Spatial analysis of Twitter sentiment and district-level housing prices. Issue 2 (6th August 2019)

• Record Type: Journal Article
• Authors: Hannum, Christopher; Arslanli, Kerem Yavuz; Kalay, Ali Furkan
• Is Part Of: Journal of European real estate research. Volume 12:Issue 2(2019)
• Page Start: 173
• Page End: 189
• Publication Date: 2019-08-06
• Subjects: C31 spatial models -- G40 general behavioural finance -- R31 housing supply and markets; Real property -- Economic aspects -- Europe -- Periodicals; Commercial real estate -- Europe -- Periodicals; Housing -- Finance -- Periodicals; Housing -- Europe -- Periodicals; 333.5068
• DOI: 10.1108/JERER-08-2018-0036
• Languages: English
• ISSNs: 1753-9269

Abstract

Abstract : Purpose: 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/approach: The 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. Findings: The 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/implications: The 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 implications: The 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/value: This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.