Abstract
In order to test the prediction effect of big data technology on regional economy, this paper uses Engle-Granger test to explore the relationship between house prices in Xiangyang City and big data represented by search index. This paper selects the house price data from April 2015 to February 2020, and uses the Baidu Index with a correlation coefficient greater than 0.5 to build a search index, so as to test it empirically. The empirical results show that big data has a strong prediction for house prices in small and medium-sized cities, and the search index constructed by the weight of correlation coefficient is better.
Highlights
As we all know, house price is an important means of government macroeconomic regulation and control
As both house price P1 and search index SS16 are firstorder single integer sequences, in order to determine whether there is a linear relationship, we use the Eviews 10 software to carry out the Engle Granger test, and the results are shown in the figure above
Through Engle-Granger test of house price index and search index, it is found that there is a co integration relationship between them, and the correlation coefficient is as high as 0.76, which shows that search index has strong prediction ability for Xiangyang house price market
Summary
House price is an important means of government macroeconomic regulation and control. The stability of house price affects the stable growth of economy to a certain extent. There are many domestic research literatures on house price [1]. Most of the research literature on housing prices is concentrated in large and medium-sized cities, while little attention is paid to small and medium-sized cities. How to analyze and predict the changes of house prices in small and medium-sized cities?. Because big data has better prediction ability in the economic field, this paper attempts to explore the relationship between big data and real estate, hoping to provide a way for the economic prediction of small and medium-sized cities
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