Insurers stake their businesses on their ability to accurately price risk when writing policies. Many data points go into calculating the premium for a home or auto policy, but a key factor is location, whether it’s due to the area’s vehicle density or crime statistics or distance of homes from a coastline. Insurers pay close attention to location for these reasons, but the current industry standard methods for determining a location—whether by zip code or street segment data—often substitute an estimated location for the actual location. In many cases, the gap between the estimated and actual location is small enough to be insignificant, but where it’s not, there’s room for error—and that error can be costly.
A recent Forbes Insights report, “Close Enough Is Not Good Enough: Why Hyper-Accurate Location Data Matters for Insurance,” sponsored by Pitney Bowes, examines this issue.
Studies conducted by Perr&Knight—an actuarial consulting and insurance operations solutions firm—for Pitney Bowes looked into the gap between the generally used estimated location and a more accurate method for insurers, to find out what impact the difference had on policy premium pricing. The studies found that around 5% of homeowner policies and a portion of auto policies—as many as 10% when looking at zip-code level data—could be priced incorrectly because of imprecise location data. Crucially, the research discovered that the range of incorrect pricing—in both under- and overpriced premiums—could vary significantly. And that opens insurers up to adverse selection, in which they lose less-risky business to better-priced competitors and attract riskier policies with their own underpricing.
Currently, industry standard location data for homeowner policies rely typically on interpolated street data. What that means is that streets will be split into segments of varying length, and homes within that segment are priced at the same risk. However, the more precise method is to use latitude and longitude measured in the center of the parcel, where the house is. That can be a difference of a few feet from the segment, or it can be a difference of 500 feet, a mile or more.
And that flows into pricing, because when underwriters can more accurately assess the risk of a location—whether it’s where a home is located or where a car is garaged—policies can be priced according to the risk that location actually represents.
It’s tempting to look at the portion of underpriced policies and assume that they’re zeroed out by the overpriced policies an insurer is carrying, but that’s the wrong way to view this. In fact, any insurer with some overpriced policies is likely to sell fewer of those, losing out to a competitor with more accurate data and pricing.
A key point here is reducing underpricing, because when the underlying data leads to policies that are priced at a lower rate than they should be, not only does it open an insurer up to paying out on a policy it hasn’t received adequate premiums for, but underpriced policies may also end up constituting a larger and larger portion of the overall book. This is essentially adverse selection.
We’ll return to this subject in an upcoming blog post and explore the implications for insurers looking to improve their location data.