太原市商品住宅价格预测研究

发布时间：2018-01-30 23:08

本文关键词： 商品住宅价格影响因素 BP神经网络预测　出处：《山西财经大学》2017年硕士论文　论文类型：学位论文

【摘要】：商品住宅市场是房地产市场的重要构成部分,商品住宅价格与经济社会发展关系密切。无论是对区域经济发展、居民生活水平提高,还是维护社会和谐稳定,商品住宅价格的变化及其发展趋势都处于十分重要的地位。1998年住房分配制度改革后,太原市商品住宅市场得到了迅速发展,商品住宅价格也出现了持续增长的势头,其增长幅度已超过居民收入的增长幅度,商品住宅价格也由此被社会所广泛关注。因此,分析太原市商品住宅价格的影响因素并预测未来房价的变化趋势,可以为太原市政府宏观调控、开发商投资决策及百姓消费提供一些参考依据,同时可通过采取相应措施继续稳定商品住宅价格,促进房地产市场健康发展。本文依据供求价格理论,以BP神经网络为研究工具,以太原市商品住宅价格为研究对象,对其影响因素及未来走势进行研究。首先,简要介绍了太原商品住宅市场供需及价格状况。其次,从定性及定量角度对太原商品住宅价格的影响因素展开分析。定性方面,分别从供给性因素、需求性因素以及对住宅供给、需求构成影响的宏观经济因素三个角度进行分析。在此基础上,选取了10个指标作为影响因子,通过因子相关性分析得出:GDP、总户数、住宅房屋施工面积、城镇居民人均可支配收入及房地产开发投资额是影响太原市商品住宅价格的主要因素。再次,以神经网络理论为依据,建立了太原市商品住宅价格的BP神经网络预测模型,对未来3年商品住宅价格进行仿真与预测。预测结果显示,太原市2017、2018、2019年商品住宅均价分别为8043.27元/平方米、8458.47元/平方米、8767.83元/平方米。由此可以得出,未来几年,太原市商品住宅价格会继续呈现上涨趋势,但是上涨幅度将趋于放缓且相对平稳。最后,针对太原市商品住宅价格的发展趋势,从政府、房地产开发企业及其他市场要素三个角度,对稳定太原市商品住宅价格提出建议,如:政府应当正确发挥宏观调控作用;开发商应控制开发规模、积极降低开发成本;消费者应保持理性,舆论媒体发挥正确的舆论导向作用等。
[Abstract]:Commercial housing market is an important part of the real estate market, commodity housing prices and economic and social development is closely related to regional economic development, the improvement of living standards, or to maintain social harmony and stability. In 1998, after the reform of housing distribution system, the commodity housing market in Taiyuan has developed rapidly. Commodity housing prices have also appeared the momentum of sustained growth, its growth rate has exceeded the growth rate of residents' income, commodity housing prices have also been widely concerned by the society. This paper analyzes the influencing factors of commodity housing price in Taiyuan and predicts the change trend of house price in the future, which can provide some reference basis for the macro-control of Taiyuan municipal government, the investment decision of developers and the consumption of common people. At the same time, we can continue to stabilize the commodity housing prices and promote the healthy development of the real estate market by taking corresponding measures. This paper takes BP neural network as the research tool according to the supply and demand price theory. Taking the commodity housing price of Taiyuan as the research object, this paper studies the influencing factors and the future trend. Firstly, it briefly introduces the supply and demand of Taiyuan commodity housing market and the price situation. Secondly. From the perspective of qualitative and quantitative analysis of Taiyuan commodity housing price impact factors. Qualitative aspects, respectively from the supply factor, demand factor and housing supply. On the basis of this, 10 indicators are selected as the influencing factors, and the total number of households is obtained by the correlation analysis of the factors. Housing construction area, per capita disposable income of urban residents and real estate investment are the main factors affecting the commodity housing prices in Taiyuan. Thirdly, the neural network theory is the basis. The BP neural network forecasting model of the commodity housing price in Taiyuan is established, and the simulation and forecast of the commodity housing price in the next three years are carried out. The forecast result shows that the Taiyuan city 20172018. In 2019, the average price of commercial residences was 8043.27 yuan / square meter respectively 8458.47 yuan / square meter or 8767.83 yuan / square meter. Taiyuan commodity housing prices will continue to show an upward trend, but the rise will tend to slow down and relatively stable. Finally, in view of the development trend of commodity housing prices in Taiyuan, from the government. From three aspects of real estate development enterprises and other market elements, this paper puts forward some suggestions for stabilizing the commodity housing prices in Taiyuan, such as: the government should give full play to the role of macro-control; Developers should control the scale of development and actively reduce the cost of development; Consumers should be rational and the media of public opinion should play the right role in guiding public opinion.
【学位授予单位】：山西财经大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F299.23

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