本文選題：批量房產(chǎn)價(jià)格評估 + 多元回歸分析　； 參考：《蘭州大學(xué)》2013年碩士論文

【摘要】：隨著國家房產(chǎn)稅政策的不斷出臺和完善,中國的房產(chǎn)稅征收已經(jīng)進(jìn)入實(shí)質(zhì)性階段,而作為稅基的房產(chǎn)價(jià)格成為稅收的關(guān)鍵,采用目前人工的評估方法去完成整個(gè)城市的房產(chǎn)評估幾乎是不可能的,中國需要類似于西方國家CAMA(計(jì)算機(jī)輔助批量評估系統(tǒng))系統(tǒng)的批量評估系統(tǒng)。本文詳細(xì)介紹了CAMA系統(tǒng)的核心算法——多元回歸分析在批量房地產(chǎn)估價(jià)中的應(yīng)用,闡述了多元回分析歸算法在目前中國批量房地產(chǎn)估價(jià)中的優(yōu)劣性。同時(shí)本文提出的粒子群算法是對當(dāng)前中國批量房地產(chǎn)估價(jià)系統(tǒng)算法的探索,它可以有效避免目前數(shù)據(jù)資料不足的缺點(diǎn)完成評估工作,是CAMA系統(tǒng)建立之初的臨時(shí)性解決方案,與此同時(shí)它可以為CAMA系統(tǒng)積累原始數(shù)據(jù)資料,幫助盡快建立中國的CAMA系統(tǒng)。文章用MATLAB實(shí)現(xiàn)了此粒子群算法,對蘭州市的若干處房地產(chǎn)實(shí)例進(jìn)行評估,評估結(jié)果顯示粒子群算法能夠比較準(zhǔn)確的完成少量樣本數(shù)據(jù)條件下的房產(chǎn)評估。雖然此方法還存在一定的局限性,但是應(yīng)用時(shí)間序列分析技術(shù)后粒子群算法的局限性可以被大大減小。所以說,采用粒子群算法作為現(xiàn)階段的批量房產(chǎn)評估系統(tǒng)核心算法是切實(shí)可行的。
[Abstract]:With the continuous introduction and improvement of the national property tax policy, the collection of real estate tax in China has entered a substantial stage, and the price of real estate as the tax base has become the key to taxation. It is almost impossible to use the present artificial evaluation method to complete the real estate evaluation of the whole city. China needs a batch evaluation system similar to the CAMA (Computer-Aided batch Evaluation system) system in western countries. In this paper, the application of multivariate regression analysis, the core algorithm of CAMA system, in batch real estate evaluation is introduced in detail, and the advantages and disadvantages of multivariate return analysis algorithm in batch real estate evaluation in China are expounded. At the same time, the particle swarm optimization algorithm proposed in this paper is an exploration of the current batch real estate valuation system algorithm in China. It can effectively avoid the shortcomings of the current data shortage to complete the evaluation work. It is also a temporary solution to the CAMA system at the beginning of its establishment. At the same time, it can accumulate raw data for CAMA system and help establish CAMA system in China as soon as possible. In this paper, the particle swarm optimization (PSO) algorithm is implemented with MATLAB to evaluate some real estate cases in Lanzhou City. The results show that PSO algorithm can accurately complete the real estate evaluation under the condition of a small number of sample data. Although this method has some limitations, the limitation of PSO can be greatly reduced by using time series analysis. Therefore, it is feasible to use particle swarm optimization (PSO) as the core algorithm of batch real estate evaluation system.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;F299.23

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 龍啟云,詹長根,姜武漢;多元線性回歸模型在市場比較法中的應(yīng)用[J];國土資源科技管理;2003年06期

，

本文編號：2032004