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Is This a Rental? Comparing Methods for Identifying Rental Units

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  • Preis, Benjamin

Abstract

Researchers regularly attempt to identify individual housing units as either owner-occupied or renter-occupied. But the data sources available to do so are rarely purpose-built for answering that question. This paper explores the most common approaches used in the literature to identify rental properties in the United States, namely by identifying properties based on characteristics listed within a tax assessment database. This study shows the possible problems associated with the current approaches to identify rental properties based on homestead exemptions or address matching. An underutilized data source — local rental registries — are introduced as a possible alternative in the cities that have them. Differences between rental registries and tax assessment databases are discussed, and the number, count, and type of rental units are compared in five cities. I identify possible sources of disagreement between data sources. This paper cautions researchers who opt to use tax assessment databases, or proprietary data sources, to identify rental units.

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  • Preis, Benjamin, 2024. "Is This a Rental? Comparing Methods for Identifying Rental Units," OSF Preprints afzdx, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:afzdx
    DOI: 10.31219/osf.io/afzdx
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    References listed on IDEAS

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    1. Hangen, Forrest & O'Brien, Daniel T., 2022. "Linking Landlords to Uncover Ownership Obscurity," SocArXiv anvke, Center for Open Science.
    2. David Robinson & Justin Steil, 2021. "Eviction Dynamics in Market-Rate Multifamily Rental Housing," Housing Policy Debate, Taylor & Francis Journals, vol. 31(3-5), pages 647-669, September.
    3. Takahiro Yabe & Bernardo García Bulle Bueno & Xiaowen Dong & Alex Pentland & Esteban Moro, 2023. "Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Boeing, Geoff & Wegmann, Jake & Jiao, Junfeng, 2020. "Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data," SocArXiv phgqt, Center for Open Science.
    5. Boeing, Geoff, 2017. "New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings," SocArXiv v54w4, Center for Open Science.
    6. Keith Ihlanfeldt, 2021. "Property Tax Homestead Exemptions: An Analysis of the Variance in Take-Up Rates Across Neighborhoods," National Tax Journal, University of Chicago Press, vol. 74(2), pages 405-430.
    7. Dirk W. Early & Paul E. Carrillo & Edgar O. Olsen, 2019. "Racial rent differences in U.S. housing markets: Evidence from the housing voucher program," Journal of Regional Science, Wiley Blackwell, vol. 59(4), pages 669-700, September.
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