How Data & Analytics Are Helping Insurers With Flood-Damaged Vehicles
Initial estimates of the damage caused by Hurricane Florence are expected to total approximately $20 billion, as residents of the Carolinas and Virginia are faced with cleanup and recovery. The Carolinas experienced historic flood levels, according to Moody’s Analytics. Mark Zandi, chief economist for the firm, said the flooding was more extensive than anticipated because it was such a slow-moving system.
The analyst firm also said Florence will end up being one of the ten costliest hurricanes America has ever seen.
While homes and businesses face their own cleanup from the flooding, personal transportation causes great headaches as well. Early estimates from Black Book show that roughly 20,000 vehicles will be either damaged or destroyed by the floods. This pales in comparison to the 700,000 vehicles damaged or destroyed from last year’s hurricanes in Texas (Harvey) and Florida (Irma), where the flooding occurred in heavier populated metropolitan areas.
Water from a hurricane, especially one that causes major flooding, can render a vehicle totally useless. Modern electronic equipment can be short-circuited when water breaches in. Salt water is also extremely corrosive and destroys a car’s engine and internal parts.
When insurance companies need to calculate payouts, a critical aspect of the entire process is having access to the most accurate valuation on each vehicle in the portfolio. However, not all valuation services are created equal, and leveraging the wrong value, multiplied over several thousand vehicles with payouts, can be financially devastating to an insurer.
Today’s advanced valuations include data analytics to provide more precise valuations. These VIN-specific value resources take into account each individual vehicle’s unique history footprint, helping insurance professionals determine the impact a vehicle’s history has on its value. Even with the use of vehicle history reports, insurance professionals are still reliant on automotive values based on an unscientific, educated guess as to the impact a vehicle’s history has on its value, which often leads to mistakes in the valuation and payout process.
Vehicle values that take into account a VIN-specific history can be as much as 31 percent more precise when compared to the auction transaction price than valuations without a history adjustment included. This is possible since today’s analytics process leverages data that can help professionals quickly pinpoint a more precise valuation on two individual vehicles of the same year, make and model, based on data inputs that take specific vehicle history events into account. Making an inaccurate estimation on appraisal values can decrease margins for a dealer, as well as increase losses for insurance companies and auto lenders.
History-adjusted vehicle valuations analyze multiple factors and events in a vehicle’s history such as number of owners, vehicle usage, accident and accident severity, title issues, flood/hail/fire damage, certified pre-owned (CPO) history, and other variables that are not obvious when physically inspecting a vehicle.
Insurers have long relied on vehicle valuation data in order to help determine the right payout for each vehicle damaged or destroyed in an accident or natural disaster. such as a hurricane. Over the last few years, these natural disasters have been responsible for the large numbers of vehicles that have been destroyed, and there’s a good chance this pattern will continue in the years to come. With the right tools and resources, such as data- and analytics-driven valuation insight that takes into account each vehicle’s unique identifiable history, insurers can be even more precise in determining the exact vehicle payout to keep clients happy and preserve the right margin in each vehicle portfolio.