Predictive Modeling of Multi-Peril Homeowners Insurance

By Edward W. Frees, Glenn G. Meyers, A. David Cummings

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Abstract

Predictive models are used by insurers for underwriting and ratemaking in personal lines insurance. Focusing on homeowners insurance, this paper examines many predictive generalized linear models, including those for pure premium (Tweedie), frequency (logistic) and severity (gamma). We compare predictions from models based on a single peril, or cause of loss, to those based on multiple perils. For multi-peril models, we introduce an instrumental variable approach to account for dependencies among perils. We calibrate these models using a database of detailed individual policyholder experience. To evaluate these many alternatives, we emphasize out-of-sample model comparisons. We utilize Gini indices for global comparisons of models and, for local comparisons, introduce nonparametric regression techniques. We find that using several different comparison approaches can help the actuary critically evaluate the effectiveness of alternative prediction procedures.

Keywords: Instrumental variables, Tweedie distribution, Gini index, insurance pricing

Citation

Frees, Edward W., Glenn G. Meyers, and A. David Cummings, "Predictive Modeling of Multi-Peril Homeowners Insurance," Variance 6:1, 2012, pp. 11-31.

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Mission Statement

Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.