Lessons from the Texas Homeowners Insurance Crisis Bob Puelz ...

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of exposure relative to the peril. 28 Exponentiation of the empirical equation ln Prem = f(X) yields Prem = e f(X) ! . Thus, for example, ∂Prem/∂Fire = " 1 ⋅ e f(X) . Rearranging e f(X) to the left- ! hand side implies " 1 = ∂Prem/∂Fire ⋅ (1/Prem) and the elasticity is obtained by multiplying both sides of the equation by the mean value of the Fire variable. Mean values from the overall sample were used. In this instance, 0.00882 ⋅ 0.8466 = 0.007467 — which reveals the percentage change in premium per $1,000 of exposure for a 1% change in the dollar amount of current year fire losses per $1,000 of exposure — is 0.747%, on average, over the 1996 to 2001 period. The association between all the perils and the premium can be considered in a similar fashion. [Table 8 about here] Evaluation of the relative magnitudes of the individual perils permits an exploration of which perils were important during this crisis. To set a standard for comparison, the elasticity on Fire is used as a benchmark to assess relative impact of the perils on homeowners premiums. Table 8 lists the elasticities of the statistically significant property perils with respect to the premium and the differences are contrasted with the fire peril, Fire. As can be seen from the table, VMM and Liability categories provide less of an impact compared to the fire peril for the sample period. Notable are wind and water. The estimated elasticity for the Wind peril is about 53% smaller than the Fire peril elasticity, while the Water peril is about 125% larger. The elasticity of the Water peril is about 374% greater than the elasticity of the Wind peril. Thus, 28 See http://garnet.acns.fsu.edu/~dmacpher/teaching/ECO5421/Pdf/chap6b.pdf for a discussion of the elasticity calculation. 20
while variations in perils help explain variations in premiums, the Water peril played an extremely important role during the sample period. Among the control variables, the coefficient on lnHous is negative, supporting the hypothesis that larger counties (reflected by the number of housing units per county) are associated with lower premiums, perhaps reflecting cost efficiencies in handling claims among larger counties. The coefficient on the urbanization variable, lnRHous, is also negative, supporting the hypothesis that as an increasing percentage of a county’s total housing units are classified as rural that premiums per $1,000 are lower, reflecting density and geographic diversification not found among more urban environments. The proposition that higher proportions of housing that are classified as vacant are associated with higher premiums per $1,000 of exposure is supported by the positive coefficient associated with lnVacant. This finding may be indicative of the additional uncertainty insurers confront with non-occupied housing. Two variables, average income per capita by Texas county and the percentage of a county’s housing that is rental, were hypothesized to be associated with the wealth impact argument of Klein and Grace (2001). It was hypothesized that income (proportions of renters) would be negatively (positively) related to premiums per $1,000 of exposure. While the proportion of renters was not found to be related to premium, higher levels of income were found to be positively related to premiums contrary to prediction. Finally, while household size and the proportion of the county that is non-white were not found to be significantly related to premium, higher levels of crime rates were found to be statistically associated to higher premiums, consistent with prediction. VI. Concluding Remarks 21
Page 1 and 2: Lessons from the Texas Homeowners I
Page 3 and 4: I. Introduction Lessons from the Te
Page 5 and 6: Illinois with company-level data to
Page 7 and 8: [Table 1 about here] might have bee
Page 9 and 10: with the NAIC, and were sourced for
Page 11 and 12: To further explore the Texas settin
Page 13 and 14: amounts are summarized in Table 4.
Page 15 and 16: [Figure 4 about here] One glimpse i
Page 17 and 18: the average household size of an oc
Page 19 and 20: units that have been classified as
Page 21: as equations in levels. The Hansen
Page 26 and 27: Table 1 Texas Homeowners Premiums W
Page 28 and 29: "Combined" Ratio $1.80 $1.60 $1.40
Page 30 and 31: Table 5 Texas Statewide Homeowners
Page 32 and 33: 35% 30% 25% 20% 15% 10% 5% 0% 60% 5
Page 34 and 35: Cumulative % of Texas Counties 100%
Page 36 and 37: Table 7 GMM Estimates of Empirical
Page 38: Harrington, Scott E. and Greg Nieha

Lessons from the Texas Homeowners Insurance Crisis Bob Puelz ...

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