The Sdudy Of Impact Of Educational Facilities On Housing Price

With the urban residents in China having the increasing standard of living, the demand for people has been beyond the basic necessities of life, and pursuing a higher level of spiritual needs, such as education. Educational resources, especially public education institutions which are local public goods, are often limited and subject to geographical restrictions. For their children to get a high quality education resources, parents often pay all kinds of costs to purchase educational opportunities, and purchase of housing to enjoy the convenience or quality education resources is the most common and effective ways. With the depth of the real estate market research, the mechanism of the impact of education on housing prices is gradually unfolded. Although researchers having similar research ideas, the result preached a great difference in the breadth and depth. For further accurate, detailed and comprehensive presentation of the impact of education on the residential market, this paper focuses on refining education variable and eliminating the neighborhood effects, construct hedonic price model and spatial econometric model to analyze the impact mechanism of different types of educational facilities on residential prices. The main conclusions are:(1) As many factors affect the capitalization of school, by combing the research progress and research ideas of existing literature, this article conclusively chooses educational variables and the elimination of neighborhood effects as two major focus of this study. Educational variables in current domestic and foreign literature can be roughly divided into input variables, output variables and other variables, and this paper elaborates and analyzes commonly used variables of the above three types. Neighborhood effect is one of the main obstacles to influence the results of the school capitalization. This article summarizes the method of eliminating or reducing neighborhood effects into three categories:spatial econometric model, the boundary fixed effects method, instrumental variables, and then analyzes the development, usage, the advantages and disadvantages of the three methods.(2) According to the different accesses to educational opportunities and the diversity of quantify, the author chooses11educational variables to construct12models. By compare the empirical results of11education variables, the combination of the5variables are chosen, which are number of kindergartens, quality of primary and secondary schools, neighboring high school and neighboring universities. The empirical results show that education facilities have a significant positive effect on residential prices, and consumers willing to pay an additional fee to get better or more educational resources in the purchase. (3) The marginal price of education characterized of standard community have been calculated, and the impact and ranking of all sort of educational facilities have been determined by standardized regression coefficients of the model. For example, the elementary school in school district up a notch, residential average price increases by3.666%, which equal to770.97Yuan/square meter. Making standardized regression coefficient as standard, we can get the ranking of impact of five education variables on housing prices. The junior high school quality and primary school quality rank first and second; the influence of kindergarten number follows; the neighboring high school and university rank last.(4) The spatial correlation of the residential market in Hangzhou has been confirmed by GeoDa software, then according to the spatial econometric model and the actual situation of Hangzhou, we build spatial lag model and spatial error model. The results show that the space model, especially spatial error model is better than the basic hedonic price model. Almost all education variables in the model are significant. The problem which school capitalization is unrealistically high, result from neighborhood effects or spatial correlation, is solved to a certain extent, and the regression coefficient decreases slightly. Finally, the author quantitatively estimates the level of education capitalization in residential market after eliminating the spatial correlation.