SPATIAL EFFECT OF MARKET SENTIMENT ON HOUSING PRICE: EVIDENCE FROM SOCIAL MEDIA DATA IN CHINA

Junjie Li,Yu Wang,Chunlu Liu

doi:10.3846/ijspm.2022.16255

Junjie Li, Yu Wang + Show 1 more

Open Access

https://doi.org/10.3846/ijspm.2022.16255

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Abstract

Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.

Highlights

Fluctuation in housing prices touches the nerves of policymakers, ordinary residents, and financial institutions (Dietzel, 2015) and has become an issue of general social concern
We used text sentiment analysis techniques to capture housing market sentiment in social media and constructed a housing market sentiment index for 30 large and medium-sized cities in China, which uses the advantages of big data such as timeliness and foresight to make up for the shortcomings of using indirect indicators such as macroeconomic indicators to characterize sentiment
Considering the existence of correlation in the housing market and the phenomenon of propagation of market sentiment, an spatial Durbin model (SDM) has been constructed and empirically analyzed to verify the spatial effect of market sentiment on housing price volatility, while avoiding the problem of omission of spatial utility found in most studies and improving the precision of parameter estimation, which is novel in this research area in relation to studying the spatial spillover effect of market sentiment

Summary

Introduction

Fluctuation in housing prices touches the nerves of policymakers, ordinary residents, and financial institutions (Dietzel, 2015) and has become an issue of general social concern. Any imbalance or contraction in the housing market can lead to financial instability, which in turn poses a macroeconomic threat (Lee & Park, 2018). Previous studies have shown that housing prices are influenced by economic fundamentals such as GDP, income level, population, and interest rates (Case et al, 2012). These standard economic explanations are difficult to reconcile with high volatility in housing prices over a corresponding period (Alkay et al, 2018). In a standard macroeconomic model with fully rational expectations, high volatility in housing prices is difficult to generate (Granziera & Kozicki, 2015)

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