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Understanding Price-To-Rent Ratios Through Simulation-Based Distributions And Explainable Machine Learning

Author

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  • Vogt Jonas

    (Finance and Data-Science, Duale Hochschule Baden Wurttemberg Mannheim, Coblitzallee 1-9, 68163 Mannheim; Germany)

Abstract

Index-level price-to-rent (PTR) ratios are a widely used metric for analyzing housing markets, employed by both real estate practitioners and policymakers. This article seeks to improve the contextualization of observed PTR values by examining the interplay between these ratios and macroeconomic and housing-market developments in a non-linear framework. We analyze historical data on housing prices, rents and macroeconomic developments from 18 advanced economies, spanning from 1870, using Boosted Regression Trees and explainable machine learning techniques. As a precursor to this analysis, we also present the empirical distribution of the price-to-rent ratio and the implied housing risk premia across all years and countries.

Suggested Citation

  • Vogt Jonas, 2025. "Understanding Price-To-Rent Ratios Through Simulation-Based Distributions And Explainable Machine Learning," Real Estate Management and Valuation, Sciendo, vol. 33(3), pages 36-48.
    • Handle: RePEc:vrs:remava:v:33:y:2025:i:3:p:36-48:n:1004
    DOI: 10.2478/remav-2025-0024
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    References listed on IDEAS

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    1. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    3. Okunev, John & Wilson, Patrick & Zurbruegg, Ralf, 2000. "The Causal Relationship between Real Estate and Stock Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 21(3), pages 251-261, November.
    4. Axel Cabrol & Wolfgang Drobetz & Tizian Otto & Tatjana Puhan, 2024. "Predicting Corporate Bond Illiquidity via Machine Learning," Financial Analysts Journal, Taylor & Francis Journals, vol. 80(3), pages 103-127, July.
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    Keywords

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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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