南宁市二手房价格影响因素分析及房价走势的预测  

Analysis and Prediction of the Factors That Influence the Price of the Second-hand House in Nanning

作　　者：邝文竹[1] 刘琳[1] 

机构地区：[1]广西大学数学与信息科学学院,广西南宁530004

出　　处：《广西科学院学报》2012年第2期110-112,共3页Journal of Guangxi Academy of Sciences

基　　金：国家自然科学基金项目(11061002);广西自然科学基金项目(2012GXNSFBA053010)资助

摘　　要：选择南宁市江南区、西乡塘区、青秀区以及兴宁区的二手房销售数据,以地理因素为例,运用方差分析和Fisher最小显著差异法研究南宁市二手房售价的影响因素,并用回归分析和BP神经网络预测与其它城区有显著差异的城区的二手房售价随房龄变化的情况。结果发现,西乡塘区和江南区二手房平均售价的均值无显著差异(P>0.05),青秀区二手房平均售价的均值与江南区、西乡塘区、兴宁区的有显著差异(P<0.05),均值差分别为0.23000、0.24310、0.08553,地理因素对二手房价格有显著影响,青秀区的二手房平均售价在4个城区中是最高的,房价随房龄的增加,呈不规则变化。According to the study of second-hand house sale data with taking geography factors as sample, by variance analysis and Fisher least significant difference method, the factors that influence the price of second-hand house in Jiangnan, Xixiangtang, Qingxiu and Xingning districts are studied. The regression analysis and BP neural network are used to predict the relationship of second-hand house price changes with the house age. The method is valuable for reference and the research results are based on the facts.

关 键 词：房价 回归分析 BP神经网络 

分 类 号：O212.1[理学—概率论与数理统计]