Bootstrap regression, Macroeconomic factors, U.S. home prices, Nonparametric methods.


This study investigates the impact of macroeconomic indicators on US home prices, underscoring the importance of understanding these dynamics due to their signifcant socioeconomic consequences. Utilizing a dataset from Kaggle, originally collected by FRED, the research examines variables like the Consumer Price Index, Population, Unemployment, GDP, Stock Prices, Income, and Mortgage Rate to discern their efect on housing market fuctuations. The analysis identifes multicollinearity among predictors, necessitating a shift from traditional multiple linear regression to a more robust bootstrap regression method due to violations of parametric assumptions. Key fndings reveal that Real Disposable Income is a signifcant predictor of home prices, although the presence of multicollinearity complicates the model-building process. The bootstrap regression approach, favored for its resilience to assumption violations, confrms the infuence of selected macroeconomic factors on home prices. The study concludes that bootstrap regression provides a reliable alternative to parametric methods in cases of assumption non-compliance and highlights the critical role of addressing multicollinearity in regression analysis. This research ofers valuable insights for stakeholders involved in the housing market, emphasizing the need for careful econometric modeling in economic policy and investment decisions.


Spring 2024

Course Name

STA 6366 Data Science 1

Instructor Name

Xie, Rui

Accessibility Status

PDF accessibility verified using Adobe Acrobat Pro Accessibility Checker

Included in

Data Science Commons