A Sentiment Index of the Housing Market in China: Text Mining of Narratives on Social Media.

Many efforts have been made to investigate the sentiment in financial and commercial real estate markets, but only a few studies focus on residential markets because of the lack of appropriate sentiment measuring approaches. In this study, we utilize social media narratives to build sentiment indexes for the housing market in China, where house-price-related narratives are abundantly documented on social media. With the help of the latest text analysis technologies from the deep learning and natural language processing fields, our indexes are built on a solid basis for understanding the semantic meanings of textual data. Highlighting the semantic temporality of text, we build separate future and past sentiment indexes to capture people's prior beliefs and posterior feelings about price movements, respectively. The future sentiment index could serve as an alternative to survey-based expectations, measure the impacts of policies on people's beliefs, and have remarkable power in predicting the future movements of both listed developers' stock prices and house prices. [ABSTRACT FROM AUTHOR]