Charles Nyce

Are Territorial Rating Models Outdated in Residential Property Insurance Markets? Evidence from the Florida Property Insurance Market

The fundamental shift in rating methodology from historical loss costs to catastrophe modeling for windstorm coverage calls into question the accuracy of rates developed using rating territories. Using premiums and modeled average annual loss (AAL) estimates from Citizens Property Insurance Corporation (Citizens) in Florida, this article analyzes the use of distance to coast (DtC) as a rating variable in providing coverage for the windstorm peril in homeowners insurance. Catastrophe models used to generate AAL costs do not rely on the same application of the law of large numbers as using historical loss costs and thus allows for more granular pricing of the windstorm peril. The results show that DtC, a rating variable that is property specific, more closely aligns premiums and AALs than territorial rating, and allows more granular pricing of the windstorm peril. More granular risk based pricing provides better incentives for homeowners regarding location and mitigation choices and may help reduce aggregate exposure to windstorm damages in the long run.

Market Implications of Public Policy Interventions: The Case of Florida's Property Insurance Market

This article asserts that the market for property insurance, particularly homeowners insurance, in the State of Florida is experiencing failures, and that a combination of market problems, externalities, and interventions unique to Florida led to these failures. The authors provide evidence of market failures in the form of undesirable market outcomes, both over time and in comparison to other coastal states. Also, they provide a narrative description of the market events, problems, and policies preceding these adverse market developments and link the narrative to the evidence. Recommendations for a return to risk-based pricing and incentives for appropriate property mitigation are made.

An Industrial Organization Theory of Risk Sharing

Examining the global reinsurance market for catastrophic losses, we propose a new theory of optimal risk sharing that finds its inspiration in the economic theory of the firm. Our model offers a theoretical foundation for the vertical and horizontal tranching of insurance contracts (also known respectively as proportional and excess of loss reinsurance contracts). Using a two-factor production model popular in industrial economics, we show how reinsurance should be optimally layered (with attachment and detachment points) for a given book of business. This allows us to find the minimum insurance premium necessary to cover the cost of catastrophic events. We conclude with public policy implications by showing the conditions under which government intervention in the catastrophic loss insurance industry can reduce the cost to society of bearing risk and increase its welfare.

The Impact of Hurricane Mitigation Features and Inspection Information on House Prices

This paper examines the effect of hurricane mitigation features and their verification on the transaction prices of single-family homes. Some of these features are obvious to buyers and sellers (visible) and others are not easily observed (hidden). Prior research on the relationship between these features and house prices has implicitly assumed that the features are known and that buyers and sellers are equally informed. This paper contributes to the literature by examining the potentially different effects of the visible and hidden features, and the verification of each, on prices in an environment of incomplete and asymmetric buyer-seller information. We assemble a dataset that includes sale information on all detached single-family residential properties in Miami-Dade County merged with a dataset of insured properties from Citizens Property Insurance Corporation. Using a treatment effects model, we find that properties with both visible and hidden mitigation features that have been verified by inspection sell, on average, at prices approximately 10% higher than properties where the features have not been verified, ceteris paribus. The results also indicate that although visible and hidden features differ significantly in their effects on price, the magnitude of the effect of the inspection on price is surprisingly similar whether features are visible or hidden. Our findings indicate that the price effect of the inspection is due to a combination of the risk mitigating benefits of the features and the insurance premium credits they represent.

Demographic Factors and Price Distortions in Insurance: Demographic Factors and Price Distortions in Insurance

Few papers have analyzed the potential linkages between price distortions and the specific demographic and political traits of customers. The existence of price distortions may have adverse and potentially unintended impacts on certain demographic groups, leading to significant public policy concerns. The current study uses census tract data and rating factors to examine age, income, and race demographics in an effort to determine if any subgroups of the population are adversely impacted by the price distortions of property insurance. The results suggest that Hispanics, lower income households, and specific age groups pay relatively more for insurance coverage than comparison groups.

Demographic Factors and Price Distortions in Insurance

Few papers have analyzed the potential linkages between price distortions and the specific demographic and political traits of customers. The existence of price distortions may have adverse and potentially unintended impacts on certain demographic groups, leading to significant public policy concerns. The current study uses census tract data and rating factors to examine age, income, and race demographics in an effort to determine if any subgroups of the population are adversely impacted by the price distortions of property insurance. The results suggest that Hispanics, lower income households, and specific age groups pay relatively more for insurance coverage than comparison groups.