Housing Price Dynamics within the U.S.: Evidence from Zip Codes with Different Demographics

Abstract

We study time-series fluctuations in the United States housing market from 2010 to 2019 using the Gordon growth model. We apply a vector autoregressive model (VAR) with fixed coefficients to measure expectations at each point in time. Our results show that, using zip code level data, we are able to explain the broad movements in housing volatility with higher prediction power compared to previous studies. Using variance decomposition analysis, we find that the housing premium is the main driver of housing market fluctuations. Motivated by previous studies and using impulse response functions, we show how different components of the housing market respond over time to a shock in the interest rate in regions with different levels of income or demographics. Our findings suggest that the impact of monetary policy is bigger in the U.S. housing market when households have less income, more female members, more African Americans, or less well-educated members; a combination of these demographics and lower income in households results in a bigger impact of monetary policy in housing market, due to the necessity of housing for these families.