Market Sentiments and the Housing Markets

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 cha...

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Main Author: Huang, Yao
Other Authors: Not found
Format: Others
Published: Virginia Tech 2020
Subjects:
Online Access:http://hdl.handle.net/10919/97518
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spelling ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-975182020-09-26T05:31:22Z Market Sentiments and the Housing Markets Huang, Yao Not found Tsang, Kwok Ping Luo, Shaowen Smith, Alexander Charles Ge, Suqin House prices default mortgage loan forecasting sentiment textual analysis 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. 2020-04-04T08:00:25Z 2020-04-04T08:00:25Z 2020-04-03 Dissertation vt_gsexam:23761 http://hdl.handle.net/10919/97518 In Copyright http://rightsstatements.org/vocab/InC/1.0/ ETD application/pdf application/pdf Virginia Tech
collection NDLTD
format Others
sources NDLTD
topic House prices
default
mortgage
loan
forecasting
sentiment
textual analysis
spellingShingle House prices
default
mortgage
loan
forecasting
sentiment
textual analysis
Huang, Yao
Market Sentiments and the Housing Markets
description 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.
author2 Not found
author_facet Not found
Huang, Yao
author Huang, Yao
author_sort Huang, Yao
title Market Sentiments and the Housing Markets
title_short Market Sentiments and the Housing Markets
title_full Market Sentiments and the Housing Markets
title_fullStr Market Sentiments and the Housing Markets
title_full_unstemmed Market Sentiments and the Housing Markets
title_sort market sentiments and the housing markets
publisher Virginia Tech
publishDate 2020
url http://hdl.handle.net/10919/97518
work_keys_str_mv AT huangyao marketsentimentsandthehousingmarkets
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