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Outperforming the benchmark: online information demand and REIT market performance

Author

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  • Karim Rochdi
  • Marian Dietzel

Abstract

Purpose - – The purpose of this paper is to investigate whether there is a relationship between asset-specific online search interest and movements in the US REIT market. Design/methodology/approach - – The authors collect search volume (SV) data from “Google Trends” for a set of keywords representing the information demand of real estate (equity) investors. On this basis, the authors test hypothetical investment strategies based on changes in internet SV, to anticipate REIT market movements. Findings - – The results reveal that people’s information demand can indeed serve as a successful predictor for the US REIT market. Among other findings, evidence is provided that there is a significant relationship between asset-specific keywords and the US REIT market. Specifically, investment strategies based on weekly changes in Google SV would have outperformed a buy-and-hold strategy (0.1 percent p.a.) for the Morgan Stanley Capital International US REIT Index by a remarkable 15.4 percent p.a. between 2006 and 2013. Furthermore, the authors find that real-estate-related terms are more suitable than rather general, finance-related terms for predicting REIT market movements. Practical implications - – The findings should be of particular interest for REIT market investors, as the established relationships can potentially be utilized to anticipate short-term REIT market movements. Originality/value - – This is the first paper which applies Google search query data to the REIT market.

Suggested Citation

  • Karim Rochdi & Marian Dietzel, 2015. "Outperforming the benchmark: online information demand and REIT market performance," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 33(2), pages 169-195, March.
    • Handle: RePEc:eme:jpifpp:v:33:y:2015:i:2:p:169-195
    DOI: 10.1108/JPIF-11-2014-0069
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    Citations

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    Cited by:

    1. Bleher, Johannes & Dimpfl, Thomas, 2022. "Knitting Multi-Annual High-Frequency Google Trends to Predict Inflation and Consumption," Econometrics and Statistics, Elsevier, vol. 24(C), pages 1-26.
    2. Fang, Jianchun & Gozgor, Giray & Lau, Chi-Keung Marco & Lu, Zhou, 2020. "The impact of Baidu Index sentiment on the volatility of China's stock markets," Finance Research Letters, Elsevier, vol. 32(C).
    3. Chaiyuth Padungsaksawasdi & Sirimon Treepongkaruna & Robert Brooks, 2019. "Investor Attention and Stock Market Activities: New Evidence from Panel Data," IJFS, MDPI, vol. 7(2), pages 1-19, June.

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