基于主成分分析法的商品住宅特征价格模型改进

特征价格模型作为一种对商品住宅价格进行有效分析和评估的工具而广泛应用于房地产领域。针对特征价格模型在实际应用中自变量数目较多、选择难度大且存在多重线性相关的问题,提出采用主成分分析法对商品住宅特征模型进行改进,排除变量间的线性相关对特征价格模型产生的误差。同时,以武汉市轻轨沿线商品住宅楼盘作为研究对象,构建了基于主成分分析的商品住宅特征价格模型,并用该模型对商品住宅价格的预测结果与实际销售价格进行对比,结果显示多数样本预测误差在5%以下,验证了改进模型的优越性。

Improvement of Commercial Housing Price Model Based on Principal Component Analysis

Hedonic price model,a tool to analyze and assess commercial housing price,is widely used in the field of real estate.Based on the fact that there are more multiple choice linear and more independent variables in practical application,this paper proposes that principal component analysis should be adopted to improve characteristic model of commercial housing and exclude the error generated by the linear correlation between the variables of the hedonic price model.Meanwhile,this paper chooses commercial residential real estate along Wuhan rail as the research object,constructs the commercial housing hedonic price model based on principal component analysis and compares the predicting results of commercial housing price and actual sales prices.The result shows that the majority of the sample forecast errors is under 5%,which verified the advantages of the improved model.