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Online Housing Search and the Geography of Submarkets

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  • Alasdair Rae

Abstract

The importance of search behaviour has long been recognised in the study of housing markets, but research in this area has frequently been hampered by lack of data. In many nations, the vast majority of initial housing search queries are now conducted online and the data this generates could, in theory, provide us with better insights into how housing market search operates spatially, in addition to generating new knowledge on the geography of local housing submarkets. This paper seeks to explore these propositions by discussing existing conceptions of search before developing a framework for understanding housing search in the digital age. A large, user-generated housing market search data-set is then introduced and analysed with respect to area definition, submarket geography and search pressure locations. The results indicate that this kind of 'big data' approach to housing research could generate important new insights for housing market analysts.

Suggested Citation

  • Alasdair Rae, 2015. "Online Housing Search and the Geography of Submarkets," Housing Studies, Taylor & Francis Journals, vol. 30(3), pages 453-472, June.
    • Handle: RePEc:taf:chosxx:v:30:y:2015:i:3:p:453-472
    DOI: 10.1080/02673037.2014.974142
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    Citations

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    Cited by:

    1. J.W.A.M. Steegmans & Jonathan de Bruin, 2019. "Online housing search and gravity models," Working Papers 19-17, Utrecht School of Economics.
    2. 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.
    3. Renigier-Biłozor, Małgorzata & Janowski, Artur & Walacik, Marek & Chmielewska, Aneta, 2022. "Modern challenges of property market analysis- homogeneous areas determination," Land Use Policy, Elsevier, vol. 119(C).
    4. Berna Keskin & Craig Watkins, 2017. "Defining spatial housing submarkets: Exploring the case for expert delineated boundaries," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1446-1462, May.
    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. Hozer Józef & Gnat Sebastian & Kokot Sebastian & Kuźmiński Wojciech, 2019. "The Problem of Designating Elementary Terrains for the Purpose of Szczecin Algorithm of Real Estate Mass Appraisal," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 42-58, September.
    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. Fahim Ullah & Samad M. E. Sepasgozar & Changxin Wang, 2018. "A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms," Sustainability, MDPI, vol. 10(9), pages 1-44, September.
    9. Gnat Sebastian, 2019. "Measurement of entropy in the assessment of homogeneity of areas valued with the Szczecin Algorithm of Real Estate Mass Appraisal," Journal of Economics and Management, Sciendo, vol. 38(4), pages 89-106, December.
    10. Joep Steegmans & Jonathan de Bruin, 2021. "Online housing search: A gravity model approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-31, March.
    11. Vandenbergh, Joren, 2024. "Seller and search behavior in the Belgian housing market," Journal of Housing Economics, Elsevier, vol. 64(C).

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