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Dating housing booms fueled by credit: A Markov switching approach

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  • Cañizares Martínez, Carlos

Abstract

This study aims to empirically identify the state of the US housing market. I do so by estimating a Markov switching model of housing prices, in which mortgage debt affects house prices nonlinearly and drives state transition probabilities. Second, I compute a state-contingent housing risk measure fed with the probability of being in each state. Finally, I show that such risk measure contains early warning information in a forecasting exercise to predict the charge-off rates of real estate residential loans and a financial stress index. The significance of this study is that it informs economic agents and policymakers about the state of the housing market mechanically.

Suggested Citation

  • Cañizares Martínez, Carlos, 2025. "Dating housing booms fueled by credit: A Markov switching approach," Journal of Financial Stability, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:finsta:v:78:y:2025:i:c:s1572308925000415
    DOI: 10.1016/j.jfs.2025.101412
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    More about this item

    Keywords

    House prices; Non-linear modeling; Markov switching model; Household debt;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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