Abstract/Summary

Purpose In mainstream economics and finance literature, market sentiment is considered ‘irrational’. This leads to significant challenges in capturing the effect of sentiment on economic relationships. Real estate is even more complex due to the fact that the sector exhibits several market inefficiencies. In this paper, we explore the literature and present a simple test for the potential of using three different sentiment indicators to improve a basic cap rate model. We establish the case using commercial real estate data for London West End. Design/methodology/approach The three indicators differ in their underlying source and method. We used orthogonalization and principal component analysis for a macroeconomic sentiment indicator. Further, online search volume data has been used to mirror the market sentiment for the London West End market. Finally, textual analysis based on wordlists has been applied to corpus of market reports. Findings Our results indicate considerable improvement in our ability to capture the effect of sentiment. Further, has the consideration of a human factor lead to improvement in our basic yield model. Practical implications The methods suggest that sentiment extracted from more forward-looking sources, such as online searches, could be a significant information gain for investors, lenders or other market participants. The additional information could be used to adjust their behavior within the market. Originality/value To our knowledge, this is the first study, which applies textual analysis to market reports for the commercial real estate market in the UK.