苏州商品住房价格影响因素分析及房价预测
发布时间：2018-07-18 08:49
【摘要】：房价问题一直是全社会的热点，与个人、家庭、单位乃至国家都息息相关。我国房地产市场的发展是我国经济水平不断发展的一大助力。一路飙升的房价令人望而生畏。虽然政府为了抑制房价，不断出台新的政策，但是房价总体趋势还是保持着上涨，只是速度放缓。今年年初，杭州几处楼盘价格下跌，大家便又开始担心房地产泡沫，甚至楼市崩盘的发生。而一直与杭州“捆绑”的苏州，房价的走势会如何，值得探讨。本文对苏州商品住房价格问题进行定性和定量分析，并对2013年和2014年的商品住房价格进行预测。从而，更加深刻且全面的对房价影响因素进行了解，并给每个在商品住房市场上的参与者提供参考。 本文基于供给和需求理论，对房价影响因素从房屋自身、经济环境、社会状态和政府政策进行定性分析，再将苏州商品住房均价做为因变量，把地区生产总值、户籍人口城镇化等9个指标作为自变量，通过多元线性回归分析建立模型，最后得到仅包含户籍人口城镇化和建筑业工程结算成本这两个指标的最优模型。再根据最优模型，对政府，房地产开发商，购房者提出相关建议。最后运用所得回归模型进行房价的预测，得到2013年和2014年房价分别为每平米11257.92元和每平米12466.55元。而当2013年商品住宅均价已知时，发现预测值与实际值接近，，所得回归模型得到一定肯定。
[Abstract]:Housing price problem has been the hot spot of the whole society, and individuals, families, units and even the country are closely linked. The development of our country's real estate market is a great help of our country's economic level. Soaring house prices are daunting. Although the government has continued to introduce new policies to curb house prices, the overall trend of house prices has continued to rise, but at a slower pace. Earlier this year, several property prices in Hangzhou fell, and people began to worry about a real estate bubble or even a housing crash. And has been with Hangzhou, "bundled" Suzhou, the trend of house prices will be, it is worth discussing. This paper makes a qualitative and quantitative analysis of commodity housing prices in Suzhou, and forecasts the commodity housing prices in 2013 and 2014. Thus, a more profound and comprehensive understanding of the impact of housing prices, and to provide a reference for each participant in the commodity housing market. Based on the theory of supply and demand, this paper makes a qualitative analysis of the influencing factors of housing price from the housing itself, the economic environment, the social state and the government policy, and then takes the average price of Suzhou commodity housing as the dependent variable, and puts the regional gross domestic product (GDP) as a dependent variable. Nine indexes, such as urbanization of household registration population, as independent variables, are established by multiple linear regression analysis. Finally, the optimal model which only includes the urbanization of household registration population and the cost of construction project settlement is obtained. According to the optimal model, the government, real estate developers, home buyers put forward relevant recommendations. Finally, using the regression model to forecast the house price, the house price is 11257.92 yuan per square meter and 12466.55 yuan per square meter in 2013 and 2014, respectively. When the average price of commercial housing is known in 2013, it is found that the predicted value is close to the actual value, and the regression model is confirmed.
【学位授予单位】：苏州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F299.27
本文编号：2131383
[Abstract]:Housing price problem has been the hot spot of the whole society, and individuals, families, units and even the country are closely linked. The development of our country's real estate market is a great help of our country's economic level. Soaring house prices are daunting. Although the government has continued to introduce new policies to curb house prices, the overall trend of house prices has continued to rise, but at a slower pace. Earlier this year, several property prices in Hangzhou fell, and people began to worry about a real estate bubble or even a housing crash. And has been with Hangzhou, "bundled" Suzhou, the trend of house prices will be, it is worth discussing. This paper makes a qualitative and quantitative analysis of commodity housing prices in Suzhou, and forecasts the commodity housing prices in 2013 and 2014. Thus, a more profound and comprehensive understanding of the impact of housing prices, and to provide a reference for each participant in the commodity housing market. Based on the theory of supply and demand, this paper makes a qualitative analysis of the influencing factors of housing price from the housing itself, the economic environment, the social state and the government policy, and then takes the average price of Suzhou commodity housing as the dependent variable, and puts the regional gross domestic product (GDP) as a dependent variable. Nine indexes, such as urbanization of household registration population, as independent variables, are established by multiple linear regression analysis. Finally, the optimal model which only includes the urbanization of household registration population and the cost of construction project settlement is obtained. According to the optimal model, the government, real estate developers, home buyers put forward relevant recommendations. Finally, using the regression model to forecast the house price, the house price is 11257.92 yuan per square meter and 12466.55 yuan per square meter in 2013 and 2014, respectively. When the average price of commercial housing is known in 2013, it is found that the predicted value is close to the actual value, and the regression model is confirmed.
【学位授予单位】：苏州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F299.27
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