Neighborhood Rental Database: Real-Time Data for Research

Urban Institute, Washington DC

Investigators

Abstract

There is no source of up-to-date, representative data on local rent levels in the US. The Neighborhood Rental Database fills this gap. The database uses data from major online rental platforms. It helps decision-makers and researchers in the public and private sectors understand housing trends, design housing policies, and respond to economic changes. Scientists collect real-time rental data from a variety of online sources. The database provides users with the mean, median, and standard deviation of listed rents in a neighborhood. It reports the 40th percentile rent that is used in setting rent limits for federally funded housing assistance programs. Scientists developed a rent index that can be compared across time and space. The index describes the rent paid by a tenant in a typical rental unit. By aggregating and standardizing this information, the Neighborhood Rental Database enables researchers to model housing dynamics, estimate the impacts of macro- and microeconomic changes, and evaluate housing policies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.