基于网络搜索数据的房地产价格预测被引量：84

Real Estate Price Prediction based on Web Search Data

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摘要本文以北京、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。 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.

作者董倩孙娜娜李伟

机构地区国家统计局统计科学研究所

出处《统计研究》 CSSCI 北大核心 2014年第10期81-88,共8页 Statistical Research

关键词网络搜索数据房地产价格预测交叉验证支持向量机随机森林 Web Search Data Real Estate Price Prediction Cross Validation SVM Random Forest

分类号 C812 [社会学—统计学]

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