大数据下基于房屋交易网站的数据获取的二手房价格走势分析——以上海为例

随着城市化进程的不断加深,互联网技术的飞速发展,以及人们对于便于工作的住房需求,致使房屋交易日趋活跃。房价是房地产市场健康稳定的重要指标。预测房价的变化有助于对房地产市场有一个清醒的认识。本文通过利用Python语言,自动提取二手房屋交易信息,并针对房屋成交量、成交户型、看房人数等多种因素进行分析,通过统计图表分析上海市某一地区的房屋交易的价格变化以及相应地区的交易活跃度,从而对二手房价走势作出预测。

Housing-transactions are getting more and more active resulting from the deepening urbanization,the rapid develop- ment of Internet technology, the demand for convenient traffic. Housing price is an important signal of the health and stability of the property market. Predicting the changes of the housing price will help people have a clear understanding of the property market.This paper uses Python language to extract second-hand housing transaction information automatically and analyzes a variety of data such as the housing trading volume, the housing type,the number of the people visited the house and so forth. By analyzing the changes of transaction price and activity for a certain area in Shanghai, we made the price tendency analysis for second-hand housing transaction in this paper .