Faculty
Science
Supervisor Name
Katsuichiro Goda, Jiandong Ren
Keywords
earthquake risk, insurance, prediction model, earthquake insurance take-up rates
Description
Maintaining an adequate level of earthquake take-up rate could protect the insurance industry from systemic failure. Past research has shown that British Columbia and Quebec have significant differences in earthquake insurance take-up rate. This report investigates key factors from the structure (default options and various types) of the insurance plan and personal characteristics along with socioeconomic/demographic profiles that affect the demand for earthquake protection in the form of insurance. The report also provides a prediction model for earthquake insurance take-up rate. The results show an importance ranking of key factors of earthquake insurance take up, the most important three are "annual expected loss ratio", “age” and “average household size”. An optimal prediction model constructed by random forest with 15 predictors provides 69.4% testing accuracy. An important finding is that there exist cognitive biases among participants. Possible explanations of this finding are discussed.
Acknowledgements
Thanks to the Western USRI program for such a great research oppotunity. And huge thank you to my supervisors Dr. Jiandong Ren and Dr. Katsuichiro Goda for their support.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Document Type
Paper
Included in
Applied Statistics Commons, Data Science Commons, Earth Sciences Commons, Statistical Models Commons
Investigation of key factors to earthquake insurance take-up rates in Quebec and British Columbia households and prediction model building
Maintaining an adequate level of earthquake take-up rate could protect the insurance industry from systemic failure. Past research has shown that British Columbia and Quebec have significant differences in earthquake insurance take-up rate. This report investigates key factors from the structure (default options and various types) of the insurance plan and personal characteristics along with socioeconomic/demographic profiles that affect the demand for earthquake protection in the form of insurance. The report also provides a prediction model for earthquake insurance take-up rate. The results show an importance ranking of key factors of earthquake insurance take up, the most important three are "annual expected loss ratio", “age” and “average household size”. An optimal prediction model constructed by random forest with 15 predictors provides 69.4% testing accuracy. An important finding is that there exist cognitive biases among participants. Possible explanations of this finding are discussed.