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Information Content and Forecasting Ability of Sentiment Indicators: Case of Real Estate Market

Author

Listed:
  • Gianluca Marcato

    (University of Reading)

  • Anupam Nanda

    (University of Reading)

Abstract

We evaluate a number of real estate sentiment indices to ascertain current and forward-looking information content that may be useful for forecasting demand and supply activities. Analyzing the dynamic relationships within a Vector Auto-Regression (VAR) framework and using the quarterly US data over 1988-2010, we test the efficacy of several sentiment measures by comparing them with other coincident economic indicators. Overall, our analysis suggests that the sentiment in real estate convey valuable information that can help predict changes in real estate returns. These findings have important implications for investment decisions, from consumers' as well as institutional investors' perspectives.

Suggested Citation

  • Gianluca Marcato & Anupam Nanda, 2016. "Information Content and Forecasting Ability of Sentiment Indicators: Case of Real Estate Market," Journal of Real Estate Research, American Real Estate Society, vol. 38(2), pages 165-204.
    • Handle: RePEc:jre:issued:v:38:n:2:2016:p:165-204
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    Citations

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    Cited by:

    1. Basse, Tobias & Desmyter, Steven & Saft, Danilo & Wegener, Christoph, 2023. "Leading indicators for the US housing market: New empirical evidence and thoughts about implications for risk managers and ESG investors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Frömel, Pascal & Kolmeder, Severin & Wagner, Dominik, 2023. "Where prices are not lazy: Evidence from REITs and the financial sector," Finance Research Letters, Elsevier, vol. 53(C).
    3. Jacek Maślankowski, 2017. "Automatic Analysis of Unstructured Content as an Example of a Data Source for the Public Administration," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 46, pages 161-172.
    4. Christopoulos, Andreas D. & Barratt, Joshua G. & Ilut, Daniel C., 2024. "Synthetic cap rate indices (1991-Covid era)," Global Finance Journal, Elsevier, vol. 60(C).
    5. Hamid Baghestani & Ajalavat Viriyavipart, 2019. "Do factors influencing consumer home-buying attitudes explain output growth?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(5), pages 1104-1115, August.
    6. Enwei Zhu & Jing Wu & Hongyu Liu & Keyang Li, 2023. "A Sentiment Index of the Housing Market in China: Text Mining of Narratives on Social Media," The Journal of Real Estate Finance and Economics, Springer, vol. 66(1), pages 77-118, January.
    7. 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.
    8. Qiulin Ke & Karen Sieracki, 2018. "Exploring sentiment-driven trading behavior of different types of investors in London office market," ERES eres2018_112, European Real Estate Society (ERES).
    9. 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.
    10. Sergiy Saydometov & Sanjiv Sabherwal & Ramya Rajajagadeesan Aroul, 2020. "Sentiment and its asymmetric effect on housing returns," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 580-600, October.
    11. Gianluca Marcato & Anupam Nanda, 2022. "Asymmetric Patterns of Demand-Supply Mismatch in Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 64(3), pages 440-472, April.

    More about this item

    JEL classification:

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

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