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

as equations in levels. The Hansen J-test for over-identification was not rejected (χ 2 = 10.18),
indicating that the instruments as a group are not correlated with the error term and, therefore, are
reasonable. T-statistics are based on robust standard errors due to Windmeijer (2005).
[Table 7 about here]
Results in Table 7 indicate the importance of the association between the value of insured
peril losses and the current premium as coefficients are positive and statistical significance
predominates. In addition, we find evidence of a statistically significant positive relationship
between the majority of one-year lagged representations of these perils and the current
premiums. In other words, when insurers experience higher prior year losses in a given county
for a given peril, it is associated with higher premiums. For our measure of uncertainty,
deviation, the findings indicate a negative and statistically significant relationship between
deviations and current premiums. The results on loss lags and our uncertainty measure indicate
that homeowners insurance prices during this crisis were changing in association with factors
perhaps not readily understood by insurance customers, in addition to changes in actual claim
experiences. Thus, the evidence suggests that when insurers confronted a dynamic and
unexpected claims environment, that risk information among market participants became more
skewed, which likely exacerbated discontent in this insurance market.
Next, we can focus on the coefficient values themselves to gauge the level of association
each of these statistically significant perils has to the variation in premiums. The empirical
equation is in “semi-log” form; therefore, we can calculate the elasticity of premium per $1,000
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