The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions

TL;DR

This paper investigates how US monetary policy shocks influence regional housing prices, revealing significant regional differences in magnitude and duration, especially in coastal states like California, Arizona, and Florida.

Contribution

It introduces a novel application of factor-augmented vector autoregressions with Bayesian estimation to analyze regional housing responses to monetary policy shocks.

Findings

Monetary policy shocks generally increase regional housing prices.

Largest effects are observed in California, Arizona, and Florida.

Regional differences in response magnitude and duration are significant.

Abstract

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.