylSd,VinylSd,BrkFace,197,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,325,1189,GasA,Excellent,Y,SBrkr,1189,0,0,1189,1,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,2,392,Typical,Typical,Paved,0,122,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,160500,-93.608197,41.993059 +Split_Foyer,Residential_Low_Density,150,14137,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Below_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,98,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,247,88,1200,GasA,Good,Y,SBrkr,1200,0,0,1200,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,More_Than_Two_Types,Fin,3,850,Typical,Typical,Paved,0,119,0,0,171,0,No_Pool,No_Fence,None,0,11,2008,ConLD,Normal,173000,-93.606374,41.993185 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,4224,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,271,1040,GasA,Good,Y,SBrkr,1040,0,0,1040,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,499,Typical,Typical,Paved,0,100,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,124000,-93.605351,41.99261 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,264,264,GasA,Typical,Y,SBrkr,616,688,0,1304,0,0,1,1,3,1,Typical,4,Typ,1,Good,BuiltIn,Fin,1,336,Typical,Typical,Paved,141,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,115000,-93.604649,41.991372 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Good,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,232,550,GasA,Typical,Y,SBrkr,925,550,0,1475,0,0,2,0,4,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,336,Typical,Typical,Paved,92,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2008,WD ,Normal,129500,-93.604184,41.990899 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1974,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Good,1973,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,25,526,GasA,Good,Y,SBrkr,526,462,0,988,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,BuiltIn,RFn,1,297,Typical,Typical,Paved,120,101,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,105000,-93.603601,41.991883 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1596,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SLvl,Below_Average,Above_Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,25,462,GasA,Typical,Y,SBrkr,526,462,0,988,1,0,1,0,1,1,Typical,4,Typ,1,Poor,BuiltIn,RFn,1,297,Typical,Typical,Paved,0,101,0,120,0,0,No_Pool,Good_Wood,None,0,7,2008,WD ,Normal,94900,-93.604269,41.991779 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17979,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,328,1113,GasA,Excellent,Y,SBrkr,1160,0,0,1160,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,257,Typical,Typical,Paved,81,0,0,0,0,0,No_Pool,Good_Wood,Shed,500,2,2008,WD ,Normal,152500,-93.601444,41.993249 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1477,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Above_Average,Excellent,1970,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,188,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,187,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2008,WD ,Normal,98000,-93.603178,41.992133 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Good,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Good,Typical,Av,ALQ,1,Unf,0,135,630,GasA,Good,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,88,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,81000,-93.603458,41.991933 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,21750,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Mitchell,Artery,Norm,OneFam,One_Story,Average,Average,1954,1954,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,988,988,GasA,Excellent,Y,FuseA,988,0,0,988,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,2,520,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2008,WD ,Normal,113000,-93.610109,41.988675 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,6490,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1983,1983,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,GLQ,3,Unf,0,282,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,315,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2008,WD ,Normal,128500,-93.6051072,41.987109 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,37,6951,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1984,1985,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,218,876,GasA,Typical,Y,SBrkr,923,0,0,923,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,362,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,119500,-93.604761,41.988934 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Excellent,1982,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,175,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,1,1,0,3,1,Good,5,Typ,1,Excellent,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2008,WD ,Normal,130500,-93.603917,41.98811 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Very_Good,1982,2003,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,PConc,Typical,Good,No,GLQ,3,Unf,0,0,816,GasA,Excellent,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Fin,2,816,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2008,WD ,Normal,138000,-93.603916,41.988076 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1982,2005,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,207,845,GasA,Good,Y,SBrkr,845,0,0,845,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2008,WD ,Normal,134500,-93.603886,41.987289 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,12508,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1985,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,323,983,GasA,Excellent,Y,SBrkr,983,767,0,1750,1,0,2,0,4,1,Typical,7,Mod,0,No_Fireplace,Attchd,Unf,1,423,Typical,Typical,Paved,245,0,156,0,0,0,No_Pool,No_Fence,Shed,1300,7,2008,WD ,Normal,160000,-93.608291,41.98658 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,12395,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1984,1984,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,217,864,GasA,Typical,Y,SBrkr,889,0,0,889,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2008,WD ,Normal,137500,-93.603722,41.986498 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11075,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Below_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,LwQ,193,29,784,GasA,Excellent,Y,SBrkr,1168,800,0,1968,0,1,2,1,4,1,Typical,7,Min2,1,Poor,Attchd,RFn,2,530,Typical,Typical,Paved,305,189,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,9,2008,WD ,Normal,172000,-93.600066,41.991885 +Two_Story_1945_and_Older,I_all,0,56600,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_and_Half_Unf,Average,Very_Poor,1900,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,686,686,GasA,Excellent,Y,SBrkr,1150,686,0,1836,0,0,2,0,4,1,Typical,7,Maj1,0,No_Fireplace,Detchd,Unf,1,288,Typical,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2008,WD ,Normal,103000,-93.577427,42.022745 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10667,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1971,1971,Gable,CompShg,MetalSd,MetalSd,BrkFace,302,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,749,1587,GasA,Typical,Y,SBrkr,1587,0,0,1587,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,167300,-93.618504,42.051297 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10628,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1970,1970,Flat,Tar&Grv,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Good,Gd,GLQ,3,Unf,0,499,1277,GasA,Typical,Y,SBrkr,1277,0,0,1277,1,0,1,0,2,1,Typical,5,Typ,1,Poor,Attchd,Unf,2,526,Typical,Typical,Paved,0,0,0,0,176,0,No_Pool,Good_Wood,None,0,4,2007,WD ,Normal,167000,-93.618503,42.051357 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,13651,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1973,1973,Gable,CompShg,Plywood,Plywood,BrkFace,1115,Typical,Good,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,343,2223,GasA,Excellent,Y,SBrkr,2223,0,0,2223,1,0,2,0,3,1,Typical,8,Typ,2,Good,Attchd,Fin,2,516,Typical,Typical,Paved,300,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,244000,-93.617273,42.049671 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,15865,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1970,1970,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Good,Good,PConc,Typical,Good,Gd,ALQ,1,Rec,823,1043,2217,GasA,Excellent,Y,SBrkr,2217,0,0,2217,1,0,2,0,4,1,Good,8,Typ,1,Typical,Attchd,Unf,2,621,Typical,Typical,Paved,81,207,0,0,224,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,268000,-93.61728,42.050443 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12394,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,886,0,1733,0,0,2,1,3,1,Good,7,Typ,1,Good,BuiltIn,Fin,2,433,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Family,225000,-93.63988,42.061275 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10364,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,806,806,GasA,Good,Y,SBrkr,806,766,0,1572,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,373,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,168000,-93.63736,42.060466 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13869,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,612,794,GasA,Good,Y,SBrkr,794,676,0,1470,0,1,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,388,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,177000,-93.636146,42.061664 +Two_Story_1946_and_Newer,Residential_Low_Density,57,8773,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,916,916,GasA,Good,Y,SBrkr,916,684,0,1600,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,460,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,169000,-93.636972,42.061326 +Split_or_Multilevel,Residential_Low_Density,56,8872,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,754,630,0,1384,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,390,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,161500,-93.637189,42.061242 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,5330,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2000,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,298,1494,GasA,Excellent,Y,SBrkr,1652,0,0,1652,1,0,2,0,2,1,Excellent,6,Typ,0,No_Fireplace,Attchd,RFn,2,499,Typical,Typical,Paved,96,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,251000,-93.634341,42.063067 +Split_or_Multilevel,Residential_Low_Density,0,10147,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Above_Average,Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,32,Typical,Typical,PConc,Good,Typical,No,LwQ,4,Unf,0,206,392,GasA,Good,Y,SBrkr,924,770,0,1694,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,398,Typical,Typical,Paved,256,64,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,160000,-93.636345,42.059592 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8637,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,52,923,GasA,Good,Y,SBrkr,947,767,0,1714,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,451,Typical,Typical,Paved,256,48,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Abnorml,180000,-93.640613,42.058953 +Two_Story_1946_and_Newer,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,277,691,GasA,Good,Y,SBrkr,691,862,0,1553,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,178750,-93.637865,42.059562 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,38,Typical,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1237,1237,GasA,Good,Y,SBrkr,1253,0,0,1253,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,402,Typical,Typical,Paved,220,21,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,176000,-93.637883,42.059517 +Two_Story_1946_and_Newer,Residential_Low_Density,60,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,938,938,GasA,Excellent,Y,SBrkr,957,1342,0,2299,0,0,3,1,5,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,482,Typical,Typical,Paved,188,30,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,204000,-93.639068,42.059242 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9556,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,1992,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,52,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1168,1168,GasA,Good,Y,SBrkr,1187,0,0,1187,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,420,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,160000,-93.637848,42.059293 +Split_or_Multilevel,Residential_Low_Density,0,10784,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1991,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,76,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,802,670,0,1472,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,402,Typical,Typical,Paved,164,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,160000,-93.637299,42.057006 +Split_or_Multilevel,Residential_Low_Density,0,9125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,170,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Good,Y,SBrkr,812,670,0,1482,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,392,Typical,Typical,Paved,100,25,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,163900,-93.637917,42.05817 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7655,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,903,903,GasA,Good,Y,SBrkr,910,732,0,1642,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,392,Typical,Typical,Paved,290,84,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,168000,-93.635833,42.057533 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,160,18160,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkCmn,138,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,752,1302,GasA,Fair,Y,SBrkr,1128,0,0,1128,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,480,Typical,Typical,Partial_Pavement,0,108,246,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Alloca,154204,-93.633329,42.057022 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3696,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1986,1986,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1074,1074,GasA,Excellent,Y,SBrkr,1088,0,0,1088,0,0,1,1,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,461,Typical,Typical,Paved,0,74,137,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,170000,-93.634513,42.059375 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,0,5062,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,TwnhsE,Two_Story,Good,Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,CBlock,Good,Typical,Mn,GLQ,3,LwQ,182,180,1190,GasA,Good,Y,SBrkr,1190,900,0,2090,1,0,2,0,3,1,Good,6,Min1,1,Typical,Attchd,Fin,2,577,Typical,Typical,Paved,219,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,207500,-93.632917,42.059239 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,38,4740,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1988,1988,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,918,1166,GasA,Good,Y,SBrkr,1179,0,0,1179,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,480,Typical,Typical,Paved,0,108,0,0,135,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,182000,-93.631622,42.059309 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,35,5118,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1990,1990,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,386,1312,GasA,Good,Y,SBrkr,1321,0,0,1321,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,0,64,140,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,212000,-93.631633,42.059304 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,14157,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,Stone,200,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,673,1922,GasA,Excellent,Y,SBrkr,1922,0,0,1922,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,676,Typical,Typical,Paved,178,51,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,377426,-93.629016,42.060431 +Two_Story_1946_and_Newer,Residential_Low_Density,98,12328,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,Stone,146,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,163,1149,GasA,Excellent,Y,SBrkr,1164,1377,0,2541,1,0,3,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,729,Typical,Typical,Paved,120,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,349265,-93.629075,42.060144 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,52,51974,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,PosN,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,710,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1559,2660,GasA,Excellent,Y,SBrkr,2338,0,0,2338,1,0,2,1,4,1,Good,8,Typ,2,Good,Attchd,Fin,3,1110,Good,Typical,Paved,0,135,0,0,322,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,591587,-93.626366,42.061202 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,195,41600,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Gilbert,Norm,Norm,TwoFmCon,One_Story,Average,Average,1969,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,53,1100,GasW,Typical,Y,SBrkr,1424,0,0,1424,1,0,1,1,3,1,Typical,7,Mod,0,No_Fireplace,More_Than_Two_Types,Unf,3,828,Typical,Typical,Dirt_Gravel,144,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,155000,-93.622874,42.060096 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,8035,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2006,2006,Gable,CompShg,MetalSd,MetalSd,BrkFace,165,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,815,1612,GasA,Excellent,Y,SBrkr,1612,0,0,1612,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,556,Typical,Typical,Paved,0,164,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,319900,-93.629538,42.059743 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8089,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,2007,2007,Gable,CompShg,MetalSd,MetalSd,BrkFace,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,474,1419,GasA,Excellent,Y,SBrkr,1419,0,0,1419,1,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,RFn,2,567,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,392000,-93.630074,42.058893 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14082,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,945,Good,Typical,PConc,Excellent,Good,Gd,GLQ,3,Unf,0,662,2220,GasA,Excellent,Y,SBrkr,2234,0,0,2234,1,0,1,1,1,1,Good,7,Typ,1,Good,Attchd,RFn,2,724,Typical,Typical,Paved,390,80,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,441929,-93.628754,42.058998 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,13870,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,PosA,PosA,OneFam,One_Story,Very_Excellent,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,250,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,590,1742,GasA,Excellent,Y,SBrkr,2042,0,0,2042,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,724,Typical,Typical,Paved,240,52,0,0,174,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,455000,-93.628723,42.058996 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12546,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1981,1981,Gable,CompShg,MetalSd,MetalSd,BrkFace,310,Good,Good,CBlock,Good,Typical,No,BLQ,2,Unf,0,762,1440,GasA,Excellent,Y,SBrkr,1440,0,0,1440,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,467,Typical,Typical,Paved,0,0,99,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,182900,-93.637875,42.054368 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10960,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1984,1984,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,1028,1284,GasA,Typical,Y,SBrkr,1284,0,0,1284,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,4,2007,COD,Abnorml,174000,-93.638853,42.053916 +Split_or_Multilevel,Residential_Low_Density,78,12090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Good,1984,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,74,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,585,585,GasA,Excellent,Y,SBrkr,1140,728,0,1868,0,0,3,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,477,Typical,Typical,Paved,268,112,0,0,147,0,No_Pool,No_Fence,None,0,1,2007,WD ,Abnorml,178000,-93.637876,42.055545 +Two_Story_1946_and_Newer,Residential_Low_Density,78,12090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1981,2003,Gable,CompShg,MetalSd,MetalSd,BrkFace,306,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,404,725,GasA,Excellent,Y,SBrkr,725,754,0,1479,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,167,72,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,176000,-93.637816,42.0545 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,12299,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1978,1985,Gable,CompShg,Plywood,Plywood,Stone,67,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,201,1529,GasA,Typical,Y,SBrkr,1664,0,0,1664,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,663,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,213000,-93.637158,42.054395 +Two_Story_1946_and_Newer,Residential_Low_Density,61,11339,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Average,1979,1979,Hip,WdShake,HdBoard,Plywood,BrkFace,549,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,22,780,GasA,Typical,Y,SBrkr,1085,845,0,1930,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,2,481,Typical,Typical,Paved,192,72,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,192000,-93.634665,42.054941 +Two_Story_1946_and_Newer,Residential_Low_Density,92,11952,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Above_Average,1977,1977,Mansard,WdShake,WdShing,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,808,808,GasA,Typical,Y,SBrkr,1161,808,0,1969,0,0,2,1,3,1,Typical,8,Typ,1,Good,Attchd,RFn,2,534,Typical,Typical,Paved,0,0,0,0,276,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,190000,-93.633973,42.055217 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12046,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,298,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,692,848,GasA,Typical,Y,SBrkr,1118,912,0,2030,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,2,551,Typical,Typical,Paved,0,224,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,195000,-93.631528,42.054427 +Split_or_Multilevel,Residential_Low_Density,0,10395,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,233,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,427,1032,GasA,Typical,Y,SBrkr,1032,0,0,1032,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,564,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,500,7,2007,WD ,Normal,148000,-93.632669,42.05622 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,11850,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1984,1984,Gable,CompShg,Plywood,Plywood,BrkFace,98,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,372,1153,GasA,Typical,Y,SBrkr,1177,0,0,1177,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,495,Typical,Typical,Paved,204,103,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,151500,-93.631795,42.056211 +Two_Story_1946_and_Newer,Residential_Low_Density,80,11584,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Good,Above_Average,1979,1979,Hip,CompShg,HdBoard,HdBoard,BrkFace,96,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,110,114,539,GasA,Typical,Y,SBrkr,1040,685,0,1725,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,0,88,216,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,197000,-93.638554,42.052353 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1979,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,253,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,356,1259,GasA,Excellent,Y,SBrkr,1353,0,0,1353,1,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,478,Typical,Typical,Paved,240,141,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,192350,-93.635411,42.053271 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10793,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Average,Average,1969,1969,Mansard,CompShg,WdShing,HdBoard,BrkFace,263,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,287,0,780,GasA,Excellent,Y,SBrkr,780,840,0,1620,0,0,2,1,4,1,Typical,7,Min1,0,No_Fireplace,Attchd,Fin,2,462,Typical,Typical,Paved,208,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2007,WD ,Normal,152000,-93.63724,42.05026 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,128,13001,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Above_Average,Average,1971,1971,Hip,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,BLQ,121,1012,1625,GasA,Typical,Y,SBrkr,1220,0,0,1220,0,1,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,944,Typical,Typical,Paved,0,0,249,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,170000,-93.634714,42.049832 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,12243,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1971,1971,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,486,1484,GasA,Good,Y,SBrkr,1484,0,0,1484,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,487,Typical,Typical,Paved,224,0,0,0,180,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,175000,-93.634581,42.050075 +Two_Story_1946_and_Newer,Residential_Low_Density,90,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Mansard,CompShg,HdBoard,AsphShn,BrkFace,365,Good,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,175,143,702,GasA,Good,Y,SBrkr,1041,702,0,1743,0,1,1,2,3,1,Typical,7,Typ,1,Good,Attchd,Unf,2,539,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,175000,-93.636497,42.050372 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12384,Pave,No_Alley_Access,Regular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Good,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,233,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,793,793,GasA,Typical,Y,SBrkr,1142,793,0,1935,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,0,113,252,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,197900,-93.633898,42.049956 +Split_or_Multilevel,Residential_Low_Density,64,8991,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,SLvl,Good,Above_Average,1976,1976,Gable,CompShg,Plywood,Plywood,Stone,130,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Rec,604,0,1228,GasA,Typical,Y,SBrkr,1324,0,0,1324,0,1,2,0,3,1,Good,5,Typ,1,Fair,Attchd,Fin,2,585,Typical,Typical,Paved,407,36,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,174000,-93.633757,42.051039 +Two_Story_1946_and_Newer,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Above_Average,1974,1974,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,153,0,1084,GasA,Typical,Y,SBrkr,1084,793,0,1877,1,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,488,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,178900,-93.632191,42.048765 +Split_or_Multilevel,Residential_Low_Density,70,10500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1971,2005,Gambrel,CompShg,MetalSd,AsphShn,BrkFace,82,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,23,372,GasA,Typical,Y,SBrkr,576,533,0,1109,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,BuiltIn,Unf,1,288,Typical,Typical,Paved,35,0,0,0,0,0,No_Pool,Good_Wood,None,0,12,2007,WD ,Normal,139000,-93.627736,42.055392 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10530,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1971,1971,Hip,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,35,664,981,GasA,Typical,Y,SBrkr,981,0,0,981,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,312,40,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,143250,-93.626331,42.055264 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7472,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Excellent,1972,2004,Gable,CompShg,HdBoard,HdBoard,BrkFace,138,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,99,725,GasA,Good,Y,SBrkr,725,754,0,1479,1,0,1,1,4,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,484,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,184000,-93.626565,42.054603 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,9457,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1970,1990,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,359,925,GasA,Typical,Y,SBrkr,1422,0,0,1422,1,0,1,0,3,1,Typical,7,Min2,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,252,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2007,WD ,Normal,155000,-93.629118,42.053407 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,7920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1970,2002,Gable,CompShg,HdBoard,HdBoard,BrkFace,32,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,GLQ,619,214,914,GasA,Excellent,Y,SBrkr,914,0,0,914,0,0,1,0,3,1,Excellent,5,Typ,0,No_Fireplace,Attchd,RFn,1,368,Typical,Good,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,133000,-93.626722,42.054612 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,9758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,287,251,950,GasA,Typical,Y,SBrkr,950,0,0,950,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,119500,-93.627112,42.053547 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8294,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1971,1971,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,858,858,GasA,Typical,Y,SBrkr,872,0,0,872,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,4,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2007,WD ,Normal,123000,-93.628047,42.05346 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7340,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,536,858,GasA,Typical,Y,SBrkr,858,0,0,858,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,684,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,110000,-93.62805,42.053503 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17199,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Good,1961,1961,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,600,914,GasA,Excellent,Y,SBrkr,914,0,0,914,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Basment,Unf,1,270,Fair,Typical,Paved,140,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Normal,128000,-93.6247461,42.0562952 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,248,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2007,WD ,Normal,120500,-93.621646,42.056395 +One_Story_PUD_1946_and_Newer,Residential_High_Density,34,4058,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwnhsE,SFoyer,Good,Average,1998,1998,Gable,CompShg,MetalSd,MetalSd,BrkFace,182,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,LwQ,139,0,723,GasA,Excellent,Y,SBrkr,767,0,0,767,1,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,367,Typical,Typical,Paved,120,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,133000,-93.624798,42.05467 +One_Story_PUD_1946_and_Newer,Residential_High_Density,33,4113,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2001,2001,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1337,1337,GasA,Excellent,Y,SBrkr,1337,0,0,1337,0,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,511,Typical,Typical,Paved,136,68,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,155000,-93.624798,42.054789 +One_Story_PUD_1946_and_Newer,Residential_High_Density,26,10943,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1997,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,475,1405,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,522,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,177000,-93.624431,42.055319 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2205,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,567,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,213,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,113500,-93.629747,42.052669 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,285,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,316,672,GasA,Typical,Y,SBrkr,672,546,0,1218,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,144,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,113000,-93.628656,42.052698 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2058,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,265,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,672,546,0,1218,0,0,1,1,4,1,Excellent,7,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,113700,-93.627855,42.052696 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2016,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,304,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Typical,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,106000,-93.629777,42.052294 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,376,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,294,765,GasA,Excellent,Y,SBrkr,765,600,0,1365,1,0,1,1,2,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,240,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,122500,-93.627242,42.051833 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,4928,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,958,1078,GasA,Typical,Y,SBrkr,958,0,0,958,0,0,2,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,205,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,128000,-93.627238,42.050397 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,Two_Story,Above_Average,Above_Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,248,804,GasA,Typical,Y,SBrkr,804,744,0,1548,1,0,2,1,3,1,Good,7,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,48,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,155000,-93.624575,42.05057 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2304,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Good,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,6,423,1061,GasA,Typical,Y,SBrkr,1055,0,0,1055,0,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,319,Typical,Typical,Paved,108,32,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,142500,-93.625672,42.049996 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,1966,Hip,CompShg,HdBoard,HdBoard,BrkFace,52,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,263,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,360,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,129250,-93.6257971,42.0488205 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,96,12469,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,378,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,639,1790,GasA,Excellent,Y,SBrkr,1816,0,0,1816,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,730,Typical,Typical,Paved,186,36,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,356000,-93.6588809,42.062574 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11694,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,452,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1774,1822,GasA,Excellent,Y,SBrkr,1828,0,0,1828,0,0,2,0,3,1,Good,9,Typ,1,Good,Attchd,Unf,3,774,Typical,Typical,Paved,0,108,0,0,260,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,314813,-93.658015,42.063296 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,12030,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,254,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1694,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,3,818,Typical,Typical,Paved,168,228,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,318000,-93.657679,42.063298 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11825,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,CemntBd,CmentBd,Stone,302,Good,Typical,PConc,Excellent,Typical,Mn,Unf,7,Unf,0,1694,1694,GasA,Excellent,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,856,Typical,Typical,Paved,0,112,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,322400,-93.6569765,42.0632689 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,12085,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,328,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,730,1734,GasA,Excellent,Y,SBrkr,1734,0,0,1734,1,0,2,0,3,1,Excellent,7,Typ,1,Good,Attchd,RFn,3,928,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,318000,-93.6566403,42.0632722 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14333,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,590,2108,GasA,Excellent,Y,SBrkr,2122,0,0,2122,1,0,2,1,2,1,Good,7,Typ,1,Excellent,Attchd,Fin,3,938,Typical,Typical,Paved,130,142,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,345474,-93.654243,42.063141 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,14450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2007,Gable,CompShg,CemntBd,CmentBd,BrkFace,315,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,2121,2121,GasA,Excellent,Y,SBrkr,2121,0,0,2121,0,0,2,1,3,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,732,Typical,Typical,Paved,124,98,0,0,142,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,415298,-93.6570383,42.0619544 +Two_Story_1946_and_Newer,Residential_Low_Density,107,13641,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,456,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,630,1934,GasA,Excellent,Y,SBrkr,1943,713,0,2656,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,1040,Typical,Typical,Paved,268,58,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,492000,-93.6566568,42.061976 +Two_Story_1946_and_Newer,Residential_Low_Density,110,13440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,190,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1108,1108,GasA,Excellent,Y,SBrkr,1148,1402,0,2550,0,0,2,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,670,Typical,Typical,Paved,120,39,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,New,Partial,338931,-93.6562702,42.0619709 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,15431,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,400,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,564,1994,GasA,Excellent,Y,SBrkr,2046,0,0,2046,1,0,2,1,2,1,Excellent,7,Typ,2,Good,Attchd,Fin,3,878,Typical,Typical,Paved,188,65,0,0,175,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,450000,-93.657967,42.061284 +Two_Story_1946_and_Newer,Residential_Low_Density,109,14154,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,350,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1063,1063,GasA,Excellent,Y,SBrkr,1071,1101,0,2172,0,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,RFn,3,947,Typical,Typical,Paved,192,62,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,280000,-93.654774,42.062259 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,456,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,740,2552,GasA,Excellent,Y,SBrkr,2552,0,0,2552,1,0,2,0,3,1,Excellent,8,Typ,2,Excellent,Attchd,Fin,3,932,Typical,Typical,Paved,130,28,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,479069,-93.6570542,42.0628153 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14226,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,375,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1935,1935,GasA,Good,Y,SBrkr,1973,0,0,1973,0,0,2,0,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,895,Typical,Typical,Paved,315,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,395000,-93.6562861,42.0624082 +Two_Story_1946_and_Newer,Residential_Low_Density,105,13693,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,772,Excellent,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,2153,2153,GasA,Excellent,Y,SBrkr,2069,574,0,2643,0,0,2,1,3,1,Excellent,9,Typ,1,Good,BuiltIn,Fin,3,694,Typical,Typical,Paved,414,84,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,380000,-93.657444,42.062434 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,11428,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,248,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1626,1626,GasA,Excellent,Y,SBrkr,1634,0,0,1634,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,866,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,250000,-93.65331,42.061504 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14977,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,304,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,626,1976,GasA,Excellent,Y,SBrkr,1976,0,0,1976,1,0,2,0,2,1,Good,7,Typ,1,Excellent,Attchd,RFn,3,908,Typical,Typical,Paved,250,63,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,440000,-93.657524,42.061131 +Two_Story_1946_and_Newer,Residential_Low_Density,118,13654,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Gable,CompShg,MetalSd,MetalSd,BrkFace,365,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1704,1704,GasA,Excellent,Y,SBrkr,1722,1036,0,2758,0,0,2,1,4,1,Excellent,9,Typ,1,Excellent,BuiltIn,Fin,3,814,Typical,Typical,Paved,282,55,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,418000,-93.657211,42.06027 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,17169,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,970,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,636,2320,GasA,Excellent,Y,SBrkr,2290,0,0,2290,2,0,2,1,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1174,Typical,Typical,Paved,192,30,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,500067,-93.657235,42.060269 +Two_Story_1946_and_Newer,Residential_Low_Density,134,16659,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1582,1582,GasA,Excellent,Y,SBrkr,1582,570,0,2152,0,0,2,1,3,1,Good,7,Typ,1,Good,Detchd,Unf,2,728,Typical,Typical,Paved,0,368,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,260116,-93.6554248,42.061133 +Two_Story_1946_and_Newer,Residential_Low_Density,76,9591,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,344,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1143,1143,GasA,Excellent,Y,SBrkr,1143,1330,0,2473,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,RFn,3,852,Typical,Typical,Paved,192,151,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,317000,-93.654689,42.060576 +Two_Story_1946_and_Newer,Residential_Low_Density,82,9709,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,120,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,140,918,GasA,Excellent,Y,SBrkr,958,1142,0,2100,1,0,2,1,3,1,Excellent,8,Typ,2,Good,BuiltIn,Fin,3,786,Typical,Typical,Paved,172,104,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,319500,-93.654617,42.060427 +Two_Story_1946_and_Newer,Residential_Low_Density,86,10562,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,300,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,294,1582,GasA,Excellent,Y,SBrkr,1610,551,0,2161,1,0,1,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,789,Typical,Typical,Paved,178,120,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,325624,-93.650917,42.059561 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,13615,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,Stone,510,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1802,1802,GasA,Excellent,Y,SBrkr,1802,0,0,1802,0,0,2,1,3,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,843,Typical,Typical,Paved,158,105,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,372000,-93.657492,42.06113 +Two_Story_1946_and_Newer,Residential_Low_Density,99,13069,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,502,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1706,1706,GasA,Excellent,Y,SBrkr,1718,1238,0,2956,0,0,2,1,5,1,Excellent,11,Typ,1,Excellent,BuiltIn,RFn,3,916,Typical,Typical,Paved,194,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,342000,-93.656249,42.059761 +Two_Story_1946_and_Newer,Residential_Low_Density,99,12099,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,388,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,166,1136,GasA,Excellent,Y,SBrkr,1136,1332,0,2468,1,0,2,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,872,Typical,Typical,Paved,184,154,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,354000,-93.6565389,42.059677 +Two_Story_1946_and_Newer,Residential_Low_Density,110,14277,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,280,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,379,1317,GasA,Excellent,Y,SBrkr,1217,1168,0,2385,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,818,Typical,Typical,Paved,192,228,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,350000,-93.65699,42.058634 +Two_Story_1946_and_Newer,Residential_Low_Density,93,11999,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,340,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1181,1181,GasA,Excellent,Y,SBrkr,1234,1140,0,2374,0,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,656,Typical,Typical,Paved,104,100,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,285000,-93.6545189,42.058619 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12568,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,246,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,226,895,GasA,Excellent,Y,SBrkr,895,923,0,1818,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,774,Typical,Typical,Paved,196,104,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,281500,-93.650597,42.059211 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9926,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,436,1614,GasA,Excellent,Y,SBrkr,1614,0,0,1614,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,878,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,245700,-93.650499,42.059202 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9254,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1602,1721,GasA,Excellent,Y,SBrkr,1721,0,0,1721,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,554,Typical,Typical,Paved,168,27,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,232698,-93.65045,42.059197 +Two_Story_1946_and_Newer,Residential_Low_Density,92,10732,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1298,1298,GasA,Excellent,Y,SBrkr,1298,530,0,1828,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,RFn,3,876,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,250000,-93.650406,42.059194 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,61,7577,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,1342,1362,GasA,Excellent,Y,SBrkr,1362,0,0,1362,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,460,Typical,Typical,Paved,192,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,194700,-93.649838,42.059175 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,3901,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,182,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,436,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,631,Typical,Typical,Paved,110,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,204000,-93.649828,42.059175 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,3903,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,182,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,272,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,631,Typical,Typical,Paved,110,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,200000,-93.649808,42.059176 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,41,6289,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,256,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,600,1362,GasA,Excellent,Y,SBrkr,1362,0,0,1362,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,460,Typical,Typical,Paved,192,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,207000,-93.649798,42.059176 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,4590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,108,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,1530,1554,GasA,Excellent,Y,SBrkr,1554,0,0,1554,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,627,Typical,Typical,Paved,156,73,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,209500,-93.649397,42.05826 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,34,4590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,Twnhs,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,108,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,1530,1554,GasA,Excellent,Y,SBrkr,1554,0,0,1554,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,627,Typical,Typical,Paved,156,73,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,CWD,Normal,209500,-93.649319,42.058276 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,44,6442,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,178,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,1346,1370,GasA,Excellent,Y,SBrkr,1370,0,0,1370,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,120,49,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,202500,-93.649291,42.058284 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,7841,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,394,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,729,1577,GasA,Excellent,Y,SBrkr,1577,0,0,1577,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,564,Typical,Typical,Paved,203,39,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,280000,-93.653414,42.056827 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,60,7313,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,246,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,408,1561,GasA,Excellent,Y,SBrkr,1561,0,0,1561,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,556,Typical,Typical,Paved,203,47,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,277500,-93.652565,42.057124 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,68,7820,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2007,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,362,Excellent,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1869,1869,GasA,Excellent,Y,SBrkr,1869,0,0,1869,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,617,Typical,Typical,Paved,210,54,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,318061,-93.652054,42.05738 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1300,1324,GasA,Excellent,Y,SBrkr,1324,0,0,1324,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,221370,-93.6514105,42.0568529 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,3242,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,235,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,405,1405,GasA,Excellent,Y,SBrkr,1405,0,0,1405,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,478,Typical,Typical,Paved,136,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,200000,-93.650608,42.057209 +Two_Story_1946_and_Newer,Residential_Low_Density,59,15810,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,768,728,0,1496,0,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,572,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,181755,-93.646937,42.063388 +Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,768,768,0,1536,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,100,38,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,176000,-93.646429,42.063381 +Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2005,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,783,783,GasA,Excellent,Y,SBrkr,783,701,0,1484,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,178900,-93.645875,42.062483 +Two_Story_1946_and_Newer,Residential_Low_Density,58,13204,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,44,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,608,608,GasA,Excellent,Y,SBrkr,608,850,0,1458,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,BuiltIn,Fin,2,454,Typical,Typical,Paved,100,33,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,168165,-93.64573,42.062279 +Two_Story_1946_and_Newer,Residential_Low_Density,62,8857,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,757,0,1495,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,440,Typical,Typical,Paved,100,30,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,New,Partial,171925,-93.644788,42.0626729 +Two_Story_1946_and_Newer,Residential_Low_Density,63,9729,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,698,698,GasA,Excellent,Y,SBrkr,698,1048,0,1746,1,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Unf,3,350,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,198444,-93.644415,42.06208 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,12216,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,408,1326,GasA,Excellent,Y,SBrkr,1326,0,0,1326,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,388,Typical,Typical,Paved,120,23,0,0,0,0,No_Pool,No_Fence,Shed,2000,6,2007,WD ,Normal,203000,-93.6443,42.062067 +Two_Story_1946_and_Newer,Residential_Low_Density,65,9018,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,179540,-93.645732,42.062533 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8993,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1302,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,436,Typical,Typical,Paved,0,22,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,176485,-93.644377,42.062887 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8899,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1316,1340,GasA,Excellent,Y,SBrkr,1340,0,0,1340,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,396,Typical,Typical,Paved,100,30,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,181134,-93.644376,42.062931 +Two_Story_1946_and_Newer,Residential_Low_Density,73,8499,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,616,616,GasA,Excellent,Y,SBrkr,616,796,0,1412,0,0,2,1,3,1,Good,6,Typ,1,Good,BuiltIn,Fin,2,432,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,New,Partial,156932,-93.644375,42.062974 +Two_Story_1946_and_Newer,Residential_Low_Density,72,8229,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,22,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,752,752,GasA,Excellent,Y,SBrkr,752,752,0,1504,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,100,32,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,166000,-93.644374,42.063019 +Two_Story_1946_and_Newer,Residential_Low_Density,64,7713,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,16,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,177594,-93.644447,42.063106 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7697,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1246,1246,GasA,Excellent,Y,SBrkr,1258,0,0,1258,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,173500,-93.644366,42.063342 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3922,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,WdShing,Wd Shng,BrkFace,72,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1258,1258,GasA,Excellent,Y,SBrkr,1258,0,0,1258,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,144,16,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,172500,-93.641795,42.062584 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3922,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,72,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1258,1258,GasA,Excellent,Y,SBrkr,1402,0,0,1402,0,2,0,2,2,1,Good,7,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,120,16,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,194201,-93.641716,42.062581 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3621,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,72,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,322,1406,GasA,Excellent,Y,SBrkr,1589,0,0,1589,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,3,630,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,226500,-93.640513,42.062946 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,53,3710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1266,1266,GasA,Excellent,Y,SBrkr,1266,0,0,1266,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,388,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,185485,-93.642177,42.063301 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,182,14572,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,230,1530,GasA,Excellent,Y,SBrkr,1530,0,0,1530,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,630,Typical,Typical,Paved,144,36,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Family,259000,-93.644256,42.061713 +Split_or_Multilevel,Residential_Low_Density,65,16219,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,56,835,GasA,Excellent,Y,SBrkr,1119,0,0,1119,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,437,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,188500,-93.644064,42.061429 +Split_or_Multilevel,Residential_Low_Density,87,11084,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,192,384,GasA,Excellent,Y,SBrkr,744,630,0,1374,1,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Family,175000,-93.643062,42.061483 +Two_Story_1946_and_Newer,Residential_Low_Density,59,11796,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1112,0,1959,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,215000,-93.643961,42.061541 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,59,10936,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1510,1510,GasA,Excellent,Y,SBrkr,1525,0,0,1525,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,534,Typical,Typical,Paved,100,18,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,206580,-93.643954,42.061531 +Two_Story_1946_and_Newer,Residential_Low_Density,62,8244,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,880,0,1720,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,183500,-93.641781,42.061318 +Split_or_Multilevel,Residential_Low_Density,0,11950,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,754,640,0,1394,0,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,Fin,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,165500,-93.639977,42.061392 +Two_Story_1946_and_Newer,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1101,0,1948,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,211000,-93.64021,42.060021 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8063,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,924,924,GasA,Excellent,Y,SBrkr,948,742,0,1690,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,463,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Abnorml,181000,-93.642288,42.058858 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8740,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,280,854,GasA,Excellent,Y,SBrkr,864,1131,0,1995,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,435,Typical,Typical,Paved,264,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,219500,-93.64334,42.059181 +Two_Story_1946_and_Newer,Residential_Low_Density,62,7917,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,953,953,GasA,Excellent,Y,SBrkr,953,694,0,1647,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,175000,-93.641279,42.057981 +Split_or_Multilevel,Residential_Low_Density,76,9967,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,SLvl,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,384,384,GasA,Excellent,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,Typical,8,Typ,1,Typical,BuiltIn,RFn,2,400,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,170000,-93.642836,42.058401 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,660,660,GasA,Excellent,Y,SBrkr,660,660,0,1320,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,162000,-93.643641,42.059526 +Two_Story_1946_and_Newer,Residential_Low_Density,58,9487,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,395,915,GasA,Excellent,Y,SBrkr,940,750,0,1690,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,442,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,187000,-93.641323,42.057836 +Two_Story_1946_and_Newer,Residential_Low_Density,59,9649,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,941,941,GasA,Excellent,Y,SBrkr,961,683,0,1644,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,460,42,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,185000,-93.640658,42.058897 +Two_Story_1946_and_Newer,Residential_Low_Density,160,15623,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,1996,1996,Hip,CompShg,Wd Sdng,ImStucc,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,300,2396,GasA,Excellent,Y,SBrkr,2411,2065,0,4476,1,0,3,1,4,1,Excellent,10,Typ,2,Typical,Attchd,Fin,3,813,Typical,Typical,Paved,171,78,0,0,0,555,Excellent,Minimum_Privacy,None,0,7,2007,WD ,Abnorml,745000,-93.6575919,42.0533209 +Two_Story_1946_and_Newer,Residential_Low_Density,100,12191,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1997,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,515,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,598,1779,GasA,Excellent,Y,SBrkr,1779,772,0,2551,1,0,2,1,4,1,Good,8,Typ,2,Typical,Attchd,Fin,3,925,Typical,Typical,Paved,76,61,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,384500,-93.655711,42.053364 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,72,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,969,969,GasA,Excellent,Y,SBrkr,997,1288,0,2285,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,3,648,Typical,Typical,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,248000,-93.652894,42.055751 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2001,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,705,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1351,2633,GasA,Excellent,Y,SBrkr,2633,0,0,2633,1,0,2,1,2,1,Excellent,8,Typ,2,Good,Attchd,RFn,3,804,Typical,Typical,Paved,314,140,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,466500,-93.651096,42.055627 +Two_Story_1946_and_Newer,Residential_Low_Density,89,10557,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,1998,1998,Gable,CompShg,MetalSd,MetalSd,BrkFace,422,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,736,1408,GasA,Excellent,Y,SBrkr,1671,1407,0,3078,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,3,806,Typical,Typical,Paved,108,87,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,410000,-93.652641,42.05359 +Two_Story_1946_and_Newer,Residential_Low_Density,74,11002,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,341,1389,GasA,Excellent,Y,SBrkr,1411,1171,0,2582,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,3,758,Typical,Typical,Paved,286,60,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,322500,-93.652777,42.053436 +Two_Story_1946_and_Newer,Residential_Low_Density,83,10790,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,275,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1066,1066,GasA,Excellent,Y,SBrkr,1108,1277,0,2385,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,3,600,Typical,Typical,Paved,120,38,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,252000,-93.649901,42.053467 +Two_Story_1946_and_Newer,Residential_Low_Density,104,21535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Excellent,Above_Average,1994,1995,Gable,WdShngl,HdBoard,HdBoard,BrkFace,1170,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,989,2444,GasA,Excellent,Y,SBrkr,2444,1872,0,4316,0,1,3,1,4,1,Excellent,10,Typ,2,Excellent,Attchd,Fin,3,832,Typical,Typical,Paved,382,50,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,755000,-93.657271,42.05198 +Two_Story_1946_and_Newer,Residential_Low_Density,92,9920,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,255,1117,GasA,Excellent,Y,SBrkr,1127,886,0,2013,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,455,Typical,Typical,Paved,180,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,269790,-93.655159,42.052361 +Two_Story_1946_and_Newer,Residential_Low_Density,95,11787,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,594,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,660,1379,GasA,Excellent,Y,SBrkr,1383,1015,0,2398,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,3,834,Typical,Typical,Paved,239,60,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,315750,-93.655905,42.052247 +Two_Story_1946_and_Newer,Residential_Low_Density,82,9950,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,290,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,638,1203,GasA,Excellent,Y,SBrkr,1214,1306,0,2520,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,721,Typical,Typical,Paved,224,114,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Abnorml,290000,-93.653708,42.051813 +Two_Story_1946_and_Newer,Residential_Low_Density,65,12257,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,513,Good,Typical,PConc,Good,Typical,Av,LwQ,4,ALQ,64,1198,1318,GasA,Excellent,Y,SBrkr,1328,1203,0,2531,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,3,752,Typical,Typical,Paved,222,98,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,290000,-93.653712,42.05048 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1992,1993,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1969,3200,GasA,Excellent,Y,SBrkr,3228,0,0,3228,1,0,3,0,4,1,Good,10,Typ,1,Good,Attchd,RFn,2,546,Typical,Typical,Paved,264,75,291,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,430000,-93.65334,42.049518 +Two_Story_1946_and_Newer,Residential_Low_Density,88,11762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,309,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,770,1105,GasA,Excellent,Y,SBrkr,1105,1097,0,2202,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,2,517,Typical,Typical,Paved,0,65,0,0,144,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,263000,-93.652946,42.049509 +Two_Story_1946_and_Newer,Residential_Low_Density,82,9044,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,526,Good,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,100,1325,GasA,Excellent,Y,SBrkr,1335,1203,0,2538,0,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,933,Typical,Typical,Paved,198,92,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,330000,-93.653864,42.052416 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9910,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1369,1369,GasA,Excellent,Y,SBrkr,1369,0,0,1369,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,605,Typical,Typical,Paved,0,203,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,213133,-93.644118,42.054408 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11830,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,164,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,322,1542,GasA,Excellent,Y,SBrkr,1542,0,0,1542,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Unf,3,852,Typical,Typical,Paved,168,110,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,260261,-93.643769,42.055757 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,11670,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,RRNn,Norm,OneFam,One_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,ImStucc,Stone,302,Excellent,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1905,1905,GasA,Excellent,Y,SBrkr,1905,0,0,1905,0,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,788,Typical,Typical,Paved,0,191,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,320000,-93.642069,42.054318 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,49,15256,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,84,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,556,1485,GasA,Excellent,Y,SBrkr,1464,0,0,1464,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,3,754,Typical,Typical,Paved,168,160,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,282922,-93.64176,42.054177 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,10612,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,248,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1496,1524,GasA,Good,Y,SBrkr,1534,0,0,1534,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,484,Typical,Typical,Paved,168,46,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Family,215000,-93.642261,42.054417 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,12291,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,754,Excellent,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,394,1966,GasA,Excellent,Y,SBrkr,1966,0,0,1966,1,0,2,0,1,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,1092,Typical,Typical,Paved,76,52,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,419005,-93.642243,42.054354 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,116,13501,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,208,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1560,1623,GasA,Excellent,Y,SBrkr,1636,0,0,1636,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,865,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,255000,-93.642234,42.054309 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9986,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,428,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1795,1795,GasA,Excellent,Y,SBrkr,1795,0,0,1795,0,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,RFn,3,895,Typical,Typical,Paved,0,49,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,147000,-93.64396,42.054514 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,9965,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1528,1528,GasA,Excellent,Y,SBrkr,1528,0,0,1528,0,0,3,2,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,480,Typical,Typical,Paved,0,228,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,235876,-93.644086,42.054154 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,9416,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Hip,CompShg,CemntBd,CmentBd,Stone,205,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,600,1726,GasA,Excellent,Y,SBrkr,1726,0,0,1726,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,786,Typical,Typical,Paved,171,138,0,0,266,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,311872,-93.643836,42.054153 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,83,9849,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Above_Average,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1689,1689,GasA,Excellent,Y,SBrkr,1689,0,0,1689,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,954,Typical,Typical,Paved,0,56,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,248328,-93.64248,42.054153 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,410,Good,Typical,PConc,Good,Good,Av,Unf,7,Unf,0,1588,1588,GasA,Excellent,Y,SBrkr,1588,0,0,1588,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,825,Typical,Typical,Paved,144,45,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,227000,-93.642344,42.054157 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8847,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,148,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,769,1538,GasA,Excellent,Y,SBrkr,1538,0,0,1538,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,146,40,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,260000,-93.643544,42.053185 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,9158,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,Stone,140,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1496,1496,GasA,Excellent,Y,SBrkr,1496,0,0,1496,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,474,Typical,Typical,Paved,168,130,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,225000,-93.643449,42.053186 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8251,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,143,Good,Typical,PConc,Good,Good,Av,GLQ,3,Unf,0,716,1494,GasA,Excellent,Y,SBrkr,1506,0,0,1506,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,672,Typical,Typical,Paved,192,35,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,249000,-93.642197,42.053227 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,Stone,186,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,317,1686,GasA,Excellent,Y,SBrkr,1694,0,0,1694,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,636,Typical,Typical,Paved,255,57,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,307000,-93.641251,42.053108 +Two_Story_1946_and_Newer,Residential_Low_Density,70,9605,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,982,982,GasA,Excellent,Y,SBrkr,982,995,0,1977,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,574,Typical,Typical,Paved,240,53,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Family,250000,-93.643748,42.053032 +Two_Story_1946_and_Newer,Residential_Low_Density,75,8778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1302,1302,GasA,Excellent,Y,SBrkr,1302,528,0,1830,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,RFn,3,859,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,221300,-93.642275,42.053067 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1314,1338,GasA,Excellent,Y,SBrkr,1338,0,0,1338,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,598,Typical,Typical,Paved,0,141,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,208900,-93.650341,42.051802 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1335,1335,GasA,Excellent,Y,SBrkr,1335,0,0,1335,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,575,Typical,Typical,Paved,0,210,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,210400,-93.65042,42.051795 +Two_Story_1946_and_Newer,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,546,896,GasA,Excellent,Y,SBrkr,896,896,0,1792,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,590,Typical,Typical,Paved,184,96,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,255000,-93.650299,42.051655 +Two_Story_1946_and_Newer,Floating_Village_Residential,81,10411,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,CBlock,Good,Typical,No_Basement,Unf,7,Unf,0,725,725,GasA,Excellent,Y,SBrkr,725,863,0,1588,0,0,3,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Unf,2,561,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,212109,-93.650279,42.051657 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1088,1088,GasA,Excellent,Y,SBrkr,1088,871,0,1959,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,1025,Typical,Typical,Paved,208,46,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,270000,-93.647584,42.050206 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1120,1184,GasA,Excellent,Y,SBrkr,1184,1426,0,2610,0,0,2,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,2,550,Typical,Typical,Paved,208,364,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,303477,-93.647933,42.050301 +Two_Story_1946_and_Newer,Floating_Village_Residential,112,12217,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Hip,CompShg,WdShing,Wd Shng,None,0,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,210,955,GasA,Excellent,Y,SBrkr,955,925,0,1880,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,880,Typical,Typical,Paved,168,127,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,310013,-93.648742,42.051681 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,84,11096,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1632,1656,GasA,Excellent,Y,SBrkr,1656,0,0,1656,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,826,Typical,Typical,Paved,0,146,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,249700,-93.643974,42.052269 +Two_Story_1946_and_Newer,Floating_Village_Residential,84,10207,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Above_Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,874,874,GasA,Excellent,Y,SBrkr,874,887,0,1761,0,0,3,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,578,Typical,Typical,Paved,144,105,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,227875,-93.64397,42.052119 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,84,10440,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Excellent,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1574,1574,GasA,Excellent,Y,SBrkr,1584,0,0,1584,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,594,Typical,Typical,Paved,0,256,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,261329,-93.643792,42.051377 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,100,11824,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,298,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1685,1685,GasA,Excellent,Y,SBrkr,1685,0,0,1685,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,3,658,Typical,Typical,Paved,112,63,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,253000,-93.642046,42.052267 +Two_Story_1946_and_Newer,Floating_Village_Residential,85,10625,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,353,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1158,1158,GasA,Excellent,Y,SBrkr,1158,1285,0,2443,0,0,2,1,4,1,Good,9,Min2,1,Good,BuiltIn,RFn,3,744,Typical,Typical,Paved,193,127,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,265000,-93.641233,42.052146 +Two_Story_1946_and_Newer,Floating_Village_Residential,102,11143,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1580,1580,GasA,Excellent,Y,SBrkr,1580,886,0,2466,0,0,3,0,4,1,Good,8,Typ,1,Good,Attchd,RFn,2,610,Typical,Typical,Paved,159,214,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,340000,-93.639574,42.051054 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,SBrkr,1100,0,0,1100,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,136,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,146000,-93.69179,42.037586 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1143,1143,GasA,Excellent,Y,SBrkr,1143,0,0,1143,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,55,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,147000,-93.692245,42.037636 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,12450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Average,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,126,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,365,1094,GasA,Excellent,Y,SBrkr,1094,0,0,1094,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,149000,-93.68912,42.037643 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7441,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1461,1461,GasA,Excellent,Y,SBrkr,1486,0,0,1486,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,566,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,181000,-93.691244,42.03722 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8462,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,105,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,114,928,GasA,Excellent,Y,SBrkr,936,785,0,1721,0,1,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,471,Typical,Typical,Paved,300,87,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,201000,-93.689026,42.036028 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11613,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,384,864,GasA,Excellent,Y,SBrkr,920,900,0,1820,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,492,Typical,Typical,Paved,144,85,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,191750,-93.689767,42.035259 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1349,1349,GasA,Excellent,Y,SBrkr,1349,0,0,1349,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,539,Typical,Typical,Paved,120,55,0,0,0,0,No_Pool,Good_Privacy,None,0,12,2007,WD ,Normal,179000,-93.688804,42.036038 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16196,Pave,No_Alley_Access,Irregular,Low,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,39,1482,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Good,5,Typ,1,Fair,Attchd,RFn,2,514,Typical,Typical,Paved,402,25,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,215000,-93.689449,42.035071 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1992,1992,Gable,CompShg,Plywood,ImStucc,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1145,1575,GasA,Good,Y,SBrkr,1575,0,0,1575,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,529,Typical,Typical,Paved,0,0,52,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,179200,-93.686428,42.036464 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,50,8012,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,630,630,GasA,Excellent,Y,SBrkr,630,636,0,1266,0,0,1,1,2,1,Typical,5,Typ,2,Typical,Attchd,RFn,1,283,Typical,Typical,Paved,340,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,128000,-93.686413,42.034569 +Split_Foyer,Residential_Low_Density,0,9180,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer_West,Norm,Norm,OneFam,SFoyer,Average,Good,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,93,0,840,GasA,Good,Y,SBrkr,884,0,0,884,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Good,Paved,240,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2007,WD ,Normal,144000,-93.685185,42.035678 +Duplex_All_Styles_and_Ages,Residential_High_Density,60,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,Duplex,One_Story,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,320,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,2020,0,0,2020,0,0,2,0,4,2,Typical,10,Typ,2,Typical,Detchd,Unf,2,630,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,144000,-93.6791142,42.0362103 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6285,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Rec,351,405,894,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,120750,-93.677007,42.034678 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,340,970,GasA,Typical,Y,SBrkr,970,0,0,970,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,24,0,0,192,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,132500,-93.674475,42.035946 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7420,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,640,1057,GasA,Typical,Y,SBrkr,1057,0,0,1057,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,132000,-93.673987,42.035871 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1962,2001,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,411,999,GasA,Good,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,132,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,132500,-93.673889,42.035857 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,6970,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,108,1040,GasA,Typical,Y,SBrkr,1120,0,0,1120,1,0,1,1,3,1,Fair,5,Typ,0,No_Fireplace,Attchd,RFn,2,544,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,Shed,400,5,2007,WD ,Normal,129000,-93.669559,42.034532 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,40,13673,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1140,1140,GasA,Typical,Y,SBrkr,1696,0,0,1696,0,0,1,1,3,1,Typical,8,Min2,1,Typical,Attchd,RFn,1,349,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,143900,-93.669552,42.034724 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,7476,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,228,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Fin,2,686,Typical,Typical,Paved,188,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,145000,-93.672318,42.035675 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9945,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,57,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,161,988,GasA,Typical,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,572,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,128500,-93.673518,42.034652 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6173,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,HdBoard,Wd Sdng,BrkFace,75,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,277,876,GasA,Typical,Y,SBrkr,902,0,0,902,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,125500,-93.671941,42.034819 +Two_Story_1946_and_Newer,Residential_Low_Density,0,19522,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,272,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,496,1223,GasA,Good,Y,SBrkr,1271,1232,0,2503,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,99,0,0,182,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,300000,-93.661846,42.037652 +Two_Story_1946_and_Newer,Residential_Low_Density,0,17542,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,Two_Story,Good,Good,1974,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,Gd,LwQ,4,ALQ,1031,36,1192,GasA,Typical,Y,SBrkr,1516,651,0,2167,1,0,2,1,3,1,Good,9,Typ,2,Good,Attchd,RFn,2,518,Typical,Typical,Paved,220,47,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,294000,-93.661848,42.037502 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,44,10751,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Average,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,44,Typical,Typical,CBlock,Fair,Typical,Gd,ALQ,1,Unf,0,250,1037,GasA,Typical,Y,SBrkr,1037,0,0,1037,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,431,Typical,Typical,Paved,136,47,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,129000,-93.660522,42.03466 +Split_Foyer,Residential_Low_Density,0,16647,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,SFoyer,Average,Average,1975,1981,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,0,1390,GasA,Typical,Y,SBrkr,1701,0,0,1701,1,0,2,0,3,1,Typical,6,Min2,2,Typical,Basment,Fin,2,611,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,171000,-93.661983,42.034644 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,12712,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Above_Average,Good,1973,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,76,1044,GasA,Typical,Y,SBrkr,1055,0,0,1055,1,0,1,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,542,Typical,Typical,Paved,455,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Abnorml,147000,-93.662134,42.034673 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9572,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1990,1990,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,336,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,971,1453,GasA,Excellent,Y,SBrkr,1453,1357,0,2810,0,0,2,1,4,1,Good,9,Typ,1,Excellent,Attchd,RFn,2,750,Good,Good,Paved,500,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,302000,-93.653082,42.047384 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,85,10678,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,One_and_Half_Fin,Very_Good,Average,1992,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,337,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,983,1683,GasA,Excellent,Y,SBrkr,2129,743,0,2872,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,2,541,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,285000,-93.652658,42.045776 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,45,4379,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,527,1378,GasA,Excellent,Y,SBrkr,1378,0,0,1378,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,540,Typical,Typical,Paved,160,56,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,214000,-93.647974,42.047618 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,80,3523,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,30,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1081,1141,GasA,Excellent,Y,SBrkr,1151,0,0,1151,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,484,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,166000,-93.64733,42.0474249 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,32,3784,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,36,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1451,1511,GasA,Excellent,Y,SBrkr,1565,0,0,1565,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,476,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,New,Partial,193800,-93.647166,42.047406 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,47,4230,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1352,1352,GasA,Excellent,Y,SBrkr,1352,0,0,1352,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,RFn,2,466,Typical,Typical,Paved,0,241,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,208900,-93.646653,42.047711 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,40,3606,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,415,1352,GasA,Excellent,Y,SBrkr,1352,0,0,1352,1,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,RFn,2,466,Typical,Typical,Paved,0,241,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,194000,-93.646637,42.047705 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,5330,Pave,Paved,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1550,1550,GasA,Excellent,Y,SBrkr,1550,0,0,1550,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,528,Typical,Typical,Paved,0,102,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,207500,-93.646607,42.047699 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4274,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,135,1241,GasA,Excellent,Y,SBrkr,1241,0,0,1241,1,0,1,1,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,569,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,199900,-93.646576,42.047687 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,4251,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2006,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,625,625,GasA,Excellent,Y,SBrkr,625,625,0,1250,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,528,Typical,Typical,Paved,0,54,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,164700,-93.646478,42.047667 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2280,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,342,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,179,744,GasA,Good,Y,SBrkr,757,744,0,1501,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,176500,-93.644107,42.047104 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3230,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,1129,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,310,729,GasA,Good,Y,SBrkr,729,729,0,1458,0,0,2,1,2,1,Typical,5,Typ,1,Fair,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,176000,-93.644096,42.047104 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,336,756,GasA,Excellent,Y,SBrkr,756,756,0,1512,0,0,2,1,2,1,Good,4,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,168500,-93.645671,42.046144 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,339,756,GasA,Excellent,Y,SBrkr,769,804,0,1573,0,0,2,1,3,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,177000,-93.645639,42.046145 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2117,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,320,756,GasA,Excellent,Y,SBrkr,769,756,0,1525,0,0,2,1,3,1,Good,5,Typ,1,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,177000,-93.645595,42.046145 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,5105,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,312,551,GasA,Excellent,Y,SBrkr,551,551,0,1102,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,480,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,148800,-93.645482,42.046405 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2645,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Very_Good,Average,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,776,776,GasA,Excellent,Y,SBrkr,764,677,0,1441,0,0,2,1,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,492,Typical,Typical,Paved,206,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,174000,-93.641813,42.047171 +Two_Story_1946_and_Newer,Floating_Village_Residential,66,7399,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,1600,Good,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,326,975,GasA,Excellent,Y,SBrkr,975,975,0,1950,0,0,2,1,3,1,Good,7,Typ,1,Typical,Detchd,RFn,2,576,Typical,Typical,Paved,0,10,0,0,198,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,239000,-93.642182,42.046318 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,73,7321,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,1999,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1339,1339,GasA,Excellent,Y,SBrkr,1358,0,0,1358,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,625,Typical,Typical,Paved,176,174,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,COD,Normal,204000,-93.639931,42.046137 +Two_Story_1946_and_Newer,Floating_Village_Residential,0,8010,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2003,2004,Hip,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,90,1054,GasA,Excellent,Y,SBrkr,1072,976,0,2048,1,0,2,1,3,1,Good,8,Typ,2,Good,Detchd,Unf,2,552,Typical,Typical,Paved,0,48,0,0,180,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,301000,-93.639878,42.046135 +Two_Story_1946_and_Newer,Floating_Village_Residential,106,8413,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,319,1220,GasA,Excellent,Y,SBrkr,1220,1142,0,2362,1,0,2,1,3,1,Good,8,Typ,2,Typical,Attchd,RFn,2,1105,Good,Typical,Paved,147,0,36,0,144,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,312500,-93.643456,42.046411 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,9466,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1994,1995,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,PConc,Good,Typical,Gd,LwQ,4,ALQ,1037,0,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,Fin,2,478,Typical,Typical,Paved,0,30,0,0,217,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,279700,-93.648204,42.044383 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Good,Above_Average,1980,1980,Hip,CompShg,VinylSd,MetalSd,BrkFace,600,Good,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,270,2002,GasA,Excellent,Y,SBrkr,2362,0,0,2362,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,546,Good,Typical,Paved,180,16,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,255000,-93.645798,42.044762 +Split_or_Multilevel,Residential_Low_Density,0,16157,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,OneFam,SLvl,Average,Good,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Gd,ALQ,1,Rec,391,289,1360,GasA,Excellent,Y,SBrkr,1432,0,0,1432,1,0,1,1,2,1,Good,5,Typ,1,Typical,Attchd,Unf,2,588,Typical,Typical,Paved,168,180,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,194000,-93.645589,42.044989 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,10768,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Average,Very_Good,1976,2004,Gable,CompShg,Plywood,Plywood,None,0,Good,Good,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,280,1437,GasA,Typical,Y,SBrkr,1437,0,0,1437,1,0,2,0,3,1,Good,6,Typ,1,Fair,Attchd,RFn,2,528,Typical,Typical,Paved,0,21,0,0,180,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,218000,-93.64577,42.042994 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3840,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Above_Average,1978,1998,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,LwQ,176,0,1057,GasA,Typical,Y,SBrkr,1295,0,0,1295,1,0,1,0,1,1,Good,4,Typ,2,Typical,Attchd,Fin,2,571,Typical,Typical,Paved,133,89,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,213750,-93.648172,42.043754 +Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,OneFam,SLvl,Above_Average,Good,1976,1994,Hip,CompShg,Plywood,Plywood,BrkFace,360,Good,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,528,528,GasA,Excellent,Y,SBrkr,1094,761,0,1855,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,512,Typical,Typical,Paved,113,100,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,187000,-93.645547,42.043048 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,OneFam,One_Story,Above_Average,Very_Good,1976,1976,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,284,1262,GasA,Excellent,Y,SBrkr,1262,0,0,1262,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,298,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,181500,-93.645544,42.042961 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,17778,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Very_Good,Average,1981,1981,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Typical,Gd,ALQ,1,Rec,829,0,2461,GasA,Good,Y,SBrkr,2497,0,0,2497,1,0,2,0,2,1,Good,7,Typ,2,Good,Attchd,RFn,2,676,Typical,Typical,Paved,266,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,373000,-93.658232,42.037103 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,18890,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,RRAe,Duplex,One_and_Half_Fin,Average,Average,1977,1977,Shed,CompShg,Plywood,Plywood,None,1,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Rec,211,652,1361,GasA,Excellent,Y,SBrkr,1361,1259,0,2620,0,0,2,2,4,2,Typical,12,Typ,1,Typical,BuiltIn,RFn,2,600,Typical,Typical,Dirt_Gravel,155,24,145,0,0,0,No_Pool,No_Fence,Gar2,8300,8,2007,WD ,Normal,190000,-93.6574973,42.0349702 +Two_Story_1946_and_Newer,Floating_Village_Residential,0,7050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,319,1057,GasA,Excellent,Y,SBrkr,1057,872,0,1929,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,650,Typical,Typical,Paved,0,235,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,226000,-93.639515,42.048488 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,BrkFace,41,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,912,912,GasA,Excellent,Y,SBrkr,1152,0,0,1152,1,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,412,Typical,Typical,Paved,0,126,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,CWD,Normal,143450,-93.634545,42.046549 +Two_Story_1946_and_Newer,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Above_Average,Above_Average,1970,1970,Gable,CompShg,VinylSd,VinylSd,BrkFace,525,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,93,1008,GasA,Typical,Y,SBrkr,1403,1008,0,2411,1,0,2,1,4,1,Typical,8,Typ,1,Poor,Attchd,RFn,2,570,Typical,Typical,Paved,0,192,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,240050,-93.635094,42.047758 +Split_Foyer,Residential_Low_Density,0,8723,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosN,Norm,OneFam,SFoyer,Above_Average,Above_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,BLQ,2,Unf,0,0,973,GasA,Excellent,Y,SBrkr,1082,0,0,1082,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,160,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,162500,-93.634528,42.048067 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,130,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,196,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,385,1295,GasA,Fair,Y,SBrkr,1295,0,0,1295,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,528,Typical,Typical,Paved,0,194,0,0,200,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,164000,-93.635899,42.04849 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,108,11358,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1972,1987,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,778,1124,GasA,Typical,Y,SBrkr,1610,0,0,1610,0,0,2,0,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,515,Typical,Typical,Paved,202,0,0,0,256,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,185000,-93.631554,42.047615 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9547,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,112,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1594,1594,GasA,Excellent,Y,SBrkr,1594,0,0,1594,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,RFn,2,472,Typical,Typical,Paved,190,80,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,188500,-93.638051,42.043363 +Two_Story_1946_and_Newer,Residential_Low_Density,78,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1993,1993,Gable,CompShg,MetalSd,MetalSd,BrkFace,194,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,329,1148,GasA,Excellent,Y,SBrkr,1091,984,0,2075,1,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Unf,2,473,Typical,Typical,Paved,235,86,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,255000,-93.63687,42.043005 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Feedr,RRAn,Duplex,One_Story,Average,Above_Average,1976,1976,Gable,CompShg,VinylSd,VinylSd,BrkFace,164,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1680,1680,GasA,Fair,Y,SBrkr,1680,0,0,1680,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,136905,-93.633748,42.045483 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,10738,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1966,1966,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,301,1093,GasA,Good,Y,SBrkr,1093,0,0,1093,1,0,2,0,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,484,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,11,2007,WD ,Normal,158500,-93.631673,42.04259 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1973,1973,Hip,CompShg,HdBoard,HdBoard,BrkFace,320,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,326,1242,GasA,Fair,Y,SBrkr,1242,0,0,1242,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,175500,-93.631302,42.04389 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,203,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,264,171,1052,GasA,Typical,Y,SBrkr,1052,0,0,1052,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,311,Typical,Typical,Paved,0,133,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,COD,Normal,127000,-93.6299222,42.0421698 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,38,437,949,GasA,Typical,Y,SBrkr,1107,0,0,1107,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,88,64,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,127000,-93.62919,42.047811 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10355,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,196,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,519,1214,GasA,Typical,Y,SBrkr,1214,0,0,1214,0,0,2,0,3,1,Typical,5,Typ,1,Fair,Attchd,RFn,1,318,Typical,Typical,Paved,0,111,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,143000,-93.626518,42.048292 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10289,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1965,1965,Hip,CompShg,MetalSd,MetalSd,BrkFace,168,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,237,1073,GasA,Typical,Y,SBrkr,1073,0,0,1073,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,515,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,156000,-93.625898,42.046504 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9503,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1958,1983,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,374,193,1024,GasA,Typical,Y,SBrkr,1344,0,0,1344,1,0,1,0,2,1,Typical,6,Min1,1,Typical,Detchd,Unf,1,484,Typical,Typical,Paved,316,28,0,0,0,0,No_Pool,Good_Wood,None,0,6,2007,WD ,Normal,144000,-93.624758,42.044758 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,10624,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Below_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,84,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Rec,264,1424,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,1,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,155,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,119000,-93.624592,42.043984 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,10899,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Below_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1224,0,0,1224,0,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,CarPort,Unf,3,530,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,103000,-93.624541,42.043929 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12342,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1978,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,978,978,GasA,Typical,Y,SBrkr,1422,0,0,1422,0,0,1,0,3,1,Typical,6,Min1,1,Typical,Attchd,RFn,1,286,Typical,Typical,Paved,0,0,36,0,0,0,No_Pool,Good_Wood,Shed,600,8,2007,WD ,Normal,139900,-93.624745,42.043121 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12772,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1960,1998,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,460,958,GasA,Typical,Y,SBrkr,958,0,0,958,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,301,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Gar2,15500,4,2007,WD ,Normal,151500,-93.623636,42.042124 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8892,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkCmn,66,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1065,1065,GasA,Good,Y,SBrkr,1065,0,0,1065,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,461,Typical,Typical,Paved,74,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,COD,Normal,126500,-93.624606,42.04227 +Split_or_Multilevel,Residential_Low_Density,65,10482,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Very_Good,1958,1958,Hip,CompShg,VinylSd,VinylSd,BrkFace,63,Typical,Good,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,81,588,GasA,Excellent,Y,SBrkr,1138,0,0,1138,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,6,2007,WD ,Normal,145000,-93.623104,42.044036 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1958,1985,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,279,522,912,GasA,Fair,Y,SBrkr,912,0,0,912,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,1,297,Typical,Typical,Paved,12,285,0,0,0,0,No_Pool,Minimum_Wood_Wire,Shed,480,6,2007,WD ,Normal,120000,-93.621352,42.042577 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,206,250,894,GasA,Good,Y,SBrkr,1074,0,0,1074,0,0,1,0,2,1,Good,6,Min1,1,Good,Detchd,Unf,2,396,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,Good_Wood,None,0,1,2007,WD ,Normal,124000,-93.621344,42.042485 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8339,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,1959,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,882,0,0,882,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,294,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,1200,7,2007,WD ,Normal,106500,-93.62232,42.042479 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14357,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,167,386,864,GasA,Typical,Y,SBrkr,1187,0,0,1187,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,128,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,CWD,Normal,140500,-93.622834,42.044921 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,8243,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkFace,56,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,264,964,GasA,Excellent,Y,SBrkr,964,0,0,964,0,0,1,0,3,1,Typical,5,Typ,1,Fair,Detchd,Fin,2,784,Typical,Typical,Paved,170,0,0,0,0,0,No_Pool,Good_Privacy,None,0,2,2007,WD ,Normal,137500,-93.621452,42.043609 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,1960,Hip,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,894,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,117600,-93.62144,42.043627 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1966,1966,Hip,CompShg,HdBoard,Plywood,BrkFace,202,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,520,1174,GasA,Excellent,Y,SBrkr,1200,0,0,1200,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2007,CWD,Normal,163500,-93.626945,42.039141 +Split_or_Multilevel,Residential_Low_Density,80,9200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,BrkFace,98,Typical,Typical,CBlock,Typical,Typical,Gd,GLQ,3,Unf,0,548,1042,GasA,Typical,Y,SBrkr,1042,0,0,1042,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,161000,-93.630023,42.040143 +Split_or_Multilevel,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Good,1964,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,108,Typical,Typical,CBlock,Good,Typical,Gd,LwQ,4,ALQ,580,452,1302,GasA,Excellent,Y,SBrkr,1302,0,0,1302,0,1,2,0,3,1,Good,7,Min1,0,No_Fireplace,Attchd,RFn,1,309,Typical,Typical,Paved,333,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2007,CWD,Normal,158000,-93.6279875,42.0417369 +Two_Story_1946_and_Newer,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1964,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,306,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,431,845,GasA,Excellent,Y,SBrkr,845,1309,0,2154,0,0,2,1,5,1,Typical,8,Typ,1,Good,Attchd,RFn,2,539,Typical,Typical,Paved,0,0,0,0,161,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,224500,-93.626864,42.039069 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11382,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1964,1964,Gable,CompShg,Wd Sdng,Plywood,BrkFace,212,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Rec,543,533,1130,GasA,Typical,Y,SBrkr,1374,0,0,1374,0,1,1,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,1,286,Typical,Typical,Paved,0,28,84,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,147000,-93.6276375,42.0413937 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,22002,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1959,1991,Gable,CompShg,MetalSd,MetalSd,BrkFace,136,Typical,Good,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,206,1592,GasA,Good,Y,SBrkr,1652,0,0,1652,1,0,1,1,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,510,Typical,Typical,Paved,0,0,0,0,201,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,200000,-93.6249615,42.0417604 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14585,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1987,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,85,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,219,331,1144,GasA,Excellent,Y,SBrkr,1429,0,0,1429,0,1,1,0,3,1,Good,7,Typ,2,Good,Attchd,Unf,2,572,Typical,Typical,Paved,216,110,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,181900,-93.624484,42.04074 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,7388,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,658,1063,GasA,Good,Y,SBrkr,1327,0,0,1327,1,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,150750,-93.62326,42.041245 +Duplex_All_Styles_and_Ages,Residential_Low_Density,70,9842,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Below_Average,Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1224,0,0,1224,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,101800,-93.621368,42.041332 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,8280,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1950,1950,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,932,932,GasA,Excellent,Y,FuseA,932,0,0,932,0,0,1,0,2,1,Good,4,Typ,1,Good,Attchd,Unf,1,306,Typical,Typical,Paved,0,0,214,0,0,0,No_Pool,Good_Privacy,None,0,11,2007,WD ,Normal,124000,-93.622804,42.039212 +One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,85,12172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Unf,Average,Good,1940,1996,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Rec,259,433,822,GasA,Typical,Y,SBrkr,908,0,0,908,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,512,Typical,Typical,Paved,284,24,0,0,192,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,138500,-93.621503,42.0392 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5000,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Brookside,Norm,Norm,OneFam,One_Story,Very_Poor,Fair,1946,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Fair,Fair,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,N,FuseF,334,0,0,334,0,0,1,0,1,1,Fair,2,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,39300,-93.629661,42.036575 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Fair,Fair,1946,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Fair,Fair,CBlock,Fair,Fair,No,LwQ,4,Unf,0,367,666,GasA,Fair,N,SBrkr,666,0,0,666,0,1,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,52,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,64500,-93.629545,42.035684 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,3500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Fair,Average,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,226,370,GasA,Typical,N,FuseA,442,228,0,670,1,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,21,0,0,0,0,No_Pool,Minimum_Privacy,Shed,2000,7,2007,WD ,Normal,64000,-93.628654,42.036023 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Feedr,OneFam,One_Story,Average,Very_Good,1958,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,ALQ,404,254,808,GasA,Excellent,Y,SBrkr,808,0,0,808,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,143,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,COD,Normal,112000,-93.628582,42.036223 +Two_Story_1946_and_Newer,Residential_Low_Density,100,9500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Above_Average,Above_Average,1964,1978,Gable,CompShg,VinylSd,VinylSd,BrkCmn,272,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,374,816,GasA,Typical,Y,SBrkr,1127,850,0,1977,0,1,1,1,4,1,Typical,9,Typ,1,Typical,Attchd,RFn,2,540,Typical,Typical,Paved,0,52,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,165000,-93.625632,42.034615 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1955,Hip,CompShg,HdBoard,HdBoard,BrkFace,176,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,710,1078,GasA,Excellent,Y,SBrkr,1150,0,0,1150,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,288,Typical,Typical,Paved,0,0,0,0,175,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,144000,-93.623704,42.0362 +Duplex_All_Styles_and_Ages,Residential_Low_Density,95,11345,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,Duplex,Two_Story,Average,Average,1948,1950,Gable,Roll,AsbShng,AsbShng,Stone,567,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,708,928,GasA,Good,Y,FuseA,928,992,0,1920,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,137000,-93.621485,42.03465 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,79,9492,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Average,1941,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,41,359,768,GasA,Typical,Y,SBrkr,968,408,0,1376,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,105000,-93.620497,42.034906 +Two_Story_1945_and_Older,Residential_Low_Density,79,9480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Average,Good,1942,1995,Gable,CompShg,MetalSd,MetalSd,Stone,224,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,342,728,GasA,Excellent,Y,SBrkr,888,756,0,1644,0,0,1,1,3,1,Good,7,Typ,2,Good,Attchd,Unf,1,312,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,146500,-93.620491,42.034755 +Duplex_All_Styles_and_Ages,Residential_Low_Density,63,8668,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1560,1560,GasA,Typical,Y,SBrkr,1560,0,0,1560,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,3,792,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,126000,-93.619383,42.049216 +Split_Foyer,Residential_Low_Density,0,10050,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Above_Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,87,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,191,793,GasA,Excellent,Y,SBrkr,1280,0,0,1280,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Basment,Fin,2,432,Typical,Typical,Paved,140,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,175000,-93.61706,42.048934 +Split_or_Multilevel,Residential_Low_Density,100,9600,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1961,1961,Hip,CompShg,WdShing,Wd Shng,BrkFace,291,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,618,1218,GasA,Typical,Y,SBrkr,1254,0,0,1254,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,525,Typical,Typical,Paved,0,0,0,0,168,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,158500,-93.615687,42.046714 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1958,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,1,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,456,912,GasA,Excellent,Y,FuseA,912,0,0,912,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,275,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,COD,Normal,114500,-93.616099,42.045809 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,11344,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,BrkFace,180,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,414,874,GasW,Typical,Y,FuseA,874,650,0,1524,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,315,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Normal,144000,-93.618143,42.0459325 +Two_Story_1946_and_Newer,Residential_Low_Density,0,18450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1965,1979,Flat,Tar&Grv,Plywood,Plywood,BrkCmn,113,Typical,Good,CBlock,Good,Typical,No,LwQ,4,Rec,723,111,1021,GasA,Typical,Y,SBrkr,1465,915,0,2380,0,0,2,1,3,1,Typical,7,Sev,1,Poor,CarPort,Unf,2,596,Typical,Typical,Paved,0,265,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Abnorml,129000,-93.615535,42.046882 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1957,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,63,936,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,1,315,Typical,Typical,Paved,0,0,0,219,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,141000,-93.618786,42.044053 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6860,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,54,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,100,1008,GasA,Excellent,Y,SBrkr,1008,0,0,1008,1,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,131000,-93.618404,42.043143 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1963,1963,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,765,1053,GasA,Good,Y,SBrkr,1053,0,0,1053,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,More_Than_Two_Types,RFn,2,692,Typical,Typical,Paved,240,0,0,0,109,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,142100,-93.619352,42.044735 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8176,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,210,1056,GasA,Fair,Y,SBrkr,1056,0,0,1056,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,139000,-93.617521,42.043932 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,BrkFace,243,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,834,1442,GasA,Good,Y,SBrkr,1442,0,0,1442,0,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,1,301,Typical,Typical,Paved,0,0,275,0,0,0,No_Pool,No_Fence,Shed,500,4,2007,COD,Normal,157900,-93.615281,42.043946 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1954,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,468,108,1056,GasA,Typical,Y,SBrkr,1056,0,0,1056,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,260,Typical,Typical,Paved,390,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,139400,-93.6168658,42.0423095 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11988,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1957,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,467,1244,GasA,Excellent,Y,FuseA,1244,0,0,1244,0,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,336,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,150000,-93.613046,42.044218 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Hip,CompShg,HdBoard,HdBoard,BrkCmn,69,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1144,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,0,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,120000,-93.611697,42.045669 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9736,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1969,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,289,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,138,525,1331,GasA,Good,Y,SBrkr,1721,0,0,1721,0,0,1,0,4,1,Typical,8,Typ,3,Typical,Attchd,Unf,2,464,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,174850,-93.612347,42.043952 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,9770,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,410,922,GasA,Typical,Y,FuseA,922,0,0,922,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,Good_Wood,None,0,4,2007,WD ,Normal,116000,-93.613457,42.043233 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10152,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,MetalSd,MetalSd,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,462,1048,GasA,Typical,Y,SBrkr,1048,0,0,1048,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,20,0,0,192,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,135000,-93.613983,42.0426969 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,12155,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Above_Average,Fair,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,LwQ,4,Unf,0,420,1657,GasA,Good,Y,SBrkr,1657,0,0,1657,0,1,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,147,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,163500,-93.610875,42.042728 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,12198,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1975,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,Unf,0,424,1204,GasA,Typical,Y,SBrkr,1411,0,0,1411,0,0,1,0,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,1,310,Typical,Typical,Paved,278,82,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,COD,Normal,130000,-93.612301,42.04302 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,928,1216,GasA,Typical,Y,SBrkr,1216,0,0,1216,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,129500,-93.6192677,42.0418697 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11556,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Excellent,1953,2006,Gable,CompShg,VinylSd,MetalSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,BLQ,2,Unf,0,456,864,GasA,Good,Y,SBrkr,1154,0,0,1154,0,0,1,1,3,1,Excellent,6,Typ,0,No_Fireplace,Detchd,Unf,1,336,Typical,Typical,Paved,63,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,142000,-93.617242,42.041243 +Duplex_All_Styles_and_Ages,Residential_Low_Density,70,8078,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,260,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1560,1560,GasA,Typical,Y,SBrkr,1560,0,0,1560,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,128600,-93.61848,42.040773 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,10950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,507,948,GasA,Typical,Y,SBrkr,948,0,0,948,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,410,Typical,Typical,Dirt_Gravel,0,48,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,125000,-93.615622,42.040451 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,7942,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,955,0,1040,GasA,Typical,Y,FuseF,1040,0,0,1040,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,293,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,136000,-93.618428,42.03981 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,8923,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1953,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,365,1008,GasA,Good,Y,SBrkr,1008,0,0,1008,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,18,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,134500,-93.617075,42.039369 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,8540,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1956,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,ALQ,691,120,925,GasA,Typical,Y,SBrkr,925,0,0,925,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,252,Typical,Typical,Paved,152,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,127000,-93.612414,42.039482 +Split_or_Multilevel,Residential_Low_Density,60,7134,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,0,384,GasA,Typical,Y,SBrkr,1360,0,0,1360,0,0,1,0,3,1,Typical,6,Min1,1,Typical,Detchd,Unf,2,572,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,130000,-93.612401,42.03846 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,55,7150,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,1040,1190,GasA,Good,Y,SBrkr,1040,500,0,1540,1,0,1,0,4,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,115000,-93.61245,42.03837 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,130,923,GasA,Typical,Y,SBrkr,925,0,0,925,1,0,1,0,3,1,Typical,5,Typ,2,Typical,Attchd,Unf,1,390,Typical,Typical,Paved,81,0,0,0,0,0,No_Pool,Good_Wood,None,0,3,2007,WD ,Normal,116900,-93.612075,42.0384781 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,13300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1956,2000,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,551,928,GasA,Typical,Y,SBrkr,928,0,0,928,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,252,Typical,Typical,Paved,261,0,156,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,132000,-93.612252,42.038545 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9532,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1953,1953,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,354,156,1105,GasA,Good,Y,SBrkr,1647,0,0,1647,1,0,1,0,3,1,Typical,6,Min1,1,Fair,Attchd,Fin,1,280,Typical,Typical,Paved,225,0,0,0,0,368,Typical,Good_Privacy,None,0,2,2007,WD ,Normal,153000,-93.618606,42.034789 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15783,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Average,1952,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,632,924,GasA,Typical,Y,SBrkr,924,0,0,924,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,420,Typical,Typical,Paved,0,324,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,6,2007,WD ,Normal,112500,-93.6197518,42.0347206 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,14190,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1890,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,CBlock,Fair,Typical,No,Unf,7,Unf,0,925,925,GasA,Good,Y,SBrkr,1000,544,0,1544,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,231,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,138000,-93.620343,42.034812 +One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,60,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Unf,Average,Good,1949,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,625,845,GasA,Typical,Y,SBrkr,893,0,0,893,0,1,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,135000,-93.617191,42.035932 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,12099,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1953,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,1018,1216,GasA,Excellent,Y,SBrkr,1216,0,512,1728,1,0,1,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,1,371,Typical,Typical,Paved,200,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2007,WD ,Normal,156000,-93.618445,42.036077 +Two_Story_1945_and_Older,Residential_Low_Density,113,21281,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Below_Average,1935,2007,Gable,CompShg,Wd Sdng,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,666,666,GasA,Good,Y,SBrkr,1308,1778,0,3086,0,0,3,1,4,1,Good,9,Min1,0,No_Fireplace,BuiltIn,Unf,3,1200,Typical,Typical,Paved,0,208,290,0,156,0,No_Pool,No_Fence,None,0,11,2007,WD ,Family,301600,-93.615616,42.036027 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10134,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,735,735,GasA,Good,Y,FuseA,735,299,0,1034,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,109000,-93.615485,42.03725 +One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,55,5500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Unf,Below_Average,Above_Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,882,882,GasA,Excellent,Y,SBrkr,882,0,0,882,0,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,103200,-93.6133009,42.0380982 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10284,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1925,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,LwQ,66,55,1151,GasA,Excellent,Y,SBrkr,845,436,0,1281,1,0,2,0,1,1,Typical,6,Mod,0,No_Fireplace,Detchd,Unf,2,580,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Abnorml,128500,-93.615536,42.0357 +One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1927,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,656,656,GasA,Typical,Y,SBrkr,968,0,0,968,0,0,2,0,4,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Fair,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,64500,-93.612418,42.035779 +Two_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Excellent,1895,1999,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Good,CBlock,Good,Typical,Av,Unf,7,Unf,0,736,736,GasA,Excellent,Y,SBrkr,751,783,0,1534,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,112,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,148000,-93.6139,42.035854 +One_Story_1945_and_Older,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,691,691,GasA,Excellent,Y,FuseA,691,0,0,691,0,0,1,0,2,1,Excellent,4,Typ,0,No_Fireplace,Detchd,Unf,1,216,Fair,Typical,Dirt_Gravel,0,20,94,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Abnorml,86000,-93.613899,42.034878 +Split_or_Multilevel,Residential_Low_Density,93,10090,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Good,Average,1963,1999,Gable,CompShg,Plywood,Plywood,BrkFace,364,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,ALQ,483,0,725,GasA,Typical,Y,SBrkr,1035,616,0,1651,0,1,2,0,4,1,Typical,6,Typ,2,Typical,BuiltIn,Unf,1,276,Typical,Typical,Paved,460,46,0,0,165,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,172000,-93.607117,42.039338 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1961,1961,Gable,CompShg,HdBoard,HdBoard,BrkFace,53,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,728,920,GasA,Good,Y,SBrkr,888,0,0,888,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,COD,Abnorml,120000,-93.609148,42.039983 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,8300,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,86,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,0,952,GasA,Good,Y,SBrkr,952,0,0,952,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,128500,-93.610561,42.040146 +Two_Story_1946_and_Newer,Residential_Low_Density,70,11606,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Sev,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Average,1969,1969,Gable,CompShg,Plywood,Plywood,BrkFace,192,Typical,Typical,PConc,Good,Typical,Av,Rec,6,Unf,0,390,1040,GasA,Typical,Y,SBrkr,1040,1040,0,2080,0,1,1,2,5,1,Fair,9,Typ,2,Typical,Attchd,Unf,2,504,Typical,Typical,Paved,335,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Family,135000,-93.605388,42.038888 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8064,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1949,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,672,252,0,924,0,0,1,0,3,1,Typical,6,Typ,1,Poor,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,2000,7,2007,WD ,Normal,122900,-93.610609,42.035733 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,11664,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Above_Average,Average,1948,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,206,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,746,1082,GasA,Typical,Y,SBrkr,1082,0,0,1082,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,130,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,119200,-93.610611,42.035921 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,78,10496,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1949,1950,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,320,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,844,1040,GasA,Excellent,Y,SBrkr,1168,678,0,1846,0,0,2,0,3,1,Typical,7,Typ,1,Good,Attchd,Unf,1,315,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,1,2007,WD ,Normal,143000,-93.610623,42.037329 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,96,246,876,GasA,Typical,Y,SBrkr,988,0,0,988,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,276,Typical,Typical,Paved,0,80,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,119000,-93.607957,42.037203 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1950,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,432,864,GasA,Fair,Y,FuseA,1238,0,0,1238,0,0,1,1,3,1,Typical,6,Min2,1,Typical,Attchd,Unf,1,357,Typical,Typical,Paved,0,171,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,102000,-93.607963,42.035954 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Very_Good,1950,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,149,317,864,GasA,Good,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,RFn,2,720,Typical,Typical,Paved,194,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,129000,-93.607963,42.035876 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,2002,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,45,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,422,1010,GasA,Excellent,Y,SBrkr,1134,0,0,1134,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,254,Typical,Typical,Paved,0,16,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,4,2007,WD ,Family,135000,-93.606932,42.037106 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1959,2003,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,466,1040,GasA,Excellent,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,0,7,2007,WD ,Normal,152000,-93.605968,42.035862 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7315,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1958,1958,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,545,1170,GasA,Typical,Y,SBrkr,1170,0,0,1170,0,1,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,338,Typical,Typical,Paved,0,0,0,0,225,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,140000,-93.60674,42.036002 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7903,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,503,1242,GasA,Good,Y,FuseA,1242,0,0,1242,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,324,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Family,152000,-93.606782,42.037084 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1960,1960,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,154,125,1377,GasA,Typical,Y,SBrkr,1377,0,0,1377,1,0,1,0,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,1,351,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2007,WD ,Normal,156500,-93.605942,42.034685 +Duplex_All_Styles_and_Ages,Residential_Low_Density,80,8000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,Duplex,One_Story,Average,Below_Average,1961,1961,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1800,1800,GasA,Excellent,N,SBrkr,1800,0,0,1800,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,141000,-93.606746,42.034614 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,925,0,0,925,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,99000,-93.606746,42.034564 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,North_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1964,1993,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,340,Typical,Typical,CBlock,Typical,Typical,Gd,BLQ,2,Rec,117,169,822,GasA,Good,Y,SBrkr,1020,831,0,1851,0,0,2,1,3,1,Good,7,Typ,1,Fair,Attchd,RFn,2,440,Typical,Typical,Paved,239,42,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,7,2007,WD ,Normal,187000,-93.604836,42.036949 +Two_Story_1946_and_Newer,Residential_Low_Density,69,7590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,Two_Story,Average,Average,1962,1962,Gable,CompShg,VinylSd,VinylSd,BrkFace,288,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,324,864,GasA,Typical,Y,SBrkr,876,936,0,1812,0,0,2,0,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,1,264,Typical,Typical,Paved,0,168,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,169500,-93.603701,42.035423 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,6600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,One_Story,Average,Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,442,312,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,294,Typical,Typical,Paved,58,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,125500,-93.603703,42.0346 +Split_Foyer,Residential_Low_Density,66,6760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Average,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,162,896,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,1,288,Typical,Typical,Paved,24,90,160,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,130000,-93.604562,42.035034 +One_Story_1945_and_Older,Residential_Medium_Density,60,6978,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Good,1926,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,850,850,GasA,Typical,Y,SBrkr,960,0,0,960,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,2,576,Typical,Typical,Paved,0,0,116,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,103000,-93.618969,42.034403 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1910,2002,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Good,BrkTil,Fair,Fair,No,Unf,7,Unf,0,264,264,GasA,Excellent,Y,SBrkr,768,664,0,1432,0,0,2,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,360,Typical,Good,Paved,270,0,112,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Abnorml,132000,-93.616988,42.032254 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,569,845,GasA,Typical,Y,SBrkr,866,430,0,1296,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,175,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,127500,-93.6190545,42.0314733 +One_Story_1945_and_Older,Residential_Medium_Density,56,4480,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Average,Average,1922,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,PConc,Fair,Fair,No,LwQ,4,Unf,0,240,1022,GasA,Typical,N,FuseF,1022,0,0,1022,1,0,1,0,2,1,Fair,4,Typ,1,Good,Detchd,Unf,1,184,Typical,Fair,Dirt_Gravel,0,122,20,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,89500,-93.62024,42.03131 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,60,7200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,SFoyer,Average,Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,BrkFace,180,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,936,GasA,Typical,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,49,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,140000,-93.617135,42.031369 +One_Story_1945_and_Older,Residential_Medium_Density,56,3153,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1920,1990,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,967,967,GasA,Good,Y,SBrkr,967,0,0,967,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,180,Fair,Typical,Dirt_Gravel,0,0,26,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,99900,-93.6176729,42.0311108 +One_Story_1945_and_Older,Residential_Medium_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1885,1995,Mansard,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,641,641,GasA,Good,Y,SBrkr,1047,0,0,1047,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,273,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,Shed,450,8,2007,WD ,Normal,100000,-93.615432,42.033405 +One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Very_Good,1940,1950,Gable,CompShg,VinylSd,VinylSd,Stone,279,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,808,808,GasA,Excellent,Y,SBrkr,1072,0,0,1072,0,0,1,0,2,1,Typical,5,Typ,2,Good,Detchd,Unf,2,379,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,130000,-93.615446,42.032343 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,120,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1900,2006,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Fair,No,BLQ,2,Unf,0,550,680,GasA,Excellent,Y,SBrkr,680,494,0,1174,0,0,1,0,2,1,Good,6,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,116,26,40,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,135000,-93.60861,42.033385 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Good,1948,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,318,649,GasA,Excellent,Y,SBrkr,679,504,0,1183,0,0,1,1,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,176,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,120000,-93.608868,42.033489 +One_Story_1945_and_Older,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Good,1937,2000,Hip,CompShg,Stucco,Stucco,BrkCmn,435,Typical,Typical,BrkTil,Fair,Typical,No,Rec,6,Unf,0,739,907,GasA,Typical,Y,SBrkr,1131,0,0,1131,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,672,Typical,Typical,Paved,0,72,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Alloca,89471,-93.606857,42.033313 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5925,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Rec,448,0,570,GasA,Good,N,SBrkr,761,380,0,1141,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,252,Fair,Fair,Paved,0,0,96,0,0,0,No_Pool,No_Fence,None,0,5,2007,ConLw,Normal,85000,-93.606842,42.032276 +One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1920,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,710,960,GasA,Good,Y,FuseA,960,0,0,960,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,168,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,108500,-93.607723,42.032357 +Two_Story_1945_and_Older,Residential_Medium_Density,57,9639,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Below_Average,Very_Good,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,1075,1075,GasA,Excellent,Y,SBrkr,1156,642,0,1798,0,0,2,1,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,342,Typical,Typical,Dirt_Gravel,0,0,160,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,137000,-93.61047,42.030579 +One_Story_1945_and_Older,Residential_Medium_Density,40,3880,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Excellent,1945,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,357,686,GasA,Good,Y,SBrkr,866,0,0,866,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,58,42,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,110500,-93.6082631,42.0318018 +One_Story_1945_and_Older,Residential_Medium_Density,60,8520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1923,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,968,968,GasA,Typical,Y,SBrkr,968,0,0,968,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,480,Fair,Typical,Dirt_Gravel,0,0,184,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,100000,-93.606789,42.030311 +Two_Story_1945_and_Older,Residential_Medium_Density,0,10337,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Very_Good,Excellent,1910,1999,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,725,725,GasA,Excellent,N,SBrkr,909,863,0,1772,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,816,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,184900,-93.618662,42.03013 +Two_Story_1945_and_Older,Residential_Medium_Density,53,9863,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1927,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Rec,210,322,728,GasA,Typical,Y,SBrkr,914,728,0,1642,0,1,1,1,4,1,Typical,9,Typ,1,Good,Detchd,Unf,1,374,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Abnorml,145000,-93.6190848,42.0299846 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1925,1994,Gambrel,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Unf,7,Unf,0,702,702,GasA,Good,Y,SBrkr,842,630,0,1472,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Fair,Partial_Pavement,0,0,84,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Normal,125000,-93.617061,42.029218 +Two_Story_1945_and_Older,Residential_Medium_Density,35,4571,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1910,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,448,616,GasA,Excellent,Y,SBrkr,616,616,0,1232,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,480,Fair,Fair,Paved,280,0,143,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,114000,-93.618294,42.029198 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,85,13600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Average,Average,1900,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,662,662,GasA,Typical,N,SBrkr,1422,915,0,2337,0,0,2,0,5,2,Typical,10,Min2,0,No_Fireplace,Detchd,Unf,2,560,Typical,Typical,Paved,0,57,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,90000,-93.616911,42.029223 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,56,8398,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1910,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Good,No,BLQ,2,Unf,0,667,926,GasA,Typical,Y,SBrkr,991,659,0,1650,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,468,Typical,Typical,Dirt_Gravel,128,103,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,144100,-93.6188503,42.0280396 +Two_Story_1945_and_Older,Residential_Medium_Density,90,9900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Below_Average,1880,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,1008,1008,GasW,Typical,Y,SBrkr,1178,1032,0,2210,0,0,2,0,5,1,Fair,8,Typ,0,No_Fireplace,Detchd,Unf,1,205,Fair,Typical,Dirt_Gravel,0,48,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,117500,-93.617037,42.028076 +Two_Story_1945_and_Older,Residential_Medium_Density,60,3600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Good,1930,2005,Gambrel,CompShg,VinylSd,VinylSd,None,0,Typical,Good,BrkTil,Typical,Fair,No,Rec,6,Unf,0,371,687,GasA,Good,Y,SBrkr,687,671,0,1358,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,336,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Partial,124500,-93.615578,42.028847 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,75,13500,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Very_Good,1879,1987,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,819,819,GasA,Typical,Y,FuseA,1312,1142,0,2454,0,0,2,0,3,1,Typical,8,Typ,1,Good,Attchd,Unf,2,576,Typical,Typical,Dirt_Gravel,0,148,150,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,185000,-93.612061,42.02819 +Two_Family_conversion_All_Styles_and_Ages,C_all,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_and_Half_Unf,Above_Average,Above_Average,1910,1998,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Rec,6,Unf,0,168,1214,GasW,Excellent,N,SBrkr,1260,1031,0,2291,0,1,2,0,4,2,Typical,9,Typ,1,Good,Detchd,Unf,2,506,Typical,Typical,Paved,0,0,0,0,99,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,133900,-93.612191,42.027154 +One_Story_with_Finished_Attic_All_Ages,Residential_Medium_Density,40,5400,Pave,Paved,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Good,1926,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,Mn,LwQ,4,Unf,0,779,1149,GasA,Good,Y,FuseA,1149,467,0,1616,0,0,2,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,183,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,152000,-93.608882,42.0282 +One_Story_1945_and_Older,Residential_Medium_Density,90,8100,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1948,1973,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,1221,1559,GasA,Good,Y,SBrkr,1559,0,0,1559,1,0,1,0,2,1,Typical,5,Min2,0,No_Fireplace,Detchd,Unf,2,812,Typical,Typical,Paved,0,116,230,0,0,0,No_Pool,Good_Wood,None,0,6,2007,COD,Normal,153500,-93.6094532,42.0277574 +Two_Story_1945_and_Older,Residential_Medium_Density,60,10800,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1890,1998,Gable,CompShg,Wd Sdng,VinylSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,718,718,GasA,Excellent,Y,SBrkr,1576,978,0,2554,0,0,1,1,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,704,Typical,Typical,Partial_Pavement,0,48,143,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,159500,-93.608678,42.026089 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,9439,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,588,912,GasA,Good,Y,FuseA,912,336,0,1248,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,160,Fair,Fair,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,87000,-93.6047622,42.0268089 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,52,8626,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,1,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,968,0,0,968,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,331,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,104500,-93.6041765,42.0272605 +Split_or_Multilevel,Residential_Medium_Density,76,11800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,SLvl,Below_Average,Good,1949,2002,Gable,CompShg,Stucco,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1382,0,0,1382,0,0,2,0,1,1,Typical,6,Mod,1,Typical,Attchd,RFn,1,384,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,110000,-93.6051157,42.0272703 +One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,55,6854,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1925,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Typical,Typical,No,LwQ,4,Rec,227,212,756,GasA,Typical,N,FuseA,916,144,0,1060,1,0,1,0,1,1,Typical,6,Mod,1,Good,Detchd,Unf,1,308,Fair,Typical,Paved,0,65,0,0,150,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,136500,-93.6281873,42.033311 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,55,8674,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Brookside,RRNn,Artery,OneFam,One_and_Half_Fin,Average,Above_Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Rec,6,Unf,0,0,910,GasA,Typical,Y,SBrkr,910,525,0,1435,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,33,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,128250,-93.6262716,42.034339 +One_Story_1945_and_Older,Residential_Low_Density,98,8731,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Average,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Fair,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,270,915,GasA,Typical,Y,SBrkr,1167,0,0,1167,0,0,1,0,3,1,Typical,6,Maj1,1,Good,Detchd,Unf,2,495,Typical,Typical,Paved,0,0,216,0,126,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,144000,-93.627605,42.031846 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,8737,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1923,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Rec,6,Unf,0,765,1065,GasA,Excellent,Y,FuseA,915,720,0,1635,0,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,0,38,0,144,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,210000,-93.627487,42.030823 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1939,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,422,728,GasA,Excellent,Y,SBrkr,728,546,0,1274,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,224,Fair,Typical,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,3,2007,CWD,Normal,135000,-93.624715,42.033482 +Two_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1939,1950,Gable,CompShg,MetalSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,324,600,GasA,Excellent,Y,SBrkr,608,624,0,1232,0,0,1,1,3,1,Typical,6,Typ,2,Typical,Attchd,Unf,1,217,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,2,2007,WD ,Normal,128000,-93.623521,42.033612 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Good,No,ALQ,1,Unf,0,360,735,GasA,Excellent,Y,SBrkr,869,349,0,1218,0,1,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,131000,-93.621501,42.033336 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Average,Good,1939,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,884,884,GasA,Excellent,Y,SBrkr,884,0,0,884,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,136,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,113000,-93.623643,42.032399 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1938,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,809,861,GasA,Good,Y,SBrkr,861,548,0,1409,1,0,1,1,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,225,0,84,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,126000,-93.623631,42.031433 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1939,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,Unf,0,624,672,GasA,Excellent,Y,SBrkr,899,423,0,1322,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,132000,-93.624539,42.031438 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,966,966,GasA,Excellent,Y,SBrkr,1014,412,0,1426,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,230,Fair,Typical,Paved,174,0,96,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,131750,-93.622551,42.03142 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,405,756,GasA,Good,Y,FuseA,903,378,0,1281,1,0,1,0,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,379,Typical,Typical,Paved,25,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,132500,-93.621504,42.032505 +Two_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1920,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,939,939,GasA,Excellent,Y,SBrkr,939,574,0,1513,0,0,1,1,4,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,24,0,150,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,137500,-93.621502,42.032386 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Good,Very_Good,1929,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,832,832,GasA,Excellent,Y,FuseA,854,0,0,854,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,48,112,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,132000,-93.6224,42.031321 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1931,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,LwQ,4,Unf,0,459,884,GasA,Typical,Y,FuseA,959,408,0,1367,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,560,Typical,Typical,Paved,0,0,0,0,120,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,127500,-93.622402,42.031483 +One_Story_1945_and_Older,Residential_Medium_Density,60,6180,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Above_Average,Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Typical,N,SBrkr,986,0,0,986,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,180,Typical,Typical,Paved,0,128,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,102000,-93.6212119,42.0314873 +Two_Story_1945_and_Older,Residential_Medium_Density,47,7755,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1918,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,FuseA,1100,1164,0,2264,0,0,2,1,4,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,408,Typical,Typical,Paved,0,152,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,200000,-93.6209132,42.0304477 +Two_Story_1945_and_Older,Residential_Low_Density,60,13515,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1919,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,764,764,GasA,Excellent,Y,FuseA,1060,764,0,1824,0,0,1,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,520,Typical,Typical,Dirt_Gravel,0,0,126,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,180500,-93.62576,42.029451 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8850,Pave,Paved,Regular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,752,624,0,1376,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,54,144,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,165000,-93.625435,42.026912 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_and_Half_Fin,Average,Average,1926,1950,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,750,750,GasA,Typical,Y,SBrkr,960,356,0,1316,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,ConLw,Family,90000,-93.625285,42.026998 +Two_Story_1945_and_Older,Residential_Low_Density,60,8730,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,Two_Story,Above_Average,Good,1915,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,698,698,GasA,Excellent,Y,FuseA,698,698,0,1396,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,0,0,0,259,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,153575,-93.625284,42.027088 +Two_Story_1945_and_Older,Residential_Medium_Density,0,5700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Old_Town,Feedr,Norm,OneFam,Two_Story,Good,Above_Average,1929,1990,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,336,672,GasA,Good,N,FuseA,672,672,0,1344,1,0,1,1,3,1,Good,6,Typ,1,Good,Attchd,Unf,2,456,Typical,Typical,Paved,0,0,70,0,0,0,No_Pool,Good_Privacy,None,0,9,2007,WD ,Normal,140000,-93.6241649,42.0299664 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,8520,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1916,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Good,BrkTil,Typical,Typical,No,Rec,6,LwQ,546,0,714,GasW,Typical,N,SBrkr,1664,862,0,2526,0,0,2,0,5,1,Good,10,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,88,15,0,0,0,0,No_Pool,Good_Wood,None,0,8,2007,CWD,Family,136000,-93.62033,42.02911 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,40,5680,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1901,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,592,592,GasA,Typical,N,FuseA,933,240,0,1173,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Fair,Paved,0,25,77,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,WD ,AdjLand,113000,-93.622147,42.02752 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,40,5680,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Fair,1901,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,969,969,GasA,Typical,N,FuseA,969,245,0,1214,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,0,0,77,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,WD ,AdjLand,117000,-93.6220032,42.0275775 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,7758,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Good,Below_Average,1931,1950,Gable,CompShg,Stucco,Stucco,BrkFace,600,Typical,Fair,PConc,Typical,Typical,No,LwQ,4,Unf,0,816,1040,GasA,Excellent,Y,FuseF,1226,592,0,1818,0,0,1,1,4,1,Typical,7,Typ,2,Typical,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,184,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,169500,-93.628447,42.025229 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,57,7449,Pave,Gravel,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Unf,Good,Good,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,637,637,GasA,Excellent,Y,FuseF,1108,0,0,1108,0,0,1,0,3,1,Good,6,Typ,1,Good,Attchd,Unf,1,280,Typical,Typical,Dirt_Gravel,0,0,205,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,139400,-93.628301,42.02543 +Split_or_Multilevel,Residential_Medium_Density,120,13200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,234,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,366,741,GasA,Fair,Y,SBrkr,1497,797,0,2294,0,0,3,0,5,1,Typical,9,Typ,1,Good,Attchd,Unf,2,658,Typical,Typical,Paved,0,110,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,202500,-93.629496,42.021576 +Two_Story_1945_and_Older,Residential_Medium_Density,60,6882,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1914,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,684,684,GasA,Typical,Y,SBrkr,773,582,0,1355,0,0,1,1,3,1,Good,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,136,0,115,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,127000,-93.6277573,42.0249982 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,9000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1937,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Typical,Y,SBrkr,780,595,0,1375,0,0,1,1,3,1,Good,6,Typ,1,Good,Detchd,Unf,1,544,Typical,Typical,Partial_Pavement,0,162,0,0,126,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,141000,-93.6275137,42.0242321 +Two_Story_1946_and_Newer,Residential_Medium_Density,60,9780,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Good,Excellent,1950,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,LwQ,4,Rec,398,224,976,GasA,Excellent,Y,SBrkr,976,976,0,1952,0,0,1,1,4,1,Good,8,Typ,2,Typical,Detchd,Fin,1,299,Typical,Typical,Paved,285,0,0,0,216,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,212300,-93.629501,42.0227 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,763,1138,GasA,Good,Y,SBrkr,1138,1042,0,2180,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,720,Typical,Typical,Dirt_Gravel,0,0,170,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,161000,-93.6274918,42.0238856 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,52,4330,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Above_Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Rec,6,ALQ,127,0,808,GasA,Typical,Y,SBrkr,838,477,0,1315,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,2,436,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,COD,Abnorml,99500,-93.628263,42.022963 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_and_Half_Unf,Average,Good,1920,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,530,530,GasA,Typical,N,SBrkr,581,530,0,1111,0,0,1,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,288,Typical,Typical,Dirt_Gravel,0,0,144,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,101000,-93.6263765,42.0238391 +One_Story_1945_and_Older,Residential_Medium_Density,40,4800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Good,1916,1990,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,999,1196,GasA,Excellent,Y,FuseA,1196,0,0,1196,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,109900,-93.625297,42.023518 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,12358,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,360,720,GasA,Typical,Y,SBrkr,854,0,528,1382,0,0,1,1,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,660,Typical,Typical,Paved,237,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,128500,-93.622793,42.026723 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,10120,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,TwoFmCon,Two_and_Half_Unf,Good,Below_Average,1910,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,925,925,GasA,Typical,N,FuseF,964,925,0,1889,0,0,1,1,4,2,Typical,9,Typ,1,Good,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,264,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,122000,-93.622025,42.025893 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,55,4388,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Unf,Average,Good,1930,1950,Gable,CompShg,WdShing,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,556,672,GasA,Excellent,Y,SBrkr,840,0,0,840,0,0,1,0,3,1,Typical,5,Typ,1,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,87000,-93.62188,42.0261614 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Below_Average,Very_Poor,1910,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,CBlock,Typical,Fair,No,Unf,7,Unf,0,771,771,GasA,Fair,Y,SBrkr,866,504,114,1484,0,0,2,0,3,1,Typical,6,Sal,0,No_Fireplace,Detchd,Unf,1,264,Typical,Fair,Dirt_Gravel,14,211,0,0,84,0,No_Pool,No_Fence,None,0,9,2007,COD,Abnorml,50000,-93.623814,42.022951 +Two_Story_1945_and_Older,Residential_Low_Density,107,12888,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,Two_Story,Good,Very_Good,1937,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,717,1005,GasA,Typical,Y,SBrkr,1262,1005,0,2267,1,0,1,1,3,1,Typical,7,Typ,2,Good,Attchd,Fin,2,498,Typical,Typical,Paved,521,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,219000,-93.655699,42.033524 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,138,18030,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1946,1994,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,469,977,1598,GasA,Typical,Y,SBrkr,1636,971,479,3086,0,0,3,0,3,1,Excellent,12,Maj1,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,122,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,200500,-93.656124,42.033285 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,444,756,GasA,Fair,N,FuseF,756,378,0,1134,1,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,AdjLand,126000,-93.657089,42.028048 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,59,4484,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Above_Average,1942,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,187,672,GasA,Typical,N,SBrkr,778,504,0,1282,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,108500,-93.655924,42.028004 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,75,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,TwoFmCon,SFoyer,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,0,925,GasA,Typical,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,40,176,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,130000,-93.675549,42.033272 +Split_Foyer,Residential_Low_Density,75,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Average,Average,1964,1980,Gable,CompShg,HdBoard,HdBoard,BrkFace,30,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,Unf,0,635,1420,GasA,Good,Y,SBrkr,1452,0,0,1452,1,0,1,0,2,1,Typical,6,Min2,1,Typical,Detchd,Unf,2,572,Typical,Typical,Paved,92,0,88,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,158450,-93.675559,42.032426 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,14299,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Fair,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,144,348,1005,GasA,Typical,Y,SBrkr,1005,0,0,1005,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,115400,-93.672207,42.034453 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7943,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkCmn,192,Typical,Fair,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,126,1029,GasA,Good,Y,SBrkr,1029,0,0,1029,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,261,Typical,Typical,Paved,64,0,39,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,118500,-93.669741,42.034429 +Split_or_Multilevel,Residential_Low_Density,65,14149,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Very_Good,1964,2001,Hip,CompShg,HdBoard,HdBoard,BrkFace,50,Good,Good,CBlock,Typical,Typical,Gd,LwQ,4,BLQ,722,190,980,GasA,Typical,Y,SBrkr,1020,0,0,1020,0,1,2,0,3,1,Typical,5,Typ,1,Poor,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,165000,-93.674417,42.033193 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11677,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Below_Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,442,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,BLQ,761,30,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2007,WD ,Normal,123000,-93.67369,42.032385 +Split_Foyer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SFoyer,Above_Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,LwQ,4,Rec,627,0,814,GasA,Good,Y,SBrkr,913,0,0,913,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,252,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,129000,-93.677504,42.032098 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,99,7094,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,374,340,894,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,384,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,125000,-93.676953,42.031533 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8978,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,948,948,GasA,Typical,Y,SBrkr,948,0,0,948,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,300,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Family,108000,-93.677778,42.031203 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,8425,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Sawyer,Feedr,Norm,TwoFmCon,One_Story,Average,Above_Average,1971,1990,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,20,768,GasA,Good,Y,SBrkr,868,0,0,868,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,138,0,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,119900,-93.678149,42.029562 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,8665,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,89,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,BLQ,288,420,876,GasA,Typical,Y,SBrkr,897,0,0,897,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,115000,-93.675714,42.031215 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8398,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,323,Typical,Good,CBlock,Typical,Typical,No,LwQ,4,BLQ,529,300,943,GasA,Typical,Y,SBrkr,943,0,0,943,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,134500,-93.677997,42.029679 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,7742,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1966,1966,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,192,955,GasA,Excellent,Y,SBrkr,955,0,0,955,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,386,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,127000,-93.677601,42.031139 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,109,8724,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,402,894,GasA,Good,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,1,Poor,Attchd,Fin,2,450,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,129000,-93.676681,42.029786 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,8197,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,148,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,660,660,GasA,Excellent,Y,SBrkr,1285,0,0,1285,0,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,138,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,143500,-93.672119,42.030591 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8169,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Good,1966,1966,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,ALQ,435,261,912,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,315,Typical,Typical,Paved,204,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,129000,-93.668544,42.034422 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,81,14175,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Sawyer,PosA,Norm,OneFam,One_Story,Average,Average,1956,1998,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,ALQ,522,332,1240,GasA,Good,Y,SBrkr,1375,0,0,1375,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,323,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,187000,-93.664037,42.032528 +Two_Story_1946_and_Newer,Residential_Low_Density,99,16779,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,Two_Story,Average,Below_Average,1920,1996,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,356,Typical,Fair,CBlock,Good,Typical,No,BLQ,2,Unf,0,404,671,GasA,Fair,Y,SBrkr,1567,1087,0,2654,0,0,3,0,4,1,Typical,11,Mod,1,Good,Attchd,Unf,2,638,Typical,Typical,Paved,128,570,0,0,0,0,No_Pool,No_Fence,Shed,500,5,2007,WD ,Normal,158000,-93.662239,42.034421 +One_Story_1945_and_Older,Residential_Low_Density,0,25339,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Good,1918,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,1416,0,0,1416,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Dirt_Gravel,0,0,112,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,112000,-93.660671,42.033557 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,6960,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Good,Very_Good,1940,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,422,680,GasA,Excellent,Y,FuseA,798,504,0,1302,0,0,1,1,2,1,Good,6,Typ,2,Good,Attchd,Unf,1,224,Typical,Typical,Paved,0,0,0,0,126,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,165250,-93.660691,42.032638 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Above_Average,Average,1954,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,231,967,GasA,Typical,Y,SBrkr,1299,0,0,1299,0,0,1,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,494,Typical,Typical,Paved,81,0,280,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,150000,-93.661983,42.033726 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,13770,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1958,1998,Gable,CompShg,Plywood,Plywood,BrkFace,340,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,BLQ,873,95,1158,GasA,Typical,Y,SBrkr,1176,0,0,1176,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,303,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,VWD,Normal,137000,-93.661985,42.034018 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Good,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,FuseA,998,0,0,998,0,0,1,0,3,1,Typical,5,Min2,0,No_Fireplace,More_Than_Two_Types,Unf,2,460,Fair,Typical,Paved,0,0,140,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,89500,-93.659355,42.032699 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,115149,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1971,2002,Gable,CompShg,Plywood,Plywood,Stone,351,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,424,1643,GasA,Typical,Y,SBrkr,1824,0,0,1824,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,Unf,2,739,Typical,Typical,Paved,380,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,302000,-93.676272,42.02868 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11075,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1984,1984,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,136,Typical,Typical,PConc,Good,Typical,No,BLQ,2,LwQ,891,0,1190,GasA,Excellent,Y,SBrkr,1522,0,0,1522,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,552,Typical,Typical,Paved,0,77,0,0,168,0,No_Pool,Good_Privacy,None,0,2,2007,WD ,Normal,182000,-93.673958,42.02384 +Two_Story_1946_and_Newer,Residential_Low_Density,97,10029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1988,1989,Gable,CompShg,Plywood,Plywood,BrkFace,268,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,320,1151,GasA,Typical,Y,SBrkr,1164,896,0,2060,0,1,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,Unf,2,521,Typical,Typical,Paved,0,228,0,0,192,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,211000,-93.674582,42.024087 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1948,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,CBlock,Typical,Good,Mn,BLQ,2,Unf,0,109,409,GasA,Excellent,Y,SBrkr,1325,0,0,1325,0,0,2,0,3,1,Good,6,Typ,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,216000,-93.669606,42.026185 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,22692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,486,1073,GasA,Typical,Y,SBrkr,1630,0,0,1630,0,0,2,0,3,1,Typical,6,Mod,1,Typical,Detchd,Unf,2,649,Typical,Typical,Partial_Pavement,0,64,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,COD,Normal,130000,-93.671221,42.023229 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,17808,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1946,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,484,484,GasA,Typical,N,SBrkr,1242,0,0,1242,0,0,1,0,2,1,Typical,4,Mod,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,109900,-93.671511,42.023021 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,12671,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Good,1954,1994,Hip,CompShg,MetalSd,MetalSd,Stone,300,Typical,Good,CBlock,Good,Fair,No,LwQ,4,Unf,0,935,1288,GasA,Excellent,Y,SBrkr,2422,0,0,2422,0,0,3,0,4,1,Good,6,Min2,2,Good,Attchd,Fin,2,527,Typical,Typical,Paved,0,63,0,0,144,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,255000,-93.66504,42.028424 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,12615,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Good,1950,2001,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Good,Av,ALQ,1,Unf,0,725,1202,GasA,Typical,Y,SBrkr,2158,0,0,2158,1,0,2,0,4,1,Good,7,Typ,1,Good,Attchd,Unf,2,576,Typical,Typical,Paved,0,29,39,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,243000,-93.663415,42.028236 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,10512,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,497,988,GasA,Excellent,Y,SBrkr,988,638,0,1626,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,332,Typical,Typical,Paved,366,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,154000,-93.66712,42.024949 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,411,864,GasA,Typical,Y,SBrkr,864,0,0,864,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,399,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,118000,-93.66061,42.025818 +One_Story_1945_and_Older,Residential_Low_Density,0,11515,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1958,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,943,0,0,943,0,0,1,0,3,1,Good,5,Min2,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,60,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,80000,-93.663517,42.025001 +Duplex_All_Styles_and_Ages,Residential_Low_Density,42,7711,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Fair,1977,1977,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1440,GasA,Typical,Y,SBrkr,1440,0,0,1440,2,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,321,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,Oth,Abnorml,150000,-93.6644275,42.0205201 +One_Story_1945_and_Older,Residential_Low_Density,58,9098,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Good,1920,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,ALQ,1,Unf,0,180,528,GasA,Excellent,Y,SBrkr,605,0,0,605,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,144,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,86000,-93.66468,42.020186 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,39,3869,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,TwnhsE,One_Story,Average,Above_Average,1984,1984,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,149,Good,Excellent,CBlock,Typical,Typical,No,LwQ,4,GLQ,755,0,1038,GasA,Good,Y,SBrkr,1038,0,0,1038,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,105,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,130000,-93.6643138,42.0246868 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,58,9280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,ALQ,1,Unf,0,785,1342,GasA,Excellent,Y,SBrkr,1342,0,0,1342,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,256,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,125000,-93.658545,42.023874 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,11100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1951,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,Mn,LwQ,4,Unf,0,0,1080,GasA,Typical,N,SBrkr,1080,400,0,1480,1,0,1,0,4,1,Typical,7,Typ,1,Good,Attchd,Unf,1,253,Typical,Typical,Paved,0,0,68,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,136500,-93.66057,42.023945 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1920,1950,Gambrel,CompShg,MetalSd,MetalSd,None,0,Fair,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,951,951,GasW,Fair,N,SBrkr,986,376,0,1362,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Fair,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,96000,-93.659659,42.023451 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,One_Story,Average,Above_Average,1957,2006,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,98,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,1340,0,0,1340,0,0,1,0,3,1,Typical,7,Typ,1,Good,Attchd,RFn,1,252,Typical,Typical,Paved,116,0,0,180,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,120000,-93.658537,42.022824 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,104,23920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1984,1984,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1105,1105,GasA,Excellent,Y,SBrkr,1105,717,0,1822,0,0,2,0,4,1,Good,7,Min2,1,Poor,Attchd,Unf,2,515,Typical,Typical,Partial_Pavement,0,195,1012,0,0,444,Typical,No_Fence,None,0,4,2007,WD ,Normal,228500,-93.693153,42.034453 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,114,10357,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Feedr,Norm,OneFam,One_Story,Good,Average,1990,1991,Hip,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,172,910,GasA,Good,Y,SBrkr,1442,0,0,1442,1,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,719,Typical,Typical,Paved,0,244,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,179900,-93.684102,42.033662 +Two_Story_1946_and_Newer,Residential_Low_Density,116,13474,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Feedr,Norm,OneFam,Two_Story,Good,Average,1990,1991,Gable,CompShg,HdBoard,Plywood,BrkFace,246,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,0,700,GasA,Good,Y,SBrkr,1122,1121,0,2243,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,746,Typical,Typical,Paved,127,44,224,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,225000,-93.684102,42.033576 +Two_Story_1946_and_Newer,Residential_Low_Density,86,10380,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1986,1987,Gable,CompShg,Plywood,Plywood,BrkFace,172,Good,Typical,CBlock,Typical,Typical,Gd,LwQ,4,ALQ,1474,0,1502,GasA,Excellent,Y,SBrkr,1553,1177,0,2730,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,Fin,2,576,Typical,Typical,Paved,201,96,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,301000,-93.68347,42.031546 +Two_Story_1946_and_Newer,Residential_Low_Density,103,13125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Excellent,Typical,Mn,BLQ,2,GLQ,634,422,1104,GasA,Excellent,Y,SBrkr,912,1215,0,2127,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,833,Typical,Typical,Paved,72,192,224,0,0,0,No_Pool,Good_Privacy,None,0,11,2007,WD ,Normal,238000,-93.683309,42.032467 +Two_Story_1946_and_Newer,Residential_Low_Density,82,11287,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1989,1989,Gable,CompShg,Plywood,Plywood,BrkFace,340,Good,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,386,807,GasA,Good,Y,SBrkr,1175,807,0,1982,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,575,Typical,Typical,Paved,0,84,0,196,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,228500,-93.683226,42.031566 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9317,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1994,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,282,779,GasA,Excellent,Y,SBrkr,1029,929,0,1958,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,499,Typical,Typical,Paved,202,93,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,CWD,Normal,220000,-93.681061,42.030801 +Two_Story_1946_and_Newer,Residential_Low_Density,77,9965,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,220,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,280,1264,GasA,Excellent,Y,SBrkr,1282,1414,0,2696,1,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,792,Typical,Typical,Paved,120,184,0,0,168,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,383970,-93.686828,42.02667 +Two_Story_1946_and_Newer,Residential_Low_Density,77,9965,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,340,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,316,1466,GasA,Excellent,Y,SBrkr,1466,1362,0,2828,1,0,3,0,4,1,Good,11,Typ,1,Typical,BuiltIn,RFn,3,1052,Typical,Typical,Paved,125,144,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,424870,-93.686941,42.026642 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9178,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,306,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1643,1643,GasA,Excellent,Y,SBrkr,1651,0,0,1651,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,870,Typical,Typical,Paved,204,64,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,250000,-93.690342,42.025666 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,10481,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,2140,2140,GasA,Excellent,Y,SBrkr,2140,0,0,2140,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,3,894,Typical,Typical,Paved,136,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,360000,-93.69067,42.025707 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,10652,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,306,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1494,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,840,Typical,Typical,Paved,160,33,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,New,Partial,279500,-93.691248,42.025594 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,103,11175,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1316,1316,GasA,Excellent,Y,SBrkr,1316,0,0,1316,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,0,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,200141,-93.691213,42.025264 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,10235,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,306,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1643,1643,GasA,Excellent,Y,SBrkr,1651,0,0,1651,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,870,Typical,Typical,Paved,192,64,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,246500,-93.691015,42.025281 +Two_Story_1946_and_Newer,Residential_Low_Density,69,9588,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2007,2007,Gable,CompShg,CemntBd,CmentBd,Stone,270,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1482,1482,GasA,Excellent,Y,SBrkr,1482,1092,0,2574,0,0,2,1,3,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,868,Typical,Typical,Paved,0,148,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,276000,-93.690234,42.025558 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8814,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,349,1274,GasA,Excellent,Y,SBrkr,1274,0,0,1274,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,508,Typical,Typical,Paved,264,98,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,New,Partial,203000,-93.689071,42.024598 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11103,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,155835,-93.689071,42.024594 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8556,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1240,1240,GasA,Excellent,Y,SBrkr,1240,0,0,1240,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,3,826,Typical,Typical,Paved,140,93,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,194000,-93.69003,42.024633 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,544,1208,GasA,Excellent,Y,SBrkr,1208,0,0,1208,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,628,Typical,Typical,Paved,105,54,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,195400,-93.69135,42.024614 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,204,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1526,1546,GasA,Excellent,Y,SBrkr,1546,0,0,1546,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,796,Typical,Typical,Paved,144,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,217000,-93.688941,42.025726 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,132,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1453,1489,GasA,Excellent,Y,SBrkr,1500,0,0,1500,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,674,Typical,Typical,Paved,144,38,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,212999,-93.688923,42.025156 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7242,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1270,1270,GasA,Excellent,Y,SBrkr,1270,0,0,1270,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,524,Typical,Typical,Paved,0,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,175900,-93.689884,42.024635 +Two_Story_1946_and_Newer,Residential_Low_Density,78,9316,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,879,879,GasA,Excellent,Y,SBrkr,879,916,0,1795,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,164,111,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,191000,-93.688917,42.024485 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9317,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1290,1314,GasA,Good,Y,SBrkr,1314,0,0,1314,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,440,Typical,Typical,Paved,0,22,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,176432,-93.688931,42.023226 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14364,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1988,1989,Gable,CompShg,Plywood,Plywood,BrkFace,128,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,92,1157,GasA,Excellent,Y,SBrkr,1180,882,0,2062,1,0,2,1,3,1,Typical,7,Typ,1,Good,Attchd,Fin,2,454,Typical,Typical,Paved,60,55,0,0,154,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,277000,-93.685142,42.029694 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8883,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,360,Good,Typical,PConc,Good,Typical,No,GLQ,3,LwQ,321,0,929,GasA,Excellent,Y,SBrkr,946,927,0,1873,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,619,Typical,Typical,Paved,108,48,0,0,144,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,207000,-93.684944,42.029803 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,159000,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1958,2006,Gable,CompShg,Wd Sdng,HdBoard,BrkCmn,472,Good,Typical,CBlock,Good,Typical,Gd,Rec,6,Unf,0,747,1444,GasA,Good,Y,SBrkr,1444,700,0,2144,0,1,2,0,4,1,Good,7,Typ,2,Typical,Attchd,Fin,2,389,Typical,Typical,Paved,0,98,0,0,0,0,No_Pool,No_Fence,Shed,500,6,2007,WD ,Normal,277000,-93.682439,42.027956 +Two_Story_1946_and_Newer,Residential_Low_Density,0,53107,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Corner,Mod,Clear_Creek,Feedr,Norm,OneFam,Two_Story,Above_Average,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,595,1580,GasA,Excellent,Y,SBrkr,1079,874,0,1953,1,0,2,1,3,1,Good,9,Typ,2,Fair,Attchd,Fin,2,501,Typical,Typical,Paved,216,231,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,240000,-93.679216,42.027969 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12205,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1966,2007,Gable,CompShg,HdBoard,HdBoard,BrkFace,157,Typical,Typical,CBlock,Typical,Fair,Gd,LwQ,4,Unf,0,264,832,GasA,Good,Y,SBrkr,976,1111,0,2087,0,0,2,1,5,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,444,Typical,Typical,Paved,133,168,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,187500,-93.6825099,42.024734 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,155,20064,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Sev,Clear_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1976,1976,Shed,WdShngl,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Good,Gd,LwQ,4,GLQ,915,0,966,GasA,Excellent,Y,SBrkr,1743,0,0,1743,2,0,0,1,0,1,Good,5,Typ,2,Fair,Attchd,Fin,2,529,Typical,Typical,Paved,646,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,279000,-93.683612,42.024305 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14217,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,472,1022,GasA,Good,Y,SBrkr,1022,0,0,1022,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,747,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,139500,-93.691971,42.022228 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8775,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,495,990,GasA,Good,Y,SBrkr,990,0,0,990,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,299,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,126000,-93.692069,42.021324 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,105,11249,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,No,ALQ,1,BLQ,544,322,1200,GasA,Excellent,Y,SBrkr,1200,0,0,1200,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,521,Typical,Typical,Paved,0,26,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,177500,-93.688862,42.02121 +Two_Story_1946_and_Newer,Residential_Low_Density,57,10021,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1997,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,96,635,GasA,Excellent,Y,SBrkr,646,662,0,1308,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,497,Typical,Typical,Paved,142,54,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,175000,-93.690402,42.020966 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9531,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,88,794,GasA,Excellent,Y,SBrkr,882,914,0,1796,1,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,546,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,211000,-93.690545,42.020912 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8428,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Good,No,GLQ,3,Unf,0,570,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,146000,-93.692286,42.019032 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,16561,Pave,No_Alley_Access,Moderately_Irregular,Low,AllPub,Inside,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,548,1097,GasA,Excellent,Y,SBrkr,1097,0,0,1097,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,242,Typical,Typical,Paved,306,0,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,147900,-93.692696,42.019083 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Below_Average,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,402,990,GasA,Excellent,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,123600,-93.692415,42.019028 +Two_Story_1946_and_Newer,Residential_Low_Density,69,9337,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,GLQ,3,Unf,0,176,824,GasA,Excellent,Y,SBrkr,905,881,0,1786,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,684,Typical,Typical,Paved,0,162,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,204750,-93.690633,42.018378 +Two_Story_1946_and_Newer,Residential_Low_Density,47,10820,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,646,988,GasA,Excellent,Y,SBrkr,988,885,0,1873,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,597,Typical,Typical,Paved,202,123,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,235500,-93.688952,42.017907 +Two_Story_1946_and_Newer,Residential_Low_Density,43,12352,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,290,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,215,853,GasA,Excellent,Y,SBrkr,853,900,0,1753,1,0,2,1,3,1,Typical,7,Typ,1,Fair,Attchd,RFn,2,534,Typical,Typical,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,217000,-93.68892,42.017826 +Two_Story_1946_and_Newer,Residential_Low_Density,68,9543,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,845,845,GasA,Excellent,Y,SBrkr,845,845,0,1690,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,517,Typical,Typical,Paved,0,103,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,190550,-93.689344,42.016896 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8826,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,144,985,GasA,Excellent,Y,SBrkr,985,857,0,1842,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,486,Typical,Typical,Paved,193,96,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,217500,-93.689518,42.016829 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10665,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,167,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,440,1453,GasA,Excellent,Y,SBrkr,1479,0,0,1479,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,558,Typical,Typical,Paved,144,29,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,226000,-93.691469,42.016297 +Two_Story_1946_and_Newer,Residential_Low_Density,72,11317,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,101,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,840,840,GasA,Excellent,Y,SBrkr,840,828,0,1668,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,500,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,180000,-93.692052,42.016214 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,97,11800,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1974,1974,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,Unf,0,201,864,GasA,Typical,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Family,131000,-93.684962,42.021992 +Split_or_Multilevel,Residential_Low_Density,59,8660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Average,1976,1976,Gable,CompShg,VinylSd,VinylSd,BrkFace,113,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,513,1015,GasA,Typical,Y,SBrkr,1025,0,0,1025,0,0,2,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,2,370,Typical,Typical,Paved,127,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,153500,-93.686678,42.021386 +Split_or_Multilevel,Residential_Low_Density,72,9720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Good,1977,1977,Gable,CompShg,Plywood,VinylSd,BrkFace,51,Typical,Typical,CBlock,Typical,Excellent,Av,ALQ,1,Unf,0,240,995,GasA,Typical,Y,SBrkr,1009,0,0,1009,0,0,2,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,160000,-93.6874333,42.0218643 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,45,8982,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,501,1040,GasA,Typical,Y,SBrkr,1040,0,0,1040,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,748,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,134900,-93.687734,42.022239 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,39,16300,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Below_Average,1977,1977,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,Rec,6,BLQ,417,399,876,GasA,Typical,Y,SBrkr,907,0,0,907,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,130000,-93.684837,42.01972 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,432,102,864,GasA,Typical,Y,SBrkr,879,0,0,879,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,80,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,Con,Normal,120000,-93.684993,42.021163 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Rec,468,276,882,GasA,Typical,Y,SBrkr,882,0,0,882,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,461,Typical,Typical,Paved,96,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,112500,-93.683235,42.021056 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,10356,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,253,969,GasA,Typical,Y,SBrkr,969,0,0,969,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,122000,-93.684354,42.021025 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1972,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,193,864,GasA,Good,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Excellent,5,Typ,0,No_Fireplace,Detchd,Fin,2,576,Good,Excellent,Paved,155,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,127000,-93.683158,42.020861 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Below_Average,Very_Good,1972,2006,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,0,385,GasA,Good,Y,SBrkr,875,0,0,875,0,0,1,0,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,728,Typical,Typical,Paved,352,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,117000,-93.683169,42.021737 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10386,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2005,Gable,CompShg,CemntBd,CmentBd,Stone,246,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,536,2000,GasA,Excellent,Y,SBrkr,2000,0,0,2000,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,3,888,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,305900,-93.687856,42.018518 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,149,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1284,1284,GasA,Excellent,Y,SBrkr,1284,885,0,2169,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,647,Typical,Typical,Paved,192,87,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,228500,-93.686575,42.018424 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11354,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Typical,Typical,Gd,GLQ,3,Unf,0,261,1673,GasA,Excellent,Y,SBrkr,1673,0,0,1673,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,583,Typical,Typical,Paved,306,113,0,0,116,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,270000,-93.682805,42.018849 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8749,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,100,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1459,1459,GasA,Excellent,Y,SBrkr,1459,0,0,1459,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,2,527,Typical,Typical,Paved,192,39,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,192000,-93.686795,42.016973 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8749,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,325,979,GasA,Excellent,Y,SBrkr,992,940,0,1932,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,610,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,213000,-93.687786,42.016961 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8158,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,214,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,860,860,GasA,Excellent,Y,SBrkr,860,869,0,1729,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,542,Typical,Typical,Paved,386,63,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,200000,-93.68675,42.016212 +Two_Story_1946_and_Newer,Residential_Low_Density,66,16226,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,747,1028,GasA,Excellent,Y,SBrkr,1210,1242,0,2452,0,0,2,1,4,1,Good,9,Typ,1,Typical,BuiltIn,Fin,2,683,Typical,Typical,Paved,208,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,267000,-93.683813,42.0172 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,11927,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Hip,CompShg,HdBoard,HdBoard,BrkFace,519,Good,Typical,PConc,Good,Typical,Gd,BLQ,2,GLQ,465,683,1556,GasA,Excellent,Y,SBrkr,1592,0,0,1592,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,120,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,228000,-93.679583,42.018564 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,256,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,333,1531,GasA,Excellent,Y,SBrkr,1531,908,0,2439,1,0,2,1,4,1,Good,7,Typ,1,Typical,Attchd,Fin,2,560,Typical,Typical,Paved,184,121,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,275000,-93.681196,42.018864 +Two_Story_1946_and_Newer,Residential_Low_Density,0,15295,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1996,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,254,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,98,860,GasA,Excellent,Y,SBrkr,1212,780,0,1992,1,0,2,1,3,1,Good,7,Min2,2,Typical,Attchd,RFn,2,608,Typical,Typical,Paved,225,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,211000,-93.6800822,42.0185298 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11248,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Excellent,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,Stone,215,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,567,1626,GasA,Excellent,Y,SBrkr,1668,0,0,1668,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,702,Typical,Typical,Paved,257,45,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,285000,-93.679805,42.018017 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,438,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,141,1220,GasA,Excellent,Y,SBrkr,1220,870,0,2090,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,556,Typical,Typical,Paved,0,65,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,240000,-93.681274,42.017856 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,36,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,189,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,131500,-93.681085,42.016277 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,17227,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Mod,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,158,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,426,1341,GasA,Excellent,Y,SBrkr,1341,0,0,1341,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,RFn,2,482,Typical,Typical,Paved,240,84,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,246900,-93.679505,42.017753 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1081,0,1928,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,219500,-93.687684,42.015974 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,638,1337,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,531,Typical,Typical,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,185000,-93.686903,42.014038 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8145,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,738,0,1476,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,552,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,New,Partial,179400,-93.684118,42.014039 +Two_Story_1946_and_Newer,Residential_Low_Density,79,9245,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,939,939,GasA,Excellent,Y,SBrkr,939,858,0,1797,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,639,Typical,Typical,Paved,144,53,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,213500,-93.684137,42.014823 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,8500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,106,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1422,1422,GasA,Excellent,Y,SBrkr,1422,0,0,1422,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,626,Typical,Typical,Paved,192,60,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,179600,-93.684714,42.013576 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8769,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,460,1169,GasA,Excellent,Y,SBrkr,1190,0,0,1190,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,100,41,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,173500,-93.684625,42.013544 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,8334,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1330,1330,GasA,Excellent,Y,SBrkr,1330,0,0,1330,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,437,Typical,Typical,Paved,0,23,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,New,Partial,167840,-93.684285,42.01343 +Two_Story_1946_and_Newer,Residential_Low_Density,64,8333,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Good,Y,SBrkr,738,753,0,1491,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,100,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,168675,-93.684271,42.013467 +Two_Story_1946_and_Newer,Residential_Low_Density,64,9045,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,768,768,GasA,Excellent,Y,SBrkr,768,768,0,1536,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,400,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,167000,-93.683732,42.01469 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9170,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Average,Good,1965,1965,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,GLQ,96,420,1214,GasA,Excellent,Y,SBrkr,1214,0,0,1214,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,461,Fair,Fair,Paved,0,0,184,0,0,0,No_Pool,Good_Privacy,Shed,400,4,2007,WD ,Normal,140000,-93.6783521,42.0210331 +Split_Foyer,Residential_Low_Density,75,9825,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SFoyer,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,162,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,0,936,GasA,Good,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,Typical,4,Typ,1,Fair,Attchd,Unf,1,384,Typical,Typical,Paved,405,0,0,0,0,0,No_Pool,No_Fence,Shed,450,8,2007,WD ,Abnorml,118500,-93.677545,42.020703 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8308,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1963,1963,Gable,CompShg,VinylSd,VinylSd,Stone,20,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,LwQ,841,115,1088,GasA,Typical,Y,SBrkr,1088,0,0,1088,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,520,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,COD,Normal,110000,-93.676222,42.021221 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,10921,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1965,1965,Hip,CompShg,HdBoard,HdBoard,BrkFace,48,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,440,960,GasA,Typical,Y,FuseF,960,0,0,960,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,432,Typical,Typical,Partial_Pavement,120,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,94750,-93.671455,42.021195 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,74,16287,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1925,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,BLQ,105,666,901,GasA,Typical,Y,SBrkr,901,450,0,1351,1,0,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Dirt_Gravel,0,0,43,0,100,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,122000,-93.671454,42.02103 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1960,1960,Hip,CompShg,HdBoard,HdBoard,Stone,198,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1179,1179,GasA,Good,Y,SBrkr,1179,0,0,1179,0,0,1,0,2,1,Typical,5,Min2,0,No_Fireplace,Attchd,Fin,2,622,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,110000,-93.671453,42.020893 +Duplex_All_Styles_and_Ages,Residential_Low_Density,81,11841,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1990,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,104,Typical,Good,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,816,GasA,Typical,Y,SBrkr,816,0,0,816,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,118500,-93.673197,42.019926 +Split_or_Multilevel,Residential_Low_Density,65,6285,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1976,1976,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Fair,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,456,960,GasA,Typical,Y,SBrkr,1044,0,0,1044,1,0,1,0,3,1,Typical,7,Typ,1,Fair,Detchd,Unf,2,528,Typical,Fair,Paved,228,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,133500,-93.6747048,42.0196959 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7917,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Good,1976,1976,Hip,CompShg,HdBoard,HdBoard,BrkFace,174,Typical,Good,CBlock,Typical,Good,No,BLQ,2,Unf,0,392,1143,GasA,Typical,Y,SBrkr,1113,0,0,1113,1,0,1,1,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,1,504,Typical,Good,Paved,370,30,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,148000,-93.67525,42.019902 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,9555,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1100,1133,0,2233,0,0,2,1,5,2,Typical,11,Typ,0,No_Fireplace,Attchd,Fin,2,579,Typical,Good,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,141000,-93.674327,42.01917 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,8536,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1125,1125,GasA,Good,Y,SBrkr,1125,0,0,1125,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,430,Typical,Typical,Paved,80,64,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,155000,-93.674307,42.018283 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7024,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Excellent,Good,No,ALQ,1,Unf,0,110,1090,GasA,Good,Y,SBrkr,1090,0,0,1090,1,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,450,Typical,Typical,Paved,0,49,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,157000,-93.673253,42.018873 +Two_Story_1946_and_Newer,Residential_Low_Density,60,7023,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Good,No,GLQ,3,Unf,0,123,734,GasA,Good,Y,SBrkr,734,674,0,1408,1,0,2,1,3,1,Typical,6,Typ,0,No_Fireplace,BuiltIn,Fin,2,489,Typical,Typical,Paved,0,85,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,171500,-93.67318,42.018922 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,128,39290,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2008,2009,Hip,CompShg,CemntBd,CmentBd,Stone,1224,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1085,5095,GasA,Excellent,Y,SBrkr,5095,0,0,5095,1,1,2,1,2,1,Excellent,15,Typ,2,Good,Attchd,Fin,3,1154,Typical,Typical,Paved,546,484,0,0,0,0,No_Pool,No_Fence,Elev,17000,10,2007,New,Partial,183850,-93.6762195,42.0164532 +Two_Story_1946_and_Newer,Residential_Low_Density,130,40094,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Edwards,PosN,PosN,OneFam,Two_Story,Very_Excellent,Average,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,762,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,878,3138,GasA,Excellent,Y,SBrkr,3138,1538,0,4676,1,0,3,1,3,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,884,Typical,Typical,Paved,208,406,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,184750,-93.676241,42.016642 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,76,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,80,547,GasA,Excellent,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,140000,-93.670587,42.018973 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1959,2000,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,ALQ,831,52,960,GasA,Excellent,Y,SBrkr,960,0,0,960,1,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,392,Typical,Typical,Paved,144,0,35,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,131750,-93.665737,42.018965 +Duplex_All_Styles_and_Ages,Residential_Low_Density,74,6882,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Below_Average,Fair,1955,1955,Gable,CompShg,AsbShng,Plywood,BrkCmn,128,Typical,Typical,PConc,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1152,0,0,1152,0,0,2,0,2,2,Fair,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,61500,-93.664768,42.020062 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10215,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,132,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,372,864,GasA,Excellent,Y,SBrkr,948,0,0,948,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,248,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,111000,-93.663509,42.020783 +Duplex_All_Styles_and_Ages,Residential_Low_Density,52,8741,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,One_Story,Average,Above_Average,1946,1950,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1195,1195,GasA,Typical,N,SBrkr,1195,0,0,1195,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,118,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2007,WD ,Abnorml,98500,-93.664686,42.020504 +One_Story_1945_and_Older,Residential_High_Density,70,4270,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Fair,Above_Average,1931,2006,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,0,544,GasA,Excellent,Y,SBrkr,774,0,0,774,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,286,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,79000,-93.661542,42.022655 +One_Story_with_Finished_Attic_All_Ages,Residential_Low_Density,62,10042,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1920,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,ALQ,278,238,660,GasA,Typical,Y,SBrkr,740,125,0,865,1,0,1,0,2,1,Typical,4,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,84,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,108500,-93.660303,42.021436 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9450,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,342,894,GasA,Excellent,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Abnorml,110000,-93.665875,42.017962 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,70,11767,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Below_Average,Good,1910,2000,Gable,CompShg,MetalSd,HdBoard,None,0,Typical,Typical,CBlock,Fair,Typical,No,Unf,7,Unf,0,560,560,GasA,Good,N,SBrkr,796,550,0,1346,0,0,1,1,2,1,Typical,6,Min2,0,No_Fireplace,Detchd,Unf,1,384,Fair,Typical,Paved,168,24,0,0,0,0,No_Pool,Good_Wood,None,0,5,2007,WD ,Normal,112000,-93.664821,42.01965 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,224,768,GasA,Typical,Y,SBrkr,768,0,0,768,0,0,1,0,2,1,Typical,4,Typ,1,Fair,Detchd,Unf,1,355,Typical,Typical,Paved,0,0,196,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,125000,-93.66346,42.018943 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1955,1955,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,182,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Dirt_Gravel,196,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,COD,Family,79275,-93.663455,42.01873 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8190,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,216,948,GasA,Excellent,Y,SBrkr,948,0,0,948,1,0,1,0,3,1,Typical,5,Typ,1,Typical,Detchd,Unf,1,280,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,119000,-93.663329,42.019937 +Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,60,10896,Pave,Paved,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,TwoFmCon,Two_and_Half_Fin,Above_Average,Good,1914,1995,Hip,CompShg,VinylSd,VinylSd,None,0,Fair,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,1184,1440,GasA,Excellent,Y,FuseA,1440,1440,515,3395,0,0,2,0,8,2,Fair,14,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,110,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Abnorml,200000,-93.650162,42.01902 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,10890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1923,1950,Hip,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Rec,6,Unf,0,925,1296,Grav,Fair,N,FuseA,1296,1296,0,2592,2,0,2,0,6,2,Typical,12,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,742,240,0,0,0,No_Pool,No_Fence,Shed,1512,1,2007,WD ,AdjLand,150000,-93.65181,42.018761 +Two_Story_1945_and_Older,Residential_Low_Density,55,10592,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1923,1996,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Good,PConc,Typical,Fair,No,Unf,7,Unf,0,602,602,GasA,Typical,Y,SBrkr,900,602,0,1502,0,0,1,1,3,1,Good,7,Typ,2,Typical,Detchd,Unf,1,180,Typical,Typical,Paved,96,0,112,0,53,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,165000,-93.646624,42.01804 +One_and_Half_Story_Unfinished_All_Ages,Residential_Low_Density,55,10594,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Unf,Average,Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,768,768,Grav,Fair,N,SBrkr,789,0,0,789,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,200,Poor,Poor,Paved,0,0,112,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,96500,-93.646688,42.018039 +One_Story_1945_and_Older,Residential_Low_Density,54,7223,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Average,1926,1950,Hip,CompShg,Stucco,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,Mn,BLQ,2,Unf,0,971,1290,GasA,Typical,Y,SBrkr,1422,0,0,1422,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,352,Typical,Typical,Paved,0,0,64,0,0,0,No_Pool,Minimum_Privacy,None,0,4,2007,WD ,Normal,136500,-93.646817,42.017888 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,6821,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1921,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,538,651,GasA,Good,Y,SBrkr,759,539,0,1298,0,0,2,0,2,1,Typical,8,Typ,1,Good,Detchd,Unf,1,240,Typical,Typical,Partial_Pavement,216,0,168,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,146500,-93.64672,42.017889 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Good,1937,1950,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,252,Typical,Typical,BrkTil,Good,Typical,No,ALQ,1,Unf,0,162,731,GasA,Excellent,Y,SBrkr,981,787,0,1768,1,0,1,1,3,1,Good,7,Typ,2,Typical,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,264,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,175000,-93.6461654,42.018921 +Two_Story_1945_and_Older,Residential_Low_Density,63,4000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1930,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,GLQ,3,Unf,0,285,531,GasA,Typical,Y,SBrkr,567,531,0,1098,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,148500,-93.644544,42.017103 +Two_Story_1945_and_Older,Residential_Low_Density,53,6720,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1921,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Typical,N,SBrkr,851,585,0,1436,0,0,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,228,Typical,Typical,Paved,184,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,CWD,Normal,141500,-93.644669,42.017106 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,55,7642,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Very_Good,1918,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,912,912,GasA,Good,Y,SBrkr,912,514,0,1426,0,0,1,1,3,1,Good,7,Typ,1,Good,Detchd,Unf,1,216,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,189950,-93.6447083,42.0162813 +Two_Story_1945_and_Older,Residential_Low_Density,53,7155,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1926,1991,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,686,686,GasA,Typical,Y,SBrkr,686,775,0,1461,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,225,Typical,Typical,Dirt_Gravel,0,0,116,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,135000,-93.644511,42.016216 +Two_and_Half_Story_All_Ages,Residential_Low_Density,53,7128,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,554,918,GasA,Good,Y,SBrkr,918,728,0,1646,0,0,2,0,4,1,Typical,7,Typ,2,Good,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,126,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,164000,-93.644698,42.016217 +One_Story_1945_and_Older,Residential_Low_Density,40,4280,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Above_Average,1913,2002,Gable,CompShg,WdShing,Stucco,None,0,Typical,Typical,PConc,Typical,Typical,No,LwQ,4,Unf,0,75,440,GasA,Typical,N,SBrkr,694,0,0,694,0,0,1,0,2,1,Good,4,Typ,1,Good,Detchd,Unf,1,352,Good,Typical,Partial_Pavement,0,0,34,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Normal,90350,-93.64476,42.016217 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,53,5362,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1910,2003,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,661,661,GasA,Excellent,Y,SBrkr,661,589,0,1250,0,0,2,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,2,552,Typical,Typical,Paved,242,0,81,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,139000,-93.646289,42.016853 +One_Story_1945_and_Older,Residential_Low_Density,50,6305,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Good,1938,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,PConc,Fair,Fair,No,Unf,7,Unf,0,920,920,GasA,Excellent,Y,SBrkr,954,0,0,954,0,0,1,0,2,1,Fair,5,Typ,1,Good,Basment,Unf,1,240,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,119750,-93.646416,42.016067 +Duplex_All_Styles_and_Ages,Residential_High_Density,82,7136,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,Duplex,Two_Story,Above_Average,Above_Average,1946,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,423,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,495,979,GasA,Typical,N,FuseF,979,979,0,1958,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,492,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,145000,-93.6435257,42.0199537 +Duplex_All_Styles_and_Ages,Residential_High_Density,82,6270,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,Duplex,Two_Story,Average,Above_Average,1949,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,717,1001,GasA,Typical,N,FuseA,1001,1001,0,2002,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,871,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,145000,-93.643215,42.019952 +Two_Story_1945_and_Older,Residential_Low_Density,60,7230,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Good,1927,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,851,851,GasA,Good,Y,SBrkr,867,851,0,1718,0,0,2,1,4,1,Good,8,Typ,1,Typical,Detchd,Unf,2,264,Typical,Typical,Paved,291,0,60,0,153,0,No_Pool,Good_Privacy,None,0,10,2007,WD ,Normal,238000,-93.64281,42.018796 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,126,13108,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Average,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Fair,Y,SBrkr,1226,0,0,1226,0,0,1,1,2,1,Typical,7,Min1,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,174,24,120,0,228,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,153500,-93.641948,42.017572 +Two_Story_1945_and_Older,Residential_Low_Density,86,22420,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Feedr,Norm,OneFam,Two_Story,Above_Average,Above_Average,1918,1950,Hip,CompShg,Wd Sdng,Stucco,None,0,Typical,Typical,BrkTil,Good,Typical,No,BLQ,2,Unf,0,242,1370,GasW,Typical,N,FuseA,1370,1254,0,2624,1,0,2,1,4,1,Typical,10,Typ,1,Good,Detchd,Unf,3,864,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,239000,-93.644426,42.020037 +Two_Story_1945_and_Older,Residential_Low_Density,78,12168,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Above_Average,1934,1998,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,537,965,GasA,Typical,Y,SBrkr,1940,1254,0,3194,0,0,2,1,4,1,Typical,10,Typ,2,Good,Basment,Unf,2,380,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Alloca,359100,-93.641322,42.018504 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,110,7810,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1930,2003,Gable,CompShg,AsbShng,CmentBd,None,0,Typical,Good,BrkTil,Typical,Good,No,GLQ,3,Unf,0,741,930,GasA,Excellent,Y,SBrkr,1230,525,0,1755,0,0,2,0,4,1,Good,7,Typ,1,Typical,Detchd,Unf,1,231,Fair,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,188000,-93.642359,42.018941 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,11275,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1932,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,480,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,LwQ,557,0,854,GasA,Typical,Y,SBrkr,1096,895,0,1991,0,0,1,1,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,432,Typical,Fair,Paved,0,0,19,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,220000,-93.6428531,42.0168895 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,79,6221,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Average,1941,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,195,728,GasA,Excellent,Y,SBrkr,760,595,0,1355,0,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,144,0,No_Pool,Minimum_Privacy,None,0,10,2007,WD ,Normal,140000,-93.640497,42.017573 +Two_Story_1945_and_Older,Residential_Low_Density,70,10570,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Very_Good,1932,1994,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Good,No,Rec,6,Unf,0,556,853,GasA,Typical,Y,SBrkr,1549,1178,0,2727,0,0,2,1,3,1,Good,10,Maj1,2,Typical,Detchd,Unf,2,440,Typical,Typical,Paved,0,74,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,315000,-93.641156,42.016961 +Two_Story_1945_and_Older,Residential_Low_Density,61,7259,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1945,2002,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,150,104,1028,GasA,Excellent,Y,SBrkr,1436,884,0,2320,1,0,2,1,3,1,Good,9,Typ,1,Typical,Detchd,Unf,1,180,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,259500,-93.642663,42.01629 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,14442,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Good,1957,2004,Hip,CompShg,CemntBd,CmentBd,BrkFace,106,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,291,1477,GasA,Excellent,Y,SBrkr,1839,0,0,1839,1,0,2,0,3,1,Good,7,Typ,2,Typical,Attchd,Fin,2,416,Typical,Typical,Paved,0,87,0,0,200,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,257500,-93.642883,42.013003 +Two_Story_1945_and_Older,Residential_Low_Density,75,13125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1940,1984,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,390,800,GasA,Typical,Y,SBrkr,960,780,0,1740,0,0,1,1,3,1,Typical,6,Typ,2,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,CWD,Normal,219500,-93.6440062,42.0145503 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,21000,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Average,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,184,Typical,Good,CBlock,Good,Typical,Mn,ALQ,1,Rec,869,905,1809,GasA,Typical,Y,SBrkr,2259,0,0,2259,1,0,2,0,3,1,Good,7,Typ,2,Good,Basment,Unf,2,450,Typical,Typical,Paved,166,120,192,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2007,COD,Abnorml,217000,-93.639393,42.01265 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,25485,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Below_Average,1960,1960,Gable,CompShg,Wd Sdng,MetalSd,BrkFace,423,Typical,Fair,CBlock,Typical,Good,Mn,LwQ,4,Rec,1020,0,1560,GasA,Typical,Y,SBrkr,1560,0,0,1560,0,0,1,1,3,1,Typical,6,Typ,3,Typical,Attchd,RFn,2,580,Typical,Typical,Paved,0,75,584,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,201000,-93.640326,42.012488 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,21579,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1968,1968,Hip,CompShg,HdBoard,BrkFace,None,0,Typical,Typical,PConc,Good,Typical,No,BLQ,2,Unf,0,675,1488,GasA,Excellent,Y,SBrkr,1488,0,0,1488,0,1,2,0,3,1,Typical,7,Typ,2,Good,Attchd,RFn,2,552,Typical,Typical,Paved,0,0,216,0,0,0,No_Pool,No_Fence,None,0,9,2007,CWD,Normal,215000,-93.641849,42.011506 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,1782,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,Two_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,186,516,GasA,Good,Y,SBrkr,529,516,0,1045,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Detchd,Unf,2,462,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2007,WD ,Normal,123900,-93.645743,42.009489 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,17871,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Below_Average,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1680,1680,GasA,Good,Y,SBrkr,1680,0,0,1680,0,0,2,0,4,1,Good,7,Typ,0,No_Fireplace,Attchd,Unf,2,628,Typical,Typical,Paved,152,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,170000,-93.644923,42.011836 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,35,3907,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Mod,Blueste,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,427,1004,GasA,Good,Y,SBrkr,1020,0,0,1020,1,0,1,0,1,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,509,Typical,Typical,Paved,135,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,200000,-93.645785,42.009321 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20693,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Good,Average,1971,1971,Gable,CompShg,Plywood,Plywood,BrkFace,652,Typical,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,1262,1696,GasA,Excellent,Y,SBrkr,1696,0,0,1696,0,0,2,0,3,1,Typical,7,Typ,2,Typical,Attchd,Fin,2,625,Typical,Typical,Paved,0,146,0,0,0,0,No_Pool,Good_Wood,None,0,2,2007,WD ,Normal,198000,-93.643548,42.011343 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,32668,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Fair,1957,1975,Hip,CompShg,Wd Sdng,Stone,None,0,Good,Typical,PConc,Typical,Typical,No,Rec,6,Unf,0,816,2035,GasA,Typical,Y,SBrkr,2515,0,0,2515,1,0,3,0,4,2,Typical,9,Maj1,2,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,0,0,200,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Alloca,200624,-93.643429,42.010333 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,18044,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Very_Good,Average,1986,1986,Gable,CompShg,WdShing,Plywood,None,0,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,279,279,GasA,Good,Y,SBrkr,2726,0,0,2726,0,0,2,1,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,691,Good,Good,Paved,216,64,169,0,0,228,Excellent,No_Fence,None,0,8,2007,WD ,Normal,315000,-93.640182,42.010076 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,85,8500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1919,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,793,793,GasW,Typical,N,FuseF,997,520,0,1517,0,0,2,0,3,1,Fair,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,144,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Normal,115000,-93.628412,42.022836 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,345,720,GasA,Good,Y,FuseA,720,495,0,1215,0,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Fin,2,720,Typical,Typical,Paved,0,0,30,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,141000,-93.6295,42.022474 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,RRAe,Norm,OneFam,One_and_Half_Unf,Average,Above_Average,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,604,720,GasA,Poor,N,FuseF,803,0,0,803,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,244,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,87000,-93.596569,42.022658 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,7288,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_and_Half_Fin,Average,Good,1925,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,BrkTil,Typical,Poor,No,Unf,7,Unf,0,936,936,GasA,Excellent,Y,SBrkr,936,665,0,1601,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,176,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,129850,-93.597005,42.022658 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1939,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,732,732,GasA,Good,Y,SBrkr,772,351,0,1123,0,0,1,0,3,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Partial_Pavement,0,0,140,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,100000,-93.626875,42.022566 +Two_Story_1945_and_Older,Residential_Medium_Density,50,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1923,1999,Gable,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,BrkTil,Good,Typical,No,ALQ,1,Unf,0,311,859,GasA,Excellent,Y,SBrkr,942,886,0,1828,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Detchd,No_Garage,0,0,No_Garage,No_Garage,Paved,174,0,212,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2007,WD ,Alloca,150909,-93.62826,42.022685 +One_Story_1945_and_Older,Residential_Medium_Density,46,3672,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_Story,Average,Good,1922,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,100,Fair,Fair,Dirt_Gravel,0,0,96,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,75200,-93.625313,42.0228 +One_Story_1945_and_Older,Residential_Medium_Density,59,8263,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Average,1920,1950,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1012,1012,GasA,Typical,Y,FuseA,1012,0,0,1012,0,0,1,0,2,1,Typical,6,Typ,1,Good,Detchd,Unf,1,308,Typical,Typical,Paved,0,22,112,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,118400,-93.629496,42.021547 +One_Story_1945_and_Older,Residential_Medium_Density,60,8967,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Poor,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,BrkTil,Fair,Poor,No,Unf,7,Unf,0,961,961,GasA,Good,Y,Mix,1077,0,0,1077,0,0,1,0,2,1,Typical,6,Maj2,0,No_Fireplace,Detchd,Unf,1,338,Poor,Poor,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Abnorml,67000,-93.628236,42.021278 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,13710,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,490,910,GasA,Typical,Y,FuseA,910,648,0,1558,0,0,1,1,4,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,282,Typical,Typical,Paved,289,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,152000,-93.626689,42.021497 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,64,11067,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Poor,Below_Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,640,0,205,845,0,0,1,0,1,1,Typical,4,Maj2,0,No_Fireplace,Detchd,Unf,1,256,Typical,Fair,Dirt_Gravel,48,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,68104,-93.628222,42.020161 +Two_Family_conversion_All_Styles_and_Ages,C_all,75,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,TwoFmCon,Two_Story,Average,Above_Average,1895,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,957,957,GasA,Fair,N,SBrkr,1034,957,0,1991,0,0,2,0,4,2,Typical,9,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,133,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,119600,-93.616969,42.020077 +One_Story_1946_and_Newer_All_Styles,C_all,65,6565,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1957,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,106,1073,GasA,Good,Y,FuseA,1073,0,0,1073,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,720,Typical,Typical,Paved,0,444,0,0,0,0,No_Pool,No_Fence,None,0,8,2007,WD ,Abnorml,140000,-93.6150637,42.0214218 +One_Story_1945_and_Older,C_all,60,6060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Excellent,1930,2007,Hip,CompShg,MetalSd,MetalSd,None,0,Good,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,100,837,GasA,Excellent,Y,SBrkr,1001,0,0,1001,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Poor,Dirt_Gravel,154,0,42,86,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,124900,-93.613886,42.021555 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,59,5568,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,CemntBd,CmentBd,Stone,473,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1573,GasA,Good,Y,SBrkr,1625,0,0,1625,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,495,Typical,Typical,Paved,123,0,0,0,153,0,No_Pool,No_Fence,None,0,10,2007,New,Partial,375000,-93.615697,42.009401 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,4750,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,481,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,1573,GasA,Excellent,Y,SBrkr,1625,0,0,1625,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,538,Typical,Typical,Paved,123,0,0,0,153,0,No_Pool,No_Fence,None,0,12,2007,WD ,Family,235000,-93.615618,42.009401 +Split_Foyer,Residential_Low_Density,0,12150,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1979,1979,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,1001,GasA,Typical,Y,SBrkr,1299,0,0,1299,1,0,2,0,2,1,Good,5,Typ,1,Poor,BuiltIn,RFn,2,486,Typical,Typical,Paved,84,0,222,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2007,WD ,Normal,183500,-93.606577,41.994357 +Split_Foyer,Residential_Low_Density,0,7540,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1978,1978,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,115,888,GasA,Excellent,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,470,Typical,Typical,Paved,0,0,0,0,192,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,156000,-93.606672,41.994207 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9187,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Average,1983,1983,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,ALQ,1,Unf,0,748,1084,GasA,Typical,Y,SBrkr,1080,0,0,1080,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,120,0,158,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,134000,-93.607597,41.994206 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,166,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,856,1441,GasA,Excellent,Y,SBrkr,1392,0,0,1392,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,3,650,Typical,Typical,Paved,168,49,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,215000,-93.607551,41.995218 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,44,12864,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,17,1409,GasA,Excellent,Y,SBrkr,1409,0,0,1409,1,0,1,1,1,1,Good,4,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,0,144,0,0,145,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,245000,-93.606933,41.995973 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9928,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Good,Average,1991,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,215,1454,GasA,Typical,Y,SBrkr,1478,0,0,1478,1,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,506,Typical,Typical,Paved,114,22,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,210000,-93.602505,41.995931 +Split_or_Multilevel,Residential_Low_Density,0,8750,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Good,Above_Average,1975,1975,Gable,CompShg,Plywood,Plywood,Stone,50,Typical,Typical,CBlock,Typical,Typical,Av,Rec,6,GLQ,530,98,852,GasA,Typical,Y,SBrkr,918,0,0,918,0,1,1,0,3,0,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,360,Typical,Typical,Paved,192,84,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,148000,-93.60104,41.995512 +Split_Foyer,Residential_Low_Density,82,8410,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1974,1974,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,Unf,0,46,970,GasA,Typical,Y,SBrkr,1026,0,0,1026,1,0,1,0,2,1,Typical,5,Typ,1,Poor,Attchd,Unf,2,528,Typical,Typical,Paved,193,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,143000,-93.601779,41.994188 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,46,4054,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,TwnhsE,One_Story,Good,Above_Average,1987,1987,Gable,CompShg,VinylSd,VinylSd,BrkFace,352,Good,Typical,BrkTil,Good,Typical,Av,GLQ,3,Unf,0,552,1501,GasA,Excellent,Y,SBrkr,1501,0,0,1501,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,Fin,2,512,Typical,Typical,Paved,240,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2007,WD ,Normal,244000,-93.63966,42.005692 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,9763,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Green_Hills,Norm,Norm,TwnhsE,One_Story,Good,Average,1998,1998,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,239,Good,Typical,PConc,Good,Typical,No,GLQ,3,ALQ,72,328,1502,GasA,Excellent,Y,SBrkr,1502,0,0,1502,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,Fin,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,WD ,Normal,330000,-93.647403,42.001694 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,115,16905,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,Gd,BLQ,2,Unf,0,383,1350,GasA,Good,Y,SBrkr,1328,0,0,1328,0,1,1,1,2,1,Typical,5,Typ,2,Good,Attchd,RFn,1,308,Typical,Typical,Partial_Pavement,0,104,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,170000,-93.649757,41.99854 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,149,19958,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1958,1995,Hip,CompShg,HdBoard,HdBoard,BrkFace,1224,Typical,Good,CBlock,Typical,Typical,No,Unf,7,Unf,0,585,585,GasA,Good,Y,SBrkr,2279,0,0,2279,0,0,2,1,4,1,Good,7,Typ,1,Good,Attchd,RFn,2,461,Typical,Typical,Paved,274,0,0,0,138,0,No_Pool,Good_Privacy,None,0,7,2007,WD ,Normal,257000,-93.6494462,42.0004101 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8368,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1689,1689,GasA,Excellent,Y,SBrkr,1689,0,0,1689,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,433,Typical,Typical,Paved,100,39,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,New,Partial,231713,-93.650588,41.994241 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,8298,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,963,1546,GasA,Excellent,Y,SBrkr,1564,0,0,1564,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,502,Typical,Typical,Paved,132,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,267300,-93.650569,41.994137 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,9037,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,32,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1048,1476,GasA,Excellent,Y,SBrkr,1484,0,0,1484,0,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,472,Typical,Typical,Paved,120,33,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,265900,-93.650561,41.9941 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,10991,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1571,1571,GasA,Excellent,Y,SBrkr,1571,0,0,1571,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,722,Typical,Typical,Paved,100,36,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,239000,-93.651732,41.994824 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,10656,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,274,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1638,1638,GasA,Excellent,Y,SBrkr,1646,0,0,1646,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,RFn,3,870,Typical,Typical,Paved,192,80,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,New,Partial,248900,-93.650417,41.995088 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,42,10331,Pave,No_Alley_Access,Regular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Good,1985,1985,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,BLQ,80,970,1265,GasA,Good,Y,SBrkr,1240,0,0,1240,0,1,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,232,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,174000,-93.646617,41.998023 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,44,13758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1990,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,117,Good,Good,CBlock,Good,Typical,Mn,LwQ,4,Unf,0,254,1156,GasA,Excellent,Y,SBrkr,1187,530,0,1717,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,400,Typical,Typical,Paved,168,36,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,187500,-93.644366,41.997992 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9303,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1996,1997,Hip,CompShg,VinylSd,VinylSd,BrkFace,42,Good,Typical,PConc,Excellent,Typical,No,ALQ,1,Unf,0,130,872,GasA,Excellent,Y,SBrkr,888,868,0,1756,1,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,Fin,2,422,Typical,Typical,Paved,144,122,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,204000,-93.646782,41.997364 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,6718,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,86,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,1017,1267,GasA,Excellent,Y,SBrkr,1312,0,0,1312,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,471,Typical,Typical,Paved,256,28,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,180500,-93.646791,41.997126 +Split_or_Multilevel,Residential_Low_Density,73,9590,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,SLvl,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,442,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,82,868,GasA,Excellent,Y,SBrkr,1146,0,0,1146,1,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,438,Typical,Typical,Paved,160,22,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,187500,-93.646788,41.996089 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11305,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,886,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,593,1922,GasA,Excellent,Y,SBrkr,1922,0,0,1922,1,0,2,0,2,1,Good,6,Typ,1,Excellent,Attchd,Fin,3,692,Typical,Typical,Paved,201,64,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,282000,-93.646894,41.995508 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,7777,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,203,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1491,1491,GasA,Excellent,Y,SBrkr,1491,0,0,1491,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,571,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,178000,-93.646634,41.997299 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14536,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,236,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,316,1616,GasA,Excellent,Y,SBrkr,1629,0,0,1629,1,0,2,0,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,808,Typical,Typical,Paved,0,252,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,WD ,Normal,270000,-93.647585,41.995341 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,94,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,356,1122,GasA,Excellent,Y,SBrkr,1146,1340,0,2486,1,0,3,1,5,1,Good,10,Typ,1,Good,BuiltIn,Fin,2,452,Typical,Typical,Paved,143,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,279900,-93.647199,41.993986 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,35133,Grvl,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Timberland,Norm,Norm,OneFam,One_Story,Average,Below_Average,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,226,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,Unf,0,413,1572,GasA,Good,Y,SBrkr,1572,0,0,1572,1,0,1,1,3,1,Typical,5,Typ,2,Typical,More_Than_Two_Types,RFn,3,995,Typical,Typical,Paved,0,263,0,0,263,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,186700,-93.662162,41.997352 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,93,15306,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,100,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,1652,1732,GasA,Excellent,Y,SBrkr,1776,0,0,1776,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,712,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,New,Partial,283463,-93.653109,41.993288 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,12633,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Timberland,PosA,PosA,OneFam,One_Story,Very_Excellent,Average,2006,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,242,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1824,1824,GasA,Excellent,Y,SBrkr,1824,0,0,1824,0,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,932,Typical,Typical,Paved,160,36,0,0,108,0,No_Pool,No_Fence,None,0,9,2007,New,Partial,392000,-93.652429,41.992975 +Two_Story_1946_and_Newer,Residential_Low_Density,88,12665,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,245,Good,Typical,PConc,Good,Good,Gd,Unf,7,Unf,0,1094,1094,GasA,Excellent,Y,SBrkr,1133,1349,0,2482,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,642,Typical,Typical,Paved,144,39,0,0,0,0,No_Pool,No_Fence,None,0,2,2007,WD ,Normal,281213,-93.647569,41.993261 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8402,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Feedr,Norm,OneFam,One_Story,Average,Average,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,914,1120,GasA,Excellent,Y,SBrkr,1120,0,0,1120,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,New,Partial,147000,-93.610102,41.990054 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,200,43500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Artery,Norm,OneFam,One_Story,Fair,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Excellent,Y,SBrkr,2034,0,0,2034,0,0,1,0,2,1,Typical,9,Min1,0,No_Fireplace,More_Than_Two_Types,RFn,4,1041,Typical,Typical,Dirt_Gravel,483,266,0,0,0,561,Typical,Good_Privacy,None,0,6,2007,WD ,Normal,130000,-93.610118,41.992318 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,62,6710,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Mitchell,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,134,Typical,Typical,PConc,Excellent,Typical,Av,Rec,6,GLQ,904,0,920,GasA,Excellent,Y,SBrkr,936,0,0,936,2,0,0,1,0,1,Typical,3,Typ,0,No_Fireplace,Attchd,Fin,2,460,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,140000,-93.608192,41.993356 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,94,25286,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Average,1963,1963,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,Gd,ALQ,1,Unf,0,431,1064,GasA,Good,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,648,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,132250,-93.605507,41.993107 +Duplex_All_Styles_and_Ages,Residential_Low_Density,100,25000,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Average,Below_Average,1967,1967,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Av,Unf,7,Unf,0,1632,1632,GasA,Typical,Y,SBrkr,1632,0,0,1632,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,143000,-93.6072813,41.9927922 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,0,32463,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Mitchell,Norm,Norm,TwoFmCon,One_Story,Below_Average,Below_Average,1961,1975,Gable,CompShg,MetalSd,MetalSd,Stone,149,Typical,Good,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,90,1249,GasA,Excellent,Y,SBrkr,1622,0,0,1622,1,0,1,0,3,1,Typical,7,Typ,1,Typical,More_Than_Two_Types,Fin,4,1356,Typical,Typical,Paved,439,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2007,WD ,Normal,168000,-93.607395,41.991211 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,4224,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,One_Story,Average,Good,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,200,1040,GasA,Typical,Y,SBrkr,1060,0,0,1060,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,572,Typical,Typical,Paved,100,110,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,129900,-93.605355,41.992917 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,4224,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,One_Story,Average,Average,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,268,1142,GasA,Typical,Y,SBrkr,1142,0,0,1142,1,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,Fin,2,528,Typical,Typical,Paved,536,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2007,WD ,Normal,134000,-93.605354,41.99284 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1504,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Below_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,294,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,253,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,85500,-93.603546,41.992145 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,0,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SLvl,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,27,530,GasA,Typical,Y,SBrkr,530,462,0,992,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,BuiltIn,Fin,1,297,Typical,Typical,Paved,112,97,0,0,0,0,No_Pool,No_Fence,None,0,7,2007,WD ,Normal,106500,-93.601862,41.992516 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1495,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Above_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,BrkFace,189,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,162,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,93900,-93.601827,41.992479 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,2001,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,BrkFace,80,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Fair,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2007,WD ,Normal,75000,-93.601507,41.991709 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Above_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,GLQ,499,0,630,GasA,Good,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,12,2007,WD ,Normal,84500,-93.602094,41.991641 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Fair,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Excellent,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2007,COD,Normal,75190,-93.603422,41.991868 +Split_or_Multilevel,Residential_Low_Density,0,11333,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,490,1029,GasA,Typical,Y,SBrkr,1062,0,0,1062,1,0,1,0,3,1,Typical,5,Typ,2,Typical,Attchd,RFn,2,539,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2007,WD ,Normal,146800,-93.604923,41.991612 +Split_Foyer,Residential_Low_Density,72,9129,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,144,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,0,923,GasA,Typical,Y,SBrkr,1008,0,0,1008,1,0,1,0,1,1,Good,4,Typ,1,Fair,Attchd,Fin,2,678,Typical,Typical,Paved,201,66,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2007,WD ,Normal,153500,-93.604644,41.991098 +Split_or_Multilevel,Residential_Low_Density,0,15957,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Corner,Mod,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1977,1977,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,96,1244,GasA,Typical,Y,SBrkr,1356,0,0,1356,2,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,528,Typical,Typical,Paved,1424,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,WD ,Normal,188000,-93.604521,41.989581 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,33983,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1994,Gable,CompShg,Plywood,Plywood,None,0,Typical,Fair,PConc,Typical,Typical,Mn,ALQ,1,Unf,0,48,1160,GasA,Typical,Y,SBrkr,1676,0,0,1676,1,0,1,1,3,1,Good,6,Mod,2,Typical,Attchd,RFn,2,672,Typical,Typical,Partial_Pavement,690,90,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2007,WD ,Normal,196000,-93.60678,41.990264 +Two_Story_1946_and_Newer,Residential_Low_Density,68,8286,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,Two_Story,Average,Good,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,Rec,6,Unf,0,185,716,GasA,Excellent,Y,SBrkr,716,716,0,1432,1,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,Fin,2,531,Typical,Typical,Paved,0,136,0,0,240,0,No_Pool,Good_Privacy,None,0,6,2007,WD ,Normal,157000,-93.604524,41.989488 +Split_Foyer,Residential_Low_Density,50,6723,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Good,1971,1971,Gable,CompShg,Wd Sdng,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,0,796,GasA,Typical,Y,SBrkr,796,0,0,796,0,1,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,129,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2007,WD ,Normal,138000,-93.602559,41.991144 +Split_Foyer,Residential_Low_Density,54,7244,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Good,1970,1970,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Good,Typical,Av,ALQ,1,Unf,0,149,768,GasA,Excellent,Y,SBrkr,768,0,0,768,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,104,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2007,WD ,Normal,129500,-93.6015199,41.991371 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,124,27697,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Fair,1961,1961,Shed,CompShg,Plywood,Plywood,CBlock,198,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,585,1396,GasA,Typical,N,SBrkr,1608,0,0,1608,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,444,Typical,Fair,Paved,152,38,0,0,0,0,No_Pool,No_Fence,None,0,11,2007,COD,Abnorml,80000,-93.607114,41.989905 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,7599,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1982,2006,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,280,845,GasA,Typical,Y,SBrkr,845,0,0,845,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,129500,-93.604858,41.987606 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11000,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1976,2003,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,PConc,Good,Typical,No,LwQ,4,Unf,0,0,1090,GasA,Typical,Y,SBrkr,1178,0,0,1178,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,502,Typical,Typical,Paved,0,44,0,0,88,0,No_Pool,Minimum_Privacy,None,0,6,2007,WD ,Normal,135000,-93.605119,41.989768 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,76,8314,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1982,1982,Gable,CompShg,HdBoard,ImStucc,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,270,816,GasA,Typical,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,264,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2007,WD ,Normal,124500,-93.604146,41.98822 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,11625,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,220,816,GasA,Typical,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,264,Typical,Typical,Paved,330,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2007,WD ,Normal,139000,-93.6030815,41.9881729 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,10712,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1991,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,Mn,BLQ,2,Unf,0,762,974,GasA,Typical,Y,SBrkr,974,0,0,974,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,28,0,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2007,Oth,Abnorml,93500,-93.606641,41.98651 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,10447,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Above_Average,1984,1984,Gable,CompShg,Plywood,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,348,864,GasA,Typical,Y,SBrkr,887,0,0,887,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2007,WD ,Normal,124500,-93.604858,41.986588 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11027,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,28,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,539,171,1178,GasA,Good,Y,SBrkr,1293,0,0,1293,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,452,Typical,Typical,Paved,280,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,149900,-93.618462,42.053406 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10533,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1956,1956,Hip,CompShg,VinylSd,VinylSd,BrkFace,244,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,235,1008,GasA,Typical,Y,SBrkr,1024,0,0,1024,1,0,1,0,2,1,Good,5,Typ,2,Typical,Attchd,Unf,1,313,Typical,Typical,Paved,0,0,0,0,280,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,157500,-93.618182,42.053327 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11765,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,Stone,302,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,490,1617,GasA,Fair,Y,SBrkr,1797,0,0,1797,0,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,3,963,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,169000,-93.6192621,42.0531078 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,39384,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Sev,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,Stone,902,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,595,1705,GasA,Excellent,Y,SBrkr,1390,0,0,1390,1,0,1,1,1,1,Excellent,4,Min1,2,Good,Attchd,Unf,2,550,Typical,Typical,Paved,0,189,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,252000,-93.616439,42.052519 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,11727,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Above_Average,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,434,Typical,Good,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1851,1851,GasA,Good,Y,SBrkr,1851,0,0,1851,0,0,2,0,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,0,146,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,192100,-93.617432,42.050507 +Two_Story_1946_and_Newer,Residential_Low_Density,60,8238,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,GLQ,3,Unf,0,113,813,GasA,Excellent,Y,SBrkr,813,712,0,1525,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,400,Typical,Typical,Paved,421,72,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,183500,-93.639067,42.059909 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13006,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1997,Gable,CompShg,HdBoard,HdBoard,BrkFace,285,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,964,964,GasA,Good,Y,SBrkr,993,1243,0,2236,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,2,642,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,222000,-93.637871,42.061407 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13041,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1995,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,781,781,GasA,Good,Y,SBrkr,781,890,0,1671,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,423,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,170000,-93.637482,42.060425 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13031,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1995,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,99,691,GasA,Good,Y,SBrkr,691,807,0,1498,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,409,Typical,Typical,Paved,315,44,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,187500,-93.637566,42.06048 +Two_Story_1946_and_Newer,Residential_Low_Density,54,9783,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1996,1996,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,821,821,GasA,Good,Y,SBrkr,821,955,0,1776,0,0,2,1,3,1,Typical,7,Typ,1,Typical,BuiltIn,Fin,2,443,Typical,Typical,Paved,286,116,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,202000,-93.636378,42.060736 +Two_Story_1946_and_Newer,Residential_Low_Density,70,11207,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,FR2,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,88,802,GasA,Good,Y,SBrkr,802,709,0,1511,1,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,413,Typical,Typical,Paved,95,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,185000,-93.636373,42.061879 +Two_Story_1946_and_Newer,Residential_Low_Density,134,19378,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,456,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,1335,1392,GasA,Excellent,Y,SBrkr,1392,1070,0,2462,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,RFn,2,576,Typical,Typical,Paved,239,132,0,168,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,320000,-93.636052,42.062828 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,14859,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,27,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1670,1670,GasA,Excellent,Y,SBrkr,1670,0,0,1670,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,3,690,Typical,Typical,Paved,144,60,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,240000,-93.635967,42.06281 +Two_Story_1946_and_Newer,Residential_Low_Density,50,13128,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,216,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1074,1074,GasA,Excellent,Y,SBrkr,1074,990,0,2064,0,0,2,1,4,1,Good,7,Typ,1,Good,Attchd,Fin,2,527,Typical,Typical,Paved,0,119,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,250000,-93.635935,42.062802 +Two_Story_1946_and_Newer,Residential_Low_Density,72,10463,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,893,893,GasA,Excellent,Y,SBrkr,901,900,0,1801,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,800,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,239900,-93.635837,42.062798 +Two_Story_1946_and_Newer,Residential_Low_Density,42,13751,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,248,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1640,1700,GasA,Excellent,Y,SBrkr,1700,512,0,2212,1,0,2,1,3,1,Good,9,Typ,1,Good,Attchd,Fin,3,773,Typical,Typical,Paved,237,38,0,0,115,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,309000,-93.635798,42.062891 +Two_Story_1946_and_Newer,Residential_Low_Density,168,23257,Pave,No_Alley_Access,Irregular,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,868,868,GasA,Excellent,Y,SBrkr,887,1134,0,2021,0,0,2,1,3,1,Good,9,Typ,1,Good,BuiltIn,RFn,2,422,Typical,Typical,Paved,0,100,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,274725,-93.635932,42.062956 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,68,13108,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1994,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,2062,2062,GasA,Excellent,Y,SBrkr,2079,608,0,2687,0,0,2,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,Fin,2,618,Typical,Typical,Paved,168,12,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,270000,-93.631604,42.060625 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8076,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,112,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,455,1160,GasA,Good,Y,SBrkr,1169,0,0,1169,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,402,Typical,Typical,Paved,0,26,0,0,144,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,183500,-93.635716,42.0591 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7685,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,112,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,179,697,GasA,Good,Y,SBrkr,697,804,0,1501,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,Fin,2,420,Typical,Typical,Paved,190,63,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,165600,-93.636949,42.058421 +Two_Story_1946_and_Newer,Residential_Low_Density,63,9084,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,935,935,GasA,Good,Y,SBrkr,955,677,0,1632,0,0,2,1,3,1,Typical,8,Typ,1,Typical,Attchd,Fin,2,462,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,176500,-93.639293,42.058201 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,30,3701,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1987,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1191,1191,GasA,Typical,Y,SBrkr,1204,0,0,1204,0,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,461,Typical,Typical,Paved,120,70,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,130000,-93.634231,42.059599 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,44,5306,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,Two_Story,Good,Good,1987,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Rec,215,354,1064,GasA,Good,Y,SBrkr,1064,703,0,1767,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,504,Good,Typical,Paved,441,35,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,239000,-93.631572,42.059332 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,6563,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Mod,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,1985,1985,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,594,1742,GasA,Typical,Y,SBrkr,1742,0,0,1742,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,114,28,234,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,275000,-93.632571,42.058136 +Two_Story_1946_and_Newer,Residential_Low_Density,59,16023,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,600,Good,Excellent,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,180,1398,GasA,Excellent,Y,SBrkr,1414,1384,0,2798,1,0,3,1,3,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,670,Typical,Typical,Paved,182,37,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,457347,-93.629601,42.060459 +Two_Story_1946_and_Newer,Residential_Low_Density,60,18062,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2006,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,662,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1528,1528,GasA,Excellent,Y,SBrkr,1528,1862,0,3390,0,0,3,1,5,1,Excellent,10,Typ,1,Excellent,BuiltIn,Fin,3,758,Typical,Typical,Paved,204,34,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,545224,-93.629337,42.060527 +Two_Story_1946_and_Newer,Residential_Low_Density,63,12292,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,184,Good,Typical,PConc,Excellent,Good,Gd,GLQ,3,Unf,0,889,1094,GasA,Excellent,Y,SBrkr,1102,1371,0,2473,0,0,2,1,4,1,Good,11,Typ,1,Good,BuiltIn,Fin,3,675,Typical,Typical,Paved,246,39,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,356383,-93.629174,42.060516 +Two_Story_1946_and_Newer,Residential_Low_Density,85,16056,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,Stone,208,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,1752,1992,GasA,Excellent,Y,SBrkr,1992,876,0,2868,0,0,3,1,4,1,Excellent,11,Typ,1,Good,BuiltIn,Fin,3,716,Typical,Typical,Paved,214,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,556581,-93.628607,42.061478 +Two_Story_1946_and_Newer,Residential_Low_Density,82,12438,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,466,Excellent,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1234,1234,GasA,Excellent,Y,SBrkr,1264,1312,0,2576,0,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,666,Typical,Typical,Paved,324,100,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,361919,-93.62877,42.061008 +Two_Story_1946_and_Newer,Residential_Low_Density,82,16052,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,734,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,644,1850,GasA,Excellent,Y,SBrkr,1850,848,0,2698,1,0,2,1,4,1,Excellent,11,Typ,1,Good,Attchd,RFn,3,736,Typical,Typical,Paved,250,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,535000,-93.627767,42.060111 +Two_Story_1946_and_Newer,Residential_Low_Density,92,15922,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,550,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1390,1390,GasA,Excellent,Y,SBrkr,1390,1405,0,2795,0,0,3,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,660,Typical,Typical,Paved,272,102,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,New,Partial,401179,-93.626337,42.059957 +Two_Story_1946_and_Newer,Residential_Low_Density,66,13682,Pave,No_Alley_Access,Moderately_Irregular,HLS,AllPub,CulDSac,Gtl,Stone_Brook,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,1031,Excellent,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1410,1410,GasA,Excellent,Y,SBrkr,1426,1519,0,2945,0,0,3,1,3,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,641,Typical,Typical,Paved,192,0,37,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,438780,-93.626477,42.061096 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14762,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Feedr,Norm,OneFam,Two_Story,Average,Above_Average,1948,1950,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1547,720,53,2320,0,0,2,0,2,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,672,Typical,Typical,Partial_Pavement,120,144,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,169000,-93.6220148,42.0593867 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,100,34650,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Gilbert,Norm,Norm,TwoFmCon,One_Story,Average,Average,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,0,1056,GasA,Typical,N,SBrkr,1056,0,0,1056,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,572,Typical,Typical,Paved,264,0,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,145000,-93.625169,42.056702 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,60,8147,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,230,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,523,1714,GasA,Excellent,Y,SBrkr,1714,0,0,1714,1,0,2,0,2,1,Good,7,Typ,1,Good,Attchd,Fin,2,517,Typical,Typical,Paved,156,55,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,318000,-93.63007,42.058563 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,11302,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,Other,BrkFace,238,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,392,1814,GasA,Excellent,Y,SBrkr,1826,0,0,1826,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,758,Typical,Typical,Paved,180,75,0,0,120,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,319000,-93.624062,42.059947 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,18261,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Stone_Brook,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,420,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,494,1910,GasA,Excellent,Y,SBrkr,2000,0,0,2000,1,0,2,1,3,1,Excellent,8,Typ,2,Good,Attchd,Unf,3,722,Typical,Typical,Paved,351,102,0,0,123,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,470000,-93.628269,42.058816 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,14145,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Good,1984,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,995,1208,GasA,Excellent,Y,SBrkr,1621,0,0,1621,1,0,2,0,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,440,Typical,Typical,Paved,108,45,0,0,0,0,No_Pool,No_Fence,Shed,400,5,2006,WD ,Normal,202500,-93.638026,42.055607 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,13837,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Good,Average,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,178,Good,Good,PConc,Good,Good,No,GLQ,3,LwQ,202,0,1204,GasA,Good,Y,SBrkr,1377,806,0,2183,0,0,2,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,Unf,3,786,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,229000,-93.637874,42.054367 +Two_Story_1946_and_Newer,Residential_Low_Density,0,16659,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Good,1977,1994,Gable,CompShg,Plywood,Plywood,BrkFace,34,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,0,795,GasA,Fair,Y,SBrkr,1468,795,0,2263,1,0,2,1,3,1,Good,9,Typ,1,Typical,Attchd,Fin,2,539,Typical,Typical,Paved,0,250,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,235000,-93.6326645,42.0543376 +Two_Story_1946_and_Newer,Residential_Low_Density,0,18800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,84,796,GasA,Typical,Y,SBrkr,790,784,0,1574,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,566,Typical,Typical,Paved,306,111,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,190000,-93.631515,42.054956 +Split_Foyer,Residential_Low_Density,0,10464,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Above_Average,Above_Average,1980,1980,Gable,CompShg,HdBoard,HdBoard,BrkFace,130,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,138,988,GasA,Typical,Y,SBrkr,1102,0,0,1102,1,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,RFn,2,582,Typical,Typical,Paved,140,22,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,169000,-93.633002,42.0562619 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1988,1988,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,102,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,916,1845,GasA,Good,Y,SBrkr,1872,0,0,1872,0,1,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,604,Typical,Typical,Paved,197,39,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,241500,-93.635486,42.05327 +Two_Story_1946_and_Newer,Residential_Low_Density,81,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Good,Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,68,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,945,945,GasA,Typical,Y,SBrkr,945,912,0,1857,0,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,482,Typical,Typical,Paved,400,105,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,188900,-93.6353259,42.0524029 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,10240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1980,1980,Gable,CompShg,Plywood,Plywood,BrkFace,157,Typical,Good,CBlock,Good,Typical,No,BLQ,2,LwQ,1061,0,1686,GasA,Typical,Y,SBrkr,1686,0,0,1686,1,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,612,Typical,Typical,Paved,384,131,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,207500,-93.636044,42.053418 +Two_Story_1946_and_Newer,Residential_Low_Density,80,16692,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Good,Average,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,184,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,LwQ,469,133,1392,GasA,Typical,Y,SBrkr,1392,1392,0,2784,1,0,3,1,5,1,Good,12,Typ,2,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,112,0,0,440,519,Fair,Minimum_Privacy,TenC,2000,7,2006,WD ,Normal,250000,-93.637387,42.050514 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11475,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,Two_Story,Above_Average,Above_Average,1975,1975,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,163,713,GasA,Typical,Y,SBrkr,811,741,0,1552,1,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,434,Typical,Typical,Paved,209,208,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2006,WD ,Normal,179900,-93.637191,42.050176 +Split_Foyer,Residential_Low_Density,0,9927,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SFoyer,Good,Average,1976,1976,Gable,CompShg,VinylSd,Wd Shng,Stone,252,Good,Typical,CBlock,Excellent,Typical,Gd,GLQ,3,Unf,0,42,1047,GasA,Typical,Y,SBrkr,1083,0,0,1083,1,0,1,0,2,1,Typical,5,Typ,1,Fair,Attchd,RFn,2,596,Typical,Typical,Paved,444,0,40,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,172000,-93.635367,42.049771 +Two_Story_1946_and_Newer,Floating_Village_Residential,75,9512,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,172,960,GasA,Excellent,Y,SBrkr,960,1358,0,2318,1,0,2,1,3,1,Good,8,Typ,1,Excellent,BuiltIn,Fin,2,541,Typical,Typical,Paved,0,246,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,294323,-93.639516,42.051077 +Split_or_Multilevel,Residential_Low_Density,81,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1975,2000,Gable,CompShg,Plywood,Plywood,BrkFace,248,Typical,Typical,CBlock,Typical,Fair,No,ALQ,1,Unf,0,127,675,GasA,Typical,Y,SBrkr,1109,766,0,1875,0,0,3,0,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,485,Typical,Typical,Paved,48,28,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,184500,-93.632369,42.052362 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,268,1056,GasA,Excellent,Y,SBrkr,1074,0,0,1074,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,495,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,153500,-93.630348,42.052522 +Two_Story_1946_and_Newer,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Good,Average,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,638,832,GasA,Typical,Y,SBrkr,832,832,0,1664,0,0,2,1,4,1,Typical,8,Typ,1,Typical,Attchd,RFn,2,528,Typical,Typical,Paved,0,28,0,0,259,0,No_Pool,Good_Wood,None,0,3,2006,WD ,Normal,162900,-93.632355,42.052359 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,1999,Hip,CompShg,HdBoard,HdBoard,BrkFace,99,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,377,0,1040,GasA,Fair,Y,SBrkr,1309,0,0,1309,1,0,1,1,3,1,Good,5,Typ,1,Fair,Attchd,RFn,2,484,Typical,Typical,Paved,265,0,0,0,0,648,Fair,Good_Privacy,None,0,1,2006,WD ,Normal,181000,-93.630289,42.04977 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1974,1974,Gable,CompShg,HdBoard,Plywood,BrkFace,176,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,348,1103,GasA,Typical,Y,SBrkr,1103,0,0,1103,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,462,Typical,Typical,Paved,295,84,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,155000,-93.631162,42.049608 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,15870,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1969,1969,Gable,CompShg,VinylSd,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Rec,791,230,1096,GasA,Excellent,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,299,Typical,Typical,Paved,240,32,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Abnorml,138800,-93.62626,42.055131 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9353,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1970,1970,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,864,864,GasA,Good,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,0,7,2006,Oth,Abnorml,116050,-93.625373,42.053459 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8125,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,244,858,GasA,Typical,Y,SBrkr,858,0,0,858,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,88000,-93.627981,42.05446 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,620,224,864,GasA,Typical,Y,SBrkr,874,0,0,874,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,63,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,97500,-93.627849,42.054385 +One_Story_PUD_1946_and_Newer,Residential_High_Density,26,8773,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,North_Ames,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2002,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,536,1487,GasA,Excellent,Y,SBrkr,1419,0,0,1419,1,0,2,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,543,Typical,Typical,Paved,196,68,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,190000,-93.624411,42.055319 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,514,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,525,525,GasA,Typical,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,105500,-93.629906,42.052655 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2160,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,363,765,GasA,Good,Y,SBrkr,765,600,0,1365,0,0,1,1,3,1,Good,7,Min1,1,Fair,Attchd,Unf,2,440,Typical,Typical,Paved,0,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,125500,-93.6292319,42.0524739 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1953,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,408,Typical,Typical,CBlock,Typical,Fair,No,BLQ,2,Unf,0,174,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,72,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,83000,-93.627932,42.0527019 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Good,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,380,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,212,494,GasA,Excellent,Y,SBrkr,494,536,0,1030,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,116000,-93.627103,42.051798 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,CemntBd,CmentBd,BrkFace,236,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Typical,Y,SBrkr,672,546,0,1218,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,201,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,91500,-93.627328,42.051839 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Good,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,281,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,355,672,GasA,Good,Y,SBrkr,672,546,0,1218,0,1,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,118000,-93.627366,42.051691 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,207,483,GasA,Typical,Y,SBrkr,483,465,0,948,0,0,1,1,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,89000,-93.629877,42.051672 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,504,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,143,525,GasA,Good,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,264,Typical,Typical,Paved,352,0,0,0,0,0,No_Pool,Good_Privacy,None,0,10,2006,WD ,Normal,108000,-93.629846,42.051673 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Briardale,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,297,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,483,483,GasA,Typical,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Typical,5,Typ,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,94500,-93.629516,42.051664 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4043,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1975,1975,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,BLQ,156,186,1069,GasA,Good,Y,SBrkr,1069,0,0,1069,0,1,2,0,2,1,Typical,4,Typ,1,Poor,Attchd,RFn,2,440,Typical,Typical,Paved,0,55,0,0,225,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,146300,-93.627169,42.050595 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,7514,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1967,1975,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Rec,108,462,943,GasA,Typical,Y,SBrkr,1387,0,0,1387,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,300,Typical,Typical,Paved,0,0,240,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,145000,-93.628857,42.048947 +Split_Foyer,Residential_Low_Density,68,7838,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,95,864,GasA,Typical,Y,SBrkr,900,0,0,900,1,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,1,288,Typical,Typical,Paved,175,144,0,0,0,0,No_Pool,Minimum_Wood_Wire,None,0,12,2006,WD ,Normal,123000,-93.62895,42.048946 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2280,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Northpark_Villa,Norm,Norm,Twnhs,One_Story,Good,Good,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,492,443,1055,GasA,Typical,Y,SBrkr,1055,0,0,1055,1,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,319,Typical,Typical,Paved,0,29,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,137500,-93.624767,42.050705 +Two_Story_PUD_1946_and_Newer,Residential_Low_Density,24,2179,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northpark_Villa,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,785,855,GasA,Good,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,144000,-93.625709,42.050441 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,129,16737,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,66,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,533,1980,GasA,Excellent,Y,SBrkr,1980,0,0,1980,1,0,2,0,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,3,770,Typical,Typical,Paved,194,45,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,315000,-93.658864,42.061472 +Two_Story_1946_and_Newer,Residential_Low_Density,72,16387,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,215,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,369,1738,GasA,Good,Y,SBrkr,1738,851,0,2589,1,0,2,1,4,1,Excellent,11,Typ,1,Good,Attchd,RFn,3,831,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,412083,-93.658413,42.063307 +Two_Story_1946_and_Newer,Residential_Low_Density,111,16259,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,370,Typical,Typical,PConc,Excellent,Good,Av,Unf,7,Unf,0,1249,1249,GasA,Excellent,Y,SBrkr,1249,1347,0,2596,0,0,3,1,4,1,Good,9,Typ,0,No_Fireplace,Attchd,RFn,3,840,Typical,Typical,Paved,240,154,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,342643,-93.658226,42.061901 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,110,16163,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,232,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1618,1618,GasA,Excellent,Y,SBrkr,1618,0,0,1618,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,3,880,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,252000,-93.657991,42.061284 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,107,13891,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Very_Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Excellent,Typical,PConc,Excellent,Good,Gd,GLQ,3,Unf,0,690,2076,GasA,Excellent,Y,SBrkr,2076,0,0,2076,1,0,2,1,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,850,Typical,Typical,Paved,216,229,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,465000,-93.6566537,42.0628074 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,108,12228,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,206,Good,Typical,PConc,Excellent,Good,No,Unf,7,Unf,0,1721,1721,GasA,Excellent,Y,SBrkr,1740,0,0,1740,0,0,2,0,4,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,3,874,Typical,Typical,Paved,0,43,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,293000,-93.654774,42.062109 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,14780,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,568,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,363,1868,GasA,Excellent,Y,SBrkr,1868,0,0,1868,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,1085,Typical,Typical,Paved,354,56,0,0,156,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,415000,-93.657984,42.061134 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,98,16033,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,378,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,572,1833,GasA,Excellent,Y,SBrkr,1850,0,0,1850,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,3,772,Typical,Typical,Paved,519,112,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,326000,-93.658788,42.060342 +Two_Story_1946_and_Newer,Residential_Low_Density,101,14215,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,380,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1158,1158,GasA,Excellent,Y,SBrkr,1158,1218,0,2376,0,0,3,1,4,1,Good,9,Typ,1,Good,BuiltIn,RFn,3,853,Typical,Typical,Paved,240,154,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,325300,-93.657118,42.060272 +Two_Story_1946_and_Newer,Residential_Low_Density,108,13418,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,Stone,132,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1117,1117,GasA,Excellent,Y,SBrkr,1132,1320,0,2452,0,0,3,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,691,Typical,Typical,Paved,113,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,309000,-93.657149,42.060271 +Two_Story_1946_and_Newer,Residential_Low_Density,120,13975,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,525,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1090,1090,GasA,Excellent,Y,SBrkr,1117,1089,0,2206,0,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,670,Typical,Typical,Paved,148,95,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,300000,-93.656864,42.060425 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,178,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1030,1030,GasA,Good,Y,SBrkr,1038,1060,0,2098,0,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,3,878,Typical,Typical,Paved,192,52,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,275500,-93.653016,42.060835 +Two_Story_1946_and_Newer,Residential_Low_Density,82,9942,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Gable,CompShg,MetalSd,MetalSd,BrkFace,385,Excellent,Typical,PConc,Excellent,Good,Av,GLQ,3,Unf,0,316,1606,GasA,Excellent,Y,SBrkr,1625,466,0,2091,1,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,RFn,2,521,Typical,Typical,Paved,194,84,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,338500,-93.654642,42.060577 +Two_Story_1946_and_Newer,Residential_Low_Density,85,11924,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,WdShing,Wd Shng,Stone,286,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,177,1175,GasA,Excellent,Y,SBrkr,1182,1142,0,2324,1,0,3,0,4,1,Excellent,11,Typ,2,Good,BuiltIn,Fin,3,736,Typical,Typical,Paved,147,21,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,345000,-93.652334,42.060866 +Two_Story_1946_and_Newer,Residential_Low_Density,103,12867,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Excellent,Typical,Av,Unf,7,Unf,0,1209,1209,GasA,Excellent,Y,SBrkr,1209,1044,0,2253,0,0,2,1,3,1,Excellent,8,Typ,1,Good,Attchd,Fin,2,575,Typical,Typical,Paved,243,142,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,344133,-93.654831,42.060424 +Two_Story_1946_and_Newer,Residential_Low_Density,82,10672,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Gd,Unf,7,Unf,0,1054,1054,GasA,Good,Y,SBrkr,1054,1335,0,2389,0,0,2,1,4,1,Good,10,Typ,1,Good,BuiltIn,Fin,3,672,Typical,Typical,Paved,176,64,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,298236,-93.653452,42.060357 +Two_Story_1946_and_Newer,Residential_Low_Density,82,11643,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,142,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,644,1524,GasA,Excellent,Y,SBrkr,1544,814,0,2358,1,0,2,1,4,1,Excellent,10,Typ,1,Good,BuiltIn,Fin,3,784,Typical,Typical,Paved,120,34,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,329900,-93.6531488,42.060377 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,12378,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,622,1896,GasA,Excellent,Y,SBrkr,1944,0,0,1944,1,0,2,0,3,1,Excellent,8,Typ,3,Excellent,Attchd,Fin,3,708,Typical,Typical,Paved,208,175,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,360000,-93.643412,42.061544 +Two_Story_1946_and_Newer,Residential_Low_Density,86,11065,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,788,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1085,1085,GasA,Excellent,Y,SBrkr,1120,850,0,1970,0,0,2,1,3,1,Excellent,8,Typ,1,Good,BuiltIn,Fin,3,753,Typical,Typical,Paved,177,74,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,281000,-93.643862,42.061377 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,121,13758,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,430,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,560,1792,GasA,Excellent,Y,SBrkr,1792,0,0,1792,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,925,Typical,Typical,Paved,204,49,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,372397,-93.654997,42.059636 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,131,14828,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2004,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,674,Excellent,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,397,1780,GasA,Excellent,Y,SBrkr,1780,0,0,1780,1,0,2,0,2,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,816,Typical,Typical,Paved,144,68,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,378000,-93.655461,42.059825 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,11846,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,One_Story,Excellent,Average,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,562,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,225,1792,GasA,Excellent,Y,SBrkr,1792,0,0,1792,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,874,Typical,Typical,Paved,206,49,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,374000,-93.657883,42.058544 +Two_Story_1946_and_Newer,Residential_Low_Density,56,20431,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Excellent,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,870,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,438,1848,GasA,Excellent,Y,SBrkr,1848,880,0,2728,1,0,2,1,4,1,Excellent,10,Typ,2,Excellent,Attchd,Fin,3,706,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,New,Partial,437154,-93.653084,42.05869 +Two_Story_1946_and_Newer,Residential_Low_Density,74,10927,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,280,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,512,1058,GasA,Excellent,Y,SBrkr,1058,846,0,1904,1,0,2,1,3,1,Excellent,8,Typ,1,Good,BuiltIn,Fin,2,736,Typical,Typical,Paved,179,60,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,275000,-93.650626,42.059216 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13215,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,112,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,426,1420,GasA,Excellent,Y,SBrkr,1426,488,0,1914,1,0,2,1,3,1,Good,9,Typ,1,Typical,BuiltIn,RFn,3,746,Typical,Typical,Paved,168,127,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,279000,-93.6534099,42.059017 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,7052,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,240,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,705,1364,GasA,Excellent,Y,SBrkr,1364,0,0,1364,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,192,36,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,185850,-93.649714,42.059184 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,5911,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,278,Excellent,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,1088,1560,GasA,Excellent,Y,SBrkr,1565,0,0,1565,1,0,2,0,2,1,Excellent,6,Typ,1,Good,Attchd,RFn,2,556,Typical,Typical,Paved,196,56,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,282500,-93.654138,42.057164 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6955,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,94,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1368,1368,GasA,Excellent,Y,SBrkr,1368,0,0,1368,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,474,Typical,Typical,Paved,132,35,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,202500,-93.649553,42.058231 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,6792,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,94,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1368,1368,GasA,Excellent,Y,SBrkr,1368,0,0,1368,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,474,Typical,Typical,Paved,132,35,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,202665,-93.649525,42.058238 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,61,7740,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,518,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,663,1686,GasA,Excellent,Y,SBrkr,1686,0,0,1686,1,0,2,0,1,1,Excellent,6,Typ,1,Good,Attchd,Fin,3,899,Typical,Typical,Paved,266,100,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,332200,-93.654124,42.057164 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,48,6373,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Excellent,Average,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,572,Excellent,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,1251,1666,GasA,Excellent,Y,SBrkr,1666,0,0,1666,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,575,Typical,Typical,Paved,228,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,310090,-93.65414,42.057164 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3136,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,163,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1405,1405,GasA,Excellent,Y,SBrkr,1405,0,0,1405,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,478,Typical,Typical,Paved,148,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,171750,-93.650617,42.05758 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1288,1316,GasA,Excellent,Y,SBrkr,1316,0,0,1316,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,397,Typical,Typical,Paved,100,0,0,23,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,169990,-93.646459,42.063304 +Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,New,Partial,170440,-93.645841,42.062442 +Two_Story_1946_and_Newer,Residential_Low_Density,65,10237,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,754,0,1492,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,169985,-93.645774,42.062361 +Two_Story_1946_and_Newer,Residential_Low_Density,58,17104,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,GLQ,3,Unf,0,100,654,GasA,Excellent,Y,SBrkr,664,832,0,1496,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,426,Typical,Typical,Paved,100,24,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,179665,-93.644411,42.061915 +Two_Story_1946_and_Newer,Residential_Low_Density,58,14054,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,879,879,GasA,Excellent,Y,SBrkr,879,984,0,1863,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,660,Typical,Typical,Paved,100,17,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,219210,-93.644366,42.061928 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,11660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1326,1326,GasA,Excellent,Y,SBrkr,1326,0,0,1326,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,427,Typical,Typical,Paved,100,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,174190,-93.644627,42.063031 +Two_Story_1946_and_Newer,Residential_Low_Density,105,15578,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Good,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Typical,8,Typ,0,No_Fireplace,Attchd,RFn,2,429,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,172785,-93.645755,42.062576 +Two_Story_1946_and_Newer,Residential_Low_Density,96,11631,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,236,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1052,1052,GasA,Excellent,Y,SBrkr,1052,1321,0,2373,0,0,2,1,4,1,Good,9,Typ,1,Good,BuiltIn,Fin,3,632,Typical,Typical,Paved,120,46,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,258000,-93.644213,42.061984 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9073,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,738,738,GasA,Excellent,Y,SBrkr,738,754,0,1492,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,100,32,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,185101,-93.644306,42.061987 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3087,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,767,1220,GasA,Excellent,Y,SBrkr,1364,0,0,1364,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,437,Typical,Typical,Paved,100,16,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,210250,-93.641662,42.062574 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3196,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,18,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1374,1374,GasA,Excellent,Y,SBrkr,1557,0,0,1557,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,420,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,234000,-93.641946,42.063298 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3196,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1273,1273,GasA,Excellent,Y,SBrkr,1456,0,0,1456,0,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,400,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,215000,-93.640287,42.063329 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,2938,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,330,1368,GasA,Excellent,Y,SBrkr,1511,0,0,1511,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Fin,2,398,Typical,Typical,Paved,130,30,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,246990,-93.640227,42.063185 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,3072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,18,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1375,1375,GasA,Excellent,Y,SBrkr,1414,0,0,1414,0,0,2,0,2,1,Good,6,Typ,1,Typical,Attchd,Fin,2,398,Typical,Typical,Paved,144,20,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,178740,-93.641321,42.062374 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,3072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,18,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,306,1365,GasA,Excellent,Y,SBrkr,1548,0,0,1548,1,0,2,0,2,1,Good,7,Typ,1,Typical,Attchd,Fin,2,388,Typical,Typical,Paved,143,20,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,225000,-93.6419,42.0623 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1232,1248,GasA,Excellent,Y,SBrkr,1248,0,0,1248,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,438,Typical,Typical,Paved,108,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,167240,-93.642261,42.063301 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1126,1142,GasA,Excellent,Y,SBrkr,1142,0,0,1142,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,440,Typical,Typical,Paved,90,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,156820,-93.642241,42.063301 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,43,3013,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,145,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1346,1362,GasA,Excellent,Y,SBrkr,1506,0,0,1506,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,2,440,Typical,Typical,Paved,142,20,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,213490,-93.642116,42.063301 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,3982,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,GLQ,3,Unf,0,366,1520,GasA,Excellent,Y,SBrkr,1567,0,0,1567,1,0,2,0,1,1,Excellent,7,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,312,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,264561,-93.641518,42.062831 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,53,4045,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Bloomington_Heights,Norm,Norm,TwnhsE,One_Story,Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,45,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,286,1356,GasA,Excellent,Y,SBrkr,1500,0,0,1500,1,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,Fin,3,648,Typical,Typical,Paved,161,20,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,246578,-93.641522,42.062873 +Two_Story_1946_and_Newer,Residential_Low_Density,60,21930,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,Unf,7,Unf,0,732,732,GasA,Excellent,Y,SBrkr,734,1104,0,1838,0,0,2,1,4,1,Typical,7,Typ,1,Good,BuiltIn,Fin,2,372,Typical,Typical,Paved,100,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,192140,-93.64458,42.060239 +Two_Story_1946_and_Newer,Residential_Low_Density,0,7861,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2002,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,326,783,GasA,Excellent,Y,SBrkr,807,702,0,1509,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,100,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,183200,-93.642588,42.060293 +Two_Story_1946_and_Newer,Residential_Low_Density,74,9056,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,Av,Unf,7,Unf,0,707,707,GasA,Excellent,Y,SBrkr,707,707,0,1414,0,0,2,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,403,Typical,Typical,Paved,100,35,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,178000,-93.6436049,42.059803 +Two_Story_1946_and_Newer,Residential_Low_Density,59,9171,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,848,848,GasA,Excellent,Y,SBrkr,848,750,0,1598,0,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,433,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,201490,-93.644094,42.061727 +Two_Story_1946_and_Newer,Residential_Low_Density,106,11194,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1406,1406,GasA,Excellent,Y,SBrkr,1454,482,0,1936,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,504,Typical,Typical,Paved,188,124,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,230500,-93.641467,42.060836 +Two_Story_1946_and_Newer,Residential_Low_Density,63,7875,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,783,783,GasA,Excellent,Y,SBrkr,807,702,0,1509,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,393,Typical,Typical,Paved,0,75,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,180000,-93.640211,42.060074 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8121,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,953,953,GasA,Excellent,Y,SBrkr,953,711,0,1664,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,460,Typical,Typical,Paved,100,40,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,172400,-93.642713,42.05865 +Two_Story_1946_and_Newer,Residential_Low_Density,0,8658,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,250,982,GasA,Excellent,Y,SBrkr,1008,881,0,1889,0,0,2,1,3,1,Typical,9,Typ,1,Typical,Attchd,Fin,2,431,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,215000,-93.642913,42.05853 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11214,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,930,930,GasA,Good,Y,SBrkr,956,930,0,1886,0,0,2,1,4,1,Good,10,Typ,1,Typical,Attchd,Fin,2,431,Typical,Typical,Paved,89,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,199900,-93.641281,42.057905 +Two_Story_1946_and_Newer,Residential_Low_Density,58,10852,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,OneFam,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,173,959,GasA,Excellent,Y,SBrkr,959,712,0,1671,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,472,Typical,Typical,Paved,0,38,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,173000,-93.64136,42.057838 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12104,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR3,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1035,1035,GasA,Good,Y,SBrkr,1082,1240,0,2322,0,0,3,1,4,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,3,617,Typical,Typical,Paved,400,45,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,287602,-93.641283,42.05696 +Two_Story_1946_and_Newer,Residential_Low_Density,66,8738,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,302,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,975,975,GasA,Excellent,Y,SBrkr,1005,1286,0,2291,0,0,2,1,4,1,Good,8,Typ,1,Typical,BuiltIn,Fin,2,429,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,230000,-93.641053,42.058461 +Two_Story_1946_and_Newer,Residential_Low_Density,82,9452,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,423,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,322,1396,GasA,Excellent,Y,SBrkr,1407,985,0,2392,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,3,870,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,348000,-93.655739,42.05443 +Two_Story_1946_and_Newer,Residential_Low_Density,84,9660,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,242,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,253,1044,GasA,Excellent,Y,SBrkr,1079,897,0,1976,1,0,2,1,3,1,Good,7,Typ,1,Excellent,Attchd,Fin,3,885,Typical,Typical,Paved,210,31,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,286000,-93.654519,42.05384 +Two_Story_1946_and_Newer,Residential_Low_Density,83,9545,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,322,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,655,1160,GasA,Excellent,Y,SBrkr,1205,1029,0,2234,1,0,2,1,3,1,Good,7,Typ,1,Typical,BuiltIn,RFn,3,768,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,300000,-93.654444,42.053801 +Two_Story_1946_and_Newer,Residential_Low_Density,118,35760,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Excellent,Average,1995,1996,Hip,CompShg,HdBoard,HdBoard,BrkFace,1378,Good,Good,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,543,1930,GasA,Excellent,Y,SBrkr,1831,1796,0,3627,1,0,3,1,4,1,Good,10,Typ,1,Typical,Attchd,Fin,3,807,Typical,Typical,Paved,361,76,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,625000,-93.657851,42.053314 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9233,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,877,Good,Typical,PConc,Excellent,Typical,Av,GLQ,3,Unf,0,358,1540,GasA,Excellent,Y,SBrkr,1540,1315,0,2855,1,0,2,1,4,1,Excellent,9,Typ,1,Typical,Attchd,RFn,3,774,Typical,Typical,Paved,247,55,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,405000,-93.6532,42.055601 +Two_Story_1946_and_Newer,Residential_Low_Density,78,12011,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,530,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,130,1086,GasA,Excellent,Y,SBrkr,1086,838,0,1924,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,592,Typical,Typical,Paved,208,75,0,0,374,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,280000,-93.65227,42.053154 +Two_Story_1946_and_Newer,Residential_Low_Density,93,12090,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,650,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1141,1141,GasA,Good,Y,SBrkr,1165,1098,0,2263,0,0,2,1,4,1,Good,10,Typ,1,Typical,BuiltIn,Fin,2,420,Typical,Typical,Paved,144,123,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,258000,-93.650059,42.053466 +Two_Story_1946_and_Newer,Residential_Low_Density,83,10019,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1995,1995,Hip,CompShg,VinylSd,VinylSd,BrkFace,397,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,815,1342,GasA,Excellent,Y,SBrkr,1358,1368,0,2726,0,0,2,1,4,1,Good,9,Typ,1,Excellent,Attchd,RFn,3,725,Typical,Typical,Paved,307,169,168,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,350000,-93.653915,42.05178 +Two_Story_1946_and_Newer,Residential_Low_Density,114,17242,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Excellent,Average,1993,1994,Hip,CompShg,MetalSd,MetalSd,BrkFace,738,Good,Good,PConc,Excellent,Typical,Gd,Rec,6,GLQ,1393,48,1733,GasA,Excellent,Y,SBrkr,1933,1567,0,3500,1,0,3,1,4,1,Excellent,11,Typ,1,Typical,Attchd,RFn,3,959,Typical,Typical,Paved,870,86,0,0,210,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,584500,-93.655997,42.049423 +Two_Story_1946_and_Newer,Residential_Low_Density,0,10236,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,501,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,BLQ,168,742,1290,GasA,Excellent,Y,SBrkr,1305,1189,0,2494,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Fin,3,803,Typical,Typical,Paved,200,95,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,321000,-93.655704,42.051073 +Two_Story_1946_and_Newer,Residential_Low_Density,92,10120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1994,1994,Hip,CompShg,VinylSd,VinylSd,BrkFace,391,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,425,1165,GasA,Excellent,Y,SBrkr,1203,1323,0,2526,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,3,844,Typical,Typical,Paved,309,78,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,290000,-93.653608,42.049687 +Two_Story_1946_and_Newer,Residential_Low_Density,0,12585,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1993,Gable,CompShg,HdBoard,ImStucc,BrkFace,420,Good,Typical,PConc,Good,Typical,No,LwQ,4,GLQ,1039,0,1286,GasA,Excellent,Y,SBrkr,1565,1234,0,2799,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,3,704,Typical,Typical,Paved,432,136,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,315000,-93.653479,42.049528 +Two_Story_1946_and_Newer,Residential_Low_Density,75,12447,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,RRAn,Norm,OneFam,Two_Story,Very_Good,Average,2005,2006,Gable,CompShg,CemntBd,CmentBd,Stone,192,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,SBrkr,1116,848,0,1964,0,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,760,Typical,Typical,Paved,200,70,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,New,Partial,252000,-93.642317,42.055056 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,49,15218,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Good,No,GLQ,3,Unf,0,108,1670,GasA,Excellent,Y,SBrkr,1670,0,0,1670,1,0,2,1,2,1,Good,6,Typ,1,Good,Attchd,RFn,3,928,Typical,Typical,Paved,0,240,200,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,336820,-93.642082,42.054318 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,49,20896,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRAn,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,356,2077,GasA,Excellent,Y,SBrkr,2097,0,0,2097,1,0,1,1,1,1,Excellent,8,Typ,1,Excellent,Attchd,Fin,3,1134,Typical,Typical,Paved,192,267,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,New,Partial,423000,-93.64178,42.054176 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10182,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Somerset,RRNn,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,420,Good,Typical,PConc,Excellent,Typical,Mn,GLQ,3,Unf,0,440,1660,GasA,Excellent,Y,SBrkr,1660,0,0,1660,1,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,500,Typical,Typical,Paved,322,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,290000,-93.641793,42.054175 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8688,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,228,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1616,1616,GasA,Excellent,Y,SBrkr,1616,0,0,1616,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,834,Typical,Typical,Paved,208,59,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,232000,-93.643635,42.053183 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,10936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Feedr,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,60,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1504,1504,GasA,Excellent,Y,SBrkr,1504,0,0,1504,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,510,Typical,Typical,Paved,144,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,199000,-93.643811,42.053033 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1254,1278,GasA,Excellent,Y,SBrkr,1278,0,0,1278,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,584,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,198600,-93.650361,42.0518 +Two_Story_1946_and_Newer,Floating_Village_Residential,100,13162,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Somerset,Feedr,Norm,OneFam,Two_Story,Excellent,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,200,2036,GasA,Excellent,Y,SBrkr,2036,604,0,2640,1,0,3,1,3,1,Excellent,11,Typ,1,Good,Attchd,RFn,3,792,Typical,Typical,Paved,0,265,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,405749,-93.64407,42.052265 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,81,11216,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1489,1489,GasA,Excellent,Y,SBrkr,1489,0,0,1489,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,776,Typical,Typical,Paved,0,140,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,232600,-93.643898,42.052272 +Two_Story_1946_and_Newer,Floating_Village_Residential,84,10728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1095,1095,GasA,Good,Y,SBrkr,1095,844,0,1939,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,3,1053,Typical,Typical,Paved,192,51,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,266000,-93.642862,42.052346 +Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,756,756,GasA,Excellent,Y,SBrkr,756,797,0,1553,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,615,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,186500,-93.642309,42.051355 +Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,858,858,GasA,Excellent,Y,SBrkr,858,858,0,1716,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,615,Typical,Typical,Paved,0,53,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,200825,-93.642374,42.051426 +Two_Story_1946_and_Newer,Floating_Village_Residential,65,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,770,770,GasA,Excellent,Y,SBrkr,778,798,0,1576,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,614,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,197000,-93.64357,42.051382 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,60,7200,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,68,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,357,1262,GasA,Good,Y,SBrkr,1262,0,0,1262,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Fin,2,572,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,185000,-93.643173,42.051245 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,62,7500,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1257,1257,GasA,Excellent,Y,SBrkr,1266,0,0,1266,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,Unf,2,453,Typical,Typical,Paved,38,144,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,176000,-93.643859,42.051225 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,60,7200,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,No,Unf,7,Unf,0,1293,1293,GasA,Excellent,Y,SBrkr,1301,0,0,1301,1,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,RFn,2,572,Typical,Typical,Paved,216,121,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,187750,-93.643977,42.051224 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7733,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Mn,GLQ,3,Unf,0,1118,1142,GasA,Excellent,Y,SBrkr,1142,0,0,1142,0,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,4,50,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,139500,-93.692575,42.037843 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,91,11024,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,118,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1400,1400,GasA,Excellent,Y,SBrkr,1400,0,0,1400,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,612,Typical,Typical,Paved,144,55,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,186800,-93.691403,42.036149 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1131,1131,GasA,Excellent,Y,SBrkr,1131,0,0,1131,0,0,1,1,3,1,Good,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,39,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,132000,-93.691787,42.037915 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1141,1141,GasA,Excellent,Y,SBrkr,1141,0,0,1141,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Abnorml,142500,-93.691754,42.037911 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,63,13072,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,217,1158,GasA,Excellent,Y,SBrkr,1158,0,0,1158,1,0,1,1,3,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,158000,-93.690959,42.037827 +Two_Story_1946_and_Newer,Residential_Low_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,172,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,891,891,GasA,Excellent,Y,SBrkr,891,795,0,1686,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,Fin,2,462,Typical,Typical,Paved,144,101,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,184000,-93.691246,42.036605 +Two_Story_1946_and_Newer,Residential_Low_Density,74,7632,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,96,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,784,784,GasA,Excellent,Y,SBrkr,831,754,0,1585,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,449,Typical,Typical,Paved,100,77,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,184900,-93.691247,42.03718 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8304,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,941,941,GasA,Excellent,Y,SBrkr,941,896,0,1837,0,0,2,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,688,Typical,Typical,Paved,150,165,0,0,0,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,187000,-93.689021,42.036849 +Two_Story_1946_and_Newer,Residential_Low_Density,65,7153,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Typical,No,ALQ,1,Unf,0,374,761,GasA,Excellent,Y,SBrkr,810,793,0,1603,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,0,124,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,175900,-93.68782,42.034595 +Two_Story_1946_and_Newer,Residential_Low_Density,70,9370,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1992,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,78,836,GasA,Excellent,Y,SBrkr,844,887,0,1731,1,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,462,Typical,Typical,Paved,307,85,0,0,224,0,No_Pool,No_Fence,Othr,3000,10,2006,WD ,Family,248500,-93.687695,42.03674 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1990,1991,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,494,1398,GasA,Good,Y,SBrkr,1398,0,0,1398,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,542,Typical,Typical,Paved,0,46,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,172000,-93.686267,42.035555 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,50,7175,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1991,1991,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,939,1217,GasA,Good,Y,SBrkr,1217,0,0,1217,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,484,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,151000,-93.686267,42.035629 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9019,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,RRAe,Norm,OneFam,Two_Story,Above_Average,Average,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,362,636,GasA,Excellent,Y,SBrkr,636,684,0,1320,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,472,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,150000,-93.68665,42.03748 +One_Story_1946_and_Newer_All_Styles,Residential_High_Density,0,8900,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1966,1966,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,0,1056,GasA,Typical,Y,SBrkr,1056,0,0,1056,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,42,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Normal,107000,-93.681595,42.035795 +One_Story_1946_and_Newer_All_Styles,Residential_High_Density,60,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,187,828,GasA,Good,Y,SBrkr,965,0,0,965,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,300,Typical,Typical,Paved,421,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Abnorml,119900,-93.681407,42.035235 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Below_Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,51,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,952,988,GasA,Excellent,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,129500,-93.674167,42.036051 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,One_Story,Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,336,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,396,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,1,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,ConLI,Abnorml,125000,-93.675963,42.036087 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,68,8927,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,One_and_Half_Fin,Above_Average,Above_Average,1977,1977,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1286,368,0,1654,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,Attchd,RFn,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,119500,-93.677123,42.036206 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9240,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1962,2002,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,252,864,GasA,Good,Y,SBrkr,1211,0,0,1211,0,0,1,0,2,1,Typical,6,Min1,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,161,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,144000,-93.675723,42.035302 +Split_or_Multilevel,Residential_Low_Density,88,8471,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Good,1977,1995,Gable,CompShg,HdBoard,Plywood,BrkFace,46,Typical,Typical,CBlock,Good,Good,Av,ALQ,1,Unf,0,0,506,GasA,Typical,Y,SBrkr,1212,0,0,1212,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,492,Typical,Typical,Paved,292,12,0,0,0,0,No_Pool,Good_Wood,None,0,7,2006,WD ,Normal,151000,-93.677012,42.035094 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9308,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRNe,Norm,OneFam,One_Story,Average,Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,430,984,GasA,Typical,Y,SBrkr,984,0,0,984,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,310,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,126000,-93.669553,42.034692 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8450,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Above_Average,1968,1968,Gable,CompShg,Plywood,Plywood,BrkFace,90,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,270,450,882,GasA,Typical,Y,SBrkr,909,0,0,909,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,294,Typical,Typical,Paved,0,155,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,COD,Normal,116000,-93.671037,42.035696 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8638,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,744,925,GasA,Good,Y,SBrkr,925,0,0,925,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,203,74,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,133500,-93.672411,42.035841 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13052,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,312,1024,GasA,Typical,Y,SBrkr,1024,0,0,1024,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2006,WD ,Normal,120875,-93.670919,42.035325 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,13526,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1965,1965,Hip,CompShg,HdBoard,Plywood,BrkFace,114,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,375,0,935,GasA,Typical,Y,SBrkr,935,0,0,935,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,180,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Normal,137000,-93.670998,42.035536 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8020,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,268,912,GasA,Typical,N,SBrkr,912,0,0,912,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2006,WD ,Normal,124000,-93.669708,42.03457 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,6993,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_Story,Average,Good,1961,1994,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,447,912,GasA,Typical,Y,SBrkr,1236,0,0,1236,0,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,135000,-93.669757,42.03458 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8789,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,253,912,GasA,Typical,Y,SBrkr,941,0,0,941,0,0,1,0,3,1,Typical,6,Typ,1,Poor,Attchd,Unf,1,288,Typical,Typical,Paved,64,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,129200,-93.671926,42.034663 +Split_or_Multilevel,Residential_Low_Density,100,14330,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Corner,Gtl,Veenker,Norm,Norm,OneFam,SLvl,Good,Below_Average,1974,1974,Gable,CompShg,WdShing,Wd Sdng,BrkFace,145,Good,Fair,CBlock,Good,Typical,Gd,ALQ,1,BLQ,497,228,1748,GasA,Good,Y,SBrkr,2151,495,0,2646,1,2,2,0,3,1,Good,9,Mod,4,Typical,Attchd,RFn,2,550,Typical,Typical,Paved,641,100,0,0,0,800,Good,Good_Privacy,None,0,1,2006,WD ,Normal,260000,-93.660643,42.037065 +Two_Story_1946_and_Newer,Residential_Low_Density,121,16059,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1991,1992,Hip,CompShg,HdBoard,HdBoard,BrkFace,284,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1288,1288,GasA,Excellent,Y,SBrkr,1301,1116,0,2417,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,462,Typical,Typical,Paved,127,82,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,260000,-93.653876,42.045763 +Two_Story_1946_and_Newer,Residential_Low_Density,105,11025,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1993,Gable,CompShg,HdBoard,ImStucc,BrkFace,692,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,216,1334,GasA,Excellent,Y,SBrkr,1520,1306,0,2826,1,0,2,1,3,1,Good,9,Typ,3,Typical,Attchd,RFn,3,888,Typical,Typical,Paved,177,208,186,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,334000,-93.653512,42.048608 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14541,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1993,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,PConc,Good,Good,No,GLQ,3,Unf,0,326,1338,GasA,Excellent,Y,SBrkr,1352,1168,0,2520,1,0,2,1,5,1,Good,10,Typ,1,Typical,Attchd,RFn,3,796,Typical,Typical,Paved,209,55,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Abnorml,310000,-93.652348,42.048371 +Two_Story_1946_and_Newer,Residential_Low_Density,0,13346,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northridge,Norm,Norm,OneFam,Two_Story,Good,Average,1992,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,367,1095,GasA,Excellent,Y,SBrkr,1166,1129,0,2295,1,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,RFn,2,590,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,268000,-93.650474,42.047997 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,12461,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northridge,Norm,Norm,OneFam,One_Story,Very_Good,Average,1994,1995,Gable,CompShg,ImStucc,ImStucc,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,168,1624,GasA,Excellent,Y,SBrkr,1624,0,0,1624,1,0,2,0,2,1,Good,5,Typ,1,Fair,Attchd,RFn,3,757,Typical,Typical,Paved,0,114,192,0,0,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,262000,-93.650057,42.048058 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,34,3628,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Good,Average,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1143,1143,GasA,Excellent,Y,SBrkr,1143,0,0,1143,0,0,1,1,1,1,Good,5,Typ,1,Good,Attchd,RFn,2,588,Typical,Typical,Paved,0,191,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,176400,-93.647832,42.047592 +One_Story_PUD_1946_and_Newer,Floating_Village_Residential,37,3316,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,208,1247,GasA,Excellent,Y,SBrkr,1247,0,0,1247,1,0,1,1,1,1,Good,4,Typ,1,Good,Attchd,Fin,2,550,Typical,Typical,Paved,0,84,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,197000,-93.646526,42.04768 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,600,80,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,151000,-93.644891,42.047665 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,623,80,1223,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,147400,-93.644891,42.047642 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,24,2544,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,623,80,1223,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,149900,-93.644891,42.047618 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,30,3180,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,600,600,GasA,Excellent,Y,SBrkr,520,600,80,1200,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,RFn,2,480,Typical,Typical,Paved,0,166,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,144152,-93.644891,42.047595 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2998,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Above_Average,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,403,756,GasA,Excellent,Y,SBrkr,768,756,0,1524,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,166000,-93.645617,42.046145 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,35,3735,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,1999,1999,Hip,CompShg,MetalSd,MetalSd,BrkFace,218,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,241,691,GasA,Excellent,Y,SBrkr,713,739,0,1452,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,34,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,183900,-93.645482,42.046431 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,2651,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,Twnhs,Two_Story,Good,Average,2000,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,32,673,GasA,Excellent,Y,SBrkr,673,709,0,1382,1,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,490,Typical,Typical,Paved,153,50,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,165000,-93.645479,42.046127 +Two_Story_PUD_1946_and_Newer,Floating_Village_Residential,0,4447,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,FR2,Gtl,Somerset,Norm,Norm,TwnhsE,Two_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,530,530,GasA,Excellent,Y,SBrkr,530,550,0,1080,0,0,2,1,2,1,Good,4,Typ,0,No_Fireplace,Attchd,RFn,2,496,Typical,Typical,Paved,0,50,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,139000,-93.645478,42.046107 +Two_Story_1946_and_Newer,Floating_Village_Residential,114,8314,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Somerset,Norm,Norm,OneFam,Two_Story,Good,Average,1997,1998,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,569,569,GasA,Excellent,Y,SBrkr,854,840,0,1694,0,0,2,1,3,1,Good,6,Typ,1,Typical,BuiltIn,Unf,1,434,Typical,Typical,Paved,0,382,0,0,110,0,No_Pool,Good_Privacy,None,0,11,2006,WD ,Normal,200000,-93.643373,42.046429 +One_Story_1946_and_Newer_All_Styles,Floating_Village_Residential,60,7180,Pave,Paved,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Somerset,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1568,1568,GasA,Excellent,Y,SBrkr,1568,0,0,1568,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,564,Typical,Typical,Paved,0,266,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,221000,-93.64114,42.046416 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,30,9549,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1995,1996,Hip,CompShg,BrkFace,BrkFace,None,0,Good,Good,PConc,Good,Good,Av,LwQ,4,GLQ,1057,0,1494,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,1,1,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,481,Typical,Typical,Paved,0,30,0,0,216,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,270000,-93.648931,42.044609 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,441,1208,GasA,Typical,Y,SBrkr,1208,0,0,1208,1,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,546,Typical,Typical,Paved,198,42,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,165000,-93.646065,42.043779 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,3760,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,LwQ,182,44,1235,GasA,Good,Y,SBrkr,1235,0,0,1235,1,0,1,0,1,1,Typical,4,Typ,3,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,133,78,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,214000,-93.649329,42.042369 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,36,3640,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,176,1036,GasA,Typical,Y,SBrkr,1036,0,0,1036,1,0,1,0,1,1,Good,4,Typ,1,Typical,Detchd,Fin,2,484,Typical,Typical,Paved,133,108,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,188000,-93.649439,42.042481 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3874,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,1980,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,419,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,1,Fair,Attchd,Fin,2,484,Typical,Typical,Paved,133,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,190000,-93.649352,42.042826 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,40,3876,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Greens,Norm,Norm,Twnhs,One_Story,Very_Good,Average,1978,1978,Gable,CompShg,Wd Sdng,Plywood,None,0,Good,Typical,CBlock,Good,Typical,Gd,GLQ,3,Rec,526,48,1226,GasA,Typical,Y,SBrkr,1226,0,0,1226,1,0,1,0,1,1,Good,4,Typ,1,Typical,Attchd,Fin,2,484,Typical,Typical,Paved,133,60,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,170000,-93.649329,42.043351 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,50271,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Gtl,Veenker,Norm,Norm,OneFam,One_Story,Excellent,Average,1981,1987,Gable,WdShngl,WdShing,Wd Shng,None,0,Good,Typical,CBlock,Excellent,Typical,Gd,GLQ,3,Unf,0,32,1842,GasA,Good,Y,SBrkr,1842,0,0,1842,2,0,0,1,0,1,Good,5,Typ,1,Good,Attchd,Fin,3,894,Typical,Typical,Paved,857,72,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,385000,-93.658237,42.037409 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,79,13110,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,RRAn,Feedr,TwoFmCon,One_Story,Average,Above_Average,1972,1972,Gable,CompShg,Plywood,Plywood,BrkFace,144,Typical,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,191,1153,GasA,Excellent,Y,SBrkr,1193,0,0,1193,1,0,2,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,2,501,Typical,Typical,Paved,140,153,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,146500,-93.633752,42.045632 +Split_or_Multilevel,Residential_Low_Density,102,10192,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,SLvl,Good,Above_Average,1968,1992,Gable,CompShg,MetalSd,MetalSd,BrkFace,143,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,570,570,GasA,Good,Y,SBrkr,1222,698,0,1920,0,0,3,0,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,487,Typical,Typical,Paved,0,98,0,0,0,0,No_Pool,Good_Privacy,None,0,9,2006,WD ,Normal,170000,-93.6346,42.048199 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20781,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,PosN,Norm,OneFam,One_Story,Good,Good,1968,2003,Hip,CompShg,BrkFace,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,68,1203,1568,GasA,Typical,Y,SBrkr,2156,0,0,2156,0,0,2,0,3,1,Typical,9,Typ,1,Good,Attchd,RFn,2,508,Good,Typical,Paved,0,80,0,290,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,262500,-93.634738,42.048997 +Two_Story_1946_and_Newer,Residential_Low_Density,90,11700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,420,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,304,708,GasA,Good,Y,SBrkr,708,708,0,1416,0,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,776,Typical,Typical,Paved,0,169,0,0,119,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,179900,-93.635873,42.048602 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11029,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Northwest_Ames,PosA,Norm,OneFam,Two_Story,Above_Average,Good,1968,1984,Gable,CompShg,HdBoard,HdBoard,BrkFace,220,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,435,1054,GasA,Typical,Y,SBrkr,1512,1142,0,2654,1,0,2,1,4,1,Good,9,Typ,1,Good,Attchd,Unf,2,619,Typical,Typical,Paved,0,65,0,0,222,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,250000,-93.633466,42.046472 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,288,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,485,732,GasA,Good,Y,SBrkr,1012,778,0,1790,1,0,1,2,4,1,Typical,8,Min2,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,148,0,0,0,147,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,165150,-93.630258,42.04806 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10140,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,RRAn,Norm,OneFam,One_Story,Good,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,264,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,68,713,1334,GasA,Good,Y,SBrkr,1334,0,0,1334,1,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,477,Typical,Typical,Paved,0,20,35,0,264,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,164500,-93.632472,42.043548 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Northwest_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1969,1969,Hip,CompShg,HdBoard,HdBoard,BrkFace,168,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1164,1164,GasA,Typical,Y,SBrkr,1164,0,0,1164,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,COD,Normal,140000,-93.633098,42.044669 +Split_or_Multilevel,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1967,1967,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Av,ALQ,1,Rec,480,100,980,GasA,Good,Y,SBrkr,980,0,0,980,0,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,384,Typical,Typical,Paved,68,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,135500,-93.627935,42.046496 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,293,1051,GasA,Good,Y,SBrkr,1051,0,0,1051,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,142000,-93.625976,42.045706 +Two_Story_1946_and_Newer,Residential_Low_Density,72,8640,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,Two_Story,Average,Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,300,Typical,Typical,CBlock,Good,Fair,Mn,ALQ,1,Rec,483,56,900,GasA,Excellent,Y,SBrkr,884,886,0,1770,1,0,1,1,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,2,530,Typical,Typical,Paved,0,60,0,0,270,0,No_Pool,No_Fence,Shed,455,6,2006,WD ,Normal,155000,-93.626097,42.045728 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9360,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Good,1968,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,203,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,216,976,GasA,Typical,Y,SBrkr,976,0,0,976,1,0,1,0,2,1,Good,4,Typ,1,Fair,Attchd,RFn,2,504,Typical,Typical,Paved,94,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Abnorml,157500,-93.626161,42.045728 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,HdBoard,HdBoard,BrkFace,168,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,36,1052,GasA,Good,Y,SBrkr,1052,0,0,1052,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,356,0,0,0,0,0,No_Pool,Good_Wood,None,0,11,2006,WD ,Normal,138500,-93.627517,42.045885 +Split_Foyer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Feedr,Norm,OneFam,SFoyer,Average,Above_Average,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,89,833,GasA,Good,Y,SBrkr,898,0,0,898,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,326,Typical,Typical,Paved,143,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,140000,-93.628069,42.045878 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9759,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1966,1966,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,252,1051,GasA,Typical,Y,SBrkr,1051,0,0,1051,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,182,88,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,124400,-93.6269,42.04795 +Split_or_Multilevel,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,140,Typical,Typical,PConc,Typical,Typical,Av,ALQ,1,Rec,402,137,1141,GasA,Good,Y,SBrkr,1141,0,0,1141,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,568,Typical,Typical,Paved,0,78,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,158000,-93.626345,42.04657 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,71,9230,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Very_Good,1965,1998,Hip,CompShg,MetalSd,MetalSd,BrkFace,166,Typical,Typical,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,203,864,GasA,Good,Y,SBrkr,1200,0,0,1200,1,0,1,1,1,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,2,884,Typical,Typical,Paved,0,64,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2006,WD ,Normal,146000,-93.624612,42.045685 +Two_Story_1946_and_Newer,Residential_Low_Density,0,14803,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Northwest_Ames,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,252,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,409,825,GasA,Good,Y,SBrkr,1097,896,0,1993,0,0,2,1,4,1,Typical,8,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,66,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,190000,-93.629155,42.044308 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10659,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,135,1050,GasA,Typical,Y,SBrkr,1050,0,0,1050,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,368,Typical,Typical,Paved,0,319,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,COD,Normal,136500,-93.623414,42.043214 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1958,1958,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,174,169,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,129900,-93.623564,42.044978 +Duplex_All_Styles_and_Ages,Residential_Low_Density,70,8393,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1959,2005,Gable,CompShg,MetalSd,MetalSd,BrkFace,122,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,1098,1626,GasA,Excellent,Y,SBrkr,1712,0,0,1712,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,2,588,Typical,Typical,Paved,272,54,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Family,145000,-93.622312,42.042114 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,RRAn,Norm,TwoFmCon,One_Story,Above_Average,Good,1965,2000,Hip,CompShg,BrkFace,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,Mn,ALQ,1,BLQ,252,34,1187,GasA,Excellent,Y,SBrkr,1565,0,0,1565,1,0,2,0,3,1,Good,7,Min1,2,Typical,Attchd,RFn,1,299,Typical,Typical,Paved,200,25,211,0,0,0,No_Pool,Minimum_Privacy,Shed,460,6,2006,WD ,Abnorml,185900,-93.630156,42.040072 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,8800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Hip,CompShg,HdBoard,HdBoard,BrkFace,425,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,698,1251,GasA,Typical,Y,SBrkr,1251,0,0,1251,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,RFn,1,461,Typical,Typical,Paved,0,116,0,0,0,0,No_Pool,Minimum_Privacy,Shed,700,3,2006,WD ,Normal,160000,-93.626765,42.038434 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,10368,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1964,1964,Hip,CompShg,HdBoard,HdBoard,BrkFace,112,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,LwQ,748,0,1008,GasA,Excellent,Y,SBrkr,1488,0,0,1488,1,0,1,1,3,1,Typical,7,Typ,1,Good,Attchd,Fin,2,430,Typical,Typical,Paved,154,60,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,167000,-93.627065,42.04102 +Two_Story_1946_and_Newer,Residential_Low_Density,85,9350,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,360,720,GasA,Good,Y,SBrkr,720,720,0,1440,0,0,1,1,4,1,Typical,7,Typ,1,Poor,Attchd,Fin,2,480,Typical,Typical,Paved,0,32,240,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,157500,-93.6282949,42.0414084 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,75,10382,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,TwoFmCon,SLvl,Above_Average,Average,1958,1958,Hip,CompShg,HdBoard,HdBoard,BrkFace,105,Typical,Fair,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,75,588,GasA,Typical,Y,SBrkr,1095,0,0,1095,1,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,ConLD,Normal,140000,-93.621621,42.041314 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,8973,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1958,1991,Gable,CompShg,Plywood,Plywood,BrkFace,85,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,28,413,1008,GasA,Typical,Y,FuseA,1053,0,0,1053,0,1,1,1,3,1,Excellent,6,Typ,0,No_Fireplace,More_Than_Two_Types,RFn,2,750,Typical,Typical,Paved,0,80,0,180,0,0,No_Pool,Minimum_Wood_Wire,None,0,7,2006,WD ,Abnorml,150000,-93.621362,42.040273 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,88,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,616,1248,GasA,Excellent,Y,SBrkr,1248,0,0,1248,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,151500,-93.622017,42.040143 +One_Story_1945_and_Older,Residential_Low_Density,60,8550,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1934,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,242,816,GasA,Excellent,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,Typical,4,Typ,1,Fair,Attchd,Unf,1,240,Typical,Typical,Paved,228,0,40,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,129800,-93.62357,42.039195 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11425,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1954,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,522,1008,GasA,Good,Y,SBrkr,1008,0,0,1008,0,0,1,0,2,1,Typical,4,Typ,1,Good,Attchd,RFn,1,275,Typical,Typical,Paved,0,0,120,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,137000,-93.622649,42.038351 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,68,9724,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1947,1950,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,938,938,GasA,Excellent,Y,SBrkr,1043,0,0,1043,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,273,Typical,Typical,Paved,125,48,0,0,0,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,137000,-93.621399,42.039202 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,4712,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1946,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,363,747,GasA,Typical,Y,SBrkr,774,456,0,1230,1,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,305,Typical,Typical,Paved,0,57,0,0,63,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Abnorml,121600,-93.628568,42.035836 +One_Story_1945_and_Older,Residential_Low_Density,51,5900,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Below_Average,Good,1923,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,440,440,GasA,Typical,Y,FuseA,869,0,0,869,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,85500,-93.629185,42.03537 +One_Story_1945_and_Older,Residential_Low_Density,52,5825,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Below_Average,Average,1926,1953,Gable,CompShg,MetalSd,MetalSd,BrkFace,108,Typical,Good,PConc,Fair,Typical,Mn,Unf,7,Unf,0,600,600,GasA,Good,Y,SBrkr,747,0,0,747,0,0,1,0,1,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,32,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,79900,-93.629256,42.035379 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,92,7438,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Feedr,OneFam,One_and_Half_Fin,Average,Very_Good,1908,1991,Gable,CompShg,AsbShng,Plywood,None,0,Typical,Typical,PConc,Fair,Typical,No,Unf,7,Unf,0,504,504,GasA,Good,Y,SBrkr,936,316,0,1252,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Paved,104,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,127000,-93.628176,42.035666 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1961,1990,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,336,1251,GasA,Typical,Y,SBrkr,1433,0,0,1433,1,0,1,0,3,1,Typical,7,Min1,1,Good,Attchd,Unf,2,441,Typical,Typical,Paved,144,0,205,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,161000,-93.6270261,42.0380632 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,10858,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Above_Average,1952,1952,Gable,CompShg,Wd Sdng,Plywood,Stone,150,Typical,Good,CBlock,Typical,Typical,Mn,LwQ,4,Unf,0,1404,1444,GasA,Excellent,Y,SBrkr,1624,0,0,1624,1,0,1,0,2,1,Typical,6,Min1,1,Good,Attchd,RFn,1,240,Typical,Typical,Paved,0,40,324,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Partial,146500,-93.624738,42.034612 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,9600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1951,1951,Gable,CompShg,HdBoard,HdBoard,Stone,144,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,60,1056,GasA,Excellent,Y,FuseA,1216,0,0,1216,1,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,1,280,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,135000,-93.623704,42.036024 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,89,10680,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1951,1951,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,44,Typical,Typical,CBlock,Typical,Fair,No,LwQ,4,Unf,0,1380,2136,GasA,Typical,N,FuseA,2136,0,0,2136,0,0,2,0,4,1,Typical,7,Mod,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2006,WD ,Normal,137900,-93.622645,42.037463 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,79,9490,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Rec,165,238,806,GasA,Typical,Y,FuseA,958,620,0,1578,1,0,1,0,3,1,Fair,5,Typ,2,Typical,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,32,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Normal,133000,-93.620494,42.034831 +Two_Story_1946_and_Newer,Residential_Low_Density,79,9462,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Average,Above_Average,1949,1973,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,704,704,GasA,Good,Y,FuseA,1024,704,0,1728,0,0,1,1,3,1,Typical,7,Min1,1,Good,Attchd,Unf,1,234,Typical,Typical,Paved,245,60,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,137000,-93.621426,42.035987 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,82,9888,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1954,1975,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,BLQ,2,Unf,0,450,936,GasA,Typical,Y,FuseA,936,0,0,936,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,160,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,121000,-93.62143,42.037247 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,8917,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1584,1584,GasA,Typical,Y,SBrkr,1584,0,0,1584,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,506,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,119000,-93.619673,42.0493 +Split_or_Multilevel,Residential_Low_Density,0,12700,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1964,1964,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,307,1246,GasA,Typical,Y,SBrkr,1246,0,0,1246,1,0,1,0,3,1,Typical,6,Typ,2,Good,Attchd,RFn,2,441,Typical,Typical,Paved,0,69,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,172000,-93.616852,42.049031 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,8500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Fair,1961,1961,Hip,CompShg,HdBoard,HdBoard,BrkCmn,203,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,635,1235,GasA,Typical,Y,SBrkr,1235,0,0,1235,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,480,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,12,2006,WD ,Abnorml,98600,-93.616012,42.04665 +Two_Story_1946_and_Newer,Residential_Low_Density,88,14200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1966,1966,Gable,CompShg,MetalSd,MetalSd,BrkFace,309,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,479,924,GasA,Excellent,Y,SBrkr,1216,941,0,2157,0,0,2,1,4,1,Good,8,Typ,2,Good,Attchd,Fin,2,487,Typical,Typical,Paved,105,66,0,0,0,0,No_Pool,Good_Privacy,None,0,5,2006,WD ,Normal,226000,-93.615629,42.048528 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,124,936,GasA,Typical,Y,SBrkr,1128,0,0,1128,0,0,1,0,2,1,Typical,5,Min1,0,No_Fireplace,Attchd,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Family,118000,-93.6181153,42.0422973 +Two_and_Half_Story_All_Ages,Residential_Low_Density,174,25419,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,Two_Story,Very_Good,Below_Average,1918,1990,Gable,CompShg,Stucco,Stucco,None,0,Good,Good,PConc,Typical,Typical,No,GLQ,3,LwQ,184,140,1360,GasA,Good,Y,SBrkr,1360,1360,392,3112,1,1,2,0,4,1,Good,8,Typ,1,Excellent,Detchd,Unf,2,795,Typical,Typical,Paved,0,16,552,0,0,512,Excellent,Good_Privacy,None,0,3,2006,WD ,Abnorml,235000,-93.620355,42.042156 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,109,9723,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Good,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,332,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1008,1008,GasA,Typical,Y,SBrkr,1008,0,0,1008,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,430,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,135000,-93.618389,42.043129 +Duplex_All_Styles_and_Ages,Residential_Low_Density,70,7728,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,SLvl,Average,Above_Average,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,303,1106,GasA,Typical,Y,SBrkr,1190,0,0,1190,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,540,Typical,Typical,Paved,0,18,0,0,0,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,132500,-93.619334,42.044735 +Split_or_Multilevel,Residential_Low_Density,70,8163,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Above_Average,1959,1959,Gable,CompShg,HdBoard,HdBoard,BrkFace,128,Typical,Good,CBlock,Typical,Typical,Av,ALQ,1,BLQ,294,102,1144,GasA,Typical,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,796,Typical,Typical,Paved,86,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,143000,-93.617499,42.043897 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Fair,Typical,Mn,BLQ,2,Unf,0,556,1179,GasA,Good,Y,SBrkr,1364,0,0,1364,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,1,331,Typical,Typical,Paved,0,60,0,0,0,0,No_Pool,Good_Privacy,None,0,3,2006,WD ,Normal,132000,-93.618625,42.04498 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,11600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,175,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,818,1383,GasA,Typical,Y,SBrkr,1383,0,0,1383,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,Attchd,RFn,1,292,Typical,Typical,Paved,0,45,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,145250,-93.612886,42.044975 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9610,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Feedr,Norm,OneFam,One_Story,Above_Average,Above_Average,1958,1958,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,632,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,918,1121,GasA,Excellent,Y,FuseA,1336,0,0,1336,0,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,488,Typical,Typical,Paved,80,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,162000,-93.612566,42.044254 +Split_or_Multilevel,Residential_Low_Density,125,10000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Above_Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,272,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Rec,281,99,1058,GasA,Excellent,Y,SBrkr,1370,0,0,1370,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Basment,RFn,1,300,Typical,Typical,Paved,191,0,0,0,120,0,No_Pool,Minimum_Privacy,None,0,2,2006,WD ,Normal,148000,-93.615882,42.042105 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,14850,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,197,1092,GasA,Typical,Y,FuseA,1092,0,0,1092,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,Fin,1,299,Typical,Typical,Paved,268,0,0,0,122,0,No_Pool,Minimum_Wood_Wire,None,0,5,2006,WD ,Normal,141000,-93.613296,42.043626 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10152,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1956,1994,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,210,1124,GasA,Excellent,Y,SBrkr,1124,0,0,1124,1,0,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,1,353,Typical,Typical,Paved,0,211,180,0,142,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,153000,-93.614251,42.0427 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,72,10011,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,64,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,710,1070,GasA,Typical,Y,SBrkr,1236,0,0,1236,0,1,1,0,2,1,Good,6,Min1,1,Fair,Attchd,Unf,1,447,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,134450,-93.612462,42.042905 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,7032,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,SFoyer,Average,Average,1979,1979,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,943,GasA,Typical,Y,SBrkr,943,0,0,943,1,0,1,0,2,1,Typical,4,Typ,2,Typical,Detchd,Unf,2,600,Typical,Typical,Paved,42,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,135960,-93.618319,42.041821 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8092,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1954,2000,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,176,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,226,1050,GasA,Excellent,Y,SBrkr,1050,0,0,1050,1,0,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Abnorml,156000,-93.618481,42.040745 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11310,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1954,1954,Hip,CompShg,Wd Sdng,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1367,1367,GasA,Excellent,Y,SBrkr,1375,0,0,1375,0,0,1,0,2,1,Typical,5,Typ,1,Typical,Attchd,Unf,2,451,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,140000,-93.618593,42.040252 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,12778,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1952,2003,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,350,1008,GasA,Excellent,Y,FuseA,1008,0,0,1008,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,RFn,1,280,Typical,Typical,Paved,0,154,0,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2006,WD ,Normal,139500,-93.617225,42.039447 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,10170,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1951,1951,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,522,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,216,216,GasA,Typical,Y,SBrkr,1575,0,0,1575,0,0,1,1,2,1,Good,5,Typ,1,Good,Attchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,155000,-93.617069,42.038357 +Split_or_Multilevel,Residential_Low_Density,55,7700,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Good,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Good,Typical,No,BLQ,2,Unf,0,30,301,GasA,Excellent,Y,FuseA,1145,0,0,1145,0,0,1,0,3,1,Typical,6,Min2,0,No_Fireplace,Detchd,Unf,2,684,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,9,2006,WD ,Normal,127000,-93.612464,42.039387 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,11050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,517,1005,GasA,Excellent,Y,SBrkr,1005,0,0,1005,0,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,Unf,1,319,Typical,Typical,Paved,0,0,0,0,288,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,133500,-93.613491,42.038602 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,13600,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1955,1955,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,912,0,1056,GasA,Good,Y,SBrkr,1056,0,0,1056,1,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,650,11,2006,WD ,Normal,125000,-93.6136767,42.0383475 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,15428,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1951,1991,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,143,884,GasA,Excellent,Y,SBrkr,884,0,0,884,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Fin,1,270,Typical,Typical,Paved,0,0,0,0,195,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,142000,-93.615474,42.038836 +One_Story_1945_and_Older,Residential_Low_Density,118,21299,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Good,Average,1941,1963,Hip,WdShake,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,929,929,GasA,Excellent,Y,SBrkr,2039,0,0,2039,1,0,1,1,3,1,Typical,7,Min1,3,Good,More_Than_Two_Types,Unf,3,791,Typical,Typical,Paved,0,0,90,0,0,0,No_Pool,No_Fence,None,0,12,2006,COD,Abnorml,167000,-93.61547,42.040276 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,13300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1956,2001,Hip,CompShg,Wd Sdng,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,521,1015,GasA,Good,Y,SBrkr,1384,0,0,1384,1,0,1,0,2,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,2,896,Typical,Typical,Paved,75,0,0,323,0,0,No_Pool,No_Fence,Shed,400,6,2006,WD ,Normal,159000,-93.612263,42.039351 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,94,22136,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,TwoFmCon,One_and_Half_Fin,Average,Average,1925,1975,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Fair,CBlock,Typical,Typical,Mn,GLQ,3,Unf,0,1153,2171,GasA,Typical,Y,SBrkr,1392,1248,0,2640,2,0,2,1,5,1,Typical,10,Maj1,1,Good,Attchd,RFn,3,1008,Typical,Typical,Dirt_Gravel,631,48,148,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,180000,-93.620354,42.034934 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1947,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,784,784,GasA,Excellent,Y,FuseA,900,412,0,1312,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,649,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,127000,-93.617182,42.034617 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,0,617,GasA,Good,Y,SBrkr,865,445,0,1310,0,0,2,0,2,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,1,398,Typical,Typical,Paved,0,0,126,0,0,0,No_Pool,No_Fence,None,0,5,2006,COD,Normal,112000,-93.6168701,42.0347269 +One_Story_1945_and_Older,Residential_Low_Density,60,10410,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Very_Good,1930,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,713,713,GasA,Excellent,Y,SBrkr,713,0,0,713,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,371,Fair,Fair,Dirt_Gravel,0,75,161,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,99800,-93.6143498,42.0378602 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1954,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,418,833,GasA,Excellent,Y,SBrkr,833,0,0,833,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Detchd,Unf,1,326,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Normal,117000,-93.6133726,42.0371796 +One_Story_1945_and_Older,Residential_Low_Density,60,5400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Sev,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1921,1968,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1073,1073,GasA,Excellent,Y,SBrkr,1073,0,0,1073,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,326,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,108480,-93.613965,42.037001 +One_Story_1945_and_Older,Residential_Low_Density,60,10914,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Fair,Fair,1929,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,715,715,GasA,Fair,N,FuseP,715,0,0,715,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,660,Fair,Typical,Dirt_Gravel,0,0,75,0,112,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,68000,-93.610772,42.037097 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,7008,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Very_Good,1900,1998,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Fair,Fair,No,Unf,7,Unf,0,448,448,GasA,Excellent,Y,SBrkr,448,272,0,720,0,0,1,0,1,1,Fair,5,Typ,0,No_Fireplace,Attchd,Unf,1,280,Fair,Typical,Paved,0,0,70,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,86900,-93.612239,42.037041 +One_Story_1945_and_Older,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Above_Average,1920,1950,Hip,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,299,1120,GasA,Excellent,Y,SBrkr,1130,0,0,1130,1,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,2,720,Typical,Typical,Paved,229,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,120000,-93.6139,42.0358 +Two_Story_1945_and_Older,Residential_Low_Density,60,7200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Excellent,CBlock,Typical,Typical,No,Rec,6,Unf,0,325,663,GasA,Excellent,Y,SBrkr,774,821,0,1595,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,49,0,231,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,155500,-93.6116427,42.0367836 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10818,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,1077,1077,GasA,Typical,Y,FuseA,981,779,0,1760,0,0,1,1,4,1,Typical,7,Typ,1,Typical,Detchd,Unf,2,648,Fair,Typical,Paved,120,0,96,0,0,0,No_Pool,No_Fence,None,0,2,2006,COD,Abnorml,80000,-93.612266,42.035857 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10410,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,One_and_Half_Fin,Fair,Below_Average,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Typical,Y,SBrkr,694,520,0,1214,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,3,936,Typical,Typical,Paved,216,0,160,0,0,0,No_Pool,Minimum_Privacy,None,0,1,2006,WD ,Family,105000,-93.614049,42.034574 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Below_Average,1900,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Fair,No,BLQ,2,Unf,0,290,954,GasA,Typical,N,FuseA,1766,648,0,2414,0,0,2,0,3,2,Typical,10,Mod,1,Good,Attchd,Unf,2,520,Typical,Fair,Dirt_Gravel,142,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,ConLD,Normal,160000,-93.612404,42.035277 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10800,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Very_Good,1936,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Typical,No,ALQ,1,Unf,0,170,796,GasA,Good,Y,SBrkr,1096,370,0,1466,0,1,2,0,3,1,Good,7,Min1,1,Typical,Attchd,Unf,2,566,Typical,Typical,Paved,436,21,0,0,0,0,No_Pool,No_Fence,Shed,500,4,2006,WD ,Normal,170000,-93.613899,42.034761 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,8658,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1965,1965,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,101,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,445,1088,GasA,Excellent,Y,SBrkr,1324,0,0,1324,0,0,2,0,3,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,138,0,0,0,0,No_Pool,Good_Wood,None,0,12,2006,WD ,Abnorml,160000,-93.608909,42.040944 +Split_or_Multilevel,Residential_Low_Density,85,13400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,1047,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,BLQ,128,380,1024,GasA,Typical,Y,SBrkr,1086,0,0,1086,1,0,1,0,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,159950,-93.608984,42.040811 +Split_or_Multilevel,Residential_Low_Density,83,10184,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SLvl,Above_Average,Average,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,379,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,503,1083,GasA,Typical,Y,SBrkr,1146,0,0,1146,0,1,1,0,3,1,Typical,6,Typ,1,Good,Attchd,Unf,1,294,Typical,Typical,Paved,345,75,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,165000,-93.607582,42.040105 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,77,9786,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1962,1981,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,312,912,GasA,Typical,Y,SBrkr,1085,649,0,1734,0,0,1,1,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,440,Typical,Typical,Paved,0,0,0,0,128,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,159000,-93.60942,42.040081 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,9510,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Average,1962,1985,Gable,CompShg,HdBoard,HdBoard,BrkCmn,161,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,434,1135,GasA,Excellent,Y,SBrkr,1207,0,0,1207,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,0,240,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,147000,-93.610278,42.040297 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,66,7800,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,LwQ,600,0,912,GasA,Typical,Y,SBrkr,912,0,0,912,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Abnorml,115000,-93.610501,42.040148 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,8910,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Unf,0,0,655,GasA,Excellent,Y,SBrkr,1194,0,0,1194,0,1,1,0,3,1,Typical,6,Typ,1,Fair,BuiltIn,Fin,2,539,Typical,Typical,Paved,0,0,192,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,159500,-93.607257,42.0385 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7332,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1959,1959,Gable,CompShg,WdShing,Wd Shng,BrkFace,207,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,450,864,GasA,Excellent,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,168,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,120000,-93.608422,42.038497 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,7100,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1957,1957,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,108,816,GasA,Typical,Y,FuseA,816,0,0,816,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,129900,-93.609073,42.038297 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Above_Average,Above_Average,1961,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,104,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,400,1313,GasA,Typical,Y,SBrkr,1773,0,0,1773,1,0,2,0,3,1,Typical,6,Min2,2,Typical,Attchd,RFn,2,418,Typical,Typical,Paved,355,98,0,0,144,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,183000,-93.606508,42.041874 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,11650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,PosA,Norm,OneFam,One_Story,Good,Average,1959,1959,Hip,CompShg,Plywood,Plywood,BrkCmn,58,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,FuseA,1472,0,0,1472,0,0,2,0,2,1,Good,5,Typ,1,Good,Attchd,Unf,2,484,Typical,Typical,Paved,0,68,0,0,227,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,157500,-93.605384,42.0416529 +Two_Story_1946_and_Newer,Residential_Low_Density,0,18275,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1962,1998,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,802,1438,GasA,Typical,Y,SBrkr,1900,548,0,2448,1,0,3,0,3,1,Typical,9,Typ,2,Good,Attchd,RFn,2,441,Typical,Typical,Paved,520,102,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,277500,-93.60674,42.040768 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,47,16321,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1957,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,BLQ,2,Unf,0,207,1484,GasA,Typical,Y,SBrkr,1600,0,0,1600,1,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,RFn,1,319,Typical,Typical,Paved,288,258,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,207500,-93.605727,42.03902 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,12144,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1950,1950,Gable,CompShg,BrkComm,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,455,910,GasA,Good,Y,SBrkr,910,611,0,1521,0,0,1,1,3,1,Good,6,Min2,0,No_Fireplace,Detchd,Unf,1,597,Fair,Typical,Paved,199,0,168,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,147500,-93.609054,42.036098 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1949,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,720,720,GasA,Typical,N,FuseA,720,472,0,1192,0,0,1,1,4,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,135000,-93.610503,42.035782 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Good,1950,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseF,1040,0,0,1040,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,625,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,109500,-93.608903,42.035996 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Below_Average,Average,1950,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1040,0,0,1040,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,420,Typical,Typical,Paved,0,29,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,109900,-93.608904,42.036099 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7560,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Average,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,671,1040,GasA,Typical,Y,FuseA,1040,0,0,1040,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,286,Typical,Typical,Paved,140,0,252,0,0,0,No_Pool,Good_Wood,None,0,7,2006,WD ,Normal,133700,-93.606743,42.036053 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1950,1950,Gable,CompShg,BrkFace,Stone,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,81400,-93.609053,42.034805 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1949,1950,Gable,CompShg,Stucco,Stucco,BrkFace,340,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,Wall,Fair,N,FuseA,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,87500,-93.609053,42.03471 +Duplex_All_Styles_and_Ages,Residential_Low_Density,60,8544,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Fair,Below_Average,1950,1950,Gable,CompShg,Stucco,Stone,None,0,Typical,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,93500,-93.609053,42.034662 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8512,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Norm,Norm,Duplex,One_Story,Average,Average,1960,1960,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Fair,No,Unf,7,Unf,0,1556,1556,GasA,Typical,Y,SBrkr,1556,0,0,1556,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,119000,-93.606892,42.034829 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7945,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Above_Average,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,BLQ,506,465,1150,GasA,Excellent,Y,FuseA,1150,0,0,1150,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,141000,-93.606893,42.034757 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,One_Story,Average,Below_Average,1961,1961,Hip,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,369,1150,GasA,Typical,Y,SBrkr,1150,0,0,1150,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,162,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,146000,-93.606744,42.034708 +Two_Story_1946_and_Newer,Residential_Low_Density,69,7590,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,266,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,148,660,GasA,Typical,Y,SBrkr,660,688,0,1348,0,0,1,1,3,1,Typical,6,Typ,1,Fair,Attchd,RFn,2,453,Typical,Typical,Paved,188,108,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,155000,-93.603704,42.036792 +Split_Foyer,Residential_Low_Density,69,10205,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,North_Ames,Norm,Norm,OneFam,SFoyer,Average,Average,1962,1962,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,141,925,GasA,Typical,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,300,Typical,Typical,Paved,150,72,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,134500,-93.605839,42.03548 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,74,7400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Good,Above_Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,236,1045,GasA,Good,Y,SBrkr,1045,0,0,1045,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,120000,-93.604687,42.034618 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,OneFam,One_Story,Average,Above_Average,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,396,864,GasA,Good,Y,SBrkr,864,0,0,864,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,2,2006,WD ,Normal,105000,-93.604568,42.034753 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,70,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,North_Ames,Artery,Norm,TwoFmCon,SFoyer,Average,Average,1962,1962,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,72,1025,GasA,Typical,Y,SBrkr,1025,0,0,1025,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,96,80,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,124000,-93.6050674,42.0345614 +Two_Story_1945_and_Older,Residential_Medium_Density,62,9856,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1900,2005,Hip,CompShg,CemntBd,CmentBd,None,0,Good,Good,PConc,Fair,Typical,No,Unf,7,Unf,0,716,716,GasA,Excellent,Y,FuseA,1007,1007,0,2014,0,0,2,0,5,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,72,167,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,169000,-93.618539,42.033267 +Two_Story_1945_and_Older,Residential_Medium_Density,57,9906,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Below_Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,686,686,GasA,Fair,N,SBrkr,810,518,0,1328,0,0,1,0,3,1,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,1,210,Typical,Typical,Paved,0,172,60,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Family,107000,-93.6195519,42.0334994 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1948,1950,Gable,CompShg,MetalSd,MetalSd,Stone,264,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,936,1212,GasA,Good,Y,FuseA,1226,442,0,1668,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,140,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,135000,-93.617164,42.033385 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,5520,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1920,1980,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Fair,No,LwQ,4,Unf,0,863,1147,GasA,Typical,N,SBrkr,1147,510,0,1657,0,0,1,0,4,1,Fair,9,Typ,1,Typical,Detchd,Unf,1,162,Fair,Fair,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,111500,-93.61852,42.032144 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1900,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,399,780,GasA,Excellent,Y,SBrkr,940,476,0,1416,0,1,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,400,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,156500,-93.617139,42.032346 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1925,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Good,No,LwQ,4,Unf,0,712,728,GasA,Excellent,Y,SBrkr,832,809,0,1641,0,1,1,1,3,1,Excellent,6,Typ,1,Good,Detchd,Unf,2,546,Fair,Typical,Paved,0,0,234,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,178000,-93.618489,42.03139 +Two_Story_1945_and_Older,Residential_Medium_Density,58,6451,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1900,1970,Gable,CompShg,AsbShng,Wd Sdng,None,0,Typical,Typical,Stone,Typical,Typical,No,Rec,6,Unf,0,504,712,GasA,Good,Y,SBrkr,848,580,0,1428,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Fin,2,576,Typical,Typical,Paved,264,0,84,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,139900,-93.619513,42.031506 +Two_Story_1945_and_Older,Residential_Medium_Density,66,3960,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Very_Good,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,502,502,GasA,Typical,N,SBrkr,502,502,0,1004,0,0,1,0,2,1,Good,5,Typ,1,Poor,Detchd,Unf,1,200,Fair,Typical,Dirt_Gravel,280,0,68,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,105000,-93.615681,42.031347 +One_Story_1945_and_Older,Residential_Medium_Density,70,5684,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1930,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,813,813,GasA,Excellent,Y,FuseA,813,0,0,813,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Detchd,Unf,1,270,Fair,Fair,Dirt_Gravel,0,113,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,110000,-93.618799,42.03121 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,56,7745,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_and_Half_Fin,Below_Average,Above_Average,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,938,938,GasA,Good,N,SBrkr,1084,867,0,1951,0,0,2,0,4,2,Fair,9,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Partial_Pavement,0,6,28,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,111500,-93.618335,42.030396 +One_Story_1945_and_Older,Residential_Medium_Density,56,7741,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,BLQ,2,Rec,72,817,1032,GasA,Good,N,FuseA,1032,0,0,1032,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,112,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,COD,Abnorml,108000,-93.618334,42.030463 +One_Story_1945_and_Older,Residential_Medium_Density,50,5633,Pave,Paved,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Good,1925,1950,Gable,CompShg,MetalSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,844,844,GasA,Typical,Y,SBrkr,844,0,0,844,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,50,81,123,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,111500,-93.615638,42.031196 +Two_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,Gravel,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Artery,Norm,OneFam,Two_Story,Average,Above_Average,1880,1991,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Fair,Fair,No,Unf,7,Unf,0,636,636,GasA,Typical,Y,FuseA,1089,661,0,1750,0,0,1,0,3,1,Excellent,8,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Poor,Dirt_Gravel,0,0,293,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Abnorml,124000,-93.614011,42.033623 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,60,7200,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Below_Average,Average,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,576,576,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,RFn,1,528,Typical,Typical,Paved,0,0,0,0,115,0,No_Pool,No_Fence,None,0,8,2006,COD,Normal,105000,-93.6145858,42.0330459 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,40,4400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,648,648,GasA,Typical,Y,FuseA,734,384,0,1118,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,116000,-93.614615,42.032305 +Two_Story_1945_and_Older,Residential_Medium_Density,42,7614,Pave,Gravel,Regular,Lvl,AllPub,Inside,Mod,Old_Town,Norm,Norm,OneFam,Two_Story,Fair,Average,1905,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,Mn,Unf,7,Unf,0,738,738,GasA,Good,Y,FuseA,714,662,0,1376,0,0,1,0,2,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Dirt_Gravel,0,0,104,0,225,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,96900,-93.613905,42.030267 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,One_Story,Average,Good,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,384,960,GasA,Typical,Y,FuseA,960,0,0,960,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,135500,-93.607862,42.032064 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1924,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,145,Typical,Good,PConc,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Excellent,Y,SBrkr,816,750,0,1566,0,0,1,1,5,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,2,450,Typical,Typical,Paved,24,0,296,0,0,0,No_Pool,Minimum_Privacy,None,0,8,2006,WD ,Normal,139000,-93.608833,42.032267 +One_Story_1945_and_Older,Residential_Medium_Density,52,7830,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Average,1921,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,76,492,GasA,Typical,Y,SBrkr,492,0,0,492,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,200,Fair,Typical,Dirt_Gravel,0,0,78,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,61500,-93.610354,42.031964 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,56,9576,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1945,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Fair,Typical,No,Rec,6,Unf,0,460,770,GasA,Typical,Y,SBrkr,885,297,0,1182,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,378,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,120000,-93.608938,42.030368 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,49,5820,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Very_Good,1955,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,906,1162,GasA,Excellent,Y,SBrkr,1163,0,0,1163,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,220,Fair,Typical,Paved,142,98,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,126175,-93.607744,42.03146 +One_Story_1945_and_Older,Residential_Medium_Density,48,5747,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Fair,Below_Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,798,798,GasA,Good,Y,SBrkr,840,0,0,840,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,Detchd,Unf,1,250,Typical,Fair,Dirt_Gravel,112,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,64000,-93.606803,42.031138 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,69,9142,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,TwoFmCon,Two_Story,Above_Average,Very_Good,1910,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,Stone,Fair,Typical,No,Unf,7,Unf,0,1020,1020,GasA,Good,N,FuseP,908,1020,0,1928,0,0,2,0,4,2,Fair,9,Typ,0,No_Fireplace,Detchd,Unf,1,440,Poor,Poor,Paved,0,60,112,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,137000,-93.618293,42.029142 +Two_Story_1945_and_Older,Residential_Medium_Density,60,9600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Below_Average,Poor,1900,1950,Gable,CompShg,AsbShng,Stucco,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1095,1095,GasW,Fair,N,SBrkr,1095,679,0,1774,1,0,2,0,4,2,Typical,8,Min2,0,No_Fireplace,More_Than_Two_Types,Unf,3,779,Fair,Fair,Dirt_Gravel,0,0,90,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,87000,-93.616886,42.027993 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,60,10800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Good,1914,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Rec,6,Unf,0,490,880,GasW,Fair,N,SBrkr,880,888,0,1768,0,0,1,1,2,1,Typical,6,Typ,2,Typical,Detchd,Unf,2,320,Typical,Typical,Dirt_Gravel,0,341,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,163000,-93.613889,42.029136 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,70,6300,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Above_Average,1910,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1226,1226,GasA,Excellent,Y,SBrkr,1226,878,0,2104,0,0,2,0,5,1,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,2,432,Fair,Typical,Partial_Pavement,0,341,88,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,155000,-93.615361,42.028951 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,63,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1900,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,540,540,GasA,Good,N,FuseA,889,551,0,1440,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,352,Fair,Typical,Paved,0,0,77,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,79000,-93.615365,42.029281 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,65,8850,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Above_Average,1916,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,815,815,GasA,Excellent,Y,SBrkr,815,875,0,1690,0,0,1,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,225,Typical,Typical,Paved,0,0,330,0,0,0,No_Pool,No_Fence,None,0,7,2006,ConLw,Normal,144000,-93.615337,42.027953 +Two_Story_1945_and_Older,Residential_Medium_Density,60,3600,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1910,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,684,684,GasA,Excellent,N,FuseA,684,684,0,1368,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Fair,Dirt_Gravel,0,158,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,114504,-93.6127958,42.0281148 +Two_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,Two_Story,Good,Good,1920,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,596,596,GasA,Excellent,Y,SBrkr,998,764,0,1762,1,0,1,1,4,1,Good,8,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Dirt_Gravel,36,0,221,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,157000,-93.613763,42.027953 +One_Story_1945_and_Older,Residential_Medium_Density,84,11340,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Above_Average,Average,1923,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1200,1200,GasA,Typical,Y,FuseA,1200,0,0,1200,0,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,312,Fair,Fair,Paved,0,0,228,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Family,125000,-93.608901,42.028963 +Two_and_Half_Story_All_Ages,Residential_Medium_Density,90,22950,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,Two_and_Half_Fin,Very_Excellent,Excellent,1892,1993,Gable,WdShngl,Wd Sdng,Wd Sdng,None,0,Good,Good,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,1107,1107,GasA,Excellent,Y,SBrkr,1518,1518,572,3608,0,0,2,1,4,1,Excellent,12,Typ,2,Typical,Detchd,Unf,3,840,Excellent,Typical,Paved,0,260,0,0,410,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,475000,-93.610438,42.029025 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,33,5976,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,Duplex,Two_Story,Average,Good,1920,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,624,624,GasA,Good,N,FuseA,624,624,0,1248,0,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,130,256,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,93500,-93.609665,42.027992 +One_Story_1946_and_Newer_All_Styles,Residential_Medium_Density,65,9750,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Norm,Norm,OneFam,One_Story,Average,Average,1958,1958,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,960,960,GasA,Excellent,Y,SBrkr,960,0,0,960,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,624,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Gar2,4500,7,2006,WD ,Normal,125000,-93.606195,42.0272703 +One_and_Half_Story_Finished_All_Ages,C_all,63,4761,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Unf,Fair,Fair,1918,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fair,Fair,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1020,1020,GasA,Fair,N,FuseP,1020,0,0,1020,0,0,1,0,2,1,Fair,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,105,0,0,0,No_Pool,No_Fence,None,0,10,2006,ConLD,Normal,64500,-93.606869,42.02297 +One_Story_1945_and_Older,Residential_Low_Density,0,7446,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_Story,Below_Average,Average,1941,1950,Gable,CompShg,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,522,788,GasA,Typical,Y,FuseA,788,0,0,788,0,0,1,0,2,1,Typical,4,Typ,2,Typical,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,4,2006,WD ,Abnorml,100000,-93.628478,42.03405 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,65,6435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,972,972,GasA,Good,Y,SBrkr,972,605,0,1577,0,0,1,0,3,1,Fair,6,Typ,1,Good,Detchd,Unf,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,140200,-93.626022,42.03179 +Two_Story_1945_and_Older,Residential_Low_Density,69,11737,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1924,1996,Gambrel,CompShg,BrkComm,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,848,848,GasW,Typical,N,SBrkr,1017,810,0,1827,0,0,1,0,2,1,Typical,9,Typ,1,Good,Detchd,Unf,1,240,Fair,Typical,Paved,27,36,42,0,0,0,No_Pool,Good_Privacy,None,0,5,2006,WD ,Normal,202500,-93.627385,42.030786 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,5000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Brookside,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1947,1950,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Good,CBlock,Typical,Typical,No,ALQ,1,Unf,0,605,1004,GasA,Excellent,Y,SBrkr,1004,660,0,1664,0,0,2,0,3,1,Typical,7,Typ,2,Good,Detchd,Unf,2,420,Typical,Typical,Paved,0,24,36,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,159000,-93.625576,42.033306 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,65,7800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,TwoFmCon,One_and_Half_Fin,Average,Good,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Good,Typical,Mn,Rec,6,Unf,0,286,793,GasA,Typical,Y,SBrkr,793,325,0,1118,1,0,1,0,3,1,Typical,5,Typ,1,Good,Detchd,Unf,2,410,Typical,Typical,Paved,0,0,0,0,271,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,119900,-93.620433,42.033369 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1934,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,Y,SBrkr,816,0,360,1176,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,2,528,Typical,Typical,Paved,112,0,0,0,0,0,No_Pool,Minimum_Privacy,Shed,400,9,2006,WD ,Normal,114500,-93.623642,42.032299 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,0,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Good,Typical,No,Unf,7,Unf,0,896,896,GasA,Good,Y,FuseA,896,448,0,1344,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Paved,200,114,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,115000,-93.62454,42.031499 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Average,1930,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Good,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,381,728,GasA,Excellent,Y,SBrkr,728,434,0,1162,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,258,Fair,Poor,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,ConLI,Abnorml,75000,-93.624553,42.032396 +One_Story_1945_and_Older,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,279,768,GasA,Typical,N,SBrkr,1015,0,0,1015,0,0,1,0,3,1,Typical,6,Min1,0,No_Fireplace,Detchd,Unf,1,450,Typical,Typical,Paved,0,0,112,0,120,0,No_Pool,Minimum_Privacy,Shed,620,7,2006,WD ,Abnorml,88000,-93.624553,42.032478 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1947,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,N,SBrkr,672,240,0,912,0,0,1,0,2,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,80500,-93.623481,42.031415 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,52,6240,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1928,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1042,1042,GasA,Excellent,Y,SBrkr,1042,534,0,1576,0,0,1,0,3,1,Typical,8,Typ,1,Good,Detchd,Unf,1,225,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Family,149000,-93.621484,42.031436 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,51,6120,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1930,1984,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,Unf,7,Unf,0,741,741,GasA,Good,Y,SBrkr,741,583,0,1324,0,0,1,0,3,1,Good,7,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Typical,Paved,0,0,55,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,110000,-93.622415,42.032418 +One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Old_Town,Artery,Norm,OneFam,One_Story,Above_Average,Above_Average,1925,1980,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,706,1103,GasA,Good,Y,SBrkr,1103,0,0,1103,0,0,1,0,2,1,Good,5,Typ,1,Good,Detchd,Unf,2,440,Typical,Typical,Paved,166,120,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,110500,-93.620411,42.032417 +One_Story_1945_and_Older,Residential_Medium_Density,50,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Good,1921,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,0,616,GasA,Good,Y,SBrkr,616,0,0,616,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,205,Typical,Typical,Paved,0,0,129,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,89000,-93.6243778,42.0310818 +One_and_Half_Story_Unfinished_All_Ages,Residential_Medium_Density,58,6380,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_and_Half_Unf,Average,Above_Average,1922,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,993,993,GasA,Typical,Y,FuseA,1048,0,0,1048,0,0,1,0,2,1,Typical,5,Typ,1,Good,Detchd,Unf,1,280,Typical,Typical,Paved,0,0,116,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,113000,-93.622386,42.030389 +One_Story_1945_and_Older,Residential_Low_Density,50,11672,Pave,Paved,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,Brookside,Norm,Norm,OneFam,One_Story,Average,Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,816,816,GasA,Typical,Y,FuseA,816,0,0,816,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,210,Fair,Fair,Dirt_Gravel,168,0,112,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,109000,-93.62577,42.029526 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,90,33120,Pave,No_Alley_Access,Irregular,Lvl,AllPub,Inside,Gtl,Old_Town,RRAn,Feedr,OneFam,One_and_Half_Fin,Above_Average,Average,1962,1962,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1595,1595,GasA,Typical,Y,SBrkr,1611,875,0,2486,0,0,2,0,5,1,Typical,8,Typ,1,Good,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,220000,-93.622773,42.02686 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,60,9873,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Brookside,RRAn,Norm,TwoFmCon,One_Story,Below_Average,Average,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,160,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,171,960,GasW,Typical,N,SBrkr,960,0,0,960,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,288,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,129000,-93.625436,42.028544 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,59,5310,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Old_Town,Feedr,Norm,OneFam,One_and_Half_Fin,Above_Average,Very_Good,1910,2003,Hip,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Fair,No,Unf,7,Unf,0,485,485,GasA,Good,Y,SBrkr,1001,634,0,1635,0,0,1,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,1,255,Fair,Typical,Paved,394,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,117000,-93.621562,42.026886 +One_Story_1945_and_Older,Residential_Medium_Density,153,4118,Pave,Gravel,Slightly_Irregular,Bnk,AllPub,Corner,Mod,Old_Town,Feedr,Norm,OneFam,One_Story,Below_Average,Below_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,693,693,Grav,Fair,N,FuseA,693,0,0,693,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,20,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,52500,-93.622624,42.02689 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,10320,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,596,596,GasA,Poor,Y,FuseF,834,596,0,1430,0,0,2,0,3,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,2,370,Fair,Fair,Paved,218,0,0,0,210,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,107000,-93.626858,42.025258 +Two_Story_1945_and_Older,Residential_Medium_Density,60,7518,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR3,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1910,2004,Gable,CompShg,AsbShng,Plywood,None,0,Fair,Good,BrkTil,Fair,Fair,No,Unf,7,Unf,0,396,396,GasA,Good,Y,SBrkr,665,665,0,1330,0,0,1,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,1,390,Typical,Typical,Dirt_Gravel,0,72,45,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,116500,-93.626631,42.025459 +One_Story_1945_and_Older,Residential_Medium_Density,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Average,Below_Average,1919,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,610,610,GasA,Excellent,N,FuseA,819,0,0,819,0,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Abnorml,72000,-93.6275675,42.0249549 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,8600,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Above_Average,1937,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,780,780,GasA,Typical,Y,SBrkr,780,596,0,1376,0,0,2,0,3,1,Typical,7,Typ,1,Good,Detchd,Unf,1,198,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,119500,-93.6273219,42.0242386 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1938,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,803,803,GasA,Excellent,Y,SBrkr,803,557,0,1360,0,0,1,1,2,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,297,Typical,Typical,Paved,0,65,190,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,125500,-93.6255067,42.0242578 +One_Story_1945_and_Older,Residential_Medium_Density,60,9786,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Fair,Below_Average,1922,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Fair,No,Unf,7,Unf,0,1007,1007,GasA,Fair,N,SBrkr,1077,0,0,1077,0,0,1,0,3,1,Typical,6,Typ,1,Good,Detchd,Unf,1,210,Typical,Fair,Partial_Pavement,0,100,48,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,91000,-93.628416,42.023117 +Two_Family_conversion_All_Styles_and_Ages,Residential_Medium_Density,60,6780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Very_Good,1935,1982,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,30,520,GasA,Good,N,SBrkr,520,0,234,754,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,53,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,84500,-93.629501,42.022825 +One_Story_1945_and_Older,Residential_Medium_Density,60,7200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1930,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,641,984,GasA,Typical,Y,FuseF,984,0,0,984,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Dirt_Gravel,0,0,164,0,0,0,No_Pool,No_Fence,None,0,3,2006,ConLI,Family,90000,-93.6265888,42.0238089 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,82,12375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Average,1951,1951,Gable,CompShg,HdBoard,HdBoard,Stone,41,Typical,Fair,CBlock,Typical,Typical,No,BLQ,2,Unf,0,477,806,GasA,Typical,Y,SBrkr,1081,341,0,1422,1,0,1,0,3,1,Typical,7,Typ,1,Typical,Detchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,137500,-93.657017,42.034411 +Duplex_All_Styles_and_Ages,Residential_Low_Density,120,11136,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Feedr,Duplex,One_Story,Above_Average,Average,1964,1964,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1921,1921,GasA,Typical,Y,SBrkr,1921,0,0,1921,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,180,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,150000,-93.65565,42.034576 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,21370,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1950,1950,Gable,CompShg,Wd Sdng,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1640,0,0,1640,0,0,1,0,3,1,Typical,7,Min1,1,Good,Attchd,RFn,2,394,Typical,Typical,Paved,0,0,225,0,0,0,No_Pool,No_Fence,Shed,600,6,2006,WD ,Normal,131000,-93.65815,42.033296 +Split_or_Multilevel,Residential_Low_Density,92,6930,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,SLvl,Average,Below_Average,1958,1958,Hip,CompShg,Wd Sdng,ImStucc,BrkFace,120,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Rec,294,468,1062,GasA,Excellent,Y,FuseF,1352,0,0,1352,0,1,1,0,3,1,Good,6,Min2,0,No_Fireplace,BuiltIn,Unf,1,288,Typical,Typical,Paved,168,0,294,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,130000,-93.6578344,42.0331453 +One_Story_1945_and_Older,Residential_Low_Density,55,8250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1935,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,1032,0,0,1032,0,0,1,0,2,1,Typical,6,Typ,1,Typical,Detchd,Unf,1,260,Typical,Typical,Paved,0,0,121,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,125000,-93.6600396,42.0277962 +One_Story_1945_and_Older,Residential_Low_Density,50,5220,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Average,Fair,1936,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,830,830,GasA,Good,Y,SBrkr,879,0,0,879,0,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,180,Typical,Typical,Partial_Pavement,0,108,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,80000,-93.655885,42.028002 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,5500,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_Story,Good,Average,2004,2004,Shed,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Good,Mn,GLQ,3,LwQ,373,190,1073,GasA,Excellent,Y,SBrkr,1073,0,0,1073,1,0,2,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,246,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,157000,-93.655786,42.027997 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,11235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Below_Average,Average,1963,1979,Gable,CompShg,HdBoard,HdBoard,BrkFace,51,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,504,1051,GasA,Good,Y,SBrkr,1382,0,0,1382,0,0,1,1,3,1,Typical,6,Typ,1,Poor,Attchd,Unf,2,459,Typical,Typical,Paved,0,82,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,148000,-93.67555,42.033521 +Duplex_All_Styles_and_Ages,Residential_Low_Density,72,10791,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Below_Average,Average,1967,1967,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,SBrkr,1296,0,0,1296,0,0,2,0,2,2,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,2,516,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,Shed,500,10,2006,WD ,Normal,90000,-93.671213,42.033388 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8414,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Above_Average,Very_Good,1963,2003,Hip,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,No,GLQ,3,Unf,0,396,1059,GasA,Typical,Y,SBrkr,1068,0,0,1068,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,RFn,1,264,Typical,Typical,Paved,192,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,2,2006,WD ,Normal,154500,-93.675399,42.0333 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11327,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1967,1967,Hip,CompShg,HdBoard,HdBoard,BrkFace,305,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,285,1064,GasA,Typical,Y,SBrkr,1064,0,0,1064,0,1,1,0,3,1,Typical,6,Typ,1,Typical,Attchd,Unf,2,528,Typical,Typical,Paved,314,48,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,153600,-93.674456,42.03224 +Split_or_Multilevel,Residential_Low_Density,96,11777,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Average,Above_Average,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,97,Typical,Typical,CBlock,Typical,Typical,Av,LwQ,4,ALQ,551,285,1164,GasA,Excellent,Y,SBrkr,1320,0,0,1320,1,0,1,0,3,1,Typical,6,Typ,2,Fair,Attchd,RFn,2,564,Typical,Typical,Paved,160,68,240,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Abnorml,164500,-93.674517,42.032304 +Split_or_Multilevel,Residential_Low_Density,80,10366,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Good,1964,1964,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Good,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,456,912,GasA,Typical,Y,SBrkr,934,0,0,934,0,1,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,1,336,Typical,Typical,Paved,77,0,0,0,0,0,No_Pool,Good_Privacy,Shed,500,7,2006,WD ,Normal,132000,-93.675408,42.032383 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1966,1966,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,286,1059,GasA,Good,Y,SBrkr,1059,0,0,1059,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Fin,1,286,Typical,Typical,Paved,0,88,0,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Abnorml,142500,-93.675606,42.031356 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11553,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1968,1968,Hip,CompShg,Plywood,Plywood,BrkFace,188,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,378,1051,GasA,Typical,Y,SBrkr,1159,0,0,1159,0,0,1,1,3,1,Typical,7,Typ,1,Fair,Attchd,Unf,1,336,Typical,Typical,Paved,466,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,158000,-93.676455,42.030611 +Two_Story_1946_and_Newer,Residential_Low_Density,74,7844,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkFace,203,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,463,672,GasA,Typical,Y,SBrkr,672,728,0,1400,0,0,1,1,3,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,149500,-93.672278,42.032216 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,9535,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,450,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,LwQ,982,0,1176,GasA,Typical,Y,SBrkr,1458,0,0,1458,1,0,1,1,3,1,Typical,7,Typ,1,Typical,Attchd,Unf,2,512,Typical,Typical,Paved,284,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,165000,-93.674385,42.030702 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10335,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1968,1993,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,891,1461,GasA,Good,Y,SBrkr,1721,0,0,1721,0,0,2,1,3,1,Typical,7,Min1,1,Typical,Attchd,RFn,2,440,Typical,Typical,Paved,0,96,180,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,180000,-93.673601,42.031362 +Split_or_Multilevel,Residential_Low_Density,0,7176,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,Typical,Typical,CBlock,Typical,Good,Gd,GLQ,3,Unf,0,166,960,GasA,Fair,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Typical,6,Typ,1,Fair,Detchd,Unf,2,616,Typical,Typical,Paved,131,0,0,0,180,0,No_Pool,Good_Privacy,None,0,7,2006,WD ,Normal,160500,-93.672645,42.031347 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,9662,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Average,Below_Average,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1967,1967,GasA,Typical,Y,SBrkr,1967,0,0,1967,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Attchd,Fin,2,580,Typical,Typical,Paved,170,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,156500,-93.670204,42.030231 +Duplex_All_Styles_and_Ages,Residential_Low_Density,75,8235,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,One_Story,Average,Below_Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,99,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,1466,1949,GasA,Typical,Y,SBrkr,1949,0,0,1949,0,0,2,0,6,2,Typical,10,Typ,0,No_Fireplace,Attchd,RFn,2,586,Typical,Typical,Paved,32,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,157000,-93.670514,42.030232 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,0,13650,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Good,Good,CBlock,Typical,Typical,No,ALQ,1,BLQ,441,554,1052,GasA,Excellent,Y,SBrkr,1252,668,0,1920,1,0,2,0,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,451,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,172500,-93.665569,42.033285 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,13125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Below_Average,1957,2000,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,67,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,BLQ,682,284,1134,GasA,Excellent,Y,SBrkr,1803,0,0,1803,1,0,2,0,3,1,Typical,8,Maj1,1,Typical,Attchd,RFn,2,484,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,1,2006,WD ,Normal,155000,-93.665505,42.03247 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,102,17920,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Below_Average,1955,1974,Hip,CompShg,Wd Sdng,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,ALQ,1,Rec,1085,372,1763,GasA,Typical,Y,SBrkr,1779,0,0,1779,1,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,Unf,2,454,Typical,Typical,Paved,0,418,0,0,312,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,170000,-93.66563,42.032291 +One_Story_1945_and_Older,Residential_Low_Density,0,17529,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1924,1950,Gable,CompShg,BrkFace,Wd Sdng,Stone,65,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,872,872,GasA,Fair,N,FuseF,872,0,0,872,0,0,1,0,2,1,Fair,5,Mod,1,Good,Detchd,Unf,1,322,Fair,Fair,Dirt_Gravel,0,0,116,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,105000,-93.667081,42.032307 +Split_or_Multilevel,Residential_Low_Density,0,10246,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,OneFam,SLvl,Below_Average,Excellent,1965,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Typical,Good,Av,GLQ,3,Unf,0,0,648,GasA,Excellent,Y,SBrkr,960,0,0,960,1,1,0,0,0,1,Typical,3,Typ,0,No_Fireplace,Attchd,Unf,1,364,Typical,Typical,Paved,88,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,145000,-93.669544,42.033686 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14175,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Mod,Sawyer,Norm,Norm,OneFam,One_Story,Average,Above_Average,1956,1987,Gable,CompShg,CemntBd,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,200,1188,GasA,Good,Y,SBrkr,1437,0,0,1437,1,0,1,1,3,1,Typical,6,Min2,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,304,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,168000,-93.66412,42.032478 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,20355,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Above_Average,1967,1967,Gable,Tar&Grv,Plywood,Plywood,BrkFace,123,Typical,Typical,CBlock,Good,Typical,Av,Rec,6,ALQ,826,229,1865,GasA,Typical,Y,SBrkr,1830,0,0,1830,1,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,Unf,2,521,Typical,Typical,Paved,0,115,168,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,278000,-93.668392,42.031635 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,87,13050,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1963,1963,Flat,Tar&Grv,WdShing,Wd Shng,None,0,Typical,Typical,CBlock,Good,Typical,Av,Rec,6,ALQ,850,46,1000,GasA,Excellent,Y,SBrkr,1000,0,0,1000,1,0,1,0,1,1,Typical,4,Typ,2,Typical,Attchd,Unf,2,575,Typical,Typical,Paved,238,0,148,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,164000,-93.667082,42.032157 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,81,15593,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Clear_Creek,Norm,Norm,OneFam,One_and_Half_Fin,Good,Below_Average,1953,1953,Gable,CompShg,BrkFace,AsbShng,None,0,Good,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,701,1304,GasW,Typical,Y,SBrkr,1304,983,0,2287,0,0,2,0,3,1,Typical,7,Typ,1,Typical,Attchd,Fin,2,667,Typical,Typical,Paved,0,21,114,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,225000,-93.662039,42.032575 +Split_Foyer,Residential_Low_Density,72,10820,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,OneFam,SFoyer,Average,Good,1971,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,153,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Rec,159,88,782,GasA,Excellent,Y,SBrkr,810,0,0,810,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,130000,-93.678165,42.029453 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,100,13350,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,OneFam,One_Story,Average,Average,1974,1974,Hip,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,102,864,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,1,Fair,Attchd,Unf,2,440,Typical,Typical,Paved,241,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,142500,-93.676352,42.028836 +One_and_Half_Story_PUD_All_Ages,Residential_Low_Density,0,1700,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,Twnhs,One_and_Half_Fin,Good,Average,1980,1981,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,33,430,GasA,Typical,Y,SBrkr,880,680,140,1700,1,0,2,1,2,1,Good,7,Typ,0,No_Fireplace,Basment,Fin,1,450,Good,Typical,Paved,188,36,0,0,200,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,148400,-93.674402,42.023651 +One_Story_PUD_1946_and_Newer,Residential_Low_Density,0,5271,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1986,1986,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,371,1453,GasA,Good,Y,SBrkr,1453,0,0,1453,1,0,1,1,2,1,Good,6,Typ,1,Typical,Attchd,RFn,2,445,Typical,Typical,Paved,0,80,0,0,184,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,190000,-93.676681,42.024705 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,9375,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,No,Rec,6,Unf,0,168,967,GasA,Excellent,Y,SBrkr,1350,0,0,1350,0,0,1,1,3,1,Typical,6,Typ,1,Good,Attchd,RFn,2,504,Typical,Typical,Paved,237,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,146000,-93.666035,42.02653 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,62,6488,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1942,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,LwQ,4,Unf,0,569,799,GasA,Excellent,N,FuseA,799,351,0,1150,0,0,1,0,3,1,Typical,6,Mod,2,Typical,BuiltIn,Unf,1,215,Typical,Typical,Paved,264,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Family,128000,-93.665153,42.027664 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,7800,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,Two_Story,Average,Very_Good,1948,2002,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Good,No,GLQ,3,Unf,0,293,896,GasA,Excellent,Y,SBrkr,1112,896,0,2008,1,0,3,0,3,1,Excellent,8,Typ,0,No_Fireplace,Attchd,Unf,1,230,Typical,Typical,Paved,103,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,225000,-93.662765,42.026667 +Split_or_Multilevel,Residential_Low_Density,0,19690,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Above_Average,Good,1966,1966,Flat,Tar&Grv,Plywood,Plywood,None,0,Good,Good,CBlock,Good,Typical,Av,Unf,7,Unf,0,697,697,GasA,Typical,Y,SBrkr,1575,626,0,2201,0,0,2,0,4,1,Good,8,Typ,1,Good,Attchd,Unf,2,432,Good,Good,Paved,586,236,0,0,0,738,Good,Good_Privacy,None,0,8,2006,WD ,Alloca,274970,-93.663475,42.026614 +Two_Story_1945_and_Older,Residential_Low_Density,114,19950,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1928,1950,Gable,CompShg,WdShing,Plywood,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,672,672,GasA,Excellent,Y,SBrkr,1337,672,0,2009,0,0,2,0,4,1,Typical,8,Typ,2,Good,More_Than_Two_Types,Unf,3,795,Typical,Typical,Partial_Pavement,0,42,0,0,180,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,235000,-93.661562,42.02821 +Two_and_Half_Story_All_Ages,Residential_Low_Density,60,19800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_and_Half_Unf,Above_Average,Very_Good,1935,1990,Gable,CompShg,BrkFace,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Rec,6,Unf,0,1411,1836,GasA,Good,Y,SBrkr,1836,1836,0,3672,0,0,3,1,5,1,Good,7,Typ,2,Good,Detchd,Unf,2,836,Typical,Typical,Paved,684,80,32,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,415000,-93.660664,42.028191 +Split_or_Multilevel,Residential_Low_Density,78,11679,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,SLvl,Average,Average,1962,1962,Gable,CompShg,Plywood,Plywood,Stone,96,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Rec,1164,0,1776,GasA,Excellent,Y,SBrkr,1560,0,0,1560,0,1,2,0,3,1,Typical,6,Min2,1,Fair,Attchd,Fin,2,528,Typical,Typical,Paved,453,253,144,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,182000,-93.660227,42.026458 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12048,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Above_Average,1952,2002,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,232,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1488,0,0,1488,0,0,1,0,3,1,Typical,7,Typ,1,Excellent,Attchd,RFn,2,569,Typical,Typical,Paved,0,189,36,0,348,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,135000,-93.666255,42.025846 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,10519,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Very_Good,1955,1999,Hip,CompShg,MetalSd,MetalSd,Stone,164,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1057,1057,GasA,Good,Y,SBrkr,1057,0,0,1057,0,1,1,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,137000,-93.667188,42.025524 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Below_Average,1954,1972,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Fair,No,Rec,6,Unf,0,550,994,GasA,Good,Y,SBrkr,1216,639,0,1855,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,325,Typical,Typical,Paved,182,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,136900,-93.666095,42.025102 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,75,9525,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,532,1000,GasA,Typical,Y,SBrkr,1068,541,0,1609,0,0,1,1,5,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,305,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,149900,-93.666096,42.025197 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,57,6420,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1952,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Excellent,Good,Mn,LwQ,4,ALQ,551,219,980,GasA,Fair,Y,FuseA,1148,0,0,1148,0,1,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,123500,-93.660187,42.026308 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,8335,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Average,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1124,0,0,1124,0,0,1,0,3,1,Typical,5,Min2,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,36,190,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,93000,-93.6627359,42.0247523 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,50,7585,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Average,Fair,1948,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Fair,Fair,Mn,Unf,7,Unf,0,810,810,GasA,Fair,Y,FuseA,1002,454,0,1456,1,1,1,0,4,1,Typical,7,Typ,1,Typical,Detchd,Unf,1,280,Typical,Typical,Partial_Pavement,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,91500,-93.658506,42.022824 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11200,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,One_Story,Above_Average,Average,1985,1985,Gable,CompShg,Wd Sdng,Wd Shng,BrkFace,85,Good,Typical,CBlock,Good,Typical,No,GLQ,3,Unf,0,40,1298,GasA,Typical,Y,SBrkr,1298,0,0,1298,1,0,2,0,3,1,Good,5,Typ,1,Typical,Attchd,Unf,2,403,Typical,Typical,Paved,165,26,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,180000,-93.685537,42.032082 +Two_Story_1946_and_Newer,Residential_Low_Density,88,12128,Pave,No_Alley_Access,Slightly_Irregular,Bnk,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Above_Average,Below_Average,1989,1989,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,Good,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,319,868,GasA,Excellent,Y,SBrkr,1313,1246,0,2559,0,0,2,1,4,1,Excellent,9,Typ,1,Typical,Attchd,RFn,2,506,Typical,Typical,Paved,0,245,0,0,168,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Abnorml,209000,-93.683235,42.032493 +Two_Story_1946_and_Newer,Residential_Low_Density,80,9554,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,BrkFace,125,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,397,777,GasA,Excellent,Y,SBrkr,1065,846,0,1911,0,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,2,471,Typical,Typical,Paved,182,81,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,215000,-93.68218,42.030933 +Duplex_All_Styles_and_Ages,Residential_Low_Density,73,9069,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,Duplex,SFoyer,Above_Average,Very_Good,1993,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Good,PConc,Good,Typical,Av,LwQ,4,GLQ,1083,0,1344,GasA,Good,Y,SBrkr,1440,0,0,1440,2,0,2,0,2,2,Good,8,Typ,0,No_Fireplace,Attchd,Unf,4,920,Typical,Typical,Paved,288,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,224500,-93.6801515,42.0315156 +Two_Story_1946_and_Newer,Residential_Low_Density,133,11003,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1308,1308,GasA,Excellent,Y,SBrkr,1308,568,0,1876,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,BuiltIn,RFn,3,848,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,229800,-93.689071,42.024601 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7488,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,815,1208,GasA,Excellent,Y,SBrkr,1208,0,0,1208,0,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,105,58,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Abnorml,183600,-93.689816,42.024635 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,7406,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,84,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,515,1199,GasA,Excellent,Y,SBrkr,1220,0,0,1220,1,0,2,0,2,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,632,Typical,Typical,Paved,105,54,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,194000,-93.690366,42.024555 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,64,6762,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Av,Unf,7,Unf,0,1286,1286,GasA,Excellent,Y,SBrkr,1294,0,0,1294,0,0,2,0,2,1,Good,6,Typ,1,Good,Attchd,RFn,2,662,Typical,Typical,Paved,168,55,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,193879,-93.691234,42.024615 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,86,11210,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,240,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1594,1614,GasA,Excellent,Y,SBrkr,1614,0,0,1614,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,865,Typical,Typical,Paved,144,59,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,221500,-93.688937,42.025687 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,8990,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,1498,1498,GasA,Excellent,Y,SBrkr,1498,0,0,1498,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,2,675,Typical,Typical,Paved,351,33,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,New,Partial,204900,-93.688925,42.025218 +Two_Story_1946_and_Newer,Residential_Low_Density,73,8760,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Mn,GLQ,3,Unf,0,927,1391,GasA,Excellent,Y,SBrkr,1391,571,0,1962,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,868,Typical,Typical,Paved,0,90,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,239799,-93.688924,42.025194 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,13377,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2006,2006,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,260,1836,GasA,Good,Y,SBrkr,1846,0,0,1846,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,RFn,2,495,Typical,Typical,Paved,0,32,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,233555,-93.689485,42.02437 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11645,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,434,1734,GasA,Excellent,Y,SBrkr,1734,0,0,1734,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,Fin,2,660,Typical,Typical,Paved,160,24,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,260000,-93.689067,42.024481 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,11645,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,198,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,448,1570,GasA,Excellent,Y,SBrkr,1590,0,0,1590,1,0,2,1,2,1,Excellent,6,Typ,0,No_Fireplace,Attchd,Fin,3,754,Typical,Typical,Paved,176,80,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,294900,-93.689084,42.023306 +Two_Story_1946_and_Newer,Residential_Low_Density,91,10984,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,945,945,GasA,Excellent,Y,SBrkr,945,864,0,1809,0,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,Attchd,RFn,2,638,Typical,Typical,Paved,144,54,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,New,Partial,209700,-93.688786,42.024376 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,9316,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,140,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,1558,1614,GasA,Excellent,Y,SBrkr,1614,0,0,1614,0,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,576,Typical,Typical,Paved,100,45,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,225000,-93.688934,42.023308 +Two_Story_1946_and_Newer,Residential_Low_Density,78,9316,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,532,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,784,784,GasA,Excellent,Y,SBrkr,784,812,0,1596,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,610,Typical,Typical,Paved,144,45,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,181000,-93.688929,42.023138 +Two_Story_1946_and_Newer,Residential_Low_Density,80,10041,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Sawyer_West,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,119,908,GasA,Excellent,Y,SBrkr,927,988,0,1915,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,2,506,Typical,Typical,Paved,120,150,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Abnorml,220000,-93.68216,42.029884 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,36500,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,621,Typical,Good,CBlock,Typical,Typical,Av,Rec,6,Unf,0,812,1624,GasA,Fair,Y,SBrkr,1582,0,0,1582,0,1,2,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,2,390,Typical,Typical,Dirt_Gravel,168,198,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,190000,-93.681349,42.028168 +Split_or_Multilevel,Residential_Low_Density,0,21453,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,CulDSac,Sev,Clear_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Average,1969,1969,Flat,Metal,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,ALQ,1,Unf,0,0,938,GasA,Excellent,Y,SBrkr,988,0,0,988,1,0,1,0,1,1,Typical,4,Typ,2,Typical,Attchd,Unf,2,540,Typical,Typical,Paved,0,130,0,130,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,180000,-93.682095,42.025573 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,62,70761,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Clear_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1975,1975,Gable,WdShngl,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,878,1533,GasA,Typical,Y,SBrkr,1533,0,0,1533,1,0,2,0,2,1,Good,5,Typ,2,Typical,Attchd,Unf,2,576,Typical,Typical,Paved,200,54,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,280000,-93.683877,42.02438 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Clear_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,Typical,Fair,CBlock,Good,Fair,No,LwQ,4,Unf,0,535,1388,GasA,Good,Y,SBrkr,1388,0,0,1388,1,0,2,0,3,1,Typical,6,Typ,1,Poor,Attchd,RFn,2,522,Typical,Typical,Paved,0,58,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,COD,Abnorml,158900,-93.680401,42.024592 +Split_Foyer,Residential_Low_Density,57,8846,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,SFoyer,Average,Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,572,870,GasA,Excellent,Y,SBrkr,914,0,0,914,0,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,148000,-93.691896,42.022227 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,13015,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,No,Unf,7,Unf,0,1100,1100,GasA,Excellent,Y,SBrkr,1100,0,0,1100,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,462,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,145000,-93.691873,42.022077 +Two_Story_1946_and_Newer,Residential_Low_Density,65,12438,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,PosN,Norm,OneFam,Two_Story,Above_Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,781,781,GasA,Excellent,Y,SBrkr,795,704,0,1499,0,0,2,1,3,1,Good,6,Typ,1,Typical,Attchd,RFn,2,473,Typical,Typical,Paved,413,91,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,187000,-93.688302,42.021209 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8685,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,579,1425,GasA,Excellent,Y,SBrkr,1425,0,0,1425,1,0,2,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,2,591,Typical,Typical,Paved,0,130,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,193000,-93.690363,42.0209 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,43,13568,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Average,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,ALQ,1,Unf,0,274,990,GasA,Excellent,Y,SBrkr,990,0,0,990,0,1,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,576,Typical,Typical,Paved,224,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,156000,-93.692005,42.019047 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,244,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,125,1525,GasA,Excellent,Y,SBrkr,1525,0,0,1525,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,541,Typical,Typical,Paved,219,36,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,235000,-93.692606,42.018043 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,79,9236,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,PConc,Good,Typical,Gd,GLQ,3,Unf,0,279,1479,GasA,Excellent,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,576,Typical,Typical,Paved,168,27,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,217000,-93.692221,42.018123 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,78,10264,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,183,Good,Typical,PConc,Good,Typical,Av,ALQ,1,Unf,0,426,1588,GasA,Excellent,Y,SBrkr,1588,0,0,1588,0,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,472,Typical,Typical,Paved,158,29,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,214000,-93.690215,42.017631 +Two_Story_1946_and_Newer,Residential_Low_Density,68,9272,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,342,842,GasA,Excellent,Y,SBrkr,856,893,0,1749,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,515,Typical,Typical,Paved,140,85,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,196000,-93.691931,42.0168729 +Two_Story_1946_and_Newer,Residential_Low_Density,72,13426,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1999,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,57,951,GasA,Excellent,Y,SBrkr,951,828,0,1779,1,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,586,Typical,Typical,Paved,208,107,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,217000,-93.689083,42.017929 +Two_Story_1946_and_Newer,Residential_Low_Density,95,13450,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,216,916,GasA,Excellent,Y,SBrkr,920,941,0,1861,1,0,2,1,3,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,492,Typical,Typical,Paved,146,91,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,200000,-93.68923,42.01686 +Two_Story_1946_and_Newer,Residential_Low_Density,65,14006,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Good,Typical,PConc,Good,Typical,No_Basement,Unf,7,Unf,0,936,936,GasA,Excellent,Y,SBrkr,936,840,0,1776,0,0,2,1,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,474,Typical,Typical,Paved,144,96,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,192500,-93.691243,42.016217 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,118,13704,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1541,1541,GasA,Excellent,Y,SBrkr,1541,0,0,1541,0,0,2,0,3,1,Good,6,Typ,1,Typical,Attchd,RFn,3,843,Typical,Typical,Paved,468,81,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,205000,-93.688577,42.016913 +Split_or_Multilevel,Residential_Low_Density,55,10780,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Average,Average,1976,1976,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Av,ALQ,1,Unf,0,428,911,GasA,Good,Y,SBrkr,954,0,0,954,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,576,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,132500,-93.686629,42.021388 +Two_Story_1946_and_Newer,Residential_Low_Density,50,8340,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Above_Average,1977,1977,Gable,CompShg,HdBoard,Plywood,BrkFace,62,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,166,675,GasA,Typical,Y,SBrkr,686,702,0,1388,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,317,Typical,Typical,Paved,406,36,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,157500,-93.686825,42.020145 +Split_or_Multilevel,Residential_Low_Density,42,10385,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,123,Typical,Typical,CBlock,Typical,Good,Av,ALQ,1,LwQ,400,0,995,GasA,Typical,Y,SBrkr,1282,0,0,1282,0,1,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,3,672,Fair,Typical,Paved,386,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,174000,-93.687158,42.02007 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,9920,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Above_Average,1969,1969,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,ALQ,1,Unf,0,448,971,GasA,Typical,Y,SBrkr,971,0,0,971,0,0,1,1,3,1,Typical,5,Typ,1,Poor,Attchd,Unf,1,300,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,128500,-93.684982,42.019697 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,120,9560,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Good,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,504,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,128500,-93.683468,42.021811 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9800,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Good,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,0,894,GasA,Typical,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,552,Typical,Typical,Paved,256,0,0,0,0,0,No_Pool,Good_Wood,None,0,7,2006,WD ,Abnorml,149900,-93.683246,42.021889 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,7200,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Average,Very_Good,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,427,864,GasA,Excellent,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,297,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,119900,-93.682979,42.020998 +Two_Story_PUD_1946_and_Newer,Residential_High_Density,0,3612,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Landmark,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,292,630,GasA,Excellent,Y,SBrkr,630,690,0,1320,0,0,2,1,3,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,484,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,137000,-93.681378,42.021785 +Two_Story_1946_and_Newer,Residential_Low_Density,90,11367,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,133,1065,GasA,Excellent,Y,SBrkr,1091,898,0,1989,1,0,2,1,3,1,Good,7,Typ,1,Good,Attchd,Fin,2,586,Typical,Typical,Paved,199,60,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,255000,-93.687703,42.01834 +Two_Story_1946_and_Newer,Residential_Low_Density,0,9930,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,199,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,370,826,GasA,Excellent,Y,SBrkr,878,884,0,1762,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Fin,2,591,Typical,Typical,Paved,320,54,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,222000,-93.686288,42.018664 +Two_Story_1946_and_Newer,Residential_Low_Density,45,9468,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,148,Typical,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,201,840,GasA,Excellent,Y,SBrkr,840,915,0,1755,1,0,2,1,3,1,Typical,7,Typ,1,Typical,Attchd,RFn,2,530,Typical,Typical,Paved,176,73,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,214000,-93.684421,42.017854 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,11088,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,Stucco,Stucco,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,476,1348,GasA,Excellent,Y,SBrkr,1358,0,0,1358,1,0,1,1,1,1,Good,5,Typ,1,Typical,Attchd,Unf,2,418,Typical,Typical,Paved,68,166,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,220000,-93.685504,42.017055 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,14781,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,CulDSac,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,178,Good,Typical,PConc,Good,Typical,Gd,Unf,7,Unf,0,1753,1753,GasA,Excellent,Y,SBrkr,1787,0,0,1787,0,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,3,748,Typical,Typical,Paved,198,150,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,275000,-93.685697,42.018188 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8726,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,872,872,GasA,Excellent,Y,SBrkr,872,1037,0,1909,0,0,2,1,4,1,Good,8,Typ,0,No_Fireplace,BuiltIn,RFn,2,529,Typical,Typical,Paved,0,108,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,197000,-93.687675,42.014494 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8125,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,Stone,295,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,668,1654,GasA,Excellent,Y,SBrkr,1654,0,0,1654,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,3,900,Typical,Typical,Paved,0,136,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,230000,-93.685571,42.016979 +Two_Story_1946_and_Newer,Residential_Low_Density,67,10566,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,261,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,170,1090,GasA,Excellent,Y,SBrkr,1090,1124,0,2214,1,0,2,1,3,1,Good,8,Typ,1,Typical,Attchd,Fin,3,646,Typical,Typical,Paved,197,80,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,268500,-93.683718,42.016904 +Two_Story_1946_and_Newer,Residential_Low_Density,0,21533,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Feedr,Norm,OneFam,Two_Story,Good,Average,1996,1997,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1065,1065,GasA,Excellent,Y,SBrkr,1065,984,0,2049,0,0,2,1,4,1,Good,9,Typ,1,Typical,Attchd,Unf,2,467,Typical,Typical,Paved,120,48,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,210900,-93.679147,42.01869 +Two_Story_1946_and_Newer,Residential_Low_Density,90,11250,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,227,Typical,Typical,PConc,Good,Typical,Mn,ALQ,1,Unf,0,258,1054,GasA,Excellent,Y,SBrkr,1070,869,0,1939,0,1,2,1,3,1,Good,8,Typ,1,Typical,Attchd,RFn,3,555,Typical,Typical,Paved,128,84,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,227000,-93.681479,42.017856 +Two_Story_1946_and_Newer,Residential_Low_Density,90,11250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,1995,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Good,PConc,Good,Good,Av,GLQ,3,Unf,0,245,930,GasA,Excellent,Y,SBrkr,950,1045,0,1995,1,0,2,1,4,1,Good,8,Typ,1,Typical,Attchd,RFn,2,610,Typical,Typical,Paved,275,170,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,237000,-93.679803,42.017867 +Two_Story_1946_and_Newer,Residential_Low_Density,75,9750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,133,1108,GasA,Excellent,Y,SBrkr,1108,989,0,2097,1,0,2,1,3,1,Good,8,Typ,1,Typical,Detchd,RFn,2,583,Typical,Typical,Paved,253,170,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,274300,-93.682686,42.017858 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9100,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2004,2005,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,1836,1860,GasA,Excellent,Y,SBrkr,1836,0,0,1836,0,0,2,0,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,484,Typical,Typical,Paved,120,33,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,216837,-93.681895,42.017186 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,37,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,131,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,4,Typ,0,No_Fireplace,Attchd,Fin,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,133000,-93.681191,42.016275 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,37,4435,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,186,848,GasA,Excellent,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Good,3,Typ,1,Good,Attchd,RFn,2,420,Typical,Typical,Paved,140,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,155900,-93.683728,42.016252 +Two_Story_1946_and_Newer,Residential_Low_Density,120,15611,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Very_Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,1079,1079,GasA,Excellent,Y,SBrkr,1079,840,0,1919,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,RFn,2,685,Good,Typical,Paved,0,51,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,233230,-93.683777,42.016618 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,8810,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,GLQ,3,Unf,0,390,1390,GasA,Excellent,Y,SBrkr,1390,0,0,1390,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,545,Typical,Typical,Paved,0,68,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,207000,-93.687684,42.015914 +Two_Story_1946_and_Newer,Residential_Low_Density,41,12393,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,847,847,GasA,Excellent,Y,SBrkr,847,1101,0,1948,0,0,2,1,4,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,434,Typical,Typical,Paved,100,48,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,195000,-93.687685,42.016092 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9135,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Above_Average,Average,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1342,1682,GasA,Excellent,Y,SBrkr,1700,0,0,1700,1,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,544,Typical,Typical,Paved,192,23,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,200000,-93.687826,42.014636 +Two_Story_1946_and_Newer,Residential_Low_Density,74,8581,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Good,Mn,Unf,7,Unf,0,851,851,GasA,Excellent,Y,SBrkr,851,886,0,1737,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,578,Typical,Typical,Paved,0,105,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,203160,-93.684089,42.014109 +Two_Story_1946_and_Newer,Residential_Low_Density,70,8400,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,Unf,7,Unf,0,784,784,GasA,Excellent,Y,SBrkr,784,827,0,1611,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,572,Typical,Typical,Paved,144,36,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,195800,-93.684116,42.015793 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,84,10084,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1528,1552,GasA,Excellent,Y,SBrkr,1552,0,0,1552,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,3,782,Typical,Typical,Paved,144,20,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,212900,-93.684116,42.016004 +Two_Story_1946_and_Newer,Residential_Low_Density,89,11645,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,860,860,GasA,Excellent,Y,SBrkr,860,860,0,1720,0,0,2,1,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,565,Typical,Typical,Paved,0,70,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Normal,196500,-93.685369,42.01397 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8772,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Mn,GLQ,3,Unf,0,340,1336,GasA,Excellent,Y,SBrkr,1336,0,0,1336,1,0,2,0,3,1,Good,6,Typ,0,No_Fireplace,Attchd,Unf,2,502,Typical,Typical,Paved,136,43,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,198000,-93.685872,42.013991 +Two_Story_1946_and_Newer,Residential_Low_Density,68,8846,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,750,750,GasA,Excellent,Y,SBrkr,750,750,0,1500,0,0,2,1,3,1,Good,6,Typ,0,No_Fireplace,Attchd,RFn,2,564,Typical,Typical,Paved,0,35,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,New,Partial,173900,-93.683435,42.015765 +Two_Story_1946_and_Newer,Residential_Low_Density,65,8461,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,Two_Story,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,728,728,GasA,Excellent,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Good,8,Typ,1,Good,Attchd,Fin,2,390,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,163990,-93.684332,42.013519 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,65,8767,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,College_Creek,Norm,Norm,OneFam,One_Story,Good,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,1286,1310,GasA,Excellent,Y,SBrkr,1310,0,0,1310,0,0,2,0,3,1,Good,6,Typ,1,Good,Attchd,Fin,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,New,Partial,164990,-93.683758,42.014593 +Two_Story_1945_and_Older,Residential_Low_Density,67,8777,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,Two_Story,Below_Average,Above_Average,1910,2000,Gable,CompShg,Plywood,Plywood,None,0,Typical,Good,CBlock,Good,Typical,No,Rec,6,BLQ,337,166,676,GasA,Good,Y,SBrkr,760,676,0,1436,1,0,2,0,3,1,Typical,6,Min1,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,147,0,0,0,0,0,No_Pool,No_Fence,Shed,420,10,2006,WD ,Normal,98000,-93.678267,42.0195855 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,134,17755,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Below_Average,1959,1959,Gable,CompShg,HdBoard,Plywood,BrkFace,132,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,1290,1466,GasA,Typical,Y,SBrkr,1466,0,0,1466,0,0,1,1,3,1,Fair,6,Typ,2,Good,Attchd,Fin,2,528,Typical,Typical,Paved,0,140,0,0,100,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,149900,-93.676369,42.020995 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,67,8877,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Average,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Fair,No,LwQ,4,Unf,0,0,836,GasA,Typical,Y,FuseF,1220,0,0,1220,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,396,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,COD,Normal,102000,-93.676361,42.020342 +Duplex_All_Styles_and_Ages,Residential_Low_Density,38,7840,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1975,1975,Flat,Tar&Grv,Plywood,Wd Shng,BrkFace,355,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,976,GasA,Typical,Y,SBrkr,1012,0,0,1012,0,2,2,0,4,0,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,AdjLand,127500,-93.675297,42.020592 +Duplex_All_Styles_and_Ages,Residential_Low_Density,35,9400,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1975,1975,Flat,Tar&Grv,WdShing,Plywood,BrkFace,250,Typical,Typical,CBlock,Good,Good,Gd,GLQ,3,Unf,0,0,945,GasA,Typical,Y,SBrkr,980,0,0,980,0,2,2,0,4,0,Typical,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,AdjLand,127500,-93.675548,42.020547 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,73,16133,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Below_Average,1969,1969,Gable,CompShg,HdBoard,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,Unf,0,329,1176,GasA,Typical,Y,SBrkr,1176,0,0,1176,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,360,Typical,Typical,Paved,0,92,0,0,112,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,119900,-93.671454,42.021113 +Two_Story_1946_and_Newer,Residential_Low_Density,62,7162,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2004,Hip,CompShg,HdBoard,Stucco,BrkFace,190,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,796,796,GasA,Excellent,Y,SBrkr,806,918,0,1724,0,0,2,1,3,1,Good,8,Typ,1,Good,BuiltIn,Fin,2,616,Typical,Typical,Paved,168,57,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,196500,-93.672379,42.01899 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,75,8050,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Above_Average,Average,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,ALQ,297,142,914,GasA,Excellent,Y,SBrkr,914,0,0,914,1,0,1,0,2,1,Good,4,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,32,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,117250,-93.671304,42.021168 +Two_Story_1946_and_Newer,Residential_Low_Density,90,11060,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,OneFam,Two_Story,Good,Average,2003,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1150,1150,GasA,Excellent,Y,SBrkr,1164,1150,0,2314,0,0,2,1,3,1,Good,9,Typ,1,Excellent,BuiltIn,Fin,2,502,Typical,Typical,Paved,0,274,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,ConLD,Normal,229000,-93.671612,42.018648 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SLvl,Average,Average,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,145000,-93.670025,42.018961 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,82,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,New,Partial,140000,-93.669977,42.01896 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,35,3675,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,Above_Average,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,82,Typical,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,0,547,GasA,Good,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Basment,Fin,2,525,Typical,Typical,Paved,0,44,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,New,Partial,142500,-93.669927,42.018959 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,134000,-93.670063,42.018813 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,130000,-93.670039,42.018812 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Above_Average,Average,2004,2006,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,130000,-93.669985,42.01881 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,24,2522,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,970,970,GasA,Excellent,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,137500,-93.66996,42.01881 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,3363,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,Two_Story,Good,Average,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,117,Good,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,976,976,GasA,Excellent,Y,SBrkr,976,732,0,1708,0,0,2,0,3,1,Good,7,Maj1,0,No_Fireplace,Detchd,Unf,2,380,Typical,Typical,Paved,0,40,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,130000,-93.669924,42.018809 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,56,6956,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Good,CBlock,Fair,Typical,Mn,Unf,7,Unf,0,624,624,GasA,Excellent,Y,SBrkr,624,312,0,936,0,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,265,Typical,Poor,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,97900,-93.661951,42.02011 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,72,7822,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,Edwards,Artery,Norm,OneFam,One_and_Half_Fin,Above_Average,Fair,1915,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Fair,BrkTil,Fair,Fair,No,Unf,7,Unf,0,832,832,GasA,Typical,Y,FuseF,846,492,0,1338,0,0,2,0,3,1,Typical,7,Typ,0,No_Fireplace,Detchd,Unf,2,528,Typical,Typical,Dirt_Gravel,0,0,208,0,0,0,No_Pool,Good_Privacy,None,0,5,2006,WD ,AdjLand,92000,-93.659182,42.022676 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,62,8707,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Edwards,Feedr,Norm,OneFam,One_and_Half_Fin,Below_Average,Average,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1093,1093,GasA,Typical,N,FuseF,1093,576,0,1669,0,0,1,1,4,1,Typical,9,Min2,0,No_Fireplace,Attchd,Unf,1,288,Fair,Typical,Paved,0,0,56,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,AdjLand,107000,-93.659222,42.022659 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,58,8410,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Edwards,Feedr,Norm,OneFam,One_and_Half_Fin,Average,Fair,1910,1996,Gambrel,CompShg,Wd Sdng,VinylSd,None,0,Typical,Fair,PConc,Typical,Typical,No,Unf,7,Unf,0,658,658,GasA,Typical,Y,SBrkr,658,526,0,1184,0,0,1,0,5,1,Typical,8,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,151,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,AdjLand,81000,-93.659222,42.022641 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,9738,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1924,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Good,BrkTil,Typical,Typical,No,BLQ,2,Unf,0,392,784,GasA,Good,Y,SBrkr,949,272,0,1221,1,0,1,0,4,1,Typical,7,Typ,0,No_Fireplace,Attchd,Unf,1,392,Typical,Typical,Paved,0,0,236,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,104900,-93.660119,42.021432 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,8172,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Average,Good,1955,1990,Hip,CompShg,WdShing,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,697,864,GasA,Typical,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,2,572,Typical,Typical,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,115000,-93.663458,42.018848 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,60,16012,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1954,1968,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,60,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,263,954,GasA,Excellent,Y,SBrkr,1482,0,0,1482,0,1,2,0,3,1,Typical,6,Min1,1,Good,More_Than_Two_Types,Unf,2,609,Typical,Typical,Paved,0,30,0,0,0,0,No_Pool,Minimum_Privacy,None,0,10,2006,WD ,Abnorml,125000,-93.661995,42.017727 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,8248,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Average,Good,1918,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,686,686,GasW,Good,Y,SBrkr,686,564,0,1250,0,1,1,1,3,1,Fair,7,Typ,0,No_Fireplace,Detchd,Unf,1,280,Typical,Typical,Partial_Pavement,207,0,96,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,116000,-93.656753,42.022089 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,45,8248,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Below_Average,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,864,864,GasA,Typical,N,SBrkr,964,0,450,1414,0,0,1,0,3,1,Typical,8,Typ,1,Good,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,112,0,0,0,No_Pool,No_Fence,None,0,9,2006,COD,Abnorml,83000,-93.6567,42.022088 +Two_and_Half_Story_All_Ages,Residential_Low_Density,60,6204,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_and_Half_Fin,Below_Average,Average,1912,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Good,Typical,PConc,Typical,Fair,No,Unf,7,Unf,0,795,795,GasA,Typical,N,SBrkr,954,795,481,2230,1,0,1,0,5,1,Typical,10,Typ,0,No_Fireplace,Detchd,Unf,1,440,Typical,Good,Paved,0,188,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,118500,-93.65564,42.02197 +One_Story_1945_and_Older,Residential_Low_Density,60,8088,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,OneFam,One_Story,Poor,Fair,1922,1955,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,498,498,GasA,Typical,N,FuseF,498,0,0,498,0,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,Detchd,Unf,1,216,Fair,Fair,Dirt_Gravel,0,0,100,0,0,0,No_Pool,No_Fence,None,0,2,2006,ConLD,Normal,35000,-93.659043,42.021559 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,76,11388,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Edwards,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Good,1910,1993,Gable,CompShg,VinylSd,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,616,616,GasA,Typical,N,SBrkr,1055,218,0,1273,0,0,1,0,3,1,Good,5,Min2,0,No_Fireplace,Detchd,Unf,1,275,Typical,Fair,Dirt_Gravel,212,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,121000,-93.655631,42.020871 +Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,0,7082,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,TwoFmCon,Two_Story,Average,Very_Good,1916,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Typical,Typical,Mn,Unf,7,Unf,0,686,686,GasA,Good,Y,SBrkr,948,980,0,1928,0,0,2,0,5,2,Typical,10,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,228,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,160000,-93.648562,42.020286 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,8400,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1940,2000,Gable,CompShg,Wd Sdng,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,LwQ,4,Unf,0,552,940,GasA,Excellent,Y,SBrkr,1192,403,0,1595,0,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,Unf,1,240,Typical,Typical,Paved,0,0,108,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,140000,-93.651811,42.018837 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,10890,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1938,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,128,1058,GasA,Typical,Y,SBrkr,1058,493,0,1551,1,0,2,0,3,1,Fair,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Fair,Typical,Paved,0,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,7,2006,WD ,Normal,137000,-93.651285,42.018282 +Two_Family_conversion_All_Styles_and_Ages,Residential_High_Density,58,6430,Pave,No_Alley_Access,Regular,Bnk,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,TwoFmCon,One_and_Half_Fin,Above_Average,Above_Average,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,0,780,GasA,Typical,N,FuseF,816,524,0,1340,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,1,440,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,128000,-93.646942,42.01924 +Two_Story_1945_and_Older,Residential_Low_Density,43,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Feedr,Norm,OneFam,Two_Story,Good,Very_Good,1926,1997,Gable,CompShg,Wd Sdng,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,200,624,GasA,Excellent,Y,SBrkr,743,736,0,1479,1,0,1,0,3,1,Good,6,Typ,2,Good,Detchd,Unf,1,312,Typical,Typical,Paved,530,0,56,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,160000,-93.646619,42.019093 +Two_Story_1945_and_Older,Residential_Low_Density,96,13132,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Average,Average,1914,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,Mn,Unf,7,Unf,0,747,747,GasA,Good,Y,FuseF,892,892,0,1784,0,0,1,1,4,1,Typical,9,Typ,0,No_Fireplace,Detchd,Unf,1,180,Fair,Fair,Dirt_Gravel,203,40,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,138887,-93.646438,42.01895 +Two_Story_1945_and_Older,Residential_Low_Density,69,4899,Pave,No_Alley_Access,Regular,HLS,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,BLQ,2,Unf,0,450,755,GasA,Excellent,Y,SBrkr,755,755,0,1510,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,216,Typical,Typical,Paved,0,0,164,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,149000,-93.648417,42.018808 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,60,6000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Fair,BrkTil,Typical,Typical,No,Unf,7,Unf,0,1008,1008,GasA,Excellent,Y,SBrkr,1008,0,514,1522,0,0,2,0,4,1,Typical,7,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Partial_Pavement,0,0,138,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,102000,-93.648416,42.018857 +Two_Story_1945_and_Older,Residential_Low_Density,54,9399,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_and_Half_Unf,Good,Very_Good,1919,1950,Gable,CompShg,MetalSd,Stucco,None,0,Typical,Typical,BrkTil,Typical,Typical,Mn,Unf,7,Unf,0,818,818,GasA,Typical,Y,SBrkr,818,818,0,1636,0,0,1,1,4,1,Good,7,Typ,1,Good,Detchd,Unf,1,288,Fair,Typical,Dirt_Gravel,0,0,212,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Abnorml,167000,-93.64644,42.016941 +Two_Story_1945_and_Older,Residential_Low_Density,54,7588,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Above_Average,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,Typical,Typical,BrkTil,Fair,Typical,No,LwQ,4,Unf,0,441,793,GasA,Good,Y,SBrkr,901,901,0,1802,0,0,1,1,4,1,Typical,9,Typ,1,Good,Detchd,Unf,1,216,Fair,Typical,Paved,0,0,40,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,200100,-93.6447118,42.0165794 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,84,10164,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Average,1939,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Fair,Av,LwQ,4,Unf,0,346,992,GasA,Fair,Y,SBrkr,992,473,0,1465,0,0,2,0,3,1,Typical,6,Typ,2,Typical,Detchd,Unf,1,240,Typical,Typical,Paved,0,126,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,131000,-93.648415,42.016086 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,51,6191,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,South_and_West_of_Iowa_State_University,Norm,Norm,OneFam,One_and_Half_Fin,Average,Below_Average,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Fair,Fair,No,LwQ,4,Unf,0,440,824,GasA,Typical,N,SBrkr,824,464,0,1288,0,0,1,0,4,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,240,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,112000,-93.647027,42.016072 +Two_Story_1945_and_Older,Residential_Low_Density,60,9550,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Average,1915,1970,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,BrkTil,Typical,Good,No,ALQ,1,Unf,0,540,756,GasA,Good,Y,SBrkr,961,756,0,1717,1,0,1,0,3,1,Good,7,Typ,1,Good,Detchd,Unf,3,642,Typical,Typical,Paved,0,35,272,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Abnorml,140000,-93.644307,42.017609 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,66,21780,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Average,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,Typical,Typical,PConc,Typical,Fair,No,Unf,7,Unf,0,817,817,GasA,Good,Y,FuseF,940,610,0,1550,0,0,1,1,3,1,Typical,7,Min2,1,Typical,Detchd,Unf,1,318,Typical,Typical,Partial_Pavement,0,0,429,0,0,0,No_Pool,Minimum_Privacy,None,0,9,2006,WD ,Normal,195000,-93.644258,42.01874 +Two_Story_1945_and_Older,Residential_Low_Density,0,11435,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Corner,Mod,Crawford,Norm,Norm,OneFam,Two_Story,Very_Good,Good,1929,1950,Gable,CompShg,BrkFace,Stucco,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,792,792,GasA,Fair,Y,SBrkr,792,725,0,1517,0,0,1,0,3,1,Good,7,Typ,2,Good,Detchd,Unf,2,400,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,230000,-93.642431,42.019059 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,80,12400,Pave,No_Alley_Access,Regular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Average,Above_Average,1940,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,CBlock,Good,Typical,Mn,BLQ,2,Unf,0,299,901,GasA,Typical,Y,SBrkr,1125,592,0,1717,0,0,1,1,2,1,Typical,7,Typ,1,Good,Attchd,Unf,1,410,Typical,Typical,Paved,0,0,0,0,113,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,194000,-93.64048,42.019759 +One_Story_1945_and_Older,Residential_Low_Density,80,11600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Below_Average,Average,1922,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,700,700,GasA,Excellent,Y,SBrkr,1180,0,0,1180,0,0,1,0,2,1,Fair,5,Typ,1,Good,Detchd,Unf,1,252,Typical,Fair,Paved,0,0,67,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,137500,-93.643443,42.017131 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,86,11500,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1936,1987,Gable,CompShg,BrkFace,BrkFace,None,0,Good,Typical,CBlock,Good,Typical,No,Rec,6,Unf,0,794,1017,GasA,Good,Y,SBrkr,1020,1037,0,2057,0,0,1,1,3,1,Good,6,Typ,1,Good,Attchd,Fin,1,180,Fair,Typical,Paved,0,0,0,0,322,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,250000,-93.641155,42.016827 +One_and_Half_Story_Finished_All_Ages,Residential_Low_Density,81,8170,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_and_Half_Fin,Good,Good,1929,1950,Gable,CompShg,Stucco,Wd Sdng,BrkFace,270,Good,Good,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,496,1022,GasA,Excellent,Y,FuseA,1122,549,0,1671,0,0,2,0,4,1,Typical,7,Typ,1,Good,Detchd,Unf,2,451,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,2,2006,WD ,Normal,218000,-93.642319,42.016385 +Two_Story_1945_and_Older,Residential_Low_Density,80,16560,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Very_Good,1932,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Good,Typical,No,Rec,6,Unf,0,449,952,GasA,Typical,Y,SBrkr,1170,1175,0,2345,0,0,2,1,4,1,Typical,9,Typ,1,Good,Detchd,Unf,2,360,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,239000,-93.641002,42.017589 +Two_Story_1945_and_Older,Residential_Low_Density,70,12320,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Good,Good,1932,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,637,637,GasA,Excellent,Y,SBrkr,959,650,0,1609,0,0,1,1,3,1,Good,8,Typ,2,Good,More_Than_Two_Types,Unf,3,579,Typical,Typical,Paved,0,0,0,0,104,0,No_Pool,Good_Wood,None,0,5,2006,WD ,Normal,199500,-93.6416718,42.0157866 +Two_Story_1945_and_Older,Residential_Low_Density,70,14210,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Above_Average,Good,1930,1959,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,PConc,Typical,Typical,No,Unf,7,Unf,0,697,697,GasA,Excellent,Y,SBrkr,1104,697,0,1801,0,0,1,1,3,1,Typical,8,Typ,1,Good,Attchd,Unf,2,365,Fair,Typical,Paved,0,90,0,0,0,0,No_Pool,Minimum_Privacy,None,0,11,2006,WD ,Normal,210000,-93.640612,42.016124 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,14115,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Good,1956,2004,Gable,CompShg,Stone,Stone,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,GLQ,547,230,1073,GasA,Excellent,Y,SBrkr,1811,0,0,1811,0,0,1,0,2,1,Excellent,6,Typ,1,Good,Attchd,Fin,2,470,Typical,Typical,Paved,0,0,280,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,230000,-93.642884,42.013296 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,12984,Pave,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Average,Above_Average,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,459,Typical,Typical,CBlock,Good,Typical,Mn,ALQ,1,LwQ,147,0,1430,GasA,Excellent,Y,SBrkr,1647,0,0,1647,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,Fin,2,621,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,217500,-93.644203,42.013495 +Two_Story_1946_and_Newer,Residential_Low_Density,78,15600,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,Two_Story,Average,Good,1950,1963,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,405,Typical,Good,CBlock,Good,Typical,No,GLQ,3,Unf,0,408,1168,GasA,Good,Y,SBrkr,1278,1037,0,2315,1,0,2,0,4,1,Typical,9,Typ,3,Good,Attchd,Fin,1,342,Typical,Typical,Paved,0,0,0,0,192,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,257000,-93.639622,42.014936 +Two_Family_conversion_All_Styles_and_Ages,Residential_Low_Density,90,15750,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Crawford,Norm,Norm,TwoFmCon,One_Story,Average,Average,1953,1953,Hip,CompShg,MetalSd,MetalSd,BrkFace,56,Typical,Typical,CBlock,Typical,Typical,Mn,BLQ,2,Unf,0,324,1165,GasA,Typical,Y,SBrkr,1336,0,0,1336,1,0,1,0,2,1,Typical,5,Typ,2,Good,Attchd,Unf,1,375,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,157000,-93.639428,42.01359 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16381,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Crawford,Norm,Norm,OneFam,One_Story,Above_Average,Average,1969,1969,Gable,CompShg,Plywood,Plywood,BrkFace,312,Good,Good,CBlock,Typical,Typical,Av,Rec,6,Unf,0,734,1844,GasA,Good,Y,SBrkr,1844,0,0,1844,1,0,2,0,3,1,Good,7,Typ,1,Typical,Attchd,RFn,2,540,Typical,Typical,Paved,0,73,216,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,223000,-93.643489,42.011793 +One_Story_1945_and_Older,Residential_Medium_Density,50,7288,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Artery,Norm,OneFam,One_Story,Average,Above_Average,1942,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Rec,6,Unf,0,671,976,GasA,Typical,N,SBrkr,976,0,0,976,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Detchd,Unf,1,215,Typical,Typical,Dirt_Gravel,160,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,AdjLand,102000,-93.628467,42.022786 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,50,7000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Good,1926,1950,Hip,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Unf,0,487,861,GasA,Excellent,Y,SBrkr,861,424,0,1285,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Fin,2,506,Typical,Typical,Paved,96,0,132,0,0,0,No_Pool,Minimum_Privacy,None,0,5,2006,WD ,Normal,145400,-93.62841,42.022709 +One_Story_1945_and_Older,Residential_Medium_Density,61,8534,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Below_Average,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Fair,Typical,No,Unf,7,Unf,0,432,432,GasA,Typical,N,FuseA,672,0,0,672,0,0,1,0,2,1,Typical,4,Min1,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,112,0,0,0,No_Pool,Good_Wood,None,0,6,2006,WD ,Normal,72000,-93.628298,42.020441 +One_Story_1945_and_Older,Residential_Medium_Density,50,7030,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,BrkTil,Typical,Typical,No,Unf,7,Unf,0,641,641,GasA,Good,Y,SBrkr,641,0,0,641,0,0,1,0,2,1,Fair,4,Typ,0,No_Fireplace,Detchd,Unf,1,272,Typical,Typical,Dirt_Gravel,184,0,70,0,0,0,No_Pool,Minimum_Privacy,None,0,3,2006,WD ,Normal,85000,-93.629496,42.020855 +One_Story_1945_and_Older,Residential_Medium_Density,50,8765,Pave,Gravel,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Below_Average,Above_Average,1936,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,BrkTil,Typical,Typical,No,ALQ,1,Unf,0,666,951,GasA,Excellent,N,SBrkr,951,0,0,951,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,1,327,Typical,Typical,Paved,0,28,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,106500,-93.626683,42.020207 +One_and_Half_Story_Finished_All_Ages,Residential_Medium_Density,75,9060,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Above_Average,Average,1957,1957,Gable,CompShg,MetalSd,MetalSd,BrkFace,327,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,967,967,GasA,Good,Y,SBrkr,967,671,0,1638,0,0,2,0,4,1,Good,6,Typ,0,No_Fireplace,Detchd,Unf,1,384,Typical,Typical,Paved,0,21,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,139000,-93.625147,42.02153 +One_and_Half_Story_Finished_All_Ages,C_all,60,11040,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Below_Average,Above_Average,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,0,637,GasA,Good,Y,SBrkr,897,439,0,1336,0,0,1,1,3,1,Typical,7,Typ,0,No_Fireplace,CarPort,Unf,1,570,Typical,Typical,Paved,0,47,120,0,0,0,No_Pool,No_Fence,None,0,9,2006,COD,Normal,108000,-93.615307,42.019025 +One_Story_1945_and_Older,C_all,69,12366,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Feedr,Norm,OneFam,One_Story,Fair,Average,1945,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Typical,Typical,Slab,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,N,SBrkr,729,0,0,729,0,0,1,0,2,1,Typical,5,Mod,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,23,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,51689,-93.61385,42.019349 +One_Story_1946_and_Newer_All_Styles,C_all,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_Story,Poor,Fair,1949,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Typical,Typical,CBlock,Typical,Typical,Av,BLQ,2,Unf,0,430,480,GasA,Typical,N,FuseA,480,0,0,480,1,0,0,0,1,1,Typical,4,Typ,0,No_Fireplace,Detchd,Unf,1,308,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,35311,-93.615012,42.019099 +Two_Family_conversion_All_Styles_and_Ages,C_all,50,9000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,TwoFmCon,One_and_Half_Fin,Average,Above_Average,1951,1951,Gable,CompShg,WdShing,Wd Shng,None,0,Fair,Fair,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,660,660,GasA,Typical,N,SBrkr,1060,336,0,1396,0,0,2,0,4,2,Typical,8,Min2,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Abnorml,115000,-93.615024,42.019099 +One_and_Half_Story_Finished_All_Ages,C_all,60,8520,Grvl,No_Alley_Access,Regular,Bnk,AllPub,Inside,Gtl,Iowa_DOT_and_Rail_Road,Norm,Norm,OneFam,One_and_Half_Fin,Fair,Average,1916,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,PConc,Fair,Fair,No,Unf,7,Unf,0,216,216,GasA,Fair,N,SBrkr,576,360,0,936,0,0,1,0,2,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Dirt_Gravel,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,78000,-93.604344,42.022603 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,5748,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,CemntBd,CmentBd,Stone,473,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,0,1573,GasA,Excellent,Y,SBrkr,1778,0,0,1778,2,0,2,0,2,1,Excellent,5,Typ,1,Good,Attchd,Fin,2,495,Typical,Typical,Paved,123,53,0,0,153,0,No_Pool,No_Fence,None,0,2,2006,New,Partial,375000,-93.616026,42.009398 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,44,3842,Pave,No_Alley_Access,Slightly_Irregular,HLS,AllPub,Inside,Mod,Crawford,Norm,Norm,TwnhsE,One_Story,Very_Good,Average,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,186,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,30,1594,GasA,Excellent,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Good,5,Typ,1,Good,Attchd,Fin,2,525,Typical,Typical,Paved,128,53,0,0,155,0,No_Pool,No_Fence,None,0,12,2006,WD ,Normal,300000,-93.616242,42.008375 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,69,23580,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,849,1625,GasA,Typical,Y,SBrkr,1625,0,0,1625,0,1,2,0,3,1,Fair,6,Typ,1,Typical,Attchd,Fin,2,576,Typical,Typical,Paved,136,28,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Normal,242500,-93.599969,41.996849 +Duplex_All_Styles_and_Ages,Residential_Low_Density,65,8385,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Above_Average,Average,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Mn,Unf,7,Unf,0,1664,1664,GasA,Typical,Y,SBrkr,1664,0,0,1664,0,0,2,0,4,2,Typical,10,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,2,616,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,145000,-93.606736,41.994357 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,70,9116,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Very_Good,Average,2001,2001,Hip,CompShg,VinylSd,VinylSd,None,0,Good,Typical,PConc,Excellent,Typical,No,Unf,7,Unf,0,1491,1491,GasA,Excellent,Y,SBrkr,1491,0,0,1491,0,0,2,0,3,1,Good,7,Typ,0,No_Fireplace,Attchd,RFn,2,490,Typical,Typical,Paved,120,100,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,209000,-93.603671,41.995987 +Duplex_All_Styles_and_Ages,Residential_Low_Density,78,10530,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Above_Average,Average,1977,1977,Gable,CompShg,Plywood,ImStucc,BrkFace,90,Typical,Typical,CBlock,Good,Typical,Gd,GLQ,3,Unf,0,0,975,GasA,Typical,Y,SBrkr,1004,0,0,1004,1,0,1,0,2,1,Typical,4,Typ,0,No_Fireplace,Attchd,Unf,2,504,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,135000,-93.603173,41.996907 +Split_or_Multilevel,Residential_Low_Density,140,11080,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1975,1975,Gable,CompShg,Plywood,Plywood,BrkFace,257,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,552,1128,GasA,Typical,Y,SBrkr,1210,0,0,1210,1,0,2,0,3,1,Typical,6,Typ,0,No_Fireplace,Attchd,Unf,2,528,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,148000,-93.601191,41.995702 +Duplex_All_Styles_and_Ages,Residential_Low_Density,82,11070,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Good,Average,1988,1989,Gable,CompShg,VinylSd,VinylSd,BrkFace,70,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,1907,1907,GasA,Good,Y,SBrkr,1959,0,0,1959,0,0,3,0,5,2,Typical,9,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,766,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Family,171000,-93.603854,41.996063 +Two_Story_1946_and_Newer,Residential_Low_Density,42,26178,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Mod,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,1989,1990,Hip,CompShg,MetalSd,MetalSd,BrkFace,293,Good,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,245,1210,GasA,Excellent,Y,SBrkr,1238,1281,0,2519,1,0,2,1,4,1,Good,9,Typ,2,Good,Attchd,RFn,2,628,Typical,Typical,Paved,320,27,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,335000,-93.644527,42.004509 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,0,8239,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Green_Hills,Norm,Norm,TwnhsE,One_Story,Good,Average,1986,1986,Gable,CompShg,BrkFace,Wd Sdng,None,0,Good,Typical,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Good,Y,SBrkr,1295,0,0,1295,0,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,RFn,1,312,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,230000,-93.645586,42.000966 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,50102,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,1958,1958,Gable,Tar&Grv,Plywood,Plywood,None,0,Typical,Typical,CBlock,Good,Typical,Gd,BLQ,2,Unf,0,723,1632,GasA,Typical,Y,SBrkr,1650,0,0,1650,1,0,1,0,2,1,Typical,6,Typ,2,Good,Attchd,Unf,2,518,Typical,Typical,Paved,0,0,0,0,138,0,No_Pool,No_Fence,None,0,3,2006,WD ,Alloca,250764,-93.651077,41.99984 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,16669,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Above_Average,1981,1981,Hip,WdShake,Plywood,Plywood,BrkFace,653,Good,Typical,CBlock,Good,Typical,No,Unf,7,Unf,0,1686,1686,GasA,Typical,Y,SBrkr,1707,0,0,1707,0,0,2,1,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,511,Typical,Typical,Paved,574,64,0,0,0,0,No_Pool,No_Fence,None,0,1,2006,WD ,Normal,228000,-93.649773,41.997906 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,54,13811,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Above_Average,1987,1987,Gable,CompShg,HdBoard,HdBoard,BrkFace,72,Typical,Typical,CBlock,Good,Good,No,GLQ,3,LwQ,40,92,1112,GasA,Good,Y,SBrkr,1137,0,0,1137,1,0,2,0,2,1,Good,5,Typ,1,Typical,Attchd,Unf,2,551,Typical,Typical,Paved,125,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,176000,-93.646099,41.999553 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,50,8049,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Good,Average,1990,1990,Hip,CompShg,HdBoard,HdBoard,BrkFace,54,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,256,1309,GasA,Typical,Y,SBrkr,1339,0,0,1339,1,0,2,0,2,1,Typical,6,Typ,1,Typical,Attchd,Fin,2,484,Good,Good,Paved,0,58,0,0,90,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,180000,-93.646575,41.998023 +Two_Story_1946_and_Newer,Residential_Low_Density,0,11170,Pave,No_Alley_Access,Moderately_Irregular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,Two_Story,Good,Average,1990,1991,Gable,CompShg,MetalSd,MetalSd,None,0,Typical,Typical,Wood,Good,Typical,No,LwQ,4,Unf,0,0,1216,GasA,Excellent,Y,SBrkr,1298,1216,0,2514,0,0,2,1,4,1,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,2,693,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,Good_Privacy,None,0,4,2006,WD ,Normal,250000,-93.646172,41.999342 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8098,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Above_Average,Average,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Good,Typical,Wood,Good,Typical,Av,GLQ,3,BLQ,116,129,1381,GasA,Excellent,Y,SBrkr,1403,0,0,1403,1,0,2,0,2,1,Good,5,Typ,0,No_Fireplace,Attchd,Unf,2,470,Typical,Typical,Paved,0,173,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,202000,-93.646643,41.996331 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,85,14331,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,630,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,526,1800,GasA,Excellent,Y,SBrkr,1800,0,0,1800,1,0,2,0,3,1,Good,7,Typ,1,Good,Attchd,Fin,3,765,Typical,Typical,Paved,270,78,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,312500,-93.646758,41.994347 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,95,13618,Pave,No_Alley_Access,Regular,Lvl,AllPub,Corner,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,198,Good,Typical,PConc,Excellent,Good,Av,GLQ,3,Unf,0,378,1728,GasA,Excellent,Y,SBrkr,1960,0,0,1960,1,0,2,0,3,1,Good,8,Typ,2,Good,Attchd,Fin,3,714,Typical,Typical,Paved,172,38,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,New,Partial,320000,-93.64789,41.994229 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11443,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Very_Good,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,208,Good,Typical,PConc,Excellent,Typical,Gd,GLQ,3,Unf,0,408,1868,GasA,Excellent,Y,SBrkr,2028,0,0,2028,1,0,2,0,2,1,Good,7,Typ,2,Good,Attchd,RFn,3,880,Typical,Typical,Paved,326,66,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,New,Partial,369900,-93.64898,41.993258 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,88,11577,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Timberland,Norm,Norm,OneFam,One_Story,Excellent,Average,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,382,Excellent,Typical,PConc,Good,Typical,Gd,GLQ,3,Unf,0,383,1838,GasA,Excellent,Y,SBrkr,1838,0,0,1838,1,0,2,0,3,1,Excellent,9,Typ,1,Good,Attchd,Fin,3,682,Typical,Typical,Paved,161,225,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,New,Partial,359900,-93.647474,41.993047 +One_Story_1946_and_Newer_All_Styles,A_agr,125,31250,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Artery,Norm,OneFam,One_Story,Very_Poor,Fair,1951,1951,Gable,CompShg,CBlock,VinylSd,None,0,Typical,Fair,CBlock,No_Basement,No_Basement,No_Basement,No_Basement,5,No_Basement,0,0,0,GasA,Typical,Y,FuseA,1600,0,0,1600,0,0,1,1,3,1,Typical,6,Mod,0,No_Fireplace,Attchd,Unf,1,270,Fair,Typical,Dirt_Gravel,0,0,135,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,81500,-93.610268,41.99222 +Duplex_All_Styles_and_Ages,Residential_Medium_Density,78,7020,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,SFoyer,Good,Average,1997,1997,Gable,CompShg,MetalSd,MetalSd,BrkFace,200,Typical,Good,PConc,Good,Typical,Gd,GLQ,3,Unf,0,45,1288,GasA,Good,Y,SBrkr,1368,0,0,1368,2,0,2,0,2,2,Typical,8,Typ,0,No_Fireplace,Attchd,Fin,4,784,Typical,Typical,Paved,0,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,215000,-93.608343,41.99329 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,Twnhs,One_Story,Above_Average,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,116,Typical,Typical,PConc,Excellent,Typical,No,GLQ,3,Unf,0,319,1216,GasA,Excellent,Y,SBrkr,1216,0,0,1216,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,402,Typical,Typical,Paved,0,125,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,164000,-93.608195,41.993173 +One_Story_PUD_1946_and_Newer,Residential_Medium_Density,32,4500,Pave,No_Alley_Access,Regular,Lvl,AllPub,FR2,Gtl,Mitchell,Norm,Norm,TwnhsE,One_Story,Above_Average,Average,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,443,Typical,Good,PConc,Excellent,Good,No,GLQ,3,Unf,0,36,1237,GasA,Excellent,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Fin,2,405,Typical,Typical,Paved,0,199,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,153500,-93.608195,41.99315 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,90,17217,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,PConc,Good,Typical,No,Unf,7,Unf,0,1140,1140,GasA,Excellent,Y,SBrkr,1140,0,0,1140,0,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,36,56,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Abnorml,84500,-93.607142,41.990054 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Good,Typical,Mn,Unf,7,Unf,0,264,264,GasA,Typical,Y,SBrkr,616,688,0,1304,0,0,1,1,3,1,Typical,5,Typ,1,Typical,BuiltIn,RFn,1,336,Typical,Typical,Paved,141,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,104500,-93.604391,41.990993 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,41,2665,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Average,Above_Average,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,PConc,Typical,Typical,No,ALQ,1,Rec,173,36,757,GasA,Excellent,Y,SBrkr,925,550,0,1475,0,0,2,0,4,1,Typical,6,Typ,1,Typical,Attchd,RFn,1,336,Typical,Typical,Paved,104,26,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,127000,-93.603922,41.990523 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,42,3964,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Above_Average,Below_Average,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,No,ALQ,1,Unf,0,105,942,GasA,Good,Y,SBrkr,1291,1230,0,2521,1,0,2,1,5,1,Typical,10,Maj1,1,Good,Attchd,Fin,2,576,Typical,Typical,Paved,728,20,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,151400,-93.603518,41.991731 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,58,10172,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1968,2003,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,423,864,GasA,Excellent,Y,SBrkr,874,0,0,874,1,0,1,0,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,RFn,1,288,Typical,Typical,Paved,0,120,0,0,0,0,No_Pool,No_Fence,None,0,10,2006,WD ,Normal,126500,-93.604279,41.993062 +Duplex_All_Styles_and_Ages,Residential_Low_Density,0,11836,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Corner,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Average,Average,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,BLQ,2,Unf,0,1503,1652,GasA,Typical,Y,SBrkr,1652,0,0,1652,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,More_Than_Two_Types,Unf,3,928,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,3,2006,WD ,Normal,146500,-93.601089,41.991574 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1470,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Below_Average,Above_Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,108,630,GasA,Typical,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Typical,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,73000,-93.603181,41.992303 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1484,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Below_Average,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,294,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,1,253,Typical,Fair,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,79400,-93.603524,41.992159 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,13384,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1969,1979,Gable,CompShg,Plywood,Plywood,BrkFace,194,Typical,Typical,PConc,Typical,Typical,Av,Rec,6,BLQ,344,641,1104,GasA,Fair,Y,SBrkr,1360,0,0,1360,1,0,1,0,3,1,Typical,8,Typ,1,Typical,Attchd,RFn,1,336,Typical,Typical,Paved,160,0,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,140000,-93.600439,41.991708 +PUD_Multilevel_Split_Level_Foyer,Residential_Medium_Density,21,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,SFoyer,Average,Good,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Good,Typical,Av,GLQ,3,Unf,0,77,630,GasA,Excellent,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Excellent,3,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,8,2006,WD ,Abnorml,92000,-93.602043,41.992533 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1533,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,138,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,CarPort,Unf,1,286,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,12,2006,WD ,Abnorml,87550,-93.6019,41.992522 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1526,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,34,0,0,0,0,No_Pool,Good_Privacy,None,0,6,2006,WD ,Normal,79500,-93.601844,41.992497 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1936,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,Twnhs,Two_Story,Below_Average,Good,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Unf,7,Unf,0,546,546,GasA,Good,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,5,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,6,2006,WD ,Normal,90500,-93.601615,41.99171 +Two_Story_PUD_1946_and_Newer,Residential_Medium_Density,21,1894,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Meadow_Village,Norm,Norm,TwnhsE,Two_Story,Below_Average,Average,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,Typical,Typical,CBlock,Typical,Typical,No,Rec,6,Unf,0,294,546,GasA,Typical,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,Typical,6,Typ,0,No_Fireplace,CarPort,Unf,1,286,Typical,Typical,Paved,0,24,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Abnorml,71000,-93.602345,41.991532 +Duplex_All_Styles_and_Ages,Residential_Low_Density,55,12640,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Above_Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,Gd,Rec,6,LwQ,396,396,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,0,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Attchd,Unf,2,574,Typical,Typical,Paved,40,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Normal,150900,-93.6044746,41.9900428 +Duplex_All_Styles_and_Ages,Residential_Low_Density,63,9297,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,Duplex,One_Story,Average,Average,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,122,1728,GasA,Typical,Y,SBrkr,1728,0,0,1728,2,0,2,0,4,2,Typical,8,Typ,0,No_Fireplace,Detchd,Unf,2,560,Typical,Typical,Paved,0,0,0,0,0,0,No_Pool,No_Fence,None,0,7,2006,WD ,Family,188000,-93.603534,41.990134 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,80,17400,Pave,No_Alley_Access,Regular,Low,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1977,1977,Gable,CompShg,BrkFace,BrkFace,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,190,1126,GasA,Fair,Y,SBrkr,1126,0,0,1126,1,0,2,0,3,1,Typical,5,Typ,1,Good,Attchd,RFn,2,484,Typical,Typical,Partial_Pavement,295,41,0,0,0,0,No_Pool,No_Fence,None,0,5,2006,WD ,Normal,160000,-93.6086884,41.9887374 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,160,20000,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,One_Story,Average,Good,1960,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Typical,Typical,CBlock,Typical,Typical,No,ALQ,1,Unf,0,0,1224,GasA,Excellent,Y,SBrkr,1224,0,0,1224,1,0,1,0,4,1,Typical,7,Typ,1,Typical,Detchd,Unf,2,576,Typical,Typical,Paved,474,0,0,0,0,0,No_Pool,No_Fence,None,0,9,2006,WD ,Abnorml,131000,-93.606842,41.987686 +Split_or_Multilevel,Residential_Low_Density,37,7937,Pave,No_Alley_Access,Slightly_Irregular,Lvl,AllPub,CulDSac,Gtl,Mitchell,Norm,Norm,OneFam,SLvl,Above_Average,Above_Average,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Typical,Typical,Av,GLQ,3,Unf,0,184,1003,GasA,Typical,Y,SBrkr,1003,0,0,1003,1,0,1,0,3,1,Typical,6,Typ,0,No_Fireplace,Detchd,Unf,2,588,Typical,Typical,Paved,120,0,0,0,0,0,No_Pool,Good_Privacy,None,0,3,2006,WD ,Normal,142500,-93.604776,41.988964 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,0,8885,Pave,No_Alley_Access,Slightly_Irregular,Low,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,BLQ,2,ALQ,324,239,864,GasA,Typical,Y,SBrkr,902,0,0,902,1,0,1,0,2,1,Typical,5,Typ,0,No_Fireplace,Attchd,Unf,2,484,Typical,Typical,Paved,164,0,0,0,0,0,No_Pool,Minimum_Privacy,None,0,6,2006,WD ,Normal,131000,-93.60268,41.988314 +Split_Foyer,Residential_Low_Density,62,10441,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Gtl,Mitchell,Norm,Norm,OneFam,SFoyer,Average,Average,1992,1992,Gable,CompShg,HdBoard,Wd Shng,None,0,Typical,Typical,PConc,Good,Typical,Av,GLQ,3,Unf,0,575,912,GasA,Typical,Y,SBrkr,970,0,0,970,0,1,1,0,3,1,Typical,6,Typ,0,No_Fireplace,No_Garage,No_Garage,0,0,No_Garage,No_Garage,Paved,80,32,0,0,0,0,No_Pool,Minimum_Privacy,Shed,700,7,2006,WD ,Normal,132000,-93.606847,41.98651 +One_Story_1946_and_Newer_All_Styles,Residential_Low_Density,77,10010,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,One_Story,Average,Average,1974,1975,Gable,CompShg,HdBoard,HdBoard,None,0,Typical,Typical,CBlock,Good,Typical,Av,ALQ,1,LwQ,123,195,1389,GasA,Good,Y,SBrkr,1389,0,0,1389,1,0,1,0,2,1,Typical,6,Typ,1,Typical,Attchd,RFn,2,418,Typical,Typical,Paved,240,38,0,0,0,0,No_Pool,No_Fence,None,0,4,2006,WD ,Normal,170000,-93.60019,41.990921 +Two_Story_1946_and_Newer,Residential_Low_Density,74,9627,Pave,No_Alley_Access,Regular,Lvl,AllPub,Inside,Mod,Mitchell,Norm,Norm,OneFam,Two_Story,Good,Average,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,94,Typical,Typical,PConc,Good,Typical,Av,LwQ,4,Unf,0,238,996,GasA,Excellent,Y,SBrkr,996,1004,0,2000,0,0,2,1,3,1,Typical,9,Typ,1,Typical,Attchd,Fin,3,650,Typical,Typical,Paved,190,48,0,0,0,0,No_Pool,No_Fence,None,0,11,2006,WD ,Normal,188000,-93.599996,41.989265 diff --git a/Bonus 1/ames_description.txt b/Bonus/Bonus 1/ames_description.txt similarity index 100% rename from Bonus 1/ames_description.txt rename to Bonus/Bonus 1/ames_description.txt diff --git a/Bonus 1/app_overview.py b/Bonus/Bonus 1/app_overview.py similarity index 100% rename from Bonus 1/app_overview.py rename to Bonus/Bonus 1/app_overview.py diff --git a/Bonus 1/overall_funktions.py b/Bonus/Bonus 1/overall_funktions.py similarity index 100% rename from Bonus 1/overall_funktions.py rename to Bonus/Bonus 1/overall_funktions.py diff --git a/Bonus/Bonus 2/Bonus2.ipynb b/Bonus/Bonus 2/Bonus2.ipynb new file mode 100644 index 0000000..eed4efa --- /dev/null +++ b/Bonus/Bonus 2/Bonus2.ipynb @@ -0,0 +1,3377 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from tqdm import tqdm\n", + "import ast" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten umwandeln" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_csv(file):\n", + " with open(file + \".csv\", \"w\") as file_dst:\n", + " with open(file, \"r\") as file_src:\n", + " for line in file_src:\n", + " #line = line.replace(\"[\",\"[\")\n", + " #line = line.replace(\"]\",\"]\")\n", + " line=line.replace(\";\",\"]|[\")\n", + " line=line.replace(\" \",\",\")\n", + " #line = line.replace(\" \",\";\")\n", + "\n", + " file_dst.write(line)\n", + "\n", + "path_to_data = \"PATH_TO_DATA\"\n", + "convert_csv(path_to_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten visualisieren" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"data/cleared_train.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Time_135 Time_136 Time_137 \\\n", + "Day ... \n", + "1 0.016636 0.015670 0.015471 ... 0.032394 0.031319 0.028765 \n", + "2 0.011208 0.010492 0.010309 ... 0.028921 0.027965 0.026302 \n", + "3 0.011708 0.011067 0.010871 ... 0.031339 0.030152 0.028004 \n", + "4 0.011602 0.010949 0.010836 ... 0.032229 0.030881 0.028563 \n", + "5 0.011929 0.011299 0.011071 ... 0.034259 0.033036 0.030995 \n", + "6 0.012806 0.012239 0.012085 ... 0.040805 0.039977 0.038003 \n", + "7 0.016160 0.015266 0.015050 ... 0.041784 0.040897 0.038813 \n", + "\n", + " Time_138 Time_139 Time_140 Time_141 Time_142 Time_143 Time_144 \n", + "Day \n", + "1 0.027599 0.024511 0.024229 0.022588 0.020483 0.020352 0.017908 \n", + "2 0.025014 0.022831 0.022933 0.021297 0.019727 0.019567 0.017172 \n", + "3 0.026624 0.023919 0.023962 0.022270 0.020422 0.020221 0.017662 \n", + "4 0.027203 0.024441 0.024533 0.022709 0.020756 0.020626 0.017895 \n", + "5 0.029382 0.026510 0.026522 0.024494 0.022432 0.022151 0.019288 \n", + "6 0.036558 0.033518 0.033415 0.031257 0.028667 0.028189 0.024794 \n", + "7 0.037492 0.034467 0.034240 0.032441 0.029791 0.029187 0.025957 \n", + "\n", + "[7 rows x 144 columns]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.drop(columns=[\"Unnamed: 0\"], axis=1)\n", + "df = df.groupby(['Day']).mean()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "df_01 = pd.DataFrame(data=df.iloc[0])\n", + "df_01.columns = [\"values\"]\n", + "df_01 = df_01.reset_index()\n", + "df_01.columns = [\"times\", \"values\"]\n", + "\n", + "df_02 = pd.DataFrame(data=df.iloc[1])\n", + "df_02.columns = [\"values\"]\n", + "df_02 = df_02.reset_index()\n", + "df_02.columns = [\"times\", \"values\"]\n", + "\n", + "df_03 = pd.DataFrame(data=df.iloc[2])\n", + "df_03.columns = [\"values\"]\n", + "df_03 = df_03.reset_index()\n", + "df_03.columns = [\"times\", \"values\"]\n", + "\n", + "df_04 = pd.DataFrame(data=df.iloc[3])\n", + "df_04.columns = [\"values\"]\n", + "df_04 = df_04.reset_index()\n", + "df_04.columns = [\"times\", \"values\"]\n", + "\n", + "df_05 = pd.DataFrame(data=df.iloc[4])\n", + "df_05.columns = [\"values\"]\n", + "df_05 = df_05.reset_index()\n", + "df_05.columns = [\"times\", \"values\"]\n", + "\n", + "df_06 = pd.DataFrame(data=df.iloc[5])\n", + "df_06.columns = [\"values\"]\n", + "df_06 = df_06.reset_index()\n", + "df_06.columns = [\"times\", \"values\"]\n", + "\n", + "df_07 = pd.DataFrame(data=df.iloc[6])\n", + "df_07.columns = [\"values\"]\n", + "df_07 = df_07.reset_index()\n", + "df_07.columns = [\"times\", \"values\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.0, 0, 'Time_0'),\n", + " Text(1.0, 0, 'Time_1'),\n", + " Text(2.0, 0, 'Time_2'),\n", + " Text(3.0, 0, 'Time_3'),\n", + " Text(4.0, 0, 'Time_4'),\n", + " Text(5.0, 0, 'Time_5'),\n", + " 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure, axis = plt.subplots(figsize=(20,6))\n", + "figure.suptitle(f\"Aggregierte Sensormesswerte von 7 Wochentagen\", fontsize=20)\n", + "nr_y_01 = df_01[\"values\"]\n", + "nr_y_02 = df_02[\"values\"]\n", + "nr_y_03 = df_03[\"values\"]\n", + "nr_y_04 = df_04[\"values\"]\n", + "nr_y_05 = df_05[\"values\"]\n", + "nr_y_06 = df_06[\"values\"]\n", + "nr_y_07 = df_07[\"values\"]\n", + "\n", + "nr_x = np.arange(0, len(df_01))\n", + "axis.set_xlabel(\"Zeitpunkt der Messung\", fontsize = 15)\n", + "axis.set_ylabel(\"Messwerte\", fontsize = 15)\n", + "axis.plot(nr_x, nr_y_01, color=\"darkgrey\", label='Tag 1')\n", + "axis.plot(nr_x, nr_y_02, color=\"green\", label='Tag 2')\n", + "axis.plot(nr_x, nr_y_03, color=\"orange\", label='Tag 3')\n", + "axis.plot(nr_x, nr_y_04, color=\"blue\", label='Tag 4')\n", + "axis.plot(nr_x, nr_y_05, color=\"purple\", label='Tag 5')\n", + "axis.plot(nr_x, nr_y_06, color=\"red\", label='Tag 6')\n", + "axis.plot(nr_x, nr_y_07, color=\"black\", label='Tag 7')\n", + "\n", + "plt.rc('xtick', labelsize=5) \n", + "plt.rc('ytick', labelsize=12) \n", + "\n", + "# Erstelle Legende\n", + "plt.legend()\n", + "# Anzahl an Ticks\n", + "label_am = 145\n", + "pos = np.arange(0, len(df_01) + 1, len(df_01) / (label_am - 1))\n", + "# Beschriftung \n", + "labels = ['Time_' + str(i) for i in range(0, label_am)]\n", + "# setzt ticks\n", + "axis.set_xticks(pos)\n", + "# Beschriftung mit Winkeln\n", + "axis.set_xticklabels(labels, rotation=90)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten gruppiert nach Sensoren" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"data/cleared_train.csv\")\n", + "df_test = pd.read_csv(\"data/df_cleared_test.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SensorSensor_1Sensor_10Sensor_100Sensor_101Sensor_102Sensor_103Sensor_104Sensor_105Sensor_106Sensor_107...Sensor_958Sensor_959Sensor_96Sensor_960Sensor_961Sensor_962Sensor_963Sensor_97Sensor_98Sensor_99
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Time_50.0173880.0201610.0060160.0058490.0119010.0092340.0147790.0046220.0159320.012503...0.0190420.0216340.0069490.0163660.0183760.0153330.0193800.0118850.0155730.010257
..................................................................
Time_1400.0242360.0314300.0129300.0122750.0272350.0196060.0257560.0120180.0270510.021518...0.0230930.0287150.0156290.0295270.0301810.0290270.0311350.0246950.0269230.019426
Time_1410.0222070.0299070.0124770.0114010.0229730.0181120.0241530.0110160.0249360.016033...0.0241060.0278300.0143210.0276220.0272240.0267570.0292470.0225670.0254740.016601
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Time_1440.0193820.0245100.0089570.0085420.0174460.0128180.0195680.0078790.0209890.012915...0.0192550.0250520.0100230.0218300.0232490.0203300.0234520.0172130.0202960.012261
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144 rows × 963 columns

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" + ], + "text/plain": [ + "Sensor Sensor_1 Sensor_10 Sensor_100 Sensor_101 Sensor_102 Sensor_103 \\\n", + "Time_1 0.018636 0.023155 0.008328 0.007599 0.016455 0.012060 \n", + "Time_2 0.018240 0.022252 0.007456 0.006854 0.015287 0.011548 \n", + "Time_3 0.019257 0.021544 0.006888 0.006592 0.014439 0.010566 \n", + "Time_4 0.017563 0.019948 0.006861 0.005825 0.011932 0.009591 \n", + "Time_5 0.017388 0.020161 0.006016 0.005849 0.011901 0.009234 \n", + "... ... ... ... ... ... ... \n", + "Time_140 0.024236 0.031430 0.012930 0.012275 0.027235 0.019606 \n", + "Time_141 0.022207 0.029907 0.012477 0.011401 0.022973 0.018112 \n", + "Time_142 0.021496 0.027632 0.010717 0.010384 0.020835 0.016388 \n", + "Time_143 0.020680 0.027330 0.010437 0.010237 0.019988 0.015634 \n", + "Time_144 0.019382 0.024510 0.008957 0.008542 0.017446 0.012818 \n", + "\n", + "Sensor Sensor_104 Sensor_105 Sensor_106 Sensor_107 ... Sensor_958 \\\n", + "Time_1 0.018191 0.006955 0.019456 0.015433 ... 0.019255 \n", + "Time_2 0.017057 0.006408 0.018508 0.014245 ... 0.017930 \n", + "Time_3 0.016425 0.006028 0.018064 0.012330 ... 0.019030 \n", + "Time_4 0.015003 0.004847 0.016235 0.012390 ... 0.019746 \n", + "Time_5 0.014779 0.004622 0.015932 0.012503 ... 0.019042 \n", + "... ... ... ... ... ... ... \n", + "Time_140 0.025756 0.012018 0.027051 0.021518 ... 0.023093 \n", + "Time_141 0.024153 0.011016 0.024936 0.016033 ... 0.024106 \n", + "Time_142 0.021949 0.009717 0.023294 0.015181 ... 0.020819 \n", + "Time_143 0.021743 0.009394 0.023016 0.016027 ... 0.020105 \n", + "Time_144 0.019568 0.007879 0.020989 0.012915 ... 0.019255 \n", + "\n", + "Sensor Sensor_959 Sensor_96 Sensor_960 Sensor_961 Sensor_962 \\\n", + "Time_1 0.025540 0.009373 0.021288 0.021727 0.020037 \n", + "Time_2 0.020264 0.008557 0.019082 0.020601 0.018478 \n", + "Time_3 0.020862 0.008085 0.018992 0.020369 0.017861 \n", + "Time_4 0.022680 0.007106 0.017330 0.018502 0.016348 \n", + "Time_5 0.021634 0.006949 0.016366 0.018376 0.015333 \n", + "... ... ... ... ... ... \n", + "Time_140 0.028715 0.015629 0.029527 0.030181 0.029027 \n", + "Time_141 0.027830 0.014321 0.027622 0.027224 0.026757 \n", + "Time_142 0.028226 0.013164 0.024634 0.024559 0.023869 \n", + "Time_143 0.027234 0.012529 0.024761 0.024239 0.023975 \n", + "Time_144 0.025052 0.010023 0.021830 0.023249 0.020330 \n", + "\n", + "Sensor Sensor_963 Sensor_97 Sensor_98 Sensor_99 \n", + "Time_1 0.022980 0.016089 0.019061 0.011948 \n", + "Time_2 0.021678 0.015761 0.017788 0.011846 \n", + "Time_3 0.020859 0.014052 0.017645 0.011569 \n", + "Time_4 0.019469 0.012321 0.015824 0.010630 \n", + "Time_5 0.019380 0.011885 0.015573 0.010257 \n", + "... ... ... ... ... \n", + "Time_140 0.031135 0.024695 0.026923 0.019426 \n", + "Time_141 0.029247 0.022567 0.025474 0.016601 \n", + "Time_142 0.026294 0.020313 0.023516 0.015303 \n", + "Time_143 0.025772 0.021085 0.023004 0.015423 \n", + "Time_144 0.023452 0.017213 0.020296 0.012261 \n", + "\n", + "[144 rows x 963 columns]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pca = df.groupby(['Sensor']).mean().drop(columns=['Day', \"Unnamed: 0\"],axis=1).transpose()\n", + "df_pca = df_pca.rename(columns={\"Sensor\":\"Time\"})\n", + "df_pca" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Time_1',\n", + " 'Time_2',\n", + " 'Time_3',\n", + " 'Time_4',\n", + " 'Time_5',\n", + " 'Time_6',\n", + " 'Time_7',\n", + " 'Time_8',\n", + " 'Time_9',\n", + " 'Time_10',\n", + " 'Time_11',\n", + " 'Time_12',\n", + " 'Time_13',\n", + " 'Time_14',\n", + " 'Time_15',\n", + " 'Time_16',\n", + " 'Time_17',\n", + " 'Time_18',\n", + " 'Time_19',\n", + " 'Time_20',\n", + " 'Time_21',\n", + " 'Time_22',\n", + " 'Time_23',\n", + " 'Time_24',\n", + " 'Time_25',\n", + " 'Time_26',\n", + " 'Time_27',\n", + " 'Time_28',\n", + " 'Time_29',\n", + " 'Time_30',\n", + " 'Time_31',\n", + " 'Time_32',\n", + " 'Time_33',\n", + " 'Time_34',\n", + " 'Time_35',\n", + " 'Time_36',\n", + " 'Time_37',\n", + " 'Time_38',\n", + " 'Time_39',\n", + " 'Time_40',\n", + " 'Time_41',\n", + " 'Time_42',\n", + " 'Time_43',\n", + " 'Time_44',\n", + " 'Time_45',\n", + " 'Time_46',\n", + " 'Time_47',\n", + " 'Time_48',\n", + " 'Time_49',\n", + " 'Time_50',\n", + " 'Time_51',\n", + " 'Time_52',\n", + " 'Time_53',\n", + " 'Time_54',\n", + " 'Time_55',\n", + " 'Time_56',\n", + " 'Time_57',\n", + " 'Time_58',\n", + " 'Time_59',\n", + " 'Time_60',\n", + " 'Time_61',\n", + " 'Time_62',\n", + " 'Time_63',\n", + " 'Time_64',\n", + " 'Time_65',\n", + " 'Time_66',\n", + " 'Time_67',\n", + " 'Time_68',\n", + " 'Time_69',\n", + " 'Time_70',\n", + " 'Time_71',\n", + " 'Time_72',\n", + " 'Time_73',\n", + " 'Time_74',\n", + " 'Time_75',\n", + " 'Time_76',\n", + " 'Time_77',\n", + " 'Time_78',\n", + " 'Time_79',\n", + " 'Time_80',\n", + " 'Time_81',\n", + " 'Time_82',\n", + " 'Time_83',\n", + " 'Time_84',\n", + " 'Time_85',\n", + " 'Time_86',\n", + " 'Time_87',\n", + " 'Time_88',\n", + " 'Time_89',\n", + " 'Time_90',\n", + " 'Time_91',\n", + " 'Time_92',\n", + " 'Time_93',\n", + " 'Time_94',\n", + " 'Time_95',\n", + " 'Time_96',\n", + " 'Time_97',\n", + " 'Time_98',\n", + " 'Time_99',\n", + " 'Time_100',\n", + " 'Time_101',\n", + " 'Time_102',\n", + " 'Time_103',\n", + " 'Time_104',\n", + " 'Time_105',\n", + " 'Time_106',\n", + " 'Time_107',\n", + " 'Time_108',\n", + " 'Time_109',\n", + " 'Time_110',\n", + " 'Time_111',\n", + " 'Time_112',\n", + " 'Time_113',\n", + " 'Time_114',\n", + " 'Time_115',\n", + " 'Time_116',\n", + " 'Time_117',\n", + " 'Time_118',\n", + " 'Time_119',\n", + " 'Time_120',\n", + " 'Time_121',\n", + " 'Time_122',\n", + " 'Time_123',\n", + " 'Time_124',\n", + " 'Time_125',\n", + " 'Time_126',\n", + " 'Time_127',\n", + " 'Time_128',\n", + " 'Time_129',\n", + " 'Time_130',\n", + " 'Time_131',\n", + " 'Time_132',\n", + " 'Time_133',\n", + " 'Time_134',\n", + " 'Time_135',\n", + " 'Time_136',\n", + " 'Time_137',\n", + " 'Time_138',\n", + " 'Time_139',\n", + " 'Time_140',\n", + " 'Time_141',\n", + " 'Time_142',\n", + " 'Time_143',\n", + " 'Time_144',\n", + " 'Day',\n", + " 'Sensor']" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "column_names = ['Time_%s' % (n+1) for n in range(144)]\n", + "column_names.append(\"Day\")\n", + "column_names.append(\"Sensor\")\n", + "\n", + "column_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten gruppiert nach Zeit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"data/cleared_train.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = df.drop(columns=[\"Unnamed: 0\", \"Day\"])\n", + "y_train = df[\"Day\"]\n", + "\n", + "fets_train = X_train.drop(\"Sensor\", axis=1).columns\n", + "sensors_train = X_train.Sensor\n", + "X_train = df.loc[:, fets_train].values\n", + "\n", + "X_train = StandardScaler().fit_transform(X_train)\n", + "X_train = pd.DataFrame(X_train, columns = fets_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RandomForestClassifier(random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(random_state=1)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf.fit(X=pca_df_train.drop(\"Day\", axis=1), y=y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.2469282528706655" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf.score(X=pca_df_test.drop(\"Day\", axis=1), y=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "lr = LogisticRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n", + " warnings.warn(\n", + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression()
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" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr.fit(X=pca_df_train.drop(\"Day\", axis=1), y=y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.2284227396322907" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr.score(X=pca_df_test.drop(\"Day\", axis=1), y=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('NLP')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bonus/Bonus 2/Bonusaufgabe 2.ipynb b/Bonus/Bonus 2/Bonusaufgabe 2.ipynb new file mode 100644 index 0000000..3c8bf51 --- /dev/null +++ b/Bonus/Bonus 2/Bonusaufgabe 2.ipynb @@ -0,0 +1,8290 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn import metrics\n", + "from sklearn.decomposition import PCA\n", + "from tqdm import tqdm\n", + "import ast" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten umwandeln" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_csv(file):\n", + " with open(file + \".csv\", \"w\") as file_dst:\n", + " with open(file, \"r\") as file_src:\n", + " for line in file_src:\n", + " #line = line.replace(\"[\",\"[\")\n", + " #line = line.replace(\"]\",\"]\")\n", + " line=line.replace(\";\",\"]|[\")\n", + " line=line.replace(\" \",\",\")\n", + " #line = line.replace(\" \",\";\")\n", + "\n", + " file_dst.write(line)\n", + "\n", + "path_to_data = \"PATH_TO_DATA\"\n", + "convert_csv(path_to_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten visualisieren" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"data/cleared_train.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"df_04 = pd.DataFrame(data=df.iloc[3])\n", + "df_04.columns = [\"values\"]\n", + "df_04 = df_04.reset_index()\n", + "df_04.columns = [\"times\", \"values\"]\n", + "\n", + "df_05 = pd.DataFrame(data=df.iloc[4])\n", + "df_05.columns = [\"values\"]\n", + "df_05 = df_05.reset_index()\n", + "df_05.columns = [\"times\", \"values\"]\n", + "\n", + "df_06 = pd.DataFrame(data=df.iloc[5])\n", + "df_06.columns = [\"values\"]\n", + "df_06 = df_06.reset_index()\n", + "df_06.columns = [\"times\", \"values\"]\n", + "\n", + "df_07 = pd.DataFrame(data=df.iloc[6])\n", + "df_07.columns = [\"values\"]\n", + "df_07 = df_07.reset_index()\n", + "df_07.columns = [\"times\", \"values\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.0, 0, 'Time_0'),\n", + " Text(1.0, 0, 'Time_1'),\n", + " Text(2.0, 0, 'Time_2'),\n", + " Text(3.0, 0, 'Time_3'),\n", + " Text(4.0, 0, 'Time_4'),\n", + " Text(5.0, 0, 'Time_5'),\n", + " 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure, axis = plt.subplots(figsize=(20,6))\n", + "figure.suptitle(f\"Aggregierte Sensormesswerte von 7 Wochentagen\", fontsize=20)\n", + "nr_y_01 = df_01[\"values\"]\n", + "nr_y_02 = df_02[\"values\"]\n", + "nr_y_03 = df_03[\"values\"]\n", + "nr_y_04 = df_04[\"values\"]\n", + "nr_y_05 = df_05[\"values\"]\n", + "nr_y_06 = df_06[\"values\"]\n", + "nr_y_07 = df_07[\"values\"]\n", + "\n", + "nr_x = np.arange(0, len(df_01))\n", + "axis.set_xlabel(\"Zeitpunkt der Messung\", fontsize = 15)\n", + "axis.set_ylabel(\"Messwerte\", fontsize = 15)\n", + "axis.plot(nr_x, nr_y_01, color=\"darkgrey\", label='Tag 1')\n", + "axis.plot(nr_x, nr_y_02, color=\"green\", label='Tag 2')\n", + "axis.plot(nr_x, nr_y_03, color=\"orange\", label='Tag 3')\n", + "axis.plot(nr_x, nr_y_04, color=\"blue\", label='Tag 4')\n", + "axis.plot(nr_x, nr_y_05, color=\"purple\", label='Tag 5')\n", + "axis.plot(nr_x, nr_y_06, color=\"red\", label='Tag 6')\n", + "axis.plot(nr_x, nr_y_07, color=\"black\", label='Tag 7')\n", + "\n", + "plt.rc('xtick', labelsize=5) \n", + "plt.rc('ytick', labelsize=12) \n", + "\n", + "# Erstelle Legende\n", + "plt.legend()\n", + "# Anzahl an Ticks\n", + "label_am = 145\n", + "pos = np.arange(0, len(df_01) + 1, len(df_01) / (label_am - 1))\n", + "# Beschriftung \n", + "labels = ['Time_' + str(i) for i in range(0, label_am)]\n", + "# setzt ticks\n", + "axis.set_xticks(pos)\n", + "# Beschriftung mit Winkeln\n", + "axis.set_xticklabels(labels, rotation=90)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten gruppiert nach Zeit" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"data/cleared_train.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = df.drop(columns=[\"Unnamed: 0\", \"Day\"])\n", + "y_train = df[\"Day\"]\n", + "\n", + "fets_train = X_train.drop(\"Sensor\", axis=1).columns\n", + "sensors_train = X_train.Sensor\n", + "X_train = df.loc[:, fets_train].values\n", + "\n", + "X_train = StandardScaler().fit_transform(X_train)\n", + "X_train = pd.DataFrame(X_train, columns = fets_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RandomForestClassifier(random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(random_state=1)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf.fit(X=pca_df_train.drop(\"Day\", axis=1), y=y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.2469282528706655" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf.score(X=pca_df_test.drop(\"Day\", axis=1), y=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "lr = LogisticRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n", + " warnings.warn(\n", + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr.fit(X=pca_df_train.drop(\"Day\", axis=1), y=y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.2284227396322907" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr.score(X=pca_df_test.drop(\"Day\", axis=1), y=y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Daten gruppiert nach Sensoren" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 963/963 [00:49<00:00, 19.34it/s]\n" + ] + } + ], + "source": [ + "df=pd.read_csv('data/PEMS_train.csv',sep='|',header=None)\n", + "df.columns = ['Sensor_%s' % (n+1) for n in range(df.shape[1])]\n", + "\n", + "for x in tqdm(df.columns.to_list()):\n", + " listen=[]\n", + " for num in range(len(df.index)):\n", + " listen.append(ast.literal_eval(df[x][num]))\n", + " df[x]=listen" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df_empty=pd.DataFrame()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "86it [00:00, 284.12it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "118it [00:00, 297.12it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "150it [00:00, 304.19it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "183it [00:00, 309.38it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "215it [00:00, 310.96it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "248it [00:00, 314.12it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "281it [00:00, 316.43it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "313it [00:01, 315.81it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "345it [00:01, 315.99it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "377it [00:01, 316.30it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "409it [00:01, 315.21it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "441it [00:01, 314.85it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "473it [00:01, 314.29it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "505it [00:01, 312.69it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "537it [00:01, 314.02it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "569it [00:01, 313.67it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "601it [00:01, 313.44it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "633it [00:02, 311.42it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "665it [00:02, 311.36it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "697it [00:02, 311.35it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "729it [00:02, 307.61it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "760it [00:02, 307.43it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "792it [00:02, 308.41it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "824it [00:02, 309.32it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "855it [00:02, 308.61it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "887it [00:02, 309.84it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "918it [00:02, 307.03it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "950it [00:03, 310.09it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\288873778.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty[col]=row_fields\n", + "963it [00:03, 307.36it/s]\n" + ] + } + ], + "source": [ + "rows = []\n", + "from numpy import mean\n", + "appending=[]\n", + "for col, values in tqdm(df.iteritems()):\n", + " row_fields=[]\n", + " for row in values:\n", + " row_fields.append(mean(row))\n", + " df_empty[col]=row_fields\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "test=[4,2,7,4,3,4,3,4,1,4,1,5,1,2,7,6,2,5,7,6,4,2,6,7,7,1,5,6,6,7,3,1,3,5,2,7,2,3,6,1,1,6,7,3,4,3,5,6,1,2,6,4,5,6,6,5,1,4,1,2,2,7,7,5,6,1,7,6,4,2,7,5,7,1,2,6,1,6,2,2,3,3,1,3,1,4,5,1,1,6,6,2,4,3,3,1,3,3,5,1,2,1,3,7,5,1,7,4,4,2,5,1,5,6,1,6,3,5,4,4,4,3,2,1,5,6,5,6,7,6,7,4,3,2,7,5,5,2,3,2,5,2,2,1,4,1,2,5,1,3,3,3,6,4,2,4,7,3,7,4,6,1,6,5,2,1,3,4,1,3,3,6,6]\n", + "a=[3,4,3,5,1,2,2,5,6,5,7,6,3,7,6,5,4,5,5,5,5,2,2,6,4,2,7,4,3,4,5,5,2,3,7,2,3,7,2,5,3,5,7,1,7,2,6,7,6,1,5,2,5,5,2,4,6,1,2,7,6,5,7,1,6,7,1,3,6,6,4,1,1,4,7,7,7,7,4,7,5,1,2,5,4,5,7,6,5,7,2,1,4,1,3,4,1,6,4,3,2,5,5,2,3,4,4,4,5,1,4,4,3,5,2,6,6,4,5,4,7,5,7,4,7,1,6,6,1,2,4,4,4,1,5,5,1,7,3,6,7,5,6,1,1,3,6,4,4,3,3,2,2,6,3,2,3,2,3,4,6,1,4,7,4,3,5,3,7,4,7,3,6,3,7,5,1,3,1,7,7,1,7,6,7,6,3,6,4,7,2,7,1,4,7,4,3,2,6,7,1,5,5,5,3,3,3,3,1,5,5,7,3,2,6,5,2,6,6,4,7,1,5,7,1,6,4,3,5,7,3,1,2,4,3,7,6,5,4,4,7,6,2,2,5,6,1,3,2,4,6,3,2,1,4,4,1,3,7,4,5,7,2,5,3,7,6]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\462493660.py:1: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty['day']=a\n" + ] + } + ], + "source": [ + "df_empty['day']=a" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 963/963 [00:32<00:00, 29.40it/s]\n" + ] + } + ], + "source": [ + "df_test=pd.read_csv('data/PEMS_test.csv',sep='|',header=None)\n", + "df_test.columns = ['Sensor_%s' % (n+1) for n in range(df.shape[1])]\n", + "for x in tqdm(df_test.columns.to_list()):\n", + " listen=[]\n", + " for num in range(len(df_test.index)):\n", + " listen.append(ast.literal_eval(df_test[x][num]))\n", + " df_test[x]=listen\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "df_empty_test=pd.DataFrame()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "89it [00:00, 450.49it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "136it [00:00, 458.82it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "184it [00:00, 465.79it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "231it [00:00, 466.90it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "279it [00:00, 469.15it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "327it [00:00, 471.25it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "375it [00:00, 468.23it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "422it [00:00, 464.25it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "470it [00:01, 465.83it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "517it [00:01, 462.06it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "564it [00:01, 459.86it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "610it [00:01, 458.33it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "656it [00:01, 454.12it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "702it [00:01, 454.06it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "748it [00:01, 445.88it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "794it [00:01, 448.49it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "839it [00:01, 431.61it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "883it [00:01, 420.36it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "928it [00:02, 428.66it/s]C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:8: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test[col]=row_fields\n", + "963it [00:02, 450.52it/s]\n", + "C:\\Users\\nikla\\AppData\\Local\\Temp\\ipykernel_19368\\2832927387.py:9: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df_empty_test['day']=test\n" + ] + } + ], + "source": [ + "rows = []\n", + "from numpy import mean\n", + "appending=[]\n", + "for col, values in tqdm(df_test.iteritems()):\n", + " row_fields=[]\n", + " for row in values:\n", + " row_fields.append(mean(row))\n", + " df_empty_test[col]=row_fields\n", + "df_empty_test['day']=test " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "X=df_empty.drop(['day'],axis=1)\n", + "y=df_empty['day']\n", + "X_test=df_empty_test.drop(['day'],axis=1)\n", + "y_test=df_empty_test['day']" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/209 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pca_numlogisticRegr
60610.820809
1601610.815029
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1331340.815029
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image_idstreetcitin_citibedbathsqftprice
001317 Van Buren AvenueSalton City, CA31732.01560201900
11124 C Street WBrawley, CA4832.0713228500
222304 Clark RoadImperial, CA15231.0800273950
33755 Brawley AvenueBrawley, CA4831.01082350000
442207 R Carrillo CourtCalexico, CA5543.02547385100
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image_idn_citibedbathsqftprice
count15474.00000015474.00000015474.00000015474.00000015474.0000001.547400e+04
mean7736.500000216.5975183.5063982.4532512173.9132097.031209e+05
std4467.103368112.3729851.0348380.9587421025.3396173.769762e+05
min0.0000000.0000001.0000000.000000280.0000001.950000e+05
25%3868.250000119.0000003.0000002.0000001426.0000004.450000e+05
50%7736.500000222.5000003.0000002.1000001951.0000006.390000e+05
75%11604.750000315.0000004.0000003.0000002737.7500008.349750e+05
max15473.000000414.00000012.00000036.00000017667.0000002.000000e+06
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sb.heatmap(data.corr())\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def streudiagramm(x_achse, y_achse):\n", + " plt.plot(x_achse, y_achse, 'o')\n", + " m, b = np.polyfit(x_achse, y_achse, 1)\n", + " plt.plot(x_achse, m*x_achse+b)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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VU/ULePjhhzF9+nRMmTLF7fOioiKUlZXhxhtvdH1mMBhw3XXXYdeuXQCA/fv3o62tzW2b3r17IyMjw7WNJ62trairq3P7z59w0cukz7Y1yoBHJg8CQBY5PqIveZyMVAR1tFnnrC0iss2ktHjidiihQ59oU7fQSRBjw6FSN9G1C36sfu0rzmq2gUll5o7p73Rkf/eVtH/cdkrO52zHvSbtM3+78wJVxIXgtgtk2neowz3HcpRUNfn8/ITSZEmxgbJu3Trs378fy5Yt8/qurMypvpeYmOj2eWJiouu7srIy6PV6N8+L5zaeLFu2DBaLxfVfv379lHbbL3DGweKZw9zW5BZOS8f8SSlenhQN4wzWO3KezKDKSU9wteMJC+ClW9Lxq9F9YNS53zZrlAHvzB6Nf8y5mkbEdEPemX01kQZBoIkNVxdLwc26NuWXYsJrW/F53kU/90w9yXFm2B0sKur9L8/NGZX+Tkc+dakeuYWVXq52u4NFbmEl1uddEPxeDNL+cdspOZ/+Mc44ElIDOyHS6DqPOkJPjZp+XWmQhhIMiDX7PNnh3yOlz5q/UZRmfO7cOTz22GP47rvvYDSKXwTGY0GWZVmvzzyR2mbhwoX4wx/+4Pq7rq7Or0YKaZoxly4nlGq1cFo6nrhxiFv6193XDMDk138k8swwAN66ewy2Hr8kmtnw+08Oie598Gw10bpj72gjkqKMSEkw49OfL3Z5hgIFeHTtAaybn42ZI5MUVWL1B7dm9sZ1VyXAGmXEmAExGPXSd5L1X8TY0lFlONSep7H9Y/xexA5wTj6qO8S95mQn45WNx/ymOrvih0Ks+KHQLbNCLCODJPWTpH8axrkdt/2Sr48R9XX5HaMAOJfJ5bJhYsw6VDe2Krofnv3y53XuDnDFBp+Zlo5/7zlLdA+1GgZJFiPKalsU/R65toTuUVelGSsyUPbv34/y8nKMGTPG9Zndbse2bduwYsUKnDhxAoDTS5KU1Hki5eXlLq+K1WqFzWZDdXW1mxelvLwc1157rWC7BoMBBoNB8Dt/QLpmV9Vowysbj+HvW0+hpLIBta2dt9+sBVrszqwCDQB9GPDqxmNoI3xCWAATX/seqfFmGBWuPZfVtRANbKP7WXDtoHhkp8ajoLQOLEJnptuT2V1UhXn/2udK9QwmX+RdxJ6iKpfo1Ou3j1SV7rxu31lVxkkg03jDNAwe++RQQI7vYIGH1xzE2x3aEfMmpvjduOQyKx6YlIJ3txV5nQf3/duzR0sOHNwytFT/5vE0TfRhGsyfJH8+I/pGIcJIPoS0tjvw8JqDiu6HZ7/kzsOs16I5CAHYwUg/5wu9mfRaRffwhRnpin7HXFszRyYJ3iPSZ83fKBoJb7jhBhw5cgR5eXmu/66++mrcc889yMvLQ2pqKqxWKzZv3uzax2az4aeffnIZH2PGjIFOp3PbprS0FPn5+aIGSqAhXXOz2Vmcq2rG4Yv1bsYJADTZO1MeHQBa2kFsnHBcqrcht6gGRYTrjUo5cK4WK34oxD2r9uC1b8hmSJTgsLmgvMu8D/wUw6kZSXhn9mhYo5S5iRttBDm8HiyYMtgrtVUr4mgd0TdKcTpvu4NVdE3HDIhGtIm8DRbAM58fQbPNjuuvSsQNQ3r5JZuHf3zAmZ0hlh3DAnhs7UE8vu4gtp+87BYLxHfRPzl1KOZPShFUO+VSme0OFjtPVeD/vj2OMK0GWSkxEGNE3yhseGSi62+SLDSlmVtmvRZPTh3q9pnUcvotI5Jw19h+itrQh2m8nvVok7DRxS3xz5+UoiglW+xYcnBKyJxBIHXu3D3kmDwkUdFyv9VixFt3j8aGQ6WKMrECjSIPSmRkJDIyMtw+Cw8PR1xcnOvzxx9/HEuXLkVaWhrS0tKwdOlSmM1m3H333QAAi8WCuXPn4oknnkBcXBxiY2Pxxz/+EcOHD/cKug0WWwgrV15JtPcgNylFGhbOF+aLXxYgJ92KqRlJyEm3Ym9RFZ769BDOVvk36JVzJT8yeRDSEiLw5KeH0dDaka4q8FwyALJT4/D5Q0Oxu7ASuWcqcPJSPb7zs8dpf0mN69/RJh2mDE3Afw9ckNynqrEN6S9scsseiTSGoV+sCecqm1Hf2pk5lBhpwN3X9EdyfLizei4L5J6pxIofTosen4V8ZkqrncUXeRfxRd5FmPVazMnqLyjENXNkkpcibWKUEaP6x2BTfime/uwIcXpudmqc29+BCKxsstmx+0wlNAzjJhg2sm8MLKbzqOb1lWWBrw6Xuv7WMCBaCrK1O/D6/4yERuNso7iiCWv3ngUEMr746r184bz4CAOe+CRPUqXY0+NC8vr9v1+PxHiPxAehUAIhJdnVuWTLrbdm9sbwPhbEhutRWttMnInF6R0FGr9L3T/55JNobm7GQw89hOrqalxzzTX47rvvEBnZKRu9fPlyhIWF4fbbb0dzczNuuOEGfPDBB9Bqg1/TxtbuwFeHe56BQqHw8Xz5aDUMapttfjdOuLZemJGOzQVleGiNvBuaBVyu7YXT0jE+LR65hZV+N1D41Da3yRonrv55jAT1Le0ouOgtmV5e34o3tpzC27NHY/wg58Dz/fFLPveVT5PNLrgMwAl0eXKprkXVkh7/fgCBq6f08L8PuKU9mzsUWz3xHIyVpBtXNLZiVmYfbMovxRtbTooO7Iumd8ZgcEJmHItnDsODHddRygOhBK6AoSec2J0UpBlP3x29hC8UBrUHM8vH50T7H3/8EW+88Ybrb4ZhsHjxYpSWlqKlpQU//fSTl9fFaDTib3/7GyorK9HU1IQvv/yyyzJzSC1NCqUnwL18fKlPJUeMWYfJQxKxeMNR+Y15cEJUADBmQExA9YcC8U7wdJPbHSw+JzSCAoUv58m/H1yQrL/x1GQRMk6EUHJeZy43yIqbMQCWfC2+vMHVsEn0WC7y5RkVMvpIs2tIM56a2pQHxAdTEqHHFwvsybn1FIonaqq6KqW6qQ2rc4slXeJCcEJUcyemYn9JdbfM5vAUw6r2oUBiV8O/HyQwHescoXbb/pVbgnEpcT4vbxw8W41LHjL/ap5RbgmUKyjIoSSTK1AZT0kC/Qok/pEq7Mb05Nx6SuiQ0cf3ope+wn/5qHHjXj84njjIVO3EIJDCa8GkvL6l258D0HkfSYJkWbYz3imUqG5qQ24hWUV6sXvG1cnx1R7gZ+7wtbbEaut41tDhIBEQVcOdY/sHtS5PjzdQqFQ9pau5JiUGOUMT5TcMMDNHJrlePsUVjYr3n5jWC2/dM5poW/UTA+cQ0J2VdwFn/7v7OQCd99GzQKAY949P9jkDJjCQmRZC94ykRhApnpk7gLo6R4B41o8vmWbJ8cGd0Pf4JR6S3HoKJZBUNNiwfMupru4G3t1WhJF9Y3CqvEFxfxgGGJwQifK6FsSG61HdaBN8oXLu68EJkbAYw1Dboqw+TmbfaABkwmChiKf7PjHSgEsBULkNBnwRtaoGsnPoG23CjqcmY8eJy7j3w33yO/gIaTZPdmo8Pj1wQVLcTGx5g6RGkBDcs/B/vx6JisZWV5aSp4dCSZ0jz+UnoayfwQmRmPP+XuUdRvAnBj3eQAE6I9FXbvcuZU2hBJrCy8q9FYGABfDI2gOqfgMsC6KXHgtnhozaF2TvmODM4LghYkp6Ajb7MVuIy2DiBqEXZw0jyqIJlDCYL8flC4PFhuuJ9okN10OrYdBst0MfpnEF2fob7v7NnZAi691IshiRNTAOL8xIx4MfHRC9Js1tdmwuKPOK91CzXMlfyvFMJfZETZ0jPp5ZP3YHq0ppNtqsC2r8CUCXeFwsnJaO40tuxqLpQ3Hd4HiY9cIpzwyA/jFGWAzuVq7OD1dSA2BwLzN0HmpVYZrQW7elXJkEw0AnzcTwhD+DJYl58AXO1f6r0X1lt/V0occoyGbhhPEMIurROekJTuE8j2WRcL1W8J0Qrtdi/qQU4rpOVosR78wejXdmjxZ955Edx0S8HRdPIWWcjOgbJVkYVb4d5/17drpzmUMMBp0GI5eJYxG5f7VNbYLxHmqWKyONYbhvfDIsJr2s8JmSOkckcMVxAWXXt6apDZuDrBlGPSg89GEa9IkxYdvJClHLkgVwrrrFbZ2Q+8GphXtI/t5xTLuDdYkA8d1+6/Mu4LF1earboVC6K/yBBAhckCwD4F+/HYdrO3RKJry2VXL72HAddj51A/LO1biJdkkdnxPE485lakYSCl6yYtuJcry++SRqm9twlTUCb9wx2iUlzwnn8d8JdgeLD3cVY19xJcz6MPxqdF9cOygeWg2DJ24cgqxlW1DVKG7ExYbr8NOffgF9mAZ2B4so41FFxuPK7UV44sYh0IdpXGnfcrViMvtFy9YnS4jQ4fOHJmBzQZloXTIhHr9hEFJ6RXgtlYzqHwOL6RxqPcTXos06vHrbcDePSE66FU99eljw+J6ChtzxSTNmYsw6l7hcXUs73t9ZjPd3FsvWueEKiYp5PMSyfqTgjDHP68sw4hoyQuceaKgHhYdcLjwHC3ctA5J9+HjOsDwDozgRoFmZfVyiWUD3DwykUNQQG67DW3ePgsWkd+k/xEcEpjYXC+DkpXpoNQxRqnVVYxvyztW4fq8ahpFMn/ZMM+bYXFCGZ77IR/7FOpyrbsaWY5eRs/wn12xd6J2gD9PgtxNScP/4VPxiSALCtJ2v8/0l1ZLGCdf3/SXVAJweKbVp31x7cgO0gwXW7CmRvablDW3YW1SFqRlJ2PHUZCyaPlRye443vj8NQ5jG7Z3JTR49jRPA6RHxZMXWU4LbcgjdP32YBjcMTZDtX7WIx69UJBOHQ8rjIZb1QwJ3fdfOy8Kbd2Zi0fShkgJ3Ys9uIKEeFB5KtB/4N0qpXsT/u3MUwrQaLw+JHHKWNIVyJTKybzSWfH3M7XdmjTIg2qxDbVOb338LXEyBmrV/Nftwg6jSAm1SuhithLEdXD/UeqSUXivSeA3ueFoNg/hIcmOUP8MnmTx6bv/+zmJF/QOcE9v8C3XEfSTpiydiHg+rj1WG+Yq46/PIRAODmR5PDRQeSi+82htV1WTDrMw+ivfjLGmpQC61MABMIjLSFEpX8sOJy16fXaprdT3/nr8F7vX+vxNTsGqH8sB3LqZAzdq/0n3kUkjF3OpyRs3jU9IU9UOtd1bptSKN11BzTQH3bBal2S97i6q8lGtJ+ucPUUMSITh+jSylk1sS/B3r4g/oEg8PpRderZZBfISBSK5YCDFJZV/gHu8/3zYc2aniFUwplFCB+8WEG7SIMgkvmU4ekqjYOOGnznIeS6nXf6RRiwvVTVi1/Qw+P3AeDpaFNcoguU+0SQcHy7pizUgG0b9+d8LVxvaTl7Hws8OSuhhr956FNUq+75n9ol3nao1StmzGwPtaSZFkMWJOdrLkNWXgnc5Lcmw+Sr1CSrf3zGbxp0dB7lhiy//+QO55F7o3gYZ6UHhUN7ZKBgnxMeu1eOuHUxgQZ4Y1yuA2oxODgfPh9qx8KRck5cnUjCREGnS4Z9Ueou3lsFqMyOgThd9/nOf2QmcADE2KxKj+0fj3nnN+aYtC8SeNrXYATq9ftEmH+8en4JHJg6DVMHjpS2W1fgD31FnOYymVBlzfYscf/+seVBlt1km+C2qa23DPP/cgyWLEzRlWon699WMh0XaA00gpq2vFgimD8caWk6Lb1bfYMeyFTZg3MQWj+segRWHK79CkKLdrNXNkkqSe1MyRSdCHaUS9wGLxFCT3gQ83aSyuIFtOUupFuv/aFLf++dOj0JVxhlIeel9iXXyBelA62JRfiofWHCSugtlks2PH6Ur8e885lHUYJ1K3jbvh1U1tXsFoYnLFUohVuiTlxvQEvHlnJtbOy8LNGVZsLij3mm2yAApK63H6UmjodFC6D0kWo6J0V39Q09yG5VtO4tv8MmzKL8V7hPEEfEb1992DSJr+XFbboqqPpCTHm/GARIot4Axc/ce2IvzuowOK07Ytxs75rd3B4uOfz0tu//HP52F3sC4vsGfqtJCKKsfUjCT8/e7Rsmmx0WYdMvtF44+f5GG5hHEGdHoExgyIQW5hJcrqWhAbLp8inpYQ4fa3PwoldoV3Qgg19yaQUA8KnD+uxRt8r9yqD9OIBqdZLUY0t9kFXwJSa81i+GppX5PidBMu+Sof7+0skdx2T3HworYp3ZfV949DVbPNbW38yalDnRkitc1Y8vUxVDXaAt6PR9YeQKRR3atNKMgyUHC/+0AFvEcbdNhwiHzSo5TdRVWwtTugD9Ngd2GlrIFT09SG3YWVGJ8Wryqe4qYMKywy6sHNNjuGPr+JqP8snF6d6/7yg6IYkiVfF+CmDP+n2gbbOyFGTroVkQYdcs9UAHAuKWWl+nc5iRRqoIBLsfN9HbG13YHkGANG9o/DbaOdKYd7iqrAgkVNk01ymYRba37+8yO4cZgV+0qq4HAAVU0tOFFaj4oGG+IjDYgN1yM+Qo/KBhvMPgS1fnbwAt7bUYQLAapYS+l5nCyv91Ks3H2mErmFlThf3RQU4wRwegWkUkWlKK1twe4zldAwDHaevhywis4cgczG++5YWUD7zwJ4cUM+xqXG4YfjZGq7uWcqMD4tXlTrSQoScT7S7CUONSVO+M9IeX0LKupbibxPseE6wdRvsSV+W7vDTaJ+Tnaya0ktENgdLFZsPYX3dhajlhcs/N/957F4pvpMIV9gWJZ0USN0qKurg8ViQW1tLaKifK8CGygBNF8MCAqlu/Gb7AF4aVYGAOeS6dOfHel2tXIAZywLaTZHKDO8TxSO+CH91Z888otByOgTJZoeLTUIhpJQpcUUptgIXn5HJqxRRpTVNqOq0YbYCAOsUcLG2bKNBV6lVzSMM0aKK83iT0h+r+/4aYlHyfhNPSgIXGASNU4oPQkuhXRTfilxQGMociUYJwAQZfQtLiIQhGkYVZovQGgJVarx0FmjjKIpxHyWbSwQ9Oxw8UIA/GqkiKWse7LwsyNBVZEFaJAsAC7FLnQefgqlO/LrMf38Fs/lCQMgLlyP1389grgwXU/ngQmpsmnSwYRhgLV7SyTTozmFbiFI0r5DFQ0DjBkgH4Bta3fIFjdcub3Ib0UWlSihVze1YfeZSr+0Swo1UOBMr1o80/9uMwqlJ/F/3x33WzyXJyyAykYbeseY8T9jlIsc9jTMei0mXNXLJZEeCrAscKlePA5JTkpdbZG7UMDBwlVWQIrVucVEJQO4EgO+olRkLreQGihdAldV1Nd0MQqlp1Jc2RRwGewtBWV4V0Fgoy4EsiK6gr/ePtKtQq9YureGgU9VjAOB1DMklgbbHTwrJL8N0lIApNvJofz3GtyQVRqDwmNqRhIcDuDJTw+joVVdFgCF0lNJjjMHPE7g87wLil6R04ZbsT6AqbYA8NB1A1Fe34L/HiCrZaKGnKEJyEqNQ2y4HglRRoAFth6/hE8PnEcNLx4iMVKPF2dluMVx8FN6S2uacfBcNVgAKXHhmJOdjK3HLxHFIAQLuWdoakYSJg9J9Mpwef2746qycoIFyW+DtBQA6XZyKP29ZqfG+6VdUqiBwsMp1tZ9g/solK7kmWnp0IdpnJkKMss80aYwGHVhbtvJlXqPDdejUkGqcoxZh59OkqW/qoErc//ETVdBq2HQLzZcVhxMLfddm4Lxae6Dw/i0eDwzPZ0oXZdfFO62MX3dvhMrROdPuGvFsqxk1WQSsTKhIon/3FGEF2akY/4keGW/yKFhoLgkglJIRdjmZCfjlY3HJPvDL8fgK0oK0EabdcgiCPL1J9RA6cAZ3KdcGptCoQB9oo0wdSwVLJ4pL0v+6q9GuAl1FVc0YvmWU6LbswBmZfZWpLwqVt7eHwhJfz8yeRDW7i2RHIDVIDUw8A0PXxASTlu1oxBbjnkXalQK/1odPFstK4cvlSVCUvn5+JIhbt6VxEgDHhFIT+ZaWXHXKMSEG1znvfV4GVZuL5Y9JyU2DakImz5Mg3kTUySvEb8cg6/w5e3lePW24UEXa6MxKB04g/v8+2KhUAIBQ/iOiDbrMKKv7zpBJPx2fKekulQ8V7RZ59JT4AbXW0b0xvu7iiWPb9ZrccPQRKK+xIbr/R5L5nnJhaS/ncH2wyRjIUb0jfKKB4mR6WuwBgbPQnT/vHccctITBLcVOg+x8gbctcpJt8oq2244VCqaxSNX+RlwZgFpNQzmTkzFS7MyMHdiKm7J7IN3BOJwuH5NG9Hb7byfnT4M8yelwPOSaxhg/qQUvCMQAxNj1gnG8sTwnndSFk5Ll2zf3zoocnFK1iiD3zRQlEKF2joIJREgJTAARva1YEJaL1yTEgsNw2Dl9jP48aTvMx9K6BEbrsPOp27Au9sKJT0Oj90wCL+/YTC0GgbNNjuWbixAcWUTkuPMGNs/Br//5JDovsOSIjFjRG+crW5CSWUjdhZKlzrQMMDxJTd7zer4SrIAi+zUeGQJVGDdeaqCqPDl6vvH4cnPDku6o2PDdXjz9lGY8/5e2eOZwhhckxqHfrFmrN59Vnb7RdOHIj7SIKt8KrQEEWnUYtmtw3FLZh9BFdXNBWVYvOGo2yTJGmXA4pnDumRg4OP5/DwzLR0mvVZUDVbs89zCSty1crdse2vnZQl6hXzdX6l6rZSSq9CxAGB3YaXfJOL57feLMWNIYqRXKQl/wp1TWV0LqhpaERuuh9Vi8ntbVKhNBaEkAqQEFkDe+VpckxqLiYN7YdnGAmqcdCMYAP/67Tj8cKJcdvmCAbD0l8Nh0mvx2JTBuMoaSaTIadJrseTW4QCcL6EJr22VbOdoaT0e/kU4fveLQQDEhaM4xFzOWg2D8YPiMX6QdGCd84Uuz57iStlqq0t/ORxVzWRxKktvG4Ffju6L9XkXiAyU+EgDZmXKpzjL1ZkRWpZRU5smWPCfHz5iy0tin5NmjIht5+v+SpfD9GEat9INJMcanxbvFSukFq59IYOXRHlXKf5aLvQn1EDpwCnWZui2yzwrtxfh0cmDZUV+KKHFA5NSMHFwL0wc3AvjUmJFAxWFXkhqBjVS3YNF6/NdBdE4l3LgpLdJB2FGNKDTyrs+pFoNVosJAPnkRMkkRs3LPhQHCH/i63UOxH0KdUhibrrawxZIqIHSAbd+3F0luh0scPfK3IBHo1P8A8MAD3gM7nyDg9TNqnRQI52FVjbasLeoynXshdPS8cSNQ/D+zjPYXFAOgMWN6Vbcx4s9UUv2wDis+OE00XaAvGEml5nAZZRwbnmS7ROjDHCwLNbnXfBqT8zdH4rekGAgtpSi9L54omZ/NUUJ1eLvtuRibhi4V9++EqEGCg8uuC/QRc7Mei2a2+yiKZVqORxihcEo3hjCGDwx5SrcN0F8WSSQs2gls0tPY+b17467eVF+LqnBq5uO++xFyUqNQ7RZJ/mbizHrkJXaeV2krhM/M0FoKYgFcOfYfvjq8EXXQCK3fUu7A/f8szNOhvNoAcDiDQVu6dIRhjCEaRm38/GXS97XQdCfcRhCyC1HyC3RSWW7yN1Xz/2DtTTiS1tS90PO28kp7+4urPTbslKoQYNkBbA7WLdgpyhjGJZ+c1x2v2tTYxBh1GFschxmZw1A3rkalNe3ID7CALBARWOr6yG0O1gs+eoo0do35crCoGXw4KSBeOiGNGg1jGigIT/A9JoU52C8p6gKLMsi2qxDbLgBNU2dVVH7x5gx663tqGtpR5QxDF89OgnWaHeDxO5gMfaVzYJl3z2ZMrQXrBYTUuLCcbG2Gat2FItuO29iCp6d7m2kyAXecd9/d7QU7+8qkTh+MiYPsSoamIUGDS67h288mHQavPbL4dDrtYLbCxlOStNMuZ4qdcnzB7Diikas3XvWbRlayYCrdBBVWlFXbDnC89yF+mGNMuCucf2RHB8ueX/tDhYrtp7C+zuL3Yo6ep4HaV/8gdq25O4HaeKGWa/FX28f2W2WepSM39RAIYALLJRzLe54arLi2czYV7agSoH4FOXKIlyvRSOv6nWSxYiZI5Pw8c/n/eLFM+k0OLbkZrfPNh6+iIfWHPT52J6suHMUbsns7fpb6AXMwZ3nhkOlqsTBSAdm9wG+CW9sOSlqWIzoG4XPH5rg2j4+3IAn/nPIr7WFkhS8J6SuHwfpgKt0EJULjPZMd+XekWJ99XxHet4Xp+ElbTgJGpwmHe4fn4JHJg9yW3JT0hdfUNsWyf2wmPREWUscXZUKrBQl4zfVQSFAqkgViWtS6rgvz8rwvYOUbgvfOAGcLtt/bCvy2xJjc5sDQxd94/aZJkDr1Y+sO4hN+U6dC+4FLPbi5s5TrXJpaUeQINeeGHytlXX7zkp6PQ6fr8P81T+7NDE0GsbvhQ+liuHxkbt+HGzHf09/dgQ7T1UIaoiQ6ofYHSzsDhbbT1yWrXfkWVGXdDmCO3fuvhjCNHhjy0mv61zmcX/Frkdtcxve2HISmwvKFPdl12my7DEplJ43IH8/uPvZbnfAGmUg7sviDUdFNWS6K9RAIUSsSJWQYJMUtnYHVm0/g+fX52PV9jOYkp6I+ZPUBxoyDHDLiNC3mildR3ObA2U1zpco93IMFC9+WQBbu4O4hLsvsAAWfnaE6KVMmr205Vg5mjuMxkAVPpQzeqQGMDFqmtpwz6o9mPDaVi+jjXQQXbH1FCa8thVz3t8r27ZnRV01KcCkhpPU8+RpYCnpy2/e24tlG337Lag5b5JnsaapDXPe24sWnhEoR1ldK5Hx252gQbIK8FWrQGhN95WNxzBvYgr+fvcoPLc+nyg2AADMOi2mDbdi6W0joNUw2F9STVRPQQlDreGYNDgRB89W4+jFOq/ZPqX7cMvftuHnRTcqLq+ulNLaFqzOLQ5oG3yqm9qwYuspPDZlsOR2SoyNpRsLsOTW4QFLV61qkJYy8OUeCaWfkp67lPCfEPyKumpSgEkNJ7nnie+lyB4YR9wXFnAtY6kN8lZz3kqexVqFnlQ1RnUwM52UQg0UHrZ2B1ZuL8TaPWdR39oOizEMSVEGtDkAvU4D1gGcvlyPNjsLHRxodQANNmGTQK8BLOYwRJn0iI8w4HR5o2ChMwfr/JFMy7Bi98IpWPH9Kfw/mZTL4X2iMCdrAOpb2vH1kVJYo4xYNH2oqrgCg5bBkKQo3JxhhZ114IfjFXDY7QDDwKjTIrewgmYHXQHUtTgr3gbKK8DHX6XgSXl/ZzEemZwm+VJVYmwUVzr7r6SQmhKizXrJ7325R0Lpp4EytPgVddWkAJOeJ+nzxB1P6X1bub0IT9w4RFV9m8x+0Yq3i48gX7ZR+twpOTYQ3EwnNVADpQOhoLDa5nacrVb3srA5gMsN7bjc0I7Cy/I/sI35ZfjmuW+IHsgjF+rw5KdH3D4LF6gDQUKrncWh87U4dL5W1f6U7kGU0flTD4aIlb9KwZNS09zmptkixLiUWJh0GjS3ybvMk+Oc/ediz/ytjVTTJB0U7+s98vQoBMLQ8qyoqzQFGCA/T9LniTuekgJ4QOdylZhqrBRr9ohnnnlu5zp+INc+FRy7O4jA0RgUyEesBwtfnlu6/EKR4saMBGw/cRntdgeiTf4tpMcnyWLEnOxkJFmMxPqw/kBuNq7VMHjtl95y7UI84+dibJ7Ehkt7UDiDwtfrx10TkiB/pQiVN1Aapyd3ngzIniduO753huuLUAE/IdR6/Uj3429X0Rg4tXLSYysJnO5KeryBYmt3hIRxQqEEkjV7LmDO+3sx5729bvoR/oSBc5asD9O4BsRgQTIbnzm6r2x155z0BJg6BjW5gGIGTp0Ui0mZI5qT2BdDyqBQQnx4p7tfynhYIBO/w0euou7UjCTseGoy1s7Lwpt3ZmLtvCzseGqy4EycNDuS/zwpyaKcmpGEBVPSiM5LyEtjd7DILazE+rwLyC2sFBysSb07/O3498XfkHql1GQfdQU9fomHH4lOoYQChjANtBoGTd3IKxZt1uHV24a7BiKxmjkcvuqgcMjJo3uy4ZGJ+N8P92HLsXKv73LSE7DyN2Ndf5O8xGua2vDvudcADLCrsAIf7CyW9GZGm3VEfRW7fkkWI64dGIdPD1yQPYbnaD41IwmThyR6KcNqNQzW7TsruQRk1mmxIGcw7r1WWkkWUKaGPDUjCW8JJAhYPeIgxK6HxaTD/eOTkZNuFTz+vdemYNk3xyVLgHguVwHO5Q+S6tJzspPxysZjyo6v0Opk4MzWlHNmeHqRpCCN//mmIyOsqwJne7yBEuyAPgpFjlZeamGEQYuJafG4e9wAAE65+bzzwQla1hC8FAEgNd6MzX+4XnAGK1db6MmpQ13f7zx1Gf8lGXg7UKtB9M97x6LZZsfSjQUormxCcpwZz0xLd3lOOEhf4hWNrZiV2QfjB8VjeB+LZMxKTVMbNheUEa3ti2UNfnX4IpGBUuGRLeQcdN1l+VduL8Limemy8SN/vSMwSqWb8kux5OtjbsZJbLgei6Z7B2ly12PF1tN4f2cRaprbUNPchuVbTmHdvnOCgZ36MA3mTUxRVI17U36p4D0sq2vF7z464CaIpub4nvdFCu76y7UBKPsdkHpa/pVbgn/llnRZ4GyPN1CCHdBHoSihodWOb/IvYcaIPpg2IglhWo0idUlfIDFORve34LOHJoh+Lzeb5n9v0mkkDRRP2XnPWbYSTHotltwqHZOiJoXUQSBbIVbgTSzd0/P6FVeQTar4/RIfdFtcg65clWh/IxakWdVow8NrDuBtjXfcyuaCMkE1YKnATiXVuO0OFk9/5p6A4MnTnx1xu39Kq30rCYLmrn9OuhVmfRj+se2Ml2c1xqzDMp73kgSlgdNdFTjb4w2UOdnJWPL1sa7uBoUiySNrD2AFRgFgFNeBCST3Xut7NWOATEDOpNPirbmj3WpaBdLtrDR11u5g8dz6fNnj8jNsOEjTPe0OFu/vko+Zs0YZ3PolN+gu/OwIfn4uxyedJyXIidFxInx8Q8CX6r5cNW65woe7CytlVZxrmtq8CvSRHh8gMw6izTq8dddoZA2Mw+aCMi85fadntRdmZw1AVmqcKhVzMa+ZEF1VPbnHB8nqwzTISU/o6m5QKJI4WOChNQfx0BrvGWdX4q+0ZRJxstLaFmg0DGZl9kH2QOUvZaUoLXGxt6iKuK4Wf/lITMbdU+4dAHafkR9AAeDOsf1d/SLZp7qpDbvPVLo8NoG+xiT3mxPhI91HLrBTH6bB3ImpeGlWBuZOTBU0HpwFYuUR2o7k+ID8c8UAePW24RifFo/NBWWCz0Zjqx2b8stQ39Km+h6JBU6L0RWBsz3eQLE7WORTITIKRTGRRi0y+0UTZTvIQVrzxt+1ceRQkjqrRGCNE9RSmu7prG4tTzvvHpDuI7SdZ2kOmwLpdSlI7+P7O4sVS9j7JkZIOtgLV1om/R2QPFfBSAXmZ139JnsA0T7BEHvk6PFLPKSy0n2jjegTbXIpydra7GjpPkkWFIrfqW+xI/35TTDptW7r4moC6uTk35Vu509IS1wo8iZ1jClKvALOJSHSwYgV+TfpPsKlOV7++hh+OyEFi27xLY2c9D7yRfjUxAQpJXtgHFbIKHlz2/FRo8gq91wpfzbUwY9z+leuvPBcMMQeOXq8B4XUGmxotWPNA9nYv+gmHF48FcdfmY75k1KCKkZFoQQTBs4AvMRIcWExFvAK2hNampDjfDVZ4KecyFmgIFn6GJcSSyyCxwlqKfUKZKfGy2wJr+3U7MOJV3pOzlkAq3YUYeaK7UTHFEPJfeRL2Eebpa9vjEQaN4mHIys1jqiNrFT3+CHSJTpPpJ6r4HiMOiEVziNNZfYHPd5AIbUGOUuez8Jp6fjXb8f5tT+JCsprUyiBgntJ3X51X7AKPchK3c92B4v1hy4SHVtO5EyuHV+XoqTQahjcP54saJh77yj1CowlHBz422UNlB90o806ZHXMop01yaQDcQ+fr8OSr+QDgsVQch+VzNjF7uim/FJMeG0r7lq5G4+ty8NdK3cLVn7Wahi8ept0dtey24a7Be4u3hCYZZhgeIz4KI25CgY9fomHm/WQqGu+v/MMtp0sR31LGxwscLm+FfGRBkQaw1DfUYzNFxgAT+VchT98etjnY1EovhATrse1qXGqVZY59/PyzScwflAvjBkQg/0l1W6ubMCZNfGf/eeIqnjHheuJZ2+eKbvVja1Y8vUxoiwZIZc7acXXtIRw2b7xZ6FKM4X2FZMFKO4rrsL4QU6PCDfoSumzvMobdFfnFhOlmK/aUYKnpqarKrI3LiUW1iiDmxCaEPxrtbeoiijDRihDSknNmakZSXhn9mgvzRih52XF1lOS8TS+LMOoKcDoK07hvNEdwnmdAd+BTDeXoscbKNysZ/mWk7LbfldQDsBbgdJfsAA1TighQVWjDV8dIV+iEWPFD4VY8UOhl+hbtFkHW7tDkVrurMzebkaBmNEgpAIqRGmtUwNkwZQ0PDI5DZsLyrziCGLDdfjV6D748lCpqKqord2B1bnFKK5sxBd58p6gRdM7Z6FKi+yRBrzuOl0BDcO4rk1OurVj0JVXR1UiXqm2yJ5Ww2DxzGGyhRj5565kyYO7J4UVDfji4EXFqckkcUeb8kuxfMsp7wMLsKWgTNBAkTJ85VKBWQB3XN2PqH1SnMJ5BW7GSaRRi2dvHtIlhQMZllXqwO166urqYLFYUFtbi6go6doaJNgdLIY+v8lvEeoUCsX/rJ2X5XrJCwUlRpt0mJAWj68OKzesPEXgSMlJT8D3x8qJPA4ci6YPRXykwcuoIgmy/L9vj2PFD4WybYTrtW6S+0k8wS85T9Cq7WeItaF+kz0AL83KINpWiE35pXj6syNe154TH+P3t6K+lahf04dbsTG/TNHSJP/ZIsHuYL20SaSIMevw83M5XgYOyT0X2o6PZ5kJuX6L3X8xTxOHVA0mJSgZv6mBAuea61XPfRNS+hIUCqWTJIsRO56a7BrMpV6k3Qn+gESyjLTzVAXuWbVHcTvcUUiUQG3tDgx+7hui4y6aPlSVB4XPxsMX8ewXR1Dd1LlMbo0yYFZmb69aTQwDScNDp2XQZlf+ZLx5ZyZmZfYh3j63sFKxorOngS30DIvdp42HL+KhNQclj/+OzL2VMohy0q1EBtff7x6NaSN886QoGb97fJAs4HRTXgkvOwrlSoSrkszFgkgpkHY3+JkeJJlCWQPjEK2wejLgP90MTzyL7CllU34pHl5z0M04AZx1b/6xrchrwJSbTqsxTgDlgaZqMme4fZTqm9gdLF76Slpl2XMfT+QyjVZsPUXkDVq0Pt/vweVSUAMFQHElLRhIoYQiGgZ46+7OmSGpblF3QanhoNUwuE9leQFSJdAPdpIFRl+THKMqQJYjFIxNtamzajJnuH2UKuI6i2nK68aI3VsSg+j9ncWyxweAykZbUJVke3yQrJMrZT5GoVxZOFjA0OFJaLbZ8dYPZEGJ3QluQPrtqlzsO1uLNgcLizEMXz06CdbozoGQq8D844nLPrX36NoDcLAskmPNeOj6NDS2tSMh0ogxA2Kwr6gK7xMaKHaWxc7TFR2BuyyyU+MxekAM3ttZiLV7z6G13YGRfSx4/fZRKCitQ3l9C+LDDQADlNe1YH9JdUgYm3dc3Q/LN58A4PRgcRonu05V4NOD59Fks2Nscizuvbaztk5mv2hEGrWoV6DWuXJbIZ787yHi2JivD1/ExiMXceZyI3Ebn+w7i2/yS9EvxoQh1ihUNLRib2GlrEFEksXKEUwlWRqDAuDTn8/hif/S7BkKJVQx6TRobut5QewmnQbHltyMef/ah80FgcsgBKC4CKVnZlZ3ROicw/VatDlYr6QJhgEemOj0Xnkq7PYklAYUe6Jk/KYeFAC9Y8xd3QUKhSJBTzROAOd5py78OiiDodImroQBWugUGkVS31kWqnWBrhT0Wg1Vkg02l0PAzUihUChCXAmGAOXKwGZ30CDZYGJ3sHjmiyNd3Q0KhUKhUEKe1bnFQWurxxsoe4uq0KBAzZJCoVAoFH8yMY2soGMooERp2Fd6vIESzIhkCoVCAZyCYhQKx/WDe3V1F4gZEBu8mM0eb6D4qxIkhUKhkPL9gusRxKKwlBAmMVKPOdnJSLIYvaoIhxoMfBfnU0KPN1DGpcTSi0ChUIKGSadB/3gz5k1UJ7hGubJ4cVYG9GEavDDD9zo3gWZKeoJP4nxK6fFjs1bDYHhf37VUKBQKRQ5O1wQAFk5Lx+QhZK79jD6Bf0eF4uw9yeLu4Y4x60S3VbNsJuTFCjdoBQdhtdcnyiis5hGmYdzq50zNSMLbs0d7nTMJA+JMijxyI/pGISc9QXE7+RfqgprFQ3VQAPz7f7ORsfjbru4GhdJtSYjQobxBWTXg312XiqMXarH9dKXssV+6dTiuG5yApRsLUFzZBGOYBpuPyQuX3X51H1yobkZdSzviw3X44aR0WwCw8OYhqGtuw8WaZjTZ7Pi24BLxOcmx/I5Mt7/nTRyIrcfllWGfnZaOzH7RrvNnAGw7VaGqD4lRBrTZHV5Ksu3tDsx5f6+iYz07bShqm9sAsNBqGLz5/WnRbX87Phk3DEl0KclWNNhQ02RDm92BHScvoeCSM/hSC+Dbx6/DIGuEVwHF6sZW0aJ5pHV4jGEaZKfG4m93j4FJr8XuwkrknqmAnJLskMRIxdcHAOpb2rH8f0bi6/yLOFHWAItJhyemDMakIQle9ZamZiS5KjiX1jTj4LlqOFincVRR34xNBcLPytnKZvztzkxcqm9FSVWTm5JsaW0LNh65iNrmdlyVGIE37hyNHacv48GPDig+F05O3xehNiVQJVk4U43Tnt1I9QYoFB9IiNDhckMbkeCXhgGOvjgVk1//UVKGOzZch90Lp3jNaLly92W1LYLtMQCsvArIAFnVcgbAiZdvhj5M42rDX3LsYn0asugbyXePhgGOL7nZdQ3U9kuofT7r8y7gsXV5io557KWpMOm1sn2Sa5sEkjYYCXVbX/sg98yJEaxzJ23D1+daaeVnT2g1Y4XsLqykxgmF4iNDkizE2zpYYM2eEtmXZFVjG/aXVHt9rtUwrjV7z9cx9zdXAZljf0m17MDCdmwH+L8woVCxvv0l1bLvHgcLt2ugpl9i14SPmoSBNXtKiPokV6jQ7mCRW1iJ9XkXkFtYKbiMQNIGtxvpMyGFZ5+4/YWOLwVpkUYpfLm+nuex+4x0bR45gplYQpd4gA73HoVC8QUWwNuzR+PpT48QFR8j1VMoqxN+mXJr9i9+WeD2wrVajHhhRjpy0q3ILax0LQ9crCZrj5MeIJUgiDbpVBdbI21DzT58uGvCxTsIMS4lFkkWoyIPAXcP1ZwHx6b8Uq97mCTQX9I25o5Pxsb8MsFnQur8Sfsk9MyR4IukhdrrK3Qe0SbxOB4pOC9NMKXuqYECIDTDwyiU7kVynBlTM5IQadThnn/ukd2eVE9hyVdHYdJpBAcX/po9Z4iMS4nF5oIyLzc2Q/gz52aIxRVkBs1b94yGhmGw8/RlrPihkPj4nv/29z6Lpg9FfKTBdU3kPAecV0pJbAJ3D9WcB+AcQB/86ICXQVRW24IHPzqAt3lBpKRtTEm34pnp6V7PBKnnhKRPO56a7Dp+RX0rlnx9TPa4vnge4sMNRNvx2xA7DyXGNIcaD5Q/oEs8QNACfiiUK5lnpjnd31mpcYiWyLYAgGizjlj7oaqxDQ9+dACb8ksFv9dqnMGNszL7IHtgHDYXlOHBjw54zXBJou3Mei3GpcRiU34p3thyUnb7aLMOWalxyB4YhwU5V8med4xZ5zYD5bwWYteAgXPmzt9n63H5oF2zXov7xqe4rgnpoMJ5pRIj9bLbahhgcEIk1uddQLvdIZtFomGAMQNiXH/bHSxe/LJA0FvDffbilwWu5R7uWknBXSvPZ0LJsg5JnwC4jj8nOxk6meNzz5UaNuWX4on/5Mlux39OpM5DDRazzs1YDBbUg4LOF2pNk7hlqdMyGDsgBhl9LegVYUR8pAHx4XocL6vHueom1DXZ8MUh4RcohXKlc8OQeJj0WuLtGbjP2IXK3nvy4pcFmDwkEftLqkVnxr6+mJvb7Nhx4jKe/vQI0TH4w9K3+aWolXiHAN7nKHUN+LNWAMgtrMTF6ias3C5fUbfZZofdwXoNzJ5ZMUKehakZSZg8JBHX/fkHlIosrwGAhmEUZbVwsTTjUmKxt6gKO09XEMVVvLfjTEemEAOTTnpO3StSL2qMNLS0Y8HHB3G2uhn9Y0xYfscoRHikAJPGeizffBLjB8WjurEVCz87gjaZQKLmNuH7YWt3YHVuMUqqmjAg1ow52cluwdArtp7GcgJDGQBmjkxyHd/f8VNyz3WgUJTF8/bbb+Ptt99GcXExAGDYsGF4/vnncfPNzrx+lmXx4osv4t1330V1dTWuueYavPXWWxg2bJjrGK2trfjjH/+ItWvXorm5GTfccAP+/ve/o2/fvsSd9ncWD+C0Un8n4dp8h8B63JRfisUbjqKsrtUvfaJQuguLpg/FfeNTXAPPih/E00051s7LQvbAOGzKL8Uzn+ejqtEmu0+4XotGXu0sa5QBi2cOc/02cwsrcdfK3epPRAWr7x+HA+eqsXzLKaLtufPmIxYrcP/4ZKQlROClrwoUv1cWTR+KuRNT3drwfD95Xj/noHgK/9xeiPpWh6L2SPjt+GR84xEf4m+4zCKg0wB468fTqGr0HmSH94nEM9OGoay2GVWNNhRXNmL17rMB6Zfn/Vi2sQArtxe5BUlrGGDexBSM6h+jeCxJ4mXxkGZkKYmfCjdocfiFm3xe4lEyfisyUL788ktotVoMGjQIAPDhhx/iL3/5Cw4ePIhhw4bhtddewyuvvIIPPvgAgwcPxssvv4xt27bhxIkTiIyMBAA8+OCD+PLLL/HBBx8gLi4OTzzxBKqqqrB//35otWQzsEAYKAD3Ay5wC8oTCtbiw/0AzlQ0oLyuFQmRBuwvqcbxSw1+6xeFEuoYdQxizAZFA8/yOzLxy1HOdMX739+LH07I64GIwU0glnx5FKt2Fqs+jhpIvD98fjs+Gc/PGOb1OTdjfn9nkao4AU9+kz0AL83KAEA2AQOApz87IulJ7g7MyeqPJbcOFzQAuhL+/Vi2sQD/2CbvCVMKZ/ySGur/nnsNfi6pJvbSzBhhxd/uHuNTH5WM34qWeGbMmOH29yuvvIK3334bu3fvRnp6Ot544w08++yzuO222wA4DZjExESsWbMG8+fPR21tLVatWoXVq1djypQpAICPPvoI/fr1w5YtW3DTTTcp6Y7fEQu483Qhc99vLriEjUdKQ+YHQKF0FS1trOJZcVWDc3Zoa3f4ZJwAzoF18pBEfJ53wafjqEHpz3/NnrN4drp3sOHmgjK8seWk3+IGuABWu4PF058dkdz2D58cQrPN7re2u5KiiqaAGQC+0C/GeT9s7Q6iZTo1cJPrzH7RRNuP7BeNP/73EPHxvzxchtdvdwRN7l51DIrdbsd//vMfNDY2Ijs7G0VFRSgrK8ONN97o2sZgMOC6667Drl27MH/+fOzfvx9tbW1u2/Tu3RsZGRnYtWuXqIHS2tqK1tZOV1ddXZ3absvCBVcJIeSGpVAo6ogNdwZirs4t9vlYNU1t+HBXkaAbP9RoaXdg16kKTLyqU+be30GNGqazqNvuwkpZr0gTb9msu2PUMgEzAHxhSKJzFWF1bnHAJrWc0f/R7mKi7Zd9o3w8W51b7LZUFUgUm0FHjhxBREQEDAYDfve73+Hzzz9Heno6ysrKAACJiYlu2ycmJrq+Kysrg16vR0xMjOg2QixbtgwWi8X1X79+/ZR222e4lC1qnFAo/sFqMQEg10ORY1+xt6BbqPLpwfNuf/s7qHHexBTXLLen6TyZ9JqQ9GpXNTtjrPz1vAvBGf2kv4XD52sVtxHI/nui2EC56qqrkJeXh927d+PBBx/Evffei4KCAtf3jIfYAMuyXp95IrfNwoULUVtb6/rv3LlzSrvtE/6c3RiCWAmSQglV+CmRpHoochRervfLcYKBp8fCFxEvT8INWjw5dSjvk56l89Qaos4gTqPEX8+7EJzRH06YUWdRIdoWyP57oni01Ov1GDRoEK6++mosW7YMI0eOxJtvvgmr1QoAXp6Q8vJyl1fFarXCZrOhurpadBshDAYDoqKi3P4LJr7Obm5MT8Sbd2Zi9W/Hwdbu/8h4CqW7wRd8mpOd7JchtPBy8GZ2vjI22V0Tw5/y4Y2tdjfJ81DReWKYwJtK0Wad17UNBaJ5+jdzspMVVR4mhW/03zaaLCt23sRUWe0ePgw6lw6Dgc/TeZZl0draipSUFFitVmzevNn1nc1mw08//YRrr70WADBmzBjodDq3bUpLS5Gfn+/aJhTxdXZzTUosZmX2wclL9VdEEBqFohYNA/z97lFuWXH6MA3+d2Jy13WqC7j32mS3v+UE25TCf2eRCOepMR48AyXl2nhgYoqzLYXtKKHN7sDsrAEBMQB8gd8dfZgGNwxN8Pvx+Ub/tYPiZb0o4QYtsgfGK2rngUkpQQuQBRQaKM888wy2b9+O4uJiHDlyBM8++yx+/PFH3HPPPWAYBo8//jiWLl2Kzz//HPn5+bjvvvtgNptx9913AwAsFgvmzp2LJ554At9//z0OHjyI2bNnY/jw4a6snlDEl9kN3+IM5todhRKKrLhrNKaN6O31+eQh1oC3vWBKGuZPSgl4O3LMF3jJSxU/VAP/naXVMHj1tuGS23PGAyk56Qk49tJUrJ2XhTfvzMTaeVnY/1wO3pk9GtYo9/dlksWId2aPxsJpzjo2Vhk1WF9obLXjwNlqzFN4PmqJNuvw+A1psttVN7W5vFp2B4v8C9KJHtFmndd1FCPJYvRSedVqGLx++0jJ/V7/n5HYX1JNnFY+f1IKFnaoRQcLRVk8ly5dwpw5c1BaWgqLxYIRI0Zg06ZNyMnJAQA8+eSTaG5uxkMPPeQSavvuu+9cGigAsHz5coSFheH22293CbV98MEHxBooXYGaIlocQ5MiXS+jptZ2/3eOQukGyOkJ+TMGQwyb3YFJaQloabPj433n0NLe+WuONoUBDOP2so4L12PJrAwALBZ+dhi1LcLBDUp0UKRe8mLFD2PMOrAsWQ0VsYJuUzOS8M7s0ZI6T6P6x4hmKWoYIMoYhpszkvD8jGEuITTh5SP3q8GX2uKkHJZvPkkk5sdHp2XQZpe/0rmFlVg4LR07Tlfg6EV1cUkxZh1YQHTwjjJqMXfCQDwyeRC+OnyR6JjcM04SMlDT1IZ/zx0NjYbB5oIyfJF30U3IMMKgxcS0eMy+JhlZIlL+JPd8PWFa/l//ZyRuG0MupuovFAm1hQqBEmqTgsviAZTpHnDqgXYHi+ylm1HeEPppkBSKv3jkFwMxflAv2WJtpMJSD/9iIGLNehRVNuIjPyp+Rpt1WHrrcMSE672KDv7hk0Oyabix4Xpk9rMg71yt20Ci1Ti9Gb/J7o+5EwYSuceF5OgBuD4rrmhy1QkSksaXqpkiJ3XPfV9W14KqhlbEhuthtZiIiu2JFacT6pdSxV8GwNSMRHyTL1+H6JFfDMQfbxqCVdvPEBXxm5PVH6P7x6Cq0YbYCAOsUe7XfOX2Qvxw4rJbLSdO8XXhtHTic+FE1EhVXt+8MxOzMp1ChmIifnKGP7ev2D1X2nd/EDChtp6M2OxGCv7yzt6iKmqcUHoM3Ex+Qc5VRNLY41JiZethRZt1+EPH8XILK/1qoNQ0teGhNQfwzuzRrkFBTn2VT1WjDVuPe4vNORzOKrgp8RHEa/diWkz8z66yRni9i6wEg5WUzpPQ95xS9jf5pV61YvjIFdlj4KyllJNuhVbDYFxKLGLMOlQTLC/EmnVYettwRBp0RAZKdqozruLuawYQGSjPTEsXrSP144lLwveVhUsI7smpQyU97J5eLTWVn8VE/ISqPnsidc/lVgfEPHLBghooAohZnDnpVkQadMg9U4Gdpytx8FyN5HH4AUXBcGFTKKFAoEqz84/ky7KrFNwgCgAvrM/3+XhCg7MYJIX8OEhUr31FSCr+lY3HXJ4DPqRF9vYWVbmqC/9yVB+8R1CWYNEtTqPL1u4Aw0hXpWYYYGzHYHqghEwL5EBJNcaneQeLkii+rtxehCduHEJU8JG7N0qNAqXGnxJIi1X687lSAjVQeNgdLP72/Sn8c8cZNPCS6c16LXpF6HG5webm6tUwEBUEyuxnQbRZj4dW78WOwiq0tHW7lTQKRRUkM3lP9hZVyQbrcYGG3ACnpBIyKdwgCgCX6uWLF5LgOTgLIaRSLee+l/OG+IKYVDzfc8A3UkgnYPztctKtRAYKp+2xv6Ra0jgBnMbL/pJqZz0aQoG63DMVggYKieKrg+1UVhXysAv9FpQaBUqNP6WIrQ6o+R37G2qgdLApv1R0rbnJZkdJVbPX51IPb965WuSdU67SR6F0VyamxeOh6wepmsmrGeDEXqzhBi0afVDrCpS3U+y4YrEbJO77QKDEc8B5iNUsW3CeBLHB19OToPwZIX0GhbcjzbrktlPi1VJiFKj5bSglGB45NVADBcrWmikUijDXD+6lekavZoADhF+sDpbFPf/co6ofSvrij+MG0n2vFqWeA0BdLAPfkwDIexKUPiPZA+OIMoXEnllSxVT+dkq8WqRGgdrfhlIC6ZFTS4/XXbc7WCzeUCC/IYXSzWHgXDawRhkk55ZqhkF+cTo1yAmVcX0XCtbjXqyzMvsge2AcslLjVIuecW2MS4lFYqRexRG8keq7Evd9sFDqOQCkdVykYhk4T4KnNopVQNtD6TNCIlAXY9YhK1V4UCZRfPX1ufd8doWMUF9+G92dHm+gcCl1FMqVDH+QeP6WdNGYDQbO4G6l8IvTqUHtAKf0WHJwbWg1DF6claFwb2/k+h4M971S1HgOAGXGhud+O56a7Cb6tuOpyV7bK31GSATqlt02XPSZ0odpZAXffH3uSfDnb6O70eMNFJpdQxGiK37qgXzRcYMEAMnUywc6hMTemT2aqEYHw/hPYVLtAKfkWGLEmHV4x6MNTujKTFB4LclixPxJKUhS2Pdgue+V4IvngNTY8ITEk8AdX8kzwt1DMXVbuX4tnJaO+ZNSvK6Hxo/PPQn+/G10J3q8UJtSwSBKz+H6wb1Q39KGCzXNiDSG4bq0XqhotCHGFIZN+aUob2wDAyDGFIbLjepVgvtGm3DvtcmYnTUA1//lB1yqbxXdNlyvQXJ8BI5elJbKBoCxA6IxOzsZsSY9jl+qx87Tl/HjSfnMhndmj0ZOuhW7z1Ri1+kKXKhpRu9oEzL7RuPvP55EYUUTDGFa3J+djHnXkYmPKYHT3iipapLU3iCBn7obH2EAWOekpLy+FcdK69Bka8fY5Djce614G3YHix+PXsKSTcdQ19KG5Lhw/PM3Y3HiUr1X7AC/Pe66n6sWPw+7g8WE17bKxm7seGoyAEgKuPkzsFEsi4dD7eCsJJVaCqXPiK3dgQ93FWFfcTXC9VrcNrovrh0UT9y2P59JX/DX9etKlIzfPd5AsTtYjH91K13moQjy5p2ZePWb425xAma9Fs1tdtmUR6VEGLRu6e1+OaYxDI2t7Yr6qg/TINasd/tNiMmMp8absfkP16t6SQq9bDcXlClOt1X60laa0jvvX/uwuaDc6/OMPlF4dlq6YHtCGiJ89VHP/kgFinKeL88+cx4ufno2ibIoKWNf3ozLDcKp1moMFKnrriSDROn925Rfiqc/PeJVKiDarMOrtw33+Vp1F6MhVPpJDRSF0CweCkU9Ggb4+z3K3MxCg4yYkqyUhLuawYpUjh0QN074eLanxvsgdR4ABPssBIncPQkzV2zH4fPSXjolRorUdWfhfe/F7qHS+0fybidZ6hFDjX5NVxBK/aQGikLsDhZjXt5MXNWRQqF4Q/qiFxtkpOAvdXCzPqWDFbecIqe7wbXRbLNj6PObiPv39uzRmDwkEUMWfSOZpqthgONLbhZc7hFawpHqM8l5KKWhpR0Zi78l2vbky97n4YncdRdC6B4qvX92B4vRS75DbbP08muSymul9PnrKkKtn0rG7x4fJAuQqVhSKBRpFm84CruMgIaU7ocUnum2cvohgHNJhN8fpSm9r3ytTH7gxS8L8OEucg0RT4QCRUkq33ria2rygo8PEm/73o4zstuoPQfA/R4qvX+7z1TKGieAumul5vnrCrpLP8WgBgpoJg+F4g/K6lplX/RqBis+pCXrhQZppSm9h87XEPeLa29fMdlAR6o14su7Se2+Z6u9VbPF+PzghYD1w/MeKr1/uYWVxG19k1+K3MJK4oHaX/o1dgeL3MJKrM+7oKh9UkJRZ0cJVEkWwU3ho1CuZOQGEV8nA9xvVY1+iNKU3iijfJq1JyQpyQC51ogv7ya1+/aPMeFEWT3Rtja7I2D94ODuofKUbPLB/l+5JfhXbglxXIY/9GuCERcSijo7SqAGCgJXGZVC6WnIDSJqBytfS9bbHSwcDhbRJp1XNodYGw9MSMVOBbNwAPjVqL7YcOiibAwKpyHCjzuJNuqw+dgllFQ1ITnOjGc6MoSUvpuEZOVJ4PoyZWgCNh+TDgzmSI0zY33eBbeYmd2FlR2F+hhckxILsJC87nJw91CpnH52ajxW/FCoqC3S+kdq9Gv497q4ohHLt5ySbF8os8nuYPH+ziJsLigDANyYbsV944XF4uwOFhUSkgV8Tl1qwM5TFQADlNY0Y9PRUpTVtiDarMe8iamYkNarSzJ+qIEC59rvzJFJkpH3FEpPQk2F4BizDmMGxLh95qkfEWdSLh/PvRbvHNsfXx2+iPhwA/YUVcr2MclixJgBMXhzy0ms2nEGdS3yKdx8Rc4JV/WCPkwDW7u8lwAAwvVa7C6uxLSMJHx1pFR0O059VGgGzbH9FLB691nkpCcgo0+U4mUxMWVRMT0Pqb5I8f2JCnx/wqmtYzGFoc3OuhVcXfGDosN5wZdwV1oFOGtgnGhmmBik9Y/kCh1ybDlWhuyBccTXl2v/6c+OYPGGArdUf7Ne61XM9ueSGiz75rhLYJFD6f1c8cNp0bpFO05XwhCmwZt3ZgY96Jdm8YCmGVMonqgxUAB3F7WQFogawg1a6LQaxYHsA+JMqG1uJ95PKG2WJN1WKfMnpWBU/xjFmUyk5KQnYOVvxnp9LqbNcsPQBGwpKA9J77HQuShZGpFL+ZZi7bwsyeJ5pMce0TcKR87XBfz6cs+vmiw5UnxJyeagacYKoEJtFIr/4OabU9ITZPVDQg3PdFMlacZKYAAkRBokFYN95e93j8K0Eb1df/syUAeCaJMOtnY7mtqkvVNKUrI9vR1q0pv5vHlnJmZl9hH8ztdjBwIGQMFLUzH59R8D1i9rlBE7n1aXvs6hZPzu8Us8tFggheI/uNlOdzNOgM5sBm7WrDTNmBQWCKhxAgDPrc/HTRlJ0GoY2NodWLk9dIwTAMTxKA4WWPLVUVydHOtmiNjaHdh45CKKK53xOpn9omHSa90Ml4r6Vp8G6vhwA3ILKwWNIF+z0QIBC+czG8h+ldW5/0YCTY83UEI1eplCoQQf/vtASZpxqFHV2OYaSFbnymuzhDKrd5/F6t1nATi9XL0i9W7Lbly8zoi+Ubhcb/N5gGYAWMw6PPGfQ26TV/4yUqiOG8F4ZoN57j3eQKEpxhQKhaO4olOfRE2acSjBDSSkmivdgdLaFlEDxB+xQlzslTNuyd3Lw8+wCdVxIxjPbDDPvccLtY1LifUqxU2hUHom6/addYll/e+1KV3cG9/gBhJSzRUKkBhlcBVh9ITt+O/FLwswZkAMkiyhN278Nis5oP0K12sUp6/7Qo83ULQaBs/fMrSru0GhUEIAvqqm3kAmuhaKxIbrXAPJnOxkhGBx3ZBiytAErJ2Xhddvz5TN+iqtbcH+kmpXIcdQ4kx1E16YkY5A3e6M3pag6qH0eAMFAGLCDV3dBQrlioDp+C8nPaGru6IabmmkoiGwgawkZPRRl6U4a2Qf10CiD9Ng3kRpb1BOekJIegSCxRBrFLIHxqGcMHi5rK4FUzOSMHd8cmA7ppBz1U2YmpGEt2ePFvUE+cKRC3VBrdtDDRTQQFkKxV9YLUa8PXs0Vv5mbLc1UrilEdK19nA/e1qG94nCnKz+OPbSVOQMTVR1jL4xJre/F05Lx/xJKV6eFA3j1M9Y+Zux2PHUZKydl4VHfjFQbde7LVxWShWhUcptNyXdGrA+xagwMLjlvKkZSdj/XA4WTElDlNH9+UyM1OOd2aMFnwc5mtrsQa3b0+ODZAH3wDgKhaKOu8b1xcu3joBWw2BTfim2CKQac0GIN2dYkRJvxrp951HVaCNu4+5r+mF6Rm84WBaPrj2oWj5dCE+p9Mx+0UT7vTIjA4//95Bf+pBkMeKLhydAq2Fgd7BYu/esquOUVDV6fbZwWjqeuHGIoJIs0FlNObNftGKJ+O4MA2BssvOeRxAam9x241JifZLxF8MaZcC2Jydjf0k1yutbEGvS494P9kpmYzEABidGwu5godUw0GoYPDZlMB68fpDgPZ+akeR6Hk5drse6veeJ+hbMCX2P96D48hKgUCid6LRa18AqV+J9T1El0hKjcG9HTRpSZozog/Fp8Zg4uBde/dVw15ISH0bk31IISaWv2VNCtO+ir/IJW5Ens1809hZVwdbuwAc7i1BWp26Z6YuDFwVd8fowDeZOTMVLszIwd2KqoADa0o2B0X8JVVgA+0uqAQBbCGsQ8bdraZcvoaCUxTOHQR+mQfbAOMzK7IOJV/WSXaZjAcx5by8mvLYVm/KdpRY25Zfiur/8gCVfH8O/ckuw5OtjuO4vP7i+556HWSP7EvctmFk8Pd6DQoXaKBT/wLmXSUSsqhrbsODjPABOVdHa5jZZaW5rlMEtg4Bba/eUPbd26FUAEJREv2VEEj49cMHNc2MVkEonTc+tJ6jxQ8o3+WX4Jr8MGgY+aZfUtbQLCmpJKbCqrcfjiWfffT0XUnzRQeG8As0yyrYc3HZ7i6rQQrgPCdFmHV69bbignDxXhkGufASXDv3ApBS8u63I63clVBCR1CsSbdIFNYunxxsoNP6EQvEPXIVepb8pEuMEcM4qPTMIpmYkCVZ95bbz/K66sRVLvj7mZpzEhuuwaPpQr0GhK9Nz/TGge94HqRo2APxSv4UBsOKuUYgJN7iu+ZgBMa6livgIA8ACFY2tqKh33gu15AxNQEu7w1X5WUhJlvT4nFcgJd6MHcI189xIiXc+G/4aP25MT8S92cnIGhgnmSXDLdN9uKsIy7ec8ioeCHR6KVdu9zZOuO89CyLGR5Alitx37YCgZvH0eAMlVAV3KJTuRHZqjGu5QOlvimRQnD8pRbRIGRc7IffdpvxSPLzmoFd71Y1teHjNQbytYdzauPuaAT4NoF0N/z6IFZArq23B7z46gGizTrFx4ukZESvYB0Dw/tjaHXhl4zHFxhgXK/TOnKu9Bkv+/bY7WKzcfkZ2mYxfMfmZaeku1VopnunwZvhr/Lh/fAqxfLw+TIOMPtGCxgkfqevKwqO0A+E9GJscHIl7jh4fg8KVzaYyARSKem4fO8D1b3//phgAGw6V+pTeSBIX8+KXBW5t5J2rITp2bLgu5N4f/EGX5NyVVooGgBV3jcbaeVl4885MrJ2XhR1PTVZU6XZ/SbUq4wRwjxUSQ6thsHjmMNlj8o9l0mtls89y0hNg0ncGyfqans2/V6T4y3PjSqlvJIt1It3OX/R4A0WrYVwuzlB7yVAo3QW+GrO/f1P82Z5a5OJihNogHQR+2VHxNlTeHwzcB11/F7YLN2jxzuzRmDYiyRXEmS2zNCGEmkGWS2PPSbcit7AS6/MuILewUtR4nZqRhHdENEFizDq8w4vD4JBKkR8zIBrvzL7a9Tf3rPty7+8c21/xtfOX50ZpSn2wVxx6/BIPIB5sR6FQyNh6vMzNRT01IwkPTEpxroP7KUDSl1kj6b787UhfxlPSrRibEhsS7w+hZRZ/zbYnDIrD/EkDce2geL/EIagZ7FiWxcGz1aKxNEIeHC5OaXdhJXLPVABwLgNlpYobVSt/MxbNNjvmr/4Zu89UwmZ3PsT7S2ow4bWtbm2JjR9GnYYogDY5XnmsE+e5KattEV2d0TAAywqv3nim1JMcL8Yc3ABZgBooLriHePnmEwHRALgpPQGzs5IBFthVVIEj52phCGPAaDRIiDSg6HIDcouq/d4u5crgtlF9MCMjCY98fACNNv9lDfiLf24vxp9uGuqKQ9mUXyqYQeALvszeSPctrujUD5F7afNf8loN4xoEH15zwO+6GGIwAGLD9Xhu+lBYLSa3AGEOX2e93Hl++Ntr/BogSTIoelJW14p/bCvy/lwgM4WPVsNgfFo8xqfFE/fvp5Pl2H6qgigLRihY2+Fgcc+qPbLtqLk/nOfmwY8OuLSFOLg7NG+iM4tH7Hu+l4073u8+OiDaZnVTGzYXlClaxvOVHr/Ew8cZYEX+ACvh0PlaXDsoHhOv6oWnpg7FR/OysOr+a/DPe8di6W0j8PspVwWkXUr3xhimQWY/C749Wor7V/8cksYJ4HwBfrjLOXBIxTwIQTLkqVmn50MqurZ2b2exQKmlKrGX/Pi0eFF9Fn/DHf+VX2bgl6P7Ci6z2B0sHCyLaJO4KikDZ3qrlKYMScyHUvy5FCgWR8TH7mCJloW4bZXGLHEButySV9bAOMlYLAa+Pdec58bqEQPDLYM9OXUoHp8yGBaPe89972lo5KRbJeXxucyfYErdUw+KJwG69mV1rYK6BBxqZhOUK5+WdgfyztV2dTeI2FdcjXmTVMQ8MM6U0S0F5aLP/syRST4NkKSia56/UzmtFbElBaHlLQ0D3DA0AfkX6tyOFW3Woa3dgUaZrAxPpPoAKNM1efW24QC8dWPk2vAVfy6ve2Wm8JBKsRY6NyUxS/y2PHVmFk1Px8NrhL0cnKLy3qIqQc8XCWJp9psLyjDhta3uz5lJh/vHJ+ORyWmCbe0tqpIMlpa6voGCGigeBDJKWWotWMplR6F0B0xhzpee0pgHlgU2F5RjzIBo7C+pEdzm3W1FGNU/RnAwkRIf4yAVXRPqv5zWiidiy1sOFthSUI637nbXCalubMVDaw6K9ufXo/tgfFovJEQ6NUTKG1pR1dCK2HA9Io067DxVgYrGVrd+iaUVC/EAL4Xb8zzHDIjBvqIq/N+3x0ESu6GGqRlJmDwk0SXHXtfchi/yLqo+Hv/+2R0sVmw9jeVbTnptV9qRYr1gymA8MnmQ2zmpiVnaeLgUz63Pd9PYSbIY8cCkFGw4VOpmLDAd8SHv7SzGezuLJY0lPmLPOt9gELv3Nc1tWL7lFFJ7RWDGyN6S50J6zoGGGigeBDJKWe7YYrMJs06DJj+qFVIogSBMC+QWVuKoSo+PmHHCwReW4l7UWwrK8On+c6jhqbkmROjw0q3uapxNre3E/Yg16b0+k9Ja4Q8asWY9Fn52RNIwWPL1Mex4arLrPMa/+r3otgyAnYWVeO3XI12Gx583HRed3VujDHj+lnQs+foY8STnk5/P48mpQ131W/i6MeOWbnGbVa/44TSijFr8+dcjRQdTEoORj78UbDm49+ym/FIs3nBUVgdl+ZaTWLu3BItnDnOdEz8WiaStZRsLBGNjSmtb8O62IpdRuqWgDKt2FnulV8vF0HDnI+cFIllefXTtQRw+X41np7unYIdiJg/Dsv6KsQ8edXV1sFgsqK2tRVSUunLkYtgdLCa8tlVyqSU2XIdtf5qMj/edRXFlE/6z/5xstLY1yoCdT98ArYaBrd0hWrCL6wP/B/7R7iJ8feSSH8+SQvE/WgawB/htsnZeFmqbbUQDGpdCuim/VDL4z5Nnpw3FvEmpRNuqHVzXzstC9sA4vLnllODsXmj72mabX9RehVgwJQ2PTRns+pvkmgml6CpdSlHi6SHBrNfiyOKbsLmgTPFxGQBvzx4NALLnzgUO73hqMr7NL5X0gAHOa/DTn36B6/7yg+izwj+mp0Endp24rTjDJrewEnet3C3ZF475k1Jc8vmAc9wZ8/Jm0WUeqf4pQcn4TYNkPZALjGMALP3lcEQYwzB3YioWzxwGo06+AubztziDzJZtLMCQRd+4FW8asugbLOMV6OIHW40ZEIONhMaJVCAchRJoAm2cAHANPCQGwe/X5cHW7sCLXyorfneu2ns5SCjAkhs01NZ+2ZRfSmScAEBZXYuiwGOlvL+z2BX8aHewWLzhqOw+f/jkkFvApNj14LwDXIE6DqXB1CQ02eyue67muIs3HMXiDWTPCzdOPLdevlhkaW0LVucWK9biAZQF7CpZflm5vQi29s6J9eaCMtkYlEAES0tBl3gE4JZaPN2DiR3uU4tJj/V5F1ypZCQqjDHhBlE3oIOF6/OF09LdPCg/F1cR/9ASIvVBS2+kULqCL/IuEv8ebO0OrNpRqNiA8KzBI+QVsEYZ0NLuUD24Rht0ePqLI8TbV9S3BFRjpaa5zRX86CygKh+L12SzY9epCky8qpfsIOpZ+wXwv4Acx8tfH1V1XBYgrh79+JTBLo9FVSPZO5c0DsrTyFASsKtk+cXBAqtzizF3Yqrr/kkRY9YhJ91KfHx/QA0USdwtxZZ2B575PN/NCNBpyazJD3YW4ruCy5LbrNxehIw+0Vi68ZiqH9jJcrJ1Uwqlu8EAiAnXuQUgkvD2j8o0jTRMZ9FDQKKGDeFAJsbm45cU/carGpSdtxrKapsBKAuC/PTgeUy8qhd2n6lUnPUSqGDLw+cDn/WWrKJYIGnxSU8jQ0nw6i0jeiPJYiR+tjijicRYrG5qC2oGD0CXeAThXkplde43rKapzctD0Ubo1/624LLsbMvBOgOYulqNkkIJNVh0Ssoroa5FWeruvIkprniwQCxBcCjJKgKAf+7w9rz6myVfH8Om/FJFs/Amm91ZhPHfZDE+apR6leKp+xEIlErEx4XrMSc7WZUuipLgVX6IAgmc0RSKGTwANVC8CORLiUKhqCcqCAPPqP4xrn8HagkCIJ9Nc7QFQRyrqtEZhFvdaIOOcGSINIbhwY/IlXP5g+24lFhJYTC1zJuYquq4DJxLd3KxfNE8yXfSYoG3je4DfZhGkfAfB2kb1R3exakZSfjbXaNkt9cwwJysAUBzNfo7LiBLU4BbNLm4T7sJfwpbhz+FrUM06t32Ic1u8hd0iccDOVclhUIJPgycKq/WKCMu1QVOzJAfJxHI2WLOkER8f7w8JN81L311FKSqBj8UlBLdC8/aL4GCATAuRf0SxPO3pOOZL6SDXj2Nizuu7os3vj8tuc9Xh0vx9M1DVQn/aTUMFk0bgifX7kIvphbxqEU8U4teTA3iO/7uxdSiz+d1YL9vAdNwGTPsrZhB4nh5xfm/UQDWeWfXI4Mpxr1tT7v+XrOnRFToLRBQA4XHxsOlWPBJXld3g0KheMAFMC6YMhhvbDkZMDFDfpxEIPUealrbXMKMoeStVRIoCgBVLeT6TJ7eATnlUjWwcA6iSo/LpUJbTHrZfblYDLF0dzNaeIZDjdOoaKzF5bX/gVVTh6mN5bjJVA5H+yVo7R37tgL4b8d/AkwDME3ucWQB+Dn85lP7RLe/L9XbqJJsVyCWYUOhUEKH5HhzwCuPc8GigSw/UV7XioQoAx67IQ1vfn8qpIwUf2MxheG1X43w8g5sLigj2v+RXwxCWmIETl1qwIofpD0VAHl8jwE29GJq8eCYCNw5jIW2cROO5Z/GkrDjTgODqUWvDm9FBOPxrK12/m8qAJDasac6/8kAkBen8AOmWDgiElDWHoUKWKCJSMCQtEEIi0wEIhKBiF5AeAI2nG7F7z+RTy0HqJJs0Nl4+CI1TigUH4k0aPDub8Zh5+kKooFEDfHhBoxPi3eTZC+tbsar357wWxsVDU4PAkmFV7W8svGY348Zqmg13gEtdgdLLGc/ISUSWQl2HGm9gGOa/Yhn6tALNZ1GBG+ZI4ppAg4CLylxfuV3/AdgKIChXTEqGqOBiAQgPMH5/4gEILxXhxGRgMM1ejzw2TlUwoI2iWGbEwDkowHQu+M/MeIiK4i7Gkwl2R5voNgdLJHQDoVCkWbTY9ejT6wJ41Ji8emB84EpfNmxQsCXZLc7WHy4u8RvHpWerCXEwKn3RLrME28OQ2VTO1gAOrQjrsPjwBkOvVCL+NZatH1cg8qfHIhja4GGS9C21OAAQOZ9WOP833AAqwTiJIJJHWtCNRONMocFFWwUKlgLLrPRuIxot78rEYVWODvrDwXWYQ4WzPdb0S6jQqs6xoewWzG8AOFg0OMNlL1FVcRCOxQKRRi9lkGfWBMA6cKX/L/VxJFw3g0+/Pb8YRAxHW9rEvGq7gJ3raPNOtQ3tSAWdU4DQiDockI0C1Z7CdqmcsQyDdIHdoB8iUNaBsqvtGvNON8WiQpYOowGp+Hg/DvK9e/n75iEnMyBbvtyMhOA97MLAL8dn4xVO4uJ+iGVnaMEud+Ur22U15EZ97dm9qZKssEk2HndFMqVhl7L4OQr09w+k8tWAICnPzuiOJhRzL0s1p4aOM9MINOMSdDAgRjUY6CpEZH2KkS213QGXfKCMJ3GRR3ZQUkMivKO/wdhHGpm9ahgLaiAuxFxucOoeHTGtcgYPAiISETu+VaiOjPcMsdxiTpJXFBsjkDWjNyzazHpiQ0UqewcpajJACKFVACxb4yy9Hhf6fEGSjDX0yiUruL9OVejrq2dONCQlL/9agRmjO0n+N3UjCS3WBF+ZdtN+aWKjBNPF7ZQxVx+exerm5B3vgYAg/6xZgyxRqKioRWL1h9Fg0Rl4xizDlmpypROGTgQjQbEM3UdgZWcIdH5Nz/oUsMo9POwcAYSdOHyRit0qGAtqNfGIDUlFXpLYke8hDPQcttFDV7Yetm5DUwQs27evDMTszL7yBZl5e730GsmOwU7AJTXk6WocPeN/zyU1TajqtGG2AgDrFHSFZbtDhYWkx5P3nSV4D52BysbPB1t1uGtu0Yja2CcXz0OUr8pX4iNMPh1O3/R4w0ULlI/FPUIKBR/MKJvFH4xLBEAkFtY6VcD5URVE2ZIfM+PFeFQunTi6cKWq5jrbC8Ov7raw3BiWUSiAcv+s60jPqLGK1tjjKUN2uWPAY3lmOVox6zuNn/RhHkEWgoFXXZkbxijAYZsYDMAkNLx1RkrUfS9vHeDmxCqWbJQoqjKIfT8SSH1bHF9Ien7q7cNx/i0eOJ2laD0nEiwRpFdW9Lt/EWPN1D8vX7NJyc9AZsLyuU3pFBUwACYMcIKhtFgV2EFLgvUaxnRNwobHpno8jh8d7TU+0Ae6LQMcQkHNWok8ksnLCLR7PI+pIU3454MI4aUHUD+9lPQXjyHt5kaxBvq0Au1MDBtsjoSHFMATJGaBFYrPh1VOFgGlzviIypYS0eQpQWXO+IjuL8r2CjUIAJr5l0b1BooQh4qqVm6XEq2UBCn0iULNW0oQbTuUkc15rdnj3b1KZDLLV0ByURdSIY/0DAsy3a7FPy6ujpYLBbU1tYiKirKL8cUspyNOgZDrJEoqWxCdVOnWzjSqEX/WDPOVjWhXqDWh4Zx1vRYOC0dm/JLVa21Uyhq0GqAxEgjhvWOwvI7RiHCGCb4bEthCGPQ2k72Whjf14B/35UCNF4GGsqBhku8f3N/lwMNl4H2Zl9Orcu4zMvO8Ay6bDHE4m//exPyqg242GZCQlS46GC+8XApHlqjLmWZWxoJBnIeKqn9pIJL+QM8HyXGkNo25OCWnMR+I2KZOEoNuVAmUNfWEyXjNzVQeIg9bHKfX6xpRt4559QrOS4cc7KTXQXHuOPuOHUZ976/z+c+6hgAjDMwUathkBRlBMswiDTqMDAhHJ/8fMHnNijdn3c6XibcSycCTejPXMIA5hIGMOWuf/fXlKMvQ66BEMpUsRFO70NH0GXv3v0xbvhQ76UOcxygla/VkltYqSgo01/H86UNXxHzIpAOUmqNG6V99Hcb/r7X3ZVg3D8l43ePX+LhI7a2R/L5r8b0lTzu6XKZdD1CIs16VDXaOoqHsQi3OVwPz6rtZwBQA6U7Y0ILBjDlTuOBMyKYciQzZeivUZCn2bHcMRVAUYjFUbDGaBS3RnSoW3akfLIWXO7I3uCWPeREqeTQnAWOz7vZbbKgBH9XeFWTMRisGjaAdKFUtqMv/FpFQgQqiDPQbYRqNd9gE4z7pwRqoAgg5DEBnIUEcwsrAbAY2z8WJ8rrkXumAkfOVaOu1Y52OxCmBSxGLfpEm2FzALZ2B5pb21Dfqqzsuxie6WCltS343UcHMCwpEoWX/WMEUeQxwIZ+TDmSO4wI7t/9OwyKMIa8Rkmos8cxxM1wEBKluj1rEJbcOpzoeAyAEyLuZH/iYIHVucWYOzHV6zsS17yaoEx/bMfhLw0NUuRig1i41yoSIxBBnIFuI1D3sDsSjPtHCjVQPBBycUWbdWhrd6DRxjcyCgX3t9mBy412XG6sF/w+UBwtDW573QUd2tGvY0mjv4cRMYC5BAMjnnLaXbnAxqHEkYizbCLOsgkoZhNRwlpxlk1APQKjY1BcSVb/hMOfuiVSCNVlIXVj+zsoU2ltn2AHXPZkL0KgA3C7E6EUV0MNFB5i6680wDXwaGFHH6aiczmDKe8wIsowgCmHmSGvsNpdKGNjUMImdhgTCc5/dxgVNYhAUJSy/ERynHLDh3Mnf7CzCEu+DkxtmgGx7v1Skqnhb/VOEoXdBVPSkBwf3iUDQ0/2IgRaqbW7EIwYFCXQINkO7A4W41/9XlGp8Z4IAweSUIUBms74iAFMWYdnohyRTPfM1JDiMmtxGQ4ljk4jooRNRCWi0J0MiUBx7KWpMOnV1WeVE+1Si4YBji/pjEFRm6nh75d2qA0CHKTiab7UlAl1QvXeBANfA6RJoUGyKlix9fQVZpywSES1K1NjAHPJzTMRI1djoxtSxUZ0GA5WlLAJONvhnShhE3EZFrBQFyxJkSYnPUG1cQIErnbPvIkpbgGyamMs/B042JWBiFLu+2B4Ebj2y+paUFHfgpqmNrAAYsx6xEfKq7yqPTdSgn1vPPs8ZkAM9pdUd8lz4WuAdCCgBgqcluPyLSe7qHUW8ahzZWwka9xjJOJJa2x0I+pYM0o6PBBnecsaJY5ElCEWDmpIdDkGLYNWQrG2SxKFxqQGDVu7Ax/uKsa+4iqYdBr8MrMPvj9RjlpeNWGTXotltw2HIUyDxRsKUFbnPrPtFanH4fPev5Gc9AQsnJYOW7sDq3OLUVLVhHrCKsVSMRYOFii4WIuy2mZYLSZZyXQxeQJ+wH18uFM5rtlmx9KNBSiubEJynBnPTEt3M/w8j5eeFIU/fHwAhy/UwRCmwYS0XhibHIve0cL92pRf6nUNrVFGLJ7Z6R3ISbfi8SmD8f7OIreqzhoGCDeE4T8/n8OEQb1g0mtFz23X6Qp8euA8mmztGJsch3uvTYZWw2DF1tN4b2eR2/0VwhCmwau3jcAvR/cRvYa1TW347Qd7cbG2Bb0tRtx9zQD833cnRD0fDS3teHzdARy6UAtDmAZ3jeuHeRMHQR+mEWyDXymbVHrC1u7AS1/lY/eZKui1GvxyVB/8dkIq9GEat+dwQKwZc7KTsfX4JS9vjYZxPmNC58D1h//sZKfGu+T0lcpk8PFXgLS/6fFLPM6lna1uP1oxLGhAMlPWoSXRYUR0LHUkMjU+9SMUaWQNLgOimE3EuQ6jophNRCkbBzvUz5opVxZzJ6Rg0S3pbp9JucsPnq3Gu9uLQPr2GdE3CuV1rW5ezmhTGGqaxYOcc9IT8P2xcrcXPgmeWhdSQndi7n+xc585Mgkf/3zeK64tTMOgXaCjOekJWPmbsYrF9jz7tSm/FL/7SFwk7p3ZowGAuA3P/nLntnr3WTTZ3DMWGQC6jkFaCb0i9AjTaryuYbvdIaia7Ak3BPePM6GkUnjpOSc9AfkX6gSfUcD7enDnueFQqdvnhjANWkXOb0TfKORfqHN7Dkm9gfzlFUC4wGa0WYc7ru7r1Sexvgo9sy99eRTvERRA9IdgIBVqU4C7QA+L5bq/45fanb53Moi0sroOj4TVwzORgAtsL5+0JCgUUk6+3BnvIbWeHcovHK0GOPnyNNcMU+w8+DBwX58n2UcJI/pG4cj5OsXH4/qVk27FmJc3Swb7hxu0aPSTFEJ3J9SeUQZOI6TaT8kanjElcsYrnzEDovHpg+N9ap/GoCiA786NQHNAjZN2VuPK1ChxpYA60z/Ps73Q2pXlSikUH1nx/Sn84aarZNezQxm7A2hoaYfFrJM8Dz4sOtfn0fFvf56n0BIWCSycM+6zFU2ymYjUOOkk1J5RFvCbccIdj4spmTwkUVHhzv0lNWi22X2KOVNCjzdQ+ClzDTDjl60v4gXdh7Cg0W1J45zLM5GAZlx5aXYUiq+8ve00/nDTVQTFAEOb336wF58+NF7ReXDr89y/Q4WapjYs3XS8q7tBCTG4mJLVucWKn9elGwuIRRl9pccbKONSYmGNMrpiUA6yabjV9rLXdtYoA/7y65HYU1QFJUqylQ2tuFQvv15KoXR32jsm4d1dyOtixwu7rFZZynx5fQsOnasJQI8olMAgJGQoh1JRRl/o8QaKVsNg8cx02TW4xTOHYeLgXpg4uJfrs+uGJuCB6wZK7udLgTAKpTsRrneubnd3Ia8kizOrpoIgEJNPfLgBXxy8GIguUSgBwVPIkAQ1ooxqUZTPuWzZMowdOxaRkZFISEjArbfeihMnTrhtw7IsFi9ejN69e8NkMuH666/H0aNH3bZpbW3Fo48+ivj4eISHh2PmzJk4f/6872ejkqkZSXhn9miEi6yrpSdFYefpCnz68znkFlbC7mBhd7DILazE+rwLrs8AeH2e2S8aseE0toRy5fPt478A0Ckb3l2lvB6eNAgAUNNEbqAkWYwAA1Qp2IcSWLrr88eno3g9ok3y1beVHjfJYsSc7GTFv9VnpqXLb+QnFHlQfvrpJzz88MMYO3Ys2tvb8eyzz+LGG29EQUEBwsPDAQB//vOf8de//hUffPABBg8ejJdffhk5OTk4ceIEIiMjAQCPP/44vvzyS6xbtw5xcXF44okncMstt2D//v3QarsudbXRJhwoVlBah4LSOqzGWQDOiGrAXQJfLKXLM6+dQrlS6RNrAiAt+NUdaOhYq2IUvLVfmJGOiobACj0G41p2x/slhtVihD6MEU0xDgXC9Vq3ccdzvLDyUp5JM23k4Ivu6cM0rt8qCb6KMirFpzTjy5cvIyEhAT/99BMmTZoElmXRu3dvPP7443jqqacAOL0liYmJeO211zB//nzU1taiV69eWL16Ne644w4AwMWLF9GvXz9s3LgRN910k2y7/pa6tztY2TQ8CoUiztUDovFfj/RDIe0Oa5QBLe0Ov/zWuEnB+ryLbvoo1igDFs8cplhrhYPTQdl5qgL3rNoju/1jN6RhQc7ggC3nJlmMWDR9KJZ8fSygAbgLpgzGun1nA6KDYtJrvT5Xg5QOSv9YI177VaaXINnMFdtFM6GEdFDkJpXhBi0iDWFuz5xSHRQN41Q6fnLqUGIl2U35pQHTQSHR2eE0eXwlaDoop0+fRlpaGo4cOYKMjAycOXMGAwcOxIEDBzBq1CjXdrNmzUJ0dDQ+/PBDbN26FTfccAOqqqoQExPj2mbkyJG49dZb8eKLL3q109raitbWzoehrq4O/fr185uBsvN0Be75p/yLiEKhCPPMzUME47GEVCw3F5S5ZmykLx+uDsz//XokKhpbiZUym212jFu6BfUtZFWrk3i1ZkgmLtGmMOxfdKNr+6tf3kyUEvr0TVehwWYHwCJMo8Gb358CICwvz+lV2B1sQAor8mvsAHC7lpn9ovH+jjNYs/csWu0OjOhjwZt3jlalJLu3qAoXa5qx+Mt81LfIGysMgMQoA16/PRMVDa2SSrLv3TcOFrP4MogSJdnqRhseXiP8fPK1ZXxVkuWXYSAlUEqy3LH3FlXhQnUTNh65iKOldWDA4BdXJeD5GcP85jkJioHCsixmzZqF6upqbN++HQCwa9cujB8/HhcuXEDv3r1d2z7wwAMoKSnBt99+izVr1uD+++93MzgA4MYbb0RKSgr+8Y9/eLW1ePFiQcPFXwbK/317Ait+OO3zcSiUUMUaZcSlOv8W4+NgAJzgibSRIDRji+kQoxKrA6O2WJkS4bR3PNogUWDlb7/x8EU8tOagZBvhBi0Ov3CTqoKE/i6s6O9CcCRw9wMQN1C7ol98enLRwEATFKG2Rx55BIcPH8aOHTu8vmM8Fm9ZlvX6zBOpbRYuXIg//OEPrr85D4q/YK+YVVfKlYw+jIGtnVUcJzB/UgpG9Y8JWEzIA5NSFM8GxYqybS4o814W8nFgmJqRhLdnj5Z0YceYdVh223CvNrgA+sUbjgouI3luP21Eb8w/X4N/bCsS7c/r/zPSawZLWqSOpKDfA5NSiN39vl5bNZDcj67oF5+uLOhI6USVB+XRRx/FF198gW3btiElJcX1eaCWeDzxdwzKym2FeGUjFTOieBMbrkdmPwu2n6pAG2HxPE9G9YvGofM1XmvQk4ckYPeZKjS0yi8//H7yIDw2ZbDgAC61/j1/UgoWdkTdC80K48L1GNnPgrxztahq7FzXT7IYccfV/TriDFgUVzRhY36p4Dr6Qj9H9fujKq3UcZ2VdFtR3dQKDaNB9sA4ZKXGSbahtE8bD5fiuS/y3bJ6/DkDl5vh++ruDwb8vsSHGwAGXks5lCuPgC3xsCyLRx99FJ9//jl+/PFHpKWleX3fu3dvLFiwAE8++SQAwGazISEhwStI9qOPPsLtt98OACgtLUXfvn27LEh20ReHsXr3OZ+PQ6EIsXZeFsYMiBFcg954+CKe/SJfNG5BzM3PrUPvOn0ZB87VirY9f5J7IF58hAFgIRvHIRSsZ3ewfllH7ykE2hgIJWODQiElYEs8Dz/8MNasWYP169cjMjISZWVlAACLxQKTyQSGYfD4449j6dKlSEtLQ1paGpYuXQqz2Yy7777bte3cuXPxxBNPIC4uDrGxsfjjH/+I4cOHY8qUKSpPWT12B4v/7r8Q9HYpPQMNA1Q3tkIfpsHcialu323KL8XDaw6KLrksmJKGRyaneQ06Ql4UMd7dVoT1eaVu1bo5o4dfsVerYVx/b8ovxXV/+UFwdu55DhRx+NeUI1BGhaPDaA11D0SgvWPUWLuyUORBEYsRef/993HfffcBcHpRXnzxRfzjH/9AdXU1rrnmGrz11lvIyMhwbd/S0oI//elPWLNmDZqbm3HDDTfg73//O3FcSeCqGVMo/sez2i3QGewoZmTwMys8gyl9rZQrFYAoVYVYbB8KGf4MvJRLCw3FgM5ABZ7SgNbuRdDSjLsKfxoo6/Mu4LF1ef7pGIUiQmKUAX0sRpTWtaK3xYiHJg7E3H/vl91vWFIkekebcGN6InpHm/HEfw65eUPUImQAyRlNABBlDMMLM4ahd7SpW89S/ZX2ycfuYLG7sBK5ZyoAMG6xLSSGH2lQJomR2pUGpVRqub8N32Ab1N3RU6Omz4E8T2qgKIB6UCg9mX//7zXQMAzK652Bo0o0NrrrLHXZxgKs3F7k14BfKRGtpbdmSIqsMR3bGcI0bplCUmnGJMt7Yl64QKJGnE9tP9V6IdXSHT01avoc6POkBooC7A4WGS98i+Y231UOKZTuRrRJh5pm9aquQstXocyyjQWSKcD8rCdS5LRS1CLkBVAzoeKUcQONr8uPSvtJei38cf7dcelTTZ+DcZ5Kxu8eH4Kv1TDI7Od7JhCF0h3xxTjhePHLAlexzFDG1u7Ayu3ixgkArNxeBJtIyrYQdgeLxRuOym+oAu6K8q9veb3y5T01+yjF7mDx4pcFPsVGKe0n6fa+nr/UuQndo1BATZ9D8TxVC7VdKdgdLArLG7q6GxRKt4QFUFrbgr1FVYKzVJL0ZX6a88WaZuwrqsSO0xVobXdgRF+ntHqEUfxVJRVPwpdeP3axTrZwp4MFVucWe2UrNdvsWLqxAMWVTegfa8KN6VbUNLehor7VbVnG33DXd3dhJTQaBqcu1Ss+RkKk0fVvpbE3DS3tWPDxQZytbkb/GBOW3zFK8F7sLaryuU4Qv5/+3D4+wgBA+blzz+TO0xWS5yb3G1DTtq/I3Q/P54pb4vX1PP1NjzdQ9hZVobyBFgmkUHyhrK4FuYWVsqqwnoXYxBROOb4/fhkZi79Fai8zHrthsOvYgPO3u3J7IX44cdmtIOArG49h3kSneu4fPjmkuEhdSVWT29/z/rUPmwvK3T77957g6iY9vOaAKm9XtFkHh4OF3cHiz5uOecXecNdKaFnLs8jeibJ6ZCz+FiP6RGHDoxPdtvXFS8HFijgcLNbnXSCqF8OJu5GUcHjikzwM72vB98fKic+dpHieJ2LXQCjmSaptDl8CVUnvh5rnKhgeOY4eb6AE82JTKFcqf/wkD3yh3WizTjAo0tODUVrbIhkTwnHmcpMr2y66oyicWNClg0XHMeWPK8SAWLPr30LGSVegdimupqkN96zaA7NINeHOawW3wVKqAvDhC3W47i9b8dOfJrs+U+r94MMCaG6zu1WOJq24G23WgQUkSziU1bWiTOAeip272liahEijl1Gx9fglwWVFsbb5fRALVCXJ9iK9H2qeK1/utVJ6vIESzItNoVypeFYBkKoA7CuBPDYA3H3NAADOZZ1QME78gZwXaeX2Ijxx4xDowzRoaGkXNU44Siqb8cXP53Dr1U7tqjEDYry8Y0rwvKdltS148KMDrqBMMaOhtmM/i4hBTMLK7UWYMLAXqpptiA83YPEGZbE0nAeooqEVY1/Z4lYyQo53ededQ+xcy2pb8LuPDngZ/9YoIxbPdDfmxqXEIsli9FtRSQ4N47zXwaLHB8mOS4mFXhvaeewUCiV4HDhbDQBYurGgi3sSPLjYGwB4/GPpaswcT35+xBUwub+kWrVxIgQ/KNPW7pAM3mQAMD4Mww4WmPP+Xjy2Lg/3rNqjSGeI89z0jjbi0bUHFRknAMCywIe7il1/kwSqehlzdU7DZVN+qeszrqgk10d/4WCd9zpY9HgDRathEB9p6OpuUCiUEOGj3SUAgOLKJpktryy42JvjpWSBuDa7czkDCMxSOReUuTq3WDZ4s7pJvuBmINBpGYRpgP0lNaqP8d/95/D8+nys2n4GO05cVh1svPCzI24ZNlzVaKvFfZUg2qRT3VeAxqAEnWFJUbhYQ2NRKBQK8E1+GTbllyI5zoztp7q6N8GDi71pc5CnWZfWNAMI7FK5Z9ByKGFTWeGcz4lLDThxyfdM0uqmNuw+U4nxg+Jdn03NSPKKWXGwLO755x6JI0nDZUUFgx7vQQGA5XeM6uouUCiUEOLFLwuwYMpVXd2NoKFhgDnZyQCAYdYI4v0OnnO6+6sbA5dqzQ9alkKkVFyPYtfpCq/PuKKVszL7uMovJFmM6pd+gij3Qg0UABHGMIzoS8XaKBSKk9LaFrz2bejEoAR67J03McUVqDkqmTwIstFmg93B4qWvyEskKIFhnEHLUgMqAyAuXI/up4nufy50eLSk4MenqKEigMaoJ9RA6WDDIxOpkUKhUFz8dMJ7NtpVxITrfdpfbHDXMN7y/ofO1hIfd9uJSuwtqvJLAUshWBbIO1cjGvDJ/T0rs3dA2u9usKyzAG5uYaUrHsXuYJFbWOn2+dSMJDw+ZbCqNmiacRex4ZGJaGhpx+MfH8CJsgZEGcIQF6VH8eUGlNW2wtaxNBuuYxAbYYCtzY6W1jbUtQXV60WMlDYAhUKRJpRWDBZNHwqrxYTy+hacLKvHWz8WKtrfIqAdE2kMw9JfDseMke6D+zkF8XhtDjbgQZPl9S2YldkHb88e7V2IsEMbxGLS472dxQHtR3dg/aGLWH/oIgBxEUROT6W2WVnGEQBoGbiEEoMBNVA8iDCG4Z/3jlO8XyhWRX74FwOx4gdlLzIKxRduzeyNCEMYPtpztqu74hNJFiOuH9wLa/cFVzFWDKvF5JIXzy2sJDJQFk0fivhIA4ormvDGlpNek5WGlnb8fu1B6LSMm4ZG/xgTTpSRZfIkRhoCPqPmji8U8MkvlRAI3Y/ujJgIIqenoobYcH3QKmMDdInHb3DCOKEAA+cLNjs1XnZbCsWf/PnXI/H8jGHdPmDxhRnpeH7GsK7uBgDAGmVwm7Vy7xqpmIwkixH3jU/BLSN6Y92+s4oKwClJGlj3wLUYlxILa1Tg3n2Z/aJd//YM+OQGS190P8w9bJruiwFX0WBTVEzTV6iB4ie4H0hXv5e59l+YkY6sgT5Ga1MoCpg7wRloqQ/T4IGJKV3dHVWEaRi806FeatJrkZOe0NVdwuKZw9xmrVJBjvzfv1bDEBeN4/RMAPKkgV4ResRGOGfUi2eSBV2qeRet2VPi9rdQTAUgrvshx4Ibh2LtvCy8eWcm1s7Lwog+oROLGGrvbhadgn7BgBoofoT7gXSlJ8VqMeLxKYPR2u7A3qIqLJrufzVBCkWI3rznfuG0dMyflOK35y4nPUHydxVt1iFcr/W5nb/8z0i35Y6Vvxmr2EgJN/jeD8B5TpyxJIRJ6HwZ4IFJKa59SONDPLeTSxroFaHHvudyXH9PzUjCO7NHu+ok8eHO4x0VxgPgroOyKb8UE17birtW7sZj6/Jw18rdmPDaVpeK6tSMJOx4ajLWzsvCxDQyD/K56iaXV2ZcSiwuN0jHZpj1WgRrlcNqMWLu+OTgNEZIMHVpephzK/Dw10nLaptR0dCK6sY2nK9pRkVDC34uroJUWYwYcxjuvmYAGDhdmUOtUZj3r324WNuCpCgD+sYYsfnYZTS3OXj76HDbqD6IMumwdu9ZLN9y0vVdpFGLueOT8XV+mc/l0Cndl6GJZhy7FNgXi+eLa+G0dEwY1Atz3tur6niDEyPwq9F9cf94p2fGs5ItGKCiodWtwvHuM5XYdboCF2qa0TvahOzUOGg0DHILK4jisYSWKlb+ZiyabXYs3ViA4somJMeZ8dTUoThyoVa0L3YHiw93FWNfcRWqGm34mUAe/OHrB4JhnKHt2anxyOItYfDZlF8qGkPAdhShG9U/BlMzkojjQ4S245IGHvr3z8gtrISDZZAcZ8R/fjcBsRHeWUXcu2/3mUrkFlYKngc/hmRfURVRrBKngyJVo4Zft4dbBiq4WIvtp+Qzsfg6K3IeJ8BZ12j1/eNwsrweJVVNOHqhFvvP1si2o5ScoQl4Z87V2FtUhVUhFABMqkvjDxiW7X7Z43V1dbBYLKitrUVUVOi440iQerkAwIIpaXhkcppkIJJQGe7NBWWSFTinDO2FuRMGory+BWW1zVj2zQkfz4TSnZg1woqbR/TGE5/kodEWmDXkZ24eggeuG+j2md3BYsJrWxUFL3LF13Y8NdlvAXly/QhEmxykAfRr52W5AmHFsDtYjH91q2xarzXKgJ1P3wAAXXbectjaHRiy6BvJGj4aBji+5GZoNQwmvLZV1HgQOg8lx+c0YNbnXXBVzZbizTszMSuzDwBgzqo9RIaQPkyjKH7j9V+PwK+u7qfqNxQoGAAnXr7ZrbihUpSM39SDQghnFJTVNqOq0YbYCAOsUU7joNlmx2Nr9+PnszXQahjccFUvDEyIxIWaFtgddjS02tHS5sDY5FgkRRkQoWfQYBN+1JZvOYW//3ga8yamIHtgL1Q0tCLKEIZ/7y3BueoW9I024OoBsbhQ2wIGwKh+Mdh9phLPf3FE8uHdcuwyBsSFY/KQRJy8VI8wDYN2f1b3ooQ0ze1O7YPGlnY88d/DAWmDFXgCuXiJBz86QJT27hlD4S+k+hGoNjlIKssmWYxE6ZukmiNlda3YW1SF7IFxXXbecujDNJg3MUUw04SDE5DLLawkjqXhjDwlx+dQ43EiLYmQnRqLbScriI2M3jFOT4Xcs8sK/FsKX+QnHpiU4pNxohRqoBCwKb/UK/+eQ2ig/8+Bi4LH+a7gElF7re0sVvxwBit+OOP13Ymyenx/vNNaX72bPJ1z1Y5irNpRTLx9d4RqvwgztkMdlHvpBYILIvoZXGzW4g1HUVbXqUIZbQoDGMa9dHyHRoNY3IUvcP0Q09KQalPIa0k6qPMHGDFmjkwiOp4SzRFuWyXn7ct5qmHhtHScqWjE5oJyr+9y0hNcAnJqY2m4/VduL3LzpGgYp3HCF6gD5I1JzlPDNyafmZZO9B5+Z/bV+OlkORZvKJA1Mj0NVrl7CMDrOzEdFP4+JH3h8BT0CwbUQJFBbN2Tg3ohQofu7hUiNa5MYQxa2oX8FeLHvfdaZ1YN9wImjUeKNulQ09wmvyFI1qbdBzqjLgzP3zIUMeGGoA2IUloaYghNUJIUGlJTM5LwwCTx2fy7vLgRKZRojvC3JTlvf5ynUjbllwoaJwCwuaAcm/JLfY6lWTgtHU/cOASrc4tRUtWEAbFmzMlOFvQEqPG0cdleYucBOI0tk17rug8rtp7C8i3CbhdGoA1A/h6Kfffk1KGS+0j1BQBuG5WEV3+VGVTPCQeNQZGAW/ujwaXdg57iPfl/t4/EM+uPoqGVrMT8NcnRsJgNMOs0SO9twfnqJvxLZsZn1mlxdXIMYkxhWH+4jKidkyJr02JGPvf6fdsjU4VkFs/fJtasx/Gyepyrlh58+PvuPlOJnacrcLGmGb0tJsSE6xEfoYfVYnJrT2nf+W3sOlWBTw+eR5PNjjEDYvD+zmLR2So/hgJAZzBwhAFgnfVP4sMNaLc7cN8H+2Sf84QIHXKfySE29uTOc9LgeDhYuAKED52vcQuEHZsSi9xTFXh3xxnUNLXCoAvDUGskUntFeN0P7t5drGnGc18ccQv49yRcr8W7c67G2JRYXPeXHyTfxTHmMDw/I8O19K7VMLC1O7A6txiFFQ24XNeKhEgDNBoGmX2j0TvGLGqcbjx8Ec+tz0dVY6dxHheux6zM3shJtwruN+9f+0Q9QSt/M9brcyGDMNqsw/3XpuCRyYMAALsLK5F7pgLoSJrISo2D3cG6GVt3XzMAeedqVBv5dgeLFVtP4b0dRaht6XynWIxh+O2EFNmYSKUoGb+pgSJBKKrDUrzRMJAMhLvSiDRqUd8ikQrWBYTrtTi8+CZBQ0JJcCPJLF5qyRUQd99z+z792RG3ZSVPuPZy0q2yE5QkgQDTTfml+MMnh9Akla4nwoIpaVi375zPk6Jwgxave6RMixHoiRj/fsjdOzGSLEZk9ImS9FIIbf/9sXLJd4OQh0iojwwDt2KEQvst21hAvJTEwRkH7+8sdvNURpt1sLU7vJ4hQ0egrdTrTonXi+R++NuLRg0UH+DPzE6U1uPvP1Gp+FAl0qBFfWtoDdQ9GaEsFCUZLLXNNllvBQDJJVc+nmvmchl0nm0+PiVN0vXN7zt33kraCDQMxD08fII1EctJT8CWgvKQ83Lyr5Pckj5/H/D2W7axQDIYVyx+g7Q9Jch599S0TfoskUCzeFSi1rqndA3UOAktLlQ3AXA3UEoJyr8DQFldC/686bioJDsDYPGGowAY4pf5yu1FeOLGIS4NlcUbCgj3dLa5art3kLoQ56qcQl92B4sX1ucTtxEMXvyyADnpVkkXfaAqEXtC6v0INiyAP/33EHacqsD6QxeIni/umXzxywJcNzgBK7eLGyeA+7PI4Xwmj/rdYOP3LSfdCgBeS6bo+J60bRZkz5K/oQZKB4GwZCmUnsS3R8vw66v7uf5etrEA70rMKvlUNbTKppHyM4BIcLBOWe65E1OJ03P51BEawB/vO4vbx/bD3qIqXKpXXiE2UAil3gpR1aDsul6J1LfYFRe45K7v0o0FskvM/GeRY8XW04qfaaV9W7H1NNbtO+u1ZHrn2P6KJ+Ikz5K/oQYKnJasEmuSQqF409zWOaDLubw5uBiUc9WBUbnl1G2VpOcqpb4jWLmslsxbFGzkzj023FsVlkJOcSXZs+sp2c9X/A4UQm2U1baobjuQvyMhaC0ekMkbUygUaQbEOdOMbe0OWZc30LlWvmh6OtbnCWsH+Uq/GBMAZem5SuHSqytC1BMhd+5WiylIPbkySY4j0xbinhNuQtxV+DIRD+TvSAhqoCD4ViGFciVyY8d69+rcYqKsKqNOg8enDIbFrEO1RFYNnxizTlEBwsGJkcgtrERZXQtiBArZSWExkhX9iw3XYdEXR7DnTKWi4wcaBmQKtZw2DkUZ3PV9aupQMDIPpYYB5mQnA+i+E2JStWN/Qpd4EHyrkEK5EuHSJEmrnTa3ObB8y0lEm8gNh9tG9cF7CgqnPbYuj1hozpPaFjtRCvvHP19QdXwx1EiW+yJjz4mTBTr7KKNPFPIv1AW0jWAzc2QScpb/BLlcWL6kfqAnxIHQgxITjws01IOCzhlE8KtRUChXDpyhr7TaqRIDYkq6FQ9MSgnIsYUItr7OgilpsBJ6M6wWI96ZPRrvzB7ttY/VYlSUFjo1IwmP3zBIcX9J0DDONNtnfZBJT7IYMX9SCqIJvWBJFiNy0hMC9k436TR4YFIK3t1WJOkN4c6dn2Ic6Amx1WLEgilpfjteksJnyZ9QDwqUFzSjUCjeZPaLBuB0Zb+y8ZjfB3cN42zjD5/k+ffAIQAXLPzIZGc1czElWTDOWBdSmXMlPHrDYHyQWyIpYkeKTsvg16P7YFBCpEtJ1u5gZYsmenLXuD6YObKfm2w7p67KskC02akAnBBldF0n/vk3tLQjY/G3Pp+PJzufugHT/7Zd8jyijGHY88wUmPTuS4VjBsQERFzSFMbgn/eOQ1ZHls26fedUV0BmAPz51yPQV0JtNxhQA6UDsYJmFAqFjDV7SjB3YipRFVk1OFhnG91x/V4KoeUYpamcWg3jc/qnVsPg1duG+7TUw53L3+4a5TXjVjoR7BWhx7LbMr2OMT4tHuPT4on6c+hcDdF2Svn84HnZ57CupR1552q87sv+kuqAeOZ+d32a23XxZdLNAqhrbkN5mDO1uKuMFLrE4wVd6KFQ1MCPPVk4LR3zJ6XA3+800viW7oTS5ZhAMjUjCe/MHg2znixA2HM7uXPhJoJyy1gDYk3Y91wOWaclcNax8T+kz6FQvEmgYlCS492XVsWuNeky2ZKvj+GxdXm4a+VuTHhtKzbll/qtr6RQD0oHVKiNQvENz9gTfhXZPUVV+K7gkt/b6K4smj4U8ZGGoFRwVsrUjCR8sLMYu4uqZLddMCUNGX2iFS0teVblDdeF4e8/nkJpXSt6W4x4775xsCjMuBKH7LpOGBSH1F4RYFkWq2UKaQLkz6FQvAlpDMrq+8fh+KV6fHu0FD+X1Khqa2pGEiYPSXQrLjg4IRJz3t9L1AeOstoWPPjRgaAb0tRAgTMv/YlPDlHjhEJRCT+Nko8+TIO5E1Nx3/gUTHhtq+iaOANnQTYx1zcXozEnOxn/3CEdmBjKcOdx3/iUkDJK+NjaHdhbLG+cAMC916ZIVo4Ww3NJasqwRMXHICF7YBxW/HBadrsHrx+E8YPiYXew2HKsXPI55T+HctsJpeVySRly+16bFo+JV/XCbyfI/3bE2hIq32KNMiLarENtU5siqXu+fH6wnl26xAPg8XUH0Kii8iiFQnHCT6MUgos/ALzntNzf8yamOA0Vke9fmJEOfZgGL8xIJ16IDSUTQEnqb1dCqmOj0zAhfR4AkJUaJ7ukEWPWISvVaSyRPKf851BuO6HrQ9oGt6/S7Tm4VQFPY/5SXQtqOowTJXePXzohWPR4A8XW7sCXh8u6uhuUIDFxULzf4yJ6MkJplGKIrYlzcQsLp6VLfs+5lrnjSImL5aQnCKbfcumnns+AhnHu43nMGLMOBhHDS+wx4lJiPY8VSrEmUpDGV7R1VH4PZbjAXymW3TbcbXCXe049n0M1Kd5K91W6vVT5Fs4wiTHrkBhlEO2jGMEUNmVYVk5iJvRQUq5ZjlXbz2DJ18f81DNKqDN+YBx2Foae4qfZoEVjgKozMwA+uG8s9pVUAWBwTUosNAyD0tpmfHu0DGermlBc2YTWdoei40Yatdj7TI5XGqUc9o6BTSxuQe57z+3OVzXiu4JLaG5zICXejGempbv6JHYsW7vDbV2enworVPl11+kKfHbgPBptdoxNjsG91zq1WFbnFqO4shEAkNkvBr2jTa42SM8jlHBW2M0nisMAgDfvzMSszD6q2wrU9fE8dnWjDS99VeBWMDLJYsQLM9JFDQmlz6Ga81C6L+n2uYWVuGvlbtn2//2/10DDMCivb0FFfSvRWLh2XpZPGWNKxu8eb6A8vz4f/8ot8VPPKJTQg8TDwb34dp6uIFqz5/D1ZUUJHYTiFeRQe/+F2pIzGHw99qLpQxETbuhWBqNa1uddwGPr8mS34xuYdgdLFOuy46nJPl03JeN3j1/iuVKyAigUX+CCFtMSIxTtR+tYXRmIxStIobY2i1hbXKaIL+msUsd+eM1B1DbbMCuzD7IHxl2xxglAninE305trEsg6fEGypzs5JAKpLtSoNc0dFi5vQg2wuUbpTLctI5V90cqXkEMtbVZ5GIjAGemiF2Fklkgj93dkCvfIlZI0pe4mkDQ4w0UfZhGUW2P7sp1acIznUAZElf+K6D74GCdsRIkKKlL1RXVTSn+R2l1XV9qs8i15UumSCCP3d3wxRsyNSMJO56ajLXzsvDmnZlYOy8LO56a3CXB3T3eQAGcglI56Qld3Y2A8tMp4R+lxazDijszg9sZStDZcqwMuYWVsrNH/otNiq6qbkrxP6TLdL/JHuDzYEXalpqlw0AeuzviizeEW/Lt6uUwKtQGp2tQSRlwLQPotQya29X5CSJ1QFMbwOVsaNH572BT29SGH09e7qLWKUrwpZBl7plq5J7ZjWiTDvePT8Yjk9NEXzrci00sYNJfwYwUd7oq64d0me7mjCSfA6LVxEaEwrG7K56qvd0tOJgaKCB3cXpGrNsdLD7YWaQ4TfnlX7mn5pFGXAeKTw9c6LK2KeSwcEqL9481o6rRhtgIAzYevojNx8qJj1HT3IblW07h/V3FePW24aJGRk66FZEGnVfVWKvF1K1ecN2FQGa1yEGqbOqP5TzSthwOFuvzLigaUIN5Ht0JfxSS7CqogQL1rkGthkF8pHKhG08Lvistehor0r14f1cx9ncUUdtbVIVwg7qfcE1Tm2htDaHBMjZch1kje6Oq0YayuhZYo8gGDimvQDA9BqGsSSJWB6w0SPVPpKoM+zt7Q66iMQugydaOe1btcX1GaqgF8zy6imA8x6H0W6EGCnxzDSoxLsQs+HEpsYg26VDT3EZ8LErPpKapDf/v+1P45OdzPtejYeFdW0NssKxqbMP7u9z1guQGDimvADraDobHoCu9E3LIZdAI3aNAILasZw3AdZJbQqxtbnf7W0mhumCeR7AJxnMcar+VHi/UBvgmUCO3r+dxxH5kb245ieVbTsn2NcIQhobWdtntKBRSnp02FOm9o1Be34olXx1FVSO5oSz2TIsZOlJxNNwvi9RjwM30yupaUNXQithwvXPCwAAVDa0dCqKteHjNQcF+qGlLyaySZB9Sxc9gCeLZHSx2F1ZiV2EFLtY0o3eMCdemxiMrAIGSX+VdxCPrDhJtq1QkLJS8AP5A6vcEkD/HXd0GQJVkVcHdHMDbNcgCuDkjEam9IhBt0iPWrENVkw0Vja3IP1+HupY2HJEJstVrGYzoG42rB8SgobUNDMMgOS7cTWJ7zMubUdMkPTj8MjMJQ5MsOF/TjMbWdnx24AJdpqF0KUkeAwdntKvx8JAORKSqpxqCCslq2vLFe8Tf59Ofz+GJ/x6WPAcAeP3XI/Crq/vJbucrm/JL8fRnR7zeQ0adBtekxGFSWrzrnSUGiXGw8XApHll7gKgoIZ8rTbmY5FrJ/Z78ofAajDY4lIzfdImng6kZSXhgUgpWbi8C32Tj/vlN/iUAl1Qf32Zn8XNJNX4uqXb7/JWNxzBvolOK/H/G9MXK7UWSx/k8rxSf53UqLeq1DGx2aqJQug5OW4IbOJTqavDhtCo+2FmE+EiD4EtbbKYnhNQAyNfFEBv01MSHiO0jtFSRd76G4Cyc2906um9AvQKb8kvxu45JmictbQ78dPIyfjp52e2dJXQMOcNsU34pHloj3I4cV1KKMKkRq0TfRa3xFow21EANlA425Zfi3W1FQfdGOFjgH9uKcKaiEZsLyLMxOKhxQgkFLlY3AXC+uPwxiPAz4/gvbTWqp3KI9VdNfAhJFVn3fcgMjLNVzV4zXH/GBjiLBBYQbcu9swC4GSkkhllOuhUvfknWjhBXSoqwEiM2GPouoaohQ4XaoE7q2d+oMU4olFBh1c5Oz5+/BxF+jRZfvDNiiPWXpC1PZVKlaqbJcWS1wH46eTkgtWs4uFgeJfBLKJDKzO8+U6l66e9KUS5WKskfDH2XUNWQoQYKfHNJUygU4FhpvWuwGjMgBv6MR+S/tJUOolLIDXpltc1Ex+Fvp3Qmevc1A4i2F8Kf9WXUzIz5JRRIDbPcwkrF7VwpKcIcSo1YkvIT0WadT8ab2to9gYYaKLiy1jUplK6ARedgtb+kWnHwI8nxS2ud2Tr+PKbUoFfVaCM6Dn87pTPRvHM1RNuL4a/6MmpnxiVVTQCUvEOVPxhC0ux2B4vcwkqsz7tAVMJB6faBhPRaldU2I7ewEl8dvog7x/aTvHI1TW3YXFCmuk+hWMkYoDEoAK6cdU0KpSvhBitSz4MaYsP1kmqhYgilN4cbtNhXVAWLSS8YcBpt1hMdm79dNYFRw5+J+mty5OtxxqXEwhplVOyhGhDrXKIifYdmp8bj0wMXiDzW0WYd3rprtFeKs9KsKrXaHqSpyq5099pml8KzlJAh6bVa8vUxN+OXYQCxnFvv2CblhKKGDDVQ0Oneoss8FIp6uMGK1POgBqvF5FILJYEzTITe642tdqzaWYxVO4sFB6yqRjJvDbed3cFiydfyAaCLpg91DSL+mhz5ehythsHimemiWTxCaBhgTnYyAHKZ+ayBca77J2VgMgBevW04xqfFu32uJLhUzfb8/UiMGql0dzEjiHS88fwdSQmCSGXZKNGECbXaPXSJB84fZ69IstkShUIR5pej+gIAYiOUlX9Ishix4s5RsnErGsYZ38LN9JIs8oNyQqQe4Qat7HalAgGnpIJ1lQ025BZWYvnmE0STnJjwzuvja7wOAyAuXO9aDhBbuuCWOD7dfx6LvjiCRV8cwartZ1xxQ4BzcHpn9mhEm3VEbWf0iXLpocgtEbAA7hzbD18dvohIow6/v2EQLEbh+bE1yoDHp6Shtd3hdk5Kg0vltmcBLN5w1OuacUaNXFCy2HYcQs8U4LxWM0cGxhux8/Rlt2WsTfmlmPDaVty1cjceW5eHu1buxoTXtkoGVodKJWOAelAAAM02Ow6fJ69mTKFQvPnjJwfw3m+zYI0im80/8ouBGD+oF8alxGJvUZVs3IqDdca3ZA+Mw9SMJDgcLB5aI65EesuIJOwqrERjK1mtcM+04Qs1TUT7vb+zCO9sO0O0LeC+HKMkXkesdk1low0LPjkEQPks31PTxHldgWc/P4JqmdIb+RfqYGt3uIwUsSUCS4fBI6eUbTHpMDEtHj8XV7tty52TxaRXpNVBkvxQVteKFVtP47EpaQDI08QnD0kkyvwUS0XfcMj3zCshVvxQ6Pp3tFknKPyppHRAV0M9KACWblSfl0+hUJz8fLYGQEdtKZlZeLRZhwU5V7lmaEqzX+wOFs98kS+57VeHSxUvN/EDTisbyPZtVahFxF+OIT3vueOTYSXwGCmd5XOaJss63oGb8kvx8JoDssYJty8XGM0xNSMJO56ajLXzsvDmnZlYMGUwapvaZBWyAaC2uQ1fHS71ioPhzmkLYRAod01Jg0aXbznpul6kGTarc4uJQwKUpqL7C7Fr7s/sr0BDDRQAxZVkMyUKhSJOm4KBmnMac0sPpy41EO3HDe67CyuJBj01cEG+Jr1/X49CqZqxhIG411+V4Br4l9+RidhwYQOQP/jY2h3E+k4rtxeh2WZXrAfFBUbz4ZYIbhnRG+v2nfVZX4rb//O8C0TbJ0QaYXew+CLvInEb3GBNajAWVzYSHxsAzld1bh8KWaP+yv4KNHSJB06xpO3ydfooFIoEvaOdxsPeoipZ46G6qQ0rtp7Gun1niWeT/ME990yFb52VgPO6WKNMfjumWKrm8bJ6ov2Pl9Vj4uBeyB4Yh9zCSsn4GDWzfAfr9CQrndlzgdFC+NNTwMIZExQbrkd1o00yEJdbMlTiPeMG60BldH5XcAn/M7Y/gNDKGg0FY0kK6kEB8IxATQkKhaKMX49xBsmSvvSWbzmpaACbOTKJN7gHLnCPC/Id1S/ab8cU0vIAgHPVZN5b/nak11fIuyGFUk8yg84sHiECMfjdmtnb1bZnX4BOA1BN2+X1LcSCZZn9YhQdu7mtMxiZRHgtWISSsSQENVAA6MM0MEhU55TcV8vg7mv6IVIkIp1C6SnMnTAQQOBeehsOlbrWzANZsIwL8u0dQyZDL8UjvxiEtfOysOOpyYIBiVIeCLHt4sPJsqT6xSjzAJHK7nPcMsIqWdU4EM9BTroVb88e7RWP42kAqmk7IdJILFjWO1rZtU2J77y2Um0Ei+5SOoAaKHC6Ilt56XZKsNlZDIyPQH1Lu597RaF0H3QaxuXdCNQMkb9mnpUaR5wOqwRrlMH10ubOQw3cALAgZ7Bkquac7GSi9Go3TwXhhR1ijSLuP8M4Pcmk24cbtHjjztGS2/jzOeAPqJ6BuEIGoJK2PQdrLhtJyghS+mx4eunF2kiyGBFt1gXUcOlOpQOogQLfXZFKXakUypVGW4cYFEAmm60W7req1TB49bbhstsrbW/xzGGulzZ3HnLH8EUaXB+mwbyJKZLbzJuY4uapqCCU+69qshH1HwBMOi30YRri8339f0bKnpu/PAVC11NOq4O0bbF7JWcEkT4bAJCTngCT3luLR6wN7rn29bfC7e9pyIstN4Yi1ECB767I9QfO+qknFEr3hW/oS81CF3RoTqiB/1vlhMU8dVeSLEa8M3u08zuP9s16reCLP1yvxTsCL20xUTipNpQOAAunpWP+pBQvT4qGAeZP6tQo4VBS72dqRhIenzJYdtsmmx17i6qQk27F41MGI9ok7J1K8jg3uRo3Ys+BEtQOqCRtSx1bzggiEQzMSU/Ayt+MFf1eqA2xfosJDoYbtJg/KcWrH9aOZ3T/czmS3iYhQqV2EcOyUgK6oUldXR0sFgtqa2sRFRXl8/HsDhYjFn+LRhuZoBOFQvFm7bwsIpltAJjw2lZF9XS4DI0dT032GiikpLyFvrM7WHy4qxj7iith1ofhV6P74tpB8ZIeAaVtqHGd29odWJ1bjJKqJgyINWNOdrJgjIfdwWLMy5slM6VizDr8/FwOtBoG6/Mu4LF1ebLt/3Z8Mr75/+2deXgUVbqHf91JZyV7IAlbEpAtJKyCiSEoCsiiyDCKywWXQcQZnRlQr4ML1zA6go6KjogIw4jLgIwDKCoywKDsIIYghgBCSFgTMGQlCdm67h9Ndaq7q7rOqa5eknzv8/Boqk+dvep8dc635JXYOlkL8seYlDhk9uroEF+GJ8aNNV5N1VX8+csjKFeouwFAXHgg3pg6CKVX6nVxtS4dn9gOgYAAlNbok7eY/+JtJ/D3HQWobmhRFejUwYQ/T07TvFOhPHcLcaCoHKEBfpgimbt6zUOtsYtY4Vm/uQWUHTt24K9//StycnJQXFyM9evXY/LkydbfBUHA/PnzsWzZMpSXl+OGG27Au+++i/79+1vT1NfX4+mnn8bq1atRV1eHW2+9FUuWLEHXrl11byALCzbm4/0dhS7nQxDtlQQF4UEJ0YEYoB7fVsyxtWxLuxsWASUyxIScawLK3oLLuG/5Pk1lKfW9UowblrFSGvvWOs6u9IWv4Ym28Kzf3Ec8NTU1GDhwIBYvXiz7+2uvvYY333wTixcvxoEDBxAfH48xY8agurrF3n/27NlYv349Pv30U+zatQtXrlzB7bffjuZmz+9gNDSZsXwnCScE4QrzJsrrWyhtFSttY0eFmFr1mbknYPEzU1HbaNUJYlEYVZIrxZg1v1+di1+/txvvby9w6tCNxUup5RipFyKCW/8488YHkrvfF45SxLq40hZ3wG0bO378eIwfP172N0EQ8NZbb+H555/HlClTAAAffvgh4uLisGrVKsyaNQuVlZVYsWIFPv74Y4wePRoA8Mknn6Bbt27YunUrbrvtNheaw8/He4uYY2F4k0Fdw2E0GtDQZEbeBTbnTloY0j0S4/rHIyrEhP9d+5PbyiHaFlGhjh5R1baKpZFTS6quouxKPSKDTfjlSj12nihF1dVGDOwagecn9pdVMmRBr21vXyqbNyyAqNDpLAK02juwsVlAzukK5JyuwIJvjjlN6yyyrtyciAg2YXS/TogLD0Te+SqEBZmQ3sO7QeoAtmO93SdLueIDSdmUV4zsDfk2rv3jw4OQPcn5UQrvvGJNz+rmX64t7kJX5x2FhYUoKSnB2LFjrdcCAwNx0003Yc+ePZg1axZycnLQ2Nhok6Zz585ITU3Fnj17ZAWU+vp61Ne3aK5XVekX2K+1WOCM6NURT9/Wl/k8WSv/e1tfq7dKgmBFjKYrvgTLa+rx+Kpc1TD3fkYDKusa8NqmY7Ivx5/OV+Gf+8/iURllUTXcfZburbJ5lGRFxqUm4NGRyVi+s9BGGDEagFF9OuK/x35xqU5y2AtSSscHlXWNWHuwxY394m9PIjLEhIVTtOtvuIqz8QOgGHxRCbm+eExGYCypuorHPjkoq7CtVi9X0/+FMSadJ73P6mrFU1JiCc4UFxdncz0uLs76W0lJCQICAhAVFaWYxp4FCxYgIiLC+q9bt2661ZnVUZL3sUi87vb8J06+4cnRipr8BGHPS18ftQnp/vhqR+EEcNwqVgtmJ94jDWjHglK+9sH05HB1292Vsllg9XYqdcK1Ka8Yy3YUOuyUCALcIpwAwImL1db+c3Z8IEdFbSMe06GvtOBs/B775CAeU5mvchSVtnwIN5sFzF3nfHf62XU/Ocw73nnFk76uoRl559k+/D3pfdYtZsYGg+2jIwiCwzV7nKV59tlnUVlZaf139uxZ3eo6PSPJJ1wOqzEsMQordp7Cxp+KEWRyn3W4OPn8jAY8nJnktnIIdXzch5IN9nFPnKnei1vF+05d5lq0lu0oRAODQ0VXztI35RVjxKvbbIStEa9uw8bDF5iEFk+c47N6O5UeRzirkwHumWuLvy2w9t/ibSc0xeXR0leuCJgs46eFTw+csdZj3yn1QJfltY3Yc7LU2o7dJ0uRveEI87zinYevMAr/wSajR73P6nrEEx8fD8CyS5KQ0LJ9dOnSJeuuSnx8PBoaGlBeXm6zi3Lp0iXceOONsvkGBgYiMJDNvTMvAf5GTByQgK8Oe15SZyXQ34iHPzzgVl0ZaaAtkSdu6YUP9hS5LWosIY8BlpdIa9CNcoW9BZe5Fi0BFp2xGVk9nKZjPUvfd+oyMq+LtV5XOoIorryK363KtbmmtE3uqXN8UcnYfvs+XqZeLHUSBUpx7ulJSeVVLNqqLRorb1+5erSmZ4BDKdJ2sB6f//afObhSz2Y4Yj+veOchaxymmNAAj+oG6fopnpycjPj4eGzZssV6raGhAdu3b7cKH0OHDoXJZLJJU1xcjLy8PEUBxd28fe9ghGhUwvME9U1mtwsngKM3RT+jAfdcz2b6TWjH/nGPjwjCwzcmeqUunsSsYVLL6YzZfzFLlQ6d8ejHP+ClL49gb8FlNDSZuXZzlLbVeRVYebBv55iUeGz/31GYN7EfHshIxLyJ/bD9f0c5LMSsZc3ITHLJoZoSrr66WOuvx7HeN248UmqZl2w9wiqcSBH7incesqo6XK5p8G0rnitXruDkyZPWvwsLC3Ho0CFER0eje/fumD17Nl555RX06tULvXr1wiuvvIKQkBDcf//9AICIiAjMmDEDTz31FGJiYhAdHY2nn34aaWlpVqseT+NnNGB6ene3+UK5PjESXaNCsPtkKX65wh4C3B0MTYxEwaUaVNS17IrIfXUBlgee/MO4n+s6hqJ/lwjUNjRhWFKMZTw2HPF2tdxO5VX+Z8H+RSpnCRHFGKOnpr4ZK3YXYcXuIkSHBjgcUzlDuk0+JiXeKtizns+XVtfji0PnVa0wRAuMLfkl+PzQBZs6iubY0h3Ov+8qdHiWWes0OiUez01MwZ4TpZj+wfdM93gCaf2lDt/KrtQjOjQA8RHBGJoYpXqMNf/LfIQFmhyctMnturiDsmshCjJ6xGLxtwVuKUPsK15F6jF94/DJfnWP6HWNZt+24vnhhx8watQo699PPvkkAODBBx/EypUr8cwzz6Curg6/+93vrI7aNm/ejLCwMOs9ixYtgr+/P6ZOnWp11LZy5Ur4+XlnF6PZLGDDj+6TnM+V12HFg8Mx8M+b3VYGK7lnKnBk/jgcOlvh1Oys2Swgux0skr7AiV9qcOKXGgDA5vxLXq6N5zAYDEiICGJeGAywDZqnZAmh5KXUGTzCiRT7IwhRgdWZl1yjwaJULKJ0BKG2cModvRbbWUmx1El6vOtnNCCrT0dk9IjC3lPlqu13N1JlX2f9ER1qQlmN8riLRxr/s2K/Td6TBiZg2Y5C3Y+15Ii+Zoqf3tMS6FLvo3NpX/GMOQBU1LPXxZNWPNwCys033wxnzmcNBgOys7ORnZ2tmCYoKAjvvPMO3nnnHd7i3YK7zh1FSqrq8ZuVvvFFYhaAu5fuQUJEEOLCgzCoWyT+fq4CeecrcOxCFcrrGmAWDAg2+aGkii0oGUFoITkm1Oqfg2WBeCSrxfU7iyWEp5Du3ogKrHKCk4j9Drm96TWgrA/DggDbnR2pHxR7HROl493rOoX7hICS2iXcusvhrD+cCSdKlFRe5dohlvadFl2d+IhgAC2BLp3NES1MGpjgEOiSdcxjQ9l1PHnSugoFC4RnJMILbt4+5CHvQhW2HL2ET/afwdP/PowF3xzDl4dLcKK0FqU1TSirbcR5H6ov0fYwGiy7IeNSE/DHW9mCB97cp8V9AYslBKAcYE1PymSiC9t7wwWUo9PaW1XwmuTKIe7siDgL3ijnvVVLiDZ36E5uzb/k1HOtK/ALGMoBItXabm/23RLo0naxZz2elGPDj8U2+iFcY84zdh60LtTViqe14gm77s4cW9kE0daZmZVs3Q1pYlS6+2TfaavVDaslxJi+nfC5G49vAdj4C3L2pe+slVKrClz7f1cpqayz+VvquVfNq2iHIDbBbtKAeNyaEm/jnA/QzxJIAPDy10e8+u58ICMR41MTbPrKvh/La+odLL2k2O9QAfLjMTQxCjf99VuuQJoichZPrGNeKiNkK3GJUQldD0hAAdu5sStEBvsjOpi6miAMgIxXWLanbueJX9BsFuBnNKDJzGbhEBcZxP1sq+kz2CMqnOux86Hnbq6cXo2f0cCk4OhvZBNQusd0wJ2Dulj/fs9o0F3h9PC5St3y0sL41ASHPrPvR60O5eTGQ+lohgW5+cMy5jwf6Vr1tbRARzxw7vhIDyrqmrDleKkbciaI1sVHvxnu4LI+o0esQmpbrtQ3W3cYrlxlE1BqrjZbn201RA+s+54djdUz0zE9vTvTfdEdLNv0euiydQoL0m1HV6yXFlitNOzTjUtNwK4/3YLVM9PxxKiemsuXYh9U0FPIeeSVQxRMneXD43BO6WiGBa1zZ3hyNCIYP6JdmVe8kIByDVcmBUEQ6kSHmnBDD8eFL71nDLMfIvELUc0ztYjBYLA+2wlOnm2p4mCAvxEZPWMwIa0zUxnx4UE2ddOCdDFkiT7MUy8tpPeIkdWjkRIVYgnqZ4/4xT5nTB+nfc7KzKweuuTDg5LysBw8TtFYkQp6i+4ZxKSbwiJMKeFnNOA3mclMaV2ZV7yQgCJBOil+k5mEQH/qHoLQi7KaRtz0128dtsP9jAbMzGJ7OYoWBEkxbI6lxHTjUhOw7ambMT29O/rFh8Fed7ZTWICD4qAoKDhDuiho/Xq1Xwz12NF1ZbESybrO+S7K1Ou7Ol28pe3QSpDJiBG9OnLlEx0awNRvhmv/Zo1MdhhnJeVhOdzlnE8U9H41uAsWTElTTc8iTDnjiVt6qQqleswrHmgFtkOMrvrB7iLUM8T9IAiCneJrAdc22oWWGJ7M6Pjp2vuXNYZWVEgAdp8oxRP/PIiU/9uEj/edwdGSatg76fzlSgNyz9ia1YoLrLiQ2VfDANtFobymQbVOoQF+iAsLsLkWFx7osBgq7ehGh5owIzMJc0b3dlqveRNT8H1hGVM8GnsPtRsPFyNz4TZ8eVg+eKvImh/OqR5bjEtNwJL7B2u28Jmengg/o4E5n4SIILx8ZyoAdeFO7PdnJ6RYP0zfvncQVs9Mx64/3YIxKfFOY/qI/fZzSTVTW1w5uhuTEo85o3vJ7jRGBvsrRj/mQTR/lus3ufnuCUhz0w49FN0Ior0RYjKitpFdoH9i9UEsxmBMGGA5RmG1IhDT+RkNCPA3qn5EPPnZj0z5mgVYfWJIdWTGpSbg0ZHJWL6z0CYAosFgsUSS+i353Sp1vxY1Dc0wOezMyr/w1Sww+sR3kI3FM2lgAl76mi0ejSteVCtqG7Gv4DIyeznXIZowoDMWw8DUP/Z8dbgYc8f3g5/R4DQf6S7UuNQEvGd0jFNkMNgHsbQN62Gv9Oospg9Pv8nFOeNBrawgk77LeISME7nIEBMWTElzWQjihXZQ7HC30zaCaIvwCCeARSD43apc63EPr2vufacuu2WHc/lO24jJm/KKsWxHoYNzNbNgia68Ka+Y22mc/cv/YpVyrBhx4bxzUBdk9Iyx+XqVHkmLX/7zJvbDsh2FTPFolGLX8LD3FJvy/4QBFr8fvLok9robSvnYH8mMS03AvIn9EB3acmRh79pFqd/VYvos2JjP3G88uixysIyRs/mjpSw5/0JavDPrAe2g2OFJN74E0d4RPZ7yuubeU+Aeqziz0BIxmWU3df6X+Qg1+bvktlwaK0Ya14cF6Zd/s1nAiFe3qcajGZMSb627qzvFZg4Z0X5H6HhJFZZ8d0r1Pvt3Motvj015xXh8Va6q7xn7fnc25uK15TvZXePHhQcie1J/TTsPrLv5rswf1rJczV8rtINihyecthEEYUH8QnamGCr3FXq+vA7uQoyYzGqdsTb3nMtlarH0sIfHmkSvneKqq3yCmXRHKKtXJ6Z75N7JznaWeI7p7fudpV94gvm+MXWQ5mMRnjFydf64wxJJD0hAsYNFc58gCP0Qv5B5XHN3jnTfMypGTGbdTa2pb9KtbFd2cHmsSXTbKXbhY1rNnJrVD4k9WoQvsT/03kHn8dBqj5a6aK2/uyyRXIWOeOxgCfZFEIR+SL+QWV1zZ/bsyHQ8oAUxYjLrbuo+Hb8qXdnB5dXj0YPkmFDN92oJYsiClkVUNF/XewfdE+OpR3nemDss0A6KDONSE3DXkC7qCQmC0IzSF7Kz7XuRyjr3KO2N6tvRGiNoeHK0ql8IAKi+6voOitbdAik8OxJ6OIMTAz66Am8QQxY0LaLXOoJlzOXMu+Vw93ja18mV8ty1m+UqJKAokNmro7erQBBtGgHavpCbzQL+/JWya3FXOFZczeySnBdx4WPRsdECjx6PHs7gpAEfXUHOGmnXn27RrLuhRfjiOYoJCfRT1W/Rw2cI6xjpMX94dcA8BR3xKBAbGqCeiCAIBPgbER0SgBLOKKd3DeliXYSazYLqsY6YZvfJX7jLYqW48ir+sasQlXUNOFtW55J1DgCY/ICUhHDcPqAzHrwxGduOXZT1XfLiHSm4pW8cVuw8hdNltUiMDsH0jCSrACDXPwCw52Qp1h08h5qGZgxLisKDNybjvWmOPkDEMkTnY2I+794/GC99fdTB38e8if2wMa8EXx2WN10d0DXcIaaSUj2l49jQZMbHe4ts2uhnNGBfweVrJssGxDLEepHLR+wr6dERK6XV9fji0HmUVterjnlNfTMmpMZhX2GZbFDJyGATHs5MslpLucKYlHjMHt0LH+wusgaltEdpbJUiVfOWFRVqwst3pnrcBwoAGATB3jrc96mqqkJERAQqKysRHh7uljKW7yjAXzYe0y2/IH8jrpJnWkIG8R3C++FuMAAJ4UG44GW/PUunDbHRG1mxq5ApAu0DGYn4852pqk6xANcciomMTYnD5vyLmu93lcgQExZOSZPVsXlt01Es32nrb8V4zRnc4O5RDm2PDDGhrqFZ1hfMrJHJeGZcP4cytuSXyPbzvIkpiAoNcFjYJi3eicPnqhTbM8suKrXaOC7YmO/QRoMBMPkZbXzPAEBEsD9e/fUA2UVRLh+xr+zrk73hCEqqnO+OGA38z55IdGgA7hzUGdV1jdhy9JLN0aOSczwpzgQ6uf6MDDbhoRuTMCw5GqVX6m3G1r6t8QwmzmL5W/NLsP7QecUo3ixtYYVn/SYBRYH/+yIPH+097Za8CaItEBHkj1fvsl1ENuUVMyuYz5vYD12igvHbTw46bJmL333vTRsCALJpeBmfGodv8rwnoIjYuyVfsDHf6sVWL+SEB7V+ltbppa/ysGKX+vvv55fHI8DfqJr/6JRO2JJ/ibsdvH1l3+5ms4Cp7+9BzukK7rJZsFfutf8NcOxbEWcCHSA/5+XyVHvmlNzg83rDddYWHnjWb9JBUUA0NSQIQp7Kq03IOd1iwaIWdt6eu4Z2U3WKlb3hCLI36BN6Ym/BZR1ycZ3sDUesei4NTWYs36mvcAJY3PaLuxJqzscEWJxwSevEIpwAwPvfFTDlr0U4AYC5637i6it7T8DNZgG5Zyo0lc2CmiM4wLZvRZx5q33sk4OYu+4np8/F8+vzsP7gOew+UYq5a517MZb2oVr5ztpiP088AQkoCkzPSNIc4Iog2gvLdxZhwUaLUMLrf+L1zcdUnUOVVNW7rG9igCXIXkWdfv5K7PPnoaSq3urw6uO9RZqPF9T4cI9lMWcZF6kTro/3FjGX8ffdp9waHkSM9yPWS62vRE/AIu7sXxbkHJyxeKt1pgcjALhc04A5//oR/7Niv6Juioi0D9XKV8PTztpIQFEgwN+oGgJ+TAqbJ0SCaMuIX628/ieKLte6qUaO/GqQvm4DIkNMWDptCJbKmMiyIPaV6LXWHYgLCauAJ6bjqVN9Y7PbnXeJ8X5Y6yVN587+5UHaR96I9yaNmeRq+e5SUJeDrHicIJ5lOlPIcnaOlxARhLAgP/x8scZTVSYIj2MWgKc/O4S6hmau+xKjQ7DTTXUSiQrxx4IpAxARHIAVu4t0y/fd+4ZYo/iKSq+7T5Zi8bcnme4XfXW48yi5rtEyHmWMJrRiOp46hZiMHnDeZdmjYq1XYnSIVfmz2k3+cniR9pF34r217PO5Wj7rfNIDElBUeHZCCp4a21fRpE3q+bKksg5lNQ2I7hCI+PAgbDt20S3nywTha2z4kT+SalSwySULChYC/S2vuOHJ0YgI8kelTk7VpNYaomO54cnR+HfOOdUvzPjwQKuZ8PSMJPxl41G39EFal0gAFksTFsR00zOS8NLXR5nuGZXSSTXQo6uIwRBZ+spoADqFB2HEq9t8Jip9ZIjJxsGZN+K9iX2oR/ms80kPSECR0NBkxj92ncK63HNobBaQnhyDcf3jUXG1EX0TwtE7Lgx7T13GM5/9CBiALlHBGNIlEqtzzuJseR26RQXh3usTcehcBY6VVOLvJJwQhCLvfFfg9jLEUPTvTRuCrN4dFf168CAA+N2qg1hqtLVo8DMakNY1HCX5zhfGTuGBVlPSAH8jbu2nzcJFjRHXWXZ44iOCmdKL6QL8jUgID0Ixw1Z+SnykW8ODRIWYkN4jxlovtb5K7RKOP6x2HsXY01TUNmJLfol1rrBE7o4IMaHymh6Kq22R9iFL+Wqwzic9IAHlGnLma4WltVh94CxzHsdLqrH16C96V40gCI1IQ9E3NfEdQamRveGITfj5hiYzk6Bx+FwV6hqaERzgh2azgLzzyr5GtGIwAMOufbUPTYxS3akyGizpAEs7LjIeA9wzrDsAyzFXZIhJUbnTAIsH1tr6Zq5F8S+TU639y9JXeeermPM3GoBb+3VC3vkqt++2SOcKSwyihVPSAMBl3z8AsGBKmo3DNi2O7KQM6hbpUn14ICVZuMcPAUEQvoFoSfGLghMqrUitcQA+65dXNFo+sSIIQM7pcgCW/7JYv4jpeSxf1hw4A8DSDjXLk5r6Znz0m+F4fkI/XH9NGFIjKrTFqyxLX7HUe2xKHOZN7IdjL43H8geGWd3sPzGqJ1OdtGA/V1hiEIkhAOZN7KepzISIIEUfKGL50aHqsabsWbXfc/7B2v0OSkOTGctIOCEIQgNShUMeixHRgsmdCpNi3qxlaLEsEtOylvHd8UvYmFfCLJRJ89WrryYOSMCdEqsuUYfI3cqr9vmzRO72MxoQG6bu+l9kbEocJg5IYHJ1Py41AXWNZsxZc4irHZ60jGr3AsrHe4t86rySIIjWg1ThkMf6JSkmxOF+vRHzZi1Di2WRmJa1DF5LKmm+evWVUj7uVl6Vy18UjnjvU+KG5Ggb4UuN+HD+NnvSiWm7P+LxFTt5wr3ckByF+4Z1xdDukcjsGYO7r+/s7SoROhIfrh4qvlMH/u1s52UG2lhnTM9IYr73uWsuDLRE3lVDbK9Yt+HJ0dZIyizpWZ1UGtDSZrEdzuB1fBka6GfTv2p9ZVApw76d9gxPjkYcx24FD/ZzhQdLvdQtZ4wGvjko5q02bvbcf0MiV3pXaPcCCrm0bx/sLyzH6gPnkHOmArsLLuOzHy54u0rtmjEpnXRblGeNTEb2JPVQ8X+enKZTiRayJ/W32UIP8Ddi1kjnzh0BS9uDA/wAOA9zrwVpe8W6bckvUdUPkaZncVIJAI+OTLaJIDxpoPMYLbym1DX1zXhtU4u5s7O+Ev+emZUMg5Pfpe20x89owPw7+/NVkhH7ucKDpV6pqulmZrWMB0/eL96RwjX3Dp2t4CrDFdq9gDI9I0nXrxcWOnUwuU1SJwg5Qq4tiKyInlJZFioejAaLQLH8gWF4b9oQ7q83KQa0BIdjVTpcOm2IbF8YDMCALmyBR8W+kVM+fHZCilMhZUxKJyx/YJjNNaW6O1vPEiKCMGtkskP/SdsLsMVHigwxYUxKvGw7lKogF5RPiy8cNaSxdZrNAiKCA/BwZhKi7HxxiO1+dkKK6jxwhtocmTUyGUunDZHdkZLrK2dzhQeWeknHgzfv96YNQWQw2w6jJx3NUTRjeN6KZ0ZmEsKDTVi09YTTdLNGJmPFriI0KXx6+BsBmWjrBOHA8xP6IrVLJLO306gQExZMScPxkitYtPVn1fS/u6knqusbIcCyK9k3PhxltQ2IDgnAsZIqnC2vc3ByCLSEe9954hcsYfCLMuK6GIQE+GNYUhQevNHxi9FZ+Hppmj0nSrE29xxqG5oxLCkaCeGB+Ms3trGBgkwGzMzsgeE9YrC/sAyAgIwesUjvGaP6NWwJuHcK63PPo7HZjBuTY/DCHf2tOydy2Nd9aGIUck6X41L1VcR2CAQEoLSm3qZdau3dW3AZ9y3fp9qvq2emy+pCNDSZsXJ3ITbnlwCwmBM/nOnY76zlaEGMem1vchsdasKvBnXB6JR4h3azzANnNJsF7DlZirUHz6G2oQnDkmLw4I0tc7fZLGBfweVrLuQteiTDkqJxoKjsWlBK9rnCg9zcldbLFXafKMX/rNivmk5prrDCs36TgHINXzI17hDoh4W/SkNMWBCW7TiJb4+XKqYN9Dei3gUpJT7cspNTUuU598WEZzEagGMvjUeAvxFfHDqPP356SPcy3r53EJdynj2s9XK1HDnEyK4soe1bG57qV3fNKwC4qXdH7Pj5lzY5Pt5GKsjFdgjEU/86hItV9YoO5OIjgrDrT7e4JHTxrN/t3opHZHD3KADeE1BMfgY0NlumxZX6ZvxhzSGmM9uGZotwYu/wRw1xer0woR+ecNOLhfANpGfT7rJUcDVfXksTvVCLLCs6eZM6ZGtNeKpfee7nfVcdPFPeZsfHm8jFkYsMMVn7Vc6BnDMdHnfQ7nVQAMtLau66n7xaB1E4EWFVKBME4K4hXbkjqsZHBOHd+wdjyXb3uxsnvINR5mx6eHK0JudMznBmGcEKi4WGUjnNZgF7Cy7ji0PnsbfgMpo5tDHVnH+JTt72FVzWXIY3caVf3VHOkvv5oj8bAFQ7iZ8kHR81XJknbQ1x19B+7ovu9SPsdGxYdXj0hnZQAOw7ddmplruvExLoh21P3Yx+/7eJKf2grmF4dGQvzP8yDxerG9xcO8JTGACEBflj0qDOSI4JddD3ACxa+78a1EXXyL56fFWxuP+WK0fuKzAhIggv3pHC9DJlVfh7fNVBVEgCBPKU4U209qu7yhmXmoDbUluck320twg5pysU872lb0f895h6+JDHVx3Ewl+nKY6Hq/OkLcGyaxhs8sO7M4Y46Dx5GtpBAa4pNbVeEqNDuNwPHy2+gsdXHSThpA0hvjpeu2sAXp6chhlZPRQV5y5W66dvNGd0L91e8CyWOFKUvgJLKi0BAjflqVuVsB5NSIUT3jK8DW+/ursc0TlZoL8RB50IJ2NSOuGRLDb38xV1jYrjocc8aUuw7hoajQbcOagLMnRW9OWBdlAAuB4v0nuIznle/tq5KaGU+mb3tTfYZERdI5kWeZp4xq/BhiYzNv6kzwvZaAB+e/N1uuQlwuL+G7B8BWZvcF13RGtk19am/8Dar54qx9n4AZa+zTtfhaGJUVzjYz8ebV3HSAu8oQ+8CQkoAIYlRgNonboYA7qG40BhGbpFed/h3K8Hx2Pr0VIuAaVbVCDOlpMFEQ/+RgOmXt8FkcGBMBphNWf86UwlkuZ+bU33+WOZSOseYTWHNAvAzxeruR1mKWEWgF0nSnFLv06KaRqazPh4bxFOl9XKmhmL2FsTqK1Gi7edQEmV+lfgyt2FiA0LtFkoG5rM+HBPIQ4UlSM0wA93D+mCv31bwK28KZbxfWGZg9lls1nAvlOXuUxOtfRVdHAAjl2sxtly5/cA6m7VlUxzr1xtwpw1uThTXofuUcFYdM9gdAhybelgHb+c0+XMkXflxoN1t0C8x1XzZKUxZB1bV8pgxVsK6VogM2MAy3cU4C8bj+lQM+8RHuSHqqv6hpMnWgcJEUFuDxfvjAFdw7HhiSyH6ws25mP5zkIbgchosFgVSRV35fQDpNjrCmzKK8ZjGkLFJ0QEIbVLOLbmX3IQRAL9jQgO8LPRRYsMNjkc7chhb6K7Ka8Yc9f95KDXFhliwsIp8noSPH2VveGIolsAuXtYUNLRCPA34PTlOof0SmPOouvBM35i327KK8bctT9xjwePmXWgv9ElPRWlMUztEo6881WqY8sC6zxxRrNZwIhXtynuSullTqwEz/pNOigAzpY7PoCtDRJO2i/eFE4A4PC5KkxavNPmmuhXyH63xiwA7+8oxIKNliNJJf0AKVJdARbPqEoUV17FFhnhBADqm8yoqG3EnNG98fa9g7B6Zjre/Z8hTPlKvzTFxVdO6b6ithGPyeg88PTVY58cdOqzyP4eFpTGoLjyqqxwAsiPOYuuB+/4bb3mIG5caoKm8WDdBSgqrXFJT8XZGB4+V6U6tiywzhM1WEIGeNqcWAkSUEDxeAjCVQ6fq8KVa+agDU1mLN/p3KfQ8p2FqGtoVtQPkCL+Pv/LfOw7ddmtAtmnB87g9gGdrZ5B1d7RRgMwNDEKQItehRrZG45YTVx5+orHFYLURbwznOloqCEdczVdD+Da+BXwjd+Xh0us7UjvEcNtMj2oWyRTOav2n1Gtu5JZMssYKsE6TqzzhCUvwHOK065CAgrYo3cSBKHMnDW5AICP9xap6rmYBeCVjcrHOvaIugLutrgT9REAIOd0OVM7ck6XA7DoOzjTqxApqaq3lsHaV3/5+giXKwSzYMlbDTUdDTXEMWfV9bC4hudDbIeWL39W60Znlm1SPRWl+mnV62IdJ9Z5wpKXyLjUBOz60y1YPTPdumu460+3+IxwApCAAoA9eidBEMqcuXZUerqslil90WW2dLa4X2VOtF7gtXbgsXoQ07L21Y/nKpnzFmHJ21VLDXHM2fPh/xKUtoP3y5+1f1lQauPOE+p+WpzBUkfWdvC2V1Sc9rY5sRJkxXMNUcFo2Y7CVmx0TLRGAv0MeO3ugSitrsdLXx9Vv8FH6R4VDID9yDQpJgQ7ncfLdCCjRyzWHjzv1mMeUW+B19qBx+pBTMvaVxFB/N5/WfJ21VJDHHPWfDJ6xmDtwXNc42dvochjMq3n8b1cG5vNAn64toOmFZY6srajrakr0A6KhGcnpOD4y+MxPCnK21Uh2hFb5tyMOwd1wUOZyU7P2H2dRfcMBsB2ZGo0AH8a14/5aFXUL0jvGWPd5ncHUh0GXjfxw5OjER+uvlAb0KK3wtpXM7N6MLag5Z7pGUmq6dTaqIY45qx9ld6Df/z6xoU5XGP98mfpXwOA+HBt4QC+LyzDlXrtBgqs48Q6T1jyak2QgCJDn3jHB4Ig3IG/Eegea/nqcXbG7usM6Bpu9Y3BcmQ6MysZP52v5Dq7F/ULxqUm4DeZSS7UVr0MgF/nwc9owH3Du6uWIaBFb4W1r0b07ojIEPZdFGmASGe4MuekY87TV+NSE7B02hBm3x1lddo9XrP076Mjk5E9SZtVi6tHZKzjxDpPtPpW8VXaVmtcZMHGfPR54Rt8vO+Mt6tCtAP8jcDJVybaXFM6Y5fiY8fEsj4xnp2Qglkjkx3qKg1gyPpyjwwxOegXjEmJ565nQkQQxqR0kl2IQwP9sFRGh4FX5yEplm2LXdp2lr7yMxqwcEqaar5yASLVUGpjQkQQEmOCZe+RG3OevhqXmoAPHhzGVD9Xj6FY+lerVYvWumkZJ5Z2tDXIUds1RBtzgnA3gX4GbJlzs3XnRA45z6pi4K6hiVHIOV1uPX/fduyiUxPEiWnxSI4NhVkAokICUF7TwBTFet7EfojuEIjS6quoqG1EU7MZe0+V4uSlGhgMBgxPisI79w916lXUmdfLvQWXcd/yfar1+OeMG5DZK9ahf9ScTcWFB+KNqYNQeqXeqSfZKUO64sbrYp0qCLJ6GGVt0+qZ6Q5eXVk8hFocteXbWAuFBfljSPdIjOzV0SUPpXp5kmXtK3EMlfRR9HYYxtK/vJ5km80CMhf+16lvGgCICwvAI1k9mTz+6tEOX4Zn/SYBBZYB7/PCN6Qc2w4w+RnQ6MZYRGrEhAbg++dH664tz+NhUosnST08WNrjqkdL0TEYIB891xv+HDzhpdNVd+y+hC+OIS8snnHldufaK+RJlpMP95DlTnvBHcJJiIn9MQrwN+Kxj3+wOrjSi2cnpODYS+Mxb2I/PJCRiHkT++HYS+NlhQc/owEdwwKcznnpmbteHizl6uGKR0tfdDblCS+dvm4ayoMvjiEvok6NnI5QVIiJhBMXoB0UAI98eABbj17SoWYEwY5SPBOtsG79zvzoALbkK8/3HrEh+OPo3ugUFoRB3SLR/8VNTpVZjQbg2EvjNW+Xs8RvcYaeOwp65aWlTSxlu1o/X919cVe9PNneZrNgDcwJWITI9B6uC5C+OmZaoSMeTsa9tQPHSqp1qBlB8JEcG4wJqZ1xvqIOnSODkXldLNJ7tERiZX0pKelQ2SvP1TU0o9//bWKuX1iQH6oZ4jzNm9gPMySmsLwLtLtfwiyLh1ZBSanuPLoCrEH2XBHkXL2/tSCOx5b8Enx+6ALKalqsgFpbe9vimJGAwkGzWUDai5tQ28gWw4BwLzMyk3BL3zg89dmPTG7D2yIhAX4I8DfauDZ39lJSU/CWCikvrP8Jn+zX30rtgYxE/PnOVAAtegX2LxZv6RWwRBfWWmelBWTSwARs+LGYaWFhKRuAS33qa2PiLtQiY7em9rbVMSMdFA6+Lywj4cSHWH3gLNJ7xqC2QV8djdZEbUOzw2KqFFW1ocmsan32/o6WIGI/nqvQta4iogdL1qBxSoHX9IYluvDGw8Wa6uwsCvD7OwqZIuOy9Ff2hiPI3qC9T31tTNwFS2Ts1tLe9jJmarR7V/euOtoh9KW2oRlvbj2KKp2VSFs7AixfTvO/zMeYlHjr0cS7//2Z6f65n+Wic3QHlNdod3rljHuGWRyUsQaN+76wzMHMVkTpyMT+umhufaasBv86cBZX6pvQPToEi+4ZjOAAP+w7dRl/+vdh1bo//e8fUdugfIwl1jl7Qx4MBgMSo0Nwz7DueG59HpdyvdwYqkX3FQBVE1axfvsKLjuYYwOujUlDkxkrdxdic34JAIv/mYcz9XUIpsfxHk9UZpY5yFu23seTejxHbYF2L6C46gSI0J93t5E/GjnkXkpLd5xiunfdjyVurBmw5sAZzMjqwR1gzx6eIxOjAQ7Ku8cvXkFq9n/gbzSgifHr0plwIkXqwFFrzCTpGFbWNWDu2p805SPH46sOYuGv0xy2/bWOyYKN+Q6xyX44XYEF3xyzHhu6ujjrpWOhJSqzHh+n7tIRYa2bHsfgvqyE2+4FFEv8jEDVrxSC8BWkLy/WRdjdiFFUWQX+olLHqKtKZ+7ikYk9zpruK/2ixNb8Evxjd5Gu7g0q6hrx208OOugm8AY9BNT1mt7fUYhTpTXIO1/lktKu3HiLR2E8OhZahI3YDoHc90jRs/72sI7ZS18dQbDJqLkcX1fCbfc6KH5GA7In9fd2NQiCGemLlSc+izsRdVBEgV+NTw+csTk/59mibwusyz3ntrba6yYMTYxiCjQnBjBsaDI79UwssiX/EpOejRx661ho2Ql/6l+HVOuphLt1RMpr2D6Yy2oamfpbDiWdHdYx9ATtXkABLI525ozu7e1qEAQT3xdetv7/178f6cWaWJBGUWUNmCcec4ho2aJvzZTXukfHSnqEJJJzulw1KKNZaAlg+PHeIq4gjvblA+qLM4+OBQtaojJfrKrXvBDrXX8pzWaB6whRAL8w1FqUcElAuUavTh28XQWCYOLDPafRbBbQbBZQeLkGJj/vnhfbR1FNig1luk+6Ld9eTcrdhbRveXVQxOM6rbAszq7qKtmjJSqzKwux3vWXokVY5xWG3Clg6Um710EBRIlVm7tugvA0FXWNmPr+HlyouOrVXQelWDy8Og+b8orx0ldHdK+fVqand8f1SdH4oais1UY2l44B73iIx3Wu4mxx1qIXo4boNt+ZHxR7tFrDsOta1TDnKaJVeZfnPncKWHpCAgra3/Yy0frJOV3htbJNRmBA10iM7R+HhzN7OPwubrerBcwbnhytqGjoLQwA5t3eHwH+RoxPTcA/95/hOu4INhlRx+BXKSrEhIraRre1W6rDwDMegOW47i8bj2o+5hFxtoir1Qmw9JFYJ1bGpSZgTEq81SrlxMVqLP5WPXI370LMUn8AWLT1BPrEh3EpnGq1LOW5zx0CojugIx54X0okiNZEoxnIOVOBBd8cR9953zgEC2QJmHfvsO7Y8OMFPLf+J58RTgBLMEfRxDLA34iZWclc979210BVxeXIEBP+MjkNAPtxBC8vfX3UemzBG8BQS7vt80yQCDxyiHVyNvbltY3Yks9vHi8Npph5XUeme3gXYmmfOkP0e8NzhKRFnwYAl48jljKiQ00oqbqKvQWXvaaLQgIKvC8lEkRrRSmisVKU2sgQEyJCTFi09WfMWXMIZTWOHl7tiQkNwKyRyUiwy8uZZYqfxjdbfZMZ+061KCE/OyEFs0Ymq1rBAJaQAncM7IyFU9Kcpls4JQ0TBsj3jxyBGpyi2esP8EYNFtut1OwxKZ1ggGsRm8ekxDsV5pwt7s1mAXsLLuOLQ+edLqDDk6NVBUYtOzWApU9nqxhXaNHlYBV+7Hnpa3ZBiEVnp6ymEXPWHMJ9y/dhxKvbvGLV0+5j8QCWyT7i1W10zEMQGmGJaFxUWoNFW09w571o6kD8akhXB4dSl6uv4olPDyneN2d0L1yuacBHe09zlfe7m3vimXF9ba6Jgf92nvgFOWfKbQIoRoea8PKdqZgwoLP1mpyTMwOAR+2CN4pt+vvOAvz32C82ZYrpnxnXzybQoSAIePc79WOLt+8dhDsHdbG5xuuUy5knWVd9aOwtuIz7lu9TTbd6ZrqNfghPuc1mAUNf3iIb6kAkMsSEnBfGaHJO9sWh8/ijkzkoIjcWaqjFFZLDvq/0KkPP+D886zfpoKBFmnzsk4PeropHMAAYkhjpVT0Gom1hFizmqdKIxkDLdrv4EaCF+Ihgm7wAqOZnAPDpgbN4/a6B3ALKhYo6h2sB/kbMyOqBGVk9VBf5TXnFDsKJyLIdhRjcPcr6khfblNEzxmn048xesVY39nsLLjMJKHI7w9I+ZCHA34hHb+qJR2/q6fCbvb4HrxdSLYqavM7Rvi8scyqcAJaYTFpdxrtTl0Pavxt/KsbH+9TnMa+6grSMkso6vPT1UZvozyJKoTbcDQko1xiXmoCl04bIRj31FN2jgtCrUwdcbRKQ2iUcUSEB+HfOWZz8xdHsb3hSBK42CjhyoQrNnHtgP2XfhuAAP2Qu/C950CV049QvVxR/06qILnUgBrTsAOw++QuTmSQMYFJmlNIlMtjhGuvOg5p/CWcveVEIUkN0vOZsN9++39wFr8AjhXdx19K37rZW4VVA5kXavywCCq8gJJ3XZTUNssKJiDfi/5CAImFcagJu6RuHIS9twZV6ZUdKwf4G9I4PQ955NuHAzwAM7BKOLjGhuFhRh+/tdi6UzDUXbMyXFU4A4PuiSszMSsKfxvfD3oLLAAQMS4zGQysPqL6Ic8+Uo6y2AfcN765py50g5DhWUq34m9YFQHQgltEzRtOWd+mVerx4Rwp+y7E7euN1tgH3eI4UPBHkjcfxmi8HkuNd3LX0rbutVcTd999+chAGwOFID2DTx1HDHYKQlucJ8KxRCQkoduScLncqnABAXZOAH89VMefZLAAHz1XhoN09ISY/TEiLxytTBsDPaMDegsvWL7RB3SJV3U0v31mE5TuLrH+HBZ1m+kqc/o/vmetOEKwEmZSVOV1RRL9UfVWzOXKnsCBk9IzBe9OGIHvDEdUdw6gQE9J72Oo78BwpeMK/RGvxYaEG7+Kupd3u3uEAlP2vxOsY00ZvQcgV835PGpV4VUBZsmQJ/vrXv6K4uBj9+/fHW2+9haysLG9WyaMPdW1jM9YePI+YDgEOkVrDgvy5/RBUX3WP+2yideNnBJrVXXO4TI+Oyt6YxYVCyzFPbIdAPP3Zj1wvU/uFRzxrX7ztJBZt/VnxvgVT0qwveS1HCp7wL9FafFiwwLO4a2m3p3Y4XNXHYS1DD0FIa9wrPYQ5XrwmoKxZswazZ8/GkiVLkJmZiffffx/jx49Hfn4+undXj+XhLk79wu/5zxUEQDZqKAkbhB4YDUBe9jgcPFNuPQosKq3FVz/pbzL43ARl00jpQsH6YhRfiBDAJdgoLTx+RgP+OLoX+sR3QPaGfBv3+nJHNlqOFDzxxe6JMjwJ6+Kutd2e2OEAXNPHYUUPQUiLPpiewhwPXhNQ3nzzTcyYMQOPPPIIAOCtt97Cf/7zH7z33ntYsGCBV+rUbBbw0d4ir5RN8JPZMxq7C7wbK8LXmZmVbFGIvi4WmRLdii4yZrCuMCalE4ID/JymERcKFkV06QuxlDGyq4jawsP6ktdypOCJL3ZP7Qp4EpbF3ZV2e2KHw1O4KghpOSXQW5hjxSsCSkNDA3JycjB37lyb62PHjsWePXsc0tfX16O+vuUlVVXFrv/Bw/eFZSj3kgWPL3PHgHjc0i8eu0/8gn8fPO/WstK6hCM+PAhbjl5STWs0+oafwfTkSNzcOw4L/3Pc21Wx4fYBCQ6K1yLPTkjBiJ4dMf0D1/WRxqR0wvIHhjGlbTlqOYEPdhehok7+eZO+EC07P+o8Meo6ZF4Xy7TwsLzktR6leOKL3VO7Ar6GK+32xA5Ha4B1Xs+b2A+xYYFeFea8IqCUlpaiubkZcXFxNtfj4uJQUuLo2njBggWYP3++2+vl60plPNh/YWjB3rroV4O74FhJFfIuKFtruMrka86MWASUpJgQ7NTJCCk5JgQB/kYUltagwYlpVlxYAB7J6omz5ba+KprNAj7cd9qpOWtYkB8mD+qK7tHBqKptxDsMviy0EhcWgLfvHew0zY29YrlNcAEgyN+A65NikBwbgucmpKjunNhjOWrpjSdu6WX9oo3tEAgIQGlNvcMLkXVrf86Y3rq+RF05SvGUTkJb2RXgob22Wy9Y5/VDmcle71OvKskaDLaNFwTB4RoAPPvss3jyySetf1dVVaFbt26618dXlcp4hA1xcm176mas2n8ap8tq0S0qGCt2FeFiFftCFBbkh++fG+Ow+Dw5pi9+8+EBjtqzYzRYApUBUA1WZjRYdB62Hr3EvcDK8fKv0pB5XaxVux2Q30Kef2eq7Fcay/bzX+8aaHNvg9ksq38kEhHsj8q6Fl2k+PBAZE/qDwCKxyTSerLsIijVWQ4xt7fuHaybZQLLF623jjRcLdcTX+ztdVegvbZbD1rTEaFX9shjY2Ph5+fnsFty6dIlh10VAAgMDER4eLjNP3cgSpa+ghh0K//P4zBvYj88kJGIeRP74Z37BqvGwQgO8MOMrB74852pmDmyJ7InOY+7IM3DAMtiKvdlfFOfjjD58U1c1tQzsywutFmClYm6FWrxJFiQmpbyxiyRoiXeyZL7ByM61DZWSEJEEJZOG4KD88Zi9cx0vH3vIKyemY7dc2/FuNQEjEtNQM4LYzBndC9EBtvey1JPljpHhZgcYpjw5q0nroxLayyXINxJa5nXXovFc8MNN2Do0KFYsmSJ9VpKSgruvPNOVSVZvWPxSPFk+PeEiCBMGpiAZde+ouUkWaXJoiUOhtw99h4pWWJpbMordhoWIPJaKHn7PHPPlMvuGMjFKAEsjuqW7yy0qZ+cUzutDodElsr0MW/MElfu9WRZPPkA8LltdL3a21rKJQh34o15zbN+e01AWbNmDaZPn46lS5ciIyMDy5Ytw/Lly3HkyBEkJiY6vdedAgrAt+CZ/Azo3TEENY3NOH3Z9qghyM+AJrOAJsnFDoFG3D20G8b2T7BOBq1Bt7RMLvt7hiZGIed0OfcE3ZRXjBc3HMFFieOr+PAgZE9KcXo+3NBkxge7C61h1MemxOOha8HH5HAWn0SpXZFBJmw5ehGny2qRFGPRk9j+8yUm01KCIAjCfbQKAQWwOGp77bXXUFxcjNTUVCxatAgjR45Uvc/dAgrQsuCVVNah9EoDymrrUVxxFZ0jghEVGoDYDgGIjwi2WXxd+QptjV9ora3Ora2+BEEQbY1WI6BoxRMCCkEQBEEQ+sKzfvuGIwmCIAiCIAgJJKAQBEEQBOFzkIBCEARBEITPQQIKQRAEQRA+BwkoBEEQBEH4HCSgEARBEAThc5CAQhAEQRCEz0ECCkEQBEEQPgcJKARBEARB+Bz+3q6AFkTnt1VVVV6uCUEQBEEQrIjrNosT+1YpoFRXVwMAunXr5uWaEARBEATBS3V1NSIiIpymaZWxeMxmMy5cuICwsDAYDL4R7K2qqgrdunXD2bNn21V8oPbabqD9tp3a3b7aDbTftrfXdgPua7sgCKiurkbnzp1hNDrXMmmVOyhGoxFdu3b1djVkCQ8Pb3cTGWi/7Qbab9up3e2P9tr29tpuwD1tV9s5ESElWYIgCIIgfA4SUAiCIAiC8DlIQNGJwMBAvPjiiwgMDPR2VTxKe2030H7bTu1uX+0G2m/b22u7Ad9oe6tUkiUIgiAIom1DOygEQRAEQfgcJKAQBEEQBOFzkIBCEARBEITPQQIKQRAEQRA+BwkoCixZsgTJyckICgrC0KFDsXPnTsW069atw5gxY9CxY0eEh4cjIyMD//nPf2zSrFy5EgaDweHf1atX3d0ULnja/d1338m26dixYzbp1q5di5SUFAQGBiIlJQXr1693dzM0wdP2hx56SLbt/fv3t6ZpDWO+Y8cO3HHHHejcuTMMBgM+//xz1Xu2b9+OoUOHIigoCD169MDSpUsd0rSGMedte1t5znnb3Vaec952t5VnfMGCBRg2bBjCwsLQqVMnTJ48GcePH1e9zxeecxJQZFizZg1mz56N559/Hrm5ucjKysL48eNx5swZ2fQ7duzAmDFjsHHjRuTk5GDUqFG44447kJuba5MuPDwcxcXFNv+CgoI80SQmeNstcvz4cZs29erVy/rb3r17cc8992D69On48ccfMX36dEydOhX79+93d3O44G3722+/bdPms2fPIjo6GnfffbdNOl8f85qaGgwcOBCLFy9mSl9YWIgJEyYgKysLubm5eO655/CHP/wBa9eutaZpLWPO2/a28pzztluktT/nvO1uK8/49u3b8fjjj2Pfvn3YsmULmpqaMHbsWNTU1Cje4zPPuUA4MHz4cOGxxx6zuda3b19h7ty5zHmkpKQI8+fPt/79wQcfCBEREXpV0S3wtvvbb78VAAjl5eWKeU6dOlUYN26czbXbbrtNuPfee12ur564Oubr168XDAaDUFRUZL3WGsZcCgBh/fr1TtM888wzQt++fW2uzZo1S0hPT7f+3VrGXApL2+Vojc+5FJZ2t6XnXETLeLeFZ1wQBOHSpUsCAGH79u2KaXzlOacdFDsaGhqQk5ODsWPH2lwfO3Ys9uzZw5SH2WxGdXU1oqOjba5fuXIFiYmJ6Nq1K26//XaHLy9v4kq7Bw8ejISEBNx666349ttvbX7bu3evQ5633XYbc196Aj3GfMWKFRg9ejQSExNtrvvymGtBaTx/+OEHNDY2Ok3jS2OuB63xOXeF1v6cu0pbecYrKysBwGHeSvGV55wEFDtKS0vR3NyMuLg4m+txcXEoKSlhyuONN95ATU0Npk6dar3Wt29frFy5Ehs2bMDq1asRFBSEzMxMnDhxQtf6a0VLuxMSErBs2TKsXbsW69atQ58+fXDrrbdix44d1jQlJSUu9aUncHXMi4uL8c033+CRRx6xue7rY64FpfFsampCaWmp0zS+NOZ60Bqfcy20lefcFdrKMy4IAp588kmMGDECqampiul85TlvldGMPYHBYLD5WxAEh2tyrF69GtnZ2fjiiy/QqVMn6/X09HSkp6db/87MzMSQIUPwzjvv4G9/+5t+FXcRnnb36dMHffr0sf6dkZGBs2fP4vXXX8fIkSM15elNtNZz5cqViIyMxOTJk22ut5Yx50Wun+yvt5Yx10prf855aGvPuRbayjP+xBNP4PDhw9i1a5dqWl94zmkHxY7Y2Fj4+fk5SIGXLl1ykBbtWbNmDWbMmIF//etfGD16tNO0RqMRw4YN8xlJ25V2S0lPT7dpU3x8vMt5uhtX2i4IAv7xj39g+vTpCAgIcJrW18ZcC0rj6e/vj5iYGKdpfGnMXaE1P+d60Rqfc620lWf897//PTZs2IBvv/0WXbt2dZrWV55zElDsCAgIwNChQ7Flyxab61u2bMGNN96oeN/q1avx0EMPYdWqVZg4caJqOYIg4NChQ0hISHC5znqgtd325Obm2rQpIyPDIc/Nmzdz5eluXGn79u3bcfLkScyYMUO1HF8bcy0ojef1118Pk8nkNI0vjblWWvtzrhet8TnXSmt/xgVBwBNPPIF169Zh27ZtSE5OVr3HZ55z3dRt2xCffvqpYDKZhBUrVgj5+fnC7NmzhdDQUKv29ty5c4Xp06db069atUrw9/cX3n33XaG4uNj6r6KiwpomOztb2LRpk1BQUCDk5uYKDz/8sODv7y/s37/f4+1TgrfdixYtEtavXy/8/PPPQl5enjB37lwBgLB27Vprmt27dwt+fn7CwoULhaNHjwoLFy4U/P39hX379nm8fc7gbbvItGnThBtuuEE2z9Yw5tXV1UJubq6Qm5srABDefPNNITc3Vzh9+rQgCI7tPnXqlBASEiLMmTNHyM/PF1asWCGYTCbh3//+tzVNaxlz3ra3leect91t5TnnbbdIa3/Gf/vb3woRERHCd999ZzNva2trrWl89TknAUWBd999V0hMTBQCAgKEIUOG2JhkPfjgg8JNN91k/fumm24SADj8e/DBB61pZs+eLXTv3l0ICAgQOnbsKIwdO1bYs2ePB1vEBk+7X331VaFnz55CUFCQEBUVJYwYMUL4+uuvHfL87LPPhD59+ggmk0no27evzYvNl+BpuyAIQkVFhRAcHCwsW7ZMNr/WMOaiCanS3JVr93fffScMHjxYCAgIEJKSkoT33nvPId/WMOa8bW8rzzlvu9vKc65lrreFZ1yuzQCEDz74wJrGV59zw7UGEARBEARB+Aykg0IQBEEQhM9BAgpBEARBED4HCSgEQRAEQfgcJKAQBEEQBOFzkIBCEARBEITPQQIKQRAEQRA+BwkoBEEQBEH4HCSgEARBEARhZceOHbjjjjvQuXNnGAwGfP7559x5CIKA119/Hb1790ZgYCC6deuGV155hSsPimZMEARBEISVmpoaDBw4EA8//DB+/etfa8rjj3/8IzZv3ozXX38daWlpqKysRGlpKVce5EmWIAiCIAhZDAYD1q9fj8mTJ1uvNTQ04IUXXsA///lPVFRUIDU1Fa+++ipuvvlmAMDRo0cxYMAA5OXloU+fPprLpiMegiAIgiCYefjhh7F79258+umnOHz4MO6++26MGzcOJ06cAAB8+eWX6NGjB7766iskJycjKSkJjzzyCMrKyrjKIQGFIAiCIAgmCgoKsHr1anz22WfIyspCz5498fTTT2PEiBH44IMPAACnTp3C6dOn8dlnn+Gjjz7CypUrkZOTg7vuuourLNJBIQiCIAiCiYMHD0IQBPTu3dvmen19PWJiYgAAZrMZ9fX1+Oijj6zpVqxYgaFDh+L48ePMxz4koBAEQRAEwYTZbIafnx9ycnLg5+dn81uHDh0AAAkJCfD397cRYvr16wcAOHPmDAkoBEEQBEHoy+DBg9Hc3IxLly4hKytLNk1mZiaamppQUFCAnj17AgB+/vlnAEBiYiJzWWTFQxAEQRCElStXruDkyZMALALJm2++iVGjRiE6Ohrdu3fHtGnTsHv3brzxxhsYPHgwSktLsW3bNqSlpWHChAkwm80YNmwYOnTogLfeegtmsxmPP/44wsPDsXnzZuZ6kIBCEARBEISV7777DqNGjXK4/uCDD2LlypVobGzEyy+/jI8++gjnz59HTEwMMjIyMH/+fKSlpQEALly4gN///vfYvHkzQkNDMX78eLzxxhuIjo5mrgcJKARBEARB+BxkZkwQBEEQhM9BAgpBEARBED4HCSgEQRAEQfgcJKAQBEEQBOFzkIBCEARBEITPQQIKQRAEQRA+BwkoBEEQBEH4HCSgEARBEAThc5CAQhAEQRCEz0ECCkEQBEEQPgcJKARBEARB+BwkoBAEQRAE4XP8P/k8sSqbWR+CAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "streudiagramm(prices, data[\"n_citi\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### mögliche Anpassungen:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# data = data.drop([\"n_citi\", \"street\", \"citi\"], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bilder einlesen & bearbeiten" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "normalize = transforms.Normalize(\n", + " mean = [0.485, 0.456, 0.406],\n", + " std = [0.229, 0.224, 0.225]\n", + ")\n", + "\n", + "transform = transforms.Compose([\n", + " transforms.Resize(350),\n", + " transforms.CenterCrop(350),\n", + " #transforms.Grayscale(num_output_channels=3),\n", + " transforms.ToTensor(),\n", + " #normalize\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10831, 5)\n", + "(10831,)\n", + "(4643, 5)\n", + "(4643,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(data.drop(['price', 'citi', 'street'], axis=1), data['price'], test_size=0.30, random_state=42)\n", + "print(x_train.shape)\n", + "print(y_train.shape)\n", + "print(x_test.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10831/10831 [01:33<00:00, 116.17it/s]\n" + ] + } + ], + "source": [ + "train_imgs = []\n", + "train_prices = []\n", + "train_features = []\n", + "\n", + "for i in tqdm(x_train.image_id): \n", + " price = int(data[data[\"image_id\"] == int(i)][\"price\"])\n", + " x = [float(x_train[x_train[\"image_id\"] == int(i)][\"n_citi\"]), float(x_train[x_train[\"image_id\"] == int(i)][\"bed\"]), float(x_train[x_train[\"image_id\"] == int(i)][\"bath\"]), float(x_train[x_train[\"image_id\"] == int(i)][\"sqft\"])]\n", + " img = Image.open(pics_path + str(i) + \".jpg\")\n", + " img_tensor = transform(img)\n", + " img_tensor = img_tensor.unsqueeze(0)\n", + " \n", + " train_imgs.append(img_tensor)\n", + " train_prices.append(torch.tensor(price))\n", + " train_features.append(torch.tensor(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4643/4643 [00:26<00:00, 172.51it/s]\n" + ] + } + ], + "source": [ + "test_imgs = []\n", + "test_prices = []\n", + "test_features = []\n", + "\n", + "for i in tqdm(x_test.image_id): \n", + " price = int(data[data[\"image_id\"] == int(i)][\"price\"])\n", + " x = [float(x_test[x_test[\"image_id\"] == int(i)][\"n_citi\"]), float(x_test[x_test[\"image_id\"] == int(i)][\"bed\"]), float(x_test[x_test[\"image_id\"] == int(i)][\"bath\"]), float(x_test[x_test[\"image_id\"] == int(i)][\"sqft\"])]\n", + " img = Image.open(pics_path + str(i) + \".jpg\")\n", + " img_tensor = transform(img)\n", + " img_tensor = img_tensor.unsqueeze(0)\n", + " \n", + " test_imgs.append(img_tensor)\n", + " test_prices.append(torch.tensor(price))\n", + " test_features.append(torch.tensor(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15474/15474 [01:26<00:00, 179.76it/s]\n" + ] + } + ], + "source": [ + "train_pics_list = []\n", + "train_data = []\n", + "prices = []\n", + "tab_features = []\n", + "for file in tqdm(listdir(pics_path)):\n", + " img = Image.open(pics_path + file)\n", + " img_tensor = transform(img)\n", + " #img_tensor = img_tensor.unsqueeze(0)\n", + "\n", + " train_pics_list.append(img_tensor)\n", + " img_id = file[:-4]\n", + "\n", + " prices.append(price)\n", + " tab_features.append(x)\n", + "\n", + " if len(train_pics_list):\n", + " train_data.append(((torch.stack(train_pics_list)), prices, tab_features))\n", + " prices = []\n", + " train_pics_list = []\n", + " tab_features = []\n", + "#print(train_data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Netz(\n", + " (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n", + " (conv2): Conv2d(6, 9, kernel_size=(5, 5), stride=(1, 1))\n", + " (fc1): Linear(in_features=1, out_features=256, bias=True)\n", + " (fc2): Linear(in_features=256, out_features=128, bias=True)\n", + " (fc3): Linear(in_features=128, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "class Netz(nn.Module):\n", + " def __init__(self, input_shape):\n", + " super(Netz, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 6, kernel_size=5)\n", + " self.conv2 = nn.Conv2d(6, 9, kernel_size=5)\n", + " self.fc1 = nn.Linear(input_shape, 256)\n", + " self.fc2 = nn.Linear(256, 128)\n", + " self.fc3 = nn.Linear(128, 1)\n", + "\n", + " def forward(self, x, img):\n", + " img = self.conv1(img)\n", + " img = F.max_pool2d(img, 3)\n", + " img = F.relu(img)\n", + "\n", + " img = self.conv2(img)\n", + " img = F.max_pool2d(img, 3)\n", + " img = F.relu(img)\n", + "\n", + " x = self.fc1(x)\n", + " x = F.relu(self.fc1(x))\n", + " x = self.fc2(x)\n", + " x = F.relu(self.fc2(x))\n", + "\n", + " combined = torch.cat((img, x), 1)\n", + " price = self.fc3(combined)\n", + " return price\n", + "\n", + "model = Netz()\n", + "criterion = nn.MSELoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.01)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(torch.utils.data.Dataset):\n", + " def __init__(self, x, img, y):\n", + " self.x = torch.tensor(x[['n_citi', 'bed', 'bath', 'sqft']].values).float()\n", + " self.img = img\n", + " self.y = torch.tensor(y.values).float()\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + "\n", + " \n", + " def __getitem__(self, idx):\n", + " x = self.x[idx]\n", + " y = self.y[idx]\n", + " img = self.img[idx]\n", + " img = torchvision.transforms.functional.to_tensor(img.astype(np.uint8).reshape((64, 64, 3)))\n", + " return {'x': x, 'y': y, 'img': img}\n", + " \n", + "BATCH_SIZE = 256 \n", + "train_dataset = MyDataset(x_train, x_train_images, y_train)\n", + "dataLoader_train = torch.utils.data.DataLoader(train_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True)\n", + "\n", + "test_dataset = MyDataset(x_test,x_test_images, y_test)\n", + "dataLoader_test = torch.utils.data.DataLoader(test_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (conv): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): ReLU()\n", + " (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (4): ReLU()\n", + " (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (flatten): Sequential(\n", + " (0): AdaptiveMaxPool2d(output_size=1)\n", + " (1): Flatten(start_dim=1, end_dim=-1)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=4, out_features=256, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=256, out_features=128, bias=True)\n", + " (3): ReLU()\n", + " )\n", + " (final_fc): Sequential(\n", + " (0): Linear(in_features=192, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "class Model(torch.nn.Module):\n", + " \n", + " def __init__(self, input_shape):\n", + " super().__init__()\n", + " \n", + " self.conv = torch.nn.Sequential(\n", + " torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3)),\n", + " torch.nn.ReLU(),\n", + " torch.nn.BatchNorm2d(32),\n", + " torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5, 5)),\n", + " torch.nn.ReLU(),\n", + " torch.nn.BatchNorm2d(64),\n", + " )\n", + " \n", + " self.flatten = torch.nn.Sequential(torch.nn.AdaptiveMaxPool2d(1), torch.nn.Flatten())\n", + " \n", + " self.fc = torch.nn.Sequential(\n", + " torch.nn.Linear(input_shape, 256),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(256, 128),\n", + " torch.nn.ReLU(),\n", + " )\n", + " \n", + " self.final_fc = torch.nn.Sequential(\n", + " torch.nn.Linear(128+64, 512),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(512, 1)\n", + " )\n", + " \n", + " def forward(self, x, img):\n", + " img = self.conv(img)\n", + " img = self.flatten(img) \n", + " x = self.fc(x)\n", + " #combined = torch.cat((img, x), dim=1)\n", + " combined = torch.cat((img.view(img.size(0), -1), x.view(x.size(0), -1)), dim=1)\n", + " print(x.size(), img.size())\n", + " price = self.final_fc(combined)\n", + " return price\n", + " \n", + "model = Model(4)\n", + "print(model)\n", + "criterion = torch.nn.MSELoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "started!\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Sizes of tensors must match except in dimension 1. Expected size 1 but got size 128 for tensor number 1 in the list.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\nikla\\workspace\\Machine Learning DHBW\\Bonusaufgabe 2\\Advance-Machine-Learning\\Bonus\\Bonus 3\\Bonus3.ipynb Zelle 29\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m y \u001b[39m=\u001b[39m train_prices[i]\n\u001b[0;32m 11\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m---> 12\u001b[0m outputs \u001b[39m=\u001b[39m model(x \u001b[39m=\u001b[39;49m x, img \u001b[39m=\u001b[39;49m img)\n\u001b[0;32m 13\u001b[0m loss \u001b[39m=\u001b[39m criterion(outputs[:,\u001b[39m0\u001b[39m], y)\n\u001b[0;32m 14\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "\u001b[1;32mc:\\Users\\nikla\\workspace\\Machine Learning DHBW\\Bonusaufgabe 2\\Advance-Machine-Learning\\Bonus\\Bonus 3\\Bonus3.ipynb Zelle 29\u001b[0m in \u001b[0;36mModel.forward\u001b[1;34m(self, x, img)\u001b[0m\n\u001b[0;32m 33\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc(x)\n\u001b[0;32m 34\u001b[0m \u001b[39m#combined = torch.cat((img, x), dim=1)\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m combined \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mcat((img\u001b[39m.\u001b[39;49mview(img\u001b[39m.\u001b[39;49msize(\u001b[39m0\u001b[39;49m), \u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m), x\u001b[39m.\u001b[39;49mview(x\u001b[39m.\u001b[39;49msize(\u001b[39m0\u001b[39;49m), \u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)), dim\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[0;32m 36\u001b[0m \u001b[39mprint\u001b[39m(x\u001b[39m.\u001b[39msize(), img\u001b[39m.\u001b[39msize())\n\u001b[0;32m 37\u001b[0m price \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinal_fc(combined)\n", + "\u001b[1;31mRuntimeError\u001b[0m: Sizes of tensors must match except in dimension 1. Expected size 1 but got size 128 for tensor number 1 in the list." + ] + } + ], + "source": [ + "n_epochs = 10\n", + "print('started!')\n", + "for epoch in range(n_epochs):\n", + " train_batch_loss = 0\n", + " model.train()\n", + " for i in range(len(train_imgs)):\n", + " x = train_features[i]\n", + " img = train_imgs[i]\n", + " y = train_prices[i]\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_batch_loss += loss.item()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " test_batch_loss = 0\n", + " model.eval()\n", + "\n", + " with torch.no_grad():\n", + " for i in range(len(test_imgs)):\n", + " x = test_features[i]\n", + " img = test_imgs[i]\n", + " y = test_prices[i]\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " test_batch_loss += loss.item()\n", + " \n", + "\n", + " print('epoch {}/{} finished with train loss: {} and test loss: {}'.format(epoch+1, n_epochs, train_batch_loss / len(dataLoader_train), test_batch_loss / len(dataLoader_test)))\n", + " \n", + "torch.save(model.state_dict(), './model_two_input')" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "expected 4D input (got 3D input)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\nikla\\workspace\\Machine Learning DHBW\\Bonusaufgabe 2\\Advance-Machine-Learning\\Bonus\\Bonus 3\\Bonus3.ipynb Zelle 30\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m data1 \u001b[39m=\u001b[39m data[i]\n\u001b[0;32m 10\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m---> 11\u001b[0m outputs \u001b[39m=\u001b[39m model(x \u001b[39m=\u001b[39;49m x1, img \u001b[39m=\u001b[39;49m data1)\n\u001b[0;32m 12\u001b[0m loss \u001b[39m=\u001b[39m criterion(outputs[:,\u001b[39m0\u001b[39m], price1)\n\u001b[0;32m 13\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "\u001b[1;32mc:\\Users\\nikla\\workspace\\Machine Learning DHBW\\Bonusaufgabe 2\\Advance-Machine-Learning\\Bonus\\Bonus 3\\Bonus3.ipynb Zelle 30\u001b[0m in \u001b[0;36mModel.forward\u001b[1;34m(self, x, img)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x, img):\n\u001b[1;32m---> 31\u001b[0m img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv(img)\n\u001b[0;32m 32\u001b[0m img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflatten(img) \n\u001b[0;32m 33\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc(x)\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\container.py:204\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 202\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[0;32m 203\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m--> 204\u001b[0m \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m 205\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\batchnorm.py:138\u001b[0m, in \u001b[0;36m_BatchNorm.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 138\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_check_input_dim(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m 140\u001b[0m \u001b[39m# exponential_average_factor is set to self.momentum\u001b[39;00m\n\u001b[0;32m 141\u001b[0m \u001b[39m# (when it is available) only so that it gets updated\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[39m# in ONNX graph when this node is exported to ONNX.\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmomentum \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\torch\\nn\\modules\\batchnorm.py:410\u001b[0m, in \u001b[0;36mBatchNorm2d._check_input_dim\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 408\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_input_dim\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[0;32m 409\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39minput\u001b[39m\u001b[39m.\u001b[39mdim() \u001b[39m!=\u001b[39m \u001b[39m4\u001b[39m:\n\u001b[1;32m--> 410\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mexpected 4D input (got \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39mD input)\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39minput\u001b[39m\u001b[39m.\u001b[39mdim()))\n", + "\u001b[1;31mValueError\u001b[0m: expected 4D input (got 3D input)" + ] + } + ], + "source": [ + "for epoch in range(30):\n", + " model.train()\n", + " batch_id = 0\n", + " train_batch_loss = 0\n", + " for data, price, x in train_data:\n", + " for i in range(64):\n", + " price1 = torch.Tensor(prices[i])\n", + " x1 = torch.Tensor(x[i])\n", + " data1 = data[i]\n", + " optimizer.zero_grad()\n", + " outputs = model(x = x1, img = data1)\n", + " loss = criterion(outputs[:,0], price1)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_batch_loss += loss.item()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " print(\"Train Epoch: {} [{}/{} ({:,0f}%)]\\tLoss: {:.6f}\".format(epoch, batch_id*len(data), len(train_data), 100. * batch_id / len(train_data, loss.data[0])))\n", + "\n", + " batch_id += 1\n", + " test_batch_loss = 0\n", + " model.eval()\n", + "\n", + " with torch.no_grade():\n", + " for data, price, x in train_data:\n", + " price = torch.Tensor(prices)\n", + " x = torch.Tensor(x)\n", + " optimizer.zero_grad()\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], price)\n", + " test_batch_loss += loss.item()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(649900)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_prices[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('NLP')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bonus/Bonus 3/Bonus_3_Cramer.ipynb b/Bonus/Bonus 3/Bonus_3_Cramer.ipynb new file mode 100644 index 0000000..95c4da7 --- /dev/null +++ b/Bonus/Bonus 3/Bonus_3_Cramer.ipynb @@ -0,0 +1,1860 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'skimage'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\nikla\\workspace\\Machine Learning DHBW\\Bonusaufgabe 2\\Advance-Machine-Learning\\Bonus\\Bonus 3\\Bonus_3_Cramer.ipynb Cell 1\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdata\u001b[39;00m \u001b[39mimport\u001b[39;00m Dataset, DataLoader\n\u001b[0;32m 8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m \u001b[39mimport\u001b[39;00m transforms, utils\n\u001b[1;32m----> 9\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mskimage\u001b[39;00m \u001b[39mimport\u001b[39;00m io, transform\n\u001b[0;32m 10\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m \u001b[39mimport\u001b[39;00m transforms \n\u001b[0;32m 12\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m \n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'skimage'" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.utils.data\n", + "import torch.optim as optim\n", + "\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torchvision import transforms, utils\n", + "from skimage import io, transform\n", + "from torchvision import transforms\n", + "\n", + "import pandas as pd \n", + "import numpy as np\n", + "import cv2\n", + "\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], + "source": [ + "path = \"archive/\"\n", + "data_path = path + \"socal2.csv\"\n", + "pics_path = path + \"socal2/socal_pics/\"\n", + "df = pd.read_csv(data_path)\n", + "prices = df[\"price\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idstreetcitin_citibedbathsqftprice
001317 Van Buren AvenueSalton City, CA31732.01560201900
11124 C Street WBrawley, CA4832.0713228500
222304 Clark RoadImperial, CA15231.0800273950
33755 Brawley AvenueBrawley, CA4831.01082350000
442207 R Carrillo CourtCalexico, CA5543.02547385100
\n", + "
" + ], + "text/plain": [ + " image_id street citi n_citi bed bath sqft \\\n", + "0 0 1317 Van Buren Avenue Salton City, CA 317 3 2.0 1560 \n", + "1 1 124 C Street W Brawley, CA 48 3 2.0 713 \n", + "2 2 2304 Clark Road Imperial, CA 152 3 1.0 800 \n", + "3 3 755 Brawley Avenue Brawley, CA 48 3 1.0 1082 \n", + "4 4 2207 R Carrillo Court Calexico, CA 55 4 3.0 2547 \n", + "\n", + " price \n", + "0 201900 \n", + "1 228500 \n", + "2 273950 \n", + "3 350000 \n", + "4 385100 " + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of images: 15474\n" + ] + } + ], + "source": [ + "X_house_images=np.zeros((15474,64,64,3),dtype='uint32')\n", + "cnt=0\n", + "for i in range(15474):\n", + "\n", + " sample=cv2.imread(pics_path+'/'+str(i)+'.jpg')\n", + " imgs=cv2.resize(sample,(64,64))\n", + " \n", + " X_house_images[cnt]=imgs\n", + " cnt+=1\n", + "\n", + "print(\"No. of images: \",cnt)" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "split = train_test_split(df, X_house_images, test_size=0.25, random_state=42)\n", + "(Xatt_train,Xatt_test,Ximage_train,Ximage_test) = split\n", + "\n", + "y_train , y_test = Xatt_train['price'].values , Xatt_test['price'].values\n", + "\n", + "X1_train=Xatt_train[['n_citi','bed','bath','sqft']].values\n", + "X2_train=Ximage_train\n", + "X1_test=Xatt_test[['n_citi','bed','bath','sqft']].values\n", + "X2_test=Ximage_test" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.110e+02, 3.000e+00, 2.000e+00, 2.502e+03],\n", + " [3.070e+02, 4.000e+00, 3.000e+00, 1.895e+03],\n", + " [7.800e+01, 4.000e+00, 3.000e+00, 1.573e+03],\n", + " ...,\n", + " [8.200e+01, 4.000e+00, 3.000e+00, 1.550e+03],\n", + " [1.930e+02, 3.000e+00, 2.000e+00, 1.534e+03],\n", + " [2.650e+02, 3.000e+00, 2.100e+00, 2.580e+03]])" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X1_train" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(torch.utils.data.Dataset):\n", + " def __init__(self, x, img, y):\n", + " self.x = torch.tensor(x).float()\n", + " self.img = img\n", + " self.y = torch.tensor(y).float()\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + "\n", + " \n", + " def __getitem__(self, idx):\n", + " x = self.x[idx]\n", + " y = self.y[idx]\n", + " img = self.img[idx]\n", + " img = transforms.functional.to_tensor(img.astype(np.uint8).reshape((64, 64, 3)))\n", + " return {'x': x, 'y': y, 'img': img}\n", + " \n", + "BATCH_SIZE = 256 \n", + "train_dataset = MyDataset(X1_train,X2_train, y_train)\n", + "dataLoader_train = torch.utils.data.DataLoader(train_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True)\n", + "\n", + "test_dataset = MyDataset(X1_test,X2_test, y_test)\n", + "dataLoader_test = torch.utils.data.DataLoader(test_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataLoader_train\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idn_citibedbathsqftprice
0031732.01560201900
114832.0713228500
2215231.0800273950
334831.01082350000
445543.02547385100
\n", + "
" + ], + "text/plain": [ + " image_id n_citi bed bath sqft price\n", + "0 0 317 3 2.0 1560 201900\n", + "1 1 48 3 2.0 713 228500\n", + "2 2 152 3 1.0 800 273950\n", + "3 3 48 3 1.0 1082 350000\n", + "4 4 55 4 3.0 2547 385100" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del df['street']\n", + "del df['citi']\n", + "#del df['n_citi']\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (conv): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): ReLU()\n", + " (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (4): ReLU()\n", + " (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (flatten): Sequential(\n", + " (0): AdaptiveMaxPool2d(output_size=1)\n", + " (1): Flatten(start_dim=1, end_dim=-1)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=4, out_features=256, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=256, out_features=128, bias=True)\n", + " (3): ReLU()\n", + " )\n", + " (final_fc): Sequential(\n", + " (0): Linear(in_features=192, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "class Model(torch.nn.Module):\n", + " \n", + " def __init__(self, input_shape):\n", + " super().__init__()\n", + " \n", + " self.conv = torch.nn.Sequential(\n", + " torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3)),\n", + " torch.nn.ReLU(),\n", + " torch.nn.BatchNorm2d(32),\n", + " torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5, 5)),\n", + " torch.nn.ReLU(),\n", + " torch.nn.BatchNorm2d(64),\n", + " )\n", + " \n", + " self.flatten = torch.nn.Sequential(torch.nn.AdaptiveMaxPool2d(1), torch.nn.Flatten())\n", + " \n", + " self.fc = torch.nn.Sequential(\n", + " torch.nn.Linear(input_shape, 256),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(256, 128),\n", + " torch.nn.ReLU(),\n", + " )\n", + " \n", + " self.final_fc = torch.nn.Sequential(\n", + " torch.nn.Linear(128+64, 512),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(512, 1)\n", + " )\n", + " \n", + " def forward(self, x, img):\n", + " img = self.conv(img)\n", + " img = self.flatten(img) \n", + " x = self.fc(x)\n", + " combined = torch.cat((img, x), dim=1)\n", + " #combined = torch.cat((img.view(img.size(0), -1), x.view(x.size(0), -1)), dim=1)\n", + " #print(x.size(), img.size())\n", + " price = self.final_fc(combined)\n", + " return price\n", + " \n", + "model = Model(4)\n", + "print(model)\n", + "criterion = torch.nn.MSELoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "started!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 10%|█ | 1/10 [02:39<23:58, 159.80s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 1/10 finished with train loss: 525078970724.1739 and test loss: 207061161472.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|██ | 2/10 [05:23<21:36, 162.05s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 2/10 finished with train loss: 110927604869.56522 and test loss: 107115519744.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 30%|███ | 3/10 [08:00<18:38, 159.81s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 3/10 finished with train loss: 99138941996.52174 and test loss: 101747551488.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|████ | 4/10 [10:44<16:09, 161.63s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 4/10 finished with train loss: 98605530423.65218 and test loss: 99086805504.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|█████ | 5/10 [13:26<13:27, 161.45s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 5/10 finished with train loss: 96771056506.43478 and test loss: 100011087360.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 6/10 [16:08<10:47, 161.77s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 6/10 finished with train loss: 95817108613.56522 and test loss: 101267421184.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 70%|███████ | 7/10 [18:53<08:08, 162.91s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 7/10 finished with train loss: 95464652800.0 and test loss: 97971708160.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 80%|████████ | 8/10 [21:33<05:24, 162.01s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 8/10 finished with train loss: 95159813787.82608 and test loss: 99372399104.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 90%|█████████ | 9/10 [24:17<02:42, 162.56s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 9/10 finished with train loss: 93755081505.39131 and test loss: 99780259328.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [26:53<00:00, 161.37s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 10/10 finished with train loss: 93853830544.69565 and test loss: 99534354944.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "n_epochs = 10\n", + "print('started!')\n", + "for epoch in tqdm(range(n_epochs)):\n", + " train_batch_loss = 0\n", + " model.train()\n", + " for step, batch in enumerate(dataLoader_train):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_batch_loss += loss.item()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " test_batch_loss = 0\n", + " model.eval()\n", + "\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader_test):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " test_batch_loss += loss.item() \n", + "\n", + " print('epoch {}/{} finished with train loss: {} and test loss: {}'.format(epoch+1, n_epochs, train_batch_loss / len(dataLoader_train), test_batch_loss / len(dataLoader_test)))\n", + " \n", + "torch.save(model.state_dict(), './model_two_input.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Results :\n", + "\n", + "RSS : 380696832934998.3\n", + "RSE : 313763.5903382174\n", + "TSS : 560633939775361.56\n", + "R Squared : 0.3209529321618695\n", + "MSE : 98396700164.12643\n", + "MAE : 229841.06415578962\n", + "Accuracy with 10% : 0.18350995089170327\n", + "\n", + "Train Results :\n", + "\n", + "RSS : 1075644050759336.2\n", + "RSE : 304473.250464067\n", + "TSS : 1638210861992667.8\n", + "R Squared : 0.3434031749423533\n", + "MSE : 92687983693.1802\n", + "MAE : 224996.14421450882\n", + "Accuracy with 10% : 0.1912968548039638\n" + ] + } + ], + "source": [ + "def reg_report(true, pred, name='Test'):\n", + " print(\"\\n{} Results :\\n\".format(name))\n", + " print(\"RSS :\",sum((pred-true)**2))\n", + " print(\"RSE :\",math.sqrt(sum((pred-true)**2)*(1/(len(pred)-2))))\n", + " print(\"TSS :\",sum((true-true.mean())**2))\n", + " print(\"R Squared :\",1-(sum((pred-true)**2)/sum((true-true.mean())**2)))\n", + " print(\"MSE :\",((pred-true)**2).mean())\n", + " print('MAE :',(abs(pred-true)).mean())\n", + " print('Accuracy with 10% :', ((pred<=true*1.1) & (true*0.9<=pred)).mean())\n", + " \n", + "\n", + "def eval_report(y_train, pred_train,y_test, pred_test):\n", + " reg_report(y_train, pred_train, name='Train')\n", + " reg_report(y_test, pred_test, name='Test')\n", + " \n", + "def res(dataLoader, name = 'Test'): \n", + " trues = []\n", + " preds = []\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + " outputs = model(x = x, img = img)\n", + "\n", + " trues = trues + y.tolist()\n", + " preds = preds + outputs[:,0].tolist()\n", + "\n", + "\n", + " reg_report(true = np.array(trues), pred = np.array(preds), name=name)\n", + "\n", + "res(dataLoader_test, name = 'Test')\n", + "res(dataLoader_train, name = 'Train')" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (conv): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): ReLU()\n", + " (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (4): ReLU()\n", + " (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (flatten): Sequential(\n", + " (0): AdaptiveMaxPool2d(output_size=1)\n", + " (1): Flatten(start_dim=1, end_dim=-1)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=4, out_features=256, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=256, out_features=128, bias=True)\n", + " (3): ReLU()\n", + " )\n", + " (final_fc): Sequential(\n", + " (0): Linear(in_features=192, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "class Netz(nn.Module):\n", + " def __init__(self, input_shape):\n", + " super(Netz, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 6, kernel_size=5)\n", + " self.conv2 = nn.Conv2d(6, 9, kernel_size=5)\n", + " #self.flatten=nn.Sequential(nn.AdaptiveMaxPool2d(1), nn.Flatten())\n", + " self.fc1 = nn.Linear(input_shape, 256)\n", + " self.fc2 = nn.Linear(256, 128)\n", + " self.fc3 = nn.Linear(137, 1)\n", + "\n", + "\n", + " def forward(self, x, img):\n", + " img = self.conv1(img)\n", + " img = F.max_pool2d(img, 3)\n", + " img = F.relu(img)\n", + "\n", + " img = self.conv2(img)\n", + " img = F.max_pool2d(img, 3)\n", + " img = F.relu(img)\n", + " #\n", + " # img=self.flatten(img)\n", + "\n", + " x = self.fc1(x)\n", + " x = F.relu(x)\n", + " x = self.fc2(x)\n", + " x = F.relu(x)\n", + " #rint(x.shape,img.shape)\n", + " #combined = torch.cat((img.view(img.size(0), -1), x.view(x.size(0), -1)), dim=1)\n", + " combined = torch.cat((x, img), 1)\n", + " price = self.fc3(combined)\n", + " return price\n", + " #pass\n", + "\n", + "model_2 = Netz(4)\n", + "criterion = nn.MSELoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.01)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "started!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/10 [00:00 14\u001b[0m outputs \u001b[39m=\u001b[39m model_2(x \u001b[39m=\u001b[39;49m x, img \u001b[39m=\u001b[39;49m img)\n\u001b[1;32m 15\u001b[0m loss \u001b[39m=\u001b[39m criterion(outputs[:,\u001b[39m0\u001b[39m], y)\n\u001b[1;32m 16\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn [118], line 29\u001b[0m, in \u001b[0;36mNetz.forward\u001b[0;34m(self, x, img)\u001b[0m\n\u001b[1;32m 26\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(x)\n\u001b[1;32m 27\u001b[0m \u001b[39m#rint(x.shape,img.shape)\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[39m#combined = torch.cat((img.view(img.size(0), -1), x.view(x.size(0), -1)), dim=1)\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m combined \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mcat((x, img), \u001b[39m1\u001b[39;49m)\n\u001b[1;32m 30\u001b[0m price \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc3(combined)\n\u001b[1;32m 31\u001b[0m \u001b[39mreturn\u001b[39;00m price\n", + "\u001b[0;31mRuntimeError\u001b[0m: Tensors must have same number of dimensions: got 2 and 4" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "n_epochs = 10\n", + "print('started!')\n", + "for epoch in tqdm(range(n_epochs)):\n", + " train_batch_loss = 0\n", + " model_2.train()\n", + " for step, batch in enumerate(dataLoader_train):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model_2(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_batch_loss += loss.item()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " test_batch_loss = 0\n", + " model_2.eval()\n", + "\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader_test):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + " outputs = model_2(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " test_batch_loss += loss.item() \n", + "\n", + " print('epoch {}/{} finished with train loss: {} and test loss: {}'.format(epoch+1, n_epochs, train_batch_loss / len(dataLoader_train), test_batch_loss / len(dataLoader_test)))\n", + " \n", + "torch.save(model_2.state_dict(), './mmodel_2odel_two_input.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Results :\n", + "\n", + "RSS : 2488957288173446.0\n", + "RSE : 802272.0013507858\n", + "TSS : 560633939775360.8\n", + "R Squared : -3.439540868986171\n", + "MSE : 643307647498.9537\n", + "MAE : 705963.4366304198\n", + "Accuracy with 10% : 0.0\n", + "\n", + "Train Results :\n", + "\n", + "RSS : 7362237494844666.0\n", + "RSE : 796562.3317061164\n", + "TSS : 1638210861992678.0\n", + "R Squared : -3.4940719571895817\n", + "MSE : 634402196884.5054\n", + "MAE : 702295.735096819\n", + "Accuracy with 10% : 0.0\n" + ] + } + ], + "source": [ + "def reg_report(true, pred, name='Test'):\n", + " print(\"\\n{} Results :\\n\".format(name))\n", + " print(\"RSS :\",sum((pred-true)**2))\n", + " print(\"RSE :\",math.sqrt(sum((pred-true)**2)*(1/(len(pred)-2))))\n", + " print(\"TSS :\",sum((true-true.mean())**2))\n", + " print(\"R Squared :\",1-(sum((pred-true)**2)/sum((true-true.mean())**2)))\n", + " print(\"MSE :\",((pred-true)**2).mean())\n", + " print('MAE :',(abs(pred-true)).mean())\n", + " print('Accuracy with 10% :', ((pred<=true*1.1) & (true*0.9<=pred)).mean())\n", + " \n", + "\n", + "def eval_report(y_train, pred_train,y_test, pred_test):\n", + " reg_report(y_train, pred_train, name='Train')\n", + " reg_report(y_test, pred_test, name='Test')\n", + " \n", + "def res(dataLoader, name = 'Test'): \n", + " trues = []\n", + " preds = []\n", + " model_2.eval()\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + " outputs = model_2(x = x, img = img)\n", + "\n", + " trues = trues + y.tolist()\n", + " preds = preds + outputs[:,0].tolist()\n", + "\n", + "\n", + " reg_report(true = np.array(trues), pred = np.array(preds), name=name)\n", + "\n", + "res(dataLoader_test, name = 'Test')\n", + "res(dataLoader_train, name = 'Train')" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "class TwoInputsNet(nn.Module):\n", + " def __init__(self):\n", + " super(TwoInputsNet, self).__init__()\n", + " self.conv = nn.Conv2d(3,8,kernel_size=3) \n", + " self.conv1 = nn.Conv2d(8,8,kernel_size=3)\n", + " self.conv2 = nn.Conv2d(8,8,kernel_size=3) \n", + " self.fc1 = nn.Linear(3,3)\n", + " self.fc2 = nn.Linear(26915,1024)\n", + " self.fc3 = nn.Linear(1024,32) \n", + " self.fc4 = nn.Linear(32,1) \n", + "\n", + " def forward(self, input1, input2):\n", + " c = self.conv(input1)\n", + " c = self.conv1(c)\n", + " c = F.relu(c)\n", + " c = self.conv2(c)\n", + " c = F.relu(c)\n", + " f = self.fc1(input2)\n", + " # now we can reshape `c` and `f` to 2D and concat them\n", + " #combined = torch.cat((c.view(c.size(0), -1), f.view(f.size(0), -1)), dim=1)\n", + " combined = torch.cat((img, x), dim=1)\n", + " out = self.fc2(combined)\n", + " out = F.relu(out)\n", + " out = self.fc3(out)\n", + " out = F.relu(out)\n", + " out = self.fc4(out)\n", + "\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TwoInputsNet(\n", + " (conv): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv2): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1))\n", + " (fc1): Linear(in_features=3, out_features=3, bias=True)\n", + " (fc2): Linear(in_features=26915, out_features=1024, bias=True)\n", + " (fc3): Linear(in_features=1024, out_features=32, bias=True)\n", + " (fc4): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "network = TwoInputsNet()\n", + "print(network)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "momentum = 0.9\n", + "n_epochs = 10\n", + "\n", + "optimizer = optim.SGD(network.parameters(), lr=learning_rate,momentum=momentum)\n", + "\n", + "loss_func = torch.nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "def train(dataloader,epoch):\n", + " batch_idx = 0\n", + " for items in dataloader:\n", + " image = torch.FloatTensor(batch['img'])\n", + " features = torch.FloatTensor(items['x'])\n", + " price = torch.FloatTensor(items['y'])\n", + "\n", + " if torch.cuda.is_available():\n", + " image = image.cuda()\n", + " features = features.cuda()\n", + " price = price.cuda()\n", + " \n", + " output = network(image,features)\n", + " output = output.reshape(4)\n", + " loss = loss_func(output, price)\n", + " optimizer.zero_grad() \n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " if batch_idx % 4 == 0: #every 25 * batchsize sample we print results\n", + " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", + " epoch+1, batch_idx * image.shape[0], len(dataloader.dataset),\n", + " 100. * batch_idx / len(dataloader), loss.item()))\n", + " \n", + " batch_idx = batch_idx + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "mat1 and mat2 shapes cannot be multiplied (1x4 and 3x3)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [134], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n_epochs):\n\u001b[1;32m 2\u001b[0m \u001b[39m# train \u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m train(train_dataset,epoch)\n", + "Cell \u001b[0;32mIn [133], line 13\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(dataloader, epoch)\u001b[0m\n\u001b[1;32m 10\u001b[0m features \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mcuda()\n\u001b[1;32m 11\u001b[0m price \u001b[39m=\u001b[39m price\u001b[39m.\u001b[39mcuda()\n\u001b[0;32m---> 13\u001b[0m output \u001b[39m=\u001b[39m network(image,features)\n\u001b[1;32m 14\u001b[0m output \u001b[39m=\u001b[39m output\u001b[39m.\u001b[39mreshape(\u001b[39m4\u001b[39m)\n\u001b[1;32m 15\u001b[0m loss \u001b[39m=\u001b[39m loss_func(output, price)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn [130], line 18\u001b[0m, in \u001b[0;36mTwoInputsNet.forward\u001b[0;34m(self, input1, input2)\u001b[0m\n\u001b[1;32m 16\u001b[0m c \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(c)\n\u001b[1;32m 17\u001b[0m c \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(c)\n\u001b[0;32m---> 18\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc1(input2)\n\u001b[1;32m 19\u001b[0m \u001b[39m# now we can reshape `c` and `f` to 2D and concat them\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[39m#combined = torch.cat((c.view(c.size(0), -1), f.view(f.size(0), -1)), dim=1)\u001b[39;00m\n\u001b[1;32m 21\u001b[0m combined \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat((img, x), dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m--> 114\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n", + "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (1x4 and 3x3)" + ] + } + ], + "source": [ + "for epoch in range(n_epochs):\n", + " # train \n", + " train(train_dataset,epoch)" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idn_citibedbathsqftprice
000.23430032.00.9263820.996177
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220.63285031.00.9700930.956260
330.88405831.00.9538740.914127
440.86715043.00.8696150.894681
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" + ], + "text/plain": [ + " image_id n_citi bed bath sqft price\n", + "0 0 0.234300 3 2.0 0.926382 0.996177\n", + "1 1 0.884058 3 2.0 0.975096 0.981440\n", + "2 2 0.632850 3 1.0 0.970093 0.956260\n", + "3 3 0.884058 3 1.0 0.953874 0.914127\n", + "4 4 0.867150 4 3.0 0.869615 0.894681" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idn_citibedbathsqftprice
0103050.81159442.10.8954390.693629
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\n", + "
" + ], + "text/plain": [ + " image_id n_citi bed bath sqft price\n", + "0 10305 0.811594 4 2.1 0.895439 0.693629\n", + "1 7918 0.328502 2 2.0 0.938805 0.582271\n", + "2 8316 0.236715 3 2.0 0.949272 0.994460\n", + "3 6262 0.016908 4 3.0 0.855984 0.858726\n", + "4 1938 0.321256 3 2.0 0.952091 0.803380" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.sample(256)\n", + "df.reset_index(inplace = True )\n", + "del df['index']\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'street'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'street'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [226], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mdel\u001b[39;00m df[\u001b[39m'\u001b[39m\u001b[39mstreet\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 2\u001b[0m \u001b[39mdel\u001b[39;00m df[\u001b[39m'\u001b[39m\u001b[39mciti\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m cols \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39msqft\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mprice\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mn_citi\u001b[39m\u001b[39m'\u001b[39m]:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/core/generic.py:4243\u001b[0m, in \u001b[0;36mNDFrame.__delitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4238\u001b[0m deleted \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 4239\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m deleted:\n\u001b[1;32m 4240\u001b[0m \u001b[39m# If the above loop ran and didn't delete anything because\u001b[39;00m\n\u001b[1;32m 4241\u001b[0m \u001b[39m# there was no match, this call should raise the appropriate\u001b[39;00m\n\u001b[1;32m 4242\u001b[0m \u001b[39m# exception:\u001b[39;00m\n\u001b[0;32m-> 4243\u001b[0m loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49maxes[\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4244\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mgr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mgr\u001b[39m.\u001b[39midelete(loc)\n\u001b[1;32m 4246\u001b[0m \u001b[39m# delete from the caches\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3807\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'street'" + ] + } + ], + "source": [ + "del df['street']\n", + "del df['citi']\n", + "for cols in ['sqft','price','n_citi']:\n", + " df[cols] = (df[cols].max() - df[cols])/(df[cols].max() - df[cols].min())\n", + " \n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SocalDataset(Dataset):\n", + " \n", + " def __init__(self, dataframe, root_dir, transform=None):\n", + " self.features = dataframe\n", + " self.root_dir = root_dir\n", + " self.transform = transform\n", + "\n", + " def __len__(self):\n", + " return len(self.features)\n", + "\n", + " def __getitem__(self, idx):\n", + " if torch.is_tensor(idx):\n", + " idx = idx.tolist()\n", + " try:\n", + " img_name = '{}{}.jpg'.format(str(self.root_dir), str(self.features.loc[idx,'image_id']))\n", + " image = io.imread(img_name)\n", + " image = image / 255.0\n", + " \n", + " if len(image.shape) == 3:\n", + " house_features = self.features.iloc[idx, 1:]\n", + " house_features = np.array([house_features]).reshape(5)\n", + " sample = {'image': image, 'house_features': house_features}\n", + "\n", + " if self.transform:\n", + " sample['image'] = transform.resize(sample['image'],(64,64)).reshape(3,64,64)\n", + " \n", + " sample['image'] = torch.from_numpy(sample['image']).float()\n", + " sample['house_features'] = torch.from_numpy(sample['house_features']).float()\n", + "\n", + " return sample\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "house_dataset = SocalDataset(dataframe=df,\n", + " root_dir=pics_path,\n", + " transform = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 torch.Size([3, 64, 64]) torch.Size([5])\n", + "1 torch.Size([3, 64, 64]) torch.Size([5])\n", + "2 torch.Size([3, 64, 64]) torch.Size([5])\n", + "3 torch.Size([3, 64, 64]) torch.Size([5])\n", + "4 torch.Size([3, 64, 64]) torch.Size([5])\n", + "5 torch.Size([3, 64, 64]) torch.Size([5])\n", + "6 torch.Size([3, 64, 64]) torch.Size([5])\n", + "7 torch.Size([3, 64, 64]) torch.Size([5])\n", + "8 torch.Size([3, 64, 64]) torch.Size([5])\n", + "9 torch.Size([3, 64, 64]) torch.Size([5])\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " sample = house_dataset[i]\n", + " print(i, sample['image'].shape, sample['house_features'].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "test_size = 0.3\n", + "\n", + "test_amount = int(house_dataset.__len__() * test_size)\n", + "\n", + "train_set, test_set = torch.utils.data.random_split(house_dataset,[\n", + " (house_dataset.__len__() - test_amount ), test_amount ])" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataloader = torch.utils.data.DataLoader(\n", + " train_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")\n", + "\n", + "test_dataloader = torch.utils.data.DataLoader(\n", + " test_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "it = iter(train_dataloader)\n", + "items = next(it)\n", + "print(type(items))" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([5, 3, 64, 64])\n", + "torch.Size([5, 5])\n", + "tensor([[0.8019, 3.0000, 3.0000, 0.9127, 0.9668],\n", + " [0.8744, 2.0000, 1.0000, 0.9431, 0.6931],\n", + " [0.3599, 2.0000, 1.0000, 0.9435, 0.7618],\n", + " [0.5338, 3.0000, 2.0000, 0.9416, 0.9778],\n", + " [0.4372, 3.0000, 2.1000, 0.9174, 0.8643]])\n" + ] + } + ], + "source": [ + "print(items['image'].shape)\n", + "print(items['house_features'].shape)\n", + "print(items['house_features'])" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "class TwoInputsNet(nn.Module):\n", + " def __init__(self):\n", + " super(TwoInputsNet, self).__init__()\n", + " self.conv = nn.Conv2d(3,6,kernel_size=3) \n", + " self.conv1 = nn.Conv2d(6,9,kernel_size=3)\n", + " self.conv2 = nn.Conv2d(9,12,kernel_size=3) \n", + " self.fc1 = nn.Linear(4,3)\n", + " self.fc2 = nn.Linear(40371,1024)\n", + " self.fc3 = nn.Linear(1024,32) \n", + " self.fc4 = nn.Linear(32,1) \n", + "\n", + " def forward(self, input1, input2):\n", + " c = self.conv(input1)\n", + " c = self.conv1(c)\n", + " c = F.relu(c)\n", + " c = self.conv2(c)\n", + " c = F.relu(c)\n", + " f = self.fc1(input2)\n", + " \n", + " # now we can reshape `c` and `f` to 2D and concat them\n", + " combined = torch.cat((c.view(c.size(0), -1),\n", + " f.view(f.size(0), -1)), dim=1)\n", + " out = self.fc2(combined)\n", + " out = F.relu(out)\n", + " out = self.fc3(out)\n", + " out = F.relu(out)\n", + " out = self.fc4(out)\n", + "\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TwoInputsNet(\n", + " (conv): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv1): Conv2d(6, 9, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv2): Conv2d(9, 12, kernel_size=(3, 3), stride=(1, 1))\n", + " (fc1): Linear(in_features=4, out_features=3, bias=True)\n", + " (fc2): Linear(in_features=40371, out_features=1024, bias=True)\n", + " (fc3): Linear(in_features=1024, out_features=32, bias=True)\n", + " (fc4): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "network = TwoInputsNet()\n", + "print(network)" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "momentum = 0.9\n", + "n_epochs = 5\n", + "\n", + "optimizer = optim.SGD(network.parameters(), lr=learning_rate,momentum=momentum)\n", + "\n", + "loss_func = torch.nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataloader = torch.utils.data.DataLoader(\n", + " train_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")\n", + "\n", + "test_dataloader = torch.utils.data.DataLoader(\n", + " test_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "def train(dataloader,epoch):\n", + " batch_idx = 0\n", + " for items in dataloader:\n", + " image = torch.FloatTensor(items['image'])\n", + " features = torch.FloatTensor(items['house_features'][:,:4])\n", + " price = torch.FloatTensor(items['house_features'][:,4])\n", + "\n", + " if torch.cuda.is_available():\n", + " image = image.cuda()\n", + " features = features.cuda()\n", + " price = price.cuda()\n", + " \n", + " output = network(image,features)\n", + " output = output.reshape(5)\n", + " loss = loss_func(output, price)\n", + " optimizer.zero_grad() \n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " if batch_idx % 4 == 0: #every 25 * batchsize sample we print results\n", + " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", + " epoch+1, batch_idx * image.shape[0], len(dataloader.dataset),\n", + " 100. * batch_idx / len(dataloader), loss.item()))\n", + " \n", + " batch_idx = batch_idx + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Epoch: 1 [0/180 (0%)]\tLoss: 0.452995\n", + "Train Epoch: 1 [20/180 (11%)]\tLoss: 0.126921\n", + "Train Epoch: 1 [40/180 (22%)]\tLoss: 0.093897\n", + "Train Epoch: 1 [60/180 (33%)]\tLoss: 0.059054\n", + "Train Epoch: 1 [80/180 (44%)]\tLoss: 0.018912\n", + "Train Epoch: 1 [100/180 (56%)]\tLoss: 0.026393\n", + "Train Epoch: 1 [120/180 (67%)]\tLoss: 0.073876\n", + "Train Epoch: 1 [140/180 (78%)]\tLoss: 0.019582\n", + "Train Epoch: 1 [160/180 (89%)]\tLoss: 0.071252\n", + "Train Epoch: 2 [0/180 (0%)]\tLoss: 0.009247\n", + "Train Epoch: 2 [20/180 (11%)]\tLoss: 0.005556\n", + "Train Epoch: 2 [40/180 (22%)]\tLoss: 0.057850\n", + "Train Epoch: 2 [60/180 (33%)]\tLoss: 0.207975\n", + "Train Epoch: 2 [80/180 (44%)]\tLoss: 0.030397\n", + "Train Epoch: 2 [100/180 (56%)]\tLoss: 0.086041\n", + "Train Epoch: 2 [120/180 (67%)]\tLoss: 0.005431\n", + "Train Epoch: 2 [140/180 (78%)]\tLoss: 0.035753\n", + "Train Epoch: 2 [160/180 (89%)]\tLoss: 0.133349\n", + "Train Epoch: 3 [0/180 (0%)]\tLoss: 0.015860\n", + "Train Epoch: 3 [20/180 (11%)]\tLoss: 0.035526\n", + "Train Epoch: 3 [40/180 (22%)]\tLoss: 0.020284\n", + "Train Epoch: 3 [60/180 (33%)]\tLoss: 0.011326\n", + "Train Epoch: 3 [80/180 (44%)]\tLoss: 0.131106\n", + "Train Epoch: 3 [100/180 (56%)]\tLoss: 0.052148\n", + "Train Epoch: 3 [120/180 (67%)]\tLoss: 0.026631\n", + "Train Epoch: 3 [140/180 (78%)]\tLoss: 0.043114\n", + "Train Epoch: 3 [160/180 (89%)]\tLoss: 0.045804\n", + "Train Epoch: 4 [0/180 (0%)]\tLoss: 0.101069\n", + "Train Epoch: 4 [20/180 (11%)]\tLoss: 0.044998\n", + "Train Epoch: 4 [40/180 (22%)]\tLoss: 0.021710\n", + "Train Epoch: 4 [60/180 (33%)]\tLoss: 0.131808\n", + "Train Epoch: 4 [80/180 (44%)]\tLoss: 0.060891\n", + "Train Epoch: 4 [100/180 (56%)]\tLoss: 0.026389\n", + "Train Epoch: 4 [120/180 (67%)]\tLoss: 0.022358\n", + "Train Epoch: 4 [140/180 (78%)]\tLoss: 0.061502\n", + "Train Epoch: 4 [160/180 (89%)]\tLoss: 0.013414\n", + "Train Epoch: 5 [0/180 (0%)]\tLoss: 0.036915\n", + "Train Epoch: 5 [20/180 (11%)]\tLoss: 0.067229\n", + "Train Epoch: 5 [40/180 (22%)]\tLoss: 0.020201\n", + "Train Epoch: 5 [60/180 (33%)]\tLoss: 0.048129\n", + "Train Epoch: 5 [80/180 (44%)]\tLoss: 0.056789\n", + "Train Epoch: 5 [100/180 (56%)]\tLoss: 0.161134\n", + "Train Epoch: 5 [120/180 (67%)]\tLoss: 0.017697\n", + "Train Epoch: 5 [140/180 (78%)]\tLoss: 0.057869\n", + "Train Epoch: 5 [160/180 (89%)]\tLoss: 0.024027\n" + ] + } + ], + "source": [ + "for epoch in range(n_epochs):\n", + " # train \n", + " train(train_dataloader,epoch)" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Results :\n", + "\n", + "RSS : 6.53765385536986\n", + "RSE : 0.19164652996049286\n", + "TSS : 6.631032657354906\n", + "R Squared : 0.014082090499354383\n", + "MSE : 0.03632029919649922\n", + "MAE : 0.13936191845441656\n", + "Accuracy with 10% : 0.35555555555555557\n", + "\n", + "Train Results :\n", + "\n", + "RSS : 3.6185278102910923\n", + "RSE : 0.22113123809944435\n", + "TSS : 3.641416601597961\n", + "R Squared : 0.006285683241193718\n", + "MSE : 0.047612208030145944\n", + "MAE : 0.16736403372334807\n", + "Accuracy with 10% : 0.27631578947368424\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[W NNPACK.cpp:53] Could not initialize NNPACK! Reason: Unsupported hardware.\n" + ] + } + ], + "source": [ + "import math\n", + "def reg_report(true, pred, name='Test'):\n", + " print(\"\\n{} Results :\\n\".format(name))\n", + " print(\"RSS :\",sum((pred-true)**2))\n", + " print(\"RSE :\",math.sqrt(sum((pred-true)**2)*(1/(len(pred)-2))))\n", + " print(\"TSS :\",sum((true-true.mean())**2))\n", + " print(\"R Squared :\",1-(sum((pred-true)**2)/sum((true-true.mean())**2)))\n", + " print(\"MSE :\",((pred-true)**2).mean())\n", + " print('MAE :',(abs(pred-true)).mean())\n", + " print('Accuracy with 10% :', ((pred<=true*1.1) & (true*0.9<=pred)).mean())\n", + " \n", + "\n", + "def eval_report(y_train, pred_train,y_test, pred_test):\n", + " reg_report(y_train, pred_train, name='Train')\n", + " reg_report(y_test, pred_test, name='Test')\n", + " \n", + "def res(dataLoader, name = 'Test'): \n", + " trues = []\n", + " preds = []\n", + " network.eval()\n", + " with torch.no_grad():\n", + " for items in dataLoader:\n", + " image = torch.FloatTensor(items['image'])\n", + " features = torch.FloatTensor(items['house_features'][:,:4])\n", + " price = torch.FloatTensor(items['house_features'][:,4])\n", + "\n", + " outputs = network(input2 = features, input1 = image)\n", + "\n", + " trues = trues + price.tolist()\n", + " preds = preds + outputs[:,0].tolist()\n", + "\n", + "\n", + " reg_report(true = np.array(trues), pred = np.array(preds), name=name)\n", + "\n", + "res(train_dataloader, name = 'Test')\n", + "res(test_dataloader, name = 'Train')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('NLP')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bonus/Bonus 4/bonus4 (1).ipynb b/Bonus/Bonus 4/bonus4 (1).ipynb new file mode 100644 index 0000000..d396f79 --- /dev/null +++ b/Bonus/Bonus 4/bonus4 (1).ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"b8SLZ23uygVb"},"source":["## Tensflow "]},{"cell_type":"markdown","metadata":{"id":"Hu673-QdygVd"},"source":[]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":3691,"status":"ok","timestamp":1671230954342,"user":{"displayName":"Niclas Cramer","userId":"02053576108445824683"},"user_tz":-60},"id":"iXx26FLsygVd"},"outputs":[],"source":["# Import libraries\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import json\n","import os\n","from tqdm import tqdm, tqdm_notebook\n","import random\n","\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential, Model\n","from tensorflow.keras.layers import *\n","from tensorflow.keras.optimizers import *\n","from tensorflow.keras.applications import *\n","from tensorflow.keras.callbacks import *\n","from tensorflow.keras.initializers import *\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","\n","from numpy.random import seed\n","seed(42)"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1671230954343,"user":{"displayName":"Niclas Cramer","userId":"02053576108445824683"},"user_tz":-60},"id":"6PL6YLmaygVe"},"outputs":[],"source":["tf.random.set_seed(42)"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"CUx5kFwAygVe","outputId":"89025814-4360-45de-e300-e71731dea25e"},"outputs":[{"data":{"text/plain":["(50, 8)"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["artists = pd.read_csv('data/artists.csv')\n","artists.shape"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"z-SIQj7FygVe","outputId":"43890756-3db4-4fd1-d85a-359229051989"},"outputs":[{"data":{"text/html":["
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idnameyearsgenrenationalitybiowikipediapaintings
00Amedeo Modigliani1884 - 1920ExpressionismItalianAmedeo Clemente Modigliani (Italian pronunciat...http://en.wikipedia.org/wiki/Amedeo_Modigliani193
11Vasiliy Kandinskiy1866 - 1944Expressionism,AbstractionismRussianWassily Wassilyevich Kandinsky (Russian: Васи́...http://en.wikipedia.org/wiki/Wassily_Kandinsky88
22Diego Rivera1886 - 1957Social Realism,MuralismMexicanDiego María de la Concepción Juan Nepomuceno E...http://en.wikipedia.org/wiki/Diego_Rivera70
33Claude Monet1840 - 1926ImpressionismFrenchOscar-Claude Monet (; French: [klod mɔnɛ]; 14 ...http://en.wikipedia.org/wiki/Claude_Monet73
44Rene Magritte1898 - 1967Surrealism,ImpressionismBelgianRené François Ghislain Magritte (French: [ʁəne...http://en.wikipedia.org/wiki/René_Magritte194
\n","
"],"text/plain":[" id name years genre \\\n","0 0 Amedeo Modigliani 1884 - 1920 Expressionism \n","1 1 Vasiliy Kandinskiy 1866 - 1944 Expressionism,Abstractionism \n","2 2 Diego Rivera 1886 - 1957 Social Realism,Muralism \n","3 3 Claude Monet 1840 - 1926 Impressionism \n","4 4 Rene Magritte 1898 - 1967 Surrealism,Impressionism \n","\n"," nationality bio \\\n","0 Italian Amedeo Clemente Modigliani (Italian pronunciat... \n","1 Russian Wassily Wassilyevich Kandinsky (Russian: Васи́... \n","2 Mexican Diego María de la Concepción Juan Nepomuceno E... \n","3 French Oscar-Claude Monet (; French: [klod mɔnɛ]; 14 ... \n","4 Belgian René François Ghislain Magritte (French: [ʁəne... \n","\n"," wikipedia paintings \n","0 http://en.wikipedia.org/wiki/Amedeo_Modigliani 193 \n","1 http://en.wikipedia.org/wiki/Wassily_Kandinsky 88 \n","2 http://en.wikipedia.org/wiki/Diego_Rivera 70 \n","3 http://en.wikipedia.org/wiki/Claude_Monet 73 \n","4 http://en.wikipedia.org/wiki/René_Magritte 194 "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["artists.head()"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"BmmKnBpfygVe","outputId":"4ddcafc5-685d-4295-f70c-47087711cabf"},"outputs":[{"data":{"text/html":["
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namepaintingsclass_weight
0Vincent van Gogh8770.445631
1Edgar Degas7020.556721
2Pablo Picasso4390.890246
3Pierre-Auguste Renoir3361.163149
4Albrecht Dürer3281.191519
5Paul Gauguin3111.256650
6Francisco Goya2911.343018
7Rembrandt2621.491672
8Alfred Sisley2591.508951
9Titian2551.532620
10Marc Chagall2391.635223
\n","
"],"text/plain":[" name paintings class_weight\n","0 Vincent van Gogh 877 0.445631\n","1 Edgar Degas 702 0.556721\n","2 Pablo Picasso 439 0.890246\n","3 Pierre-Auguste Renoir 336 1.163149\n","4 Albrecht Dürer 328 1.191519\n","5 Paul Gauguin 311 1.256650\n","6 Francisco Goya 291 1.343018\n","7 Rembrandt 262 1.491672\n","8 Alfred Sisley 259 1.508951\n","9 Titian 255 1.532620\n","10 Marc Chagall 239 1.635223"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Sort artists by number of paintings\n","artists = artists.sort_values(by=['paintings'], ascending=False)\n","\n","# Create a dataframe with artists having more than 200 paintings\n","artists_top = artists[artists['paintings'] >= 200].reset_index()\n","artists_top = artists_top[['name', 'paintings']]\n","#artists_top['class_weight'] = max(artists_top.paintings)/artists_top.paintings\n","artists_top['class_weight'] = artists_top.paintings.sum() / (artists_top.shape[0] * artists_top.paintings)\n","artists_top"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"zE7ik-VMygVe"},"outputs":[],"source":["images_dir = 'data/images'\n","artists_dirs = os.listdir(images_dir)\n","artists_top_name = artists_top['name'].str.replace(' ', '_').values"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"vHK5vEDuygVe","outputId":"c1588338-8abe-4955-e83a-a9394ce5b54f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 3181 images belonging to 11 classes.\n","Found 790 images belonging to 11 classes.\n","Total number of batches = 198 and 49\n"]}],"source":["# Augment data\n","batch_size = 16\n","train_input_shape = (224, 224, 3)\n","n_classes = artists_top.shape[0]\n","\n","train_datagen = ImageDataGenerator(validation_split=0.2,\n"," rescale=1./255.,\n"," #rotation_range=45,\n"," #width_shift_range=0.5,\n"," #height_shift_range=0.5,\n"," shear_range=5,\n"," #zoom_range=0.7,\n"," horizontal_flip=True,\n"," vertical_flip=True,\n"," )\n","\n","train_generator = train_datagen.flow_from_directory(directory=images_dir,\n"," class_mode='categorical',\n"," target_size=train_input_shape[0:2],\n"," batch_size=batch_size,\n"," subset=\"training\",\n"," shuffle=True,\n"," classes=artists_top_name.tolist()\n"," )\n","\n","valid_generator = train_datagen.flow_from_directory(directory=images_dir,\n"," class_mode='categorical',\n"," target_size=train_input_shape[0:2],\n"," batch_size=batch_size,\n"," subset=\"validation\",\n"," shuffle=True,\n"," classes=artists_top_name.tolist()\n"," )\n","\n","STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\n","STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n","print(\"Total number of batches =\", STEP_SIZE_TRAIN, \"and\", STEP_SIZE_VALID)"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"IP4jIFSxygVe","outputId":"e5f5e833-3190-479b-9f5d-d8971cf26b2a"},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Print a random paintings and it's random augmented version\n","fig, axes = plt.subplots(1, 2, figsize=(20,10))\n","\n","random_artist = random.choice(artists_top_name)\n","random_image = random.choice(os.listdir(os.path.join(images_dir, random_artist)))\n","random_image_file = os.path.join(images_dir, random_artist, random_image)\n","\n","# Original image\n","image = plt.imread(random_image_file)\n","axes[0].imshow(image)\n","axes[0].set_title(\"An original Image of \" + random_artist.replace('_', ' '))\n","axes[0].axis('off')\n","\n","# Transformed image\n","aug_image = train_datagen.random_transform(image)\n","axes[1].imshow(aug_image)\n","axes[1].set_title(\"A transformed Image of \" + random_artist.replace('_', ' '))\n","axes[1].axis('off')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"kCXxvtfHygVe"},"outputs":[],"source":["# Load pre-trained model\n","base_model = ResNet50(weights='imagenet', include_top=False, input_shape=train_input_shape)"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"hlbyEJmcygVe"},"outputs":[],"source":["for layer in base_model.layers:\n"," layer.trainable = True"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"Z33RTz-PygVf","outputId":"5fb29018-9730-4e6a-be52-5b5a2881548a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"resnet50\"\n","__________________________________________________________________________________________________\n"," Layer (type) Output Shape Param # Connected to \n","==================================================================================================\n"," input_1 (InputLayer) [(None, 224, 224, 3 0 [] \n"," )] \n"," \n"," conv1_pad (ZeroPadding2D) (None, 230, 230, 3) 0 ['input_1[0][0]'] \n"," \n"," conv1_conv (Conv2D) (None, 112, 112, 64 9472 ['conv1_pad[0][0]'] \n"," ) \n"," \n"," conv1_bn (BatchNormalization) (None, 112, 112, 64 256 ['conv1_conv[0][0]'] \n"," ) \n"," \n"," conv1_relu (Activation) (None, 112, 112, 64 0 ['conv1_bn[0][0]'] \n"," ) \n"," \n"," pool1_pad (ZeroPadding2D) (None, 114, 114, 64 0 ['conv1_relu[0][0]'] \n"," ) \n"," \n"," pool1_pool (MaxPooling2D) (None, 56, 56, 64) 0 ['pool1_pad[0][0]'] \n"," \n"," conv2_block1_1_conv (Conv2D) (None, 56, 56, 64) 4160 ['pool1_pool[0][0]'] \n"," \n"," conv2_block1_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_1_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block1_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_1_bn[0][0]'] \n"," n) \n"," \n"," conv2_block1_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block1_1_relu[0][0]'] \n"," \n"," conv2_block1_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_2_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block1_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_2_bn[0][0]'] \n"," n) \n"," \n"," conv2_block1_0_conv (Conv2D) (None, 56, 56, 256) 16640 ['pool1_pool[0][0]'] \n"," \n"," conv2_block1_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block1_2_relu[0][0]'] \n"," \n"," conv2_block1_0_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block1_0_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block1_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block1_3_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block1_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_0_bn[0][0]', \n"," 'conv2_block1_3_bn[0][0]'] \n"," \n"," conv2_block1_out (Activation) (None, 56, 56, 256) 0 ['conv2_block1_add[0][0]'] \n"," \n"," conv2_block2_1_conv (Conv2D) (None, 56, 56, 64) 16448 ['conv2_block1_out[0][0]'] \n"," \n"," conv2_block2_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_1_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block2_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_1_bn[0][0]'] \n"," n) \n"," \n"," conv2_block2_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block2_1_relu[0][0]'] \n"," \n"," conv2_block2_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_2_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block2_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_2_bn[0][0]'] \n"," n) \n"," \n"," conv2_block2_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block2_2_relu[0][0]'] \n"," \n"," conv2_block2_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block2_3_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block2_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_out[0][0]', \n"," 'conv2_block2_3_bn[0][0]'] \n"," \n"," conv2_block2_out (Activation) (None, 56, 56, 256) 0 ['conv2_block2_add[0][0]'] \n"," \n"," conv2_block3_1_conv (Conv2D) (None, 56, 56, 64) 16448 ['conv2_block2_out[0][0]'] \n"," \n"," conv2_block3_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_1_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block3_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_1_bn[0][0]'] \n"," n) \n"," \n"," conv2_block3_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block3_1_relu[0][0]'] \n"," \n"," conv2_block3_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_2_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block3_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_2_bn[0][0]'] \n"," n) \n"," \n"," conv2_block3_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block3_2_relu[0][0]'] \n"," \n"," conv2_block3_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block3_3_conv[0][0]'] \n"," ization) \n"," \n"," conv2_block3_add (Add) (None, 56, 56, 256) 0 ['conv2_block2_out[0][0]', \n"," 'conv2_block3_3_bn[0][0]'] \n"," \n"," conv2_block3_out (Activation) (None, 56, 56, 256) 0 ['conv2_block3_add[0][0]'] \n"," \n"," conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 32896 ['conv2_block3_out[0][0]'] \n"," \n"," conv3_block1_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_1_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block1_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_1_bn[0][0]'] \n"," n) \n"," \n"," conv3_block1_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block1_1_relu[0][0]'] \n"," \n"," conv3_block1_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_2_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block1_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_2_bn[0][0]'] \n"," n) \n"," \n"," conv3_block1_0_conv (Conv2D) (None, 28, 28, 512) 131584 ['conv2_block3_out[0][0]'] \n"," \n"," conv3_block1_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block1_2_relu[0][0]'] \n"," \n"," conv3_block1_0_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block1_0_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block1_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block1_3_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block1_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_0_bn[0][0]', \n"," 'conv3_block1_3_bn[0][0]'] \n"," \n"," conv3_block1_out (Activation) (None, 28, 28, 512) 0 ['conv3_block1_add[0][0]'] \n"," \n"," conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block1_out[0][0]'] \n"," \n"," conv3_block2_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_1_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block2_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_1_bn[0][0]'] \n"," n) \n"," \n"," conv3_block2_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block2_1_relu[0][0]'] \n"," \n"," conv3_block2_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_2_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block2_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_2_bn[0][0]'] \n"," n) \n"," \n"," conv3_block2_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block2_2_relu[0][0]'] \n"," \n"," conv3_block2_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block2_3_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block2_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_out[0][0]', \n"," 'conv3_block2_3_bn[0][0]'] \n"," \n"," conv3_block2_out (Activation) (None, 28, 28, 512) 0 ['conv3_block2_add[0][0]'] \n"," \n"," conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block2_out[0][0]'] \n"," \n"," conv3_block3_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_1_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block3_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_1_bn[0][0]'] \n"," n) \n"," \n"," conv3_block3_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block3_1_relu[0][0]'] \n"," \n"," conv3_block3_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_2_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block3_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_2_bn[0][0]'] \n"," n) \n"," \n"," conv3_block3_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block3_2_relu[0][0]'] \n"," \n"," conv3_block3_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block3_3_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block3_add (Add) (None, 28, 28, 512) 0 ['conv3_block2_out[0][0]', \n"," 'conv3_block3_3_bn[0][0]'] \n"," \n"," conv3_block3_out (Activation) (None, 28, 28, 512) 0 ['conv3_block3_add[0][0]'] \n"," \n"," conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block3_out[0][0]'] \n"," \n"," conv3_block4_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_1_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block4_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_1_bn[0][0]'] \n"," n) \n"," \n"," conv3_block4_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block4_1_relu[0][0]'] \n"," \n"," conv3_block4_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_2_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block4_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_2_bn[0][0]'] \n"," n) \n"," \n"," conv3_block4_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block4_2_relu[0][0]'] \n"," \n"," conv3_block4_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block4_3_conv[0][0]'] \n"," ization) \n"," \n"," conv3_block4_add (Add) (None, 28, 28, 512) 0 ['conv3_block3_out[0][0]', \n"," 'conv3_block4_3_bn[0][0]'] \n"," \n"," conv3_block4_out (Activation) (None, 28, 28, 512) 0 ['conv3_block4_add[0][0]'] \n"," \n"," conv4_block1_1_conv (Conv2D) (None, 14, 14, 256) 131328 ['conv3_block4_out[0][0]'] \n"," \n"," conv4_block1_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_1_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block1_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_1_bn[0][0]'] \n"," n) \n"," \n"," conv4_block1_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block1_1_relu[0][0]'] \n"," \n"," conv4_block1_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_2_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block1_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_2_bn[0][0]'] \n"," n) \n"," \n"," conv4_block1_0_conv (Conv2D) (None, 14, 14, 1024 525312 ['conv3_block4_out[0][0]'] \n"," ) \n"," \n"," conv4_block1_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block1_2_relu[0][0]'] \n"," ) \n"," \n"," conv4_block1_0_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block1_0_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block1_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block1_3_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block1_add (Add) (None, 14, 14, 1024 0 ['conv4_block1_0_bn[0][0]', \n"," ) 'conv4_block1_3_bn[0][0]'] \n"," \n"," conv4_block1_out (Activation) (None, 14, 14, 1024 0 ['conv4_block1_add[0][0]'] \n"," ) \n"," \n"," conv4_block2_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block1_out[0][0]'] \n"," \n"," conv4_block2_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_1_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block2_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_1_bn[0][0]'] \n"," n) \n"," \n"," conv4_block2_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block2_1_relu[0][0]'] \n"," \n"," conv4_block2_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_2_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block2_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_2_bn[0][0]'] \n"," n) \n"," \n"," conv4_block2_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block2_2_relu[0][0]'] \n"," ) \n"," \n"," conv4_block2_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block2_3_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block2_add (Add) (None, 14, 14, 1024 0 ['conv4_block1_out[0][0]', \n"," ) 'conv4_block2_3_bn[0][0]'] \n"," \n"," conv4_block2_out (Activation) (None, 14, 14, 1024 0 ['conv4_block2_add[0][0]'] \n"," ) \n"," \n"," conv4_block3_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block2_out[0][0]'] \n"," \n"," conv4_block3_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_1_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block3_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_1_bn[0][0]'] \n"," n) \n"," \n"," conv4_block3_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block3_1_relu[0][0]'] \n"," \n"," conv4_block3_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_2_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block3_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_2_bn[0][0]'] \n"," n) \n"," \n"," conv4_block3_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block3_2_relu[0][0]'] \n"," ) \n"," \n"," conv4_block3_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block3_3_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block3_add (Add) (None, 14, 14, 1024 0 ['conv4_block2_out[0][0]', \n"," ) 'conv4_block3_3_bn[0][0]'] \n"," \n"," conv4_block3_out (Activation) (None, 14, 14, 1024 0 ['conv4_block3_add[0][0]'] \n"," ) \n"," \n"," conv4_block4_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block3_out[0][0]'] \n"," \n"," conv4_block4_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_1_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block4_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_1_bn[0][0]'] \n"," n) \n"," \n"," conv4_block4_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block4_1_relu[0][0]'] \n"," \n"," conv4_block4_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_2_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block4_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_2_bn[0][0]'] \n"," n) \n"," \n"," conv4_block4_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block4_2_relu[0][0]'] \n"," ) \n"," \n"," conv4_block4_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block4_3_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block4_add (Add) (None, 14, 14, 1024 0 ['conv4_block3_out[0][0]', \n"," ) 'conv4_block4_3_bn[0][0]'] \n"," \n"," conv4_block4_out (Activation) (None, 14, 14, 1024 0 ['conv4_block4_add[0][0]'] \n"," ) \n"," \n"," conv4_block5_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block4_out[0][0]'] \n"," \n"," conv4_block5_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_1_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block5_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_1_bn[0][0]'] \n"," n) \n"," \n"," conv4_block5_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block5_1_relu[0][0]'] \n"," \n"," conv4_block5_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_2_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block5_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_2_bn[0][0]'] \n"," n) \n"," \n"," conv4_block5_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block5_2_relu[0][0]'] \n"," ) \n"," \n"," conv4_block5_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block5_3_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block5_add (Add) (None, 14, 14, 1024 0 ['conv4_block4_out[0][0]', \n"," ) 'conv4_block5_3_bn[0][0]'] \n"," \n"," conv4_block5_out (Activation) (None, 14, 14, 1024 0 ['conv4_block5_add[0][0]'] \n"," ) \n"," \n"," conv4_block6_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block5_out[0][0]'] \n"," \n"," conv4_block6_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_1_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block6_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_1_bn[0][0]'] \n"," n) \n"," \n"," conv4_block6_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block6_1_relu[0][0]'] \n"," \n"," conv4_block6_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_2_conv[0][0]'] \n"," ization) \n"," \n"," conv4_block6_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_2_bn[0][0]'] \n"," n) \n"," \n"," conv4_block6_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block6_2_relu[0][0]'] \n"," ) \n"," \n"," conv4_block6_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block6_3_conv[0][0]'] \n"," ization) ) \n"," \n"," conv4_block6_add (Add) (None, 14, 14, 1024 0 ['conv4_block5_out[0][0]', \n"," ) 'conv4_block6_3_bn[0][0]'] \n"," \n"," conv4_block6_out (Activation) (None, 14, 14, 1024 0 ['conv4_block6_add[0][0]'] \n"," ) \n"," \n"," conv5_block1_1_conv (Conv2D) (None, 7, 7, 512) 524800 ['conv4_block6_out[0][0]'] \n"," \n"," conv5_block1_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_1_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block1_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_1_bn[0][0]'] \n"," n) \n"," \n"," conv5_block1_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block1_1_relu[0][0]'] \n"," \n"," conv5_block1_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_2_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block1_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_2_bn[0][0]'] \n"," n) \n"," \n"," conv5_block1_0_conv (Conv2D) (None, 7, 7, 2048) 2099200 ['conv4_block6_out[0][0]'] \n"," \n"," conv5_block1_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block1_2_relu[0][0]'] \n"," \n"," conv5_block1_0_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block1_0_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block1_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block1_3_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block1_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_0_bn[0][0]', \n"," 'conv5_block1_3_bn[0][0]'] \n"," \n"," conv5_block1_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block1_add[0][0]'] \n"," \n"," conv5_block2_1_conv (Conv2D) (None, 7, 7, 512) 1049088 ['conv5_block1_out[0][0]'] \n"," \n"," conv5_block2_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_1_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block2_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_1_bn[0][0]'] \n"," n) \n"," \n"," conv5_block2_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block2_1_relu[0][0]'] \n"," \n"," conv5_block2_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_2_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block2_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_2_bn[0][0]'] \n"," n) \n"," \n"," conv5_block2_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block2_2_relu[0][0]'] \n"," \n"," conv5_block2_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block2_3_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block2_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_out[0][0]', \n"," 'conv5_block2_3_bn[0][0]'] \n"," \n"," conv5_block2_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block2_add[0][0]'] \n"," \n"," conv5_block3_1_conv (Conv2D) (None, 7, 7, 512) 1049088 ['conv5_block2_out[0][0]'] \n"," \n"," conv5_block3_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_1_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block3_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_1_bn[0][0]'] \n"," n) \n"," \n"," conv5_block3_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block3_1_relu[0][0]'] \n"," \n"," conv5_block3_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_2_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block3_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_2_bn[0][0]'] \n"," n) \n"," \n"," conv5_block3_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block3_2_relu[0][0]'] \n"," \n"," conv5_block3_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block3_3_conv[0][0]'] \n"," ization) \n"," \n"," conv5_block3_add (Add) (None, 7, 7, 2048) 0 ['conv5_block2_out[0][0]', \n"," 'conv5_block3_3_bn[0][0]'] \n"," \n"," conv5_block3_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block3_add[0][0]'] \n"," \n","==================================================================================================\n","Total params: 23,587,712\n","Trainable params: 23,534,592\n","Non-trainable params: 53,120\n","__________________________________________________________________________________________________\n"]}],"source":["base_model.summary()"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"544wpdurygVf"},"outputs":[],"source":["model_eff=EfficientNetB2(weights='imagenet', include_top=False, input_shape=train_input_shape\n",")"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"iufPCm40ygVf"},"outputs":[],"source":["for layer in model_eff.layers:\n"," layer.trainable = True"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"5vv3P8-tygVf","outputId":"0f201234-9599-461f-81ac-056018e2da9d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"efficientnetb2\"\n","__________________________________________________________________________________________________\n"," Layer (type) Output Shape Param # Connected to \n","==================================================================================================\n"," input_2 (InputLayer) [(None, 224, 224, 3 0 [] \n"," )] \n"," \n"," rescaling (Rescaling) (None, 224, 224, 3) 0 ['input_2[0][0]'] \n"," \n"," normalization (Normalization) (None, 224, 224, 3) 7 ['rescaling[0][0]'] \n"," \n"," tf.math.truediv (TFOpLambda) (None, 224, 224, 3) 0 ['normalization[0][0]'] \n"," \n"," stem_conv_pad (ZeroPadding2D) (None, 225, 225, 3) 0 ['tf.math.truediv[0][0]'] \n"," \n"," stem_conv (Conv2D) (None, 112, 112, 32 864 ['stem_conv_pad[0][0]'] \n"," ) \n"," \n"," stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] \n"," ) \n"," \n"," stem_activation (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] \n"," ) \n"," \n"," block1a_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['stem_activation[0][0]'] \n"," D) ) \n"," \n"," block1a_bn (BatchNormalization (None, 112, 112, 32 128 ['block1a_dwconv[0][0]'] \n"," ) ) \n"," \n"," block1a_activation (Activation (None, 112, 112, 32 0 ['block1a_bn[0][0]'] \n"," ) ) \n"," \n"," block1a_se_squeeze (GlobalAver (None, 32) 0 ['block1a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block1a_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1a_se_squeeze[0][0]'] \n"," \n"," block1a_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1a_se_reshape[0][0]'] \n"," \n"," block1a_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1a_se_reduce[0][0]'] \n"," \n"," block1a_se_excite (Multiply) (None, 112, 112, 32 0 ['block1a_activation[0][0]', \n"," ) 'block1a_se_expand[0][0]'] \n"," \n"," block1a_project_conv (Conv2D) (None, 112, 112, 16 512 ['block1a_se_excite[0][0]'] \n"," ) \n"," \n"," block1a_project_bn (BatchNorma (None, 112, 112, 16 64 ['block1a_project_conv[0][0]'] \n"," lization) ) \n"," \n"," block1b_dwconv (DepthwiseConv2 (None, 112, 112, 16 144 ['block1a_project_bn[0][0]'] \n"," D) ) \n"," \n"," block1b_bn (BatchNormalization (None, 112, 112, 16 64 ['block1b_dwconv[0][0]'] \n"," ) ) \n"," \n"," block1b_activation (Activation (None, 112, 112, 16 0 ['block1b_bn[0][0]'] \n"," ) ) \n"," \n"," block1b_se_squeeze (GlobalAver (None, 16) 0 ['block1b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block1b_se_reshape (Reshape) (None, 1, 1, 16) 0 ['block1b_se_squeeze[0][0]'] \n"," \n"," block1b_se_reduce (Conv2D) (None, 1, 1, 4) 68 ['block1b_se_reshape[0][0]'] \n"," \n"," block1b_se_expand (Conv2D) (None, 1, 1, 16) 80 ['block1b_se_reduce[0][0]'] \n"," \n"," block1b_se_excite (Multiply) (None, 112, 112, 16 0 ['block1b_activation[0][0]', \n"," ) 'block1b_se_expand[0][0]'] \n"," \n"," block1b_project_conv (Conv2D) (None, 112, 112, 16 256 ['block1b_se_excite[0][0]'] \n"," ) \n"," \n"," block1b_project_bn (BatchNorma (None, 112, 112, 16 64 ['block1b_project_conv[0][0]'] \n"," lization) ) \n"," \n"," block1b_drop (Dropout) (None, 112, 112, 16 0 ['block1b_project_bn[0][0]'] \n"," ) \n"," \n"," block1b_add (Add) (None, 112, 112, 16 0 ['block1b_drop[0][0]', \n"," ) 'block1a_project_bn[0][0]'] \n"," \n"," block2a_expand_conv (Conv2D) (None, 112, 112, 96 1536 ['block1b_add[0][0]'] \n"," ) \n"," \n"," block2a_expand_bn (BatchNormal (None, 112, 112, 96 384 ['block2a_expand_conv[0][0]'] \n"," ization) ) \n"," \n"," block2a_expand_activation (Act (None, 112, 112, 96 0 ['block2a_expand_bn[0][0]'] \n"," ivation) ) \n"," \n"," block2a_dwconv_pad (ZeroPaddin (None, 113, 113, 96 0 ['block2a_expand_activation[0][0]\n"," g2D) ) '] \n"," \n"," block2a_dwconv (DepthwiseConv2 (None, 56, 56, 96) 864 ['block2a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block2a_bn (BatchNormalization (None, 56, 56, 96) 384 ['block2a_dwconv[0][0]'] \n"," ) \n"," \n"," block2a_activation (Activation (None, 56, 56, 96) 0 ['block2a_bn[0][0]'] \n"," ) \n"," \n"," block2a_se_squeeze (GlobalAver (None, 96) 0 ['block2a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block2a_se_reshape (Reshape) (None, 1, 1, 96) 0 ['block2a_se_squeeze[0][0]'] \n"," \n"," block2a_se_reduce (Conv2D) (None, 1, 1, 4) 388 ['block2a_se_reshape[0][0]'] \n"," \n"," block2a_se_expand (Conv2D) (None, 1, 1, 96) 480 ['block2a_se_reduce[0][0]'] \n"," \n"," block2a_se_excite (Multiply) (None, 56, 56, 96) 0 ['block2a_activation[0][0]', \n"," 'block2a_se_expand[0][0]'] \n"," \n"," block2a_project_conv (Conv2D) (None, 56, 56, 24) 2304 ['block2a_se_excite[0][0]'] \n"," \n"," block2a_project_bn (BatchNorma (None, 56, 56, 24) 96 ['block2a_project_conv[0][0]'] \n"," lization) \n"," \n"," block2b_expand_conv (Conv2D) (None, 56, 56, 144) 3456 ['block2a_project_bn[0][0]'] \n"," \n"," block2b_expand_bn (BatchNormal (None, 56, 56, 144) 576 ['block2b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block2b_expand_activation (Act (None, 56, 56, 144) 0 ['block2b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block2b_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2b_expand_activation[0][0]\n"," D) '] \n"," \n"," block2b_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2b_dwconv[0][0]'] \n"," ) \n"," \n"," block2b_activation (Activation (None, 56, 56, 144) 0 ['block2b_bn[0][0]'] \n"," ) \n"," \n"," block2b_se_squeeze (GlobalAver (None, 144) 0 ['block2b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block2b_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2b_se_squeeze[0][0]'] \n"," \n"," block2b_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2b_se_reshape[0][0]'] \n"," \n"," block2b_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2b_se_reduce[0][0]'] \n"," \n"," block2b_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2b_activation[0][0]', \n"," 'block2b_se_expand[0][0]'] \n"," \n"," block2b_project_conv (Conv2D) (None, 56, 56, 24) 3456 ['block2b_se_excite[0][0]'] \n"," \n"," block2b_project_bn (BatchNorma (None, 56, 56, 24) 96 ['block2b_project_conv[0][0]'] \n"," lization) \n"," \n"," block2b_drop (Dropout) (None, 56, 56, 24) 0 ['block2b_project_bn[0][0]'] \n"," \n"," block2b_add (Add) (None, 56, 56, 24) 0 ['block2b_drop[0][0]', \n"," 'block2a_project_bn[0][0]'] \n"," \n"," block2c_expand_conv (Conv2D) (None, 56, 56, 144) 3456 ['block2b_add[0][0]'] \n"," \n"," block2c_expand_bn (BatchNormal (None, 56, 56, 144) 576 ['block2c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block2c_expand_activation (Act (None, 56, 56, 144) 0 ['block2c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block2c_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2c_expand_activation[0][0]\n"," D) '] \n"," \n"," block2c_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2c_dwconv[0][0]'] \n"," ) \n"," \n"," block2c_activation (Activation (None, 56, 56, 144) 0 ['block2c_bn[0][0]'] \n"," ) \n"," \n"," block2c_se_squeeze (GlobalAver (None, 144) 0 ['block2c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block2c_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2c_se_squeeze[0][0]'] \n"," \n"," block2c_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2c_se_reshape[0][0]'] \n"," \n"," block2c_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2c_se_reduce[0][0]'] \n"," \n"," block2c_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2c_activation[0][0]', \n"," 'block2c_se_expand[0][0]'] \n"," \n"," block2c_project_conv (Conv2D) (None, 56, 56, 24) 3456 ['block2c_se_excite[0][0]'] \n"," \n"," block2c_project_bn (BatchNorma (None, 56, 56, 24) 96 ['block2c_project_conv[0][0]'] \n"," lization) \n"," \n"," block2c_drop (Dropout) (None, 56, 56, 24) 0 ['block2c_project_bn[0][0]'] \n"," \n"," block2c_add (Add) (None, 56, 56, 24) 0 ['block2c_drop[0][0]', \n"," 'block2b_add[0][0]'] \n"," \n"," block3a_expand_conv (Conv2D) (None, 56, 56, 144) 3456 ['block2c_add[0][0]'] \n"," \n"," block3a_expand_bn (BatchNormal (None, 56, 56, 144) 576 ['block3a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block3a_expand_activation (Act (None, 56, 56, 144) 0 ['block3a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block3a_dwconv_pad (ZeroPaddin (None, 59, 59, 144) 0 ['block3a_expand_activation[0][0]\n"," g2D) '] \n"," \n"," block3a_dwconv (DepthwiseConv2 (None, 28, 28, 144) 3600 ['block3a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block3a_bn (BatchNormalization (None, 28, 28, 144) 576 ['block3a_dwconv[0][0]'] \n"," ) \n"," \n"," block3a_activation (Activation (None, 28, 28, 144) 0 ['block3a_bn[0][0]'] \n"," ) \n"," \n"," block3a_se_squeeze (GlobalAver (None, 144) 0 ['block3a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block3a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block3a_se_squeeze[0][0]'] \n"," \n"," block3a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block3a_se_reshape[0][0]'] \n"," \n"," block3a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block3a_se_reduce[0][0]'] \n"," \n"," block3a_se_excite (Multiply) (None, 28, 28, 144) 0 ['block3a_activation[0][0]', \n"," 'block3a_se_expand[0][0]'] \n"," \n"," block3a_project_conv (Conv2D) (None, 28, 28, 48) 6912 ['block3a_se_excite[0][0]'] \n"," \n"," block3a_project_bn (BatchNorma (None, 28, 28, 48) 192 ['block3a_project_conv[0][0]'] \n"," lization) \n"," \n"," block3b_expand_conv (Conv2D) (None, 28, 28, 288) 13824 ['block3a_project_bn[0][0]'] \n"," \n"," block3b_expand_bn (BatchNormal (None, 28, 28, 288) 1152 ['block3b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block3b_expand_activation (Act (None, 28, 28, 288) 0 ['block3b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block3b_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3b_expand_activation[0][0]\n"," D) '] \n"," \n"," block3b_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3b_dwconv[0][0]'] \n"," ) \n"," \n"," block3b_activation (Activation (None, 28, 28, 288) 0 ['block3b_bn[0][0]'] \n"," ) \n"," \n"," block3b_se_squeeze (GlobalAver (None, 288) 0 ['block3b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block3b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3b_se_squeeze[0][0]'] \n"," \n"," block3b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3b_se_reshape[0][0]'] \n"," \n"," block3b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3b_se_reduce[0][0]'] \n"," \n"," block3b_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3b_activation[0][0]', \n"," 'block3b_se_expand[0][0]'] \n"," \n"," block3b_project_conv (Conv2D) (None, 28, 28, 48) 13824 ['block3b_se_excite[0][0]'] \n"," \n"," block3b_project_bn (BatchNorma (None, 28, 28, 48) 192 ['block3b_project_conv[0][0]'] \n"," lization) \n"," \n"," block3b_drop (Dropout) (None, 28, 28, 48) 0 ['block3b_project_bn[0][0]'] \n"," \n"," block3b_add (Add) (None, 28, 28, 48) 0 ['block3b_drop[0][0]', \n"," 'block3a_project_bn[0][0]'] \n"," \n"," block3c_expand_conv (Conv2D) (None, 28, 28, 288) 13824 ['block3b_add[0][0]'] \n"," \n"," block3c_expand_bn (BatchNormal (None, 28, 28, 288) 1152 ['block3c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block3c_expand_activation (Act (None, 28, 28, 288) 0 ['block3c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block3c_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3c_expand_activation[0][0]\n"," D) '] \n"," \n"," block3c_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3c_dwconv[0][0]'] \n"," ) \n"," \n"," block3c_activation (Activation (None, 28, 28, 288) 0 ['block3c_bn[0][0]'] \n"," ) \n"," \n"," block3c_se_squeeze (GlobalAver (None, 288) 0 ['block3c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block3c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3c_se_squeeze[0][0]'] \n"," \n"," block3c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3c_se_reshape[0][0]'] \n"," \n"," block3c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3c_se_reduce[0][0]'] \n"," \n"," block3c_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3c_activation[0][0]', \n"," 'block3c_se_expand[0][0]'] \n"," \n"," block3c_project_conv (Conv2D) (None, 28, 28, 48) 13824 ['block3c_se_excite[0][0]'] \n"," \n"," block3c_project_bn (BatchNorma (None, 28, 28, 48) 192 ['block3c_project_conv[0][0]'] \n"," lization) \n"," \n"," block3c_drop (Dropout) (None, 28, 28, 48) 0 ['block3c_project_bn[0][0]'] \n"," \n"," block3c_add (Add) (None, 28, 28, 48) 0 ['block3c_drop[0][0]', \n"," 'block3b_add[0][0]'] \n"," \n"," block4a_expand_conv (Conv2D) (None, 28, 28, 288) 13824 ['block3c_add[0][0]'] \n"," \n"," block4a_expand_bn (BatchNormal (None, 28, 28, 288) 1152 ['block4a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4a_expand_activation (Act (None, 28, 28, 288) 0 ['block4a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4a_dwconv_pad (ZeroPaddin (None, 29, 29, 288) 0 ['block4a_expand_activation[0][0]\n"," g2D) '] \n"," \n"," block4a_dwconv (DepthwiseConv2 (None, 14, 14, 288) 2592 ['block4a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block4a_bn (BatchNormalization (None, 14, 14, 288) 1152 ['block4a_dwconv[0][0]'] \n"," ) \n"," \n"," block4a_activation (Activation (None, 14, 14, 288) 0 ['block4a_bn[0][0]'] \n"," ) \n"," \n"," block4a_se_squeeze (GlobalAver (None, 288) 0 ['block4a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block4a_se_squeeze[0][0]'] \n"," \n"," block4a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block4a_se_reshape[0][0]'] \n"," \n"," block4a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block4a_se_reduce[0][0]'] \n"," \n"," block4a_se_excite (Multiply) (None, 14, 14, 288) 0 ['block4a_activation[0][0]', \n"," 'block4a_se_expand[0][0]'] \n"," \n"," block4a_project_conv (Conv2D) (None, 14, 14, 88) 25344 ['block4a_se_excite[0][0]'] \n"," \n"," block4a_project_bn (BatchNorma (None, 14, 14, 88) 352 ['block4a_project_conv[0][0]'] \n"," lization) \n"," \n"," block4b_expand_conv (Conv2D) (None, 14, 14, 528) 46464 ['block4a_project_bn[0][0]'] \n"," \n"," block4b_expand_bn (BatchNormal (None, 14, 14, 528) 2112 ['block4b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4b_expand_activation (Act (None, 14, 14, 528) 0 ['block4b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4b_dwconv (DepthwiseConv2 (None, 14, 14, 528) 4752 ['block4b_expand_activation[0][0]\n"," D) '] \n"," \n"," block4b_bn (BatchNormalization (None, 14, 14, 528) 2112 ['block4b_dwconv[0][0]'] \n"," ) \n"," \n"," block4b_activation (Activation (None, 14, 14, 528) 0 ['block4b_bn[0][0]'] \n"," ) \n"," \n"," block4b_se_squeeze (GlobalAver (None, 528) 0 ['block4b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4b_se_reshape (Reshape) (None, 1, 1, 528) 0 ['block4b_se_squeeze[0][0]'] \n"," \n"," block4b_se_reduce (Conv2D) (None, 1, 1, 22) 11638 ['block4b_se_reshape[0][0]'] \n"," \n"," block4b_se_expand (Conv2D) (None, 1, 1, 528) 12144 ['block4b_se_reduce[0][0]'] \n"," \n"," block4b_se_excite (Multiply) (None, 14, 14, 528) 0 ['block4b_activation[0][0]', \n"," 'block4b_se_expand[0][0]'] \n"," \n"," block4b_project_conv (Conv2D) (None, 14, 14, 88) 46464 ['block4b_se_excite[0][0]'] \n"," \n"," block4b_project_bn (BatchNorma (None, 14, 14, 88) 352 ['block4b_project_conv[0][0]'] \n"," lization) \n"," \n"," block4b_drop (Dropout) (None, 14, 14, 88) 0 ['block4b_project_bn[0][0]'] \n"," \n"," block4b_add (Add) (None, 14, 14, 88) 0 ['block4b_drop[0][0]', \n"," 'block4a_project_bn[0][0]'] \n"," \n"," block4c_expand_conv (Conv2D) (None, 14, 14, 528) 46464 ['block4b_add[0][0]'] \n"," \n"," block4c_expand_bn (BatchNormal (None, 14, 14, 528) 2112 ['block4c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4c_expand_activation (Act (None, 14, 14, 528) 0 ['block4c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4c_dwconv (DepthwiseConv2 (None, 14, 14, 528) 4752 ['block4c_expand_activation[0][0]\n"," D) '] \n"," \n"," block4c_bn (BatchNormalization (None, 14, 14, 528) 2112 ['block4c_dwconv[0][0]'] \n"," ) \n"," \n"," block4c_activation (Activation (None, 14, 14, 528) 0 ['block4c_bn[0][0]'] \n"," ) \n"," \n"," block4c_se_squeeze (GlobalAver (None, 528) 0 ['block4c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4c_se_reshape (Reshape) (None, 1, 1, 528) 0 ['block4c_se_squeeze[0][0]'] \n"," \n"," block4c_se_reduce (Conv2D) (None, 1, 1, 22) 11638 ['block4c_se_reshape[0][0]'] \n"," \n"," block4c_se_expand (Conv2D) (None, 1, 1, 528) 12144 ['block4c_se_reduce[0][0]'] \n"," \n"," block4c_se_excite (Multiply) (None, 14, 14, 528) 0 ['block4c_activation[0][0]', \n"," 'block4c_se_expand[0][0]'] \n"," \n"," block4c_project_conv (Conv2D) (None, 14, 14, 88) 46464 ['block4c_se_excite[0][0]'] \n"," \n"," block4c_project_bn (BatchNorma (None, 14, 14, 88) 352 ['block4c_project_conv[0][0]'] \n"," lization) \n"," \n"," block4c_drop (Dropout) (None, 14, 14, 88) 0 ['block4c_project_bn[0][0]'] \n"," \n"," block4c_add (Add) (None, 14, 14, 88) 0 ['block4c_drop[0][0]', \n"," 'block4b_add[0][0]'] \n"," \n"," block4d_expand_conv (Conv2D) (None, 14, 14, 528) 46464 ['block4c_add[0][0]'] \n"," \n"," block4d_expand_bn (BatchNormal (None, 14, 14, 528) 2112 ['block4d_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4d_expand_activation (Act (None, 14, 14, 528) 0 ['block4d_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4d_dwconv (DepthwiseConv2 (None, 14, 14, 528) 4752 ['block4d_expand_activation[0][0]\n"," D) '] \n"," \n"," block4d_bn (BatchNormalization (None, 14, 14, 528) 2112 ['block4d_dwconv[0][0]'] \n"," ) \n"," \n"," block4d_activation (Activation (None, 14, 14, 528) 0 ['block4d_bn[0][0]'] \n"," ) \n"," \n"," block4d_se_squeeze (GlobalAver (None, 528) 0 ['block4d_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4d_se_reshape (Reshape) (None, 1, 1, 528) 0 ['block4d_se_squeeze[0][0]'] \n"," \n"," block4d_se_reduce (Conv2D) (None, 1, 1, 22) 11638 ['block4d_se_reshape[0][0]'] \n"," \n"," block4d_se_expand (Conv2D) (None, 1, 1, 528) 12144 ['block4d_se_reduce[0][0]'] \n"," \n"," block4d_se_excite (Multiply) (None, 14, 14, 528) 0 ['block4d_activation[0][0]', \n"," 'block4d_se_expand[0][0]'] \n"," \n"," block4d_project_conv (Conv2D) (None, 14, 14, 88) 46464 ['block4d_se_excite[0][0]'] \n"," \n"," block4d_project_bn (BatchNorma (None, 14, 14, 88) 352 ['block4d_project_conv[0][0]'] \n"," lization) \n"," \n"," block4d_drop (Dropout) (None, 14, 14, 88) 0 ['block4d_project_bn[0][0]'] \n"," \n"," block4d_add (Add) (None, 14, 14, 88) 0 ['block4d_drop[0][0]', \n"," 'block4c_add[0][0]'] \n"," \n"," block5a_expand_conv (Conv2D) (None, 14, 14, 528) 46464 ['block4d_add[0][0]'] \n"," \n"," block5a_expand_bn (BatchNormal (None, 14, 14, 528) 2112 ['block5a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5a_expand_activation (Act (None, 14, 14, 528) 0 ['block5a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5a_dwconv (DepthwiseConv2 (None, 14, 14, 528) 13200 ['block5a_expand_activation[0][0]\n"," D) '] \n"," \n"," block5a_bn (BatchNormalization (None, 14, 14, 528) 2112 ['block5a_dwconv[0][0]'] \n"," ) \n"," \n"," block5a_activation (Activation (None, 14, 14, 528) 0 ['block5a_bn[0][0]'] \n"," ) \n"," \n"," block5a_se_squeeze (GlobalAver (None, 528) 0 ['block5a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5a_se_reshape (Reshape) (None, 1, 1, 528) 0 ['block5a_se_squeeze[0][0]'] \n"," \n"," block5a_se_reduce (Conv2D) (None, 1, 1, 22) 11638 ['block5a_se_reshape[0][0]'] \n"," \n"," block5a_se_expand (Conv2D) (None, 1, 1, 528) 12144 ['block5a_se_reduce[0][0]'] \n"," \n"," block5a_se_excite (Multiply) (None, 14, 14, 528) 0 ['block5a_activation[0][0]', \n"," 'block5a_se_expand[0][0]'] \n"," \n"," block5a_project_conv (Conv2D) (None, 14, 14, 120) 63360 ['block5a_se_excite[0][0]'] \n"," \n"," block5a_project_bn (BatchNorma (None, 14, 14, 120) 480 ['block5a_project_conv[0][0]'] \n"," lization) \n"," \n"," block5b_expand_conv (Conv2D) (None, 14, 14, 720) 86400 ['block5a_project_bn[0][0]'] \n"," \n"," block5b_expand_bn (BatchNormal (None, 14, 14, 720) 2880 ['block5b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5b_expand_activation (Act (None, 14, 14, 720) 0 ['block5b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5b_dwconv (DepthwiseConv2 (None, 14, 14, 720) 18000 ['block5b_expand_activation[0][0]\n"," D) '] \n"," \n"," block5b_bn (BatchNormalization (None, 14, 14, 720) 2880 ['block5b_dwconv[0][0]'] \n"," ) \n"," \n"," block5b_activation (Activation (None, 14, 14, 720) 0 ['block5b_bn[0][0]'] \n"," ) \n"," \n"," block5b_se_squeeze (GlobalAver (None, 720) 0 ['block5b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5b_se_reshape (Reshape) (None, 1, 1, 720) 0 ['block5b_se_squeeze[0][0]'] \n"," \n"," block5b_se_reduce (Conv2D) (None, 1, 1, 30) 21630 ['block5b_se_reshape[0][0]'] \n"," \n"," block5b_se_expand (Conv2D) (None, 1, 1, 720) 22320 ['block5b_se_reduce[0][0]'] \n"," \n"," block5b_se_excite (Multiply) (None, 14, 14, 720) 0 ['block5b_activation[0][0]', \n"," 'block5b_se_expand[0][0]'] \n"," \n"," block5b_project_conv (Conv2D) (None, 14, 14, 120) 86400 ['block5b_se_excite[0][0]'] \n"," \n"," block5b_project_bn (BatchNorma (None, 14, 14, 120) 480 ['block5b_project_conv[0][0]'] \n"," lization) \n"," \n"," block5b_drop (Dropout) (None, 14, 14, 120) 0 ['block5b_project_bn[0][0]'] \n"," \n"," block5b_add (Add) (None, 14, 14, 120) 0 ['block5b_drop[0][0]', \n"," 'block5a_project_bn[0][0]'] \n"," \n"," block5c_expand_conv (Conv2D) (None, 14, 14, 720) 86400 ['block5b_add[0][0]'] \n"," \n"," block5c_expand_bn (BatchNormal (None, 14, 14, 720) 2880 ['block5c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5c_expand_activation (Act (None, 14, 14, 720) 0 ['block5c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5c_dwconv (DepthwiseConv2 (None, 14, 14, 720) 18000 ['block5c_expand_activation[0][0]\n"," D) '] \n"," \n"," block5c_bn (BatchNormalization (None, 14, 14, 720) 2880 ['block5c_dwconv[0][0]'] \n"," ) \n"," \n"," block5c_activation (Activation (None, 14, 14, 720) 0 ['block5c_bn[0][0]'] \n"," ) \n"," \n"," block5c_se_squeeze (GlobalAver (None, 720) 0 ['block5c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5c_se_reshape (Reshape) (None, 1, 1, 720) 0 ['block5c_se_squeeze[0][0]'] \n"," \n"," block5c_se_reduce (Conv2D) (None, 1, 1, 30) 21630 ['block5c_se_reshape[0][0]'] \n"," \n"," block5c_se_expand (Conv2D) (None, 1, 1, 720) 22320 ['block5c_se_reduce[0][0]'] \n"," \n"," block5c_se_excite (Multiply) (None, 14, 14, 720) 0 ['block5c_activation[0][0]', \n"," 'block5c_se_expand[0][0]'] \n"," \n"," block5c_project_conv (Conv2D) (None, 14, 14, 120) 86400 ['block5c_se_excite[0][0]'] \n"," \n"," block5c_project_bn (BatchNorma (None, 14, 14, 120) 480 ['block5c_project_conv[0][0]'] \n"," lization) \n"," \n"," block5c_drop (Dropout) (None, 14, 14, 120) 0 ['block5c_project_bn[0][0]'] \n"," \n"," block5c_add (Add) (None, 14, 14, 120) 0 ['block5c_drop[0][0]', \n"," 'block5b_add[0][0]'] \n"," \n"," block5d_expand_conv (Conv2D) (None, 14, 14, 720) 86400 ['block5c_add[0][0]'] \n"," \n"," block5d_expand_bn (BatchNormal (None, 14, 14, 720) 2880 ['block5d_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5d_expand_activation (Act (None, 14, 14, 720) 0 ['block5d_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5d_dwconv (DepthwiseConv2 (None, 14, 14, 720) 18000 ['block5d_expand_activation[0][0]\n"," D) '] \n"," \n"," block5d_bn (BatchNormalization (None, 14, 14, 720) 2880 ['block5d_dwconv[0][0]'] \n"," ) \n"," \n"," block5d_activation (Activation (None, 14, 14, 720) 0 ['block5d_bn[0][0]'] \n"," ) \n"," \n"," block5d_se_squeeze (GlobalAver (None, 720) 0 ['block5d_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5d_se_reshape (Reshape) (None, 1, 1, 720) 0 ['block5d_se_squeeze[0][0]'] \n"," \n"," block5d_se_reduce (Conv2D) (None, 1, 1, 30) 21630 ['block5d_se_reshape[0][0]'] \n"," \n"," block5d_se_expand (Conv2D) (None, 1, 1, 720) 22320 ['block5d_se_reduce[0][0]'] \n"," \n"," block5d_se_excite (Multiply) (None, 14, 14, 720) 0 ['block5d_activation[0][0]', \n"," 'block5d_se_expand[0][0]'] \n"," \n"," block5d_project_conv (Conv2D) (None, 14, 14, 120) 86400 ['block5d_se_excite[0][0]'] \n"," \n"," block5d_project_bn (BatchNorma (None, 14, 14, 120) 480 ['block5d_project_conv[0][0]'] \n"," lization) \n"," \n"," block5d_drop (Dropout) (None, 14, 14, 120) 0 ['block5d_project_bn[0][0]'] \n"," \n"," block5d_add (Add) (None, 14, 14, 120) 0 ['block5d_drop[0][0]', \n"," 'block5c_add[0][0]'] \n"," \n"," block6a_expand_conv (Conv2D) (None, 14, 14, 720) 86400 ['block5d_add[0][0]'] \n"," \n"," block6a_expand_bn (BatchNormal (None, 14, 14, 720) 2880 ['block6a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6a_expand_activation (Act (None, 14, 14, 720) 0 ['block6a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6a_dwconv_pad (ZeroPaddin (None, 17, 17, 720) 0 ['block6a_expand_activation[0][0]\n"," g2D) '] \n"," \n"," block6a_dwconv (DepthwiseConv2 (None, 7, 7, 720) 18000 ['block6a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block6a_bn (BatchNormalization (None, 7, 7, 720) 2880 ['block6a_dwconv[0][0]'] \n"," ) \n"," \n"," block6a_activation (Activation (None, 7, 7, 720) 0 ['block6a_bn[0][0]'] \n"," ) \n"," \n"," block6a_se_squeeze (GlobalAver (None, 720) 0 ['block6a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6a_se_reshape (Reshape) (None, 1, 1, 720) 0 ['block6a_se_squeeze[0][0]'] \n"," \n"," block6a_se_reduce (Conv2D) (None, 1, 1, 30) 21630 ['block6a_se_reshape[0][0]'] \n"," \n"," block6a_se_expand (Conv2D) (None, 1, 1, 720) 22320 ['block6a_se_reduce[0][0]'] \n"," \n"," block6a_se_excite (Multiply) (None, 7, 7, 720) 0 ['block6a_activation[0][0]', \n"," 'block6a_se_expand[0][0]'] \n"," \n"," block6a_project_conv (Conv2D) (None, 7, 7, 208) 149760 ['block6a_se_excite[0][0]'] \n"," \n"," block6a_project_bn (BatchNorma (None, 7, 7, 208) 832 ['block6a_project_conv[0][0]'] \n"," lization) \n"," \n"," block6b_expand_conv (Conv2D) (None, 7, 7, 1248) 259584 ['block6a_project_bn[0][0]'] \n"," \n"," block6b_expand_bn (BatchNormal (None, 7, 7, 1248) 4992 ['block6b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6b_expand_activation (Act (None, 7, 7, 1248) 0 ['block6b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1248) 31200 ['block6b_expand_activation[0][0]\n"," D) '] \n"," \n"," block6b_bn (BatchNormalization (None, 7, 7, 1248) 4992 ['block6b_dwconv[0][0]'] \n"," ) \n"," \n"," block6b_activation (Activation (None, 7, 7, 1248) 0 ['block6b_bn[0][0]'] \n"," ) \n"," \n"," block6b_se_squeeze (GlobalAver (None, 1248) 0 ['block6b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6b_se_reshape (Reshape) (None, 1, 1, 1248) 0 ['block6b_se_squeeze[0][0]'] \n"," \n"," block6b_se_reduce (Conv2D) (None, 1, 1, 52) 64948 ['block6b_se_reshape[0][0]'] \n"," \n"," block6b_se_expand (Conv2D) (None, 1, 1, 1248) 66144 ['block6b_se_reduce[0][0]'] \n"," \n"," block6b_se_excite (Multiply) (None, 7, 7, 1248) 0 ['block6b_activation[0][0]', \n"," 'block6b_se_expand[0][0]'] \n"," \n"," block6b_project_conv (Conv2D) (None, 7, 7, 208) 259584 ['block6b_se_excite[0][0]'] \n"," \n"," block6b_project_bn (BatchNorma (None, 7, 7, 208) 832 ['block6b_project_conv[0][0]'] \n"," lization) \n"," \n"," block6b_drop (Dropout) (None, 7, 7, 208) 0 ['block6b_project_bn[0][0]'] \n"," \n"," block6b_add (Add) (None, 7, 7, 208) 0 ['block6b_drop[0][0]', \n"," 'block6a_project_bn[0][0]'] \n"," \n"," block6c_expand_conv (Conv2D) (None, 7, 7, 1248) 259584 ['block6b_add[0][0]'] \n"," \n"," block6c_expand_bn (BatchNormal (None, 7, 7, 1248) 4992 ['block6c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6c_expand_activation (Act (None, 7, 7, 1248) 0 ['block6c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1248) 31200 ['block6c_expand_activation[0][0]\n"," D) '] \n"," \n"," block6c_bn (BatchNormalization (None, 7, 7, 1248) 4992 ['block6c_dwconv[0][0]'] \n"," ) \n"," \n"," block6c_activation (Activation (None, 7, 7, 1248) 0 ['block6c_bn[0][0]'] \n"," ) \n"," \n"," block6c_se_squeeze (GlobalAver (None, 1248) 0 ['block6c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6c_se_reshape (Reshape) (None, 1, 1, 1248) 0 ['block6c_se_squeeze[0][0]'] \n"," \n"," block6c_se_reduce (Conv2D) (None, 1, 1, 52) 64948 ['block6c_se_reshape[0][0]'] \n"," \n"," block6c_se_expand (Conv2D) (None, 1, 1, 1248) 66144 ['block6c_se_reduce[0][0]'] \n"," \n"," block6c_se_excite (Multiply) (None, 7, 7, 1248) 0 ['block6c_activation[0][0]', \n"," 'block6c_se_expand[0][0]'] \n"," \n"," block6c_project_conv (Conv2D) (None, 7, 7, 208) 259584 ['block6c_se_excite[0][0]'] \n"," \n"," block6c_project_bn (BatchNorma (None, 7, 7, 208) 832 ['block6c_project_conv[0][0]'] \n"," lization) \n"," \n"," block6c_drop (Dropout) (None, 7, 7, 208) 0 ['block6c_project_bn[0][0]'] \n"," \n"," block6c_add (Add) (None, 7, 7, 208) 0 ['block6c_drop[0][0]', \n"," 'block6b_add[0][0]'] \n"," \n"," block6d_expand_conv (Conv2D) (None, 7, 7, 1248) 259584 ['block6c_add[0][0]'] \n"," \n"," block6d_expand_bn (BatchNormal (None, 7, 7, 1248) 4992 ['block6d_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6d_expand_activation (Act (None, 7, 7, 1248) 0 ['block6d_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1248) 31200 ['block6d_expand_activation[0][0]\n"," D) '] \n"," \n"," block6d_bn (BatchNormalization (None, 7, 7, 1248) 4992 ['block6d_dwconv[0][0]'] \n"," ) \n"," \n"," block6d_activation (Activation (None, 7, 7, 1248) 0 ['block6d_bn[0][0]'] \n"," ) \n"," \n"," block6d_se_squeeze (GlobalAver (None, 1248) 0 ['block6d_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6d_se_reshape (Reshape) (None, 1, 1, 1248) 0 ['block6d_se_squeeze[0][0]'] \n"," \n"," block6d_se_reduce (Conv2D) (None, 1, 1, 52) 64948 ['block6d_se_reshape[0][0]'] \n"," \n"," block6d_se_expand (Conv2D) (None, 1, 1, 1248) 66144 ['block6d_se_reduce[0][0]'] \n"," \n"," block6d_se_excite (Multiply) (None, 7, 7, 1248) 0 ['block6d_activation[0][0]', \n"," 'block6d_se_expand[0][0]'] \n"," \n"," block6d_project_conv (Conv2D) (None, 7, 7, 208) 259584 ['block6d_se_excite[0][0]'] \n"," \n"," block6d_project_bn (BatchNorma (None, 7, 7, 208) 832 ['block6d_project_conv[0][0]'] \n"," lization) \n"," \n"," block6d_drop (Dropout) (None, 7, 7, 208) 0 ['block6d_project_bn[0][0]'] \n"," \n"," block6d_add (Add) (None, 7, 7, 208) 0 ['block6d_drop[0][0]', \n"," 'block6c_add[0][0]'] \n"," \n"," block6e_expand_conv (Conv2D) (None, 7, 7, 1248) 259584 ['block6d_add[0][0]'] \n"," \n"," block6e_expand_bn (BatchNormal (None, 7, 7, 1248) 4992 ['block6e_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6e_expand_activation (Act (None, 7, 7, 1248) 0 ['block6e_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1248) 31200 ['block6e_expand_activation[0][0]\n"," D) '] \n"," \n"," block6e_bn (BatchNormalization (None, 7, 7, 1248) 4992 ['block6e_dwconv[0][0]'] \n"," ) \n"," \n"," block6e_activation (Activation (None, 7, 7, 1248) 0 ['block6e_bn[0][0]'] \n"," ) \n"," \n"," block6e_se_squeeze (GlobalAver (None, 1248) 0 ['block6e_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6e_se_reshape (Reshape) (None, 1, 1, 1248) 0 ['block6e_se_squeeze[0][0]'] \n"," \n"," block6e_se_reduce (Conv2D) (None, 1, 1, 52) 64948 ['block6e_se_reshape[0][0]'] \n"," \n"," block6e_se_expand (Conv2D) (None, 1, 1, 1248) 66144 ['block6e_se_reduce[0][0]'] \n"," \n"," block6e_se_excite (Multiply) (None, 7, 7, 1248) 0 ['block6e_activation[0][0]', \n"," 'block6e_se_expand[0][0]'] \n"," \n"," block6e_project_conv (Conv2D) (None, 7, 7, 208) 259584 ['block6e_se_excite[0][0]'] \n"," \n"," block6e_project_bn (BatchNorma (None, 7, 7, 208) 832 ['block6e_project_conv[0][0]'] \n"," lization) \n"," \n"," block6e_drop (Dropout) (None, 7, 7, 208) 0 ['block6e_project_bn[0][0]'] \n"," \n"," block6e_add (Add) (None, 7, 7, 208) 0 ['block6e_drop[0][0]', \n"," 'block6d_add[0][0]'] \n"," \n"," block7a_expand_conv (Conv2D) (None, 7, 7, 1248) 259584 ['block6e_add[0][0]'] \n"," \n"," block7a_expand_bn (BatchNormal (None, 7, 7, 1248) 4992 ['block7a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block7a_expand_activation (Act (None, 7, 7, 1248) 0 ['block7a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1248) 11232 ['block7a_expand_activation[0][0]\n"," D) '] \n"," \n"," block7a_bn (BatchNormalization (None, 7, 7, 1248) 4992 ['block7a_dwconv[0][0]'] \n"," ) \n"," \n"," block7a_activation (Activation (None, 7, 7, 1248) 0 ['block7a_bn[0][0]'] \n"," ) \n"," \n"," block7a_se_squeeze (GlobalAver (None, 1248) 0 ['block7a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block7a_se_reshape (Reshape) (None, 1, 1, 1248) 0 ['block7a_se_squeeze[0][0]'] \n"," \n"," block7a_se_reduce (Conv2D) (None, 1, 1, 52) 64948 ['block7a_se_reshape[0][0]'] \n"," \n"," block7a_se_expand (Conv2D) (None, 1, 1, 1248) 66144 ['block7a_se_reduce[0][0]'] \n"," \n"," block7a_se_excite (Multiply) (None, 7, 7, 1248) 0 ['block7a_activation[0][0]', \n"," 'block7a_se_expand[0][0]'] \n"," \n"," block7a_project_conv (Conv2D) (None, 7, 7, 352) 439296 ['block7a_se_excite[0][0]'] \n"," \n"," block7a_project_bn (BatchNorma (None, 7, 7, 352) 1408 ['block7a_project_conv[0][0]'] \n"," lization) \n"," \n"," block7b_expand_conv (Conv2D) (None, 7, 7, 2112) 743424 ['block7a_project_bn[0][0]'] \n"," \n"," block7b_expand_bn (BatchNormal (None, 7, 7, 2112) 8448 ['block7b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block7b_expand_activation (Act (None, 7, 7, 2112) 0 ['block7b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block7b_dwconv (DepthwiseConv2 (None, 7, 7, 2112) 19008 ['block7b_expand_activation[0][0]\n"," D) '] \n"," \n"," block7b_bn (BatchNormalization (None, 7, 7, 2112) 8448 ['block7b_dwconv[0][0]'] \n"," ) \n"," \n"," block7b_activation (Activation (None, 7, 7, 2112) 0 ['block7b_bn[0][0]'] \n"," ) \n"," \n"," block7b_se_squeeze (GlobalAver (None, 2112) 0 ['block7b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block7b_se_reshape (Reshape) (None, 1, 1, 2112) 0 ['block7b_se_squeeze[0][0]'] \n"," \n"," block7b_se_reduce (Conv2D) (None, 1, 1, 88) 185944 ['block7b_se_reshape[0][0]'] \n"," \n"," block7b_se_expand (Conv2D) (None, 1, 1, 2112) 187968 ['block7b_se_reduce[0][0]'] \n"," \n"," block7b_se_excite (Multiply) (None, 7, 7, 2112) 0 ['block7b_activation[0][0]', \n"," 'block7b_se_expand[0][0]'] \n"," \n"," block7b_project_conv (Conv2D) (None, 7, 7, 352) 743424 ['block7b_se_excite[0][0]'] \n"," \n"," block7b_project_bn (BatchNorma (None, 7, 7, 352) 1408 ['block7b_project_conv[0][0]'] \n"," lization) \n"," \n"," block7b_drop (Dropout) (None, 7, 7, 352) 0 ['block7b_project_bn[0][0]'] \n"," \n"," block7b_add (Add) (None, 7, 7, 352) 0 ['block7b_drop[0][0]', \n"," 'block7a_project_bn[0][0]'] \n"," \n"," top_conv (Conv2D) (None, 7, 7, 1408) 495616 ['block7b_add[0][0]'] \n"," \n"," top_bn (BatchNormalization) (None, 7, 7, 1408) 5632 ['top_conv[0][0]'] \n"," \n"," top_activation (Activation) (None, 7, 7, 1408) 0 ['top_bn[0][0]'] \n"," \n","==================================================================================================\n","Total params: 7,768,569\n","Trainable params: 7,700,994\n","Non-trainable params: 67,575\n","__________________________________________________________________________________________________\n"]}],"source":["model_eff.summary()"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"V8S9muqjygVf"},"outputs":[],"source":["# Add layers at the end\n","X = base_model.output\n","X = Flatten()(X)\n","\n","X = Dense(512, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","\n","model_res = Model(inputs=base_model.input, outputs=output)\n"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"sTWbthhlygVf"},"outputs":[],"source":["X = model_eff.output\n","X = Flatten()(X)\n","\n","X = Dense(512, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","model_eff = Model(inputs=model_eff.input, outputs=output)"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"MTAzL3WNygVf"},"outputs":[],"source":["optimizer = Adam(learning_rate=0.0001)\n","model_res.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"Pzv37_2kygVf"},"outputs":[],"source":["n_epoch = 10\n","\n","early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1, \n"," mode='auto', restore_best_weights=True)\n","\n","reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, \n"," verbose=1, mode='auto')"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"Qh49OTuGygVf"},"outputs":[],"source":["class_weights = artists_top['class_weight'].to_dict()"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"pGlpUMbsygVf","outputId":"06d1545e-c4db-48b9-9cb9-03b4dea02af6"},"outputs":[{"data":{"text/plain":["{0: 0.44563076604125634,\n"," 1: 0.5567210567210568,\n"," 2: 0.8902464278318493,\n"," 3: 1.1631493506493507,\n"," 4: 1.1915188470066518,\n"," 5: 1.2566501023092662,\n"," 6: 1.3430178069353327,\n"," 7: 1.491672449687717,\n"," 8: 1.5089505089505089,\n"," 9: 1.532620320855615,\n"," 10: 1.6352225180677062}"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["class_weights"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[],"source":["from tensorflow.python.distribute.collective_all_reduce_strategy import CollectiveAllReduceExtended\n","CollectiveAllReduceExtended._enable_check_health = False"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[],"source":["class MyRunConfig(tf.estimator.RunConfig):\n"," def __init__(self, **kwargs):\n"," super().__init__(**kwargs)\n","\n"," def __deepcopy__(self, memo={}):\n"," cls = self.__class__\n"," result = cls.__new__(cls)\n"," memo[id(self)] = result\n"," for k, v in self.__dict__.items():\n"," if '_distribute' in k:\n"," setattr(result, k, v)\n"," else:\n"," setattr(result, k, deepcopy(v, memo))\n"," return result\n","\n","class MyDistributeStrategy(tf.distribute.MultiWorkerMirroredStrategy):\n"," def __init__(self, **kwargs):\n"," super().__init__(**kwargs)\n","\n"," def __deepcopy__(self, memo={}):\n"," cls = self.__class__\n"," result = cls.__new__(cls)\n"," memo[id(self)] = result\n"," for k, v in self.__dict__.items():\n"," if '_extend' in k:\n"," setattr(result, k, v)\n"," else:\n"," setattr(result, k, deepcopy(v, memo))\n"," return result"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING:tensorflow:Collective ops is not configured at program startup. Some performance features may not be enabled.\n","INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:CPU:0',), communication = CommunicationImplementation.AUTO\n","INFO:tensorflow:Initializing RunConfig with distribution strategies.\n","INFO:tensorflow:Not using Distribute Coordinator.\n"]}],"source":["strategy = MyDistributeStrategy()\n","config = MyRunConfig(train_distribute=strategy)"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"r6RIDryhygVg","outputId":"e806582a-eb78-45a3-dca3-6cdcb2bc7703"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n"]},{"name":"stderr","output_type":"stream","text":["Exception in thread Thread-58:\n","Traceback (most recent call last):\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\threading.py\", line 973, in _bootstrap_inner\n"," self.run()\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\threading.py\", line 910, in run\n"," self._target(*self._args, **self._kwargs)\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\keras\\utils\\data_utils.py\", line 759, in _run\n"," with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\site-packages\\keras\\utils\\data_utils.py\", line 736, in pool_fn\n"," pool = get_pool_class(True)(\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\context.py\", line 119, in Pool\n"," return Pool(processes, initializer, initargs, maxtasksperchild,\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\pool.py\", line 212, in __init__\n"," self._repopulate_pool()\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\pool.py\", line 303, in _repopulate_pool\n"," return self._repopulate_pool_static(self._ctx, self.Process,\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\pool.py\", line 326, in _repopulate_pool_static\n"," w.start()\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\process.py\", line 121, in start\n"," self._popen = self._Popen(self)\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\context.py\", line 327, in _Popen\n"," return Popen(process_obj)\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\popen_spawn_win32.py\", line 93, in __init__\n"," reduction.dump(process_obj, to_child)\n"," File \"c:\\Users\\nikla\\anaconda3\\envs\\NLP\\lib\\multiprocessing\\reduction.py\", line 60, in dump\n"," ForkingPickler(file, protocol).dump(obj)\n","TypeError: cannot pickle '_thread.lock' object\n"]}],"source":["history1 = model_res.fit(train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l2w0_NB7ygVg","outputId":"5539a6bd-f759-44ad-b890-c46ecece5a3c"},"outputs":[{"ename":"NameError","evalue":"name 'model_res' is not defined","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfor\u001b[39;00m layer \u001b[39min\u001b[39;00m model_res\u001b[39m.\u001b[39mlayers:\n\u001b[1;32m 2\u001b[0m layer\u001b[39m.\u001b[39mtrainable \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[39mfor\u001b[39;00m layer \u001b[39min\u001b[39;00m model_res\u001b[39m.\u001b[39mlayers[:\u001b[39m50\u001b[39m]:\n","\u001b[0;31mNameError\u001b[0m: name 'model_res' is not defined"]}],"source":["for layer in model_res.layers:\n"," layer.trainable = False\n","\n","for layer in model_res.layers[:50]:\n"," layer.trainable = True\n","\n","optimizer = Adam(lr=0.0001)\n","\n","model_res.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])\n","\n","n_epoch = 10\n","history2 = model_res.fit(train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr, early_stop],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E8uYgVo5ygVg","outputId":"0134855d-ed5c-4906-a09a-609f1adbc783"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"]}],"source":["optimizer = Adam(lr=0.0001)\n","model_eff.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kichqldcygVg","outputId":"35ca8843-ded8-4b88-b331-def3254e3ed1"},"outputs":[{"ename":"AttributeError","evalue":"module 'tensorflow' has no attribute 'contrib'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39msetattr\u001b[39m(tf\u001b[39m.\u001b[39;49mcontrib\u001b[39m.\u001b[39mrnn\u001b[39m.\u001b[39mGRUCell, \u001b[39m'\u001b[39m\u001b[39m__deepcopy__\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mlambda\u001b[39;00m \u001b[39mself\u001b[39m, _: \u001b[39mself\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[39msetattr\u001b[39m(tf\u001b[39m.\u001b[39mcontrib\u001b[39m.\u001b[39mrnn\u001b[39m.\u001b[39mBasicLSTMCell, \u001b[39m'\u001b[39m\u001b[39m__deepcopy__\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mlambda\u001b[39;00m \u001b[39mself\u001b[39m, _: \u001b[39mself\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[39msetattr\u001b[39m(tf\u001b[39m.\u001b[39mcontrib\u001b[39m.\u001b[39mrnn\u001b[39m.\u001b[39mMultiRNNCell, \u001b[39m'\u001b[39m\u001b[39m__deepcopy__\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mlambda\u001b[39;00m \u001b[39mself\u001b[39m, _: \u001b[39mself\u001b[39m)\n","\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'contrib'"]}],"source":["setattr(tf.contrib.rnn.GRUCell, '__deepcopy__', lambda self, _: self)\n","setattr(tf.contrib.rnn.BasicLSTMCell, '__deepcopy__', lambda self, _: self)\n","setattr(tf.contrib.rnn.MultiRNNCell, '__deepcopy__', lambda self, _: self)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Q6teIURygVg","outputId":"e8d7a81d-816b-4cff-ee4a-7b9651428e35"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n"]},{"name":"stderr","output_type":"stream","text":["Exception in thread Thread-35:\n","Traceback (most recent call last):\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/threading.py\", line 980, in _bootstrap_inner\n"," self.run()\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/threading.py\", line 917, in run\n"," self._target(*self._args, **self._kwargs)\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/site-packages/keras/utils/data_utils.py\", line 781, in _run\n"," with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/site-packages/keras/utils/data_utils.py\", line 756, in pool_fn\n"," pool = get_pool_class(True)(\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/context.py\", line 119, in Pool\n"," return Pool(processes, initializer, initargs, maxtasksperchild,\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/pool.py\", line 212, in __init__\n"," self._repopulate_pool()\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/pool.py\", line 303, in _repopulate_pool\n"," return self._repopulate_pool_static(self._ctx, self.Process,\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/pool.py\", line 326, in _repopulate_pool_static\n"," w.start()\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/process.py\", line 121, in start\n"," self._popen = self._Popen(self)\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/context.py\", line 284, in _Popen\n"," return Popen(process_obj)\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/popen_spawn_posix.py\", line 32, in __init__\n"," super().__init__(process_obj)\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/popen_fork.py\", line 19, in __init__\n"," self._launch(process_obj)\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/popen_spawn_posix.py\", line 47, in _launch\n"," reduction.dump(process_obj, fp)\n"," File \"/Users/niclascramer/opt/miniconda3/envs/tensorflow/lib/python3.9/multiprocessing/reduction.py\", line 60, in dump\n"," ForkingPickler(file, protocol).dump(obj)\n","TypeError: cannot pickle '_thread.lock' object\n"]}],"source":["n_epoch = 10\n","history1_eff = model_eff.fit(train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"saH85928ygVg"},"outputs":[],"source":["for layer in model_eff.layers:\n"," layer.trainable = False\n","\n","for layer in model_eff.layers[:50]:\n"," layer.trainable = True\n","\n","optimizer = Adam(lr=0.0001)\n","\n","model_eff.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])\n","\n","n_epoch = 50\n","history2_eff = model_eff.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr, early_stop],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1tcvwUIgygVg"},"outputs":[],"source":["# Merge history1 and history2\n","history = {}\n","history['loss'] = history1.history['loss'] + history2.history['loss']\n","history['acc'] = history1.history['acc'] + history2.history['acc']\n","history['val_loss'] = history1.history['val_loss'] + history2.history['val_loss']\n","history['val_acc'] = history1.history['val_acc'] + history2.history['val_acc']\n","history['lr'] = history1.history['lr'] + history2.history['lr']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zZJtggFnygVg"},"outputs":[],"source":["# Merge history1 and history2\n","history_eff = {}\n","history_eff['loss'] = history1_eff.history['loss'] + history2_eff.history['loss']\n","history_eff['acc'] = history1_eff.history['acc'] + history2_eff.history['acc']\n","history_eff['val_loss'] = history1_eff.history['val_loss'] + history2_eff.history['val_loss']\n","history_eff['val_acc'] = history1_eff.history['val_acc'] + history2_eff.history['val_acc']\n","history_eff['lr'] = history1_eff.history['lr'] + history2_eff.history['lr']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tH8PK7OmygVg"},"outputs":[],"source":["def plot_training(history):\n"," acc = history['acc']\n"," val_acc = history['val_acc']\n"," loss = history['loss']\n"," val_loss = history['val_loss']\n"," epochs = range(len(acc))\n","\n"," fig, axes = plt.subplots(1, 2, figsize=(15,5))\n"," \n"," axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n"," axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n"," axes[0].set_title('Training and Validation Accuracy')\n"," axes[0].legend(loc='best')\n","\n"," axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n"," axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n"," axes[1].set_title('Training and Validation Loss')\n"," axes[1].legend(loc='best')\n"," \n"," plt.show()\n"," \n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZfJNYGivygVg"},"outputs":[],"source":["def plot_training(history):\n"," acc = history['acc']\n"," val_acc = history['val_acc']\n"," loss = history['loss']\n"," val_loss = history['val_loss']\n"," epochs = range(len(acc))\n","\n"," fig, axes = plt.subplots(1, 2, figsize=(15,5))\n"," \n"," axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n"," axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n"," axes[0].set_title('Training and Validation Accuracy')\n"," axes[0].legend(loc='best')\n","\n"," axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n"," axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n"," axes[1].set_title('Training and Validation Loss')\n"," axes[1].legend(loc='best')\n"," \n"," plt.show()\n"," \n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Dg9MVSKKygVg"},"outputs":[],"source":["plot_training(history)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l8tw8fRtygVg"},"outputs":[],"source":["plot_training(history_eff)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2-iRjugEygVg"},"outputs":[],"source":["# Prediction accuracy on train data\n","score = model_res.evaluate_generator(train_generator, verbose=1)\n","print(\"Prediction accuracy on train data =\", score[1])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jSunpbRaygVg"},"outputs":[],"source":["score_eff = model_eff.evaluate_generator(train_generator, verbose=1)\n","print(\"Prediction accuracy on train data =\", score_eff[1])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tdAV9LMrygVg"},"outputs":[],"source":["url = 'https://www.gpsmycity.com/img/gd/2081.jpg'\n","\n","import imageio\n","import cv2\n","\n","web_image = imageio.imread(url)\n","web_image = cv2.resize(web_image, dsize=train_input_shape[0:2], )\n","web_image = image.img_to_array(web_image)\n","web_image /= 255.\n","web_image = np.expand_dims(web_image, axis=0)\n","\n","\n","prediction = model_res.predict(web_image)\n","prediction_probability = np.amax(prediction)\n","prediction_idx = np.argmax(prediction)\n","\n","print(\"Predicted artist =\", labels[prediction_idx].replace('_', ' '))\n","print(\"Prediction probability =\", prediction_probability*100, \"%\")\n","\n","plt.imshow(imageio.imread(url))\n","plt.axis('off')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f7sLT3WsygVg"},"outputs":[],"source":["url = 'https://www.gpsmycity.com/img/gd/2081.jpg'\n","\n","import imageio\n","import cv2\n","\n","web_image = imageio.imread(url)\n","web_image = cv2.resize(web_image, dsize=train_input_shape[0:2], )\n","web_image = image.img_to_array(web_image)\n","web_image /= 255.\n","web_image = np.expand_dims(web_image, axis=0)\n","\n","\n","prediction = model_eff.predict(web_image)\n","prediction_probability = np.amax(prediction)\n","prediction_idx = np.argmax(prediction)\n","\n","print(\"Predicted artist =\", labels[prediction_idx].replace('_', ' '))\n","print(\"Prediction probability =\", prediction_probability*100, \"%\")\n","\n","plt.imshow(imageio.imread(url))\n","plt.axis('off')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k5aEiFGaygVh"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jGriLpmKygVh"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"NLP","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]"},"vscode":{"interpreter":{"hash":"cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980"}}},"nbformat":4,"nbformat_minor":0} diff --git a/Bonus/Bonus 4/bonus4.ipynb b/Bonus/Bonus 4/bonus4.ipynb new file mode 100644 index 0000000..1ec5b6e --- /dev/null +++ b/Bonus/Bonus 4/bonus4.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"lX4yavP3Z8Hp"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python Platform: Windows-10-10.0.22621-SP0\n","Tensor Flow Version: 2.9.1\n","Keras Version: 2.9.0\n","\n","Python 3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]\n","Pandas 1.4.3\n","Scikit-Learn 1.1.1\n","GPU is NOT AVAILABLE\n"]}],"source":["import sys\n","\n","import tensorflow.keras\n","import pandas as pd\n","import sklearn as sk\n","import tensorflow as tf\n","import platform\n","\n","print(f\"Python Platform: {platform.platform()}\")\n","print(f\"Tensor Flow Version: {tf.__version__}\")\n","print(f\"Keras Version: {tensorflow.keras.__version__}\")\n","print()\n","print(f\"Python {sys.version}\")\n","print(f\"Pandas {pd.__version__}\")\n","print(f\"Scikit-Learn {sk.__version__}\")\n","gpu = len(tf.config.list_physical_devices('GPU'))>0\n","print(\"GPU is\", \"available\" if gpu else \"NOT AVAILABLE\")"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"KN5Hf_04Z8Hr"},"outputs":[{"ename":"TypeError","evalue":"'module' object is not callable","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[1;32mc:\\Users\\nikla\\workspace\\Machine Learning DHBW\\Bonusaufgabe 2\\Advance-Machine-Learning\\Bonus\\Bonus 4\\bonus4.ipynb Cell 2\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\"\"\u001b[39m\n\u001b[0;32m 2\u001b[0m \u001b[39mNum GPUs Available: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(tf\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mlist_physical_devices(\u001b[39m'\u001b[39m\u001b[39mGPU\u001b[39m\u001b[39m'\u001b[39m))\u001b[39m}\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[39mUse Tensorflow CUDA: \u001b[39m\u001b[39m{\u001b[39;00mtf\u001b[39m.\u001b[39mtest()\u001b[39m}\u001b[39;00m\u001b[39m\"\"\"\u001b[39m)\n","\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable"]}],"source":["print(f\"\"\"\n","Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}\n","Use Tensorflow CUDA: {tf.test()}\"\"\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6Et5mlt2Z8Hr"},"outputs":[],"source":["tf.device('mps')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NtWIL5dlZ8Hr"},"outputs":[],"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader\n","from PIL import Image\n","\n","from os import listdir\n","\n","from torchvision import transforms, utils\n","from skimage import io, transform\n","from torchvision import transforms \n","\n","import pandas as pd \n","import numpy as np\n","import seaborn as sb\n","import matplotlib.pyplot as plt\n","import cv2\n","from tqdm import tqdm\n","\n","from sklearn.model_selection import train_test_split\n","import glob"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zItZjXcTZ8Hr"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_jUSPAtGZ8Hs"},"outputs":[],"source":["df=pd.read_csv('/Users/niclascramer/Dev/Uni/Advance Machine Learning/Bonus 4/artists/artists.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T78EiBZ1Z8Hs"},"outputs":[],"source":["df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oPyvuxE1Z8Hs"},"outputs":[],"source":["len(df.name)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-V3T8viKZ8Hs"},"outputs":[],"source":["\n","mypath = 'artists/resized'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a75VKe9cZ8Ht"},"outputs":[],"source":["from os import listdir\n","from os.path import isfile, join\n","onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NwFYhaJhZ8Ht"},"outputs":[],"source":["onlyfiles"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"okXiu0Y2Z8Ht"},"outputs":[],"source":["import re\n","def get_numbers_from_filename(filename):\n"," return re.search(r'\\d+', filename).group(0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"70ukO3-JZ8Ht"},"outputs":[],"source":["num_list=[]\n","for filename in onlyfiles:\n"," num_list.append(get_numbers_from_filename(filename))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z4W8bJ6vZ8Ht"},"outputs":[],"source":["num_list"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CIrACi2nZ8Hu"},"outputs":[],"source":["data_list=[]\n","for name in tqdm(df.name.tolist()):\n"," for file in onlyfiles:\n"," if all(x in file for x in name.split(' ')):\n"," data_list.append([name,get_numbers_from_filename(file),f'{mypath}/{file}'])\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f_t8jZWdZ8Hu"},"outputs":[],"source":["df_bilder=pd.DataFrame(data=data_list,columns=['name','num','path'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EeRvMgyNZ8Hu"},"outputs":[],"source":["df=df_bilder.merge(df,on='name')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uaZ1LIDEZ8Hu"},"outputs":[],"source":["y=df.name.tolist()\n","X=df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nSY4ReheZ8Hu"},"outputs":[],"source":["random_seed=42\n","test_size=0.3\n","\n","X_train_all, X_test_all, y_train_all, y_test_all = train_test_split(X, y, random_state=random_seed, test_size=test_size)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zf3SKIPtZ8Hu"},"outputs":[],"source":["!kaggle kernels output supratimhaldar/deepartist-identify-artist-from-art -p /path/to/dest\n"]},{"cell_type":"markdown","metadata":{"id":"s3LkhKmUZ8Hu"},"source":["## Tensflow "]},{"cell_type":"markdown","metadata":{"id":"GLYB5ZqaZ8Hv"},"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ieDhJR7UZ8Hv"},"outputs":[],"source":["# Import libraries\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import json\n","import os\n","from tqdm import tqdm, tqdm_notebook\n","import random\n","\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential, Model\n","from tensorflow.keras.layers import *\n","from tensorflow.keras.optimizers import *\n","from tensorflow.keras.applications import *\n","from tensorflow.keras.callbacks import *\n","from tensorflow.keras.initializers import *\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","\n","from numpy.random import seed\n","seed(42)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"laj-GmLPZ8Hw"},"outputs":[],"source":["#tf.device('mps')\n","tf.device('/cpu:0')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_eKKqEEtZ8Hw"},"outputs":[],"source":["!where tensorflow"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lRWB7USPZ8Hw"},"outputs":[],"source":["print(\"TensorFlow version:\", tf.__version__)\n","print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n","tf.config.list_physical_devices('GPU')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SGuQJPayZ8Hw"},"outputs":[],"source":["# import tensorflow as tf\n","\n","# cifar = tf.keras.datasets.cifar100\n","# (x_train, y_train), (x_test, y_test) = cifar.load_data()\n","# model = tf.keras.applications.ResNet50(\n","# include_top=True,\n","# weights=None,\n","# input_shape=(32, 32, 3),\n","# classes=100,)\n","\n","# loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n","# model.compile(optimizer=\"adam\", loss=loss_fn, metrics=[\"accuracy\"])\n","# model.fit(x_train, y_train, epochs=5, batch_size=64)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nN-P9KL8Z8Hw"},"outputs":[],"source":["tf.random.set_seed(42)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1KR0Pxz8bqsS"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive/')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bnkqgZGVZ8Hw"},"outputs":[],"source":["artists = pd.read_csv('/content/drive/MyDrive/Bonus 4/artists.csv')\n","artists.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sAcT41iFaGCj"},"outputs":[],"source":["\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y2fpuAW_Z8Hw"},"outputs":[],"source":["artists.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fKCt5mqeZ8Hw"},"outputs":[],"source":["# Sort artists by number of paintings\n","artists = artists.sort_values(by=['paintings'], ascending=False)\n","\n","# Create a dataframe with artists having more than 200 paintings\n","artists_top = artists[artists['paintings'] >= 200].reset_index()\n","artists_top = artists_top[['name', 'paintings']]\n","#artists_top['class_weight'] = max(artists_top.paintings)/artists_top.paintings\n","artists_top['class_weight'] = artists_top.paintings.sum() / (artists_top.shape[0] * artists_top.paintings)\n","artists_top"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m_AXlYNRZ8Hw"},"outputs":[],"source":["images_dir = '/content/drive/MyDrive/Bonus 4/images'\n","artists_dirs = os.listdir(images_dir)\n","artists_top_name = artists_top['name'].str.replace(' ', '_').values"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VPhJTF-SZ8Hx"},"outputs":[],"source":["# Augment data\n","batch_size = 16\n","train_input_shape = (224, 224, 3)\n","n_classes = artists_top.shape[0]\n","\n","train_datagen = ImageDataGenerator(validation_split=0.2,\n"," rescale=1./255.,\n"," #rotation_range=45,\n"," #width_shift_range=0.5,\n"," #height_shift_range=0.5,\n"," shear_range=5,\n"," #zoom_range=0.7,\n"," horizontal_flip=True,\n"," vertical_flip=True,\n"," )\n","\n","train_generator = train_datagen.flow_from_directory(directory=images_dir,\n"," class_mode='categorical',\n"," target_size=train_input_shape[0:2],\n"," batch_size=batch_size,\n"," subset=\"training\",\n"," shuffle=True,\n"," classes=artists_top_name.tolist()\n"," )\n","\n","valid_generator = train_datagen.flow_from_directory(directory=images_dir,\n"," class_mode='categorical',\n"," target_size=train_input_shape[0:2],\n"," batch_size=batch_size,\n"," subset=\"validation\",\n"," shuffle=True,\n"," classes=artists_top_name.tolist()\n"," )\n","\n","STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\n","STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n","print(\"Total number of batches =\", STEP_SIZE_TRAIN, \"and\", STEP_SIZE_VALID)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l7QyprP4Z8Hx"},"outputs":[],"source":["# Print a random paintings and it's random augmented version\n","fig, axes = plt.subplots(1, 2, figsize=(20,10))\n","\n","random_artist = random.choice(artists_top_name)\n","random_image = random.choice(os.listdir(os.path.join(images_dir, random_artist)))\n","random_image_file = os.path.join(images_dir, random_artist, random_image)\n","\n","# Original image\n","image = plt.imread(random_image_file)\n","axes[0].imshow(image)\n","axes[0].set_title(\"An original Image of \" + random_artist.replace('_', ' '))\n","axes[0].axis('off')\n","\n","# Transformed image\n","aug_image = train_datagen.random_transform(image)\n","axes[1].imshow(aug_image)\n","axes[1].set_title(\"A transformed Image of \" + random_artist.replace('_', ' '))\n","axes[1].axis('off')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"h5Szf7QhZ8Hx"},"outputs":[],"source":["# Load pre-trained model\n","base_model = ResNet50(weights='imagenet', include_top=False, input_shape=train_input_shape)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3-JUJsLyZ8Hx"},"outputs":[],"source":["for layer in base_model.layers:\n"," layer.trainable = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mQV3ls9kZ8Hx"},"outputs":[],"source":["base_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CTCp8YfbZ8Hx"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fG9OHJHwZ8Hx"},"outputs":[],"source":["model_eff=EfficientNetB2(weights='imagenet', include_top=False, input_shape=train_input_shape\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9kjWQ94xZ8Hx"},"outputs":[],"source":["for layer in model_eff.layers:\n"," layer.trainable = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6jtNhkGzZ8Hx"},"outputs":[],"source":["model_eff.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BUjzoKa5Z8Hx"},"outputs":[],"source":["# Add layers at the end\n","X = base_model.output\n","X = Flatten()(X)\n","\n","X = Dense(512, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","\n","model_res = Model(inputs=base_model.input, outputs=output)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0G4FXUX1Z8Hx"},"outputs":[],"source":["X = model_eff.output\n","X = Flatten()(X)\n","\n","X = Dense(512, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","#X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","model_eff = Model(inputs=model_eff.input, outputs=output)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FtOg72ZNZ8Hx"},"outputs":[],"source":["optimizer = Adam(lr=0.0001)\n","model_res.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5eLGBuxrZ8Hx"},"outputs":[],"source":["n_epoch = 10\n","\n","early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1, \n"," mode='auto', restore_best_weights=True)\n","\n","reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, \n"," verbose=1, mode='auto')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v9lZ2WFqZ8Hy"},"outputs":[],"source":["class_weights = artists_top['class_weight'].to_dict()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oMVCMAW-Z8Hy"},"outputs":[],"source":["class_weights"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p9nekFoUZ8Hy"},"outputs":[],"source":["tf.config.list_physical_devices('GPU')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ymeZJaaGZ8Hy"},"outputs":[],"source":["print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFj_93VgZ8Hy"},"outputs":[],"source":["import tensorflow as tf \n","\n","if tf.test.gpu_device_name(): \n","\n"," print('Default GPU Device:{}'.format(tf.test.gpu_device_name()))\n","\n","else:\n","\n"," print(\"Please install GPU version of TF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"naPfIXc6Z8Hy"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V6KBRj99Z8Hy"},"outputs":[],"source":["# Train the model - all layers\n","history1 = model_res.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yByEY0OvZ8Hy"},"outputs":[],"source":["for layer in model_res.layers:\n"," layer.trainable = False\n","\n","for layer in model_res.layers[:50]:\n"," layer.trainable = True\n","\n","optimizer = Adam(lr=0.0001)\n","\n","model_res.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])\n","\n","n_epoch = 10\n","history2 = model_res.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr, early_stop],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WEMXg0zJZ8Hy"},"outputs":[],"source":["optimizer = Adam(lr=0.0001)\n","model_eff.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AOJ2dOdLZ8Hy"},"outputs":[],"source":["history1_eff = model_eff.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3CDmQfv1Z8Hy"},"outputs":[],"source":["for layer in model_eff.layers:\n"," layer.trainable = False\n","\n","for layer in model_eff.layers[:50]:\n"," layer.trainable = True\n","\n","optimizer = Adam(lr=0.0001)\n","\n","model_eff.compile(loss='categorical_crossentropy',\n"," optimizer=optimizer, \n"," metrics=['accuracy'])\n","\n","n_epoch = 10\n","history2_eff = model_eff.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n"," validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n"," epochs=n_epoch,\n"," shuffle=True,\n"," verbose=1,\n"," callbacks=[reduce_lr, early_stop],\n"," use_multiprocessing=True,\n"," workers=16,\n"," class_weight=class_weights\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gih4q4ScZ8Hy"},"outputs":[],"source":["# Merge history1 and history2\n","history = {}\n","history['loss'] = history1.history['loss'] + history2.history['loss']\n","history['acc'] = history1.history['acc'] + history2.history['acc']\n","history['val_loss'] = history1.history['val_loss'] + history2.history['val_loss']\n","history['val_acc'] = history1.history['val_acc'] + history2.history['val_acc']\n","history['lr'] = history1.history['lr'] + history2.history['lr']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uFznWe-tZ8Hy"},"outputs":[],"source":["# Merge history1 and history2\n","history_eff = {}\n","history_eff['loss'] = history1_eff.history['loss'] + history2_eff.history['loss']\n","history_eff['acc'] = history1_eff.history['acc'] + history2_eff.history['acc']\n","history_eff['val_loss'] = history1_eff.history['val_loss'] + history2_eff.history['val_loss']\n","history_eff['val_acc'] = history1_eff.history['val_acc'] + history2_eff.history['val_acc']\n","history_eff['lr'] = history1_eff.history['lr'] + history2_eff.history['lr']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2h4qeewPZ8Hy"},"outputs":[],"source":["def plot_training(history):\n"," acc = history['acc']\n"," val_acc = history['val_acc']\n"," loss = history['loss']\n"," val_loss = history['val_loss']\n"," epochs = range(len(acc))\n","\n"," fig, axes = plt.subplots(1, 2, figsize=(15,5))\n"," \n"," axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n"," axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n"," axes[0].set_title('Training and Validation Accuracy')\n"," axes[0].legend(loc='best')\n","\n"," axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n"," axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n"," axes[1].set_title('Training and Validation Loss')\n"," axes[1].legend(loc='best')\n"," \n"," plt.show()\n"," \n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OTUJuvPxZ8Hy"},"outputs":[],"source":["def plot_training(history):\n"," acc = history['acc']\n"," val_acc = history['val_acc']\n"," loss = history['loss']\n"," val_loss = history['val_loss']\n"," epochs = range(len(acc))\n","\n"," fig, axes = plt.subplots(1, 2, figsize=(15,5))\n"," \n"," axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n"," axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n"," axes[0].set_title('Training and Validation Accuracy')\n"," axes[0].legend(loc='best')\n","\n"," axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n"," axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n"," axes[1].set_title('Training and Validation Loss')\n"," axes[1].legend(loc='best')\n"," \n"," plt.show()\n"," \n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kIWLQzQcZ8Hz"},"outputs":[],"source":["plot_training(history)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JadxbCexZ8Hz"},"outputs":[],"source":["plot_training(history_eff)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NpNOECoyZ8Hz"},"outputs":[],"source":["# Prediction accuracy on train data\n","score = model_res.evaluate_generator(train_generator, verbose=1)\n","print(\"Prediction accuracy on train data =\", score[1])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MNLiSPNXZ8Hz"},"outputs":[],"source":["score_eff = model_eff.evaluate_generator(train_generator, verbose=1)\n","print(\"Prediction accuracy on train data =\", score_eff[1])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PlNKBvP2Z8Hz"},"outputs":[],"source":["url = 'https://www.gpsmycity.com/img/gd/2081.jpg'\n","\n","import imageio\n","import cv2\n","\n","web_image = imageio.imread(url)\n","web_image = cv2.resize(web_image, dsize=train_input_shape[0:2], )\n","web_image = image.img_to_array(web_image)\n","web_image /= 255.\n","web_image = np.expand_dims(web_image, axis=0)\n","\n","\n","prediction = model_res.predict(web_image)\n","prediction_probability = np.amax(prediction)\n","prediction_idx = np.argmax(prediction)\n","\n","print(\"Predicted artist =\", labels[prediction_idx].replace('_', ' '))\n","print(\"Prediction probability =\", prediction_probability*100, \"%\")\n","\n","plt.imshow(imageio.imread(url))\n","plt.axis('off')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kr6ayT-qZ8Hz"},"outputs":[],"source":["url = 'https://www.gpsmycity.com/img/gd/2081.jpg'\n","\n","import imageio\n","import cv2\n","\n","web_image = imageio.imread(url)\n","web_image = cv2.resize(web_image, dsize=train_input_shape[0:2], )\n","web_image = image.img_to_array(web_image)\n","web_image /= 255.\n","web_image = np.expand_dims(web_image, axis=0)\n","\n","\n","prediction = model_eff.predict(web_image)\n","prediction_probability = np.amax(prediction)\n","prediction_idx = np.argmax(prediction)\n","\n","print(\"Predicted artist =\", labels[prediction_idx].replace('_', ' '))\n","print(\"Prediction probability =\", prediction_probability*100, \"%\")\n","\n","plt.imshow(imageio.imread(url))\n","plt.axis('off')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4ibgdbrFZ8Hz"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nJMjkAgVZ8Hz"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"private_outputs":true,"provenance":[],"toc_visible":true},"gpuClass":"standard","kernelspec":{"display_name":"NLP","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980"}}},"nbformat":4,"nbformat_minor":0} diff --git a/Bonus/Bonus 5/bonus5 (1).ipynb b/Bonus/Bonus 5/bonus5 (1).ipynb new file mode 100644 index 0000000..e0f7d52 --- /dev/null +++ b/Bonus/Bonus 5/bonus5 (1).ipynb @@ -0,0 +1,663 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nikla\\anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "from pathlib import Path\n", + "from torch.utils.data import Dataset, DataLoader, sampler\n", + "from PIL import Image\n", + "import torch\n", + "from torch import nn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class CloudDataset(Dataset):\n", + " def __init__(self, r_dir, g_dir, b_dir, nir_dir, gt_dir, pytorch=True):\n", + " super().__init__()\n", + " self.files = [self.combine_files(f, g_dir, b_dir, nir_dir, gt_dir) for f in r_dir.iterdir() if not f.is_dir()]\n", + " self.pytorch = pytorch\n", + " \n", + " def combine_files(self, r_file: Path, g_dir, b_dir,nir_dir, gt_dir):\n", + " \n", + " files = {'red': r_file, \n", + " 'green':g_dir/r_file.name.replace('red', 'green'),\n", + " 'blue': b_dir/r_file.name.replace('red', 'blue'), \n", + " 'nir': nir_dir/r_file.name.replace('red', 'nir'),\n", + " 'gt': gt_dir/r_file.name.replace('red', 'gt')}\n", + "\n", + " return files\n", + " \n", + " def __len__(self):\n", + " \n", + " return len(self.files)\n", + " \n", + " def open_as_array(self, idx, invert=False, include_nir=False):\n", + "\n", + " raw_rgb = np.stack([np.array(Image.open(self.files[idx]['red'])),\n", + " np.array(Image.open(self.files[idx]['green'])),\n", + " np.array(Image.open(self.files[idx]['blue'])),\n", + " ], axis=2)\n", + " \n", + " if include_nir:\n", + " nir = np.expand_dims(np.array(Image.open(self.files[idx]['nir'])), 2)\n", + " raw_rgb = np.concatenate([raw_rgb, nir], axis=2)\n", + " \n", + " if invert:\n", + " raw_rgb = raw_rgb.transpose((2,0,1))\n", + " \n", + " return (raw_rgb / np.iinfo(raw_rgb.dtype).max)\n", + " \n", + "\n", + " def open_mask(self, idx, add_dims=False):\n", + " \n", + " raw_mask = np.array(Image.open(self.files[idx]['gt']))\n", + " raw_mask = np.where(raw_mask==255, 1, 0)\n", + " \n", + " return np.expand_dims(raw_mask, 0) if add_dims else raw_mask\n", + " \n", + " def __getitem__(self, idx):\n", + " \n", + " x = torch.tensor(self.open_as_array(idx, invert=self.pytorch, include_nir=True), dtype=torch.float32)\n", + " y = torch.tensor(self.open_mask(idx, add_dims=False), dtype=torch.torch.int64)\n", + " \n", + " return x, y" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8400" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_path = Path('archive/38-Cloud_training')\n", + "data = CloudDataset(base_path/'train_red', \n", + " base_path/'train_green', \n", + " base_path/'train_blue', \n", + " base_path/'train_nir',\n", + " base_path/'train_gt')\n", + "len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image_index = 2000\n", + "\n", + "fig, ax = plt.subplots(1,2, figsize=(10,9))\n", + "ax[0].imshow(data.open_as_array(image_index))\n", + "ax[1].imshow(data.open_mask(image_index))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset, valid_dataset = torch.utils.data.random_split(data, (6000, 2400))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataload = DataLoader(train_dataset, batch_size=3, shuffle=True)\n", + "valid_dataload = DataLoader(valid_dataset, batch_size=3, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from torch import nn\n", + "class UNET(nn.Module):\n", + " def __init__(self, in_channels, out_channels):\n", + " super().__init__()\n", + "\n", + " self.conv1 = self.contract_block(in_channels, 32, 7, 3)\n", + " self.conv2 = self.contract_block(32, 64, 3, 1)\n", + " self.conv3 = self.contract_block(64, 128, 3, 1)\n", + "\n", + " self.upconv3 = self.expand_block(128, 64, 3, 1)\n", + " self.upconv2 = self.expand_block(64*2, 32, 3, 1)\n", + " self.upconv1 = self.expand_block(32*2, out_channels, 3, 1)\n", + "\n", + " def __call__(self, x):\n", + "\n", + " conv1 = self.conv1(x)\n", + " conv2 = self.conv2(conv1)\n", + " conv3 = self.conv3(conv2)\n", + "\n", + " upconv3 = self.upconv3(conv3)\n", + "\n", + " upconv2 = self.upconv2(torch.cat([upconv3, conv2], 1))\n", + " upconv1 = self.upconv1(torch.cat([upconv2, conv1], 1))\n", + "\n", + " return upconv1\n", + "\n", + " def contract_block(self, in_channels, out_channels, kernel_size, padding):\n", + "\n", + " contract = nn.Sequential(\n", + " torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding),\n", + " torch.nn.BatchNorm2d(out_channels),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding),\n", + " torch.nn.BatchNorm2d(out_channels),\n", + " torch.nn.ReLU(),\n", + " torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n", + " )\n", + "\n", + " return contract\n", + "\n", + " def expand_block(self, in_channels, out_channels, kernel_size, padding):\n", + "\n", + " expand = nn.Sequential(torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=padding),\n", + " torch.nn.BatchNorm2d(out_channels),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Conv2d(out_channels, out_channels, kernel_size, stride=1, padding=padding),\n", + " torch.nn.BatchNorm2d(out_channels),\n", + " torch.nn.ReLU(),\n", + " torch.nn.ConvTranspose2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1, output_padding=1) \n", + " )\n", + " return expand" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "unet = UNET(4,2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([3, 2, 384, 384])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xb, yb = next(iter(train_dataload))\n", + "xb.shape, yb.shape\n", + "\n", + "pred = unet(xb)\n", + "pred.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from IPython.display import clear_output\n", + "\n", + "def train(model, train_dl, valid_dl, loss_fn, optimizer, acc_fn, epochs=1):\n", + " start = time.time()\n", + " model.cuda()\n", + "\n", + " train_loss, valid_loss = [], []\n", + "\n", + " best_acc = 0.0\n", + "\n", + " for epoch in range(epochs):\n", + " print('Epoch {}/{}'.format(epoch, epochs - 1))\n", + " print('-' * 10)\n", + "\n", + " for phase in ['train', 'valid']:\n", + " if phase == 'train':\n", + " model.train(True)\n", + " dataloader = train_dl\n", + " else:\n", + " model.train(False) \n", + " dataloader = valid_dl\n", + "\n", + " running_loss = 0.0\n", + " running_acc = 0.0\n", + "\n", + " step = 0\n", + "\n", + " for x, y in dataloader:\n", + " x = x.cuda()\n", + " y = y.cuda()\n", + " step += 1\n", + " if phase == 'train':\n", + " optimizer.zero_grad()\n", + " outputs = model(x)\n", + " loss = loss_fn(outputs, y)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + "\n", + " else:\n", + " with torch.no_grad():\n", + " outputs = model(x)\n", + " loss = loss_fn(outputs, y.long())\n", + "\n", + " acc = acc_fn(outputs, y)\n", + "\n", + " running_acc += acc*dataloader.batch_size\n", + " running_loss += loss.detach()*dataloader.batch_size\n", + "\n", + " if step % 100 == 0:\n", + " print('Current step: {} Loss: {} Acc: {} AllocMem (Mb): {}'.format(step, loss, acc, torch.cuda.memory_allocated()/1024/1024))\n", + "\n", + " epoch_loss = running_loss / len(dataloader.dataset)\n", + " epoch_acc = running_acc / len(dataloader.dataset)\n", + "\n", + " clear_output(wait=True)\n", + " print('Epoch {}/{}'.format(epoch, epochs - 1))\n", + " print('-' * 10)\n", + " print('{} Loss: {:.4f} Acc: {}'.format(phase, epoch_loss, epoch_acc))\n", + " print('-' * 10)\n", + "\n", + " train_loss.append(epoch_loss) if phase=='train' else valid_loss.append(epoch_loss)\n", + "\n", + " time_elapsed = time.time() - start\n", + " print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) \n", + " \n", + " return train_loss, valid_loss \n", + "\n", + "def acc_metric(predb, yb):\n", + " return (predb.argmax(dim=1) == yb.cuda()).float().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 49/49\n", + "----------\n", + "valid Loss: 0.2551 Acc: 0.8899015784263611\n", + "----------\n", + "Training complete in 165m 39s\n" + ] + } + ], + "source": [ + "loss_fn = nn.CrossEntropyLoss()\n", + "opt = torch.optim.Adam(unet.parameters(), lr=0.01)\n", + "train_loss, valid_loss = train(unet, train_dataload, valid_dataload, loss_fn, opt, acc_metric, epochs=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "train_loss = [x.cpu() for x in train_loss]\n", + "valid_loss = [x.cpu() for x in valid_loss]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,8))\n", + "plt.plot(train_loss, label='Train loss')\n", + "plt.plot(valid_loss, label='Valid loss')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save({'state_dict': unet.state_dict()}, 'checkpoint_50.pth.tar')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(unet, 'net/unet_50_epoch.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "UNET(\n", + " (conv1): Sequential(\n", + " (0): Conv2d(4, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n", + " (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n", + " (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " )\n", + " (conv2): Sequential(\n", + " (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " )\n", + " (conv3): Sequential(\n", + " (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " )\n", + " (upconv3): Sequential(\n", + " (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", + " )\n", + " (upconv2): Sequential(\n", + " (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): ConvTranspose2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", + " )\n", + " (upconv1): Sequential(\n", + " (0): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv2d(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): ConvTranspose2d(2, 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", + " )\n", + ")" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = torch.load(\"net/unet_50_epoch.pt\")\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class CloudDatasetTest(Dataset):\n", + " def __init__(self, r_dir, g_dir, b_dir, nir_dir, pytorch=True):\n", + " super().__init__()\n", + " self.files = [self.combine_files(f, g_dir, b_dir, nir_dir) for f in r_dir.iterdir() if not f.is_dir()]\n", + " self.pytorch = pytorch\n", + " \n", + " def combine_files(self, r_file: Path, g_dir, b_dir,nir_dir):\n", + " \n", + " files = {'red': r_file, \n", + " 'green':g_dir/r_file.name.replace('red', 'green'),\n", + " 'blue': b_dir/r_file.name.replace('red', 'blue'), \n", + " 'nir': nir_dir/r_file.name.replace('red', 'nir')}\n", + "\n", + " return files\n", + " \n", + " def __len__(self):\n", + " \n", + " return len(self.files)\n", + " \n", + " def open_as_array(self, idx, invert=False, include_nir=False):\n", + "\n", + " raw_rgb = np.stack([np.array(Image.open(self.files[idx]['red'])),\n", + " np.array(Image.open(self.files[idx]['green'])),\n", + " np.array(Image.open(self.files[idx]['blue'])),\n", + " ], axis=2)\n", + " \n", + " #if include_nir:\n", + " # nir = np.expand_dims(np.array(Image.open(self.files[idx]['nir'])), 2)\n", + " # raw_rgb = np.concatenate([raw_rgb, nir], axis=2)\n", + " \n", + " if invert:\n", + " raw_rgb = raw_rgb.transpose((2,0,1))\n", + " \n", + " # normalize\n", + " return (raw_rgb / np.iinfo(raw_rgb.dtype).max)\n", + " \n", + " \n", + " def __getitem__(self, idx):\n", + " \n", + " x = torch.tensor(self.open_as_array(idx, invert=self.pytorch, include_nir=True), dtype=torch.float32)\n", + " \n", + " return x\n", + " \n", + " def open_as_pil(self, idx):\n", + " \n", + " arr = 256*self.open_as_array(idx)\n", + " \n", + " return Image.fromarray(arr.astype(np.uint8), 'RGB')\n", + " \n", + " def __repr__(self):\n", + " s = 'Dataset class with {} files'.format(self.__len__())\n", + "\n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset class with 9201 files" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_path = Path('archive/38-Cloud_test')\n", + "data2 = CloudDatasetTest(base_path/'test_red', \n", + " base_path/'test_green', \n", + " base_path/'test_blue', \n", + " base_path/'test_nir')\n", + "data2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "test_ds = torch.utils.data.random_split(data2, (6000, 3201))\n", + "test_dl = DataLoader(test_ds, batch_size=12, shuffle=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf-gpu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "f599085227da76de7860c088523293673b66c34e63d5f725449b4547148eb02d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bonus/Bonus 5/checkpoint.pth.tar b/Bonus/Bonus 5/checkpoint.pth.tar new file mode 100644 index 0000000..53cad42 Binary files /dev/null and b/Bonus/Bonus 5/checkpoint.pth.tar differ diff --git a/Bonus/Bonus 5/checkpoint_30.pth.tar b/Bonus/Bonus 5/checkpoint_30.pth.tar new file mode 100644 index 0000000..769c238 Binary files /dev/null and b/Bonus/Bonus 5/checkpoint_30.pth.tar differ diff --git a/Bonus/Bonus 5/checkpoint_50.pth.tar b/Bonus/Bonus 5/checkpoint_50.pth.tar new file mode 100644 index 0000000..06f8506 Binary files /dev/null and b/Bonus/Bonus 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and b/Bonus/Bonus 5/net/unet_50_epoch.pt differ diff --git a/Bonus/Bonusaufgabe_3 (1).ipynb b/Bonus/Bonusaufgabe_3 (1).ipynb new file mode 100644 index 0000000..b0b951a --- /dev/null +++ b/Bonus/Bonusaufgabe_3 (1).ipynb @@ -0,0 +1,2334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Niclas Cramer, Niklas Koch, Jasmina Pascanovic" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.utils.data\n", + "import torch.optim as optim\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from PIL import Image\n", + "\n", + "from os import listdir\n", + "\n", + "from torchvision import transforms, utils\n", + "from skimage import io, transform\n", + "from torchvision import transforms \n", + "\n", + "import pandas as pd \n", + "import numpy as np\n", + "import seaborn as sb\n", + "import matplotlib.pyplot as plt\n", + "import cv2\n", + "from tqdm import tqdm\n", + "\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "path = \"archive/\"\n", + "data_path = path + \"socal2.csv\"\n", + "pics_path = path + \"socal2/socal_pics/\"\n", + "df = pd.read_csv(data_path)\n", + "prices = df[\"price\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idstreetcitin_citibedbathsqftprice
001317 Van Buren AvenueSalton City, CA31732.01560201900
11124 C Street WBrawley, CA4832.0713228500
222304 Clark RoadImperial, CA15231.0800273950
33755 Brawley AvenueBrawley, CA4831.01082350000
442207 R Carrillo CourtCalexico, CA5543.02547385100
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image_idn_citibedbathsqftprice
count15474.00000015474.00000015474.00000015474.00000015474.0000001.547400e+04
mean7736.500000216.5975183.5063982.4532512173.9132097.031209e+05
std4467.103368112.3729851.0348380.9587421025.3396173.769762e+05
min0.0000000.0000001.0000000.000000280.0000001.950000e+05
25%3868.250000119.0000003.0000002.0000001426.0000004.450000e+05
50%7736.500000222.5000003.0000002.1000001951.0000006.390000e+05
75%11604.750000315.0000004.0000003.0000002737.7500008.349750e+05
max15473.000000414.00000012.00000036.00000017667.0000002.000000e+06
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In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " sb.heatmap(df.corr())\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sb.heatmap(df.corr())\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def streudiagramm(x_achse, y_achse):\n", + " plt.plot(x_achse, y_achse, 'o')\n", + " m, b = np.polyfit(x_achse, y_achse, 1)\n", + " plt.plot(x_achse, m*x_achse+b)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "streudiagramm(prices, df[\"n_citi\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deep Learning" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of images: 15474\n" + ] + } + ], + "source": [ + "X_house_images=np.zeros((15474,64,64,3),dtype='uint32')\n", + "cnt=0\n", + "for i in range(15474):\n", + "\n", + " sample=cv2.imread(pics_path+'/'+str(i)+'.jpg')\n", + " imgs=cv2.resize(sample,(64,64))\n", + " \n", + " X_house_images[cnt]=imgs\n", + " cnt+=1\n", + "\n", + "print(\"No. of images: \",cnt)" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "split = train_test_split(df, X_house_images, test_size=0.25, random_state=42)\n", + "(Xatt_train,Xatt_test,Ximage_train,Ximage_test) = split\n", + "\n", + "y_train , y_test = Xatt_train['price'].values , Xatt_test['price'].values\n", + "\n", + "X1_train=Xatt_train[['n_citi','bed','bath','sqft']].values\n", + "X2_train=Ximage_train\n", + "X1_test=Xatt_test[['n_citi','bed','bath','sqft']].values\n", + "X2_test=Ximage_test" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.110e+02, 3.000e+00, 2.000e+00, 2.502e+03],\n", + " [3.070e+02, 4.000e+00, 3.000e+00, 1.895e+03],\n", + " [7.800e+01, 4.000e+00, 3.000e+00, 1.573e+03],\n", + " ...,\n", + " [8.200e+01, 4.000e+00, 3.000e+00, 1.550e+03],\n", + " [1.930e+02, 3.000e+00, 2.000e+00, 1.534e+03],\n", + " [2.650e+02, 3.000e+00, 2.100e+00, 2.580e+03]])" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X1_train" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(torch.utils.data.Dataset):\n", + " def __init__(self, x, img, y):\n", + " self.x = torch.tensor(x).float()\n", + " self.img = img\n", + " self.y = torch.tensor(y).float()\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + "\n", + " \n", + " def __getitem__(self, idx):\n", + " x = self.x[idx]\n", + " y = self.y[idx]\n", + " img = self.img[idx]\n", + " img = transforms.functional.to_tensor(img.astype(np.uint8).reshape((64, 64, 3)))\n", + " return {'x': x, 'y': y, 'img': img}\n", + " \n", + "BATCH_SIZE = 256 \n", + "train_dataset = MyDataset(X1_train,X2_train, y_train)\n", + "dataLoader_train = torch.utils.data.DataLoader(train_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True)\n", + "\n", + "test_dataset = MyDataset(X1_test,X2_test, y_test)\n", + "dataLoader_test = torch.utils.data.DataLoader(test_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataLoader_train\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idn_citibedbathsqftprice
0031732.01560201900
114832.0713228500
2215231.0800273950
334831.01082350000
445543.02547385100
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" + ], + "text/plain": [ + " image_id n_citi bed bath sqft price\n", + "0 0 317 3 2.0 1560 201900\n", + "1 1 48 3 2.0 713 228500\n", + "2 2 152 3 1.0 800 273950\n", + "3 3 48 3 1.0 1082 350000\n", + "4 4 55 4 3.0 2547 385100" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del df['street']\n", + "del df['citi']\n", + "#del df['n_citi']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Netz" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (conv): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): ReLU()\n", + " (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (4): ReLU()\n", + " (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (flatten): Sequential(\n", + " (0): AdaptiveMaxPool2d(output_size=1)\n", + " (1): Flatten(start_dim=1, end_dim=-1)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=4, out_features=256, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=256, out_features=128, bias=True)\n", + " (3): ReLU()\n", + " )\n", + " (final_fc): Sequential(\n", + " (0): Linear(in_features=192, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "class Model(torch.nn.Module):\n", + " \n", + " def __init__(self, input_shape):\n", + " super().__init__()\n", + " \n", + " self.conv = torch.nn.Sequential(\n", + " torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3)),\n", + " torch.nn.ReLU(),\n", + " torch.nn.BatchNorm2d(32),\n", + " torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5, 5)),\n", + " torch.nn.ReLU(),\n", + " torch.nn.BatchNorm2d(64),\n", + " )\n", + " \n", + " self.flatten = torch.nn.Sequential(torch.nn.AdaptiveMaxPool2d(1), torch.nn.Flatten())\n", + " \n", + " self.fc = torch.nn.Sequential(\n", + " torch.nn.Linear(input_shape, 256),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(256, 128),\n", + " torch.nn.ReLU(),\n", + " )\n", + " \n", + " self.final_fc = torch.nn.Sequential(\n", + " torch.nn.Linear(128+64, 512),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(512, 1)\n", + " )\n", + " \n", + " def forward(self, x, img):\n", + " img = self.conv(img)\n", + " img = self.flatten(img) \n", + " x = self.fc(x)\n", + " combined = torch.cat((img, x), dim=1)\n", + " #combined = torch.cat((img.view(img.size(0), -1), x.view(x.size(0), -1)), dim=1)\n", + " #print(x.size(), img.size())\n", + " price = self.final_fc(combined)\n", + " return price\n", + " \n", + "model = Model(4)\n", + "print(model)\n", + "criterion = torch.nn.MSELoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "started!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 10%|█ | 1/10 [02:39<23:58, 159.80s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 1/10 finished with train loss: 525078970724.1739 and test loss: 207061161472.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|██ | 2/10 [05:23<21:36, 162.05s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 2/10 finished with train loss: 110927604869.56522 and test loss: 107115519744.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 30%|███ | 3/10 [08:00<18:38, 159.81s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 3/10 finished with train loss: 99138941996.52174 and test loss: 101747551488.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|████ | 4/10 [10:44<16:09, 161.63s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 4/10 finished with train loss: 98605530423.65218 and test loss: 99086805504.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|█████ | 5/10 [13:26<13:27, 161.45s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 5/10 finished with train loss: 96771056506.43478 and test loss: 100011087360.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 6/10 [16:08<10:47, 161.77s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 6/10 finished with train loss: 95817108613.56522 and test loss: 101267421184.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 70%|███████ | 7/10 [18:53<08:08, 162.91s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 7/10 finished with train loss: 95464652800.0 and test loss: 97971708160.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 80%|████████ | 8/10 [21:33<05:24, 162.01s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 8/10 finished with train loss: 95159813787.82608 and test loss: 99372399104.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 90%|█████████ | 9/10 [24:17<02:42, 162.56s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 9/10 finished with train loss: 93755081505.39131 and test loss: 99780259328.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [26:53<00:00, 161.37s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 10/10 finished with train loss: 93853830544.69565 and test loss: 99534354944.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "n_epochs = 10\n", + "print('started!')\n", + "for epoch in tqdm(range(n_epochs)):\n", + " train_batch_loss = 0\n", + " model.train()\n", + " for step, batch in enumerate(dataLoader_train):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_batch_loss += loss.item()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " test_batch_loss = 0\n", + " model.eval()\n", + "\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader_test):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + " outputs = model(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " test_batch_loss += loss.item() \n", + "\n", + " print('epoch {}/{} finished with train loss: {} and test loss: {}'.format(epoch+1, n_epochs, train_batch_loss / len(dataLoader_train), test_batch_loss / len(dataLoader_test)))\n", + " \n", + "torch.save(model.state_dict(), './model_two_input.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Results :\n", + "\n", + "RSS : 380696832934998.3\n", + "RSE : 313763.5903382174\n", + "TSS : 560633939775361.56\n", + "R Squared : 0.3209529321618695\n", + "MSE : 98396700164.12643\n", + "MAE : 229841.06415578962\n", + "Accuracy with 10% : 0.18350995089170327\n", + "\n", + "Train Results :\n", + "\n", + "RSS : 1075644050759336.2\n", + "RSE : 304473.250464067\n", + "TSS : 1638210861992667.8\n", + "R Squared : 0.3434031749423533\n", + "MSE : 92687983693.1802\n", + "MAE : 224996.14421450882\n", + "Accuracy with 10% : 0.1912968548039638\n" + ] + } + ], + "source": [ + "def reg_report(true, pred, name='Test'):\n", + " print(\"\\n{} Results :\\n\".format(name))\n", + " print(\"RSS :\",sum((pred-true)**2))\n", + " print(\"RSE :\",math.sqrt(sum((pred-true)**2)*(1/(len(pred)-2))))\n", + " print(\"TSS :\",sum((true-true.mean())**2))\n", + " print(\"R Squared :\",1-(sum((pred-true)**2)/sum((true-true.mean())**2)))\n", + " print(\"MSE :\",((pred-true)**2).mean())\n", + " print('MAE :',(abs(pred-true)).mean())\n", + " print('Accuracy with 10% :', ((pred<=true*1.1) & (true*0.9<=pred)).mean())\n", + " \n", + "\n", + "def eval_report(y_train, pred_train,y_test, pred_test):\n", + " reg_report(y_train, pred_train, name='Train')\n", + " reg_report(y_test, pred_test, name='Test')\n", + " \n", + "def res(dataLoader, name = 'Test'): \n", + " trues = []\n", + " preds = []\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + " outputs = model(x = x, img = img)\n", + "\n", + " trues = trues + y.tolist()\n", + " preds = preds + outputs[:,0].tolist()\n", + "\n", + "\n", + " reg_report(true = np.array(trues), pred = np.array(preds), name=name)\n", + "\n", + "res(dataLoader_test, name = 'Test')\n", + "res(dataLoader_train, name = 'Train')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Netz" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (conv): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): ReLU()\n", + " (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (4): ReLU()\n", + " (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (flatten): Sequential(\n", + " (0): AdaptiveMaxPool2d(output_size=1)\n", + " (1): Flatten(start_dim=1, end_dim=-1)\n", + " )\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=4, out_features=256, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=256, out_features=128, bias=True)\n", + " (3): ReLU()\n", + " )\n", + " (final_fc): Sequential(\n", + " (0): Linear(in_features=192, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "class Netz(nn.Module):\n", + " def __init__(self, input_shape):\n", + " super(Netz, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 6, kernel_size=5)\n", + " self.conv2 = nn.Conv2d(6, 9, kernel_size=5)\n", + " #self.flatten=nn.Sequential(nn.AdaptiveMaxPool2d(1), nn.Flatten())\n", + " self.fc1 = nn.Linear(input_shape, 256)\n", + " self.fc2 = nn.Linear(256, 128)\n", + " self.fc3 = nn.Linear(137, 1)\n", + "\n", + "\n", + " def forward(self, x, img):\n", + " img = self.conv1(img)\n", + " img = F.max_pool2d(img, 3)\n", + " img = F.relu(img)\n", + "\n", + " img = self.conv2(img)\n", + " img = F.max_pool2d(img, 3)\n", + " img = F.relu(img)\n", + " #\n", + " # img=self.flatten(img)\n", + "\n", + " x = self.fc1(x)\n", + " x = F.relu(x)\n", + " x = self.fc2(x)\n", + " x = F.relu(x)\n", + " #rint(x.shape,img.shape)\n", + " #combined = torch.cat((img.view(img.size(0), -1), x.view(x.size(0), -1)), dim=1)\n", + " combined = torch.cat((x, img), 1)\n", + " price = self.fc3(combined)\n", + " return price\n", + " #pass\n", + "\n", + "model_2 = Netz(4)\n", + "criterion = nn.MSELoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.01)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "started!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/10 [00:00 6\u001b[0m model_2\u001b[39m.\u001b[39mtrain()\n\u001b[1;32m 7\u001b[0m \u001b[39mfor\u001b[39;00m step, batch \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(dataLoader_train):\n\u001b[1;32m 8\u001b[0m x \u001b[39m=\u001b[39m batch[\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'model_2' is not defined" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "n_epochs = 10\n", + "print('started!')\n", + "for epoch in tqdm(range(n_epochs)):\n", + " train_batch_loss = 0\n", + " model_2.train()\n", + " for step, batch in enumerate(dataLoader_train):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model_2(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_batch_loss += loss.item()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " test_batch_loss = 0\n", + " model_2.eval()\n", + "\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader_test):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + " outputs = model_2(x = x, img = img)\n", + " loss = criterion(outputs[:,0], y)\n", + " test_batch_loss += loss.item() \n", + "\n", + " print('epoch {}/{} finished with train loss: {} and test loss: {}'.format(epoch+1, n_epochs, train_batch_loss / len(dataLoader_train), test_batch_loss / len(dataLoader_test)))\n", + " \n", + "torch.save(model_2.state_dict(), './mmodel_2odel_two_input.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Results :\n", + "\n", + "RSS : 2488957288173446.0\n", + "RSE : 802272.0013507858\n", + "TSS : 560633939775360.8\n", + "R Squared : -3.439540868986171\n", + "MSE : 643307647498.9537\n", + "MAE : 705963.4366304198\n", + "Accuracy with 10% : 0.0\n", + "\n", + "Train Results :\n", + "\n", + "RSS : 7362237494844666.0\n", + "RSE : 796562.3317061164\n", + "TSS : 1638210861992678.0\n", + "R Squared : -3.4940719571895817\n", + "MSE : 634402196884.5054\n", + "MAE : 702295.735096819\n", + "Accuracy with 10% : 0.0\n" + ] + } + ], + "source": [ + "def reg_report(true, pred, name='Test'):\n", + " print(\"\\n{} Results :\\n\".format(name))\n", + " print(\"RSS :\",sum((pred-true)**2))\n", + " print(\"RSE :\",math.sqrt(sum((pred-true)**2)*(1/(len(pred)-2))))\n", + " print(\"TSS :\",sum((true-true.mean())**2))\n", + " print(\"R Squared :\",1-(sum((pred-true)**2)/sum((true-true.mean())**2)))\n", + " print(\"MSE :\",((pred-true)**2).mean())\n", + " print('MAE :',(abs(pred-true)).mean())\n", + " print('Accuracy with 10% :', ((pred<=true*1.1) & (true*0.9<=pred)).mean())\n", + " \n", + "\n", + "def eval_report(y_train, pred_train,y_test, pred_test):\n", + " reg_report(y_train, pred_train, name='Train')\n", + " reg_report(y_test, pred_test, name='Test')\n", + " \n", + "def res(dataLoader, name = 'Test'): \n", + " trues = []\n", + " preds = []\n", + " model_2.eval()\n", + " with torch.no_grad():\n", + " for step, batch in enumerate(dataLoader):\n", + " x = batch[\"x\"]\n", + " img = batch[\"img\"]\n", + " y = batch[\"y\"]\n", + "\n", + " outputs = model_2(x = x, img = img)\n", + "\n", + " trues = trues + y.tolist()\n", + " preds = preds + outputs[:,0].tolist()\n", + "\n", + "\n", + " reg_report(true = np.array(trues), pred = np.array(preds), name=name)\n", + "\n", + "res(dataLoader_test, name = 'Test')\n", + "res(dataLoader_train, name = 'Train')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Netz" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "class TwoInputsNet(nn.Module):\n", + " def __init__(self):\n", + " super(TwoInputsNet, self).__init__()\n", + " self.conv = nn.Conv2d(3,8,kernel_size=3) \n", + " self.conv1 = nn.Conv2d(8,8,kernel_size=3)\n", + " self.conv2 = nn.Conv2d(8,8,kernel_size=3) \n", + " self.fc1 = nn.Linear(3,3)\n", + " self.fc2 = nn.Linear(26915,1024)\n", + " self.fc3 = nn.Linear(1024,32) \n", + " self.fc4 = nn.Linear(32,1) \n", + "\n", + " def forward(self, input1, input2):\n", + " c = self.conv(input1)\n", + " c = self.conv1(c)\n", + " c = F.relu(c)\n", + " c = self.conv2(c)\n", + " c = F.relu(c)\n", + " f = self.fc1(input2)\n", + " # now we can reshape `c` and `f` to 2D and concat them\n", + " #combined = torch.cat((c.view(c.size(0), -1), f.view(f.size(0), -1)), dim=1)\n", + " combined = torch.cat((img, x), dim=1)\n", + " out = self.fc2(combined)\n", + " out = F.relu(out)\n", + " out = self.fc3(out)\n", + " out = F.relu(out)\n", + " out = self.fc4(out)\n", + "\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TwoInputsNet(\n", + " (conv): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv2): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1))\n", + " (fc1): Linear(in_features=3, out_features=3, bias=True)\n", + " (fc2): Linear(in_features=26915, out_features=1024, bias=True)\n", + " (fc3): Linear(in_features=1024, out_features=32, bias=True)\n", + " (fc4): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "network = TwoInputsNet()\n", + "print(network)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "momentum = 0.9\n", + "n_epochs = 10\n", + "\n", + "optimizer = optim.SGD(network.parameters(), lr=learning_rate,momentum=momentum)\n", + "\n", + "loss_func = torch.nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "def train(dataloader,epoch):\n", + " batch_idx = 0\n", + " for items in dataloader:\n", + " image = torch.FloatTensor(batch['img'])\n", + " features = torch.FloatTensor(items['x'])\n", + " price = torch.FloatTensor(items['y'])\n", + "\n", + " if torch.cuda.is_available():\n", + " image = image.cuda()\n", + " features = features.cuda()\n", + " price = price.cuda()\n", + " \n", + " output = network(image,features)\n", + " output = output.reshape(4)\n", + " loss = loss_func(output, price)\n", + " optimizer.zero_grad() \n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " if batch_idx % 4 == 0: #every 25 * batchsize sample we print results\n", + " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", + " epoch+1, batch_idx * image.shape[0], len(dataloader.dataset),\n", + " 100. * batch_idx / len(dataloader), loss.item()))\n", + " \n", + " batch_idx = batch_idx + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "mat1 and mat2 shapes cannot be multiplied (1x4 and 3x3)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [134], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n_epochs):\n\u001b[1;32m 2\u001b[0m \u001b[39m# train \u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m train(train_dataset,epoch)\n", + "Cell \u001b[0;32mIn [133], line 13\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(dataloader, epoch)\u001b[0m\n\u001b[1;32m 10\u001b[0m features \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mcuda()\n\u001b[1;32m 11\u001b[0m price \u001b[39m=\u001b[39m price\u001b[39m.\u001b[39mcuda()\n\u001b[0;32m---> 13\u001b[0m output \u001b[39m=\u001b[39m network(image,features)\n\u001b[1;32m 14\u001b[0m output \u001b[39m=\u001b[39m output\u001b[39m.\u001b[39mreshape(\u001b[39m4\u001b[39m)\n\u001b[1;32m 15\u001b[0m loss \u001b[39m=\u001b[39m loss_func(output, price)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn [130], line 18\u001b[0m, in \u001b[0;36mTwoInputsNet.forward\u001b[0;34m(self, input1, input2)\u001b[0m\n\u001b[1;32m 16\u001b[0m c \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(c)\n\u001b[1;32m 17\u001b[0m c \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(c)\n\u001b[0;32m---> 18\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc1(input2)\n\u001b[1;32m 19\u001b[0m \u001b[39m# now we can reshape `c` and `f` to 2D and concat them\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[39m#combined = torch.cat((c.view(c.size(0), -1), f.view(f.size(0), -1)), dim=1)\u001b[39;00m\n\u001b[1;32m 21\u001b[0m combined \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat((img, x), dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1189\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/torch/nn/modules/linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m--> 114\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n", + "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (1x4 and 3x3)" + ] + } + ], + "source": [ + "for epoch in range(n_epochs):\n", + " # train \n", + " train(train_dataset,epoch)" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idn_citibedbathsqftprice
000.23430032.00.9263820.996177
110.88405832.00.9750960.981440
220.63285031.00.9700930.956260
330.88405831.00.9538740.914127
440.86715043.00.8696150.894681
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" + ], + "text/plain": [ + " image_id n_citi bed bath sqft price\n", + "0 0 0.234300 3 2.0 0.926382 0.996177\n", + "1 1 0.884058 3 2.0 0.975096 0.981440\n", + "2 2 0.632850 3 1.0 0.970093 0.956260\n", + "3 3 0.884058 3 1.0 0.953874 0.914127\n", + "4 4 0.867150 4 3.0 0.869615 0.894681" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_idn_citibedbathsqftprice
0103050.81159442.10.8954390.693629
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\n", + "
" + ], + "text/plain": [ + " image_id n_citi bed bath sqft price\n", + "0 10305 0.811594 4 2.1 0.895439 0.693629\n", + "1 7918 0.328502 2 2.0 0.938805 0.582271\n", + "2 8316 0.236715 3 2.0 0.949272 0.994460\n", + "3 6262 0.016908 4 3.0 0.855984 0.858726\n", + "4 1938 0.321256 3 2.0 0.952091 0.803380" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.sample(256)\n", + "df.reset_index(inplace = True )\n", + "del df['index']\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'street'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'street'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [226], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mdel\u001b[39;00m df[\u001b[39m'\u001b[39m\u001b[39mstreet\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 2\u001b[0m \u001b[39mdel\u001b[39;00m df[\u001b[39m'\u001b[39m\u001b[39mciti\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m cols \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39msqft\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mprice\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mn_citi\u001b[39m\u001b[39m'\u001b[39m]:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/core/generic.py:4243\u001b[0m, in \u001b[0;36mNDFrame.__delitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4238\u001b[0m deleted \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 4239\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m deleted:\n\u001b[1;32m 4240\u001b[0m \u001b[39m# If the above loop ran and didn't delete anything because\u001b[39;00m\n\u001b[1;32m 4241\u001b[0m \u001b[39m# there was no match, this call should raise the appropriate\u001b[39;00m\n\u001b[1;32m 4242\u001b[0m \u001b[39m# exception:\u001b[39;00m\n\u001b[0;32m-> 4243\u001b[0m loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49maxes[\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4244\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mgr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mgr\u001b[39m.\u001b[39midelete(loc)\n\u001b[1;32m 4246\u001b[0m \u001b[39m# delete from the caches\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/Bonu_Aufgaben/lib/python3.8/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3807\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'street'" + ] + } + ], + "source": [ + "del df['street']\n", + "del df['citi']\n", + "for cols in ['sqft','price','n_citi']:\n", + " df[cols] = (df[cols].max() - df[cols])/(df[cols].max() - df[cols].min())\n", + " \n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SocalDataset(Dataset):\n", + " \n", + " def __init__(self, dataframe, root_dir, transform=None):\n", + " self.features = dataframe\n", + " self.root_dir = root_dir\n", + " self.transform = transform\n", + "\n", + " def __len__(self):\n", + " return len(self.features)\n", + "\n", + " def __getitem__(self, idx):\n", + " if torch.is_tensor(idx):\n", + " idx = idx.tolist()\n", + " try:\n", + " img_name = '{}{}.jpg'.format(str(self.root_dir), str(self.features.loc[idx,'image_id']))\n", + " image = io.imread(img_name)\n", + " image = image / 255.0\n", + " \n", + " if len(image.shape) == 3:\n", + " house_features = self.features.iloc[idx, 1:]\n", + " house_features = np.array([house_features]).reshape(5)\n", + " sample = {'image': image, 'house_features': house_features}\n", + "\n", + " if self.transform:\n", + " sample['image'] = transform.resize(sample['image'],(64,64)).reshape(3,64,64)\n", + " \n", + " sample['image'] = torch.from_numpy(sample['image']).float()\n", + " sample['house_features'] = torch.from_numpy(sample['house_features']).float()\n", + "\n", + " return sample\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "house_dataset = SocalDataset(dataframe=df,\n", + " root_dir=pics_path,\n", + " transform = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 torch.Size([3, 64, 64]) torch.Size([5])\n", + "1 torch.Size([3, 64, 64]) torch.Size([5])\n", + "2 torch.Size([3, 64, 64]) torch.Size([5])\n", + "3 torch.Size([3, 64, 64]) torch.Size([5])\n", + "4 torch.Size([3, 64, 64]) torch.Size([5])\n", + "5 torch.Size([3, 64, 64]) torch.Size([5])\n", + "6 torch.Size([3, 64, 64]) torch.Size([5])\n", + "7 torch.Size([3, 64, 64]) torch.Size([5])\n", + "8 torch.Size([3, 64, 64]) torch.Size([5])\n", + "9 torch.Size([3, 64, 64]) torch.Size([5])\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " sample = house_dataset[i]\n", + " print(i, sample['image'].shape, sample['house_features'].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "test_size = 0.3\n", + "\n", + "test_amount = int(house_dataset.__len__() * test_size)\n", + "\n", + "train_set, test_set = torch.utils.data.random_split(house_dataset,[\n", + " (house_dataset.__len__() - test_amount ), test_amount ])" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataloader = torch.utils.data.DataLoader(\n", + " train_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")\n", + "\n", + "test_dataloader = torch.utils.data.DataLoader(\n", + " test_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "it = iter(train_dataloader)\n", + "items = next(it)\n", + "print(type(items))" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([5, 3, 64, 64])\n", + "torch.Size([5, 5])\n", + "tensor([[0.8019, 3.0000, 3.0000, 0.9127, 0.9668],\n", + " [0.8744, 2.0000, 1.0000, 0.9431, 0.6931],\n", + " [0.3599, 2.0000, 1.0000, 0.9435, 0.7618],\n", + " [0.5338, 3.0000, 2.0000, 0.9416, 0.9778],\n", + " [0.4372, 3.0000, 2.1000, 0.9174, 0.8643]])\n" + ] + } + ], + "source": [ + "print(items['image'].shape)\n", + "print(items['house_features'].shape)\n", + "print(items['house_features'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Netz" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "class TwoInputsNet(nn.Module):\n", + " def __init__(self):\n", + " super(TwoInputsNet, self).__init__()\n", + " self.conv = nn.Conv2d(3,6,kernel_size=3) \n", + " self.conv1 = nn.Conv2d(6,9,kernel_size=3)\n", + " self.conv2 = nn.Conv2d(9,12,kernel_size=3) \n", + " self.fc1 = nn.Linear(4,3)\n", + " self.fc2 = nn.Linear(40371,1024)\n", + " self.fc3 = nn.Linear(1024,32) \n", + " self.fc4 = nn.Linear(32,1) \n", + "\n", + " def forward(self, input1, input2):\n", + " c = self.conv(input1)\n", + " c = self.conv1(c)\n", + " c = F.relu(c)\n", + " c = self.conv2(c)\n", + " c = F.relu(c)\n", + " f = self.fc1(input2)\n", + " \n", + " # now we can reshape `c` and `f` to 2D and concat them\n", + " combined = torch.cat((c.view(c.size(0), -1),\n", + " f.view(f.size(0), -1)), dim=1)\n", + " out = self.fc2(combined)\n", + " out = F.relu(out)\n", + " out = self.fc3(out)\n", + " out = F.relu(out)\n", + " out = self.fc4(out)\n", + "\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TwoInputsNet(\n", + " (conv): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv1): Conv2d(6, 9, kernel_size=(3, 3), stride=(1, 1))\n", + " (conv2): Conv2d(9, 12, kernel_size=(3, 3), stride=(1, 1))\n", + " (fc1): Linear(in_features=4, out_features=3, bias=True)\n", + " (fc2): Linear(in_features=40371, out_features=1024, bias=True)\n", + " (fc3): Linear(in_features=1024, out_features=32, bias=True)\n", + " (fc4): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "network = TwoInputsNet()\n", + "print(network)" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "momentum = 0.9\n", + "n_epochs = 5\n", + "\n", + "optimizer = optim.SGD(network.parameters(), lr=learning_rate,momentum=momentum)\n", + "\n", + "loss_func = torch.nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataloader = torch.utils.data.DataLoader(\n", + " train_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")\n", + "\n", + "test_dataloader = torch.utils.data.DataLoader(\n", + " test_set,\n", + " batch_size=5,\n", + " shuffle=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "def train(dataloader,epoch):\n", + " batch_idx = 0\n", + " for items in dataloader:\n", + " image = torch.FloatTensor(items['image'])\n", + " features = torch.FloatTensor(items['house_features'][:,:4])\n", + " price = torch.FloatTensor(items['house_features'][:,4])\n", + "\n", + " if torch.cuda.is_available():\n", + " image = image.cuda()\n", + " features = features.cuda()\n", + " price = price.cuda()\n", + " \n", + " output = network(image,features)\n", + " output = output.reshape(5)\n", + " loss = loss_func(output, price)\n", + " optimizer.zero_grad() \n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " if batch_idx % 4 == 0: #every 25 * batchsize sample we print results\n", + " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", + " epoch+1, batch_idx * image.shape[0], len(dataloader.dataset),\n", + " 100. * batch_idx / len(dataloader), loss.item()))\n", + " \n", + " batch_idx = batch_idx + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Epoch: 1 [0/180 (0%)]\tLoss: 0.452995\n", + "Train Epoch: 1 [20/180 (11%)]\tLoss: 0.126921\n", + "Train Epoch: 1 [40/180 (22%)]\tLoss: 0.093897\n", + "Train Epoch: 1 [60/180 (33%)]\tLoss: 0.059054\n", + "Train Epoch: 1 [80/180 (44%)]\tLoss: 0.018912\n", + "Train Epoch: 1 [100/180 (56%)]\tLoss: 0.026393\n", + "Train Epoch: 1 [120/180 (67%)]\tLoss: 0.073876\n", + "Train Epoch: 1 [140/180 (78%)]\tLoss: 0.019582\n", + "Train Epoch: 1 [160/180 (89%)]\tLoss: 0.071252\n", + "Train Epoch: 2 [0/180 (0%)]\tLoss: 0.009247\n", + "Train Epoch: 2 [20/180 (11%)]\tLoss: 0.005556\n", + "Train Epoch: 2 [40/180 (22%)]\tLoss: 0.057850\n", + "Train Epoch: 2 [60/180 (33%)]\tLoss: 0.207975\n", + "Train Epoch: 2 [80/180 (44%)]\tLoss: 0.030397\n", + "Train Epoch: 2 [100/180 (56%)]\tLoss: 0.086041\n", + "Train Epoch: 2 [120/180 (67%)]\tLoss: 0.005431\n", + "Train Epoch: 2 [140/180 (78%)]\tLoss: 0.035753\n", + "Train Epoch: 2 [160/180 (89%)]\tLoss: 0.133349\n", + "Train Epoch: 3 [0/180 (0%)]\tLoss: 0.015860\n", + "Train Epoch: 3 [20/180 (11%)]\tLoss: 0.035526\n", + "Train Epoch: 3 [40/180 (22%)]\tLoss: 0.020284\n", + "Train Epoch: 3 [60/180 (33%)]\tLoss: 0.011326\n", + "Train Epoch: 3 [80/180 (44%)]\tLoss: 0.131106\n", + "Train Epoch: 3 [100/180 (56%)]\tLoss: 0.052148\n", + "Train Epoch: 3 [120/180 (67%)]\tLoss: 0.026631\n", + "Train Epoch: 3 [140/180 (78%)]\tLoss: 0.043114\n", + "Train Epoch: 3 [160/180 (89%)]\tLoss: 0.045804\n", + "Train Epoch: 4 [0/180 (0%)]\tLoss: 0.101069\n", + "Train Epoch: 4 [20/180 (11%)]\tLoss: 0.044998\n", + "Train Epoch: 4 [40/180 (22%)]\tLoss: 0.021710\n", + "Train Epoch: 4 [60/180 (33%)]\tLoss: 0.131808\n", + "Train Epoch: 4 [80/180 (44%)]\tLoss: 0.060891\n", + "Train Epoch: 4 [100/180 (56%)]\tLoss: 0.026389\n", + "Train Epoch: 4 [120/180 (67%)]\tLoss: 0.022358\n", + "Train Epoch: 4 [140/180 (78%)]\tLoss: 0.061502\n", + "Train Epoch: 4 [160/180 (89%)]\tLoss: 0.013414\n", + "Train Epoch: 5 [0/180 (0%)]\tLoss: 0.036915\n", + "Train Epoch: 5 [20/180 (11%)]\tLoss: 0.067229\n", + "Train Epoch: 5 [40/180 (22%)]\tLoss: 0.020201\n", + "Train Epoch: 5 [60/180 (33%)]\tLoss: 0.048129\n", + "Train Epoch: 5 [80/180 (44%)]\tLoss: 0.056789\n", + "Train Epoch: 5 [100/180 (56%)]\tLoss: 0.161134\n", + "Train Epoch: 5 [120/180 (67%)]\tLoss: 0.017697\n", + "Train Epoch: 5 [140/180 (78%)]\tLoss: 0.057869\n", + "Train Epoch: 5 [160/180 (89%)]\tLoss: 0.024027\n" + ] + } + ], + "source": [ + "for epoch in range(n_epochs):\n", + " # train \n", + " train(train_dataloader,epoch)" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test Results :\n", + "\n", + "RSS : 6.53765385536986\n", + "RSE : 0.19164652996049286\n", + "TSS : 6.631032657354906\n", + "R Squared : 0.014082090499354383\n", + "MSE : 0.03632029919649922\n", + "MAE : 0.13936191845441656\n", + "Accuracy with 10% : 0.35555555555555557\n", + "\n", + "Train Results :\n", + "\n", + "RSS : 3.6185278102910923\n", + "RSE : 0.22113123809944435\n", + "TSS : 3.641416601597961\n", + "R Squared : 0.006285683241193718\n", + "MSE : 0.047612208030145944\n", + "MAE : 0.16736403372334807\n", + "Accuracy with 10% : 0.27631578947368424\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[W NNPACK.cpp:53] Could not initialize NNPACK! Reason: Unsupported hardware.\n" + ] + } + ], + "source": [ + "import math\n", + "def reg_report(true, pred, name='Test'):\n", + " print(\"\\n{} Results :\\n\".format(name))\n", + " print(\"RSS :\",sum((pred-true)**2))\n", + " print(\"RSE :\",math.sqrt(sum((pred-true)**2)*(1/(len(pred)-2))))\n", + " print(\"TSS :\",sum((true-true.mean())**2))\n", + " print(\"R Squared :\",1-(sum((pred-true)**2)/sum((true-true.mean())**2)))\n", + " print(\"MSE :\",((pred-true)**2).mean())\n", + " print('MAE :',(abs(pred-true)).mean())\n", + " print('Accuracy with 10% :', ((pred<=true*1.1) & (true*0.9<=pred)).mean())\n", + " \n", + "\n", + "def eval_report(y_train, pred_train,y_test, pred_test):\n", + " reg_report(y_train, pred_train, name='Train')\n", + " reg_report(y_test, pred_test, name='Test')\n", + " \n", + "def res(dataLoader, name = 'Test'): \n", + " trues = []\n", + " preds = []\n", + " network.eval()\n", + " with torch.no_grad():\n", + " for items in dataLoader:\n", + " image = torch.FloatTensor(items['image'])\n", + " features = torch.FloatTensor(items['house_features'][:,:4])\n", + " price = torch.FloatTensor(items['house_features'][:,4])\n", + "\n", + " outputs = network(input2 = features, input1 = image)\n", + "\n", + " trues = trues + price.tolist()\n", + " preds = preds + outputs[:,0].tolist()\n", + "\n", + "\n", + " reg_report(true = np.array(trues), pred = np.array(preds), name=name)\n", + "\n", + "res(train_dataloader, name = 'Test')\n", + "res(test_dataloader, name = 'Train')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Netz war am besten." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NLP", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cde137ca4d604021dfeee5cc69f15444c7734737e8b71c16850c523803c8f980" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bonus/test.ipynb b/Bonus/test.ipynb new file mode 100644 index 0000000..7ae389d --- /dev/null +++ b/Bonus/test.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow version: \n", + "Eager mode enabled: True\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "print('TensorFlow version: {version}'.format(version=tf.version))\n", + "print('Eager mode enabled: {mode}'.format(mode=tf.executing_eagerly()))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num GPUs Available: 1\n", + "GPU available: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n" + ] + } + ], + "source": [ + "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n", + "print('GPU available: {gpu_available}'.format(gpu_available=tf.config.list_physical_devices('GPU')))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf-gpu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "f599085227da76de7860c088523293673b66c34e63d5f725449b4547148eb02d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}