Textual Analysis based Real-Estate-Sentiment Evaluation of Internet Data

Mathieu (2016) finds investor sentiment to be a significant factor in explaining REIT returns and REIT return volatility in the US. As Freybote and Seagraves (2016) show, particularly institutional investors tend to rely on the sentiment of specialized real estate investors, by analyzing the buy-sell-imbalance as an indicator of the demand for a particular asset. Based on the aforementioned factors, the objective of this paper is to complement the sentiment-investigating literature by applying two methodologies of textual analysis to real-estate-related newspaper headlines, in order to create sentiment measures and test relationships to US REIT prices. Furthermore, this study analyzes, if real estate related news do reflect, cause or enhance market performance in the real estate sector.For this purpose, a set of about 130,000 newspaper articles from four different US newspapers, with a time frame from 2005 until 2015, was collected. Following the approach of Bollen et al. (2009), sentiment analysis is applied with a term based methodology, by counting words that indicate positive or negative sentiment derived from different research approaches. Moreover, this dictionary-based methodology will be supplemented by and compared to the results of a machine learning tool, the "Google Prediction API". In consequence, qualitative information from news stories and posts are converted into a quantifiable measure achieved by analyzing the positive and negative tone of the information.To test the explanatory power of the created sentiment measures on REIT market movements in the US (FTSE EPRA/NAREIT), a regression model is employed. Due to the unique characteristics of REITs, variables to control for macroeconomic changes, the general stock market and also representatives for the direct real estate market are included in the model. Results show, that the created real estate sentiment measures have significant effects on the REIT market. Different measures were found to have varying relationships. Furthermore, the created sentiment measures are more powerful in times of a decreasing REIT market than in increasing times.To the best of our knowledge, this is the first research work applying textual analysis to capture sentiment in the securitized real estate market in the US. Furthermore, the broad collection of newspaper articles from four different sources is unique, as normally only one or two different sources have been used in literature so far.