基于层次分析法的房地产价格模型研究及应用
The Study and Applications of Property Price Model Based on Hierarchical Analysis
摘要:
城市快速发展的同时,各大城市相继出现了严重的房地产泡沫现象,这成为社会密切关注的热点问题。本文首先通过比较、分析国内房地产价格数据,初步找出了影响房地产价格的九个影响因素;然后建立层次分析模型,确定了影响房地产价格的三个主要因素,并逐层进行一致性检验,验证了层次分析模型的合理性;之后,根据收集的房地产价格数据和层次分析法所得结论,建立了房地产价格的多元线性回归模型,用最小二乘法求解模型参数,进行模型检验,判断出所建立的模型具有高的精确度,能够很好地拟合房地产价格;最后,针对我国房地产市场现状以及建模结论,在土地资源相关政策、市场政策等方面提出建议,合理使用这些建议能够对房地产泡沫起到一定的抑制作用。
Abstract:
China’s real estate industry has entered a new era of sustained and rapid development. As a result, the price of real estate went up. At the same time, real estate bubble emerged in cities which made urban housing prices become a hot issue among the public. The author analyzed the factors of this issue and made suggestions as follows: firstly, a large amount of real estate price data was collected. According to analyzing difference among the data, nine factors that could affect real estate prices were found out. Then, the author used AHP (Analytic Hierarchy Process) to establish a model of level analysis. This model determined three main factors that influenced property price. Then the paper confirmed the model’s validity through applicability and consistency testing. Secondly, according to the collected data and the conclusion which made by AHP, a multiple linear regression model was established. The least square method was used to solve the model and obtain model parameters. Then, the predictions of the model were tested. Besides, the extreme testing proved its reliability. Finally, targeting at the present situation of real estate market in China and conclusion of the model, suggestions are given in this paper. These suggestions can be used in many ways: policies on land resources, market and other policies. These suggestions also gave the government ways to control real estate bubble.
参考文献
|
[1]
|
刘民权, 孙波. 商业地价形成机制、房地产泡沫及其治理[J]. 金融研究, 2009(10): 22-37.
|
|
[2]
|
吴冠岑, 王沁颖. 我国大中城市房地产泡沫测度与分化研究——以中国35个大中城市为例[J]. 价格理论与实践, 2017(5): 53-56.
|
|
[3]
|
单钰淇, 董佩鑫, 刘超. 层次分析法在制动器惯性台架电模拟效果评估中的应用[J]. 煤炭技术, 2010, 29(6): 187-188.
|
|
[4]
|
国土资源网[EB/OL]. http://www.mlr.gov.cn/
|
|
[5]
|
中华人民共和国国家统计局[EB/OL]. http://data.stats.gov.cn/
|
|
[6]
|
Li, X., Sha, J. and Wang, Z.L. (2017) A Comparative Study of Multiple Linear Regression, Artificial Neural Network and Support Vector Machine for the Prediction of Dissolved Oxygen. Hydrology Research, 48, 1214-1225.
[Google Scholar] [CrossRef]
|
|
[7]
|
Grégoire, G. (2014) Multiple Linear Regression. European Astronomical Society Publications Series, 66, 45-72.
|
|
[8]
|
Slinker, B.K. and Glantz, S.A. (2008) Multiple Linear Regression. Circulation, 117, 1732-1737.
[Google Scholar] [CrossRef]
|
|
[9]
|
王雪峰. 房地产泡沫和金融不安全——日本泡沫经济15周年评述[J]. 现代日本经济, 2007, 153(3): 25-29.
|
|
[10]
|
Levitin, A.J. and Wachter, S.M. (2013) The Commercial Real Estate Bubble.
|
|
[11]
|
彭山桂, 程道平, 张勇. 地方政府土地出让策略互动行为的检验及其影响分析[J]. 中国人口•资源与环境, 2017, 27(7): 111-119.
|
|
[12]
|
姬智. 浅谈中国当前的房地产泡沫[J]. 时代金融, 2017(6): 217.
|
|
[13]
|
顾汉龙, 冯淑怡, 王秋兵. 市场机制引入对城镇新增建设用地配置效率的影响[J]. 中国人口•资源与环境, 2017, 27(7): 101-110.
|
|
[14]
|
Chovanec, P. (2011) China’s Real Estate Bubble May Have Just Popped. Foreign Affairs.
|
|
[15]
|
Yan-Ming, Z.A.P.F. (2011) Measuring Real Estate Price Bubble Based on Kalman Filter: An Evidence from Beijing Market. Finance and Trade Research, 22, 59-65.
|