房地产价格影响因素及预测研究

发布时间：2018-04-28 15:04

本文选题：房地产价格 + 灰色关联度　；参考：《安徽财经大学》2014年硕士论文

【摘要】：房地产价格作为房地产业运行的“晴雨表”,不仅是政府宏观调控的重要目标指标,也关系着国计民生,是社会各界关注的重要民生话题。自1998年商品房改革以来,我国的房地产业得到了飞速发展,有效地带动了国民经济的快速发展,并成为国民经济的支柱产业之一。同时,飞涨的房地产价格也引发了社会资源配置失衡、产业结构失调、购房难等各种经济和社会问题。2005年以来,为了规范房地产市场,有效抑制房价过快上涨,政府出台了一系列严厉的房地产调控政策,房价大幅上涨的趋势仍未得到有效的控制。这既有调控措施方向不准、力度不够的原因,也有房地产价格的影响因素错综复杂而难以调控。因此,研究房地产价格的影响因素并对房价的未来发展趋势进行预测就显得十分重要。首先,利用HP滤波等统计方法,以上海市为例,深入分析了1999年1月至2013年3月间上海市房价的走势和波动情况,刻画了实际房价与均衡房价的偏离程度,发现上海市房价虽处于不断的波动中,然而总体走势是上涨的,带有明显的增长刚性。其次,在理论分析房地产价格的影响因素基础上,运用灰色关联度和VAR模型对1999年1月至2013年3月的上海市相关月度数据进行定量分析,实证结果表明房价的主要影响因素来自于经济基本面,而住房需求、银行信贷、地价等也是高房价的主要推动因素。此外,房地产价格与通货膨胀、证券市场也有一定的相关性。再次,在以上研究的基础上,选用三种房价预测模型——时间序列预测模型、灰色预测模型、BP神经网络模型对房价的未来发展趋势进行预测。通过比较三种模型的预测效果发现,基于多因素的BP神经网络模型预测效果要优于VAR(2)模型与灰色预测模型。同时,预测结果表明在未来的一年内,房地产价格仍将保持继续上涨的趋势。最后,在总结全文的基础上,从调整经济结构、房地产金融、土地、调节房地产供需不平衡四个方面提出了一些政策建议,以期促进房价合理回归和房地产市场的健康发展。
[Abstract]:As a "barometer" of real estate operation, real estate price is not only an important target of the government's macro control, but also the national economy and the people's livelihood. It is an important topic of people's livelihood. Since the reform of commercial housing in 1998, China's real estate industry has been developed rapidly, and it has effectively moved the rapid development of the national economy. As one of the pillar industries of the national economy, the soaring real estate prices also lead to a variety of economic and social problems, such as unbalance of the allocation of social resources, the imbalance of industrial structure, the difficulties of buying a house, and other economic and social problems. In order to standardize the real estate market and effectively restrain the rapid rise of house prices, the government has issued a series of severe real estate regulation policies, and the government has issued a series of severe real estate regulation policies. The trend of the price rise has not been effectively controlled. This has not only the reasons for the inaccuracy of the control measures, but also the complexity of the real estate prices. Therefore, it is very important to study the factors affecting the real estate price and to predict the future development trend of the house prices.
First, using HP filtering and other statistical methods, taking Shanghai as an example, the trend and fluctuation of housing prices in Shanghai from January 1999 to March 2013 were analyzed, and the deviation between real and balanced house prices was depicted. Although the housing price in Shanghai was in constant fluctuation, the overall trend was rising, with obvious growth rigidity.
Secondly, on the basis of the theoretical analysis of the influence factors of real estate prices, the monthly data of Shanghai city in Shanghai from January 1999 to March 2013 are quantitatively analyzed with the grey correlation and the model. The empirical results show that the main factors of the housing price are from the economic fundamentals, while the housing demand, the bank credit and the land price are also high prices. The main driving factors. In addition, real estate prices and inflation, the stock market also has a certain correlation.
Thirdly, on the basis of the above research, three forecasting models of house price - time series prediction model, grey prediction model and BP neural network model are used to predict the future development trend of house prices. By comparing the prediction results of the three models, it is found that the prediction effect of the BP neural network model based on multiple factors is better than that of VAR (2) model and The grey prediction model also predicts that real estate prices will continue to rise in the coming year.
Finally, on the basis of the full text, we put forward some policy suggestions from four aspects: adjusting the economic structure, real estate finance, land and regulating the imbalance of real estate supply and demand, in order to promote the rational return of house prices and the healthy development of the real estate market.

【学位授予单位】：安徽财经大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F299.23

【参考文献】