Bulletin of Earthquake Science and Engineering

Bulletin of Earthquake Science and Engineering

Assessment of Earthquake Insurance Industry's Performance Using Spatial Statistics Methods: A Case Study of the 2017 Sarpol-e Zahab Earthquake

Document Type : Propagative Article

Authors
Morteza Bastami 1
Mahdi Rahmani-Qeranqayeh 2
Asma Hamzeh 3
1 Associate Professor, Structural Engineering Research Center, International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran
2 Ph.D. Student, International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran
3 Assistant Professor Insurance Research Center, Tehran, Iran
10.48303/bese.2024.2011452.1136
Abstract
Earthquakes are a systemic risk, simultaneously affecting large geographic areas and multiple assets, thereby imposing substantial financial burdens on insurance companies. Understanding the insurance industry’s performance in past earthquakes is crucial for shaping future insurance policies and ensuring financial sustainability. By analyzing historical data and integrating it into advanced risk models, insurance companies can enhance their ability to mitigate future losses while maintaining profitability. The role of the insurance industry in compensating earthquake-induced losses has been a focal point of studies conducted by researchers, governments, and institutions worldwide. However, in Iran, the relatively short history of natural disaster insurance has resulted in a scarcity of comprehensive reports and studies. In addition, existing analyses primarily rely on classical statistical methods, which assume independence among observations. This assumption is often violated in the context of earthquake impacts, where spatial autocorrelation-where geographically proximate observations are more similar-plays a significant role. To address this limitation, spatial statistical methods, which explicitly account for spatial autocorrelation, provide a more robust analytical framework. This study investigates economic losses from earthquakes and the insurance industry’s response between 2007 and 2020 across seismic regions of Iran. Data on economic losses were collected from official government statistics, statements by authorities, provincial government announcements, and news reports. Concurrently, data on insurance payouts and the number of payouts were gathered from 19 insurance companies over the same period. A focused analysis of the 2017 Sarpol-e Zahab earthquake in Kermanshah province was also conducted, examining regional losses and the payout distribution of a specific insurance company. The study employed a two-tiered analysis approach. First, a comprehensive analysis of earthquake losses and insurance payouts was conducted using data visualization techniques to manage the complexity and volume of data. This exploration spanned temporal (annual) and spatial (provincial) dimensions over the past decade. Second, for the Sarpol-e Zahab earthquake, spatial prediction techniques, specifically kriging, were used to predict the probability of insurance payouts. Logistic regression models provided baseline probability prediction, which were refined using spatial residuals to achieve higher accuracy compared to classical statistical methods. The refined spatial models enabled a detailed mapping of insurance payouts at the provincial level. The analysis revealed significant insights into the performance of insurance companies. Over the past decade, certain companies faced substantial financial burdens due to payouts for single, high-impact earthquakes. In years marked by multiple damaging earthquakes, the cumulative payouts by the insurance sector were notably high. Encouragingly, the insurance industry’s contribution to compensating earthquake losses has shown a growing trend. In the case of the Sarpol-e Zahab earthquake, the analyzed insurance company demonstrated the highest probability of payouts in the western counties of Kermanshah province. Higher payouts were observed in central parts of these counties and surrounding areas, reflecting variations in policyholder distribution and building types. The spatial models effectively captured these regional payout distributions with greater accuracy than classical methods. This study underscores the importance of integrating spatial statistics into the analysis of earthquake losses and insurance industry performance. Such approaches not only provide deeper insights into loss distribution and payout patterns but also enhance the precision of risk modeling, paving the way for more resilient insurance frameworks in the face of natural disasters.
Keywords
Insurance Industry
Building
Spatial Correlation
Logistic Regression
Kriging Spatial Prediction
Earthquake

Subjects

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  • Receive Date 12 September 2023
  • Revise Date 28 March 2024
  • Accept Date 05 June 2024