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Heterogeneous trends in apartment rental prices

Author

Listed:
  • Metz-Peeters, Maike
  • Werenbeck-Ueding, Sven

Abstract

We introduce a novel, non-parametric approach for estimating house price indices that capture heterogeneous price developments independently of strict functional form assumptions. Utilizing the potential outcomes framework, our approach employs causal forests to effectively address changes in the composition of available housing units while mitigating the curse of dimensionality inherent in traditional matching estimators. By directly incorporating geographical coordinates into the model, the algorithm autonomously determines the adaptive spatial neighborhood for each observation, thus avoiding the imposition of fixed spatial boundaries. This flexibility makes the method particularly well-suited for densely populated areas and enables the investigation of complex heterogeneity in house price developments. We demonstrate the utility of this approach through an application to apartment rental prices in six major German cities before and during the COVID-19 pandemic, illustrating how it uncovers nuanced trends in rental price dynamics during a period of significant market change.

Suggested Citation

  • Metz-Peeters, Maike & Werenbeck-Ueding, Sven, 2025. "Heterogeneous trends in apartment rental prices," Ruhr Economic Papers 1156, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:319075
    DOI: 10.4419/96973340
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    References listed on IDEAS

    as
    1. Clapp, John M & Giaccotto, Carmelo, 1992. "Estimating Price Trends for Residential Property: A Comparison of Repeat Sales and Assessed Value Methods," The Journal of Real Estate Finance and Economics, Springer, vol. 5(4), pages 357-374, December.
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    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Zhang, Lei & Yi, Yimin, 2018. "What contributes to the rising house prices in Beijing? A decomposition approach," Journal of Housing Economics, Elsevier, vol. 41(C), pages 72-84.
    5. Alicia N. Rambaldi & Cameron S. Fletcher, 2014. "Hedonic Imputed Property Price Indexes: The Effects of Econometric Modeling Choices," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S2), pages 423-448, November.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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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