本文關(guān)鍵詞：中國房價波動特征及政策調(diào)控效應(yīng)研究　出處：《中國地質(zhì)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 房價波動 房價泡沫 調(diào)控政策 反應(yīng) 效應(yīng)

【摘要】：我國房地產(chǎn)市場化改革以來,截至2015年底,全國平均房價增長了2.57倍,除了次貸危機(jī)期間出現(xiàn)短暫下跌外,房價的上漲從未停止。2008年之后經(jīng)濟(jì)增長趨緩,但房價反而急劇上漲,引起了各界人士對房價波動的關(guān)注和對房價泡沫的擔(dān)憂。同期,我國政府出臺了大量的政策對房地產(chǎn)市場進(jìn)行調(diào)控,尤其是2008年之后,但房價卻陷入“越調(diào)越漲”的窘境之中,調(diào)控政策的有效性受到質(zhì)疑。房價波動和房價泡沫的正確判斷是制定、實(shí)施和評價調(diào)控政策的基礎(chǔ)和依據(jù),其中方法和數(shù)據(jù)是關(guān)鍵。指標(biāo)法的前提是房價決定于經(jīng)濟(jì)基本面,否則指標(biāo)法的應(yīng)用就失去了意義。在數(shù)據(jù)方面,目前大都使用官方數(shù)據(jù),但其質(zhì)量備受爭議,相對和平均意義的數(shù)據(jù)形式忽略了房地產(chǎn)的異質(zhì)性。大數(shù)據(jù)的興起為房價泡沫研究提供了包含異質(zhì)性特征的數(shù)據(jù),在提高對其判斷的正確性的同時,也能夠?yàn)檎哒{(diào)控提供更為合理的依據(jù)。本文圍繞“房價波動”和“政策調(diào)控”兩條主線,以房價決定理論與模型、大數(shù)據(jù)理論、特征價格理論和政府干預(yù)理論為基礎(chǔ),利用房天下和鏈接網(wǎng)站,抓取了30個省會城市和直轄市的房價發(fā)布信息,形成我國房價的網(wǎng)絡(luò)大數(shù)據(jù)。以此為基礎(chǔ),構(gòu)建房地產(chǎn)的特征價格模型,對房價進(jìn)行特征調(diào)整;然后通過文獻(xiàn)研究法梳理房價的影響因素,利用BMA方法和MC3抽樣技術(shù)篩選出對房價最具解釋力的影響因素集,回歸得到基礎(chǔ)房價;結(jié)合兩個數(shù)據(jù)最終得到房價泡沫的結(jié)果,從GDP、收入、區(qū)域、人口、土地等角度對房價波動特征進(jìn)行分析。將房價波動定義為市場和政府干預(yù)兩種機(jī)制作用的結(jié)果,利用30個省市1999-2015年的面板數(shù)據(jù)展開實(shí)證分析。首先利用HP濾波法分離出長期趨勢下的均衡房價,得到房價波動的總效應(yīng);其次使用變量減少法篩選出影響房價的市場因素集,回歸得到市場機(jī)制下的房價,并計(jì)算得到房價波動的市場效應(yīng);通過相減得到政府干預(yù)機(jī)制下的政策效應(yīng)。結(jié)合兩種方法下獲得的房價影響因素集,在對四象限模型擴(kuò)展的基礎(chǔ)上,建立使用市場和資產(chǎn)市場、長期與短期的分析框架,對房價波動的形成機(jī)制進(jìn)行解釋。調(diào)控政策對房價波動的作用,首先考慮政策是否將房價波動作為反應(yīng)變量,在前人研究的基礎(chǔ)上,構(gòu)建了貨幣、財(cái)政和土地政策變量對房價的反應(yīng)函數(shù),利用1998-2014年的時間序列數(shù)據(jù),并以2008年為界,使用GMM模型實(shí)證分析不同階段的調(diào)控政策對房價的立場和反應(yīng)。其次,在分析政策對房價的影響機(jī)理的基礎(chǔ)上,建立房價與政策變量的回歸模型,利用上述數(shù)據(jù)實(shí)證分析各項(xiàng)政策對房價波動的影響。最后,使用脈沖響應(yīng)函數(shù)分析政策變量對房價的動態(tài)影響,并運(yùn)用方差分解方法確定各項(xiàng)政策變量對房價波動的貢獻(xiàn)大小。本文的研究結(jié)論如下:(1)政策調(diào)控失效的原因之一是對房價泡沫的判斷出現(xiàn)了偏差,測度方法和數(shù)據(jù)選擇由于不能完全反映房地產(chǎn)的異質(zhì)性,因此很難反映出房價波動和房價泡沫的真實(shí)情況,隨著時間推移,政策調(diào)控可能會加劇房價波動。(2)影響房價的特征變量中,建筑面積對房價的彈性大于1;多數(shù)城市房價對房間數(shù)的彈性為負(fù),且絕對值小于0.5;建筑年代越早,反而總價越高;大多數(shù)城市的樓層越高,房價越低。(3)土地因素和收入因素是基礎(chǔ)房價,即長期均衡房價的決定因素,尤其是土地因素,包括土地總成本、土地供應(yīng)量和土地價格,土地市場對長期房價的影響最為突出。短期市場房價決定于收入、投資、成本、技術(shù)和勞動力等因素。(4)我國不存在全面性的房價泡沫,只存在局部泡沫,80%的城市房價均處于合理波動范圍內(nèi)。30個省會城市和直轄市中,一半城市的房價被高估,另外一半城市被低估,即實(shí)際房價沒有反映出真實(shí)價值。GDP、土地對房價波動存在閾值效應(yīng),房價波動具有異質(zhì)變異特征和收入效應(yīng),人口因素不能解釋區(qū)域間的差異。(5)2009年之后大多數(shù)省市的房價波動為正,但幅度不大,具有明顯的趨同性和空間遞進(jìn)特征。1999-2015年市場機(jī)制下的房價波動構(gòu)成了一個完整的周期,并且大部分時間內(nèi)房價被低估,市場機(jī)制對房價的決定作用并未因區(qū)域間市場成熟度的不同而不同。2008-2010年的擴(kuò)張性政策是房價上漲的重要原因,尤其對于東部省市,真正意義上的負(fù)效應(yīng)政策調(diào)控在2012年之后,但調(diào)控政策在不同區(qū)域間并未實(shí)現(xiàn)同步影響,而是由東向西轉(zhuǎn)移。(6)引入房價變量,政策的最優(yōu)反應(yīng)規(guī)則需要對產(chǎn)出、通貨膨脹及其滯后項(xiàng)和房價做出反應(yīng)。貨幣供應(yīng)量對房價做出了相反的反應(yīng),是房價高漲的重要原因;整體上,貨幣政策并未對房價做出顯著反應(yīng),反而加劇了房價的上漲,土地政策的反應(yīng)逐步走強(qiáng),固定資產(chǎn)投資的反應(yīng)趨于減弱;利率和貨幣供應(yīng)量對經(jīng)濟(jì)增長和通貨膨脹的反應(yīng)的顯著性呈現(xiàn)交錯變化特征。(7)貨幣供應(yīng)量對房價波動的影響最大,但有一定幅度的降低,是房價持續(xù)上漲的重要原因,同時利率政策推高了房價。土地供應(yīng)對房價的影響有限,并不斷下滑,固定資產(chǎn)投資對房價的影響在2008年之后變得顯著。利率在短期內(nèi)對房價負(fù)向影響,而貨幣供應(yīng)量的影響在長期,固定資產(chǎn)投資和土地供應(yīng)量對房價的短期影響明顯。4個政策變量中,貨幣供應(yīng)量對房價的影響最大,其次是固定資產(chǎn)投資、利率和土地供應(yīng)量。(8)實(shí)現(xiàn)房地產(chǎn)市場的健康有序發(fā)展以及房價穩(wěn)定,應(yīng)發(fā)揮市場機(jī)制對房價的決定作用,實(shí)施分城施策,加強(qiáng)調(diào)控政策對房價的反應(yīng)和政策的規(guī)則化和制度化建設(shè),完善土地供應(yīng)體系,發(fā)揮土地供應(yīng)對房價的作用。本文的創(chuàng)新點(diǎn)在于:(1)基于大數(shù)據(jù)理念,建立了30個省會城市和直轄市的房價大數(shù)據(jù),通過構(gòu)建HPM模型對市場房價進(jìn)行調(diào)整,得到反映異質(zhì)性特征的房價數(shù)據(jù),利用基礎(chǔ)價值法擬合并導(dǎo)出長期均衡房價,從而得到房價泡沫結(jié)果,形成了房價泡沫測度與判斷的方法和框架。(2)在市場機(jī)制和政府干預(yù)機(jī)制對房價決定的模型框架下,利用回歸方法對兩種機(jī)制產(chǎn)生的房價波動進(jìn)行分解,從而能夠判斷和評價兩種機(jī)制分別對房價波動的影響程度和效應(yīng)。(3)分析各項(xiàng)政策對房價波動的影響之前,構(gòu)建了引入房價變量的政策反應(yīng)函數(shù),并推導(dǎo)了最優(yōu)政策反應(yīng)規(guī)則,分別實(shí)證分析了貨幣、財(cái)政和土地政策對房價波動的立場和反應(yīng)。將三項(xiàng)政策置于同一框架下,實(shí)證分析它們各自對房價波動的影響,包括靜態(tài)和動態(tài)影響。
[Abstract]:Since the reform of China's real estate market, by the end of 2015, the national average price increase of 2.57 times, but fell short appears during the subprime crisis, housing prices have never stopped.2008 years after economic growth slowed, but prices rose sharply, attracted people from all walks of life on the price fluctuations and to the attention of the housing bubble worries the same period, the Chinese government issued a number of policies to regulate the real estate market, especially after 2008, but the price is in the more stressed the more up predicament, effective control policy has been questioned. Correct judgment of price volatility and price bubble is making, implementation and evaluation and the basis of regulatory policy among them, methods and data is the key precondition. Index method is the price depends on the economic fundamentals, or the application of index method is meaningless. In terms of data, most of the current use of the official number According to its quality, but controversial, and the average relative significance of data form ignores the heterogeneity of real estate. The rise of big data provides a heterogeneity of data for the study of the housing bubble in the right to improve the judgment at the same time, can also provide a more reasonable basis for policy regulation. This paper focuses on the "price fluctuations" and "policy" the two main line, with prices in decision theory and model, big data theory, the hedonic price theory and government intervention theory as the foundation, the use of real world and linked sites, grabbed 30 of the capital city and the municipality prices to release information, the formation of large data network of China's housing prices. On this basis, the hedonic price model of real estate construction, characteristic adjustment of prices; then through literature research method combing the factors affecting prices, using the BMA method and the MC3 sampling technique to screen the prices Influential explanatory factors, regression based prices; combined with the two data obtained from the GDP results of the housing bubble, area, population, income, and to analyze the characteristics of land price fluctuations and other aspects. The price fluctuations in the market and the government intervention is defined as two kinds of machine made the results of empirical analysis, using the panel data of 30 provinces during 1999-2015. The first isolated long-term equilibrium prices under the trend of the use of the HP filter, to obtain the total effect of price fluctuations; secondly use variable reduction method to find out influencing factors of market prices, to get under the market mechanism of prices, and to calculate the market effect of price fluctuations; policy the effects of government intervention mechanism under the influence of factors. By subtracting prices obtained from the two methods combined with the set, based on the expansion of the four quadrant model, the establishment of long-term market and asset market. With the analysis framework of short-term, the formation mechanism of housing price fluctuation was explained. Effects of regulatory policies on price fluctuations, first consider whether the policy will be price fluctuations as a response variable, on the basis of previous research, build the monetary reaction function, finance and land policy variables on the price, using the time series data 1998-2014 in 2008, the use of GMM model to analyze the different stages of the regulatory policy positions and responses of prices. Secondly, in the analysis of the policy impact on the price mechanism on the basis of establishing the regression model of prices and policy variables, analysis of the impact of policies on price fluctuations by the empirical data. Finally, using the impulse response function analysis of dynamic effects of policy variables on prices, and use the variance decomposition method to determine the contribution of the policy variables on the price fluctuations. The conclusion of this paper One of the reasons are as follows: (1) policy failure is the housing bubble judgment, measurement methods and data selection because of heterogeneity can not fully reflect the real estate, so it is difficult to reflect the real situation of price fluctuations and the housing bubble, with the passage of time, policy may exacerbate price fluctuations (2. The impact of price variables), the construction area of housing price elasticity is greater than 1; most of the city of elastic room number is negative, and the absolute value is less than 0.5; the building earlier, but the higher price; most of the city's higher floors, prices lower. (3) land factor and income factor is based on prices, determinants of long-term equilibrium prices, especially land factors, including the total cost of land, land supply and land prices, land prices on the market long-term effect is most prominent. The short-term market price depends on the income of investment The cost of capital, labor, technology and other factors. (4) does not exist in our country comprehensive housing bubble, there is only local bubble, 80% city house prices are at a reasonable range of.30 in the capital city and municipality directly under the central government, half of the city's housing prices are overvalued, the other half city is undervalued, the actual price was not reflect the true value of.GDP, there is a threshold effect on land price fluctuations, the price fluctuations have heterogeneous variability and the income effect, population factors cannot explain the differences between regions. (5) after the 2009 price fluctuations in most provinces is positive, but modest, has the obvious trend of price fluctuations and spatial characteristics of same-sex.1999-2015 progressive market the mechanism consists of a complete cycle, and most of the time in the price is undervalued, determine the role of the market mechanism of prices is not due to the regional market maturity varies.2008-2 010 years of expansionary policy is an important reason for rising prices, especially in the eastern provinces, the true sense of the negative effect of policy regulation in 2012, but the regulation policy in different regions did not achieve the synchronization effect, but the transfer from east to west. (6) the introduction of price variables, the optimal reaction rules and policies need to output. Inflation and its lag and prices respond. Money supply in response to housing prices is an important reason for rising prices; on the whole, the monetary policy did not make a significant response to prices, but increased prices, land policy reaction gradually strengthened, fixed asset investment response tends to weaken significantly; the money supply and interest rates in response to economic growth and inflation has staggered changes. (7) the impact of money supply on price fluctuations, but to a certain extent reduced An important reason is low, housing prices continued to rise, while the interest rate policy to push up prices. Land supply limited impact on prices, and declining prices, the impact of investment in fixed assets to become significant after 2008. The interest rate in the short term negative impact on prices, and the money supply influence in the long term, short term the impact of investment in fixed assets and land supply for housing was.4 a policy variable, the impact of money supply on the price of the largest, followed by investment in fixed assets, interest rates and the supply of land. (8), price stability and orderly development of the realization of the real estate market health, should play a decisive role in the market mechanism of prices and the implementation of the city facilities strategy, strengthen the construction of the policy of price regulation and policy rules and reaction system, improve the land supply system, play the role of land supply on house prices. The creative points of this paper are as follows: (1) base On the idea of big data, big data set up 30 provincial city and municipality directly under the central government house, by constructing a HPM model to adjust the market prices, housing prices get data to reflect the heterogeneity of the long-term equilibrium price derived by fitting value based method, so as to get the price bubble, forming method and framework of the housing bubble measure and judgment. (2) in the framework of market mechanism and government intervention mechanism of price decision, price fluctuations were produced in two different mechanisms by using regression method of decomposition, which can judge and evaluate the two mechanisms respectively on the fluctuation of the price impact and effect. (3) before analyzing the influence of policy on housing prices the volatility of the established policy reaction function into price variables, and the optimal policy rule is derived, respectively, the empirical analysis of monetary, financial and land policies on price fluctuations and position The three policies were placed in the same framework to demonstrate their impact on the volatility of house prices, including static and dynamic effects.

【學(xué)位授予單位】：中國地質(zhì)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：F299.23

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