Real estate media sentiment through textual analysis

Abstract

Purpose The purpose of this paper is to determine systematically the broader relationship between news media sentiment, extracted through textual analysis of articles published by leading US newspapers, and the securitized real estate market. Design/methodology/approach The methodology is divided into two stages. First, roughly 125,000 US newspaper article headlines from Bloomberg, The Financial Times, Forbes and The Wall Street Journal are investigated with a dictionary-based approach, and different measures of sentiment are created. Second, a vector autoregressive framework is used to analyse the relationship between media-expressed sentiment and REIT market movements over the period 2005–2015. Findings The empirical results provide significant evidence for a leading relationship between media sentiment and future REIT market movements. Furthermore, applying the dictionary-based approach for textual analysis, the results exhibit that a domain-specific dictionary is superior to a general dictionary. In addition, better results are achieved by a sentiment measure incorporating both positive and negative sentiment, rather than just one polarity. Practical implications In connection with fundamentals of the REIT market, these findings can be utilised to further improve the understanding of securitized real estate market movements and investment decisions. Furthermore, this paper highlights the importance of paying attention to new media and digitalization. The results are robust for different REIT sectors and when conventional control variables are considered. Originality/value This paper demonstrates for the first time, that textual analysis is able to capture media sentiment from news relevant to the US securitized real estate market. Furthermore, the broad collection of newspaper articles from four different sources is unique.

Is the MD&A of US REITs informative? A textual sentiment study

“…In contrast to the Henry list, this dictionary is quite extensive and contains 354 positive and 2,355 negative words [5]. Only recently, Ruscheinsky et al (2018) adjusted the LM dictionary for their investigation on the impact of media sentiment in real estate markets. Comparing headlines from Bloomberg , The Financial Times , Forbes and The Wall Street Journal to the wordlists of the LM dictionary, the researchers added words that are considered positive or negative in the context of real estate and removed other words that have a rather different or unclear classification.…”

“…For example, Doran et al (2012) and Price et al (2017) analyze transcripts and audio files of earnings conference calls for a REIT sample, to show that sentiment impacts initial reaction-window abnormal returns. In creating a custom dictionary for the real estate domain, Ruscheinsky et al (2018) find media sentiment to lead future REIT market movements. Beracha et al (2019) apply the newly developed wordlists to news abstracts of the Wall Street Journal and provide evidence that news-based sentiment has predictive power for the direct commercial real estate market in the United States.…”

“…To extract sentiment from the MD&A, we rely on a dictionary-based approach. Specifically, we employ the Loughran and Mcdonald (2011) financial dictionary and a custom wordlist for the real estate industry created by Ruscheinsky et al (2018) to determine the overall sentiment inherent in each filing. Our findings suggest that higher levels of pessimistic (optimistic) language in the MD&A are associated with lower (higher) future firm performance.…”

“…This holds even after controlling for the information released in other concurrent disclosures that may be predictive of future performance. Hereby, the use of a domain-specific real estate dictionary, namely the dictionary developed by Ruscheinsky et al (2018) leads to superior results. Moreover, we find a significant market response to pessimistic language in the MD&A at the time of the SEC filing.…”

Artificial intelligence, news sentiment, and property market liquidity

“…Walker (2014) and Soo (2015) study the influence of media on housing markets in the UK and the USA. Ruscheinsky et al (2018) furthermore argue that sentiment extracted from newspaper headlines can be used as a leading indicator for the US REIT market.…”