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LightBox and First Street Foundation Partner to Provide A New Model for Flood Data

May 19, 2020
By: Vivian Nguyen

Michael Millar,
Allison Millar,

The partnership substantially improves the data available to more accurately value and underwrite properties and evaluate risk

IRVINE, CA and BROOKLYN, NY (May 19, 2020)—LightBox, a leading provider of CRE data and workflow solutions, and First Street Foundation, a nonprofit 501(c)(3) research and technology group, announced a partnership today that will provide new data and analytic capabilities to support the valuation, underwriting, and assessment of commercial and residential property risk due to natural flood-related disasters.

The partnership offers the first predictive, probabilistic model to estimate the impact of climate change on flooding down to the individual property level. The model can be used by property owners, investors, appraisers, lenders, and insurance companies to better understand and mitigate risk, including changes in property valuation due to flood-related events.

The announcement was made as the United States prepares for the June 1 start of hurricane season. Since 1980, over $1 trillion in damages can be attributed to flood-related events in the United States with nearly $41 billion in losses from events in 2019, according to NOAA.

As part of the partnership, LightBox is providing property and location data for more than 145 million U.S. properties via SmartParcels®, the most complete parcel database in North America. First Street will provide its climate-adjusted, predictive flood data and statistics based on future potential climate states as projected by the Intergovernmental Panel on Climate Change.

“Providing access to transformative data sets like these from First Street is critical to our mission at LightBox,” says Eric Frank, CEO of LightBox. “By including forward-looking, parcel-level data in our platform, we are enabling commercial owners, investors, lenders, and other real estate professionals to make successful decisions as they are better able to anticipate, quantify, and appropriately plan for risk and its mitigation throughout the real estate lifecycle.”

Available from LightBox, estimates and related data from the First Street Foundation Flood Model are integrated into the LightBox desktop SaaS applications, through its API and via bulk delivery.

The First Street Foundation Flood Model incorporates scientific data-driven predictions on how certain climate factors—sea level rise, changes in precipitation patterns and ocean temperatures—influence future flooding, including hurricane storm surge, tidal flooding, pluvial (precipitation) flooding, and fluvial (riverine) flooding. Also, it considers parcel-specific nuances, including the position of the building footprint and the topography of the property.

Based on this information, the First Street Foundation Flood Model produces probabilistic inundation statistics, in five-year intervals, from 2020 to 2050. The estimated risk for properties is captured in a Flood Factor™ rating system that ranks on a scale from 1, minimal, to 10, extreme, the risk associated with a specific property.

“This partnership with LightBox creates the critical convergence of our scientific, data-driven model with location intelligence allowing us to make a range of reasonable, evidence-based conclusions on how a given area, down to the specific property parcel, may be impacted by flooding-related conditions,” adds Matthew Eby, Founder and Executive Director of First Street Foundation. “The conclusions that can be drawn will be meaningful and truly impactful for the real estate industry.”

Through its “Flood Lab”, First Street is also providing these data to a group of over 90 leading researchers from 20 of the country’s top academic institutions, to analyze and quantify the impacts of flooding on the national economy.

“Commercial real estate owners, investors, and the team involved in the research and due diligence process want data and information that will tell them whether flood-related events pose an investment threat, and whether returns adequately compensate for flood-related risk,” says Jacob Sagi, Professor of Finance and Wood Center in Real Estate Distinguished Scholar, University of North Carolina. “This information, when evaluated by the people who use best practices to interpret its impact, will inform prudent investment and underwriting decisions.”

Flood protection information (adaptation features) and flood statistics will be updated by First Street Foundation quarterly with a more comprehensive model and feature update annually. This new approach builds on the industry standard maintained by FEMA and provides a timely and more extensive array of mapping and flood related data to support real estate professionals as they evaluate and seek to mitigate property risk.

About LightBox

LightBox provides the real estate industry’s most reliable decision platform. Through the delivery of market-leading workflow, data and GIS capabilities, LightBox enables the success of over 100,000 CRE brokers and investors, 1,100 banks and lenders, 2,000 appraisal firms, 5,000 environmental consulting and engineering firms as well as thousands of home builders, land developers, government agencies, and energy companies. Including a broad set of capabilities to enable critical functions including marketing, prospecting, due diligence, risk management, asset management and location intelligence, the LightBox platform provides solutions to support real estate lending, investment sales, appraisals, debt capital markets, property development, environmental engineering and GIS analysis. LightBox is backed by Silver Lake and Battery Ventures. Learn more at

About First Street Foundation

First Street Foundation is a nonprofit 501(c)(3) research and technology group whose mission is to define and communicate America’s flood risk based on the highest standards of the scientific method. The Foundation has assembled a group of more than 80 world renowned experts across a variety of disciplines, to use a transparent, peer-reviewed methodology to model and analyze the past, present and future flood risk of individual homes and properties across the United States. To learn more, visit