Local house-price vulnerability: Evidence from the U.S. and Canada

• Published in: Journal of housing economics Vol. 54; p. 101791
• Main Authors: Alter, Adrian, Mahoney, Elizabeth M.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2021
• Subjects: Housing market; Local factors; Quantile regressions; Spatial dependence; R31; Spatial dependence; E37; G12; G1; Housing market; Local factors; Quantile regressions
• ISSN: 1051-1377; 1096-0791
• DOI: 10.1016/j.jhe.2021.101791

To quantify downside risks to housing markets, we apply and extend the house price-at-risk methodology to a sample of 37 cities across the United States and Canada using quarterly data from 1983 to 2018. Our findings suggest that downside risks to housing markets in the United States have seemingly fallen after the global financial crisis, while having increased in Canada. Local factors such as supply availability and valuation proxies are found to be significantly associated with future downside risks to major housing markets, but their effect varies across cities and time horizons. We find important spatial dependence in housing risks, with overvaluation in nearby cities increasing downside risks to house prices in a given city. Additionally, macro-financial drivers such as household debt, capital flows, and financial conditions play a key role in forecasting house price risks. Using micro-level data from California, we highlight the heterogeneity of tail risks across counties and the role played by local factors in forecasting housing risks. •The house price-at-risk (HaR) methodology is applied and extended to a sample of 37 cities across the United States and Canada using quarterly data from 1983 to 2018.•Local factors such as supply availability and valuation are found to be significantly associated with future downside risks to major housing markets.•The forecasting accuracy of the HaR model improves by about 5 percentage points when spatially weighted local factors are introduced and found significant.•Tail risks across counties in California are found heterogeneous while the role played by local factors in forecasting housing risks remains important.