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Forecasting Residential Real Estate Price Changes from Online Search Activity

Author

Listed:
  • Eli Beracha

    () (University of Wyoming)

  • M. Babajide Wintoki

    () (University of Kansas)

Abstract

The intention of buying a home is revealed by many potential home buyers when they turn to the Internet to search for their future residence. This paper examines the extent to which future cross-sectional differences in home price changes are predicted by online search intensity in prior periods. Our findings are economically meaningful and suggest that abnormal search intensity for real estate in a particular city can help predict the city’s future abnormal housing price change. On average, cities associated with abnormally high real estate search intensity consistently outperform cities with abnormally low real estate search volume by as much as 8.5% over a two-year period.

Suggested Citation

  • Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
  • Handle: RePEc:jre:issued:v:35:n:3:2013:p:283-312
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    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. How monitoring online ‘Brexit’ talk can weigh the EU referendum result
      by Blog Admin in British Politics and Policy at LSE on 2015-06-24 12:00:28

    Citations

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    Cited by:

    1. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    2. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    3. Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    4. Prashant Das & Alan Ziobrowski & N. Coulson, 2015. "Online Information Search, Market Fundamentals and Apartment Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 480-502, November.
    5. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    6. Steffen Heinig & Anupam Nanda & Sotiris Tsolacos, 2016. "Which Sentiment Indicators Matter? An Analysis of the European Commercial Real Estate Market," ICMA Centre Discussion Papers in Finance icma-dp2016-04, Henley Business School, Reading University.
    7. Sheridan Titman & Ko Wang & Jing Yang, 2014. "The Dynamics of Housing Prices," NBER Working Papers 20418, National Bureau of Economic Research, Inc.
    8. Theologos Dergiades & Eleni Mavragani & Bing Pan, 2017. "Arrivals of Tourists in Cyprus: Mind the Web Search Intensity," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 107, Hellenic Observatory, LSE.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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