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Using Linear Regression and Machine Learning Techniques to Predict Housing Prices Based on Economic Factors
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Reale, Michael, Ph.D., Spetka, Scott, Ph.D., Novillo, Jorge, Ph.D.
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Spring 2023
Publication Date
2023-01-13
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Abstract
When trying to predict housing prices, most studies rely on data from a specific area, and the
features of the homes there. In this study, the goal is to use linear regression and machine
learning to predict housing prices based on overarching economic factors. A mix of machine
learning and linear regression was used, including TensorFlow Keras, OLS, Ridge, Lasso,
Elastic Net, XGBoost, Random Forest and SVM. Datasets featured include Average Sales Price
of House Sold for the United States, closing stock prices (NASDAQ, S&P), 30-year mortgage
interest rates, average monthly rent, number of houses sold, number of houses constructed, mean
family income, median family income, GDP, and unemployment rate.