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New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings

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  • Boeing, Geoff

    (Northeastern University)

Abstract

Current sources of data on rental housing – such as the census or commercial databases that focus on large apartment complexes – do not reflect recent market activity or the full scope of the U.S. rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the U.S. We find some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census data.

Suggested Citation

  • 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.
    • Handle: RePEc:osf:socarx:v54w4
    DOI: 10.31219/osf.io/v54w4
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    References listed on IDEAS

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    1. Hanson, Andrew & Hawley, Zackary, 2011. "Do landlords discriminate in the rental housing market? Evidence from an internet field experiment in US cities," Journal of Urban Economics, Elsevier, vol. 70(2-3), pages 99-114, September.
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    5. Boeing, Geoff, 2017. "Honolulu Rail Transit: International Lessons from Barcelona in Linking Urban Form, Design, and Transportation," SocArXiv mzsb2, Center for Open Science.
    6. Robert Goodspeed, 2015. "Smart cities: moving beyond urban cybernetics to tackle wicked problems," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 8(1), pages 79-92.
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Danya Kim & Jangik Jin, 2019. "The Effect of Land Use on Housing Price and Rent: Empirical Evidence of Job Accessibility and Mixed Land Use," Sustainability, MDPI, vol. 11(3), pages 1-18, February.
    2. Eduardo Pérez-Molina, 2022. "Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica," Environment and Planning B, , vol. 49(3), pages 987-1004, March.
    3. 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.
    4. 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.
    5. Julia Gabriele Harten & Annette M Kim & J Cressica Brazier, 2021. "Real and fake data in Shanghai’s informal rental housing market: Groundtruthing data scraped from the internet," Urban Studies, Urban Studies Journal Limited, vol. 58(9), pages 1831-1845, July.
    6. 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).
    7. 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.
    8. 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.
    9. Hu, Lirong & He, Shenjing & Luo, Yun & Su, Shiliang & Xin, Jing & Weng, Min, 2020. "A social-media-based approach to assessing the effectiveness of equitable housing policy in mitigating education accessibility induced social inequalities in Shanghai, China," Land Use Policy, Elsevier, vol. 94(C).
    10. 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.
    11. Giuseppe Arbia & Vincenzo Nardelli, 2024. "Using Web-Data to Estimate Spatial Regression Models," International Regional Science Review, , vol. 47(2), pages 204-226, March.
    12. Geoff Boeing & Jake Wegmann & Junfeng Jiao, 2020. "Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data," Papers 2002.01578, arXiv.org.
    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. Adu, Providence & Delmelle, Elizabeth C., 2022. "Spatial Variations in Exclusionary Criteria from Online Rental Advertisements," SocArXiv 8g4sv, Center for Open Science.
    15. 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.

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