  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.
