Improving home insurance ratemaking with geographically weighted poisson regression (GWPR) model: Assessing water damage risk
This study examines the consideration of spatial heterogeneity in the development of Home Insurance rates, specifcally focusing on water damage throughout Spain. This focus arises from the need to establish a methodology that not only improves ratemaking procedures for water damage but also acknowle...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/718304 |
| Acesso em linha: | http://hdl.handle.net/10486/718304 https://dx.doi.org/10.1007/s12061-024-09632-4 |
| Access Level: | acceso abierto |
| Palavra-chave: | Geographically Weighted Poisson Regression Model (GWPR) Spatial Heterogeneity Ratemaking Water Damaged Home Insurance Claims Rainfall Taxonomy Economía |
| Resumo: | This study examines the consideration of spatial heterogeneity in the development of Home Insurance rates, specifcally focusing on water damage throughout Spain. This focus arises from the need to establish a methodology that not only improves ratemaking procedures for water damage but also acknowledges the potential impacts of climate change, allowing diferentiation in the efect of variables such as rainfall depending on the location and frequency of water claims. By using the GWPR model, spatial heterogeneity is taken into account and the ratemaking process is enhanced by identifying spatial clusters related to the frequency of water damage claims. Moreover, an empirical development has been carried out employing a database of home insurance data for water coverage in the Spanish territory. The variables selected in this process are not only associated with weather, but also with characteristics of the policies, housing, and socio-economic conditions of the policyholders |
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