U.S. Housing Prices Before and After COVID-19: Shifts in Market Dynamics and Key Drivers

Abstract

Restrictions to International travel during COVID-19 and the unrelated adjustments to the policies resulted in permanently changing the United States market ever since the work-from-home movement gained traction. This article focuses on the use of fixed effects panels as well as breakpoints to analyze the housing prices changes every metro performed from pre-pandemic to post-pandemic. It uses data from Zillow and Freddie Mac from 2015 to 2024 and distinguishes the following changes: decreased price sensitivity to raised mortgage rates after 2020, the large price geographically located in the suburbs for the work-from-home option, and the inefficiently proportioned supply and demand for rent in the stricter market regulations states such as Massachusetts and California. Machine learning, in the form of random forests, further confirms the elevated importance of price predictors such as net migration and available housing units. These shifts indicate a change in the available housing policies, stressing that with the current trends in migration and native supply changing, the monetary systems may have lost relevance.