Social Media and Real Estate: Do Twitter users predict REIT performance?

Problems and objective Social media platforms have become vibrant online platforms where people share their opinions and views on any topic (Yadav and Vishwakarma, 2020). With the increasing volume and speed of social media, the exchange of stock market-related information has become more important, which is why the effects of social media information on stock markets are becoming increasingly salient (Li et al., 2018). Business organizations need to understand these dynamics, as it reflects the interest of all kind of market participants – retail investors, institutional investors, but also clients, journalists and many others. Therefore, it is not surprising that there is evidence for public sentiment, obtained from social media, correlating with or even predicting economic indicators (e.g. Bollen et al., 2011; Sprenger et al., 2014; Xu and Cohen, 2018). Regarding real estate, Zamani and Schwartz (2017) successfully used Twitter language to forecast house price changes for a small sample at the county level. Except this limited research on real estate markets and the research for the general stock market, there is no more general study that examines the relationship between social media and real estate markets. Nevertheless, real estate markets are of particular interest, not only because of its popularity as an asset class among retail investors, but also because real estate is ubiquitous in daily life and the intransparency of the market. Sentiment indicators extracted from social media therefore promises to cover perspectives from all kinds of people and could therefore be more informative than traditional sentiment measures. However, as described by Li et al. (2018), social media-based sentiment indicators are not intended to replace traditional sentiment indicators, but rather complement them, as these are usually based on the knowledge of only a few industry insiders instead of that of the general public. Besides, the study focuses on indirect real estate (i.e. REITs) as it allows retail investors who represent the majority of social media users sharing equity-related information, to participate in real estate markets. Methodology & Data Using a dictionary-based approach, a classical machine learning approach as well as a deep learning based approach to extract the sentiment of approximately 4 million tweets, this paper compared methods of different complexity in terms of their ability to classify social media sentiment and predict indirect real estate returns on a monthly basis. The baseline for this comparison is a conventional dictionary-based approach including valence shifting properties. The dictionary used is the real estate specific dictionary developed Ruscheinsky et al. (2018). For the classical machine learning method, a support vector machine (SVM), which already has stated to be potent in a real estate context (Hausler et al., 2018), is utilized. The more complex deep learning approach is based on a Long Short-Term Memory (LSTM) model. The usefulness of deep learning-based approaches for sentiment analysis in a real estate context has been proven before by Braun et al. (2019). As high-tradevolume-stocks tend to be discussed most on Twitter, posts are collected from this platform (Xu and Cohen, 2018), including a ten-year timespan from 2013 to 2022. Hereby selection is made on the basis of cashtags representing all US REITs. The monthly total return of the FTSE Nareit allEquity Total Return states the dependent variable, whereby the created sentiment variable is the variable of interest. Contribution to science and practice The aim of this study is to create a standardized framework that enables investors of all kinds to better classify current market events and thus better navigate the opaque real estate market. This framework could be applied not only by investors, but vice versa by REITs to understand and optimize their position in society and in the investor landscape. To the authors knowledge, this is the first study to analyze the impact of social media sentiment on (indirect) real estate returns, based on a comprehensive national dataset.