There is a deeply-rooted tradition in the Chinese culture that regards excellence in education as almost the only key for children’s success in life. Motivated by a host of stories such as “Meng Mu San Qian” and “Zi Lu Bai Shi” that tell the herculean efforts of ancient elites (and their entire families) in pursuing top education, Chinese parents of all socioeconomic status are willing to make every possible effort to secure their children’s access to advanced educational resources. Because of the severely limited number of top-ranking public institutions, the fight for enrollment starts as early as for primary schools and creates great educational inequality, since the wealthy could afford admissions fees for their kids to get into better schools.
In an attempt to equalize access to public educational resources and enhance fairness in the enrollment process, the “district correspondence enrollment” policy was first adopted back in 1986 (Sixth National People’s Congress, 1986) . It divided residential estates into designated school districts in correspondence with nearby public primary and junior middle schools and stipulated that children living in the neighborhoods in a given school district enroll in the corresponding school. In 2014 (The State Council, 2012) , this policy was strengthened to apply to 100% of the enrollment for public primary schools and over 90% of the enrollment for public junior middle schools.
The policy did almost eliminate school selection, but it also set the start of the School District Housing Fever, which is the soaring housing price in districts corresponding to Key Primary Schools (KPS) and Key Middle Schools (KMS). As a folk concept that has been in use for decades, KPS and KMS refer to those schools with capable teaching staff, favorable learning environment, and promising student body. Though there aren’t clearly-defined criteria for a school to qualify as KPS or KMS, they are usually reputed locally. In October 2017, sales price averaged ¥113,000/m2 in Quandong neighborhood (Lianjia, 2017a) because the houses entail enrollment in Mingzhu primary school, the reputed best public primary school citywide. Yet in Weifang No.2 neighborhood which is out of the Mingzhu school district, the average sales price for apartments was only ¥69,800/m2 (Lianjia, 2017b) . The two neighborhoods are only one block away, and both were constructed over 30 years ago, leaving their correspondence to schools as almost the only viable explanation for the apparent disparity in housing prices.
The reason is still the lack of high-quality educational resources and its uneven distribution as a public good. At its core, the designation of school district housing connects enrollment to estates, which is a private good, so that the desire of getting into KPS and KMS turns into an excess demand in the real estate markets of hot districts. In this way, enrollment is still unequal, even more so than before as housing prices are pushed to unprecedented levels.
China’s real estate industry also set the stage of the country’s school district housing fever. Since the industry’s takeoff 30 years ago, it has been growing at an exponential level. From 2009 to 2013, the industry grew more than 22%, of which second-hand sales of school district estates accounted for more than 13% (National Data, 2018) . According to Forbes, China has one of the highest home ownership rates in the world with over 90% of families owning their homes. This universal possession of estates means that most families can and do choose to sell off their houses in dissatisfactory districts and switch to districts with better schools, a process by which the feverish hike of school district housing prices takes place.
These observations render it crucial to study the School District Housing Fever, since a balanced educational system is key to maintaining equality in educational opportunities. Also, higher social mobility will give the society a boost in human resources, as the poor will no longer be prevented from receiving quality education by unaffordable housing prices and kept at the bottom for generations.
Facing the deeply-rooted problem of school district housing, the Chinese central government has been taking a variety of measures. In February 2016, Shanghai adopted a new policy (Shanghai Education Commission, 2016) stipulating that only one enrollment opportunity is available for each estate in every five years, regardless of changes in its ownership. This means that after one child is enrolled, the house will be ineligible for enrollment for five years. In terms of the educational system, the government is trying to equalize the qualities of schools across districts. One measure was to join each district with a higher-quality school with a district corresponding to a lower-quality school and randomly enroll children in the joint district into either of the two schools. This way, the disparity in school quality can be evened out theoretically. However, these policies are still of limited effect in cooling down school district housing in real practice.
Therefore, by engaging in a scientific examination of the cause and development of the phenomenon, this paper aspires to propose several possible remedies based on supply-side economics so that the welfare of the entire society can be maximized.
2. Literature Review
As a basic societal good, public education is financed by the government with its tax revenue and is provided to all the citizens. These characteristics render it a typical public good in classical economic theory. Public education creates a positive externality: As more people are educated, the society’s labor productivity increases while its crime rate tends to fall, which benefits other members of the society. Therefore, other assets absorb the cost of public education, and the prices of these assets rise consequently. This economic phenomenon is referred to as capitalization of public goods (Liu & Yi, 2011) . China’s school district housing fever, in which housing prices are higher in districts with higher-quality public schools, is a manifestation of capitalization of public goods.
2.1. Background on China’s Contemporary Public Educational System
China’s school district housing fever arises in the context of the China’s contemporary public educational system, which underwent four stages of development (Zou, 2008) .
The first stage spanned from 1977 to 1985 and witnessed the reorganization of China’s Public Educational System. In this period, more than 230 higher education institutions were established, and a number of high-quality primary, middle and high schools were formally recognized and distinguished by the government to the general public at district and city levels. These developments created a social atmosphere highly passionate for education, and most families competed fiercely for high-quality educational resources.
The second stage was from the year of 1985 to 1989 and witnessed the reorganization of China’s Public Educational System. On July 1st, 1986, the Compulsory Education Law of the People’s Republic of China was formally promulgated, which guaranteed nine years of compulsory education for every Chinese citizen and instructed its detailed implementation: Residence estates were divided into designated school districts in correspondence with nearby public elementary and junior middle schools, and children living in a school district can enroll in the corresponding school without selection. The main purpose of the Law was to enhance educational fairness for low-income families by replacing selective enrollment fees with the relatively fixed locations of residence estates. However, since estates are private commodities available for purchase, the adoption of the Law set the start of China’s school district housing fever.
The years 1990 through 2003 can be regarded as the third stage where the industrialization of China’s Public Educational System took place. The Decision of the Central Committee of the Communist Party of China on the Acceleration of the Development of the Tertiary Industry of 1992 classified education as a tertiary industry. Education was thereby redefined as a constituent of “social productivity” instead of a former “superstructure”. Under this principle, the government prioritized educational outcome ahead of educational fairness. Also, the scale and capacity of the educational system was rapidly enlarged. This led to an industrialization of Chinese education. Meanwhile, with the legalization of private institutions for basic education in 1999, the regulation of the educational system loosened and the competition for high-quality educational resources was fueled as enrollment fees were collected by many institutions. The prices of school district housing remained high through this period.
From 2003 until this present day, the development of China’s Public Educational System is in its fourth stage. Into the 21st century, the government has reemphasized the fairness of public education. In May 2005, the Ministry of Education formally issued Opinions on Further Promoting the Balanced Development of Compulsory Education. It placed a total ban on any kind of enrollment selections such as fees or exams and ordered a stricter enforcement of the “district correspondence enrollment” policy ( Ministry of Education of People’s Republic of China, 2014a, 2014b) (refer to the Introduction of this paper for an explanation): School districts were re-specified, and children are obliged to enroll accordingly. Up until 2014 (General Office of the Ministry of Education of People’s Republic of China, 2014) , this principle has been applied to 100% of the enrollment of public primary schools and 95% of that of public junior middle schools in 19 major cities across the country.
As of 2017 when this paper is composed, an average child can enroll in high-quality elementary education either by paying for private education or by buying school districts housing for public school. However, in 2016, the average tuition fee of private primary schools in Shanghai was ¥24,500 (≈$3720) per semester and still rising (which is more than ten times the tuition fee charged by public education). For most families, this sum was a significant economic burden, so the competition for entrance into high-quality public schools and corresponding houses remained and intensified. From March 2015 to March 2016, the average price for high-quality primary school district housing in Shanghai rose by over 62.8%, equivalent to about ¥26,000 (≈$4000) per square meter (Xingdd, 2016) (Figure 1).
2.2. Western Researches―Overview and Analysis
Over the past 60 years, with the development of economic theories and its applications, a large number of Western researches were conducted on the capitalization of public education on estate prices.
In 1956, American geo-economist Charles Tiebout became the first to put forward a theoretical model (Tiebout, 1956) to describe the provision of public goods. He observed that local residents automatically move away from
Figure 1. Average price for key primary school district housing in Shanghai, 2015-2016.
communities with dissatisfactory public services to where the public goods best satisfies their preferences, a process which he called “vote with their feet”. He hypothesized a corresponding capitalization effect in the prices of the estates and suggested that a market solution can exist for public goods at the local level, where the government and consumer-voters can communicate their supply and demand of public services through the market of real estates across different communities.
Oates (1969) empirically verified the Tiebout hypothesis in 1969. By working with data from fifty-three residential communities in northeastern New Jersey, Oates concluded that local property values bear a significant negative relationship to the effective tax rate and a significant positive correlation with expenditure per pupil of the public schools.
From the 1970s onwards, as the capitalization of public education on estate prices arose in more places, many scholars took different perspectives and approaches in investigating the phenomenon. Most of the published findings confirmed the positive effect of school quality on the prices of private estates, while the precise magnitude and choice of variables were varied.
Oates used expenditure per pupil as the dominant factor in measuring a school’s quality, which focused on the school’s educational input. However, Rosen & Fullerton (1977) argued that proficiency test scores are a better measure of school quality, because the educational output effectively takes into account both the school’s efforts and the unquantifiable learning environment and peer effects the school offers. In this way, their 1977 paper obtained results with a significant level as high as 90%.
Rosen and Fullerton’s finding was cited by a large body of researches, and K-12 student achievement measures replaced expenditure to become the most commonly-used factor in subsequent studies on the relation between estate prices and public school quality. Haurin & Brasington (1996) used the pass rate on a ninth-grade statewide proficiency test and calculated that the capitalization of school quality occurred on a per lot basis rather than per square foot of land. It is worth noting that their study separated the capitalization effects in the prices of the estates caused by other variables, such as the house’s structural characteristics and its distance to the city’s CBD. In this way, they were able to isolate the precise effect of school quality on house prices.
Another measure of school quality is the value added, which refers to the marginal effect of school education on students’ achievements over a given time period. In this sense, schools with higher value added are better, meaning that they boost students’ achievements to a greater level apart from the impact of their families or innate aptitudes. Downes & Zabel (2002) used a sample of 1173 house prices data in the Chicago metropolitan area and found that higher average levels of academic achievement raise house values, but value-added did not. Brasington (2006) arrived at a similar conclusion: Using data from 310 school districts and 77,000 house transactions in 2000 in Ohio, they found that households consistently value a school’s average proficiency test scores and expenditures instead of the value-added. They calculated that the elasticity of house prices with respect to school expenditures was 0.49, and an increase in test scores by one standard deviation raised house prices by 7.1%, while the effect of value added on house prices was insignificant. Hayes & Taylor (1996) , however, found that in addition to absolute achievement levels, homebuyers are willing to pay an extra premium for value added. Other researches that discussed this method include Boardman & Murnane (1979) , Aitkin & Longford (1986) , and Hanushek, Rivkin, & Taylor (1996) .
There are other notable studies that took unique perspectives. Dubin & Goodman (1982) studied the impact of twelve variables of crime and twenty-one variables of education on house prices in Baltimore and found that these variables substantially explain house prices. Black (1999) studied housing price at the convergence of two or more school districts so that the effects of housing and geographical characteristics could be isolated. He calculated a slight capitalization effect of school quality 50% less than the average rate obtained in other studies. Dills (2004) showed that improved performance on college entrance exams was associated with increased overall housing value aggregated to the district level.
More recent researches took a more practical angle as scholars evaluated the outcomes of school district policies. In 2008, Hu & Yinger (2008) investigated the impact of school district consolidation in New York State since 1990 on the capitalization effects of public schools. They found that the policy boosted house prices by 25% in very small districts but had no marked effect in those with more than about 1700 pupils. In 2011, Nguyen-Hoang & Yinger (2011) made a comprehensive review of empirical studies on the capitalization of school quality into house prices since 1999 and confirmed that significant capitalization effects did exist especially for educational outputs. But they concluded that although challenges still remained, much progress had been made on the issue.
Although past researchers used different types and scopes of the variables in calculating the capitalization effect of school district housing, the hedonic price model, or hedonic price regression, was the commonly-used methodology to determine the relationship of each housing attribute to its transaction price and measure each of these relationships isolated from the effects of other attributes (Monson, 2009) . In this case, the model can isolate the effects of other attributes and measure the net capitalization effect of the quality of public education on housing prices.
2.3. Chinese Researches―Overview and Analysis
A large body of Chinese literature studies the capitalization effect of public education in estate prices. Due to the relatively later emergence of the capitalization effects in China, most Chinese researchers employed the hedonic price regression model and similar methodologies as Western researches. But again, their perspectives varied.
An early research conducted by Feng & Lu (2010) in 2010 studied monthly panel data of Experimental Model Senior High Schools (EMSHS) and corresponding housing prices in 52 school districts across Shanghai. Their study marked the presence of capitalization of public education and calculated an average rise of 6.9% of housing price when an extra leading EMSHS is added to district. Wen, Yang, & Qin (2013) , Huang (2010) , Meng & Jia (2014) , and Wang, Ge, & Zhang (2010) attained similar results using data in Hangzhou, Xi’an, Guangzhou, and Nanjing, respectively.
Liu & Sun (2015) based their study on the panel data of second-hand deals of school district housing in Wuhou District, Chengdu. They concluded that public education did have a marked price premium on the estates price, and the amount of premium showed the Matthew effect, where top schools have a significant positive effect on the price of estates, while price premium of average schools may be tiny or even negative since people are selling the estates off in exchange for those in better school districts. Using data from 202 school districts in Hangzhou City Area, Mao, Luo, & Pan (2014) calculated the price premiums of sought-after middle and primary schools to be 25.5% and 12.8% and concluded that the presence of private education aggravated the local capitalization effect.
2.4. Thoughts and Inspirations
After a thorough review of the background and the status quo of China’s school district housing fever in theoretical and empirical lens, we came to the understanding that the problem has been chronic and prevalent across the country, limiting low-income families’ access to high-quality educational resources economically. We found that no researches has been conducted on Shanghai’s public primary school district housing, so our research paper has practical importance in investigating the local phenomenon at the very basis of education. Also, since Shanghai has a socioeconomic development level ahead of other cities in China, this paper can also shed light on studying the problem in other areas or nationwide. Furthermore, the government’s past policy attempts to cool down the housing prices mainly took a demand-side approach but haven’t produced effective outcomes so far. Seeing that the latest Western studies have focused on evaluating and advising school district policies, we also considered some of the impacts of recent Chinese policies and used our empirical findings to discuss future policy advice.
The body of past literature guided us of the research methodology. Knowing from existing researches that a wide range of housing attributes can affect house prices, we used hedonic price model to isolate their effects so that the pure effect of public education can be obtained. Specifically, we excluded the effects of eight non-school attributes, namely distance to the corresponding public primary school, distance to the CBD of the district, property management fee, green coverage ratio, elevator, residential area, floor area ratio, and building age.
3.1. Data and Variable Description
This study selects three main urban districts (namely Xuhui, Huangpu, and Changning) and four main suburban districts (namely Minhang, Jiading, Songjiang, and Baoshan) in Shanghai as its study areas. Among the city’s total fifteen districts, the seven are chosen according to their scale of the school district housing fever, which is estimated from the size of the population as well as the number and quality of public primary schools in the district. It is believed that the capitalization effect is more pronounced and observable in districts with a larger population and more schools of higher qualities. Another factor in selecting the study areas is the accessibility and sufficiency of data. Major districts like Pudong weren’t included in consideration due to the lack of data arising from its large floating population and other factors. According to existing research, the capitalization effect is only significant for schools of relatively higher quality; it is likely to be negligent or negative for middle- or lower-quality school districts since people are not considering the schools as a factor in real estate purchase of even selling of their estates for those in better school districts. So the top ~30% of primary schools in each urban district and ~10% of primary schools in each suburban district and their corresponding neighborhoods are studied in this paper for a significant causational effect. That said, the sample of this paper covers a total of 10.08 million residents and 43 school districts1.
In this paper, the dependent variable is the average housing price of a neighborhood. The independent variables include the quality of the corresponding public primary school and eight other factors classified into three aspects according to the framework of the hedonic pricing model: structure, neighborhood, and location. The eight factors are chosen mainly based on the accessibility of standardized data for neighborhoods across the city. For example, although better interior decoration counts towards a higher housing price, there is no quantifiable data to measure or score it, while a simple dummy variable is too arbitrary. Nevertheless, the eight factors are balanced so that they reflect different parts in housing prices. For instance, property management fee accounts for the social status of the residents’ composition, while green coverage ratio reflects the quality of the neighborhood’s environment.
1Data last updated by the end of 2016; same for other data in this paper if not otherwise specified.
Since the purpose of this study is to measure the capitalization effect of public school quality into housing prices, the quality of the corresponding public primary school is the main independent variable to be studied separate from the other non-school attributes, which are treated as control variables. Similar to studies Wen & Chen (2014) ; Wen, Zhang, & Zhang (2014) ; and Wen & Tao (2015) , three methods (actual data, scoring method, and dummy variable) are adopted to quantify the variables of the sample as accurately as possible. Specifically, the actual data of housing price, residential area, building age, property management fee, floor area ratio, green coverage ratio, the distance to the corresponding public primary school, and the distance to the district’s CBD are used to measure their contents directly. Since there is no standardized test for primary school students’ academic performances in China, the quality of the corresponding public primary school is given by a score equal to its rank within the district, which can reflect its educational quality relative to other schools in Shanghai.
The variable names, classification, description, quantification, and expected signs and scales are all listed below in Table 1, and the comprehensive data is provided in the Appendix. The rankings of the schools are obtained from 51test.net, China’s largest educational portal website and Hatong-shsx, one of Shanghai’s largest Wechat Official Accounts providing information on examinations, and educational activities with over 2.63 million subscribers (Hatong Shanghai Shengxue, 2017) . The correspondence between the schools and the neighborhoods and the estimated average housing prices in each neighborhood are obtained from the study conducted by sh.bendibao.com, which is a trusted portal website providing all-rounded information for local life such as transportation and real estate trading. Referring to portal websites for these data is justified because school-related information is not released by government agencies so as not to intensify the school district housing fever. The sources cited in this paper are credible since they are universally-recognized by a large user base and the information they provide are also obtained from careful investigations and analyses. The housing information including sales prices and other relevant characteristics is obtained from Fang.com, one of the largest and most renowned real estate information platform covering 642 cities in China with over 6.5 million active subscribers (Fang, 2017) . For each neighborhood, the house whose price is closest to the neighborhood average is selected as the representative. In order to ensure the unity of data, this paper only studies high-rise housing apartments and excludes villas and townhouses. These considerations render the choice and analysis of variables in this paper considerably relevant and complete.
3.2. Descriptive Statistics Analysis of Independent Variables
In this section and the one that follows, the data with be analyzed statistically in three dimensions: citywide, urban, and suburban, so that the conclusions and implications are specific and distinguishable.
The descriptive analysis on the citywide scale is given below in Table 2. The average school district housing price is 76,949.81 RMB/m2, with a maximum value of 150,621 RMB/m2 and a minimum value of 28,641 RMB/m2. The quality of the corresponding public primary school, given by its ranks, has an average of 5, maximum of 11 and minimum of 1, which is consistent with our sampling of the data. The averages of distance to the corresponding public primary School, distance to the district’s CBD, property management fee, green coverage ratio, elevator, residential area, floor area ratio, and building age are, respectively,
Table 1. Variable description.
748.13 meters, 2141.80 meters, 2 RMB/m2 per month, 34.03%, 0.62, 92.41 m2, 2.21, and 19.54 years.
The descriptive analysis for the urban areas is given below in Table 3. Most notably, the average school district housing price is 88,311.57 RMB/m2, 14.77% higher than the average value citywide.
The descriptive analysis for the suburban areas is given below in Table 4. The average school district housing price is 49,993.49 RMB/m2, over 35% lower than the average value citywide. In comparison with the data of the urban areas, the average green coverage ratio and distance to the district’s CBD are greater in the suburban places while the property management fee is cheaper and there are fewer elevators. Also, the public schools in suburban areas generally have lower ranks than those in urban areas, reflecting the relative lack of educational resources in suburban areas. As these patterns match properly with the actual
Table 2. Descriptive analysis of independent variables, citywide.
Table 3. Descriptive analysis of independent variables, urban.
environmental and economic situations in Shanghai, they prove the validity and real-life significance of the sample and data sources of this paper and lay the foundation for following technical analyses.
The number of KPS in each district of Shanghai is listed below in Table 5 and illustrated in Figure 2. The four districts with the most KPS are Xuhui, Huangpu, Pudong, and Yangpu in order. These districts are all in the urban area and share the characteristic of advanced socioeconomic activities and educational
Table 4. Descriptive analysis of independent variables, suburban.
Table 5. Number of key primary schools (kps) in shanghai, by district.
Figure 2. Number of KPS in Shanghai, by district.
resources, and thus relatively higher housing prices. The suburban districts, by contrast, generally have less developed economies and lower-quality educational resources.
Figures 3-4 offer a clearer observation of the more pronounced capitalization effect of public school quality into housing prices in the urban districts by visualizing the distribution of housing prices. The distribution of housing prices in Huangpu District is illustrated graphically below in Figure 3 where the peaks and troughs correspond to the different housing prices of neighborhoods in different school districts and the trend of change between them. One school may correspond to several neighborhoods whose housing prices decrease as the distance to the school increases. The conspicuous peaks are likely to be caused by sought-after schools whose housing prices are significantly higher than those of others. The varying heights of the peaks illustrate the different housing prices for different schools.
Figure 4 and Figure 5 illustrate the distributions of housing prices in Xuhui and Changning Districts, respectively. Similar to Figure 3, the peaks and troughs correspond to neighborhoods in different school districts. But the unique characteristic setting these two districts apart from Huangpu is the differences between housing prices of different neighborhoods are obvious in Xuhui and Changning. In Huangpu, however, there isn’t such sharp difference.
3.3. Hedonic Price Model
To calculate the exact capitalization rates, the traditional Hedonic Price Model is employed. The basic assumption of our econometric model assumes that housing price reflect the market values of public school quality and other characteristics.
After referring to existing researches on real estate valuation and considering the quantification of the variable studied here, the logarithm functional is used to establish the basic model. Specifically, positive and continuous independent variables (e.g., distance, building age, floor area ratio) are adopted in logarithmic form, while the variables quantified through scoring or dummy variables (namely quality of corresponding public primary school and elevator) are adopted
Figure 3. Distribution of housing prices in Huangpu district.
Figure 4. Distribution of housing prices in Xuhui district.
Figure 5. Distribution of housing prices in Changning district.
in linear form. We assume a standard form for the empirical hedonic house price function:
is the natural logarithm of housing price for the representative house in the ith neighborhood that corresponds to school j.
is the quality of school j, and
is the set of the seven control variables of the representative house in the ith neighborhood that corresponds to school j.
is the error term, and
This paper uses two-stage least squares (2SLS) regression method (an extension of the OLS method) to compute the coefficients in order to avoid the endogeneity bias of education quality and quantity. The following results are obtained using the data processing tools in Excel and are analyzed on the citywide scale, in the urban areas, and in the suburban areas.
The results of regression analysis and variance analysis on the citywide scale are given in Table 6 and Table 7, respectively. The variance analysis confirms that the model fits very well with the experimental data and so the coefficients it yielded can be used to explain the sample. Every coefficient fits with its expected sign, and their P-values further verify that most coefficients are statistically significant. Specifically, the coefficient of the quality of the corresponding public primary school is −0.031, meaning that housing price increases by 3.1% for a one-rank improvement of its corresponding school (which is equivalent to its numerical rank decreasing by 1).
The results of regression analysis and variance analysis in the urban areas are given in Table 8 and Table 9, respectively. The variance analysis confirms that the model fits very well with the experimental data and so the coefficients it yielded can be used to explain the sample. The P-values of the coefficients further verify that most of them are statistically significant and fit with their expected signs. Specifically, the coefficient of the quality of the corresponding public primary school is −0.028, meaning that housing price increases by 2.8% for a one-rank improvement of its corresponding school (which is equivalent to its numerical rank decreasing by 1).
Table 6. Regression analysis, citywide.
Table 7. Variance analysis, citywide.
Table 8. Regression analysis, urban.
Table 9. Variance analysis, urban.
Figure 6. Variable “quality of the corresponding public primary school” residual plot.
Figure 7. Variable “quality of the corresponding public primary school” line fit plot.
The results of regression analysis and variance analysis in the suburban areas are given in Table 10 and Table 11, respectively. The variance analysis confirms that the model fits very well with the experimental data and so the coefficients it yielded can be used to explain the sample. The P-values of the coefficients further verify that most of them are statistically significant and fit with their expected signs. Specifically, the coefficient of the quality of the corresponding public primary school is −0.019, meaning that housing price increases by 1.9% for a one-rank improvement of its corresponding school (which is equivalent to its numerical rank decreasing by 1).
This paper establishes hedonic regression analysis to investigate the capitalization effect of public school quality into housing prices and quantitatively calculate the exact capitalization rates. Using the data of 344 residential neighborhoods over 7 main districts in Shanghai, it yielded the following empirical results:
1) The quality of the corresponding public primary school has a significant positive effect on housing prices. The compulsory educational facilities are capitalized into estate prices in Shanghai. Under the strict enforcement of the “district correspondence enrollment” policy, families are willing to pay higher prices for houses so that their children can enroll in better schools.
2) The capitalization rate of public school quality is calculated to be 3.1%, 2.8%, and 1.9% on the citywide, urban, and suburban scales, respectively. When the rank of the corresponding public primary school improves by one, the average housing price in the neighborhood increases by the given percentage.
3) The capitalization effects of public school quality into housing prices exhibit a Matthew Effect, i.e. the amount of capitalization increases rapidly as the quality of the corresponding public primary school approaches the top citywide,
Table 10. Regression analysis, suburban.
Table 11. Variance Analysis, Suburban.
Note: When the sample is divided into urban and suburban areas for separate analyses, its size diminishes, so the coefficients of a few variables disagree with their expected signs or have P-value greater than 0.1. Also, due to the particularity of the designation of school districts and the real estates in Shanghai, many old, unmodern neighborhoods are in close proximity to high-ranking public primary schools near the city center while the neighborhood and structural attributes vary quite erratically especially in suburban areas. Yet the P-value, residual plot, and line fit plot for the school quality variable in all three dimensions are statistically significant and can thus reliably reveal the causational effects and validate the conclusions of this study.
while the effect is much milder for schools at a middle or middle-lower level. Since the increase in housing price is given in percentages, the higher the rank, the more the capitalization effect compounds. On the citywide scale where a larger disparity in school quality is present, the measured capitalization rate is higher than in separate samples. These patterns reflect the serious inequality of access to public educational resources behind the school district housing fever: The gap between low- and high-quality educational resources widened and became even more unaffordable for the low-income families.
5. Further Implications
5.1. Research Significance
As one of the few existing studies conducted on Shanghai’s School District Housing Fever, especially after the strengthening of the “district correspondence enrollment” policy in 2014, this paper examines the status-quo of the local capitalization effect. Most notably, it deals with the lack of sufficient data on public school quality by synthesizing multiple sources. It also samples housing information of 344 neighborhoods in Shanghai and considers seven control variables that account for every aspect in the estate valuation.
Moreover, by using the 2SLS regression method, it avoids the endogeneity bias that has been an issue in most existing researches in the field so that it was able to yield statistically significant results which are also consistent with real-life situations.
5.2. Policy Recommendations and Action Plans to Mitigate the School District Housing Fever
The conclusions of this study reveal that the status-quo of the distribution of public educational resources in Shanghai is still considerably unequal. The “district correspondence enrollment” policy itself is a demand-side policy as it regulate the method for enrollment, and so are most of the policies and measures adopted in an attempt to improve China’s educational inequality, such as designating common school districts for higher-ranking and lower-ranking schools and enroll children in them randomly. Yet as already pointed out in the Literature Review, such policies are predicted to have only a limited effect in mitigating the phenomenon, since although the enrollment mechanism is made to be compulsory, random, or “fair”, the gap among the qualities of schools still exists, and the allocation and use of high-quality educational resources would still remain in an unfair advantage to families with better access to other resources, if not even more so.
So at its roots, the School District Housing Fever stems from a critical lack of high-quality public educational resource, and the key to promoting educational equality and cooling down the prices of the private estates also lie in equalizing and improving the qualities of public primary schools. Only in this way can families relieve their anxious competition to get their kids into top-ranking schools by all means.
To achieve this end, the government, the educational institutions, and the society as a whole must all take up specific roles and fulfill their responsibilities. Drawing from the conclusions and real-life experiences, this paper proposes the following recommendations for each party to mitigate the School District Housing Fever and the educational inequality with joint efforts.
Since China’s public education is primarily financed and regulated by the government, it should increase its educational input to provide more high-quality educational resources. In fact, this can be done without increasing the tax burden on citizen by instead drawing from higher housing price which arises from the capitalization effect studied in this paper. For those housing estates traded at a price distinctively higher than those of similar estates, a certain proportion of its sales price can be levied as tax (the exact rate of which should be designated based on the amount of capitalization of public school quality in its price). This sum of tax revenue can then be transferred into establishing a Fund for Balancing School District Housing (name only provisional, and the precise working mechanism of such a Fund also requires further discussion in greater detail). Money can then be allocated by the Fund to middle- or lower-quality public institutions in an effort to improve their educational quality. If adopted, this supply-side approach may be able to bridge the gap among the qualities of public schools and enhance educational equality. In preliminary stages, separate Funds can operate with individual districts, while a citywide program can be established as the mechanism matures.
In addition to directly allocate funds to middle- or lower-quality public institutions to improve their educational quality, the government can also work to increase their access to higher-quality educational resources by adopting and promoting programs that involve the exchange and circulation of resource or personnel. By, for example, letting inexperienced teachers and facilitator engage in training programs led by capable teaching staffs from higher-quality institutions, the disparity between educational qualities may also diminish.
But one drawback of these supply-side solutions is that it may take long before they take effect. In the immediate future, one way to increase low-income families’ access to high-quality educational resources is to give incentive to public schools. For example, a certain amount of monetary reward can be given for the school to enroll one pupil from humble family backgrounds, and the funding may as well come from the Fund collected from the feverish housing prices. This measure may propel the schools to take initiatives towards educational inequality on their parts. Also, in the housing market regulation process, the important role of public goods layout and urban planning policy should also be emphasized so that the workings of the real estate market does not interfere with or exacerbate the School District Housing Fever.
When it comes to public educational institutions, they should take a proactive part in enhancing and equalizing educational qualities among themselves. This can be done by encouraging the exchange and circulation of resources on the school level and seeking the support from local governments. Several schools, including higher- and lower-quality ones, in Changning District has already been engaging in such an initiative: Teachers and staff that participate in cross-school exchange and training programs are prioritized in promotions of their professional titles and are given extra bonuses. Being advocated by the district’s Educational Bureau, this program is expanding its scale to involve more participant schools.
The responsibility of the society as a whole is to facilitate the circulation of information and give support to the improvement and equalization of the educational qualities. One way to do this is by volunteering in programs and initiatives that trains teachers and facilitator, especially for lower-quality institutions. Another way for private entities to mitigate the fierce competition of high-quality public schools is to allocate more social capital in establishing private educational institutions, so that higher-income families may have more choices and the excess demand for public institutions can be balanced. Lastly, average families, homebuyers and investors should refrain from speculative purchases of school district housing that exacerbates their feverish prices and makes them even more inaccessible to low-income families.
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