[1]张双妮.基于逐步回归模型拟合房价预测模型[J].现代农业研究,2019,(2):124-125.
 Zhang Shuangni.Fitting house price forecasting model based on stepwise regression model[J].Modern Agricultural Research,2019,(2):124-125.
点击复制

基于逐步回归模型拟合房价预测模型
分享到:

《现代农业研究》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年2期
页码:
124-125
栏目:
推广与实践
出版日期:
2019-02-25

文章信息/Info

Title:
Fitting house price forecasting model based on stepwise regression model
作者:
张双妮
江西财经大学
Author(s):
Zhang Shuangni
Jiangxi University of Finance and Economics
关键词:
房价预测逐步回归模型
Keywords:
house price forecast stepwise regression model
摘要:
本文基于逐步回归模型来拟合房价预测模型,对全国的综合房价、以北京市为代表的 发达省份、以海南省为代表的半发达省份和以江西省为代表的发展中省份均进行拟合和预测, 再进行比较,得出结论:长期来看房价普遍会上涨,而从短期来看全国的综合房价和以江西省为 代表的发展中省份的房价将会下跌,以北京市为代表的发达省份和以海南省为代表的半发达省 份的房价将会继续上涨。
Abstract:
This paper is based on the stepwise regression model to fit the house price forecasting mod? el. The comprehensive housing prices in the country, the developed provinces represented by Beijing, the semi-developed provinces represented by Hainan Province, and the developing provinces represent? ed by Jiangxi Province are fitted. And forecast, and then compare, and conclude that housing prices will generally rise in the long run, and in the short term, the comprehensive housing prices in the country and the prices of developing provinces represented by Jiangxi Province will fall, represented by Beijing. Housing prices in developed provinces and semi-developed provinces represented by Hainan Province will continue to rise.

参考文献/References:

[1] 张荣艳.基于GM(1,N)模型的郑州市房地产价格预测[J].数 学的实践与认识,2018,48(05):82-88. [2] 袁芳.西安市房地产价格影响因素分析及预测[J].现代经 济信息,2018(01):477-478.

更新日期/Last Update: 2019-02-25