城市二手房个户交易价格的预测
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摘要
基于武汉市二手房中介发布的数据,用一种新线性模型测度了二手房个体交易价格及首付额的主要影响因素.依据二手房价的分布,按二手房所在行政区域分段建立了价格预测模型,同时给出了二手房交易价格等级的推理规则.统计学检验表明,首付额预测模型性能优良,可用于实际二手房个户交易价格的预测.
In this paper,a new linear model was used to measure the main factors that affect the individual transaction price and the down payment on a second-hand house,based on the second-hand house data obtained from some property agencies in Wuhan.in terms of the distribution of second-hand housing price,some prediction models were established piecewise corresponding to the administrative region in which second-hand houses are located,meanwhile,the inference rule model for the classification level of second-hand housing transaction price was discussed.Prediction models of down payment were excellent from the point of view of statistical test,and can be used to forecast the transaction price of real second-hand housing.
In this paper,a new linear model was used to measure the main factors that affect the individual transaction price and the down payment on a second-hand house,based on the second-hand house data obtained from some property agencies in Wuhan.in terms of the distribution of second-hand housing price,some prediction models were established piecewise corresponding to the administrative region in which second-hand houses are located,meanwhile,the inference rule model for the classification level of second-hand housing transaction price was discussed.Prediction models of down payment were excellent from the point of view of statistical test,and can be used to forecast the transaction price of real second-hand housing.
引文
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[2] GAO L Z,KWONG W,CHAU.Determinants and Sustainability of House Prices:Thee Case of Shanghai,China[J].Sustainability,2015,7(4):4524-4548.
[3]许浩.合肥市二手房市场价格研究[D].芜湖:安徽师范大学数学与统计学院,2017.
[4]郭倩蓉,张敏锋.我国一二三线城市房价与地价关系的实证研究[J].闽南师范大学学报:自然科学版,2017,(2):109-114.
[5]郑永坤,刘春.基于ARIMA模型的二手房价格预测[J].计算机与现代化,2018,0(4):122-126.
[6]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):81-87.
[7]陈怀东.基于COPULA及LPPL模型对北京二手房及租赁价格的研究[J].中国经贸导刊,2017,(8):46-51.
[8]刘越.天津市二手房销售价格指数走势分析及预测[D].天津:天津商业大学理学院,2017.