Scholarlink

<p>This study examines the consideration of spatial heterogeneity in the development of Home Insurance rates, specifically 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 differentiation in the effect 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving Home Insurance Ratemaking with Geographically Weighted Poisson Regression (GWPR) Model: Assessing Water Damage Risk

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月11日

查看全文
  • Maria-Victoria Rivas-Lopez,
  • Mariano Matilla-García,
  • Roman Minguez-Salido,
  • Miguel Angel Bravo-Ovalle

摘要

This study examines the consideration of spatial heterogeneity in the development of Home Insurance rates, specifically 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 differentiation in the effect 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.