R Street Institute: 'Determining Auto and Homeowner Insurance Prices'

WASHINGTON, Dec. 1 (TNSRep) -- The R Street Institute issued the following white paper on Nov. 30, 2021, entitled "Determining Auto and Homeowner Insurance Prices".

The white paper was written by Jerry Theodorou, policy director for finance, insurance and trade.

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SUMMARY

* Credit-based insurance scores have been found to be predictive of insurance losses. Insurers that use credit-based scores in their pricing methodology are able to calculate risk-adjusted premiums corresponding to actuarially-determined probability of loss.

* Some consumer advocacy organizations maintain that insurers' use of credit-based insurance scores in their rating is discriminatory because residents of minority communities often have lower insurance scores, resulting in higher premiums.

* Objective, fact-based research by insurance economists has not found evidence of unfair discrimination by insurers in the pricing of automobile insurance policies.

BACKGROUND

Credit-based insurance scores are among the demographic ratemaking factors. A credit-based insurance score is a rating based in whole or in part on elements in a consumer's credit information. Use of insurance scoring was developed to avoid discrimination based on categories challenged as unfair, such as race, religion and national origin.

Most insurance companies incorporate credit-based insurance scores into their automobile and homeowner insurance rating algorithms. There have been claims by some consumer organizations and regulators that the use of insurance scores is discriminatory and should be prohibited. These arguments rely on the assumption that the credit information on which the scoring is based is either inherently inaccurate or inherently discriminatory. Insurance companies disagree, asserting that insurance scores allow them to price insurance policies more appropriately in accordance with risk magnitude associated with individual policyholders.

The methodology insurance companies use to calculate rates for personal automobile insurance has been refined over the years. Most insurers have developed algorithms called Generalized Linear Models (GLMs), introduced in the 1990s. Today's GLMs incorporate numerous rating factors, or predictors, and the impact of their interrelationships. In automobile insurance, these factors can include data on driver demographics, driving history and vehicle type.

Approximately 95 percent of auto insurers make use of credit-based insurance scores in states where it is allowed. California, Hawaii, Maryland, Michigan and Massachusetts prohibit or limit insurers' ability to use credit scores in ratemaking. These states have all resisted the model laws in different ways, claiming that insurance scores, despite the data, are unfairly disadvantaging customers.

CURRENT DEBATE

Washington State has been a flash point in the effort to ban credit-based insurance scores. In October 2021, Washington's Thurston County Superior Court Judge overturned the insurance commissioner's March 2021 emergency rule prohibiting the use of credit-based insurances scores as an input in determining insurance rates. The temporary rule was based on the theory that the pandemic lockdown rendered credit information unreliable.

Many groups--including insurance regulators, legislators, federal government bodies and consumer advocacy groups--have conducted studies examining whether the use of credit-based insurance scores is discriminatory. Claims about discrimination are mostly supported by studies that report on errors in credit reports, or which claim that socio-economic factors have nothing to do with driving and are merely proxies for race. But research does not seem to find discrimination in insurance scores.

A 2007 comprehensive study found that scores permit insurers to evaluate risk more accurately, rendering them more willing to insure high-risk drivers. This also allows insurers to be more competitive, enabling them to take on such insurance buyers for lower premium than otherwise possible. Without insights made possible by the scores, insurers refrain from quoting insurance for high-risk drivers, who must obtain insurance in the state's assigned risk pool--the residual market. In fact, there has been a significant decrease in the number of drivers put into the high-risk pools: 1989 had three million high-risk drivers, and today has slightly more than 150,000 today--a 95 percent decline.

More recent research does not support allegations that insurers engage in unfair price discrimination. A November 2021 report on insurance rating variables focused on hard braking and hard acceleration found both to be associated with higher accident rates and predictive of losses. The study highlights the importance of letting insurers use all actuarially sound rating variables. Limiting the use of insurance scores can impact fairness, consumer choice and rate accuracy.

ACTION ITEMS

Insurers should be involved in discussions on how they are allowed to manage their business. In the absence of their presence, proposals by regulators and consumer advocates lacking understanding of how and why insurance is priced may have unintended consequences in the form of higher--not lower--premiums for the communities they wish to help.

A recent actuarial presentation concluded that insurers should not be excluded in public policy discussions concerning credit-based scores. With their deep knowledge in developing fair rates, insurers should be able to be influence decisions regarding inputs into rating algorithms.

There should be outreach and education to regulators and legislators to counter accusations that equate alleged insurer pricing discrimination with endemic racism. Industry critics have always suspected illegal racial discrimination by use of proxies in rating, but since the industry has been careful not to collect racial information, a general case has not been made. If the current social justice movement somehow mandates collecting this kind of information, then the measurements for a fair system will migrate from inputs to results, infecting an actuarial rating system with politics.