Abstract
Each week, thousands of newspaper articles on real estate topics are read by market participants. While the market is comparatively intransparent, readers hope to find valuable information. This raises the question of whether this investment of time pays off and whether different types of newspapers are an equivalent source of information. This paper examines the relationship between news-based ...
Abstract
Each week, thousands of newspaper articles on real estate topics are read by market participants. While the market is comparatively intransparent, readers hope to find valuable information. This raises the question of whether this investment of time pays off and whether different types of newspapers are an equivalent source of information. This paper examines the relationship between news-based coverage of real estate topics respectively news-based market sentiment and total returns of the asset classes of residential, office and retail. Using methods of natural language processing, including word embedding, topic modelling and sentiment analysis, three sentiment indicators for each asset class can be derived from 137,000 articles of two trade and two daily newspapers. Our results suggest that trade newspapers outperform daily newspapers in the prediction of future total returns and that the generated sentiment indicators Granger-cause total returns. Moreover, the results indicate that daily newspapers report more negatively on rising returns in the residential market than the trade press. To the best knowledge of the authors, this is the first study to quantify news coverage and sentiment for the main real estate asset classes through means of textual analysis, and to assess different sentiments in trade and daily press.
Altmetric