Abstract
In this chapter we present estimates of the demand for homeowners insurance derived from two-stage least squares regressions for New York and Florida. We estimate the demand at the level of the Zip code. Because we have the Zip code location of the insured house and we have access to Zip code level information from the Census, we model the demand for coverage at the level of the Zip code.1
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Notes
We recognize that some Zip codes are quite large geographically and many are diverse demographically, but this is the smallest level of aggregation that will permit analysis of our data.
Ordinance or Law Coverage will upgrade a rebuilt house after a covered loss to the current building code. Without the coverage, the house will be “repaired” or rebuilt according to code only as long as doing so does not exceed the Coverage A limit on the policy.
We actually use PRICE1 = 1 + PRICE = [(l+r)(Premiums-ILC)]/[ILC] as our price variable; adding 1 to PRICE simply assures that our price measure in equation (3) is always positive.
The decomposition of the non-catastrophe and catastrophe portions of indicated loss costs has become a standard feature of advisory loss cost filings and insurer pricing. The term “cat loading” is sometimes used to characterize the catastrophe component of the expected loss cost. Because catastrophes occur infrequently, modeling techniques must be used to calculate catastrophe loadings, as analysis of historical data is insufficient for this purpose. The cat expected loss costs used in this study were computed from the RMS catastrophe model in support of ISO loss cost estimations. While proprietary, interested readers can find more on the RMS model at http://www.rms.com/Catastrophe/Models/.
Note here that the price elasticity measures for cat and non-cat are not defined in the traditional way. For example, since we only have a price variable for the total price (the price of cat and non-cat coverage bundled together), our elasticity is actually the percentage change in total price over the percentage change in the quantity demanded of cat coverage (or non-catastrophic cover).
In Florida and New York, regulators require insurers to report statistical data to one of several designated statistical agents. ISO and the National Association of Independent Insurers (NAH) are the two principal statistical agents; other statistical agents account for only a small portion of insurers operating in these markets. An increasing number of insurers have selected ISO as their statistical agent, which has broadened the types of insurers in its database. At the same time, among the ISO reporting firms, several declined to authorize the use of their data for this study. These tended to be insurers with more unique products and portfolios of exposures.
The regression we estimate is: Probit [(ISO Reporter and Participant) =1, 0 otherwise] = f(log of total assets, log of Florida homeowners premiums, Best Capital Adequacy Ratio, business concentration ratio (top four lines), geographical four state concentration ratio, percent of claims paid within two years, percent of claim value paid within two years, Stock Dummy, Direct Writer Dummy, and year dummies).
Several large “direct writers” with significant amounts of exposures in coastal areas report their statistical data to NAH.
Indeed, in areas with a high catastrophe risk (and high catastrophe loadings in the coast of insurance), insureds may forgo replacement cost coverage on personal property in order to afford and purchase more adequate structural coverage for catastrophe losses.
As mentioned earlier, insurers typically require a homeowner to carry a Coverage A limit equal to at least 70-80 percent of the replacement cost of his home. Limits on the other property coverages are stated as percentages of the Coverage A limit. Further, the problem of inadequate coverage limits has received increasing attention and has probably prompted insureds and insurers to maintain coverage limits closer to the replacement cost of homes.
The expense load and price mark-up on lower deductibles are very high. Insureds likely become increasingly attuned to this as their premium increases, as revealed by a significant increase in the size of the deductibles chosen by policyholders in Florida and New York discussed in Chapter 2.
We should note it is likely that insurers have made the pricing of large deductibles very attractive to consumers as this viewed as one of several effective strategies to manage an insurer’s catastrophe exposure.
We define “rate suppression” as a binding regulatory ceiling on the overall rate level charged by an insurer. “Rate compression” is defined as a binding regulatory constraint on the rate differential between low and high-risk territories. In practice, regulators tend to both compress and suppress rates by imposing severe constraints on the rates for the highest-risk territories, without a compensating increase in the rates for low-risk territories to produce an adequate overall rate level.
Some insurers and agents may not periodically review their insureds’ coverage limits for adequacy. The replacement cost of a home can increase due to improvements by the homeowner, as well as general inflation in building costs.
We estimated a regression between the log of the median home value and the log of income holding other things constant such as the characteristics of the house, insurance prices, and neighborhood characteristics constant. The elasticity of median house value with respect to income, our measure of η, was estimated to be 1.04. Thus, as long as ϕα was greater than (approximately)-.04 we would expect to see a positive elasticity between income and the amount of insurance purchased.
It is interesting to note that the 12 insurers that became insolvent because of Hurricane Andrew were relatively small companies.
The company with a NR2 rating appears to be an anomaly. Category NR2 is a not rated category. One firm is in the date set with an NR2 rating. The reason the firm was not rated is because company started operation right after Hurricane Andrew, thus A.M. Best did not have the ability to properly rate the company. This firm is a wholly owned subsidiary of an A++ rated company. Thus, the company is not exactly a high-risk firm. Currently, it holds an A rating from A.M. Best. In light of these facts, if we look at the catastrophe demand, we see that consumers value a strong company, but not necessarily the strongest company.
The greater the value of the land, the greater the incentive of an owner to avoid foreclosure if his home is destroyed. This is one reason given for why lenders do not require earthquake insurance in areas of California where land prices are high.
See http://www.ncigf.org/Publications/Claim%20Parameters.xls for a summary of state fund policy limits for 2001.
Coverages in addition to Coverage A triggered by a given claim would be combined with Coverage A losses in the application of the guaranty fund claim coverage limit. For example, if a fire totally destroyed an insured’s home with a Coverage A limit of $250,000 and personal property valued at $125,000, the Florida guaranty fund would only cover $300,000, leaving $75,000 in losses not covered by the guaranty fund.
We were not able to estimate a fixed effect model here due to the fact that there were some 2000 observations above the $300,000 level. Given the fact that the A.M. Best Ratings do not change much over this period for individual firms, the ratings and the firm effects are highly collinear. If we had a longer panel and we saw ratings change over the time period, we would be able to separate the ratings effect from the firm effect.
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Grace, M.F., Klein, R.W., Kleindorfer, P.R., Murray, M.R. (2003). Demand Estimation for Homeowners Insurance Policies. In: Catastrophe Insurance. Topics in Regulatory Economics and Policy, vol 45. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9268-0_5
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DOI: https://doi.org/10.1007/978-1-4419-9268-0_5
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