The absence of a major catastrophic event in Florida in recent years has not erased memories of the 2004 and 2005 hurricane seasons and the impact of the ensuing hard property insurance market. Smart Florida business owners know they cannot afford to leave themselves exposed during what could be a very busy hurricane season. The loss of a key facility can ruin a business.

Even with this knowledge, commercial property owners can be in a vulnerable position when it comes to buying property insurance. With some lenders requiring full property coverage, no matter the price, some businesses buy too much property coverage, others too little. Is it possible to calibrate coverage more precisely?

Yes, with catastrophe risk modeling. Cat models can help Florida commercial insureds better understand their exposure to windstorm risk across their entire property schedule and down to each individual location. Using this sophisticated insight, insureds can determine the proper amount of insurance coverage to match their exposure, their financial needs, and their risk appetite.

Perhaps just as important, the modeling output can help convince underwriters to provide the coverage at reduced rates.

It's All About Quality

Property underwriters have used sophisticated software tools for modeling since the 1990s, and cat models have become institutionalized in the underwriting process. Today, more and more insurance brokers and corporate risk managers are using the models, as well. But for commercial insureds to really determine their exposure and the appropriate level of risk to retain and transfer, the focus must be on the quality of the data that is being put into the models.

There are many data points that can make a significant difference in the accuracy of modeling outputs, including address, occupancy type, year built, number of stories, construction type, and secondary characteristics. However, one of the most basic and essential data points — replacement value — is often the one not represented accurately, resulting in property coverage that is either too high or too low.

Why? A typical commercial insured might not have all of this data available for all of his properties. Or if he has some of this information, it might not be accurate. It is worth taking the time to assemble accurate data because there is a direct correlation between property values and modeling output, said Bill Churney, vice president of business development at Boston-based modeling firm AIR Worldwide.

“If business owners are using bad data when buying insurance, then they are likely to make a bad decision,” Churney said. “If property values are off 20 percent, then loss estimates will at least be off 20 percent, as well.”

In certain instances, more accurate replacement values may mean higher values and possibly higher premiums. Even if this is the case, it is far better to pay more to properly protect your property than to experience a total loss and then discover that you do not have enough coverage to rebuild.

Collecting detailed, accurate information on properties and entering it into the models, however, can often lead to lower property premiums. To understand how this works, you have to dig deeper into modeling.

Models default to a local composite of a general geographic area and type of construction that is prevalent for that occupancy type. Often, the model users (for instance, underwriters) may default the settings to the most conservative or worst-case scenarios. When it comes to property addresses, for example, these defaults help the models determine distance from the coast and elevation, both essential variables for underwriters.

The Human Element

Small commercial property owners and large companies — even those that have the internal staff and resources to collect the needed data — can benefit by utilizing engineering resources. Engineering and risk management specialists should always have a hand in data collection and review to help ensure that the data is complete and accurate.

In fact, the data-collection process is even more important for complex properties like manufacturing sites or chemical refineries, or high-value assets that drive a portfolio's overall exposure. Not only can specialists help insureds collect detailed data required to properly represent these facilities in models, but they also can help companies learn how to mitigate their cat risk through retrofitting buildings or reinforcing windows and other structural elements.

Using engineering specialists can also bridge the credibility gap for commercial insureds with underwriters and lenders. Engineers collect and validate data, giving underwriters confidence that the data accurately demonstrates a property's performance. By populating the model with quality data, property insureds speak the underwriters' language and show that their property schedule is a better risk than others, that the risk has less uncertainty and, in the case of insureds who have applied engineering and loss-control services, that the insured has mitigated its losses.

Bridging the credibility gap with lenders can mean that less insurance needs to be purchased. Generally, for windstorm coverage, lenders require businesses to purchase limits for the full replacement values of their properties. But a detailed, verified modeling report could convince lenders to allow insureds to retain an amount of risk and purchase levels of coverage that are more cost-effective for the insureds but still adequate for the lenders' interests.

For commercial insureds who do not have their insurance levels dictated by loan covenants, the use of accurate data in modeling can also help determine the appropriate levels of coverage. For example, it is usually recommended to buy up to a 250-year to 500-year probable maximum loss (the probability of a loss in any given year is one in 250 or one in 500). However, after an insured reviews a modeling report with his broker, it might be determined that because of the concentration of properties in high-risk locations it would be more appropriate to purchase at a higher return period. They may also decide to purchase at a higher return period if many of the properties in high-risk locations are not hardened against windstorm or surge.

Ultimately, though, it is up to the corporate risk manager or commercial property owner to base the insurance purchasing decision on a company's overall risk tolerance, the cost of insurance, and the company's responsibilities to lenders, investors, and other stakeholders. With the proper and accurate use of modeling, that decision can be more fully informed and result in the right amount of coverage.

Ravi Singhvi is vice president of catastrophe risk modeling at NAPCO, a wholesale broker of commercial property insurance coverage. www.NAPCOLLC.com.