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In todayβs world, we can see that housing prices are influenced by economic fundamentals such as GDP, income level, population, and interest rates. However, these standard economic explanations are difficult to reconcile with high volatility in housing prices over a corresponding period. In addition to economic fundamentals, a growing number of scholars have tried to explain the volatility of housing prices in terms of consumer psychology and irrational behavior. These unexplained βirrationalβ factors, driven by βnoise tradersβ or βirrational investorsβ, are called market sentiment.
Market sentiment has become more easily spread between cities through social media. With its high degree of openness and rich expression of ideas and opinions, Weibo provides a large amount of content-rich textual data for capturing market sentiment. This study uses Sina Weibo, a representative social media platform in China, as a data source to measure market
sentiment and construct a Housing Market Sentiment Index. Sina Weibo is the worldβs largest Chinese-language social networking platform and, according to Sina Weiboβs fourth quarter (Q4) 2020 financial results, Sina Weibo reached 521 million monthly active users in December 2020. The measurement of the housing market sentiment index based on Sina Weibo data is divided into the following four steps
1. Obtaining social media textual data. In this step, people use Python to retrieve data from Sina Weibo, then they sorted the collected textual data by city so that they obtained a corpus of homebuyer comments on the housing market in each city.
2. Data pre-processing. Before sentiment annotation, the text needed to be pre-processed by word separation, deactivation, and cleaning of meaningless words.
3. Sentiment labeling. After the data pre-processing was completed, text sentiment analysis techniques were used to calculate the sentiment tendency value in each Sina Weibo text with the help of the Baidu AI open platform.
4. Construction of the housing market sentiment index. Finally, people use equations to measure the attitude of homebuyers towards the real estate market in each city with a combination of positive and negative comments and the difference between the number of both as a percentage of the total number of comments.
Market sentiment affects housing price inflation in two ways: the first is by directly influencing the expectations of local home buyers, which in turn increases price changes in the local housing market, as reflected by the direct effect coefficient; and the second is by influencing the expectations of home buyers in neighboring cities through spatial spillover effects, leading to increased price changes in the neighbouring citiesβ housing markets, as reflected by the indirect effect coefficient. With the help of social media, people can buy, understand more about market sentiment not only in the housing industry but also in other industries.
Source: SPATIAL EFFECT OF MARKET SENTIMENT ON HOUSING PRICE: EVIDENCE FROM SOCIAL MEDIA DATA IN CHINA
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