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本文以北京、上海、天津、重庆等16个大中城市的二手房价格和新房价格为研究对象,以来自我国最大搜索引擎的百度搜索指数为数据基础,使用6种计量模型分别对16个城市的二手房价格和新房价格进行了拟合和预测,得到预测二手房和新房价格变动情况的最优模型。结果显示:网络搜索数据不但能够较好地预测房价指数,而且能够分析经济主体行为的趋势与规律,有一定的时效性。预测的月度房地产价格能够比官方数据发布提前约两周时间。
Abstract：This article provides an optimal model predicting the price trends in new and secondary housing market in 16 cities including Beijing,Shanghai,Tianjin,and some other relatively developed cities in China. Based on the Baidu Search Index( BSI),we fitted and forecasted the housing prices in both markets by using 6 analytical models. The results show that the web search data can not only predict the housing prices,but it can also derive some specific patterns and trends of economic agent behaviors. Besides,this prediction model is timely since it can predict the price trends of the real estate industry two weeks before official statistic agencies publish the data.
[1]Askitas N.,Zimmermann K.F,Google Econometrics and Unemployment Forecasting[C].Working Paper,2009.
[2]Breiman,L.Random forests[J].Machine Learning,2001,45:5-32.
[3]Breiman,L.,J.H.Friedman,R.A.Olshen,C.J.Stone.Classification and Regression Trees[M].Chapman and Hall,New York,1984.
[4]Cho H i,Varian H.Predicting the Present with Google Trends[C].Technical Report,2009,Google Inc.
[5]Ginsberg J,Mohebbi M H,Patel R S,et al.Detecting influenza epidemics using search engine Query data[J].Nature,2009,457:1012-1014.
[6]Iverson,L.R.,A.M.Prasad,S.N.Matthews,M.Peters.Estimating potential habitat for 134 eastern US tree species under six climate scenarios[J].Forest Ecology and Management,2008,254:390-406.
[7]Jurgen A.Doornik.Improving the Timeliness of Data on Influenzalike Illnesses using Google Search Data[C].Working Paper,2009.
[8]Kulkarni R.,Haynes K.,Stough R.,et al.Forecasting Housing Prices with Google Econometrics:A Demand Oriented Approach[C].Working Paper,2009.
[9]Schmidt T,Vosen S,Forecasting Private Consumption:Surveybased Indicators vs.Google Trends[C].Ruhr Economic Papers,2009.
[10]Wu L.,Brynjolfsson E.,The Future of Prediction:How Google Searches Foreshadow Housing Prices and Sales[C],Working Paper,2014.
[11]马建堂.大数据在政府统计中的探索与应用[M].北京:中国统计出版社,2013.
[12]徐继华,冯启娜,陈贞汝.智慧政府:大数据治国时代的来临[M].北京:中信出版社,2014.
[13]杨树新等.基于网络关键词搜索的房地产价格影响因素研究[J].新疆财经大学学报,2013(3):5-12.
[14]杨欣,等.基于网络搜索数据的突发事件对股票市场影响分析[J].数学的实践与认识,2013(12):17-28.
[15]吴喜之.复杂数据统计方法——基于R的应用[M].北京:中国人民大学出版社,2013.
[16]张崇,等.网络搜索数据与CPI的相关性研究[J].管理科学学报,2012(7):50-59.
基本信息:
DOI：10.19343/j.cnki.11-1302/c.2014.10.013
中图分类号:F299.23
引用信息:
[1]董倩,孙娜娜,李伟.基于网络搜索数据的房地产价格预测[J].统计研究,2014,31(10):81-88.DOI:10.19343/j.cnki.11-1302/c.2014.10.013.
2014-10-15
2014-10-15