Market Sentiments and the Housing Markets

• Main Author: Huang, Yao
• Other Authors: Not found
• Format: Others
• Published: Virginia Tech 2020
• Subjects: House prices; default; mortgage; loan; forecasting; sentiment; textual analysis
• Online Access: http://hdl.handle.net/10919/97518

This paper has three chapters. In the first chapter, we develop a measure of housing sentiment for 24 cities in China by parsing through newspaper articles from 2006 to 2017.We find that the sentiment index has strong predictive power for future house prices even after controlling for past price changes and macroeconomic fundamentals. The index leads price movements by nearly 9 months, and it is highly correlated with other survey expectations measures that come with a significant time lag. In the second chapter, we show that short term house price movement is predictable by solely using newspaper and historical price change. In the last chapter, using the sentiment index constructed from newspaper, we got empirical results to show that some people are forward-looking when deciding default and a positive sentiment (anticipated house price appreciation) will lower the Z score of probability of default by 0.028. === Doctor of Philosophy === This paper has three chapters. In the first chapter, we develop a measure of housing sentiment for 24 cities in China by parsing through newspaper articles from 2006 to 2017. Two sentiment index were created using text mining method based on keywords matching and machine learning respectively.We find that the sentiment index has strong predictive power for future house prices even after controlling for past price changes and macroeconomic fundamentals. The index leads price movements by nearly 9 months, and it is highly correlated with other survey expectations measures that come with a significant time lag. In contrast, we find much weaker feedback coming from past prices to current sentiment. In the second chapter, we show that short term house price movement is predictable by solely using newspaper and historical price change. The accuracy of the prediction could be up to 0.96 for out of sample prediction. We first use a text mining method to transfer all the text information into numerical vector space, which is able to represent the extracted full information contained in a text. Then by adopting machine learning models of Neural networks, SVM, and random forest, we classified the newspaper into 1 (up) and 0 (down) group and constructed an index as the mean label accordingly. In the last chapter, by merging the Fannie Mae loan performance data with the sentiment index constructed from newspaper as well as the macro variables about local market, we got empirical results to show that some people are forward-looking when deciding default and a positive sentiment ( anticipated house price appreciation) will lower the Z score of probability of default by 0.028. We found that during the recession period, people access more information when they try to default, on top of the traditional econ conditions and historical house price, they also consider the future house price change. Moreover, borrowers with high income, high home value, and high FICO scores tend to pay more attention to future price change. However, for those who are less experienced in this game (first time home buyer), they only pay attention to the historical price change during the recession period.