基于特征价格模型的合肥市二手房价格影响因素剖析

Influencing Factors of Second-hand Housing Prices in Hefei

利用网络爬虫(Python)软件在链家网上抓取22700条合肥市二手房微观数据的交易信息,构建特征价格模型,分析整体样本和分类样本(主城区和非主城区)分别对合肥市二手房房价的影响因素的差异。研究结果得出,特征价格模型中所包含的三种模型中,对数模型的拟合度最高;整体样本和分类样本对二手房房价影响程度具有差异性;影响因素中周边是否配备地铁对总体样本和主城区样本的影响最显著,二手房建筑面积对非主城区样本影响最显著。研究结论可为二手房交易价格特征探索以及居民投资和消费需求提供参考。

Python was used to capture 22,700 transaction information of second-hand houses in Hefei on the Lianjia network,and a characteristic price model was constructed to analyze the significant factors affecting the second-hand housing prices in Hefei.The results show that among the three models included in the feature price model,the logarithmic model has the highest fit.Among the influencing factors,whether the surrounding area is equipped with subways and the construction area of second-hand houses has the most significant impact on the price of second-hand houses.The research conclusion can provide a reference for exploring the price characteristics of second-hand housing transactions and the investment and consumption demand of residents.