The Wall Street Journal
February 27, 2011
By Erik Holm
NEW YORK (Dow Jones)–A hurricane-modeling company that helps insurers predict the cost of mega-storms will launch a new, more-sophisticated model on Monday that shows some homeowners living hundreds of miles from the nearest ocean are at greater risk than previously thought.
While the homes closest to the coast are clearly the most likely to suffer serious damage, the model from Risk Management Solutions Inc. increases the estimates for how much harm a hurricane can do in the hours after it blows ashore and begins moving inland.
The revisions could cause insurers to think twice about the areas where they operate and re-evaluate what they’ve chosen to insure.
The updated model is expected to double RMS’s 1-in-100 year estimate for insured hurricane losses in Texas, and increase estimates in the mid-Atlantic by more than 75%. Nationwide, the 1-in-100 year loss estimate will increase by 15% to 25%.
Importantly, the figures estimate the entire insurance industry’s losses. Some insurers will fall above or below the typical range, depending on their areas of geographic focus. Some, in fact, will see their loss estimates go down.
The 1-in-100 year measurement is commonly used in the industry to evaluate an insurer’s hurricane risk, putting a ceiling on storm claims that has just a 1% chance of being exceeded each year.
Advances in the tools that measure hurricanes over the past several years combined with ever-improving computing power and more information on actual losses from past events to give RMS 10 times more wind data than the last time its so-called hazard model was updated in 2003.
“We’ve really got a much more informed view because of all the hurricanes that have been happening in the past several years,” said Claire Souch, vice president of natural catastrophe at RMS.
The new model includes a better understanding of what fuels a hurricane in the warm waters of the Atlantic or Gulf of Mexico, and what causes a hurricane to lose intensity over land.
“Different hurricane degrade over land at very different rates, and that’s what we have more insight into now,” Souch said. “There are more factors that go into it than we’d previously been able to capture.”
The factors include the size of the hurricane and how much of it is still over the water, how fast it is moving, and whether a storm is strengthening or weakening just before landfall. Terrain also plays a role; a storm will be torn apart by mountains, but will be able to gather fuel from swampy areas like the Everglades in hurricane-prone Florida.
The model also accounts for the spike in the cost of construction materials after a storm and updates assumptions about the damage that even a moderate hurricane can do to commercial buildings in some regions to reflect the strictness of local building codes–and how well those codes are enforced.
Hurricane Ike, a Category 2 storm that struck the coast of Texas in 2008, illustrates why RMS needed to update its model. While homeowners living closest to the shore took the brunt of the damage, Ike didn’t lose steam quickly as it marched north toward Oklahoma. It wasn’t downgraded from a hurricane to a tropical storm until it reached Palestine, Texas, almost 200 miles inland.
How quickly insurers increase prices on home insurance if the new model shows a jump in their loss estimates is an open question. State regulators have some control over rates, and competition for customers may cause companies to forgo a rate increase. Most companies combine RMS’s analysis with other models, which would further limit the impact of the increase.
“The more qualified opinions you can factor into the equation, the more accurate you are likely to be,” Dick Luedke, a spokesman for State Farm Mutual Automobile Insurance Co., the largest home insurer in the U.S., said in January, commenting on the pending new hurricane-cost model. Whatever the results from RMS, he said, State Farm will weigh them against the other models.
Notably, a typical home-insurance policy covers far more than hurricane risk, so the potential added cost for the typical homeowner would be significantly less than the percentage increase for a 1-in-100 year loss in his or her neighborhood.
