Multiple Linear Regression Models: Predicting the Texas Windstrom Insurance Association Claim Payout and Ratio Versus the Appraised Value of Commercial Buildings from Hurricae Ike
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Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System (GIS) to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage and ratio. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.
Kim, Ji Myong (2013). Multiple Linear Regression Models: Predicting the Texas Windstrom Insurance Association Claim Payout and Ratio Versus the Appraised Value of Commercial Buildings from Hurricae Ike. Doctoral dissertation, Texas A & M University. Available electronically from