城市教育配套對(duì)住宅價(jià)格的影響研究
發(fā)布時(shí)間：2018-08-20 14:52
【摘要】：隨著我國(guó)城市居民生活水平日益提高,人們的生活需求已經(jīng)超越衣食住行,追求教育等更高層次的精神需求。教育資源,尤其是公立教育機(jī)構(gòu)屬于地方公共品,往往供給有限,且受到地域限制。為讓子女得到優(yōu)質(zhì)教育資源,父母往往通過(guò)支付各類費(fèi)用以購(gòu)買(mǎi)受教育的機(jī)會(huì),其中通過(guò)購(gòu)買(mǎi)住房享受便利或優(yōu)質(zhì)的教育資源是最有常見(jiàn)且有效的途徑之一。隨著房地產(chǎn)市場(chǎng)研究的日漸深入,教育對(duì)住宅價(jià)格的影響機(jī)制也被逐步展現(xiàn)出來(lái)。雖然研究者在研究思路上有類似之處,但得到的結(jié)果卻呈現(xiàn)出很大的差異,研究的廣度和深度參差不齊。為進(jìn)一步準(zhǔn)確、細(xì)致、全面地呈現(xiàn)教育對(duì)住宅市場(chǎng)的影響方式,本文重點(diǎn)從教育變量細(xì)化和消除鄰里效應(yīng)兩方面入手,構(gòu)建特征價(jià)格模型和空間計(jì)量模型,分析不同類型教育配套對(duì)住宅價(jià)格的影響機(jī)制。本文的主要結(jié)論有： (1)影響學(xué)校資本化研究結(jié)果的因素較多,本文通過(guò)梳理既有文獻(xiàn)的研究進(jìn)展和研究思路,總結(jié)發(fā)現(xiàn)選取教育變量和消除鄰里效應(yīng)是本研究的兩大重點(diǎn)。當(dāng)前眾多國(guó)內(nèi)外文獻(xiàn)中的教育變量可以大致分為投入變量、產(chǎn)出變量、其他變量,本文對(duì)上述三類中常用變量進(jìn)行了詳細(xì)闡述和分析。鄰里效應(yīng)是影響學(xué)校資本化結(jié)果的主要障礙之一。本文將消除或降低鄰里效應(yīng)的主要方法總結(jié)為三類：空間計(jì)量模型、邊界固定效應(yīng)法、工具變量,并分別分析了三種方法的發(fā)展、使用及優(yōu)劣勢(shì)。 (2)根據(jù)不同受教育機(jī)會(huì)的獲取方式和量化多樣化的特點(diǎn),筆者選取了11個(gè)教育變量,組合構(gòu)建了12個(gè)模型。比較11個(gè)教育變量的實(shí)證結(jié)果發(fā)現(xiàn)幼兒園數(shù)目、小學(xué)質(zhì)量、初中質(zhì)量、鄰近高中和鄰近大學(xué)等5個(gè)變量組合得到最優(yōu)結(jié)果。實(shí)證結(jié)果證明教育配套對(duì)住宅價(jià)格的正效應(yīng)明顯,且消費(fèi)者在購(gòu)房時(shí)愿意為得到更好或更多的教育資源支付額外費(fèi)用。 (3)計(jì)算得到標(biāo)準(zhǔn)小區(qū)的教育特征的邊際價(jià)格,并通過(guò)模型中標(biāo)準(zhǔn)化回歸系數(shù)確定了各類教育配套的影響程度及其排序。例如,學(xué)區(qū)內(nèi)小學(xué)提高一個(gè)等級(jí),小區(qū)均價(jià)提高3.666%,小區(qū)均價(jià)上升770.97元／平方米。以標(biāo)準(zhǔn)化后的回歸系數(shù)為標(biāo)準(zhǔn)進(jìn)行比較,得到5個(gè)教育變量對(duì)住房?jī)r(jià)格影響程度,其中初中質(zhì)量和小學(xué)質(zhì)量排名靠比較前,幼兒園數(shù)目影響力居中下水平,鄰近高中和鄰近大學(xué)排名靠后。 (4)使用GeoDa軟件證實(shí)了杭州住宅市場(chǎng)的空間相關(guān)性,然后根據(jù)空間計(jì)量模型和杭州市實(shí)際情況,構(gòu)建空間滯后模型和空間誤差模型。結(jié)果顯示：空間模型擬合效果好于基本特征價(jià)格模型,尤以空間誤差模型更優(yōu)。模型中幾乎所有教育變量顯著,鄰里效應(yīng)或空間相關(guān)系引起的學(xué)校資本化率虛高在一定程度被去除,回歸系數(shù)稍減小。最后筆者定量測(cè)算了在消除空間相關(guān)性后,教育配套對(duì)在住宅市場(chǎng)的資本化程度。
[Abstract]:With the increasing improvement of living standards of urban residents in China, people's living needs have exceeded the needs of food, clothing, housing and transportation, and the pursuit of higher spiritual needs such as education. Educational resources, especially public educational institutions, which are local public goods, are often limited in supply and subject to geographical constraints. In order to give their children access to high-quality educational resources, parents often pay all kinds of fees to buy opportunities for education, and one of the most common and effective ways is to enjoy convenient or high-quality educational resources through the purchase of housing. With the deepening of real estate market research, the impact of education on housing prices has been gradually revealed. Although there are similarities in the research ideas, the results are very different, and the breadth and depth of the research are not uniform. In order to further accurately, meticulously and comprehensively present the influence of education on the housing market, this paper focuses on two aspects: education variable refinement and neighborhood effect elimination, and builds a feature price model and a spatial measurement model. This paper analyzes the influence mechanism of different types of education on housing price. The main conclusions of this paper are as follows: (1) there are many factors influencing the research results of school capitalization. It is found that the selection of educational variables and the elimination of neighborhood effect are the two main points of this study. At present, the educational variables in many domestic and foreign literature can be roughly divided into input variables, output variables and other variables. This paper describes and analyzes the three commonly used variables in detail. Neighborhood effect is one of the main obstacles to the result of school capitalization. In this paper, the main methods to eliminate or reduce the neighborhood effect are summarized into three categories: spatial metrology model, boundary fixed effect method, tool variable, and the development of the three methods are analyzed respectively. (2) according to the characteristics of obtaining different educational opportunities and quantitative diversification, the author selects 11 educational variables and constructs 12 models. The empirical results of 11 educational variables showed that the best results were obtained from the combination of 5 variables, such as the number of kindergartens, the quality of primary school, the quality of junior high school, the adjacent high school and the adjacent university. The empirical results show that the positive effect of educational matching on housing price is obvious, and consumers are willing to pay extra cost to get better or more educational resources. (3) calculate the marginal price of the educational characteristics of the standard residential area. Through the standardized regression coefficient in the model, the influence degree and ranking of all kinds of educational matching are determined. For example, the primary school in the school district raised one grade, the average price of the district increased by 3.666 yuan, the average price of the district increased by 770.97 yuan per square meter. With the standardized regression coefficient as the standard, the influence of five educational variables on the housing price was obtained. Among them, the junior middle school quality and primary school quality ranked first, and the number of kindergartens was at the middle and lower levels. (4) the spatial correlation of Hangzhou residential market is verified by using GeoDa software, and then the spatial lag model and spatial error model are constructed according to the spatial metrology model and the actual situation of Hangzhou. The results show that the fitting effect of the spatial model is better than that of the basic feature price model, especially the spatial error model. Almost all the educational variables in the model are significant, the virtual high of school capitalization rate caused by neighborhood effect or spatial relationship is removed to a certain extent, and the regression coefficient is slightly reduced. Finally, the author quantificationally calculates the degree of capitalization in housing market after eliminating spatial correlation.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F293.3;F224
本文編號(hào)：2194037
[Abstract]:With the increasing improvement of living standards of urban residents in China, people's living needs have exceeded the needs of food, clothing, housing and transportation, and the pursuit of higher spiritual needs such as education. Educational resources, especially public educational institutions, which are local public goods, are often limited in supply and subject to geographical constraints. In order to give their children access to high-quality educational resources, parents often pay all kinds of fees to buy opportunities for education, and one of the most common and effective ways is to enjoy convenient or high-quality educational resources through the purchase of housing. With the deepening of real estate market research, the impact of education on housing prices has been gradually revealed. Although there are similarities in the research ideas, the results are very different, and the breadth and depth of the research are not uniform. In order to further accurately, meticulously and comprehensively present the influence of education on the housing market, this paper focuses on two aspects: education variable refinement and neighborhood effect elimination, and builds a feature price model and a spatial measurement model. This paper analyzes the influence mechanism of different types of education on housing price. The main conclusions of this paper are as follows: (1) there are many factors influencing the research results of school capitalization. It is found that the selection of educational variables and the elimination of neighborhood effect are the two main points of this study. At present, the educational variables in many domestic and foreign literature can be roughly divided into input variables, output variables and other variables. This paper describes and analyzes the three commonly used variables in detail. Neighborhood effect is one of the main obstacles to the result of school capitalization. In this paper, the main methods to eliminate or reduce the neighborhood effect are summarized into three categories: spatial metrology model, boundary fixed effect method, tool variable, and the development of the three methods are analyzed respectively. (2) according to the characteristics of obtaining different educational opportunities and quantitative diversification, the author selects 11 educational variables and constructs 12 models. The empirical results of 11 educational variables showed that the best results were obtained from the combination of 5 variables, such as the number of kindergartens, the quality of primary school, the quality of junior high school, the adjacent high school and the adjacent university. The empirical results show that the positive effect of educational matching on housing price is obvious, and consumers are willing to pay extra cost to get better or more educational resources. (3) calculate the marginal price of the educational characteristics of the standard residential area. Through the standardized regression coefficient in the model, the influence degree and ranking of all kinds of educational matching are determined. For example, the primary school in the school district raised one grade, the average price of the district increased by 3.666 yuan, the average price of the district increased by 770.97 yuan per square meter. With the standardized regression coefficient as the standard, the influence of five educational variables on the housing price was obtained. Among them, the junior middle school quality and primary school quality ranked first, and the number of kindergartens was at the middle and lower levels. (4) the spatial correlation of Hangzhou residential market is verified by using GeoDa software, and then the spatial lag model and spatial error model are constructed according to the spatial metrology model and the actual situation of Hangzhou. The results show that the fitting effect of the spatial model is better than that of the basic feature price model, especially the spatial error model. Almost all the educational variables in the model are significant, the virtual high of school capitalization rate caused by neighborhood effect or spatial relationship is removed to a certain extent, and the regression coefficient is slightly reduced. Finally, the author quantificationally calculates the degree of capitalization in housing market after eliminating spatial correlation.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F293.3;F224
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 溫海珍,賈生華;住宅的特征與特征的價(jià)格——基于特征價(jià)格模型的分析[J];浙江大學(xué)學(xué)報(bào)(工學(xué)版);2004年10期
，本文編號(hào)：2194037
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