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We examine whether and the extent to which news-based sentiment, captured by textual analysis, can predict the performance of the private commercial real estate market in the United States. Our results show that sentiment reflected in news abstracts of The Wall Street Journal predicts returns of commercial real estate up to four quarters in advance. These findings are statistically significant and persist even when controlling for other related factors. This suggests that news-based sentiment can serve as an early market indicator. We are the first to examine the bidirectional relationship between sentiment, measured by textual analysis, and the performance of the private U.S. commercial real estate market. The findings contribute to the academic literature, and carry practical implications for real estate professionals.

Purpose The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA. Design/methodology/approach By means of an artificial neural network, market sentiment is extracted from 66,070 US real estate market news articles from the S&P Global Market Intelligence database. For training of the network, a distant supervision approach utilizing 17,822 labeled investment ideas from the crowd-sourced investment advisory platform Seeking Alpha is applied. Findings According to the results of autoregressive distributed lag models including contemporary and lagged sentiment as independent variables, the derived textual sentiment indicator is not only significantly linked to the depth and resilience dimensions of market liquidity (proxied by Amihud’s (2002) price impact measure), but also to the breadth dimension (proxied by transaction volume). Practical implications These results suggest an intertemporal effect of sentiment on liquidity for the direct property market. Market participants should account for this effect in terms of their investment decisions, and also when assessing and pricing liquidity risk. Originality/value This paper not only extends the literature on text-based sentiment indicators in real estate, but is also the first to apply artificial intelligence for sentiment extraction from news articles in a market liquidity setting.

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