本文選題：商品住宅價(jià)格 + 影響因素��； 參考：《昆明理工大學(xué)》2014年碩士論文

【摘要】：房地產(chǎn)市場最直觀的體現(xiàn)就是市場價(jià)格,不同因素、不同地區(qū)和不同市場對房價(jià)影響會有很大的不同,尤其住宅價(jià)格與人民生活息息相關(guān)。近些年來,房價(jià)的不斷上漲更是成為人們關(guān)注的焦點(diǎn),學(xué)術(shù)界也較多選取商品住宅銷售均價(jià)作為代表商品住房市場發(fā)展程度的風(fēng)向標(biāo)。 研究結(jié)合昆明市的房地產(chǎn)市場現(xiàn)狀,從眾多影響房價(jià)的因素中選取了13個(gè)指標(biāo)作為該市房價(jià)的代表因素,通過建立灰色關(guān)聯(lián)度模型對盡可能多的因素進(jìn)行篩選和分析,并輔助MATLAB編程計(jì)算出各個(gè)因素與房價(jià)的絕對和相對關(guān)聯(lián)度值,最終綜合考慮確定出每個(gè)因素的綜合關(guān)聯(lián)度,進(jìn)而判斷其影響大小并排序；依據(jù)綜合關(guān)聯(lián)度排序的結(jié)果,從中選出綜合關(guān)聯(lián)系數(shù)較大的前七個(gè)因素與昆明市房價(jià)建立GM(1,8)模型,利用模型對2005-2011年昆明市房價(jià)做出預(yù)測,并與實(shí)際值進(jìn)行殘差檢驗(yàn)和后驗(yàn)差檢驗(yàn),證實(shí)預(yù)測誤差較小。 在后期預(yù)測應(yīng)用時(shí),雖然各個(gè)指標(biāo)的預(yù)測值是由經(jīng)改進(jìn)的GM(1,1,x(0))預(yù)測獲得,但其累積誤差不容忽視,為更大程度的提高預(yù)測的準(zhǔn)確性,隨后對構(gòu)建的GM模型進(jìn)行改進(jìn)：調(diào)用MATLAB的多項(xiàng)式曲線擬合對各指標(biāo)進(jìn)行預(yù)測,借助BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練和修正其指標(biāo)預(yù)測值的結(jié)果,將修正之后的預(yù)測值重新代入構(gòu)建的GM模型,這在很大程度上控制了偏差,得出了更具參考價(jià)值和說服力的結(jié)論,也彌補(bǔ)了影響房價(jià)變化的多個(gè)經(jīng)濟(jì)變量之間的定量關(guān)系無法用精確的數(shù)學(xué)表達(dá)式來描述的不足。最后,對昆明市商品住宅價(jià)格進(jìn)行了短期預(yù)測,并依據(jù)未來市場波動的不同情形分別予以闡述,結(jié)論部分客觀的對昆明市區(qū)域房價(jià)及影響因素做了分析,從宏觀調(diào)控層面給出了政策建議,此研究為昆明市的投資決策和房地產(chǎn)市場發(fā)展提供了一定的指導(dǎo)和借鑒意義。
[Abstract]:The most intuitive embodiment of the real estate market is the market price, different factors, different regions and different markets will have a very different impact on house prices, especially housing prices and people's lives are closely linked.In recent years, the rising of house prices has become the focus of attention, and the academic circles also choose the average price of commercial housing as the vane to represent the development of commodity housing market.According to the present situation of real estate market in Kunming, 13 indexes are selected as the representative factors of housing price from many factors, and the grey correlation model is established to screen and analyze as many factors as possible.The absolute and relative correlation degree between each factor and house price is calculated by assistant MATLAB programming. Finally, the comprehensive correlation degree of each factor is determined synthetically, and then the influence is judged and sorted, and according to the result of comprehensive correlation degree ranking,The first seven factors with large comprehensive correlation coefficient and the housing price in Kunming are selected to establish GM1 / 8) model. The model is used to predict the housing price in Kunming from 2005 to 2011, and the residual error test and posterior error test are carried out with the actual value, which proves that the prediction error is small.In the application of later prediction, although the prediction value of each index is obtained by the improved GM1 / 1X / 0) prediction, the cumulative error can not be ignored, so as to improve the accuracy of the prediction to a greater extent.Then the GM model is improved: the polynomial curve fitting of MATLAB is used to predict each index, and the modified GM model is replaced by BP neural network to train and revise the predicted value of the index.To a great extent, the deviation is controlled, and the conclusion of more reference value and persuasion is drawn, which also makes up for the deficiency that the quantitative relationship between several economic variables that affect the change of house price cannot be described by precise mathematical expression.Finally, the short-term forecast of commodity housing price in Kunming is given, and the different situations of future market fluctuation are expounded respectively. The conclusion is that the regional housing price and its influencing factors in Kunming are analyzed objectively.The policy suggestions are given from the aspect of macro-control, which provides some guidance and reference for the investment decision and the development of real estate market in Kunming.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F299.23

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