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金融研究  2021, Vol. 497 Issue (11): 97-116    
  本期目录 | 过刊浏览 | 高级检索 |
学区房溢价的影响因素:教育质量的视角
张勋, 寇晶涵, 张欣, 吕光明
北京师范大学统计学院,北京 100875;
福特汉姆大学经济学系,美国纽约
The Determinants of School District Housing Price Premiums from the Perspective of School Quality
ZHANG Xun, KOU Jinghan, ZHANG Xin, LV Guangming
School of Statistics, Beijing Normal University;
Department of Economics,Fordham University
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摘要 优质教育资源可能形成于学校的教育质量,也可能来源于生源质量。房地产的市场化定价机制为探讨优质教育资源的背后形成机制提供了便利。本文利用北京市二手房成交数据,采用特征价格模型和边界固定效应法,估算了学区房溢价。在此基础上,利用学校层面的教育质量信息,探讨了教育质量对学区房溢价的解释力。实证结果表明,以学校物质资本和教师人力资本所表征的教育质量是学区房溢价,即优质教育资源的主要来源,解释了总体学区房溢价的64.71%,这种解释力在考虑了潜在的内生性问题后依旧稳健。进一步通过量化北京市的三个教育强区(西城区、东城区和海淀区)中教育质量的解释力,发现优质教育资源既可形成于优质生源集聚,也可形成于教育经费投入长期累积所带来的教育质量的提升。义务教育均衡化改革,推动优质公共投入的公平供给,是平抑高企的学区房价格的有效手段。
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张勋
寇晶涵
张欣
吕光明
关键词:  教育质量  学区房溢价  边界固定效应  北京    
Summary:  Many studies confirm that high-quality educational resources have a capitalization effect that results in price premiums for housing in school districts. High-quality educational resources, as measured by physical capital investment in schools and human capital investment in teachers, may be determined by the quality of a district's school education or the quality of its students (the peer effect). Few studies consider the mechanisms contributing to the formation of high-quality educational resources, especially the extent to which school quality determines the quality of educational resources. However, these mechanisms can be examined by using pricing information from the real estate market.
Using records of second-hand housing transactions in Beijing and unique data regarding education quality at the school level, we are among the first to quantify the power of education quality in explaining school district housing premiums, and thus estimate the economic value of education quality. China's unique institutional background makes it possible to quantify the effect of school quality on school district housing premiums. Unlike most European and American countries, where education funding comes from property taxes, China's education investment depends mainly on general fiscal expenditures by local governments. Hence, in China, the causal influence of education quality on housing prices can be identified by relating school district housing price premiums to education quality data at the school level. This allows us to quantify the relationship between education quality and school district housing premiums and thus explore the mechanisms contributing to the formation of high-quality educational resources.
We use the hedonic model with boundary fixed effects. Specifically, we identify the housing transaction records on both sides of each elite school's attendance zone boundary to control for unobserved factors. We then use this information to calculate each school district's housing price premium. We further match the school education quality data with the second-hand housing transaction records to quantify the role of education quality in the calculated school district housing premiums.
Our results show that education quality, measured by the physical capital investment in schools and the human capital investment in teachers, explains 64.71% of the overall price premium on school district housing. Our results are robust to various confounding factors, including the potential effects of newly built primary schools, education reforms, and access to private schools. Furthermore, we quantify the power of education quality in explaining housing price premiums in three high-quality educational districts in Beijing (Xicheng District, Dongcheng District, and Haidian District). We find that high-quality educational resources come either from concentration of high-quality students within a district or from improvements in school quality due to long-term accumulation of education spending.
From a Chinese policy perspective, the education equalization reforms implemented in recent years promote high-quality equity in compulsory education, which will help curb inflation of school district housing prices and moderate price fluctuations in the real estate market. Our findings indicate that the main sources of school district housing price premiums are physical capital investment and the quality of human capital at the school level, and that recent education reforms can help stabilize housing prices. Therefore, the recent equalization reforms to compulsory education will significantly improve overall school quality by increasing both physical capital investment and human capital investment in weak schools. Furthermore, given the importance of student quality (the peer effect), increasing intergenerational mobility and continuing to advance equalization of opportunities may also be important ways to stabilize school district housing prices.
We contribute to the literature in three ways. First, we add to the literature by discussing the relationships between education quality and school district housing price premiums using a unique dataset that measures education quality at the school level. By matching the education quality data with housing transaction records in the real estate market, we quantify the power of education quality to explain school district housing premiums and explore the mechanisms that contribute to the formation of high-quality education resources. Second, in the context of China, we identify the causal relationship between school quality and housing premiums based on a hedonic model with boundary fixed effects. We take advantage of various stock indicators to measure education quality, including fixed asset value, school building area, the teacher-student ratio, and average teacher salary. Based on the heterogeneous effects identified among three high-quality educational districts in Beijing, we also examine how policy can influence high housing prices in school districts.
Keywords:  School Quality    School District Housing Price Premium    Boundary Fixed Effect    Beijing
JEL分类号:  R21   R31   H52  
基金资助: * 本文感谢国家自然科学基金项目(项目批准号:71603026和71973014)和北京大学-林肯研究院城市发展与土地政策研究中心2018—2019年度研究基金的资助。感谢匿名审稿人的宝贵意见,文责自负。
通讯作者:  张 欣,经济学博士,副教授,北京师范大学统计学院,E-mail:xin.zhang614@bnu.edu.cn.   
作者简介:  张 勋,经济学博士,副教授,北京师范大学统计学院,E-mail:zhangxun@bnu.edu.cn.
寇晶涵,博士研究生,福特汉姆大学经济学系,E-mail:jkou5@fordham.edu.
吕光明,经济学博士,教授,北京师范大学统计学院,E-mail:lgmbnu@bnu.edu.cn.
引用本文:    
张勋, 寇晶涵, 张欣, 吕光明. 学区房溢价的影响因素:教育质量的视角[J]. 金融研究, 2021, 497(11): 97-116.
ZHANG Xun, KOU Jinghan, ZHANG Xin, LV Guangming. The Determinants of School District Housing Price Premiums from the Perspective of School Quality. Journal of Financial Research, 2021, 497(11): 97-116.
链接本文:  
http://www.jryj.org.cn/CN/  或          http://www.jryj.org.cn/CN/Y2021/V497/I11/97
[1]冯皓和陆铭,2010,《通过买房而择校:教育影响房价的经验证据与政策含义》,《世界经济》第12期,第89~104页。
[2]哈巍和余韧哲,2017,《学校改革,价值几何——基于北京市义务教育综合改革的“学区房”溢价估计》,《北京大学教育评论》第3期,第137~157页。
[3]韩璇、沈艳和赵波,2020,《房价中的优质教育溢价评估——以北京为例》,《经济学(季刊)》第20卷第1期,第257~276页。
[4]胡婉旸、郑思齐和王锐,2014,《学区房的溢价究竟有多大:利用“租买不同权”和配对回归的实证估计》,《经济学(季刊)》第13期,第1195~1214页。
[5]梁若冰和汤韵,2008,《地方公共品供给中的Tiebout模型:基于中国城市房价的经验研究》,《世界经济》第10 期,第71~83页。
[6]刘祖云和毛小平,2012,《中国城市住房分层:基于2010年广州市千户问卷调查》,《中国社会科学》第2期,第94~109页。
[7]邵磊、任强和侯一麟,2020,《基础教育均等化措施的房地产资本化效应》,《世界经济》第11期,第78~101页。
[8]孙伟增和林嘉瑜,2020,《教育资源供给能够降低学区房溢价吗?——来自北京市新建小学的证据》,《经济学(季刊)》第19卷第12期,第499~520页。
[9]张浩、李仲飞和邓柏峻,2014,《教育资源配置机制与房价——我国教育资本化现象的实证分析》,《金融研究》第5期,第193~206页。
[10]张牧扬、陈杰和石薇,2016,《租金率折价视角的学区价值测度——来自上海二手房市场的证据》,《金融研究》第6期,第97~111页。
[11]周京奎和吴晓燕,2009,《公共投资对房地产市场的价格溢出效应研究——基于中国30省市数据的检验》,《世界经济文汇》第1期,第15~32页。
[12]Black, S. E. 1999. “Do Better Schools Matter? Parental Valuation of Elementary Education”, Quarterly Journal of Economics, 114: 577~599.
[13]Brasington, D. and D. R. Haurin. 2006. “Educational Outcomes and House Values: A Test of the Value Added Approach”, Journal of Regional Science, 46: 245~268.
[14]Chan, J., X. Fang, Z. Wang, X. Zai and Q. Zhang. 2020. “Valuing Primary Schools in Urban China”, Journal of Urban Economics, 115: 103183.
[15]Dhar, P. and S. L. Ross. 2012. “School District Quality and Property Values: Examining Differences along School District Boundaries”, Journal of Urban Economics, 71(1): 18~25.
[16]Downes, T. A. and E. Z. Jeffrey. 2002. “The Impact of School Characteristics on House Prices: Chicago 1987-1991”, Journal of Urban Economics, 52: 1~25.
[17]Fack, G. and J. Grenet. 2010. “When Do Better Schools Raise Housing Prices? Evidence from Paris Public and Private Schools”, Journal of Public Economics, 94: 59~77.
[18]Figlio, D. N. and M. E. Lucas. 2004. “What's in a Grade School Report Cards and the Housing Market”, The American Economic Review, 94: 591~604.
[19]Gibbons, S. and S. Machin. 2003. “Valuing English Primary Schools”, Journal of Urban Economics, 53(2): 197~219.
[20]Gibbons, S., S. Machin and O. Silva. 2013. “Valuing School Quality Using Boundary Discontinuities”, Journal of Urban Economics, 75: 15~28.
[21]Hilber, C. A. L. and C. Mayer. 2009. “Why Do Households Without Children Support Local Public Schools? Linking House Price Capitalization to School Spending”, Journal of Urban Economics, 1: 74~90.
[22]Oates, W. E. 1969. “The Effects of Property Taxes and Local Public Spending on Property Values: An Empirical Study of Tax Capitalization and the Tiebout Hypothesis”, Journal of Political Economy, 77: 957~971.
[23]Rosen, H. S. and D. Fullerton. 1977. “A Note on Local Tax Rates, Public Benefit Levels, and Property Values”, Journal of Political Economy, 85: 433~440.
[24]Rothstein, J. M. 2006. “Good Principals or Good Peers? Parental Valuation of School Characteristics, Tiebout Equilibrium, and the Incentive Effects of Competition among Jurisdictions”, American Economic Review, 96: 1333~1350.
[25]Weimer, D. L. and A. M. J. Wolkoff. 2001. “School Performance and Housing Values: Using Non-Contiguous District and Incorporation Boundaries to Identify School Effects”, National Tax Journal, 54: 231~253.
[26]Zheng, S. and M. E. Kahn. 2008. “Land and Residential Property Markets in a Booming Economy: New Evidence from Beijing”, Journal of Urban Economics, 63: 743~757.
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