Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration

Mehmet Balcilar, Elie Bouri, Rangan Gupta and Mark E. Wohar

Journal of Real Estate Portfolio Management, 2020, vol. 26, issue 2, 111-117

Abstract: Recent evidence, based on a linear framework, tends to suggest that while mortgage default risks can predict weekly and monthly housing returns of the United States, the same does not hold at the daily frequency. We, however, indicate that the relationship between daily housing returns with mortgage default risks is in fact nonlinear, and hence a linear predictive model is misspecified. Given this, we use a k-th order nonparametric causality-in-quantiles test, which in turn allows us to test for predictability over the entire conditional distribution of not only housing returns, but also volatility, by controlling for misspecification due to nonlinearity. Based on this model, we show that mortgage default risks do indeed predict housing returns and volatility, barring at the extreme upper end of the respective conditional distributions.

Date: 2020
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Working Paper: Mortgage Default Risks and High-Frequency Predictability of the US Housing Market: A Reconsideration (2018)
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DOI: 10.1080/10835547.2020.1854606

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