Applying Machine Learning Techniques to Evaluate Climate-Related Risks in Real Estate Mortgage Valuations

Abstract

Facing the escalating effects of climate change, the real estate industry faces risks from physical perils and shifting towards a low-carbon economic model. These risks have substantial consequences for the assessment and effectiveness of real estate mortgage portfolios. Conventional approaches to evaluating mortgage risk frequently fail to capture the intricate, non-linear connections between climate variables and loan results. In this paper, we present a new machine-learning framework that aims to quantify climate-related risks in real estate finance. We utilize neural networks and gradient-boosting algorithms to forecast the likelihood of mortgage defaults and the potential loss resulting from defaults in different climate stress scenarios. A robust and forwardlooking risk assessment is developed by integrating property-level exposure data, loan characteristics, and macroeconomic indicators. The empirical findings prove that our models perform superior to conventional econometric methods regarding predictive precision and computational effectiveness. The framework offers a robust instrument for investors, lenders, and regulators who aim to effectively address climate risks and enhance their ability to withstand and adapt to an unpredictable future.