Forecasting real housing price returns of the USA using machine learning: the role of climate risks

Bruno Tag Sales, Hudson S. Torrent and Rangan Gupta

International Journal of Computational Economics and Econometrics, 2025, vol. 15, issue 3, 225-246

Abstract: Climate change, a pressing global challenge, has wide-ranging implications for various aspects of our lives, including housing prices. This paper delves into the complex relationship between climate change and real housing price returns in the USA, leveraging a comprehensive dataset and advanced machine learning technique - the stepwise boosting method. This ensemble learning technique significantly enhances our analysis. Our findings suggest that climate change variables can influence real housing price returns, particularly in the short term, but the relationship is complex and varies by region. The adaptive learning capability of step-wise boosting has been crucial in uncovering these insights. This methodological approach not only underscores the importance of employing advanced predictive models in analysing the effects of climate change on urban development but also highlights the potential for informed decision-making, sustainable urban planning, and climate risk mitigation.

Keywords: climate finance; housing market; machine learning; predictive modelling; step-wise boosting; USA. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:15:y:2025:i:3:p:225-246

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