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Sentiment-based predictions of housing market turning points with Google trends

Author

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  • Marian Alexander Dietzel

Abstract

Purpose - – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts. Design/methodology/approach - – Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability. Findings - – The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes. Practical implications - – The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value - – This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

Suggested Citation

  • Marian Alexander Dietzel, 2016. "Sentiment-based predictions of housing market turning points with Google trends," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 9(1), pages 108-136, March.
    • Handle: RePEc:eme:ijhmap:v:9:y:2016:i:1:p:108-136
    DOI: 10.1108/IJHMA-12-2014-0058
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    Citations

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    Cited by:

    1. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    2. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
    3. Wendi Zhang & Bin Li & Alan Wee-Chung Liew & Eduardo Roca & Tarlok Singh, 2023. "Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-33, December.
    4. Kvam, Emilie & Molnar, Peter & Wankel, Ingvild & Odegaard, Bernt Arne, 2022. "Do sustainable company stock prices increase with ESG scrutiny? Evidence using social media," UiS Working Papers in Economics and Finance 2022/1, University of Stavanger.
    5. Takumi Ito & Fumiko Takeda, 2022. "Do sentiment indices always improve the prediction accuracy of exchange rates?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 840-852, July.
    6. Camilo Acosta & Luis Baldomero-Quintana, 2024. "Quality of communications infrastructure, local structural transformation, and inequality," Journal of Economic Geography, Oxford University Press, vol. 24(1), pages 117-144.

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