Insurers have long contemplated exiting the homeowners line of business because it's historically unprofitable. Recently, Allstate's CEO, Tom Wilson, said the carrier considered vacating the homeowners market after the 2005 hurricane season. However, consumers have demonstrated their desire to bundle insurance coverage, and Allstate ultimately realized that not offering both auto and home was too big of a competitive disadvantage. Recent studies show that homeowners is growing in both policies and premium, yet the industry has only experienced four years with combined ratios below 100 since 1990.

The trends we've seen develop in auto—like online consumer shopping and increasingly sophisticated uses of data and analytics—are starting to take hold in homeowners, and now insurers have to tangle with complex market dynamics while simultaneously trying to boost underwriting profitability. As online shopping gains momentum, pricing competition will heat up, acquisition costs will increase, and retention rates will take a hit. If you don't have the resources of a Top 20 insurer, this may represent a fundamental shift in how you run your business.

All things considered, 2014 is a pivotal year in terms of how the homeowners industry will turn around historically poor results. There is significant innovation occurring now to propel underwriting and pricing strategies forward while things are relatively stable. You don't want to fall behind when severe weather wreaks havoc on loss ratios…again.

How do I lower my loss ratio?

When we talk to insurers, they tell us their primary concern is reducing their loss ratio. A quick look at what's been done previously leads us to some exciting innovations happening right now in managing non-catastrophic loss.

One of the most successful strategies to date has been to separate catastrophic (CAT) and non-catastrophic (non-CAT) losses, and develop specific CAT management strategies:

To compensate for increased exposure, actuaries build in a catastrophe load into the rates by geographic region.
Calculating probable maximum loss (PML) provides a worst-case scenario analysis that protects against inadequate reserves in the event of a catastrophe like Hurricane Andrew, after which several insurers went out of business.

Early Non-CAT Strategies: Business Rules and By Peril

Insurers have leveraged business rules based on location (e.g., ZIP code) and property characteristics (e.g., age of roof) to review those homes that trigger an “exception rule.” Many insurers follow a rule to inspect properties at new business, while others focus on homes with high peril exposure. Much like CAT management strategies, insurers take specific pricing and underwriting actions to minimize losses in fire-prone areas, as one example.

The industry falling short in generating underwriting profits, combined with the fact that 60 percent of losses are non-CAT, shows that the strategies being used today are inadequate.

The Future of Homeowners Underwriting

To see the innovation happening now, look at the Florida market. It is well studied, carefully modeled, and subject to competitive pressures with dozens of carriers vying for the business.

There is a residual market for Florida property called Florida Citizens. Citizens offers “takeout” opportunities to private insurers and provides detailed exposure and loss history statistics on individual policies. Insurers select a “wish list” of policies they'd like to assume onto private paper with granular control.

After considering the expected hurricane loss, insurers primarily look at the non-CAT cause of loss. It turns out that over 50% of the homeowners losses in Florida are not catastrophe related. This is a new paradigm where the primary driver of profitability is reducing non-CAT loss at the policy level.

2014 Brings New Categorization: Mitigatable vs. Non-Mitigatable

In order to drive down loss ratios and deliver consistent profits, the industry needs to go deeper and answer:

What caused the loss?
Could something have been done about it?

What does “mitigatable” mean?

It's an important definition best explained through a simple example. Suppose an insurer has two identical homes on their book of business, and both of them burn to the ground. One burned because a candle was left burning overnight, and the other because of a faulty electrical box that wasn't up to code. Traditionally these would be coded as non-CAT fire losses. However, one of these losses could have been prevented by the insurer. Had an inspector visited both of these homes one day prior to the loss occurring, they could have discovered the faulty electrical box. Alternatively, there would have been no way to find the burning candle. The non-CAT loss that occurred because of the electrical box should be coded as mitigatable, or within the control of the carrier to reduce or prevent. On the other hand, the loss from the candle would be coded as non-mitigatable, because the insurer cannot reduce or prevent the cause of loss.

Why is it important to categorize losses this way? In addition to being able to predict loss ratio, it's now possible to predict the percentage of the non-CAT loss ratio that is mitigatable. An underwriter can distinguish which homes to write based on their ability to reduce or prevent loss. Competitors without this knowledge are susceptible to adverse selection by writing homes where the loss ratio cannot be influenced by the insurer.

This more sophisticated approach is successful according to a Willis Re study of the Florida Citizens data, that revealed significant variability in the predicted non-CAT loss ratio. In some parts of the state, the mitigatable losses were actually more significant in terms of profitability than the catastrophic risk, because the catastrophes are so well reinsured.

The Florida example illustrates the unique opportunity available to actively shape a future portfolio and a shift in mindset that analysis should be prospective—not retrospective. As market conditions prompt insurers to stay competitive and profitable, the concept of mitigatable and non-mitigatable classifications can help accurately assess risk, drive down loss ratio and produce profits.

Dax Craig is the co-founder, president and CEO of Valen Analytics. Based in Denver, Valen is a provider of proprietary data, analytics and predictive modeling to help insurance carriers manage and drive underwriting profitability. E-mail him at Dax.Craig@valen.com.

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Driving profitability in homeowners coverage: Examining &lsquo;mitigatable&rsquo; losses

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Driving profitability in homeowners coverage: Examining ‘mitigatable’ losses