Cityscape: Volume 25 Number 2 | Recent Reforms in Zoning | A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources

The goal of Cityscape is to bring high-quality original research on housing and community development issues to scholars, government officials, and practitioners. Cityscape is open to all relevant disciplines, including architecture, consumer research, demography, economics, engineering, ethnography, finance, geography, law, planning, political science, public policy, regional science, sociology, statistics, and urban studies.

Cityscape is published three times a year by the Office of Policy Development and Research (PD&R) of the U.S. Department of Housing and Urban Development.

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Double Issue: Reentry Housing After Jail or Prison | Recent Reforms in Zoning

Volume 25 Number 2

Mark D. Shroder

Michelle P. Matuga

A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources

Daniel Kuhlmann
University of Arizona

Jane Rongerude
Iowa State University

Lily Wang
George Mason University

GuanNan Wang
College of William and Mary

For academic researchers and practitioners alike, identifying individual rental properties can be incredibly useful but is often difficult due to insufficient and incomplete data. Although some cities have ordinances that require residential rental property owners (RRPOs) to register their properties, the availability and completeness of these registries vary dramatically from place to place. In places without rental registries, tax assessor data can provide some information but often not enough to clearly distinguish residential rentals from owner-occupied units and other commercial properties.