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Qualitative analysis of housing demand using Google trends data

Author

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  • Kun-Huang Huarng
  • Tiffany Hui-Kuang Yu
  • Maria Rodriguez-Garcia

Abstract

Big data analytics often refer to the breakdown of huge amounts of data into a more readable and useful format. This study utilises Google Trends big data as a proxy for an analysis of housing demand. We employ a qualitative method (fuzzy set/Qualitative Comparative Analysis, fsQCA), instead of a quantitative method, for our estimate and forecast. The empirical results show that fsQCA successfully forecasts seasonal time series, even though the dataset is small in size. Our findings fill the gap in the qualitative and time series forecasting literature, and the forecasting procedure herein also offers a good standard for industry.

Suggested Citation

  • Kun-Huang Huarng & Tiffany Hui-Kuang Yu & Maria Rodriguez-Garcia, 2020. "Qualitative analysis of housing demand using Google trends data," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 2007-2017, January.
    • Handle: RePEc:taf:reroxx:v:33:y:2020:i:1:p:2007-2017
    DOI: 10.1080/1331677X.2018.1547205
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    Cited by:

    1. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Humaira Kamal Pasha, 2024. "Smart access and smart protection for welfare gain in Europe during COVID‐19: An empirical investigation using real‐time data," Bulletin of Economic Research, Wiley Blackwell, vol. 76(1), pages 41-66, January.

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