Merging Federal Flooding and Housing Data to Gain Insight into Flood Impacts on Federally Assisted Households: A Case Study in Kansas City, Missouri

Mariya Shcheglovitova
U.S. Department of Housing and Urban Development

Gina Lee
U.S. Geological Survey, Maryland-Delaware-District of Colombia Water Science Center

The views expressed in this article are those of the authors and do not represent the official positions or policies of HUD’s Office of Policy Development and Research and the U.S. Department of Housing and Urban Development.

Previous research on the differential impacts of floods has found that race, ethnicity, income, gender, age, and housing tenure and type influence people’s ability to prepare for and respond to flood events. However, studying the impacts of flooding is often challenging due to data limitations, especially for storm- and snowmelt-related flooding in noncoastal areas. This article draws on Federal Emergency Management Agency (FEMA) flood risk maps, the U.S. Geological Survey (USGS) Flood Inundation Mapping (FIM) Program, and U.S. Department of Housing and Urban Development (HUD) administrative data to provide a methodological example of integrating federal flooding and housing data to gain local housing insights. The USGS FIM Program provides a unique opportunity to help communities visualize potential areas at risk for flooding near local streams and rivers. Unlike FEMA flood risk maps, USGS FIM maps allow researchers to investigate local flooding processes, such as the predicted extent and depth of housing-unit flood exposure. To demonstrate the utility of USGS data for housing research, this article presents a case study investigating the impact of flooding on housing units where households receiving federal rental assistance live in Kansas City, Missouri. The presented analysis contrasts local housing unit trends in flood exposure to census-tract-level trends of flood risk derived from FEMA riverine flood maps. This case study demonstrates how USGS and FEMA data can inform housing analyses at different scales for researchers and practitioners interested in flood impacts on local communities and vulnerable populations.