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News-based sentiment analysis in real estate: a machine learning approach

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  • Jochen Hausler
  • Jessica Ruscheinsky
  • Marcel Lang

Abstract

This paper examines the relationship between news-based sentiment, captured through a machine learning approach, and the US securitised and direct commercial real estate markets. Thus, we contribute to the literature on text-based sentiment analysis in real estate by creating and testing various sentiment measures by utilising trained support vector networks. Using a vector autoregressive framework, we find the constructed sentiment indicators to predict the total returns of both markets. The results show a leading relationship of our sentiment, even after controlling for macroeconomic factors and other established sentiment proxies. Furthermore, empirical evidence suggests a shorter response time of the indirect market in relation to the direct one. The findings make a valuable contribution to real estate research and industry participants, as we demonstrate the successful application of a sentiment-creation procedure that enables short and flexible aggregation periods. To the best of our knowledge, this is the first study to apply a machine learning approach to capture textual sentiment relevant to US real estate markets.

Suggested Citation

  • Jochen Hausler & Jessica Ruscheinsky & Marcel Lang, 2018. "News-based sentiment analysis in real estate: a machine learning approach," Journal of Property Research, Taylor & Francis Journals, vol. 35(4), pages 344-371, October.
    • Handle: RePEc:taf:jpropr:v:35:y:2018:i:4:p:344-371
    DOI: 10.1080/09599916.2018.1551923
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    Cited by:

    1. 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.
    2. 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).
    3. Pyo, Dong-Jin, 2022. "Sentiment Shock and Housing Prices: Evidence from Korea," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 44(4), pages 79-108.
    4. 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.
    5. 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.
    6. Bo‐sin Tang & Winky K.O. Ho & Siu Wai Wong, 2021. "Sustainable development scale of housing estates: An economic assessment using machine learning approach," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(4), pages 708-718, July.

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