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Fire and Disaster Response Score

Accurately Rate and Underwrite Local Fire Responses

Better Understand and More Accurately Rate for Local Fire Response

This data driven approach to fire scoring incorporates cutting edge geo-spatial technology, historic fire department responses, realistic drive times from the responding station to the insured property, historic loss data, and much more all of which converge to provide a more accurate, up to date fire score.      


  • Gain A More Predictive Fire Score - this score utilizes advanced modeling and leverages our data assets to deliver a more predictive fire score for underwriting and pricing.  
  • Save Resources - this data driven solution can be delivered via our single point of entry, reducing IT implementation times and costs.
  • Eliminate Abuse - our score is address specific and eliminates the abuse commonly found in solutions that have multiple returned values.       


Many attributes go into the Fire and Disaster Response Score including: 

  • Fire Department Location 
  • Shortest Diving Distance from Fire Station to property
  • Number of paid/volunteer Firefighters 
  • Housing Density
  • Average number of Fire Claims per 100 homes  
  • Historic Local Loss Frequency & Severity   
LexisNexis® Fire and Disaster Response Score services are not provided by “consumer reporting agencies,” as that term is defined in the Fair Credit Reporting Act (15 U.S.C. § 1681, et seq.) (“FCRA”) and does not constitute “consumer reports,” as that term is defined in the FCRA. Accordingly, the LexisNexis Fire and Disaster Response Score service may not be used in whole or in part as a factor in determining eligibility for credit, insurance, employment or for any other eligibility purpose that would qualify it as a consumer report under the FCRA. Due to the nature of the origin of public record information, the public records and commercially available data sources used in reports may contain errors. Source data is sometimes reported or entered inaccurately, processed poorly or incorrectly, and is generally not free from defect. Before relying on any data, it should be independently verified.