Spatial analysis of Twitter sentiment and district-level housing prices

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.

Citation

Hannum, C., Arslanli, K.Y. and Kalay, A.F. (2019), "Spatial analysis of Twitter sentiment and district-level housing prices", Journal of European Real Estate Research, Vol. 12 No. 2, pp. 173-189. https://doi.org/10.1108/JERER-08-2018-0036