Close Enough is Not Good Enough
Insurers stake their businesses on their ability to accurately price risk when writing policies. For some, faith in their pric-ing is a point of pride. Take Progressive, for example. The auto insurer is so confident in the accuracy of its pricing that it facilitates comparison shopping for potential customers—making the bet it can afford to lose a policy that another insurer has underpriced, effectively passing off riskier customers to someone else’s business.
There are a number of data points that go into calculating the premium of a typical home or auto insurance policy: the claim history or driving record of the insured; whether there is a security system like a smoke or burglar alarm installed; the make, model and year of the car or construction of the home. Another contributing factor, of course, 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 substitutes 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.