Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning

TL;DR

This study applies various machine learning models to predict housing prices in Chicago suburbs, demonstrating that XGBoost outperforms others in volatile post-pandemic market conditions, and analyzes influential factors using SHAP values.

Contribution

It introduces a comparative analysis of multiple machine learning models for housing price prediction in a volatile market and evaluates variable importance with SHAP.

Findings

XGBoost achieved the lowest error metrics among models.

Machine learning models effectively predict prices in volatile markets.

SHAP analysis identified key factors influencing house prices.

Abstract

The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.