本文選題：房地產(chǎn)　切入點(diǎn)：商品房銷售價(jià)格　出處：《武漢紡織大學(xué)》2013年碩士論文

【摘要】：自1998年我國(guó)對(duì)住房實(shí)物分配制度停止后，我國(guó)商品房銷售價(jià)格就開始不斷上漲，國(guó)家對(duì)商品房的價(jià)格和銷售情況不斷進(jìn)行調(diào)控，但是房地產(chǎn)的價(jià)格仍在快速上漲，它已經(jīng)極大地影響了我國(guó)居民的生活和整個(gè)國(guó)民經(jīng)濟(jì)的平穩(wěn)發(fā)展，商品房?jī)r(jià)格已經(jīng)成為了一個(gè)被廣泛關(guān)注的社會(huì)問題和經(jīng)濟(jì)問題。 影響房地產(chǎn)價(jià)格的因素錯(cuò)綜復(fù)雜，目前對(duì)商品房銷售價(jià)格影響因素的分析方法也多種多樣，本文擬采用定性分析和定量分析相結(jié)合的方法進(jìn)行研究。影響因素的分析很重要，而房地產(chǎn)的定價(jià)更是受到關(guān)注。對(duì)于房地產(chǎn)的定價(jià)，目前有很多種方法，針對(duì)影響因素分析和房地產(chǎn)定價(jià)的問題，本文主要采用多元線性回歸模型、GM(1,1)模型、BP神經(jīng)網(wǎng)絡(luò)模型和灰色神經(jīng)網(wǎng)絡(luò)模型分別進(jìn)行研究，并進(jìn)行模型的比較和選擇。本文具體分為以下六個(gè)部分。 第一部分為緒論，介紹了本文研究的背景與研究意義，提出了問題，并敘述了本文的研究思路與方法，指出了本文的重難點(diǎn)、創(chuàng)新與不足。 第二部分為綜述，在對(duì)部分有代表性的參考文獻(xiàn)進(jìn)行研究、分析和比較的基礎(chǔ)上，分別介紹了國(guó)內(nèi)外對(duì)相關(guān)問題的研究現(xiàn)狀。 第三部分對(duì)房地產(chǎn)價(jià)格的影響因素進(jìn)行了分析，首先對(duì)商品房平均銷售價(jià)格影響因素進(jìn)行定性分析，主要分為內(nèi)部影響因素和外部影響因素，內(nèi)部影響因素則包括了成本因素和營(yíng)銷目標(biāo)，而外部因素則分別從經(jīng)濟(jì)環(huán)境因素、社會(huì)環(huán)境因素、行政因素和其他因素等四大因素進(jìn)行分析，，然后在定性分析的基礎(chǔ)上，選擇了本文認(rèn)為最相關(guān)的9種因素進(jìn)行多元線性回歸的定量分析，利用Eviews6.0軟件，對(duì)多元線性回歸模型進(jìn)行逐步回歸，消除因素間的多重共線性，最后確定了竣工房屋造價(jià)和全國(guó)人口總數(shù)為對(duì)我國(guó)商品房平均銷售價(jià)格影響最大的因素，并肯定了定量分析與定性分析的結(jié)果的一致性。 第四部分對(duì)商品房定價(jià)進(jìn)行數(shù)學(xué)建模，分別利用GM(1,1)模型、BP神經(jīng)網(wǎng)絡(luò)模型和灰色神經(jīng)網(wǎng)絡(luò)模型對(duì)我國(guó)商品房銷售價(jià)格進(jìn)行模擬與預(yù)測(cè)，通過模型模擬的檢驗(yàn)，確定了三種模型在我國(guó)商品房定價(jià)中的適用性。 第五部分為模型的比較與選擇，主要通過模型自身的優(yōu)缺點(diǎn)比較和與實(shí)際的結(jié)合來(lái)對(duì)模型進(jìn)行選擇，并作出了多元線性回歸模型適合于確定影響因素的分析，GM(1,1)模型適用于樣本量少的高精度的短期預(yù)測(cè)，BP神經(jīng)網(wǎng)絡(luò)適用于大樣本的中長(zhǎng)期預(yù)測(cè)，而灰色預(yù)測(cè)模型則適合于樣本量少的高精度的預(yù)測(cè)。 第六部分是對(duì)本文的總結(jié)和展望。
[Abstract]:Since the cessation of the system of real estate distribution in our country in 1998, the selling price of commercial housing in our country has been rising continuously. The price and sales of commercial housing are constantly regulated by the state, but the price of real estate is still rising rapidly.It has greatly affected the life of Chinese residents and the steady development of the whole national economy. The price of commercial housing has become a social and economic problem that has been widely concerned.The factors affecting the real estate price are complicated, and the analysis methods of the factors affecting the sale price of commercial housing are also varied. This paper intends to use the qualitative analysis and quantitative analysis to study the factors.The analysis of influencing factors is very important, and the pricing of real estate is paid more attention.There are many methods for real estate pricing at present. Aiming at the problem of influencing factors analysis and real estate pricing, this paper mainly adopts the multivariate linear regression model (GM1 / 1) to study the BP neural network model and the grey neural network model, respectively.The model is compared and selected.This article is divided into the following six parts.The first part is the introduction, which introduces the background and significance of this study, puts forward the problems, describes the research ideas and methods, and points out the important difficulties, innovations and shortcomings of this paper.The second part is a summary. On the basis of the research, analysis and comparison of some representative references, this paper introduces the current research situation of related issues at home and abroad.The third part has carried on the analysis to the real estate price influence factor, first carries on the qualitative analysis to the commodity house average sale price influence factor, mainly divides into the internal influence factor and the external influence factor.Internal factors include cost factors and marketing objectives, while external factors are analyzed from four major factors, namely, economic environmental factors, social environmental factors, administrative factors and other factors, and then on the basis of qualitative analysis,The 9 factors considered most relevant in this paper are selected for quantitative analysis of multivariate linear regression. By using Eviews6.0 software, the multivariate linear regression model is gradually regressed to eliminate the multiple collinearity between factors.Finally, it is determined that the cost of the completed house and the total population of the country are the most important factors affecting the average selling price of the commercial housing in China, and the consistency between the quantitative analysis and the qualitative analysis is confirmed.The fourth part carries on the mathematics modeling to the commercial housing pricing, respectively uses the GMX1) model and the grey neural network model to carry on the simulation and the forecast to our country commercial housing sale price, passes the model simulation test.The applicability of the three models in the pricing of commercial housing in China is determined.The fifth part is the comparison and selection of the model, mainly through the comparison of the advantages and disadvantages of the model itself and the combination of the actual model to select the model.The multivariate linear regression model is suitable for determining the influencing factors. The model is suitable for the short term prediction with small sample size and the BP neural network is suitable for the medium and long term prediction of large sample.The grey prediction model is suitable for high precision prediction with small sample size.The sixth part is the summary and prospect of this paper.
【學(xué)位授予單位】：武漢紡織大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F299.23;F224

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