本文選題：房地產(chǎn) + 多元線性回歸��； 參考：《武漢理工大學(xué)》2014年碩士論文

【摘要】：房地產(chǎn)行業(yè)是國民經(jīng)濟(jì)的支柱產(chǎn)業(yè)之一，近年來房地產(chǎn)行業(yè)發(fā)展迅猛，為國民經(jīng)濟(jì)的發(fā)展做出了巨大的貢獻(xiàn)，在我國的經(jīng)濟(jì)社會(huì)發(fā)展中有著舉足輕重的地位。同時(shí)，商品房的供需及價(jià)格與人民的生活息息相關(guān)，房地產(chǎn)問題已經(jīng)成為最重要的民生問題之一。當(dāng)前很多學(xué)者對(duì)房地產(chǎn)行業(yè)的供需及價(jià)格問題做了大量的研究，但是大多是從政策、資金等層面的因素來考慮的，定性分析多于定量分析。通過建立數(shù)學(xué)模型從定量的角度來分析影響房地產(chǎn)行業(yè)發(fā)展的因素間的數(shù)量關(guān)系，較準(zhǔn)確的預(yù)測(cè)房地產(chǎn)供需及房價(jià)的走勢(shì)從而為進(jìn)行有效的調(diào)控提供決策支持，是一個(gè)值得探索的方向。 本文從定性分析的角度深入研究了影響房地產(chǎn)需求、供給及銷售價(jià)格的因素后，選取中華人民共和國國家統(tǒng)計(jì)局《2013年中國統(tǒng)計(jì)年鑒》中的相關(guān)數(shù)據(jù)，使用多元線性回歸方法對(duì)其進(jìn)行了定量分析。然后使用人工免疫算法及灰色預(yù)測(cè)理論，針對(duì)房地產(chǎn)的供需及房價(jià)提出了有效的預(yù)測(cè)模型。本文的主要研究成果有： （1）在定性分析的基礎(chǔ)上結(jié)合數(shù)據(jù)的可獲得性，分別選取對(duì)房地產(chǎn)需求、供給及售價(jià)影響最密切的因素利用逐步回歸方法進(jìn)行了定量分析。針對(duì)不同的樣本數(shù)據(jù)，使用了向前逐步回歸、向后逐步回歸、帶常量的逐步回歸及不帶常量的逐步回歸等方法進(jìn)行建模，并對(duì)比了各方法所求得模型的擬合精度和有效性。 （2）灰色系統(tǒng)理論著重研究小樣本、貧信息的問題，房地產(chǎn)行業(yè)的年度供需值及銷售價(jià)格數(shù)列的數(shù)據(jù)量少，適合使用灰色預(yù)測(cè)模型進(jìn)行建模。本文使用GM（1,1）模型對(duì)商品房的需求量、供給量及平均銷售價(jià)格進(jìn)行了預(yù)測(cè)。為提高標(biāo)準(zhǔn)GM（1,1）模型的擬合精度，將免疫克隆選擇算法引入灰色GM（1,1）模型，提出了兩種優(yōu)化算法。 （3）針對(duì)標(biāo)準(zhǔn)GM（1,1）模型使用相鄰累加數(shù)取均值生成的背景值不能準(zhǔn)確反映數(shù)據(jù)序列變化情況，導(dǎo)致預(yù)測(cè)值與實(shí)際值有較大的誤差的不足，本文提出了一種使用免疫克隆選擇算法對(duì)背景值參數(shù)進(jìn)行尋優(yōu)的改進(jìn)算法。 （4）針對(duì)標(biāo)準(zhǔn)GM（1,1）模型使用最小二乘法求解待估參數(shù)a、b，而最小二乘法有許多使用限制可能導(dǎo)致求解的預(yù)測(cè)值精度不高的不足，本文使用免疫克隆選擇算法在最小二乘法求得的a、b初始解的基礎(chǔ)上繼續(xù)進(jìn)行尋優(yōu)計(jì)算，，獲得更為精確的a、b值。 使用基于免疫克隆選擇算法改進(jìn)的GM（1,1）模型對(duì)商品房需求、供給及售價(jià)進(jìn)行建模，實(shí)驗(yàn)結(jié)果表明兩種改進(jìn)算法都比標(biāo)準(zhǔn)GM（1,1）算法對(duì)原始數(shù)據(jù)序列的擬合精度高，本文提出的灰色人工免疫算法可以對(duì)商品房供需及售價(jià)的變化趨勢(shì)進(jìn)行更準(zhǔn)確預(yù)測(cè)。
[Abstract]:The real estate industry is one of the pillar industries of the national economy. In recent years, the real estate industry has developed rapidly, which has made a great contribution to the development of the national economy, and has played an important role in the economic and social development of our country. At the same time, the supply and demand of commercial housing and its price are closely related to the people's life. The real estate problem has become one of the most important livelihood issues. At present, many scholars have done a lot of research on the supply and demand and price of real estate industry, but most of them are considered from the aspects of policy, capital and other factors, qualitative analysis is more than quantitative analysis. By establishing a mathematical model to analyze the quantitative relationship among the factors influencing the development of the real estate industry from a quantitative point of view, the paper predicts the trend of real estate supply and demand and house prices accurately, thus providing decision support for effective regulation and control. Is a direction worth exploring. Based on the qualitative analysis of the factors affecting real estate demand, supply and sales price, this paper selects the relevant data from the Statistical Yearbook of China 2013 of the National Bureau of Statistics of the people's Republic of China. The multivariate linear regression method was used to quantitatively analyze it. Then, using artificial immune algorithm and grey prediction theory, an effective forecasting model for real estate supply and demand and house price is proposed. The main research results of this paper are as follows: 1) on the basis of qualitative analysis, combined with the availability of data, the factors most closely affecting real estate demand, supply and selling price are selected for quantitative analysis by stepwise regression method. For different sample data, the methods of stepwise regression, backward stepwise regression, stepwise regression with constant and stepwise regression without constant are used to model the model, and the fitting accuracy and validity of the models obtained by these methods are compared. 2) the grey system theory focuses on the problem of small sample and poor information, and the annual supply and demand value of real estate industry and the quantity of data in sales price series are less, so it is suitable to use grey prediction model to model the model. In this paper, the demand, supply and average selling price of commercial housing are forecasted by GM1) model. In order to improve the fitting accuracy of the standard GM-1) model, the immune clone selection algorithm was introduced into the grey GM1 / 1) model, and two optimization algorithms were proposed. (3) the background value generated by using the mean value of adjacent accumulative number in the model can not accurately reflect the variation of data sequence, which leads to the deficiency of large error between the predicted value and the actual value. In this paper, an improved algorithm for optimizing background parameters using immune clone selection algorithm is proposed. The least square method is used to solve the parameters to be estimated, and the least square method has many limitations, which may lead to the low accuracy of the prediction value. In this paper, the immune Clone selection algorithm is used to continue the optimization calculation on the basis of the initial solution obtained by the least square method, and a more accurate value of AGB is obtained. Based on the improved GM1) model based on immune clone selection algorithm, the demand, supply and selling price of commercial housing are modeled. The experimental results show that the two improved algorithms are more accurate than the standard GM1 / 1) algorithm in fitting the original data sequence. The grey artificial immune algorithm proposed in this paper can predict the change trend of supply and demand and price of commercial housing more accurately.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F299.23

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 朱永升,王衛(wèi)華,韓伯棠;影響房地產(chǎn)市場(chǎng)需求因素的灰色關(guān)聯(lián)度分析[J];北京理工大學(xué)學(xué)報(bào);2002年06期

2 徐進(jìn)軍;王海成;白中潔;;灰色預(yù)測(cè)模型若干改進(jìn)方法[J];測(cè)繪信息與工程;2011年04期

3 賴純見;陳迅;;我國住宅銷量和價(jià)格的主要影響因素——住宅市場(chǎng)宏觀調(diào)控政策效果分析[J];重慶工商大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2012年01期

4 單銳;王淑花;李玲玲;高東蓮;;基于ARIMA、BP神經(jīng)網(wǎng)絡(luò)與GM的組合模型[J];遼寧工程技術(shù)大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年01期

5 李東月;;房價(jià)預(yù)測(cè)模型的比較研究[J];工業(yè)技術(shù)經(jīng)濟(jì);2006年09期

6 閆鵬飛;王典;燕慧慧;;基于GM(1,1)模型的鄭州市商品房房價(jià)預(yù)測(cè)[J];重慶交通大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2013年03期

7 何小亞;傅武燕;;模糊數(shù)學(xué)方法在房地產(chǎn)定價(jià)中的應(yīng)用[J];經(jīng)濟(jì)師;2006年03期

8 張永勝;左祥;;2009年房價(jià)上漲因素分析及2010年房價(jià)走勢(shì)預(yù)測(cè)[J];經(jīng)濟(jì)研究導(dǎo)刊;2010年10期

9 梁云芳;高鐵梅;;中國房地產(chǎn)價(jià)格波動(dòng)區(qū)域差異的實(shí)證分析[J];經(jīng)濟(jì)研究;2007年08期

10 曹瑞;周鋒;歐陽廣帥;徐帥帥;;基于多項(xiàng)式回歸的房價(jià)模型分析[J];科協(xié)論壇(下半月);2010年10期

本文編號(hào)：1947276