基于广义回归神经网络的沈阳房地产市场研究被引量：4

Research of Shenyang Real Estate Market Based on Generalized Regression Neural Network

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摘要通过广义回归神经网络对沈阳市房地产市场2003年至2009年相关数据进行训练,采用逼近性最好的光滑因子0.1,对2010年和2011年的数据进行预测,并与真实数据进行对比,得出沈阳市房地产开发投资额、商品房均价及空置面积均在高位运行.由此判断出沈阳市房地产市场仍处于繁荣期,但属于后期阶段,有出现房地产泡沫的可能,政府、房地产开发商、购房者应给予足够关注. Based on the relevant data of Shenyang real estate market from 2003 to 2009, the data from 2010 to 2011 were forecasted using the generalized regression neural network with a smoothing factor of 0. 1 which has excellent approximation, and were compared with the true data. The results show that the developing investment, housing average price, and vacant areas of the real estate in Shenyang are in high level. Moreover, the real estate market is still in the boom, but belongs to the later period. Government, developer, and home buyer should pay attention to the real estate bubbles which may emerge in the future.

作者赵亮王连广齐锡晶

机构地区东北大学资源与土木工程学院

出处《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2014年第8期1203-1205,共3页 Journal of Northeastern University(Natural Science)

基金住房与城乡建设部科学技术计划项目(2011-R3-27)

关键词房地产周期径向基函数广义回归神经网络房地产泡沫可持续发展 real estate period radial basis function generalized regression neural network real estate bubble sustainable development

分类号 F293.3 [经济管理—国民经济]

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