Investigating the Unintended Consequences of the High School Equalization Policy on the Housing Market

Round 1

Reviewer 1 Report

More connection with neighborhood / real estate and schools. Since map is included, some more in depth analysis with GIS (geographically weighted regression) was expected, after other value influential factors were detected (POI - points of interest).

Statistically wised the SD is not so appropriate. I would recommend MAPE (mean average percentage error) as more appropriate 

Author Response

Response to Reviewer 1:

We thank you for your constructive comment and advise. We reviewed all of your advice seriously and considered revising our manuscript. For your convenience, we repeat every comment in italics, and then respond to the comment in regular font below.

1. More connection with neighborhood / real estate and schools.

→ Thank you for your constructive comment. In the revision, we added an explanation on why the quality of educational environment can be associated with house prices. Also, we added 8 references related to the topic. The revised our second paragraph in Introduction is as follows.

“… In particular, many empirical studies in several countries commonly reveal that the quality of educational environment are a substantial factor for families with students to make their residential location choice [13-17]. It suggests that an educational policy that can influence the educational environment in a region may also affect the housing market in the region at the significant level [18-20].” (lines 39 – 43, page 1).

2. Since map is included, some more in depth analysis with GIS (geographically weighted regression) was expected, after other value influential factors were detected (POI - points of interest).

→ We thank for your suggestion. We agree that we could probably use GIS for further analysis by taking into account potential spatial interactions (i.e., interactions between geographical location and treatment). In the context of difference-in-difference analysis (DD), the spatial DD can deal with the research questions that investigate the partial treatment effects on some regions near the treatment regions in the control regions. However, in our research condition, the regions near the treatment cannot be influenced by the treatment or can be few because of the unique characteristics of HSEP. More specifically, a student should be assigned to the school in the region that the government previously set, which the regions are distinctly divided. Therefore, as all students living in the control group could lose incentive to enter the traditional high-ranked schools in the treatment group, it was difficult to assume that schools near the treatment region could be more affected by HSEP than those far from the treatment region. Therefore, we chose the DD analysis, however a spatial DD method or GIS could be one in the future study with more variables collected.

3. Statistically wised the SD is not so appropriate. I would recommend MAPE (mean average percentage error) as more appropriate 

→ Thank you for your suggestion. To our knowledge, MAPE is a useful measure of prediction accuracy. In our study, we focused on the identification of casual effect of the treatment, rather than on building the model with the highest accuracy. In this framework, avoiding the endogeneity issue could be more crucial to obtain unbiased parameter estimates and conduct statistical testing for them [1]. That’s why we tried to include all the possible covariates that represent individual characteristics of houses. Based on the assumption that omitting variable bias does not occur, parameter estimates are typically tested with standard errors or t-statistics (e.g., [2])

1. Shmueli, G.; Koppius, O.R. Predictive Analytics in Information Systems Research. MIS Q. 2011, 35, 553–572, doi:10.2307/23042796.
2. Card, D.; Krueger, A. Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania. Am. Econ. Rev. 1994, 84, 772–793.

Reviewer 2 Report

Overall, I find that the study is interesting, it deals with interesting issues, relatively rarely discussed in the scientific literature. It is quite original about the links between quite distant issues such as government policy on education and housing prices. I have a few comments that relate to specific lines.
Line 28. The literature review is relatively short and concerns mainly the presentation of the government's policy, and there are few theoretical references, a review of the achievements of economic and social sciences in the field of e.g. the effectiveness of various approaches to education or factors influencing real estate prices.
Line 68r. The methodology and use of the materials is correct.
Line 139. Correct, interesting.
Line 193. The discussion is relatively modest. Most importantly, I believe that HSEP can reduce home price inequality between regions, for research by 5% ~ 9%.
Line 229. The number of 26 publications in the bibliography is somewhat short. This remark coincides with the doubts raised at the outset about too short a review of the literature.
Summing up, the study is interesting, factually correct, it can be published in its present form, although its value may be increased by a slight expansion of the introduction, the theoretical part.

Author Response

Response to Reviewer 2:

Overall, I find that the study is interesting, it deals with interesting issues, relatively rarely discussed in the scientific literature. It is quite original about the links between quite distant issues such as government policy on education and housing prices. I have a few comments that relate to specific lines.

-- We thank you for your positive and constructive comments. We reviewed all of your advice seriously and considered revising our manuscript. For your convenience, we repeat every comment in italics, and then respond to the comment in regular font below.

Line 28. The literature review is relatively short and concerns mainly the presentation of the government's policy, and there are few theoretical references, a review of the achievements of economic and social sciences in the field of e.g. the effectiveness of various approaches to education or factors influencing real estate prices.

-- Thank you for your constructive comments. In the revision, we provided additional explanation on the relationship among school quality, educational policy, and the housing market with 8 relevant references in the Introduction, as below.

“… In particular, many empirical studies in several countries commonly reveal that the quality of educational environment are a substantial factor for families with students to make their residential location choice [13-17]. It suggests that an educational policy that can influence the educational environment in a region may also affect the housing market in the region at the significant level [18-20].” (lines 39 – 43, page 1).

Line 68r. The methodology and use of the materials is correct.
Line 139. Correct, interesting.

-- Thank you for your positive comments

Line 193. The discussion is relatively modest. Most importantly, I believe that HSEP can reduce home price inequality between regions, for research by 5% ~ 9%.

-- We explained more in detail when HSEP may not be expected to reduce home price inequality as below in Discussion.

“… However, if the HSEP in Gangwon had failed to achieve its original goal of resolving the problem of high school ranking system, the HSEP might have influenced the housing market differently in the region. In this case, as students are allowed to enter the high schools nearby their home only by HSEP, their preference for the region near the traditional elite schools could be rather reinforced, which might cause the house price gap between regions to be enlarged [35]. Therefore, we caution against generalizing our finding to the HSEPs implemented in other regions and recommend decision makers in education to make a strategic plan for HSEP insofar as it can be widely expected to succeed from the initial stage of implementation.” (lines 216 – 224, page 7).

Line 229. The number of 26 publications in the bibliography is somewhat short. This remark coincides with the doubts raised at the outset about too short a review of the literature. Summing up, the study is interesting, factually correct, it can be published in its present form, although its value may be increased by a slight expansion of the introduction, the theoretical part.

-- Thank you for your positive evaluation. As requested, we conducted an additional review of the literature, and newly added 9 publications in the list of reference as follows.

13. Downes, T.A.; Zabel, J.E. The impact of school characteristics on house prices: Chicago 1987–1991. J. Urban Econ. 2002, 52, 1–25, doi:https://doi.org/10.1016/S0094-1190(02)00010-4.
14. Rajapaksa, D.; Gono, M.; Wilson, C.; Managi, S.; Lee, B.; Hoang, V.-N. The demand for education: The impacts of good schools on property values in Brisbane, Australia. Land use policy 2020, 97, 104748, doi:https://doi.org/10.1016/j.landusepol.2020.104748.
15. Glen, J.; Nellis, J. “The Price You Pay”: The impact of state-funded secondary school performance on residential property values in England. Panoeconomicus 2010, 57, doi:10.2298/PAN1004405G.
16. Qiu, L.; Guo, D.; Zhao, X.; Zhang, W. The value of school in urban China: a spatial quantile regression with housing transactions in Beijing. Appl. Econ. 2019, 52, 1–12, doi:10.1080/00036846.2019.1683146.
17. Bae, H.; Chung, I. Impact of school quality on house prices and estimation of parental demand for good schools in Korea. KEDI J. Educ. Policy 2013, 10, 43–61.
18. Wen, H.; 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:https://doi.org/10.1016/j.cities.2017.03.008.
19. Brunner, E.J.; Cho, S.-W.; Reback, R. Mobility, housing markets, and schools: Estimating the effects of inter-district choice programs. J. Public Econ. 2012, 96, 604–614, doi:https://doi.org/10.1016/j.jpubeco.2012.04.002.
20. Agarwal, S.; Rengarajan, S.; Sing, T.F.; Yang, Y. School allocation rules and housing prices: A quasi-experiment with school relocation events in Singapore. Reg. Sci. Urban Econ. 2016, 58, 42–56, doi:https://doi.org/10.1016/j.regsciurbeco.2016.02.003.
21. Lee, Y.S. School districting and the origins of residential land price inequality. J. Hous. Econ. 2015, 28, 1–17, doi:https://doi.org/10.1016/j.jhe.2014.12.002.