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<p>The frequent occurrence of real estate bubbles and their detrimental effects on financial stability highlight the necessity of prompt and precise early-warning systems (EWS). Traditional statistical methods like credit-to-GDP gaps, house price-to-income ratios, and linear econometric models usually fall short in predicting crisis events because of their linear nature and dependence on lagging indicators (Borio and Lowe, Asset prices, financial and monetary stability: exploring the nexus (BIS Working Papers No. 114), Bank for International Settlements, Basel 2002. Available at <a href="https://www.bis.org/publ/work114.htm">https://www.bis.org/publ/work114.htm</a>). However, developments in AI and ML provide fresh perspectives on intricate, nonlinear housing market dynamics, enabling policymakers to identify bubbles sooner and with greater precision. With an emphasis on emerging real estate-driven economies like Dubai, this paper develops and evaluates an AI-driven early-warning framework for identifying real estate bubbles. The study shows that when it comes to event prediction and preparation time, AI-based models perform better than traditional early-warning indicators. This is achieved by integrating banking credit, developer financing flows, liquidity indicators, and macroeconomic variables into a supervised learning framework. The results show how machine learning methods, especially interpretable AI tools and ensemble methods, can give central banks and regulators a reliable, real-time monitoring system. The outcome will be greater success for macroprudential policy.</p>

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AI-driven early warning systems for real estate bubbles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月8日

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  • Omar Al-Amary

摘要

The frequent occurrence of real estate bubbles and their detrimental effects on financial stability highlight the necessity of prompt and precise early-warning systems (EWS). Traditional statistical methods like credit-to-GDP gaps, house price-to-income ratios, and linear econometric models usually fall short in predicting crisis events because of their linear nature and dependence on lagging indicators (Borio and Lowe, Asset prices, financial and monetary stability: exploring the nexus (BIS Working Papers No. 114), Bank for International Settlements, Basel 2002. Available at https://www.bis.org/publ/work114.htm). However, developments in AI and ML provide fresh perspectives on intricate, nonlinear housing market dynamics, enabling policymakers to identify bubbles sooner and with greater precision. With an emphasis on emerging real estate-driven economies like Dubai, this paper develops and evaluates an AI-driven early-warning framework for identifying real estate bubbles. The study shows that when it comes to event prediction and preparation time, AI-based models perform better than traditional early-warning indicators. This is achieved by integrating banking credit, developer financing flows, liquidity indicators, and macroeconomic variables into a supervised learning framework. The results show how machine learning methods, especially interpretable AI tools and ensemble methods, can give central banks and regulators a reliable, real-time monitoring system. The outcome will be greater success for macroprudential policy.