Artificial intelligence, news sentiment, and property market liquidity

Abstract

PurposeThe 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/approachBy 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.FindingsAccording 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 implicationsThese 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/valueThis 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.