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arXiv · 2506.14638

A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions

Lang Qin·Yuejin Xie·Daili Hua·Xuhui Meng
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Abstract

Extreme weather events increasingly threaten the insurance and real estate industries, creating conflicts between profitability and homeowner burdens. To address this, we propose the SSC-Insurance Model, which integrates SMOTE, SVM, and C-D-C algorithms to evaluate weather impacts on policies and investments. Our model achieves 88.3% accuracy in Zhejiang and 79.6% in Ireland, identifying a critical threshold (43% weather increase) for insurance viability. Additionally, we develop the TOA-Preservation Model using TOPSIS-ORM and AHP to prioritize building protection, with cultural value scoring highest (weight: 0.3383). Case studies on Nanxun Ancient Town show a 65.32% insurability probability and a protection score of 0.512. This work provides actionable tools for insurers, developers, and policymakers to manage climate risks sustainably.

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Lang Qin, Yuejin Xie, Daili Hua, Xuhui Meng. 2025-06-17. A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions. https://arxiv.org/abs/2506.14638

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