A Comparative Prediction Study of Housing Price Index based on Web Search Data - Evidence from Beijing and Lanzhou in China

Bohui Wang, Xin Yang, Biancheng Wang, Benfu Lv
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Abstract

Recently, using web search data to predict public health trends and economical indicators, like consumption, unemployment, tourism is arising more and more researchers interest. Based on the thought that web search data could reflect searchers' attention and concern, we study the correlation between housing price index and web search data, then make a comparative analysis of two regions with different economic levels. One the one hand, a theoretical framework illustrating the relationship between web search data and housing price index has been established. One the other hand, empirical studies of Beijing and Lanzhou has been conducted to verify the predict ability of web search data. Comparing with models not including web search data, the mean absolute percentage error decreased and the goodness-of-fit improved in models with web search data. In addition, based on the comparison of Beijing and Lanzhou, we find the predict ability of web search data has a certain relationship with economic development level of the region, and web search data has a better explanation ability for the fluctuation of housing price in developed region, like Beijing. Importantly, using web data to predict is able to achieve true real-time monitoring, and provide as references for government sector to make macro-control policy.
基于网络搜索数据的房价指数预测比较研究——以北京和兰州为例
近年来,利用网络搜索数据预测公共卫生趋势和消费、失业、旅游等经济指标正引起越来越多研究者的兴趣。基于网络搜索数据能够反映搜索者的关注程度的思想,研究了房价指数与网络搜索数据的相关性,并对两个经济水平不同的地区进行了比较分析。一方面,建立了网络搜索数据与房价指数关系的理论框架。另一方面,通过北京和兰州的实证研究,验证了网络搜索数据的预测能力。与不包含网页搜索数据的模型相比,包含网页搜索数据的模型的平均绝对百分比误差减小,拟合优度提高。此外,通过对北京和兰州的比较,我们发现网络搜索数据的预测能力与该地区的经济发展水平有一定的关系,网络搜索数据对北京等发达地区的房价波动有较好的解释能力。重要的是,利用网络数据进行预测,可以实现真正的实时监控,为政府部门制定宏观调控政策提供参考。
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