Are Territorial Rating Models Outdated in Residential Property Insurance Markets?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

ABSTRACT 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. INTRODUCTION Homeowners and insurers in hurricane-prone states share a similar problem, how to balance availability of homeowners coverage and insurer solvency while addressing concerns about affordability. The problem has developed over the last 20 years with the interaction of catastrophic events, technological advances that have allowed for changes in the rating process, insurance market developments, and regulatory responses.1 Hurricane-prone states have aggregate coastal exposure of over $4 trillion (AIR Worldwide, 2008).2 Of that $4 trillion, over $2.5 trillion is located in Florida. This makes Horida a good starting point for studies related to catastrophic storms. Florida has the largest exposed coastline, the highest percentage of its population (79 percent) living in coastal areas (AIR Worldwide, 2008),3 and the largest property value at risk of any state in the United States. As noted by Grace and Klein (2009), Florida is the epicenter of the debate regarding hurricane risk financing. The problem is exacerbated by the possible increase in hurricane activity. One expert has stated there has been "quite an intense increase in hurricane activity" and "we may be in this cycle for another 20 years" (Trenbeth, 2008). The failure to recognize changing demographics within rating territories that were established several years ago or to apply new technologies to advance rating accuracy fosters problems in insurance markets. The idea of permitting more flexible rates for homeowners and individualizing risk assessment is not new. The final report prepared by the Collins Center for Public Policy made these recommendations in 1995 (Collins Center, 1995). More flexible, risk-based pricing provides more proper incentives for individuals considering relocation and for mitigation decisions, especially in light of increased concern about the affordability of homeowners insurance in catastrophe prone areas.4 It is also important to insurers because it influences how an insurer manages its book of business. Improper pricing may be one of the contributing factors to the growth of the residual markets. The Catastrophe Landscape in Property Insurance Since Hurricane Andrew in 1992, the private insurance market has struggled to adequately respond to the catastrophic risk of hurricanes. Andrew's insured losses exceeded $16 billion (Fronstin and Holtman, 1994). Following Andrew, Iniki hit Hawaii in 1992 and California suffered the Northridge earthquake in 1994. For the decade following Northridge, there was relatively little earthquake or hurricane damage. In the fall of 2004, six hurricanes made landfall in the United States: Charley, Francis, Ivan, and Jeanne in Florida; Alex struck North Carolina; and Gaston hit South Carolina. In 2005, six more hurricanes made landfall with Katrina causing more than $80 billion in economic damages (ICAT Damage Estimator, 2009). …