Why Insurance Underwriters Need Building Footprint Data

Gain more insights into a property’s location.

To help insurance underwriters gain more insights into commercial and residential structures, BuildingFootprintUSA™ has created a link that makes it easy to tie in residential and commercial building characteristics with our footprints.

Some of the building attributes we can return include:

  • Year Built
  • Square Footage
  • Occupancy
  • Construction Type
  • Number of Stories
  • Ownership
  • Valuation
  • And More (Up to 300 building attributes)

Making Location Intelligence More Intelligent

The value of building footprints doesn’t stop with connecting to assessor and other location-based data.

There are many ways underwriters can leverage our building footprints to enhance in-house or third party data sources, allowing them to truly locate, analyze and visualize risk exposure. Below are just a few examples of how we can do this:


Example #1
Natural hazard risk assessment

A commercial lines underwriter may notice a small section of a shopping mall may fall into a low-risk B and X (shaded) flood zone, while the attached parking garage falls directly into an A zone.

Underwriters can overlay building footprints on natural hazard risk maps to visualize the impacts of various perils.

This enhanced visualization not only allows underwriters to assess natural hazard risk with more clarity, but it also allows them to protectively alert customers or prospects of potential issues.


Example #2
Aggregate risk exposure

Insurance underwriters can overlay building footprints on natural hazard risk (wildfire, storm surge, hail, wind, etc) or other man-made peril data.

Using this information underwriters can then aggregate risk to more accurately identify how many policies fall into low, medium, and high-risk zones.


Example #3
Disaggregate risk exposure

When writing new business or renewing policies on large facilities (e.g. college or commercial campuses, hospitals, power plants, refineries, etc.) insurers often struggle with assigning the correct location data to each building.

Using building footprints, insurers can now obtain precise location data for each building at a facility, resulting in more accurate risk assignment and building risk profiles.


Example #4
Identify risk to secondary buildings

One of the benefits of our building footprints product is that it also identifies secondary structures on a parcel of land. An underwriter renewing a policy in California may notice a policyholder stores classic cars in a barn that sits next to a slope with heavy vegetation.

Since wildfires burn faster going uphill, the underwriter may want to alert the policyholder to clear the brush next to the barn or have them move their car collection to a safer location.

Learn More

Learn how insurers now have the ability to drill down and match a policy’s address to an exact location with Building Footprints.

Inaccurate data means insurance underwriters are assessing risks, such as flood, hurricane storm surge, wind, hail, and wildfire based on the wrong location.

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About Our Data

BuildingFootprintUSA™ understands that every building is important to someone.

About Our Data

BuildingFootprintUSA™ understands that every building is important to someone.

That is why we have created one of the most geographically accurate and comprehensive data sets of building footprints for residential and commercial addresses in the United States. Our data is trusted by our customers within the insurance industry, across other industries such as telecommunications and by the world’s leader in GIS, Esri. Click here to learn more about how Esri is integrating BuildingFootprintUSA™ data into ArcGIS online geocoding and Basemaps.


U.S. Building Footprint Coverage

We started to build our proprietary database for the United States in 2015.

We built this database using more than 700 data sources, covering over 80% of the population of the United States. Every quarter we update our database using a variety of sources and techniques, including emerging technologies like artificial intelligence/computer vision.

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