北京市商品房销售价格的影响因素分析

发布时间：2018-04-30 20:39

本文选题：房地产市场 + 房价　；参考：《清华大学》2013年硕士论文

【摘要】：近年来，伴随着住房制度和土地使用制度改革，我国的房地产业迅速发展，，不仅改善了城镇居民的居住条件，也成为我国宏观经济发展的支柱。但是，房价的持续、过快上涨所引起的房地产泡沫的迹象也给经济的稳定发展蒙上了一层阴影。住房问题关系国计民生，面对房地产价格过快上涨所造成的不良影响，政府给予了高度重视，各种形式的限售、限购和限价措施也相继出台。这些政策指向国民经济的方方面面，利用行政手段和市场调控平抑房价，同时打击投机行为。近年来的实践表明，这些措施部分取得了明显的成果，但是也促成了“假结婚”、“假离婚”等一些社会热点问题。本文以北京市商品房销售价格为例，探究房地产价格形成机制和影响因素。究竟是哪些因素影响了商品房价格，这些因素的影响程度如何，政府怎样基于这些因素更加有效、稳妥地进行宏观调控，是本文着重进行分析，力求解决的问题。本文首先介绍了行政指令形成机制、成本利润形成机制和市场供需形成机制这三种房地产价格形成机制，列举了人口、利率、经济发展水平、居民生活水平、各种价格指数、房屋施工建设和销售情况等经济社会发展中各种可能影响房价的因素以及它们的作用形式，在收集数据的过程中对这些因素的内涵作了进一步的解释。其次，通过对数据的探索性研究分析之后，使用线性回归模型解释数据之间的关系，同时采用多元线性回归中的AIC准则、方差膨胀因子等变量选择方法使模型更加合理可靠。对于城镇家庭人均可支配收入等具有明显的季节性特征的数据，考虑到作为响应变量的房屋价格指数没有这样的特点，采用季节效应模型进行分析，去除季节影响之后再进行回归分析，力求减少因季节波动导致的反常影响。从最终建立的模型及其残差图和预测曲线图上可以看出，模型的合理性还是有一定保证的，但是与现实中的情况并不完全符合，这可能是在进行变量选择的时候遗漏了重要因素，也可能是数据中的一些隐藏的特性，比如异方差性没有被考虑到，这些也是后续研究中需要考虑的问题。
[Abstract]:In recent years, with the reform of housing system and land use system, the real estate industry in China has developed rapidly, which not only improves the living conditions of urban residents, but also becomes the mainstay of our country's macroeconomic development. But the signs of a housing bubble caused by rising housing prices have also cast a shadow over the stability of the economy. The housing problem is related to the national economy and the people's livelihood. In the face of the adverse effects caused by the rapid rise in real estate prices, the government has attached great importance to it, and various forms of restrictions on sales, purchase restrictions and price restrictions have been introduced one after another. These policies point to all aspects of the national economy, using administrative measures and market regulation to stabilize house prices, while cracking down on speculation. The practice in recent years shows that these measures have achieved some obvious results, but also contributed to some hot social issues such as "fake marriage" and "fake divorce". This paper takes the selling price of commercial housing in Beijing as an example to explore the formation mechanism and influencing factors of real estate price. Exactly which factors affect the commodity housing prices, how these factors affect the degree, and how the government based on these factors more effective, stable macro-control, is the focus of this paper analysis, and strive to solve the problem. This paper first introduces three kinds of real estate price formation mechanisms, namely, administrative instruction formation mechanism, cost-profit formation mechanism and market supply-demand formation mechanism, and enumerates population, interest rate, economic development level, residents' living standard and various price indices. In the course of collecting data, the connotation of these factors is further explained in the course of building construction, sales and other economic and social development, which may affect the housing prices and their forms of action. Secondly, after the exploratory analysis of the data, the linear regression model is used to explain the relationship between the data, and the AIC criterion in the multivariate linear regression and the variable selection method such as variance expansion factor are used to make the model more reasonable and reliable. For the data with obvious seasonal characteristics such as per capita disposable income of urban households, taking into account that the housing price index as a response variable does not have such characteristics, the seasonal effect model is used to analyze the data. After removing seasonal effects, regression analysis was carried out to reduce the anomalous effects caused by seasonal fluctuations. It can be seen from the final model, its residuals and prediction curves that the rationality of the model is guaranteed, but it is not completely consistent with the actual situation. This may be due to the omission of important factors in the selection of variables, or some hidden features in the data, such as heteroscedasticity being not taken into account, which are also issues to be considered in subsequent studies.
【学位授予单位】：清华大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F299.27;F224

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