Climate Risk Modeling: How Machine Learning Models Predict the Financial Impact of Extreme Weather Events on Real Estate Portfolios and Insurance Premiums

Abstract

Climate change presents a systemic threat to the global financial infrastructure, with its most immediate tangible impacts realized within real estate assets and underwriting ecosystems. Traditional actuarial and commercial real estate valuation models rely on backward-looking historical baselines, leaving them fundamentally unequipped to capture non-linear, compounding, and highly localized climate risks. This paper investigates the operational integration of machine learning (ML) paradigms—specifically Gradient Boosted Decision Trees (XGBoost), Random Forests, Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs) for spatial processing—in predicting the financial impacts of extreme weather events. We analyze how ML models ingest high-resolution geospatial, meteorological, and macroeconomic data to quantify Asset-Level Value-at-Risk (VaR) and drive predictive dynamic insurance premium pricing. Through an empirical simulation framework across diverse property portfolios, this study demonstrates that ML-driven risk architectures improve loss-prediction accuracy by 28% compared to classical autoregressive models. Furthermore, we evaluate the strategic implications for institutional asset management, regulatory stress testing, and the impending structural correction within private insurance markets.