本文關(guān)鍵詞： 城市軌道交通 住宅房產(chǎn) 價(jià)格預(yù)測(cè) BP神經(jīng)網(wǎng)絡(luò) 馬爾可夫鏈　出處：《北京交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：我國(guó)正處于城市化加速發(fā)展階段和社會(huì)經(jīng)濟(jì)的轉(zhuǎn)型時(shí)期,經(jīng)濟(jì)的快速增長(zhǎng)加快了城市交通的發(fā)展速度。城市軌道交通已經(jīng)成為我國(guó)解決城市交通問(wèn)題的長(zhǎng)期發(fā)展戰(zhàn)略,在各大城市掀起了建設(shè)浪潮。城市軌道交通對(duì)沿線土地利用性質(zhì)的影響使得其沿線范圍服務(wù)影響區(qū)域內(nèi)住宅、商業(yè)用地等的需求量猛增,提高了土地開(kāi)發(fā)強(qiáng)度。這些改變使得城市軌道交通沿線房地產(chǎn)的增值潛力大大提高。 目前,大多數(shù)針對(duì)城市軌道交通沿線房地產(chǎn)價(jià)格的定量研究采用的基本都是交通成本模型和特征價(jià)格模型。然而,由于模型建立所需的大量基礎(chǔ)數(shù)據(jù)可得性較差,使用這些模型研究我國(guó)城市軌道交通沿線影響范圍內(nèi)的房地產(chǎn)價(jià)格具有一定局限性。因此,在基礎(chǔ)數(shù)據(jù)有限的情況下,本文結(jié)合多元統(tǒng)計(jì)分析方法,使用BP神經(jīng)網(wǎng)絡(luò)和馬爾可夫鏈建立了城市軌道交通沿線住宅房產(chǎn)價(jià)格預(yù)測(cè)模型。 本文通過(guò)研究城市軌道交通和土地利用之間的關(guān)系,結(jié)合大量研究學(xué)者的研究成果,分析了影響城市軌道交通沿線住宅房產(chǎn)價(jià)格的因素,認(rèn)為影響城市軌道交通沿線住宅房產(chǎn)價(jià)格的因素主要包括區(qū)位因素、結(jié)構(gòu)因素和鄰里因素,并從這三類因素中選取了城市軌道交通沿線住宅房產(chǎn)價(jià)格的影響指標(biāo),并對(duì)影響指標(biāo)進(jìn)行了因子分析,將其簡(jiǎn)化為6個(gè)共性因子作為BP神經(jīng)網(wǎng)絡(luò)的輸入層神經(jīng)元。隨后,本文利用BP神經(jīng)網(wǎng)絡(luò)的理論構(gòu)造了城市軌道交通沿線住宅房產(chǎn)價(jià)格預(yù)測(cè)模型的BP神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),并建立了基于馬爾可夫鏈的修正模型,以提高預(yù)測(cè)精度。 最后,本文運(yùn)用建立的土地資源價(jià)值預(yù)測(cè)模型對(duì)10處住宅房產(chǎn)的樣本數(shù)據(jù)進(jìn)行了分析,預(yù)測(cè)了這些樣本的土地價(jià)值。經(jīng)過(guò)實(shí)際價(jià)值與模型的預(yù)測(cè)結(jié)果比對(duì),驗(yàn)證了該模型能夠有效的預(yù)測(cè)城市軌道沿線住宅的價(jià)格,從而為解決URRT規(guī)劃開(kāi)發(fā)部門(mén)部分資金短缺、市民盲目選購(gòu)房產(chǎn)、跟風(fēng)投資,房地產(chǎn)商缺乏對(duì)房地產(chǎn)開(kāi)發(fā)項(xiàng)目獲利能力了解等問(wèn)題提供有效的科學(xué)依據(jù)。圖8幅,表17個(gè),參考文獻(xiàn)54篇。
[Abstract]:China is in the period of accelerating urbanization and social and economic transformation. The rapid growth of economy has accelerated the development of urban transportation. Urban rail transit has become a long-term development strategy to solve urban traffic problems in China. The impact of urban rail transit on the nature of land use along the route makes the demand for residential and commercial land increase rapidly. These changes have greatly increased the value added potential of real estate along urban rail transit. At present, most of the quantitative research on real estate prices along the urban rail transit line is based on the transport cost model and characteristic price model. Because of the poor availability of a large number of basic data needed to establish the model, it is limited to use these models to study the real estate price along the influence range of urban rail transit in China. In the case of limited basic data, this paper uses BP neural network and Markov chain to establish the housing property price prediction model along the urban rail transit line combined with the multivariate statistical analysis method. Through the study of the relationship between urban rail transit and land use, combined with the research results of a large number of scholars, this paper analyzes the factors that affect the housing prices along the urban rail transit line. It is considered that the main factors influencing the residential property prices along the urban rail transit line include location factors, structural factors and neighborhood factors. And from these three factors selected the urban rail transit along the residential property price impact indicators and factors analysis of the impact indicators. It is simplified to six common factors as input layer neurons of BP neural network. In this paper, the BP neural network structure of residential property price prediction model along urban rail transit is constructed by using the theory of BP neural network, and the modified model based on Markov chain is established to improve the prediction accuracy. Finally, this paper uses the land resource value prediction model to analyze the sample data of 10 residential properties, and forecasts the land value of these samples. The actual value is compared with the forecast results of the model. It is verified that the model can effectively predict the housing prices along the urban track, so as to solve the URRT planning and development department part of the shortage of funds, citizens blindly buy real estate, follow the trend of investment. Real estate developers lack effective scientific evidence to understand the profitability of real estate development projects. There are 8 figures, 17 tables and 54 references.
【學(xué)位授予單位】：北京交通大學(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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