本文關(guān)鍵詞： 房地產(chǎn)市場 風險評價 BP神經(jīng)網(wǎng)絡(luò)　出處：《湘潭大學》2014年碩士論文　論文類型：學位論文

【摘要】：在我國，，房地產(chǎn)業(yè)在國民經(jīng)濟中占有舉足輕重的地位，不僅關(guān)系著人民的生活水平和住房保障，同時也影響著每個地區(qū)乃至整個國家的經(jīng)濟體系穩(wěn)定程度。近些年來，由我國房地產(chǎn)市場出現(xiàn)的種種現(xiàn)象反應，我國房地產(chǎn)市場存在著不容忽視的風險。市場上也頻繁出現(xiàn)一些關(guān)于房地產(chǎn)泡沫過高，房地產(chǎn)風險過大的言論。然而房地產(chǎn)市場自身所具有的非線性等特征，使得對其風險程度的評價變得相對困難。本文正是在此背景之下，針對房地產(chǎn)的非線性特征，構(gòu)建了完整、科學的評價指標體系，通過神經(jīng)網(wǎng)絡(luò)模型來準確的評估我國房地產(chǎn)市場存在的風險程度。 本文根據(jù)房地產(chǎn)市場的發(fā)展度以及和諧度一共12個指標構(gòu)建了我國房地產(chǎn)市場的評價指標體系，并通過BP神經(jīng)網(wǎng)絡(luò)模型針對我國31個省及直轄市的2001年至2012年的房地產(chǎn)樣本數(shù)據(jù)進行建模并預測分析。借助了Matlab7.0中人工神經(jīng)網(wǎng)絡(luò)模塊，實現(xiàn)BP神經(jīng)網(wǎng)絡(luò)模型的建立，并經(jīng)過檢測，發(fā)現(xiàn)模型具有良好的泛化能力。最后利用建立的模型對我國31個省及直轄市2013年的房地產(chǎn)市場風險情況進行了預測分析，得出我國房地產(chǎn)風險程度及分布情況：（1）我國房地產(chǎn)市場整體風險較大，31個省及直轄市中只有5個處于風險較小的情況；（2）我國房地產(chǎn)市場風險從東部沿海發(fā)達地區(qū)由大到小向西部欠發(fā)達地區(qū)分布。最后，針對我國房地產(chǎn)市場風險狀況，從經(jīng)濟手段以及法律手段兩個方面給出了對策建議。 本文的主要創(chuàng)新在于：根據(jù)房地產(chǎn)市場的發(fā)展度和和諧度構(gòu)建了全面、合理的房地產(chǎn)市場風險評價指標體系；同時利用BP神經(jīng)網(wǎng)絡(luò)模型從整個國家層面分省市研究了我國房地產(chǎn)市場的風險程度及分布情況并做出了評價分析。
[Abstract]:In our country, the real estate industry plays an important role in the national economy, which not only concerns the people's living standard and housing security, but also affects the stability of the economic system in every region and even the whole country in recent years. As a result of various phenomena in the real estate market of our country, the real estate market in our country has some risks that can not be ignored. However, it is relatively difficult to evaluate the degree of risk of real estate market because of its nonlinear characteristics. In this context, this paper aims at the nonlinear characteristics of real estate. A complete and scientific evaluation index system is constructed, and the risk degree of real estate market in our country is evaluated accurately by neural network model. According to the development degree and harmony degree of the real estate market, this paper constructs the evaluation index system of China's real estate market. The BP neural network model is used to model and predict the real estate sample data from 2001 to 2012 in 31 provinces and municipalities of China. With the help of artificial neural network module in Matlab7.0, the establishment of BP neural network model is realized. After testing, it is found that the model has good generalization ability. Finally, the risk of real estate market in 31 provinces and municipalities in China in 2013 is forecasted and analyzed by using the established model. Get the real estate risk degree and distribution of our country: 1) our country real estate market overall risk is bigger, only 5 of 31 provinces and municipalities directly under the Central Government are in the condition of relatively small risk.) the real estate market risk of our country is from the developed area of the east coast. Distributed from large to small to underdeveloped areas in the west. Finally, In view of the situation of real estate market risk in China, the countermeasures and suggestions are put forward from two aspects: economic means and legal means. The main innovation of this paper lies in: according to the development degree and harmony degree of real estate market, a comprehensive and reasonable evaluation index system of real estate market risk is constructed; At the same time, BP neural network model is used to study the risk degree and distribution of real estate market in China from the whole national level.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP183;F299.23

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