Traditionally, there would have been a single address point associated with this policy (either in the middle of the land parcel, or along the road), and the TMNAS team may not have been able to determine the number of buildings except that the limit would indicate the potential for a group of buildings (For this property, 104 buildings were only flagged once this address utilized Ecopia’s Building-Based Geocoding system).
The inability to consistently identify the number, location and estimated square footage of buildings insured results in major gaps in estimating value and related risk. Without this information, carriers can find themselves paying much higher replacement costs for insured properties than expected. For TMNAS, these challenges resulted in not only increased claims costs but also reduced premiums where values were understated.
Better Balance Portfolio Risk with Building-Based Underwriting
To fill the gaps of unreliable replacement cost values, TMNAS leveraged Ecopia AI’s Building-Based Geocoding solution to make a transformational shift from an address-based database to a building-based database. Ecopia's Building-Based Geocoding offers the first and only complete building footprint collection in the USA, paired with best-in-class address data. This unique offering was generated by leveraging artificial intelligence to mine the most up-to-date geospatial imagery available - creating a unique source of truth surrounding every building in the United States.
By gaining access to this solution, TMNAS was able to identify all insured buildings on each property. Specifically, integrating Ecopia AI’s data into TMNAS internal tools and workflows enabled the determination of replacement cost and related risk on a building-by-building basis (instead of an aggregated address-by-address basis). The example below highlights a single address with 14 different buildings on the property – each now having been assigned a unique replacement cost value and respective risk.