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Searching for housing in the digital age: Neighborhood representation on internet rental housing platforms across space, platform, and metropolitan segregation

Author

Listed:
  • Chris Hess

    (Department of Policy Analysis and Management, 5922Cornell University, USA)

  • Arthur Acolin

    (Department of Real Estate, 7284University of Washington, USA)

  • Rebecca Walter

    (Department of Real Estate, 7284University of Washington, USA)

  • Ian Kennedy

    (Department of Sociology, 7284University of Washington, USA)

  • Sarah Chasins

    (Department of Electrical Engineering and Computer Sciences, 1438University of California Berkeley, USA)

  • Kyle Crowder

    (Department of Sociology, 7284University of Washington, USA)

Abstract

Understanding residential mobility, housing affordability, and the geography of neighborhood advantage and disadvantage relies on robust information about housing search processes and housing markets. Existing data about housing markets, especially rental markets, suffer from accuracy issues and a lack of temporal and geographic flexibility. Data collected from online rental platforms that are commonly used can help address these issues and hold considerable promise for better understanding the full distribution of available rental homes. However, realizing this promise requires a careful assessment of potential sources of bias as online rental listing platforms may perpetuate inequalities similar to those found in physical spaces. This paper approaches the production of rental advertisements as a social process driven by both contextual and property level factors. We compare data from two online platforms for the 100 most populated metropolitan areas in the United States to explore inequality in digital rental listing spaces and understand what characteristics are associated with over and underrepresentation of advertisements in certain areas. We find similar associations for socioeconomic measures between platforms and across urban and suburban parts of these metropolitan areas. In contrast, the importance of racial and ethnic composition, as well as broader patterns of segregation, for online representation differs substantially across space and platform. This analysis informs our understanding of how online platforms affect housing search dynamics through their biases and segmentation, and highlights the potential and limits in using the data available on these platforms to produce small area rental estimates.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:envira:v:53:y:2021:i:8:p:2012-2032
    DOI: 10.1177/0308518X211034177
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    References listed on IDEAS

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    Cited by:

    1. Adu, Providence & Delmelle, Elizabeth C., 2022. "Spatial Variations in Exclusionary Criteria from Online Rental Advertisements," SocArXiv 8g4sv, Center for Open Science.
    2. Liton (Md) Kamruzzaman & Sanaz Nikfalazar & Fuad Yasin Huda & Dharmalingam Arunachalam & Dickson Lukose, 2025. "Data Typologies in Urban Housing Research: A Systematic Review of the Literature," Sustainability, MDPI, vol. 17(11), pages 1-25, May.
    3. Ariela Schachter & John Kuk & Max Besbris & Lydia Ho, 2024. "What’s in a name? Place misrepresentation and neighbourhood stigma in the online rental market," Urban Studies, Urban Studies Journal Limited, vol. 61(16), pages 3050-3068, December.

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