QUANTIFYING DIVISION-LEVEL HETEROGENEITY IN THE U.S. HOUSING MARKET DUE TO COVID-ERA QUANTITATIVE EASING

This study quantifies the impact of COVID-era quantitative easing (QE) on housing prices across the nine U.S. Census divisions using a Bayesian Structural Time Series model implemented through the CausalImpact package in R. Monthly data from 2016 to 2025 are used to construct a counterfactual Housing Price Index (HPI) based on key supply and demand fundamentals, including income, unemployment, building permits, and housing inventory. The results show that, following the onset of QE, observed housing prices in all divisions rose significantly above their predicted counterfactual levels. These effects are statistically significant, persistent, and economically meaningful. Importantly, the magnitude of the effect varies across divisions, providing clear evidence of heterogeneity in the transmission of monetary policy to housing markets. Overall, the findings suggest that QE contributed to elevated housing prices beyond what fundamentals alone would predict, while also producing uneven divisional outcomes across the United States.