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Online rental housing market representation and the digital reproduction of urban inequality

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  • Geoff Boeing

Abstract

As the rental housing market moves online, the internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings’ representativeness could impact different communities’ access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners’ and researchers’ ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the USA and finds that they spatially concentrate and overrepresent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities’ online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real time, it does not evenly represent all market segments. The internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies, human interactions, and planners’ ability to monitor and respond to urban challenges.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:envira:v:52:y:2020:i:2:p:449-468
    DOI: 10.1177/0308518X19869678
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    Cited by:

    1. Boeing, Geoff & Harten, Julia & Sanchez-Moyano, Rocio, 2023. "Digitalization of the Housing Search: Homeseekers, Gatekeepers, and Market Legibility," SocArXiv 643x2, Center for Open Science.
    2. Geoff Boeing & Michael Batty & Shan Jiang & Lisa Schweitzer, 2022. "Urban analytics: History, trajectory and critique," Chapters, in: Sergio J. Rey & Rachel S. Franklin (ed.), Handbook of Spatial Analysis in the Social Sciences, chapter 30, pages 503-516, Edward Elgar Publishing.
    3. Nhlabathi Mthobisi & Mgiba Freddy Marilahimbilu & Ligaraba Neo, 2022. "Social Media Marketing Attributes, Sandton’s Rental Market Brand Image, and the Millennials’ Rental Preference: An Empirical Study," Real Estate Management and Valuation, Sciendo, vol. 30(1), pages 34-52, March.
    4. 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.
    5. Geoff Boeing & Max Besbris & David Wachsmuth & Jake Wegmann, 2021. "Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies," Papers 2108.08229, arXiv.org.
    6. 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).
    7. 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.
    8. Lythreatis, Sophie & Singh, Sanjay Kumar & El-Kassar, Abdul-Nasser, 2022. "The digital divide: A review and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    9. 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.
    10. Occhini, Giulia & Tranos, Emmanouil & Wolf, Levi John, 2023. "Occupational segregation in the digital economy? A Natural Language Processing approach using UK Web Data," SocArXiv z8xta, Center for Open Science.
    11. Adu, Providence & Delmelle, Elizabeth C., 2022. "Spatial Variations in Exclusionary Criteria from Online Rental Advertisements," SocArXiv 8g4sv, Center for Open Science.
    12. 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.
    13. Ferreri, Mara & Sanyal, Romola, 2022. "Digital informalisation: rental housing, platforms, and the management of risk," LSE Research Online Documents on Economics 112794, London School of Economics and Political Science, LSE Library.

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