Forecasting House Prices
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911433115041792 |
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| author | Kohlscheen, Emanuel |
| author_facet | Kohlscheen, Emanuel |
| contents | 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 root square mean errors (RMSEs) and 45% lower mean absolute errors (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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_21460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting House Prices Kohlscheen, Emanuel General Economics Economics 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 root square mean errors (RMSEs) and 45% lower mean absolute errors (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. |
| title | Forecasting House Prices |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2509.21460 |