基于BP-Boosting算法的商品住宅价格预测模型  

The Commodity Residential House Price Prediction Based on BP-Boosting

作　　者：张彦周[1] 马秋香[1] 

机构地区：[1]河南职业技术学院基础教学部,郑州450046

出　　处：《河南科学》2014年第12期2588-2592,共5页Henan Science

基　　金：国家自然科学基金项目(U1304610);河南省科技厅重大科技攻关项目(122102310606);河南省基础与前沿技术研究项目(132300410064)

摘　　要：针对商品住宅价格预测问题,分析整理了与房价相关的经济因素,首次提出将BP-Boosting回归算法运用到商品住宅价格的预测中.以郑州市房地产相关数据为实例,进行学习预测.模型结果表明,该方法简单有效,较为准确地预测出下一个季度的房价,与BP神经网络及灰色-马尔柯夫模型相比具有较为理想的预测精度.For commodity housing price forecast problems,we analyzed the economic factors related to housingprices. The paper firstly proposed BP-Boosting regression algorithm to be applied in the commodity housing priceforecast. We carried out a simulative prediction with relevant data of Zhengzhou real estate and achieved goodresults,then more accurate predict prices in the next quarter were got. Compared with BP neural network and greymarkov model,it is more effective and accurate.

关 键 词：BP-Boosting算法 商品住宅价格 预测模型 

分 类 号：F293[经济管理—国民经济]