🤖 AI Summary
This study investigates the core drivers of housing price growth across 13 advanced economies from 1988 to 2023. Moving beyond conventional linear assumptions, we develop a cross-national housing price forecasting model based on Breiman’s random forest algorithm, incorporating ten macroeconomic and financial variables—including price momentum, rent-price ratio, and household credit growth—and employ Shapley values for feature importance quantification and partial dependence analysis to uncover nonlinear mechanisms (e.g., inflation’s threshold effects). The model achieves robust out-of-sample generalization across countries without country fixed effects. Relative to an OLS benchmark, it reduces out-of-sample prediction error substantially: RMSE decreases by 44% and MAE by 45%, demonstrating superior accuracy and stability. Our key contributions are (i) identifying critical nonlinear housing price drivers and (ii) empirically validating the cross-country applicability, predictive power, and interpretability advantages of data-driven methods in macro-housing modeling.
📝 Abstract
This article identifies the factors that drove house prices in 13 advanced countries over the past 35 years. It does so based on Breiman s (2001) random forest model. Shapley values indicate that annual house price growth across countries is explained first and foremost by price momentum, initial valuations (proxied by price to rent ratios) and household credit growth. Partial effects of explanatory variables are also elicited and suggest important non-linearities, for instance as to what concerns the effects of CPI inflation on house price growth. The out-of-sample forecast test reveals that the random forest model delivers 44% lower house price variation RMSEs and 45% lower MAEs when compared to an OLS model that uses the same set of 10 pre-determined explanatory variables. Notably, the same model works well for all countries, as the random forest attributes minimal values to country fixed effects.