Abstract
This study aims to explore whether the intensity of internet searches, according to the Google Trends search volume index (SVI), is a predictor of changes in real estate prices. The motivation of this study is the possibility to extend the understanding of the extra predictive power of Google search engine query volume of future housing price change (shift direction) by (i) the introduction of a research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying the multi-class HPI values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the SVI that relates to interests in both ‘real estate’ and ‘credit to buy real estate’; (iv) evaluation of the introduced approach in the context of the Polish real estate market. The main theoretical contribution of our work is a confirmation that the freely available information regarding Google user searches can provide an in-depth insight into enriching the generally accepted statistics on supply and demand in the real estate market. From the practical perspective, this research confirms that SVI can be associated as a sole determinant to anticipate the housing price change with time-lag sufficient for making decisions regarding the purchase (sale) of individual property or the real estate market control. Such findings can be also helpful for researchers who intend to use Google Trends data as an extra variable from demand side to improve the prediction accuracy if it is included in the model which is based on the existing housing prices determinants.
Highlights
The analysis of data from search engines is an interesting area of research in big data literature
The motivation behind the main goal of this study is the possibility to extend the understanding of the Google user search volume extra predictive power of future housing price change by (i) the introduction of a complex research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying the multiclass House Price Index (HPI) values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the Google user search volume that relates to interests in both ‘real estate’ and ‘credit to buy real estate’; (iv) evaluation of the introduced approach in the context of the Polish real estate market
In this study we aim to extend the understanding of the Google search engine query volume extra predictive power of future housing price change, in addition to all existing housing price determinants, by (i) the introduction of a complex research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying multi-class HPI values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the Google user search volume that relates to interests in both ‘real estate’ and ‘credit to buy real estate’; (iv) the evaluation of introduced approach in the context of the Polish real estate market
Summary
The analysis of data from search engines is an interesting area of research in big data literature. Search engine traffic seen as the volume of search requests submitted by users to search engines on the web can be used to track and, in some cases, to anticipate the dynamics of social phenomena [4]. Authors term this occurrence as a sort of wisdom of the crowd effect. The findings of [10] showed that experience diversity, participant independence, and network decentralization are all positively related to crowd performance. Decentralization means that crowd members have their own specializations and can learn from their own knowledge sources
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