本文選題：房地產(chǎn)市場(chǎng)　切入點(diǎn)：住房成交量　出處：《重慶大學(xué)》2014年博士論文

【摘要】：近年來，房地產(chǎn)行業(yè)的迅速蓬勃發(fā)展是大家有目共睹的。而城市商品住房作為其重要組成部分的也呈現(xiàn)了穩(wěn)步增長(zhǎng)的趨勢(shì)。在住房產(chǎn)業(yè)快速發(fā)展的過程中，住房?jī)r(jià)格作為重要的經(jīng)濟(jì)杠桿對(duì)住房產(chǎn)業(yè)化與商品化起著重要的推動(dòng)作用，住房?jī)r(jià)格也成為政府、居民和廣大房地產(chǎn)開發(fā)商普遍關(guān)注的焦點(diǎn)。尤其是近年來，持續(xù)走高的城市住房?jī)r(jià)格給城市居民以及整個(gè)社會(huì)的經(jīng)濟(jì)發(fā)展都帶來了很大的負(fù)面影響。與此同時(shí)，政府宏觀調(diào)控措施雖然取得了一定的成效，但是效果不甚明顯。基于此，本文將展開對(duì)城市住房?jī)r(jià)格預(yù)測(cè)模型的研究。通過建立價(jià)格預(yù)測(cè)模型，可以掌握房地產(chǎn)價(jià)格走勢(shì)，合理準(zhǔn)確評(píng)估預(yù)測(cè)房?jī)r(jià)，進(jìn)而可以對(duì)房地產(chǎn)市場(chǎng)的發(fā)展展開分析，這對(duì)確保我國(guó)住房市場(chǎng)穩(wěn)定健康發(fā)展有著重要的作用。本文的主要研究?jī)?nèi)容如下： ①本文首先梳理了國(guó)內(nèi)外關(guān)于住房?jī)r(jià)格預(yù)測(cè)的相關(guān)研究，并基于已有的研究提出了基于粒子群算法的最小二乘支持向量機(jī)（PSO-LSSVR模型）的住房?jī)r(jià)格預(yù)測(cè)方法。最小二乘支持向量機(jī)在建模數(shù)據(jù)過程中能很好彌補(bǔ)人工神經(jīng)網(wǎng)絡(luò)模型和支持向量機(jī)的諸多不足，而且粒子群算法能迅速快捷地對(duì)參數(shù)進(jìn)行優(yōu)化，具有精度高、速度快等優(yōu)點(diǎn)。 ②通過確定房地產(chǎn)度量指標(biāo)體系和等級(jí)劃分標(biāo)準(zhǔn)，詳細(xì)介紹城市住房?jī)r(jià)格PSO-LSSVR預(yù)測(cè)模型的運(yùn)作流程。并介紹基于PSO-LSSVR模型與模糊灰色理論的房地產(chǎn)市場(chǎng)預(yù)測(cè)系統(tǒng)架構(gòu)，通過預(yù)測(cè)系統(tǒng)構(gòu)架可以使預(yù)測(cè)模型更好的發(fā)揮作用。 ③以北京市為例展開預(yù)測(cè)模型的實(shí)證分析。通過構(gòu)建相應(yīng)的PSO-LSSVR住房?jī)r(jià)格和成交量預(yù)測(cè)模型，對(duì)北京市房地產(chǎn)市場(chǎng)發(fā)展健康狀況進(jìn)行評(píng)估。實(shí)證分析表明基于粒子群優(yōu)化最小二乘支持向量回歸預(yù)測(cè)模型優(yōu)于傳統(tǒng)的支持向量回歸模型，也證明了PSO-LSSVR預(yù)測(cè)模型用于城市住房?jī)r(jià)格預(yù)測(cè)的有效性。 ④在住房?jī)r(jià)格、住房成交量的PSO-LSSVR預(yù)測(cè)模型和基于模糊灰色理論的房地產(chǎn)市場(chǎng)評(píng)估模型基礎(chǔ)上給出了整個(gè)房地產(chǎn)市場(chǎng)預(yù)測(cè)系統(tǒng)的實(shí)現(xiàn)過程，并詳細(xì)介紹系統(tǒng)的軟件實(shí)現(xiàn)過程。 ⑤總結(jié)本研究的研究成果并對(duì)未來的研究提出建議。 本文提出城市住房?jī)r(jià)格PSO-LSSVR預(yù)測(cè)模型，并結(jié)合模糊灰色理論提出一整套房地產(chǎn)市場(chǎng)預(yù)測(cè)與評(píng)估方法，對(duì)于房地產(chǎn)市場(chǎng)預(yù)測(cè)與評(píng)估有著非常重要的價(jià)值。通過該研究既可以為各國(guó)家及城市政府管理部門制定房地產(chǎn)調(diào)控政策，保證房地產(chǎn)經(jīng)濟(jì)健康、持續(xù)、穩(wěn)定的發(fā)展提供重要的手段和決策依據(jù)，，可以為企業(yè)的投資決策提供更多的幫助；也可以更進(jìn)一步完善我國(guó)房地產(chǎn)研究的理論系統(tǒng)，具有重要的理論意義和實(shí)踐意義。
[Abstract]:In recent years, the rapid and vigorous development of the real estate industry is obvious to all.As an important part of urban commercial housing, it also shows a steady growth trend.In the process of rapid development of housing industry, housing price, as an important economic lever, plays an important role in promoting housing industrialization and commercialization. Housing price has become the focus of the government, residents and real estate developers.Especially in recent years, rising urban housing prices have brought great negative effects to urban residents and the economic development of the whole society.At the same time, although the government macro-control measures have achieved some results, but the effect is not obvious.Based on this, this paper will carry out a study on the urban housing price prediction model.Through the establishment of price forecasting model, we can grasp the trend of real estate price, reasonably and accurately evaluate and forecast the housing price, and then analyze the development of real estate market, which plays an important role in ensuring the stable and healthy development of our country's housing market.The main contents of this paper are as follows:Firstly, this paper reviews the research on housing price prediction at home and abroad, and proposes a Particle Swarm Optimization (PSO) based LS-LSSVR model for housing price prediction.The least square support vector machine (LS-SVM) can make up for many shortcomings of artificial neural network model and support vector machine in the process of modeling data, and particle swarm optimization algorithm can quickly and quickly optimize the parameters, which has the advantages of high precision and fast speed.(2) by determining the real estate measurement index system and grade classification standard, the operation process of urban housing price PSO-LSSVR forecasting model is introduced in detail.The structure of real estate market forecasting system based on PSO-LSSVR model and fuzzy grey theory is introduced.3 taking Beijing as an example, the empirical analysis of forecasting model is carried out.By constructing the corresponding PSO-LSSVR housing price and volume forecasting model, this paper evaluates the health of the real estate market in Beijing.The empirical analysis shows that the least square support vector regression forecasting model based on particle swarm optimization is superior to the traditional support vector regression model, and it also proves the validity of PSO-LSSVR forecasting model in urban housing price forecasting.4. Based on the PSO-LSSVR forecasting model of housing price, housing transaction volume and the real estate market evaluation model based on fuzzy grey theory, the realization process of the whole real estate market forecasting system is given, and the software realization process of the system is introduced in detail.5 summarize the research results of this study and put forward suggestions for future research.This paper puts forward the PSO-LSSVR forecasting model of urban housing price, and puts forward a set of real estate market forecasting and evaluation methods combined with fuzzy grey theory, which has very important value for real estate market prediction and evaluation.Through the research, it can provide important means and decision basis for the governments of various countries and cities to formulate the real estate regulation and control policies and ensure the healthy, sustainable and stable development of the real estate economy.It can provide more help for the investment decision of enterprises and perfect the theoretical system of real estate research in our country, which has important theoretical and practical significance.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：F299.23

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