本文選題：房地產(chǎn)價(jià)格 + 灰色關(guān)聯(lián)度�。� 參考：《安徽財(cái)經(jīng)大學(xué)》2014年碩士論文

【摘要】：房地產(chǎn)價(jià)格作為房地產(chǎn)業(yè)運(yùn)行的“晴雨表”,不僅是政府宏觀調(diào)控的重要目標(biāo)指標(biāo),也關(guān)系著國(guó)計(jì)民生,是社會(huì)各界關(guān)注的重要民生話題。自1998年商品房改革以來(lái),我國(guó)的房地產(chǎn)業(yè)得到了飛速發(fā)展,有效地帶動(dòng)了國(guó)民經(jīng)濟(jì)的快速發(fā)展,并成為國(guó)民經(jīng)濟(jì)的支柱產(chǎn)業(yè)之一。同時(shí),飛漲的房地產(chǎn)價(jià)格也引發(fā)了社會(huì)資源配置失衡、產(chǎn)業(yè)結(jié)構(gòu)失調(diào)、購(gòu)房難等各種經(jīng)濟(jì)和社會(huì)問(wèn)題。2005年以來(lái),為了規(guī)范房地產(chǎn)市場(chǎng),有效抑制房?jī)r(jià)過(guò)快上漲,政府出臺(tái)了一系列嚴(yán)厲的房地產(chǎn)調(diào)控政策,房?jī)r(jià)大幅上漲的趨勢(shì)仍未得到有效的控制。這既有調(diào)控措施方向不準(zhǔn)、力度不夠的原因,也有房地產(chǎn)價(jià)格的影響因素錯(cuò)綜復(fù)雜而難以調(diào)控。因此,研究房地產(chǎn)價(jià)格的影響因素并對(duì)房?jī)r(jià)的未來(lái)發(fā)展趨勢(shì)進(jìn)行預(yù)測(cè)就顯得十分重要。 首先,利用HP濾波等統(tǒng)計(jì)方法,以上海市為例,深入分析了1999年1月至2013年3月間上海市房?jī)r(jià)的走勢(shì)和波動(dòng)情況,刻畫了實(shí)際房?jī)r(jià)與均衡房?jī)r(jià)的偏離程度,發(fā)現(xiàn)上海市房?jī)r(jià)雖處于不斷的波動(dòng)中,然而總體走勢(shì)是上漲的,帶有明顯的增長(zhǎng)剛性。 其次,在理論分析房地產(chǎn)價(jià)格的影響因素基礎(chǔ)上,運(yùn)用灰色關(guān)聯(lián)度和VAR模型對(duì)1999年1月至2013年3月的上海市相關(guān)月度數(shù)據(jù)進(jìn)行定量分析,實(shí)證結(jié)果表明房?jī)r(jià)的主要影響因素來(lái)自于經(jīng)濟(jì)基本面,而住房需求、銀行信貸、地價(jià)等也是高房?jī)r(jià)的主要推動(dòng)因素。此外,房地產(chǎn)價(jià)格與通貨膨脹、證券市場(chǎng)也有一定的相關(guān)性。 再次,在以上研究的基礎(chǔ)上,選用三種房?jī)r(jià)預(yù)測(cè)模型——時(shí)間序列預(yù)測(cè)模型、灰色預(yù)測(cè)模型、BP神經(jīng)網(wǎng)絡(luò)模型對(duì)房?jī)r(jià)的未來(lái)發(fā)展趨勢(shì)進(jìn)行預(yù)測(cè)。通過(guò)比較三種模型的預(yù)測(cè)效果發(fā)現(xiàn),基于多因素的BP神經(jīng)網(wǎng)絡(luò)模型預(yù)測(cè)效果要優(yōu)于VAR(2)模型與灰色預(yù)測(cè)模型。同時(shí),預(yù)測(cè)結(jié)果表明在未來(lái)的一年內(nèi),房地產(chǎn)價(jià)格仍將保持繼續(xù)上漲的趨勢(shì)。 最后,在總結(jié)全文的基礎(chǔ)上,從調(diào)整經(jīng)濟(jì)結(jié)構(gòu)、房地產(chǎn)金融、土地、調(diào)節(jié)房地產(chǎn)供需不平衡四個(gè)方面提出了一些政策建議,以期促進(jìn)房?jī)r(jià)合理回歸和房地產(chǎn)市場(chǎng)的健康發(fā)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.

【學(xué)位授予單位】：安徽財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F299.23

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本文編號(hào)：1815770