Understanding price-to-rent ratios through simulation-based distributions and explainable machine learning
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Finance and Data-Science, Duale Hochschule Baden Wurttemberg Mannheim, Germany
Submission date: 2024-10-01
Final revision date: 2025-03-11
Acceptance date: 2025-04-25
Corresponding author
Jonas Vogt
Duale Hochschule Baden Wurttemberg Mannheim, Germany
HIGHLIGHTS
- we estimate the median housing risk-premium to be 3.6
- price-rent-ratios exceed 33 in only 10% of all years
- higher price variances seems to lead to higher PTR-ratios
- higher income variance seems to lead to lower PTR-ratios
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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.