A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918061766868992 |
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| author | Qin, Lang Xie, Yuejin Hua, Daili Meng, Xuhui |
| author_facet | Qin, Lang Xie, Yuejin Hua, Daili Meng, Xuhui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions Qin, Lang Xie, Yuejin Hua, Daili Meng, Xuhui Computational Engineering, Finance, and Science 91B30, 62P05, 90B50 I.2.6; J.4; G.3 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. |
| title | A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions |
| topic | Computational Engineering, Finance, and Science 91B30, 62P05, 90B50 I.2.6; J.4; G.3 |
| url | https://arxiv.org/abs/2506.14638 |