【摘要】：房地產(chǎn)業(yè)作為我國(guó)國(guó)民經(jīng)濟(jì)的支柱產(chǎn)業(yè)，具有資金密集性、投資回報(bào)期長(zhǎng)、高利潤(rùn)、高風(fēng)險(xiǎn)等特點(diǎn)。近年來房地產(chǎn)行業(yè)的飛速發(fā)展使商業(yè)銀行對(duì)房地產(chǎn)開發(fā)企業(yè)的貸款比重不斷增加，同時(shí)也加大了風(fēng)險(xiǎn)。因此，為降低銀行的不良貸款率，保證銀行資產(chǎn)的優(yōu)良程度，對(duì)貸款的房地產(chǎn)企業(yè)進(jìn)行有效的信貸風(fēng)險(xiǎn)評(píng)價(jià)是十分有必要的。 本文站在商業(yè)銀行的角度，在明確房地產(chǎn)信貸風(fēng)險(xiǎn)相關(guān)概念，總結(jié)信貸風(fēng)險(xiǎn)評(píng)價(jià)方法的基礎(chǔ)上，結(jié)合房地產(chǎn)開發(fā)企業(yè)的特點(diǎn)，從房地產(chǎn)信貸風(fēng)險(xiǎn)產(chǎn)生的機(jī)理著手，找出影響信貸風(fēng)險(xiǎn)的宏觀因素及微觀因素。著重分析了宏觀因素與房地產(chǎn)整體違約率的數(shù)量關(guān)系。將房地產(chǎn)背景實(shí)力和影響房地產(chǎn)行業(yè)的宏觀因子等引入信貸風(fēng)險(xiǎn)評(píng)價(jià)體系，初步建立了含有29項(xiàng)指標(biāo)的評(píng)價(jià)體系，并運(yùn)用灰色關(guān)聯(lián)分析法對(duì)指標(biāo)體系進(jìn)行約簡(jiǎn)，達(dá)到降維目的的同時(shí)也分析了這些指標(biāo)對(duì)評(píng)價(jià)結(jié)果的影響程度，最終選取20個(gè)指標(biāo)對(duì)房地產(chǎn)公司信貸風(fēng)險(xiǎn)進(jìn)行評(píng)價(jià)。 在對(duì)信貸風(fēng)險(xiǎn)的評(píng)價(jià)上，本文建立了支持向量機(jī)分類模型，用四種不同的核函數(shù)訓(xùn)練樣本，運(yùn)用網(wǎng)格搜索法和交叉驗(yàn)證法進(jìn)行核參數(shù)尋優(yōu)，建立支持向量機(jī)，，再用得到的不同決策模型對(duì)測(cè)試樣本進(jìn)行測(cè)試，比較四種核函數(shù)的分類結(jié)果發(fā)現(xiàn)，徑向基核函數(shù)的分類準(zhǔn)確率最高，建立模型難度也較低，性能最優(yōu)。 通過與單一SVM方法、Logistic回歸分析方法進(jìn)行比較，結(jié)果表明基于本文提出的GRA-SVM方法的分類準(zhǔn)確性和推廣能力明顯好于其它幾種方法，證實(shí)了該方法的有效性和可行性，為商業(yè)銀行銀行建立可靠的房地產(chǎn)公司信貸風(fēng)險(xiǎn)評(píng)價(jià)系統(tǒng)提供了依據(jù)。
[Abstract]:As the pillar industry of our national economy, the real estate industry has the characteristics of capital intensity, long return period of investment, high profit, high risk and so on. In recent years, with the rapid development of real estate industry, commercial banks have increased the proportion of loans to real estate development enterprises, but also increased the risk. Therefore, in order to reduce the non-performing loan ratio of banks and ensure the excellent degree of bank assets, it is very necessary to evaluate the credit risk of real estate enterprises. This paper stands from the point of view of commercial banks, on the basis of defining the related concepts of real estate credit risk, summing up the credit risk evaluation methods, combining with the characteristics of real estate development enterprises, starting from the mechanism of real estate credit risk. Find out the macro and micro factors that affect the credit risk. This paper focuses on the quantitative relationship between macro factors and the overall default rate of real estate. The background strength of real estate and the macro factors affecting the real estate industry are introduced into the credit risk evaluation system, and the evaluation system containing 29 indexes is initially established, and the index system is reduced by grey relational analysis. At the same time, the influence of these indexes on the evaluation results is analyzed, and 20 indexes are selected to evaluate the credit risk of real estate companies. In the evaluation of credit risk, this paper establishes a support vector machine classification model, uses four different kernel functions to train samples, uses grid search method and cross verification method to optimize the kernel parameters, and establishes the support vector machine. Then the test samples are tested with different decision models. The classification results of the four kernel functions show that the classification accuracy of the radial basis kernel function is the highest, the difficulty of establishing the model is low, and the performance is the best. Compared with the single SVM method and Logistic regression analysis method, the results show that the classification accuracy and generalization ability of the GRA-SVM method proposed in this paper are obviously better than those of other methods, and the effectiveness and feasibility of the method are verified. It provides a basis for commercial banks to establish a reliable credit risk assessment system for real estate companies.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.45;F299.23;F224

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