Predicting hurricane wind damage by claim payout based on Hurricane Ike in Texas

Abstract

The increasing occurrence of natural disasters and their related damage have led to a growing demand for models that predict financial loss. Although considerable research on the financial losses related to natural disasters has found significant predictors, there has been a lack of comprehensive study that addresses the relationship among vulnerabilities, natural disasters, and the economic losses of individual buildings. This study identifies the vulnerability indicators for hurricanes to establish a metric to predict the related financial loss. We classify hurricane-prone areas by highlighting the spatial distribution of losses and vulnerabilities. This study used a Geographical Information System (GIS) to combine and produce spatial data and a multiple regression method to establish a wind damage prediction model. As the dependent variable, we used the value of the Texas Windstorm Insurance Association (TWIA) claim payout divided by the appraised values of the buildings to predict real economic loss. As independent variables, we selected a hurricane indicator and built environment vulnerability indicators. The model we developed can be used by government agencies and insurance companies to predict hurricane wind damage.

Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction

“…Among the natural hazards, this study found that the occurrence of flooding was proportional to the increase in third-party losses in bridge construction, which is consistent with the findings of previous studies that revealed that the elements of flooding are also key risk factors in construction projects [44]. In addition, as Kim et al [49] contended, the results of this study also indicate that the size of a construction project affects the potential loss. This study classified the size of a construction project into two different elements, the construction duration and rank of the construction company and verified that the rank of the company is the more significant factor of the two.…”

Revealing building vulnerability to windstorms through an insurance claim payout prediction model: a case study in South Korea

“…The total value of the property is also statistically significant with windstorm damages. Kim et al (2016) denoted that the total value of the property has a negative relationship with the windstorm damage, as the total value of the property increases as the windstorm damages decreases. He also found that the value is not only a critical indicator, but also has the most dominant impact on the damage among the significant variables (Kim et al 2016).…”

“…On the other hand, the reverse relationship is established for vulnerability. For instance, an old building is more prone to windstorms than a new building with a lower building value (Highfield et al 2010;Kim et al 2016).…”

“…Moreover, the essential values in the insurance industry, such as the probable maximum loss, excess of loss reinsurance, and limit of liability, are created on the basis of analysing loss experiences. The values apply to reasonably sharing and allocating the risks (Cummins et al 1999;Kim et al 2016). Consequently, developing advanced vulnerability functions is the best way to assess the exact financial risk from windstorms.…”

Measuring Vulnerability of Typhoon in Residential Facilities: Focusing on Typhoon Maemi in South Korea

“…Most damage occurred in Gyeongnam and Busan, which are located on the right side of the typhoon path. The cause for this is that the typhoon landed on the coast of Gyeongnam, causing direct damage to the area and, in particular, to the area to the right side of the typhoon path due to the extremely strong winds and heavy rainfall [21]. In the right-sided area of the expected path of the typhoon, more efforts are needed to mitigate typhoon damage.…”