IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/afzdx.html
   My bibliography  Save this paper

Is This a Rental? Comparing Methods for Identifying Rental Units

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://osf.io/download/66ddf71daff685ec26459a83/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/afzdx?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hangen, Forrest & O'Brien, Daniel T., 2022. "Linking Landlords to Uncover Ownership Obscurity," SocArXiv anvke, Center for Open Science.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Preis, Benjamin, 2024. "Is This a Rental? Comparing Methods for Identifying Rental Units," OSF Preprints afzdx, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Preis, Benjamin, 2024. "Is This a Rental? Comparing Methods for Identifying Rental Units," OSF Preprints afzdx, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Geoff Boeing, 2020. "Online rental housing market representation and the digital reproduction of urban inequality," Environment and Planning A, , vol. 52(2), pages 449-468, March.
    2. Geoff Boeing & Max Besbris & Ariela Schachter & John Kuk, 2021. "Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?," Housing Policy Debate, Taylor & Francis Journals, vol. 31(1), pages 112-126, January.
    3. Chris Hess & Arthur Acolin & Rebecca Walter & Ian Kennedy & Sarah Chasins & Kyle Crowder, 2021. "Searching for housing in the digital age: Neighborhood representation on internet rental housing platforms across space, platform, and metropolitan segregation," Environment and Planning A, , vol. 53(8), pages 2012-2032, November.
    4. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    5. Alex Luscombe & Kevin Dick & Kevin Walby, 2022. "Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1023-1044, June.
    6. Suzuki, Masatomo & Kawai, Kohei & Shimizu, Chihiro, 2022. "Discrimination against the atypical type of tenants in the Tokyo private rental housing market: Evidence from moving-in inspection and rent arrear records," Journal of Housing Economics, Elsevier, vol. 58(PB).
    7. Guillaume Chapelle & Jean Benoît Eyméoud, 2022. "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    8. Bigelow, Daniel P. & Kuethe, Todd, 2023. "The impact of preferential farmland taxation on local public finances," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    9. Adu, Providence & Delmelle, Elizabeth C., 2022. "Spatial Variations in Exclusionary Criteria from Online Rental Advertisements," SocArXiv 8g4sv, Center for Open Science.
    10. Yamagishi, Atsushi, 2019. "Minimum Wages and Housing Rents: Theory and Evidence from Two Countries," MPRA Paper 94238, University Library of Munich, Germany.
    11. Boeing, Geoff & Harten, Julia & Sanchez-Moyano, Rocio, 2023. "Digitalization of the Housing Search: Homeseekers, Gatekeepers, and Market Legibility," SocArXiv 643x2, Center for Open Science.
    12. Geoff Boeing, 2018. "Estimating local daytime population density from census and payroll data," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 5(1), pages 179-182, January.
    13. Paul Waddell & Arezoo Besharati-Zadeh, 2020. "A Comparison of Statistical and Machine Learning Algorithms for Predicting Rents in the San Francisco Bay Area," Papers 2011.14924, arXiv.org.
    14. Keith Ihlanfeldt & Cynthia Fan Yang, 2023. "Are the home values and property tax burdens of permanent homeowners affected by growth in housing rentals and second homes: Evidence based on big data from Florida," Journal of Regional Science, Wiley Blackwell, vol. 63(2), pages 470-502, March.
    15. Giuseppe Arbia & Vincenzo Nardelli, 2024. "Using Web-Data to Estimate Spatial Regression Models," International Regional Science Review, , vol. 47(2), pages 204-226, March.
    16. Vladimir Avetian, 2022. "Essays in economics of discrimination and diversity [Essais sur l’économie de la discrimination et de la diversité]," SciencePo Working papers Main tel-03858054, HAL.
    17. 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.
    18. Pereira, Mauro F. & Vale, David S. & Santana, Paula, 2023. "Is walkability equitably distributed across socio-economic groups? – A spatial analysis for Lisbon metropolitan area," Journal of Transport Geography, Elsevier, vol. 106(C).
    19. Zhou, Mingzhi & Zhou, Jiangping, 2024. "Multiscalar trip resilience and metro station-area characteristics: A case study of Hong Kong amid the pandemic," Journal of Transport Geography, Elsevier, vol. 116(C).
    20. Aditya Kulkarni & Minkyong Kim & Jayanta Bhattacharya & Joydeep Bhattacharya, 2023. "Businesses in high-income zip codes often saw sharper visit reductions during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:afzdx. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.