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

2025-06-17

AI Summary

Escalating extreme weather events exacerbate tensions between insurance sustainability and historic building conservation. Method: This study proposes a climate-risk-driven insurance–real estate co-decision framework: (i) the SSC-Insurance model, which quantifies, for the first time, the critical feasibility threshold for insurability (a 43% increase in adverse weather frequency); and (ii) the TOA-Preservation model, integrating cultural value weighting (0.3383) to enable tiered heritage protection. Methodologically, it integrates SMOTE, SVM, C-D-C (clustering–detection–classification), TOPSIS-ORM (Order Relation Method), and AHP (Analytic Hierarchy Process). Contribution/Results: Empirical validation achieves prediction accuracies of 88.3% (Zhejiang, China) and 79.6% (Ireland); application to Nanxun Ancient Town yields an insurability probability of 65.32% and a composite preservation score of 0.512. The framework provides a transferable methodology for climate-resilient insurance pricing and adaptive cultural heritage conservation.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Sustainability and climate impact of web technologies
• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Security and Privacy: Large-scale security measurements

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.

Problem

• Develops model to assess climate impact on insurance and real estate
• Identifies weather threshold for viable insurance policies
• Prioritizes building protection based on cultural and risk factors

Innovation

• Integrates SMOTE SVM C-D-C for weather impact
• Uses TOPSIS-ORM AHP for building protection
• Achieves high accuracy in climate risk assessment