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Sentiment-based commercial real estate forecasting with Google search volume data

Author

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  • Marian Alexander Dietzel
  • Nicole Braun
  • Wolfgang Schäfers

Abstract

Purpose - – The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Design/methodology/approach - – This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Findings - – The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively. Practical implications - – The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value - – This is the first paper applying Google search query data to the commercial real estate sector.

Suggested Citation

  • Marian Alexander Dietzel & Nicole Braun & Wolfgang Schäfers, 2014. "Sentiment-based commercial real estate forecasting with Google search volume data," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 32(6), pages 540-569, August.
  • Handle: RePEc:eme:jpifpp:v:32:y:2014:i:6:p:540-569
    DOI: 10.1108/JPIF-01-2014-0004
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    Citations

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    Cited by:

    1. Eisfeld, Rupert-Klaas & Just, Tobias, . "Die Auswirkungen der COVID-19-Pandemie auf die deutschen Wohnungsmärkte. Eine Studie im Auftrag der Hans-Böckler-Stiftung," Beiträge zur Immobilienwirtschaft, University of Regensburg, Department of Economics, number 26, August.
    2. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    3. Mikhail Stolbov & Maria Shchepeleva, 2023. "Sentiment-based indicators of real estate market stress and systemic risk: international evidence," Annals of Finance, Springer, vol. 19(3), pages 355-382, September.
    4. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    5. Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Papers 71, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
    6. Juan Manuel García Sánchez & Xavier Vilasís Cardona & Alexandre Lerma Martín, 2022. "Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting," Forecasting, MDPI, vol. 4(3), pages 1-20, July.
    7. Wang, Ping & Han, Wei & Huang, Chengcheng & Duong, Duy, 2022. "Forecasting realised volatility from search volume and overnight sentiment: Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
    8. 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, University of Reading.
    9. Maral Taşcılar & Kerem Yavuz Arslanlı, 2022. "Forecasting commercial real estate indicators under COVID-19 by adopting human activity using social big data," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1111-1132, October.

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