FACTORS INFLUENCING THE UNITED STATES HOUSE PRICE INDEX: A MULTIPLE LINEAR REGRESSION APPROACH

Abstract

This study aims to develop a robust model for predicting the House Price Index (HPI) by analyzing its key determinants within the U.S. housing market. Utilizing a Multiple Linear Regression (MLR) approach, the analysis using R software. The findings indicate that several macroeconomic factors significantly impact the HPI. Through a stepwise selection process, Model 4, which includes the Stock Price Index, Consumer Price Index, Unemployment Rate, and Mortgage Rate, was identified as the best-fitted model. Despite the presence of some multicollinearity, this model demonstrated superior predictive power and a significantly higher Adjusted R-squared value of 0.9585 compared to alternative models. The results underscore the importance of a comprehensive analytical framework for understanding housing market dynamics. This research provides valuable insights for policymakers and investors, offering a reliable tool for anticipating HPI trends and informing decision-making to enhance market stability and affordability.