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.