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Spatial analysis of Twitter sentiment and district-level housing prices

Spatial analysis of Twitter sentiment and district-level housing prices Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices.Design/methodology/approachThe authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district.FindingsThe findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation.Research limitations/implicationsThe analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon.Practical implicationsThe findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time.Originality/valueThis is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of European Real Estate Research Emerald Publishing

Spatial analysis of Twitter sentiment and district-level housing prices

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1753-9269
DOI
10.1108/jerer-08-2018-0036
Publisher site
See Article on Publisher Site

Abstract

Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices.Design/methodology/approachThe authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district.FindingsThe findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation.Research limitations/implicationsThe analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon.Practical implicationsThe findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time.Originality/valueThis is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.

Journal

Journal of European Real Estate ResearchEmerald Publishing

Published: Sep 13, 2019

Keywords: C31 spatial models; G40 general behavioural finance; R31 housing supply and markets

References