A Modeling Study of Insurance and Real Estate Risk Assessment in the Context of Global Climate Change

This paper focuses on the problem of economic losses caused by extreme weather events in the context of global climate change, and aims to develop a risk assessment model applicable to the insurance and real estate industries. The study first uses the Spearman correlation coefficient to conduct sensitivity analysis, determines the premium as the optimization variable, and combines the idea of present value of compound interest to construct a risk rating evaluation system, which classifies the risk into A, B, and C to guide the decision to insure. Subsequently, with the help of historical climate data, the ARIMA algorithm is used to predict future climate risk, and empirical evaluation is conducted for the United States and Australia to predict the future loss trend of the two countries. In order to improve the science of real estate siting decision-making, the study introduces a GIS-based decision support system, combines the gradient boosting tree algorithm to predict the risk factors, and constructs a visual GIS model to assess the suitability of siting. In addition, the study quantifies the cultural, historical, economic, and community values of the building facilities, and ranks and weights the factors by entropy weighting method to emphasize the protection of high-value factors. This study provides powerful tools and strategic recommendations for the insurance and real estate industries to address climate risk.