Seattle Real Estate Market Analysis

Overview

This project is a comprehensive analysis of the real estate market in Seattle. Using a Jupyter Notebook, we explore various trends, pricing factors, and the impact of economic indicators on the housing market.

Objectives

• To identify patterns and trends in the real estate prices in Seattle.
• To analyze the relationship between housing features and market prices.
• Quantify the relationship between housing prices and first floor square footing.
• Compare statistical metrics of properties with vs. without waterfront views.

Data

The analysis is conducted using a dataset that includes information on housing features, sale prices, and dates of transactions. The dataset contains the following key columns:

• Price: Sale price of the home
• Date: Date of the sale
• Bedrooms: Number of bedrooms
• Bathrooms: Number of bathrooms
• Sqft_Living: Square footage of the living space
• Sqft_Lot: Square footage of the lot
• Floors: Number of floors
• Waterfront: Whether the home is on the waterfront
• View: Quality of the view from the home
• Condition: Overall condition of the home
• Grade: Overall grade given to the housing unit
• Sqft_Above: Square footage of the house apart from the basement
• Sqft_Basement: Square footage of the basement
• Year_Built: Year when the house was built
• Year_Renovated: Year when the house was renovated
• Zipcode: Zip code area
• Lat: Latitude coordinate
• Long: Longitude coordinate

Methodology

The notebook starts with

• Data cleaning
• Data wrangling, followed by
• exploratory data analysis (EDA) to understand the distribution and relationship of variables.
  • Linear regression
  • Coefficient determination
  • Ridge regression
  • Finding the min, first quartile, median, third quartile, and max through boxplots.
• Visualizations to support our analysis and employ statistical methods to draw conclusions.

Tools Used

• Jupyter Notebook: As the coding canvas for python programming.
• Excel: As the main database file to extra and manipulate a dataframe from.
• Pandas: For data manipulation and analysis.
• Matplotlib/Seaborn: For creating static, interactive, and informative visualizations.
• sklearn: For for linear and polynomial regression modeling, machine learning, and standardization.

Main Findings:

Assuming all other factors remain constant, this function means that the house price in King County increases by approximately $268.47 per each additional square foot of space above the ground level. The relationship between home price and above square footage can be modeled by the following function: f(x)=268.47x+59953.19

R^2=49.29% suggests that the variability in the house prices can be explained by the square footage of the home (independent varaible 'sqft_living'). In other words, the squre footage of the living space accounts for nearly half of the observed varaition in house prices.