premium_explorer.ipynb

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  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import bokeh\n",
    "from pathlib import Path\n",
    "import hvplot.pandas\n",
    "import os\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the list and sale price to find if buyers are buying at a premium or getting a discount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and Clean the new Data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y4/x5w7cmpd0wvg6v1klh51sxvc0000gn/T/ipykernel_9920/1300110981.py:38: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.\n",
      "  df_sale_price = df_sale_price['2018':'2021']\n",
      "/var/folders/y4/x5w7cmpd0wvg6v1klh51sxvc0000gn/T/ipykernel_9920/1300110981.py:39: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.\n",
      "  df_list_price = df_list_price['2018':'2021']\n"
     ]
    }
   ],
   "source": [
    "# Set the path for the csv files\n",
    "data_path = r'./data/'\n",
    "sale_price_filenames = glob.glob(data_path + \"/*_Sale_Price.csv\")\n",
    "list_price_filenames = glob.glob(data_path + \"/*_List_Price.csv\")\n",
    "\n",
    "# Create two lists to be iterated through  to create the dataframe\n",
    "sale_price_list = []\n",
    "list_price_list = []\n",
    "\n",
    "# Loop through each file name and append it to the corresponding list\n",
    "for filename in sale_price_filenames:\n",
    "    sale_price_list.append(pd.read_csv(filename, index_col=\"date\", parse_dates=True, infer_datetime_format=True))\n",
    "\n",
    "for filename in list_price_filenames:\n",
    "    list_price_list.append(pd.read_csv(filename, index_col='date', parse_dates=True, infer_datetime_format=True))\n",
    "\n",
    "# Create a new Dataframe by concatenating the data from each file in the list \n",
    "df_sale_price = pd.concat(sale_price_list)\n",
    "df_list_price = pd.concat(list_price_list)\n",
    "\n",
    "# Drop the 'None' Column from teh Dataframes and change the Column Names\n",
    "#df_sale_price = df_sale_price.drop(columns=['None'])\n",
    "df_sale_price = df_sale_price.rename(columns={\n",
    "    'date': 'Date',\n",
    "    'value': 'Sale Price',\n",
    "    'metro_area': 'Region'\n",
    "})\n",
    "\n",
    "#df_list_price = df_list_price.drop(columns=['None'])\n",
    "df_list_price = df_list_price.rename(columns={\n",
    "    'date': 'Date',\n",
    "    'value': 'Listing Price',\n",
    "    'metro_area': 'Region'\n",
    "})\n",
    "\n",
    "# Create a new Column for the Year\n",
    "df_sale_price['Year'] = df_sale_price.index.year\n",
    "df_list_price['Year'] = df_list_price.index.year\n",
    "\n",
    "# Set the value of the column Year to 2018 through 2021 when Zillow data was accessible\n",
    "df_sale_price = df_sale_price['2018':'2021']\n",
    "df_list_price = df_list_price['2018':'2021']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the Data Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       ":DynamicMap   [Region]\n",
       "   :Overlay\n",
       "      .Scatter.Median_List_Price :Scatter   [date]   (Listing Price)\n",
       "      .Scatter.Median_Sale_Price :Scatter   [date]   (Sale Price)"
      ]
     },
     "execution_count": 184,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "21803"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_price_plot = df_sale_price.hvplot.scatter(x='date', y='Sale Price', label=\"Median Sale Price\", groupby='Region', rot=90).opts(yformatter=\"%.0f\")\n",
    "list_price_plot = df_list_price.hvplot.scatter(x='date', y='Listing Price',label='Median List Price',groupby='Region',rot=90).opts(yformatter=\"%.0f\")\n",
    "\n",
    "list_price_plot * sale_price_plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What does the data tell us?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the ploy above, we can see that on average, houses on Zillow do not sell near their list price. This could be due to Zillow's (Now former) market strategy of purchasing a house, pricing it exponentially higher than market value hoping a potential purchaser will bite, and lowering the price continually until it sells(per (Investopedia)|(https://www.investopedia.com/articles/personal-finance/110615/why-zillow-free-and-how-it-makes-money.asp). This means that potential buyers are getting a deal on property. The only time we see that the gap between the sale price and list price is in Toledo. Potential buyers are still getting a deal, however the deals there are not always that great when compared to Minneapolis or Chicago where the gap between Sale Price and List price is consistently higher than the other states."
   ]
  },
  {
   "cell_type": "code",
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