Effect of compulsory education school quality on housing prices from a spatial perspective

doi:10.3969/j.issn.1000-5641.2022.01.012

Abstract

Abstract:

In this study, we used the geographic weighted regression (GWR) method to explore the impact of compulsory education quality on housing prices. For the purpose of this analysis, we considered Wuhan in Hubei Province as the study area and collected school and housing price data from websites such as Sofang.com, Jzb.com, Whjyj.gov.cn, etc. We also used the inverse distance weighted (IDW) method for visual analysis. The results showed that: ① The quality of compulsory education has a positive impact on housing prices, and provincial demonstration schools, in particular, create a relatively high price premium on housing prices; ② The capitalization effect of the quality of junior high school is higher than that of elementary school; ③ The quality of primary and junior high school education in new urban areas has the greatest impact on housing prices. The quality of the combination of primary and junior high schools in the central and northern areas of Jiangan District had a significant impact on housing prices, while the quality of schools in the Wuchang, Qiaokou, and Jianghan districts had relatively less impact on housing prices as a whole.

Key words: school district housing, educational resources, hedonic price model, GWR model, education capitalization

CLC Number:

K909
F299.23

Hui YANG, Jin ZENG, Nai YANG, Fanmin KONG. Effect of compulsory education school quality on housing prices from a spatial perspective[J]. Journal of East China Normal University(Natural Science), 2022, 2022(1): 97-108.

Figures/Tables 14

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References 56

1	涂然, 周锐, 王新军. 城市基础教育设施的空间可达性与公平性研究——以上海市浦西8区小学为例. 复旦学报(自然科学版), 2019, 58 (6): 747- 755.
2	李明桥. 教育发展水平与机会公平性省际差异研究. 统计与信息论坛, 2019, 34 (12): 110- 116. doi: 10.3969/j.issn.1007-3116.2019.12.014
3	张艳华. 教育机会公平性失衡及其制度性原因研究. 调研世界, 2012, (6): 53- 58. doi: 10.3969/j.issn.1004-7794.2012.06.014
4	石霏, 何晓燕. 学校教育质量对房产价格的溢出效应: 一个文献综述. 教育与经济, 2018, 34 (1): 19- 26. doi: 10.3969/j.issn.1003-4870.2018.01.007
5	张珂, 张立新, 朱道林. 城市基础教育资源对住宅价格的影响——以北京市海淀区为例. 教育与经济, 2018, 34 (1): 27- 34. doi: 10.3969/j.issn.1003-4870.2018.01.008
6	马艳, 杨晗. 学区房的教育级差地租及其不平等效应研究. 财经研究, 2020, 46 (5): 37- 51.
7	TIEBOUT C M. A pure theory of local expenditures. Journal of Political Economy, 1956, 64 (5): 416- 424. doi: 10.1086/257839
8	OATES W E. 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, 1969, 77 (6): 957- 971. doi: 10.1086/259584
9	YINGER J. Capitalization and the theory of local public finance. Journal of Political Economy, 1982, 90 (5): 917- 943. doi: 10.1086/261101
10	刘润秋, 孙潇雅. 教育质量“资本化”对住房价格的影响——基于成都市武侯区小学学区房的实证分析. 财经科学, 2015, (8): 91- 99. doi: 10.3969/j.issn.1000-8306.2015.08.009
11	LUCAS S R. Effectively maintained inequality: Education transitions, track mobility, and social background effects. American Journal of Sociology, 2001, 106 (6): 1642- 1690. doi: 10.1086/321300
12	LANCASTER K J. A new approach to consumer theory. Journal of Political Economy, 1966, 74 (2): 132- 157. doi: 10.1086/259131
13	ROSEN S. Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 1974, 82 (1): 34- 55. doi: 10.1086/260169
14	汪佳莉, 季民河, 邓中伟. 基于地理加权特征价格法的上海外环内住宅租金分布成因分析. 地域研究与开发, 2016, 35 (5): 72- 80. doi: 10.3969/j.issn.1003-2363.2016.05.013
15	JUDD G D, WATTS J M. Schools and housing values. Land Economics, 1981, 57 (3): 459- 470. doi: 10.2307/3146025
16	NGUYEN-HOANG P, YINGER J. The capitalization of school quality into house values: A review. Journal of Housing Economics, 2011, 20 (1): 30- 48. doi: 10.1016/j.jhe.2011.02.001
17	FLEISHMAN L, FOGEL N, FRIDMAN I, et al. The effect of school performance on property values: A literature review and a case study. Journal of European Real Estate Research, 2017, 10 (3): 277- 302. doi: 10.1108/JERER-09-2016-0035
18	FERNANDEZ M A, BUCARAM S. The changing face of environmental amenities: Heterogeneity across housing submarkets and time. Land Use Policy, 2019, 83, 449- 460. doi: 10.1016/j.landusepol.2019.02.024
19	BRASINGTON D M, HITE D, JAUREGUI A. House price impacts of racial, income, education, and age neighborhood segregation. Journal of Regional Science, 2015, 55 (3): 442- 467. doi: 10.1111/jors.12173
20	WEN H Z, XIAO Y, ZHANG L. School district, education quality, and housing price: Evidence from a natural experiment in Hangzhou, China. Cities, 2017, 66, 72- 80. doi: 10.1016/j.cities.2017.03.008
21	王永超, 王光宇, 董丽晶. 教育资本化背景下学区房溢价水平和价格空间集聚特征研究——以沈阳市中心城区为例. 人口与发展, 2020, 26 (1): 108- 117.
22	周业安, 王一子. 教育资源、教育政策对城市居住用地价格的影响——基于北京市土地市场的数据分析. 中国人民大学学报, 2015, 29 (5): 79- 89.
23	HU L R, HE S J, LUO Y, et al. A social-media-based approach to assessing the effectiveness of equitable housing policy in mitigating education accessibility induced social inequalities in Shanghai, China. Land Use Policy, 2020, 94, 1- 17.
24	WANG D, NICOLAU J L. Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb. com. International Journal of Hospitality Management, 2017, 62, 120- 131. doi: 10.1016/j.ijhm.2016.12.007
25	RAE A, SENER E. How website users segment a city: The geography of housing search in London. Cities, 2016, 52, 140- 147. doi: 10.1016/j.cities.2015.12.002
26	GEOFF B, PAUL W. New insights into rental housing markets across the United States: Web scraping and analyzing craigslist rental listings. Journal of Planning Education and Research, 2017, 37 (4): 457- 476. doi: 10.1177/0739456X16664789
27	HU L R, HE S J, HAN Z X, et al. Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 2019, 82, 657- 673. doi: 10.1016/j.landusepol.2018.12.030
28	KIM C W, PHIPPS T T, ANSELIN L. Measuring the benefits of air quality improvement: A spatial hedonic approach. Journal of Environmental Economics and Management, 2003, 45 (1): 24- 39. doi: 10.1016/S0095-0696(02)00013-X
29	吕志芳, 陈英. 西部中小城市住宅地价空间分异规律及影响因素研究——以天水市为例. 甘肃农业大学学报, 2017, 52 (5): 112- 120.
30	LI H, REYNOLDS J F. On definition and quantification of heterogeneity. Oikos, 1995, 73 (2): 280- 284. doi: 10.2307/3545921
31	彭薇, 冯邦彦. 经济学关于空间异质性的研究综述. 华东经济管理, 2013, 27 (3): 155- 160.
32	BRUNSDON C, FOTHERINGHAM A S, CHARLTON M. Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science, 1999, 39 (3): 497- 524. doi: 10.1111/0022-4146.00146
33	FOTHERINGHAM A S, CHARLTON M E. Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 1998, 30 (11): 1905- 1927. doi: 10.1068/a301905
34	武汉市人民政府. 地理位置[EB/OL]. (2020-03-29)[2021-02-13]. http://www.wuhan.gov.cn/zjwh/whgk/202003/t20200316_976488.shtml.
35	中华人民共和国教育部. 教育部办公厅关于进一步做好重点大城市义务教育免试就近入学工作的通知[EB/OL]. (2014-01-18)[2021-10-13]. http://www.moe.gov.cn/srcsite/A06/s3321/201401/t20140129_164088.html.
36	李博. 2020年武汉市人民政府工作报告(全文)[EB/OL]. (2020-01-03)[2020-11-16]. http://hb.china.com.cn/2020-01/13/content_41032171.htm.
37	武汉市人民政府. 固定资产投资和房地产开发[EB/OL]. (2019-12-25)[2021-02-13]. http://www.wuhan.gov.cn/zjwh/jjfz/202003/t20200316_976499.shtml, 2019-12-25.
38	梁立雨, 郭志强, 李军祥, 等. 中学学区对周边小区二手房房价的溢价分析——以深圳市为例. 北京大学学报(自然科学版), 2019, 55 (3): 537- 542.
39	HILBER C A L , MAYER C. Why do households without children support local public schools? Linking house price capitalization to school spending. Journal of Urban Economics, 2008, 65 (1): 74- 90.
40	GLEN J, NELLIS J G. “The price you pay”: The impact of state-funded secondary school performance on residential property values in England. Panoeconomicus, 2010, 57 (4): 405- 428. doi: 10.2298/PAN1004405G
41	FACK G, GRENET J. When do better schools raise housing prices? Evidence from Paris public and private schools. Journal of Public Economics, 2010, 94 (1/2): 59- 77.
42	王德, 黄万枢. Hedonic 住宅价格法及其应用. 城市规划, 2005, (3): 74- 90.
43	朱丹, 董有福. 利用局部Moran’s I 指数进行DEM地形简化. 武汉大学学报(信息科学版), 2015, 40 (2): 280- 284.
44	王珂靖, 蔡红艳, 杨小唤. 多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用. 地理科学进展, 2016, 35 (12): 1494- 1505.
45	BRUNSDON C, FOTHERINGHAM A S, CHARLTON M E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 1996, 28 (4): 281- 298.
46	张羽鹤. 基于GWR模型的北京市二手房价格空间分异及影响因素研究 [D]. 北京: 中国地质大学(北京), 2018.
47	PÁEZ A, WHEELER D C. Geographically weighted regression. International Encyclopedia of Human Geography, 2009, 47 (1): 407- 414.
48	FORTHERINGHAM A S, BRUNSDON C, CHARLTON M. Geographically Weighted Regression [M]. West Sussex, UK: John Wiley& Sons Ltd, 2002.
49	卢宾宾, 葛咏, 秦昆, 等. 地理加权回归分析技术综述. 武汉大学学报(信息科学版), 2020, 45 (9): 1356- 1366.
50	CHO S H, LAMBERT D M, CHEN Z. Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data. Applied Economics Letters, 2010, 17 (8): 767- 772. doi: 10.1080/13504850802314452
51	CAMERON A C, TRIVEDI P K. Microeconometrics: Methods and Applications [M]. New York: Cambridge University Press, 2008.
52	NAKAYA T. GWR4 User manual: Windows application for geographically weighted regression modelling [Z/OL]. [2021-10-11]. https://gwr.maynoothuniversity.ie/wp-content/uploads/2013/04/GWR4_Manual.pdf.
53	FARBER S, PÁEZ A. A systematic investigation of cross-validation in GWR model estimation: Empirical analysis and Monte Carlo simulations. Journal of Geographical Systems, 2007, 9 (4): 371- 396. doi: 10.1007/s10109-007-0051-3
54	AKAIKE H. Information theory and an extension of the maximum likelihood principle [C]// 2nd International Symposium on Information Theory. Armenia: Tsahkadsor, 1973.
55	孙逸, 卢宾宾, 谢嘉仪. 考量空间关系异质性影响的房价预测模型研究. 测绘地理信息, 2020, 45 (6): 132- 137.
56	中华人民共和国教育部. 教育部等四部门关于印发《高中阶段教育普及攻坚计划(2017—2020年)》的通知 [EB/OL]. (2017-03-30)[2021-10-13]. http://www.moe.gov.cn/srcsite/A06/s7053/201704/t20170406_301981.html.

变量类别	常用的解释变量
区位 (Location)	到城市中心、公交站点、购物中心等距离
建筑结构 (Structure)	建筑面积, 建筑年龄, 楼层, 总层数, 卧室个数, 卫生间个数
邻里环境 (Neighbourhood)	学校质量, 服务设施规模与距离, 道路等级, 景观视线 (海景、公园) , 噪声, 污染水平

变量类别	指标	解释变量	描述与定量	序号
区位	${\rm{Dc}}$	距武汉市区中心的距离	直线距离 (单位: km)	1
建筑结构	${\rm{Age}}$	房龄	实际数值 (单位: 年)	2
	${\rm{Str}}$	建筑类别	塔楼 (1分) , 塔板结合 (2分) , 板楼 (3分)	3
	${\rm{H}}$	建筑高度	低层 (1分) , 多层 (2分) , 小高层 (3分) , 高层 (4分) , 超高层 (5分)	4
	${\rm{Rp}}$	容积率	实际数值 (单位: %, 范围: 0 ~ 1)	5
	${\rm{Rg}}$	绿化率	实际数值 (单位: %, 范围: 0 ~ 1)	6
	${\rm{Fee}}$	物业费	实际数值 (单位: 元/m² × 月)	7
	${\rm{Hou}}$	总户数	实际数值 (单位: 户)	8
	${\rm{P}}$	车位数量	实际数值 (单位: 个)	9
邻里环境	${\rm{AU}}$	二本以上高校的可达性	路网距离 (单位: km)	10
	${\rm{AW}}$	水域 (长江或湖泊) 的可达性	路网距离 (单位: km)	11
	${\rm{AHot}}$	三星级以上酒店的可达性	路网距离 (单位: km)	12
	${\rm{AHos}}$	三甲医院的可达性	路网距离 (单位: km)	13
	${\rm{AM}}$	地铁的可达性	路网距离 (单位: km)	14
	${\rm{AG}}$	政府的可达性	路网距离 (单位: km)	15
	${\rm{AG}}{\text{ym} }$	综合体育馆的可达性	路网距离 (单位: km)	16
	${\rm{AS}}$	大型超市/商场的可达性	路网距离 (单位: km)	17
教育资源	${\rm{GPS}}$	对口小学的学校质量	省级示范小学 (3分) , 市级示范小学 (2分) , 普通小学 (1分)	18
	${\rm{APS}}$	对口小学的可达性	路网距离 (单位: km)	19
	${\rm{GMS}}$	对口初中的学校质量	省级示范初中 (3分) , 市级示范初中 (2分) , 普通初中 (1分)	20
	${\rm{AMS}}$	对口初中的可达性	路网距离 (单位: km)	21

核函数名称	公式表达
固定高斯函数 (Fixed Gaussian)	${w}_{ij}={\rm{exp} }\left({-({d}_{ij}/\theta )}^{2}\right)$
固定双重平方函数 (Fixed bi-square)	$ {w}_{ij}=\left\{\begin{array}{l}\begin{array}{*{20}{l}}{l{{(1 - {d_{ij}}^2/{\theta ^2})}^2}},&{{d_{ij}}<\theta },\\0,&{{d_{ij}}>\theta }\end{array} \end{array}\right. $
调整双重平方函数 (Adaptive bi-square)	$ {w}_{ij}=\left\{\begin{array}{c}\begin{array}{*{20}{l}}{{(1-{{d}_{ij}}^{2}/{{\theta }_{i\left(k\right)}}^{2})}^{2}},&{d}_{ij} < {\theta }_{i\left(k\right)},\\0,&{d}_{ij} > {\theta }_{i\left(k\right)}\end{array}\end{array}\right. $
调整高斯函数 (Adaptive Gaussian)	${w}_{ij}={\rm{exp} }\left({-({d}_{ij}/{\theta }_{i\left(k\right)})}^{2}\right)$

统计量	固定高斯函数	固定双重平方函数	调整双重平方函数	调整高斯函数
R²	0.622	0.615	0.666	0.620
最优带宽	0.025	0.064	267.000	78.000
最小AICc	21618.926	21638.238	21560.379	21573.125

小学类型	平均房价/ (元·m^–2)	与研究区域平均房价差值/ (元·m^–2)	初中类型	平均房价/ (元·m^–2)	与研究区域平均房价差值/ (元·m^–2)
省级示范小学	20380	4009	省级示范初中	17532	1161
市级示范小学	16409	38	市级示范初中	16992	627
普通小学	15985	–387	普通初中	16024	–348