本文選題：鄭州市 + 住宅房產(chǎn)��； 參考：《石河子大學(xué)》2017年碩士論文

【摘要】：改革開放以來(lái)我國(guó)房地產(chǎn)行業(yè)快速發(fā)展,不僅是一線大都市房?jī)r(jià)居高不下,二線城市和三線城市的房產(chǎn)市場(chǎng)也是異�；鸨�2016年我國(guó)房地產(chǎn)市場(chǎng)的主格調(diào)是去庫(kù)存,央行多次降息來(lái)撬動(dòng)房地產(chǎn)市場(chǎng),消化已建的房產(chǎn)。鄭州市在這場(chǎng)房?jī)r(jià)的漲潮中異�；钴S,從2015年10月開始房?jī)r(jià)一直處于上升狀態(tài),截止到2016年12月份房?jī)r(jià)總體上升4.11%,在全國(guó)各市中上漲幅度排名第五,二手房交易量75243套,成交面積699.56萬(wàn)平方米。面對(duì)如此巨大的房產(chǎn)交易量,傳統(tǒng)的單項(xiàng)資產(chǎn)評(píng)估方法很難滿足需求,再加上呼之欲出的房產(chǎn)稅,將會(huì)使房產(chǎn)評(píng)估趨于常態(tài)化,定期評(píng)估繳稅將會(huì)帶動(dòng)更大的評(píng)估需求。本文所研究的特征價(jià)格模型是針對(duì)批量評(píng)估的一種方法,通過(guò)構(gòu)建房產(chǎn)特征變量與房產(chǎn)價(jià)格之間的方程可以批量化的評(píng)估,待評(píng)估房產(chǎn)基本信息輸入特征模型即可得到需要評(píng)估房產(chǎn)的價(jià)格。特征價(jià)格模型是以計(jì)量經(jīng)濟(jì)學(xué)為基礎(chǔ),不僅可以提高評(píng)估的效率,而且可以提高評(píng)估的準(zhǔn)確性,它克服了傳統(tǒng)評(píng)估的主觀性。但目前學(xué)者對(duì)特征價(jià)格模型的研究主要集中在一線大城市,對(duì)二三線中型城市研究較少。本文以鄭州市為例研究特征價(jià)格模型對(duì)二三線城市的適用性,并分析影響鄭州市住宅房產(chǎn)價(jià)格的主要因素。本文首先分析了鄭州市住宅房地產(chǎn)市場(chǎng)的現(xiàn)狀,并通過(guò)查閱文獻(xiàn)實(shí)地調(diào)研等方式初步確定影響鄭州市住宅房地產(chǎn)價(jià)格的因素,然后運(yùn)用特征分析法將初步確定的特征因素分類、整理、量化。文章主要通過(guò)線性回歸的方式將房產(chǎn)價(jià)格作為因變量,將影響房產(chǎn)價(jià)格的特征因素作為自變量進(jìn)行回歸分析,建立特征因素與房產(chǎn)價(jià)格之間的函數(shù)關(guān)系。在分析了鄭州市住宅分布以后,筆者一共抽取鄭州市100個(gè)小區(qū),700套房產(chǎn)信息進(jìn)行回歸分析。文中共采用了三種函數(shù)形式建立回歸模型,通過(guò)比較三種函數(shù)模型的檢驗(yàn)值,確定適合鄭州市具體情況的函數(shù)方程即要建立的特征價(jià)格模型。為實(shí)際檢驗(yàn)所建特征價(jià)格模型的適用性,筆者抽取50套住宅信息帶入模型評(píng)估出價(jià)格與房產(chǎn)真實(shí)價(jià)格作對(duì)比,以驗(yàn)證模型的準(zhǔn)確性。通過(guò)對(duì)特征方程的分析得出影響鄭州市住宅房產(chǎn)價(jià)格的主要因素,并系統(tǒng)的總結(jié)在運(yùn)用這一方法時(shí)所要注意事項(xiàng)。本文以鄭州市為例對(duì)特征價(jià)格模型進(jìn)行研究得出如下相關(guān)結(jié)論,第一,特征價(jià)格估價(jià)法不僅可以用于一線城市的房產(chǎn)估價(jià),對(duì)二三線城市也有較強(qiáng)的適用性。第二,最終確定了三種函數(shù)形式中對(duì)數(shù)函數(shù)模型擬合度最好,解釋效果,可以作為房產(chǎn)評(píng)估的特征方程。第三,本文通過(guò)回歸分析建立了鄭州市房產(chǎn)價(jià)格分析體系,最終確定了距離商圈的距離、文體設(shè)施、學(xué)區(qū)房、臨近公園綠地、樓層、朝向、教育設(shè)施配套、臨近大學(xué)為影響房?jī)r(jià)的關(guān)鍵因素。同時(shí)文中為房產(chǎn)評(píng)估機(jī)構(gòu)提供新的評(píng)估方法以及此方法在評(píng)估應(yīng)用中的一些建議。
[Abstract]:Since the reform and opening up, the real estate industry in China has developed rapidly, not only the housing prices in first-tier cities remain high, but also the real estate markets in second-tier cities and third-tier cities are extremely hot. The main theme of the real estate market in China in 2016 is to go to inventory. The central bank has repeatedly cut interest rates to leverage the real estate market, digesting already built real estate. Zhengzhou has been extremely active in this upsurge in house prices, which has been on the rise since October 2015. By December 2016, house prices had risen by 4.11 overall, ranking fifth in all cities in the country, with 75243 second-hand housing transactions. The transaction area is 6.9956 million square meters. In the face of such a huge real estate transaction volume, the traditional method of single asset evaluation is difficult to meet the demand. In addition, the property tax will make the property evaluation become more and more regular, and the periodic assessment tax will lead to greater assessment needs. The characteristic price model studied in this paper is a method for batch evaluation, which can be evaluated in batches by constructing the equation between the real estate characteristic variables and the real estate price. The price of the property needed to be evaluated can be obtained by the input feature model of the basic information of the property to be evaluated. The characteristic price model is based on econometrics, which can not only improve the efficiency of evaluation, but also improve the accuracy of evaluation. It overcomes the subjectivity of traditional evaluation. But at present, the research on the characteristic price model is mainly focused on the first tier cities, and few on the second and third line medium-sized cities. This paper takes Zhengzhou as an example to study the applicability of the characteristic price model to the second and third tier cities, and analyzes the main factors affecting the real estate prices in Zhengzhou. This paper first analyzes the present situation of Zhengzhou residential real estate market, and preliminarily determines the factors that affect the price of Zhengzhou residential real estate by consulting the literature on the spot, and then classifies the initially determined characteristic factors by means of characteristic analysis. Sort, quantify. In this paper, the property price is regarded as the dependent variable by linear regression, and the characteristic factors which affect the real estate price are taken as independent variables to make regression analysis, and the functional relationship between the characteristic factors and the real estate price is established. After analyzing the residential distribution in Zhengzhou, the author extracts 700 sets of real estate information from 100 residential areas in Zhengzhou for regression analysis. In this paper, regression models are established in three functional forms. By comparing the test values of the three functional models, the characteristic price model, which is suitable for the specific conditions of Zhengzhou City, is determined. In order to verify the applicability of the characteristic price model, the author takes 50 sets of housing information into the model to evaluate the price and the real estate price, so as to verify the accuracy of the model. Through the analysis of the characteristic equation, the main factors influencing the real estate price in Zhengzhou are obtained, and the matters needing attention in the application of this method are systematically summarized. This paper takes Zhengzhou as an example to study the characteristic price model and draws the following conclusions: first, the method of feature price valuation can not only be used in the property evaluation of first-tier cities, but also has a strong applicability to the second-third-tier cities. Secondly, it is determined that the logarithmic function model is the best in the three function forms, which can be used as the characteristic equation of real estate evaluation. Thirdly, this paper establishes the real estate price analysis system of Zhengzhou by regression analysis, and finally determines the distance from the commercial area, cultural and cultural facilities, school district house, adjacent park green space, floor, orientation, and educational facilities. Nearby universities are key factors affecting housing prices. At the same time, this paper provides a new evaluation method for the real estate appraisal organization and some suggestions in the application of this method.
【學(xué)位授予單位】：石河子大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F299.23

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王娟娟;毛博;;基于特征價(jià)格模型(HPM)的房地產(chǎn)評(píng)估研究綜述[J];行政事業(yè)資產(chǎn)與財(cái)務(wù);2016年01期

2 簡(jiǎn)迎輝;徐麗;;房地產(chǎn)項(xiàng)目定價(jià)與感知風(fēng)險(xiǎn)的關(guān)系研究[J];武漢理工大學(xué)學(xué)報(bào)(信息與管理工程版);2015年06期

3 張少博;楊英俊;趙文義;張紹陽(yáng);焦紅紅;;城市出租汽車特征價(jià)格定價(jià)模型[J];長(zhǎng)安大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年04期

4 高菁菁;常峰;;特征價(jià)格模型在藥品定價(jià)中的應(yīng)用[J];衛(wèi)生經(jīng)濟(jì)研究;2011年12期

5 顧光同;王江;高麗;;筆記本電腦特征價(jià)格指數(shù)的實(shí)證研究——基于非線性Hedonic模型實(shí)證分析[J];統(tǒng)計(jì)教育;2010年01期

6 栗向鑫;周明;李庚銀;;特征價(jià)格模型在電能定價(jià)中的應(yīng)用[J];電網(wǎng)技術(shù);2008年19期

7 高建;周麗萍;;基于Box-Cox變換的住宅特征價(jià)格理論研究[J];河北科技大學(xué)學(xué)報(bào);2007年03期

8 黃桐城,楊健;模糊評(píng)判在住宅價(jià)格評(píng)估中的應(yīng)用[J];系統(tǒng)工程理論方法應(yīng)用;2000年03期

9 張宏斌,賈生華;編制城市房地產(chǎn)價(jià)格指數(shù)的理論模型和實(shí)用方法[J];中國(guó)軟科學(xué);2000年04期

10 蔣一軍，襲江輝;房地產(chǎn)價(jià)格指數(shù)與Hedonic模型[J];中國(guó)資產(chǎn)評(píng)估;1996年03期

相關(guān)博士學(xué)位論文 前1條

1 王旭育;基于Hedonic模型的上海住宅特征價(jià)格研究[D];同濟(jì)大學(xué);2006年

相關(guān)碩士學(xué)位論文 前10條

1 周林林;基于特征價(jià)格模型的城市住宅用地價(jià)格影響因素研究[D];華東師范大學(xué);2015年

2 朱晨明;房產(chǎn)稅稅基批量評(píng)估中特征價(jià)格模型的應(yīng)用研究[D];河北大學(xué);2014年

3 張圣甫;基于特征價(jià)格模型的上海小區(qū)房?jī)r(jià)研究[D];復(fù)旦大學(xué);2014年

4 田曉彬;基于HPM的西安曲江新區(qū)住宅特征價(jià)格分析[D];西安外國(guó)語(yǔ)大學(xué);2013年

5 李旭寧;城市景觀對(duì)住宅價(jià)格的影響[D];浙江大學(xué);2013年

6 羅希;房地產(chǎn)特征價(jià)格理論模型研究[D];華中科技大學(xué);2013年

7 白鈺;房地產(chǎn)評(píng)估方法改進(jìn)研究[D];西南財(cái)經(jīng)大學(xué);2011年

8 張琪;基于Hedonic模型的深圳地鐵對(duì)周邊住宅價(jià)格影響的實(shí)證研究[D];哈爾濱工業(yè)大學(xué);2010年

9 陳秉賢;基于Hedonic模型的廣州住宅特征價(jià)格研究[D];暨南大學(xué);2009年

10 張冕;基于Hedonic模型的浦東新區(qū)住宅價(jià)格特征分析[D];同濟(jì)大學(xué);2008年

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