1# RECORD 6a582c18a0b55c4939a0e4812ae293fd34b678de2# TITLE: Capitalization of Public School Quality into Housing Prices: An Empirical Analysis Based on School District Housing for Public Primary Schools in Shanghai3# YEAR: 2018.0 VENUE: nan DOI: 10.4236/chnstd.2018.740254# AUTHORS: Tiancheng Zhou5# PAGES: 42 CANDIDATE TABLE PAGES (1-based): [12, 13, 16, 17, 18]6# ABSTRACT: This paper examines the capitalization of public school quality into housing prices in the specific context of China’s compulsory education system and “district correspondence enrollment” policy, which stipulates a strict correspondence between residential estates and enrollment into public primary schools. Taking 344 neighborhoods in 7 main districts of Shanghai, China as its sample, this paper carries out detailed descriptive analysis of the data. It also employs the traditional Hedonic Price Model and 2SLS regression method to quantitatively calculate the exact capitalization rates while isolating eight non-school attributes affecting housing prices. It yields the conclusion that for every one-rank improvement in the quality of the corresponding public primary school, the average housing price of a neighborhood is projected to increase by 3.1%, 2.8%, and 1.9%, on the citywide, urban, and suburban scale, respectively. The capitalization effect of public school quality in housing prices is statistically significant, so the status-quo of the distribution of public educational resources in Shanghai is still considerably unequal. In the final section of this paper, the significance of this research is discussed and comprehensive policy recommendations and action plan are given in an attempt to mitigate the school district housing fever and educational inequality.789===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) =====1011Chinese Studies, 2018, 7, 286-327 12http://www.scirp.org/journal/chnstd 13ISSN Online: 2168-541X 14ISSN Print: 2168-5428 15 16DOI: 10.4236/chnstd.2018.74025 Nov. 13, 2018 17286 18Chinese Studies19 20 21 22 23Capitalization of Public School Quality into 24Housing Prices: An Empirical Analysis Based 25on School District Housing for Public Primary 26Schools in Shanghai 27Tiancheng Zhou 28Shanghai Pinghe School, Shanghai, China 29 30 31 32Abstract 33This paper examines the capitalization of public school quality into housing 34prices in the specific context of China’s compulsory education system and 35“district correspondence enrollment” policy, which stipulates a strict corres-36pondence between residential estates and enrollment into public primary 37schools. Taking 344 neighborhoods in 7 main districts of Shanghai, China as 38its sample, this paper carries out detailed descriptive analysis of the data. It 39also employs the traditional Hedonic Price Model and 2SLS regression me-40thod to quantitatively calculate the exact capitalization rates while isolating 41eight non-school attributes affecting housing prices. It yields the conclusion 42that for every one-rank improvement in the quality of the corresponding 43public primary school, the average housing price of a neighborhood is pro-44jected to increase by 3.1%, 2.8%, and 1.9%, on the citywide, urban, and sub-45urban scale, respectively. The capitalization effect of public school quality in 46housing prices is statistically significant, so the status-quo of the distribution 47of public educational resources in Shanghai is still considerably unequal. In 48the final section of this paper, the significance of this research is discussed 49and comprehensive policy recommendations and action plan are given in an 50attempt to mitigate the school district housing fever and educational inequa-51lity. 52 53Keywords 54Capitalization of Educational Quality, Housing Prices, School District 55Housing Fever, Shanghai, China, Hedonic Price Model, Two-Stage Least 56Squares (2SLS) Regression Method 57How to cite this paper: Zhou, T. C. (2018). 58Capitalization of Public School Quality into 59Housing Prices: An Empirical Analysis 60Based on School District Housing for Pub-61lic Primary Schools in Shanghai. Chinese 62Studies, 7, 286-327. 63https://doi.org/10.4236/chnstd.2018.74025 64Received: October 11, 2018 65Accepted: November 10, 2018 66Published: November 13, 2018 67Copyright © 2018 by author and 68Scientific Research Publishing Inc. 69This work is licensed under the Creative 70Commons Attribution International 71License (CC BY 4.0). 72http://creativecommons.org/licenses/by/4.0/73 74 75Open Access767778===== PAGE 12 (score 14, numeric-paren 0, table-words 3, var-words 4) =====7980T. C. Zhou 81 82 83DOI: 10.4236/chnstd.2018.74025 84297 85Chinese Studies86 87Table 2. Descriptive analysis of independent variables, citywide. 88Variable 89Unit 90Average 91Standard 92Deviation 93Maximum 94Value 95Minimum 96Value 97Quality of the 98Corresponding Public 99Primary School 100no unit 10120.31 10211.41 10344 1041 105Housing Price 106RMB/m2 10776,949.81 10824,548.04 109150,621 11028,641 111Distance to the 112Corresponding Public 113Primary School 114Meters 115748.13 116504.75 1174600 11810 119Distance to the 120District’s CBD 121Meters 1222141.80 1231439.08 1247700 12570 126Property Management Fee 127RMB/m2 128per month 1292 1301.72 13114.8 1320.3 133Green Coverage Ratio 134% 13534.03 1369.24 13770 13810 139Elevator 140no unit 1410.52 1420.50 1431 1440 145Residential Area 146m2 14792.41 14850.92 149404 15018 151Floor Area Ratio 152no unit 1532.21 1541.09 1558.9 1560.36 157Building Age 158Years 15919.54 16012.27 161178 1621 163 164Table 3. Descriptive analysis of independent variables, urban. 165Variable 166Unit 167Average 168Standard 169Deviation 170Maximum 171Value 172Minimum 173Value 174Quality of the 175Corresponding Public 176Primary School 177no unit 17814.53 1797.58 18029 1811 182Housing Price 183RMB/m2 18488,311.57 18518,676.47 186150,621 18750,933 188Distance to the 189Corresponding Public 190Primary School 191Meters 192650.94 193383.03 1941900 19510 196Distance to the 197District’s CBD 198Meters 1992059.59 2001376.79 2017700 20270 203Property Management Fee 204RMB/m2 205per month 2062 2071.96 20814.8 2090.13 210Green Coverage Ratio 211% 21232.72 2139.17 21470 21510 216Elevator 217no unit 2180.62 2190.49 2201 2210 222Residential Area 223m2 22492.10 22553.37 226404 22718 228Floor Area Ratio 229no unit 2302.47 2311.08 2325.68 2330.38 234Building Age 235Years 23620.80 23713.83 238178 2391 240 241environmental and economic situations in Shanghai, they prove the validity and 242real-life significance of the sample and data sources of this paper and lay the 243foundation for following technical analyses. 244The number of KPS in each district of Shanghai is listed below in Table 5 and 245illustrated in Figure 2. The four districts with the most KPS are Xuhui, Hua-246ngpu, Pudong, and Yangpu in order. These districts are all in the urban area and 247share the characteristic of advanced socioeconomic activities and educational 248249250===== PAGE 13 (score 10, numeric-paren 0, table-words 2, var-words 4) =====251252T. C. Zhou 253 254 255DOI: 10.4236/chnstd.2018.74025 256298 257Chinese Studies258 259Table 4. Descriptive analysis of independent variables, suburban. 260Variable 261Unit 262Average 263Standard 264Deviation 265Maximum 266Value 267Minimum 268Value 269Quality of the 270Corresponding Public 271Primary School 272no unit 27334.23 2745.98 27544 27622 277Housing Price 278RMB/m2 27949,993.49 28013,077.58 28180,060 28228,461 283Distance to the 284Corresponding Public 285Primary School 286Meters 287978.72 288662.43 2894600 29040 291Distance to the 292District’s CBD 293Meters 2942336.86 2951566.97 2967100 297110 298Property Management Fee 299RMB/m2 300per month 3011.07 3020.71 3033.5 3040.2 305Green Coverage Ratio 306% 30737.16 3088.72 30970 31017 311Elevator 312no unit 3130.30 3140.46 3151 3160 317Residential Area 318m2 31993.13 32044.82 321303 32229 323Floor Area Ratio 324no unit 3251.57 3260.86 3278.9 3280.36 329Building Age 330Years 33116.55 3326.45 33338 3342 335 336Table 5. Number of key primary schools (kps) in shanghai, by district. 337District 338Number 339District 340Number 341District 342Number 343Xuhui 34414 345Pudong 34614 347Huangpu 34813 349Yangpu 35010 351Minhang 3529 353Jiading 3549 355Baoshan 3568 357Jing’an 3587 359Putuo 3606 361Jinshan 3626 363Songjiang 3646 365Hongkou 3665 367Changning 3684 369Fengxian 3703 371Qingpu 3722 373 374 375Figure 2. Number of KPS in Shanghai, by district. 376 377resources, and thus relatively higher housing prices. The suburban districts, by 378contrast, generally have less developed economies and lower-quality educational 379resources. 3800381238243836384838510386123871438816389Nubmer of KPS390Districts391392393===== PAGE 14 (score 6, numeric-paren 0, table-words 0, var-words 6) =====394395T. C. Zhou 396 397 398DOI: 10.4236/chnstd.2018.74025 399299 400Chinese Studies401 402Figures 3-4 offer a clearer observation of the more pronounced capitalization 403effect of public school quality into housing prices in the urban districts by visua-404lizing the distribution of housing prices. The distribution of housing prices in 405Huangpu District is illustrated graphically below in Figure 3 where the peaks 406and troughs correspond to the different housing prices of neighborhoods in dif-407ferent school districts and the trend of change between them. One school may 408correspond to several neighborhoods whose housing prices decrease as the dis-409tance to the school increases. The conspicuous peaks are likely to be caused by 410sought-after schools whose housing prices are significantly higher than those of 411others. The varying heights of the peaks illustrate the different housing prices for 412different schools. 413Figure 4 and Figure 5 illustrate the distributions of housing prices in Xuhui 414and Changning Districts, respectively. Similar to Figure 3, the peaks and troughs 415correspond to neighborhoods in different school districts. But the unique cha-416racteristic setting these two districts apart from Huangpu is the differences be-417tween housing prices of different neighborhoods are obvious in Xuhui and 418Changning. In Huangpu, however, there isn’t such sharp difference. 4193.3. Hedonic Price Model 420To calculate the exact capitalization rates, the traditional Hedonic Price Model is 421employed. The basic assumption of our econometric model assumes that hous-422ing price reflect the market values of public school quality and other characteris-423tics. 424After referring to existing researches on real estate valuation and considering 425the quantification of the variable studied here, the logarithm functional is used 426to establish the basic model. Specifically, positive and continuous independent 427variables (e.g., distance, building age, floor area ratio) are adopted in logarithmic 428form, while the variables quantified through scoring or dummy variables 429(namely quality of corresponding public primary school and elevator) are adopted 430 431 432Figure 3. Distribution of housing prices in Huangpu district. 4330434200004354000043660000437800004381000004391200004401400004411600004421 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64443Housing Prices444445446===== PAGE 16 (score 31, numeric-paren 0, table-words 11, var-words 4) =====447448T. C. Zhou 449 450 451DOI: 10.4236/chnstd.2018.74025 452301 453Chinese Studies454 455The results of regression analysis and variance analysis on the citywide scale 456are given in Table 6 and Table 7, respectively. The variance analysis confirms 457that the model fits very well with the experimental data and so the coefficients it 458yielded can be used to explain the sample. Every coefficient fits with its expected 459sign, and their P-values further verify that most coefficients are statistically sig-460nificant. Specifically, the coefficient of the quality of the corresponding public 461primary school is −0.031, meaning that housing price increases by 3.1% for a 462one-rank improvement of its corresponding school (which is equivalent to its 463numerical rank decreasing by 1). 464The residual plot and line fit plot for the variable “Quality of the Correspond-465ing Public Primary School” are cited below in Figure 6 and Figure 7 to illustrate 466its statistical significance. 467The results of regression analysis and variance analysis in the urban areas are 468given in Table 8 and Table 9, respectively. The variance analysis confirms that 469the model fits very well with the experimental data and so the coefficients it 470yielded can be used to explain the sample. The P-values of the coefficients fur-471ther verify that most of them are statistically significant and fit with their ex-472pected signs. Specifically, the coefficient of the quality of the corresponding pub-473lic primary school is −0.028, meaning that housing price increases by 2.8% for a 474one-rank improvement of its corresponding school (which is equivalent to its 475numerical rank decreasing by 1). 476 477Table 6. Regression analysis, citywide. 478Variable 479Coefficient 480Standard 481Deviation 482t Stat 483P-value 484Intercept 48512.156 4860.199 48761.061 4882.862E−183 489Quality of the Corresponding 490Public Primary School 491−0.031 4920.001 493−33.790 4948.616E−110 495Distance to the Corresponding 496Public Primary School 497−0.027 4980.010 499−2.657 5000.008 501Distance to the District’s CBD 502−0.007 5030.011 504−0.663 5050.508 506Property Management Fee 5070.029 5080.019 5091.536 5100.125 511Green Coverage Ratio 5120.002 5130.032 5140.072 5150.942 516Elevator 5170.003 5180.022 5190.123 5200.902 521Residential Area 522−0.003 5230.024 524−0.106 5250.916 526Floor Area Ratio 527−0.016 5280.020 529−0.775 5300.439 531Building Age 532−0.034 5330.023 534−1.466 5350.144 536 537Table 7. Variance analysis, citywide. 538Name 539df 540SS 541MS 542F 543Significance F 544Regression Analysis 5459 54644.456 5474.940 548208.954 5491.9458E−131 550Residuals 551334 5527.896 5530.024 554 555 556Sum 557343 55852.352 559 560 561 562563564===== PAGE 17 (score 16, numeric-paren 0, table-words 4, var-words 4) =====565566T. C. Zhou 567 568 569DOI: 10.4236/chnstd.2018.74025 570302 571Chinese Studies572 573Table 8. Regression analysis, urban. 574Variable 575Coefficient 576Standard 577Deviation 578t Stat 579P-value 580Intercept 58112.210 5820.207 58359.068 5848.565E−142 585Quality of the Corresponding 586Public Primary School 587−0.028 5880.001 589−22.069 5906.56E−59 591Distance to the Corresponding 592Public Primary School 593−0.012 5940.010 595−1.146 5960.253 597Distance to the District’s CBD 598−0.024 5990.012 600−1.948 6010.053 602Property Management Fee 6030.016 6040.020 6050.823 6060.411 607Green Coverage Ratio 6080.032 6090.031 6101.005 6110.316 612Elevator 613−0.010 6140.024 615−0.429 6160.668 617Residential Area 618−0.001 6190.025 620−0.041 6210.968 622Floor Area Ratio 623−0.054 6240.023 625−2.381 6260.018 627Building Age 628−0.071 6290.023 630−3.122 6310.002 632 633Table 9. Variance analysis, urban. 634Name 635df 636SS 637MS 638F 639Significance F 640Regression Analysis 6419 64211.335 6431.259 64468.296 6453.085E−60 646Residuals 647232 6484.278 6490.018 650 651 652Sum 653241 65415.613 655 656 657 658 659 660Figure 6. Variable “quality of the corresponding public primary school” residual plot. 661 662 663Figure 7. Variable “quality of the corresponding public primary school” line fit plot. 664-1665-0.566606670.5668166906701067120672306734067450675Resiuals676Variable “Quality of the Corresponding Public Primary 677School” Residual Plot678679680===== PAGE 18 (score 21, numeric-paren 0, table-words 6, var-words 5) =====681682T. C. Zhou 683 684 685DOI: 10.4236/chnstd.2018.74025 686303 687Chinese Studies688 689The results of regression analysis and variance analysis in the suburban areas 690are given in Table 10 and Table 11, respectively. The variance analysis confirms 691that the model fits very well with the experimental data and so the coefficients it 692yielded can be used to explain the sample. The P-values of the coefficients fur-693ther verify that most of them are statistically significant and fit with their ex-694pected signs. Specifically, the coefficient of the quality of the corresponding pub-695lic primary school is −0.019, meaning that housing price increases by 1.9% for a 696one-rank improvement of its corresponding school (which is equivalent to its 697numerical rank decreasing by 1). 6984. Conclusion 699This paper establishes hedonic regression analysis to investigate the capitaliza-700tion effect of public school quality into housing prices and quantitatively calcu-701late the exact capitalization rates. Using the data of 344 residential neighbor-702hoods over 7 main districts in Shanghai, it yielded the following empirical re-703sults: 7041) The quality of the corresponding public primary school has a significant 705positive effect on housing prices. The compulsory educational facilities are capi-706talized into estate prices in Shanghai. Under the strict enforcement of the “dis-707trict correspondence enrollment” policy, families are willing to pay higher prices 708for houses so that their children can enroll in better schools. 7092) The capitalization rate of public school quality is calculated to be 3.1%, 7102.8%, and 1.9% on the citywide, urban, and suburban scales, respectively. When 711the rank of the corresponding public primary school improves by one, the aver-712age housing price in the neighborhood increases by the given percentage. 7133) The capitalization effects of public school quality into housing prices exhi-714bit a Matthew Effect, i.e. the amount of capitalization increases rapidly as the 715quality of the corresponding public primary school approaches the top citywide, 716 717Table 10. Regression analysis, suburban. 718Variable 719Coefficient 720Standard 721Deviation 722t Stat 723P-value 724Intercept 72510.743 7260.520 72720.645 7282.677E−36 729Quality of the Corresponding 730Public Primary School 731−0.019 7320.003 733−6.246 7341.289E−08 735Distance to the Corresponding 736Public Primary School 737−0.042 7380.021 739−2.001 7400.048 741Distance to the District’s CBD 7420.042 7430.023 7441.806 7450.074 746Property Management Fee 7470.016 7480.039 7490.407 7500.685 751Green Coverage Ratio 7520.069 7530.095 7540.725 7550.470 756Elevator 7570.075 7580.049 7591.511 7600.134 761Residential Area 7620.049 7630.057 7640.868 7650.387 766Floor Area Ratio 767−0.043 7680.043 769−0.995 7700.322 771Building Age 7720.049 7730.062 7740.786 7750.434 776777778===== PAGE 19 (score 10, numeric-paren 0, table-words 3, var-words 4) =====779780T. C. Zhou 781 782 783DOI: 10.4236/chnstd.2018.74025 784304 785Chinese Studies786 787Table 11. Variance Analysis, Suburban. 788Name 789df 790SS 791MS 792F 793Significance F 794Regression Analysis 7959 7961.850 7970.206 7987.884 7991.5289E−08 800Residuals 80192 8022.399 8030.026 804 805 806Sum 807101 8084.249 809 810 811 812Note: When the sample is divided into urban and suburban areas for separate analyses, its size diminishes, 813so the coefficients of a few variables disagree with their expected signs or have P-value greater than 0.1. Al-814so, due to the particularity of the designation of school districts and the real estates in Shanghai, many old, 815unmodern neighborhoods are in close proximity to high-ranking public primary schools near the city cen-816ter while the neighborhood and structural attributes vary quite erratically especially in suburban areas. Yet 817the P-value, residual plot, and line fit plot for the school quality variable in all three dimensions are statisti-818cally significant and can thus reliably reveal the causational effects and validate the conclusions of this 819study. 820 821while the effect is much milder for schools at a middle or middle-lower level. 822Since the increase in housing price is given in percentages, the higher the rank, 823the more the capitalization effect compounds. On the citywide scale where a 824larger disparity in school quality is present, the measured capitalization rate is 825higher than in separate samples. These patterns reflect the serious inequality of 826access to public educational resources behind the school district housing fever: 827The gap between low- and high-quality educational resources widened and be-828came even more unaffordable for the low-income families. 8295. Further Implications 8305.1. Research Significance 831As one of the few existing studies conducted on Shanghai’s School District 832Housing Fever, especially after the strengthening of the “district correspondence 833enrollment” policy in 2014, this paper examines the status-quo of the local capi-834talization effect. Most notably, it deals with the lack of sufficient data on public 835school quality by synthesizing multiple sources. It also samples housing infor-836mation of 344 neighborhoods in Shanghai and considers seven control variables 837that account for every aspect in the estate valuation. 838Moreover, by using the 2SLS regression method, it avoids the endogeneity bi-839as that has been an issue in most existing researches in the field so that it was 840able to yield statistically significant results which are also consistent with real-life 841situations. 8425.2. Policy Recommendations and Action Plans to Mitigate the 843School District Housing Fever 844The conclusions of this study reveal that the status-quo of the distribution of 845public educational resources in Shanghai is still considerably unequal. The “dis-846trict correspondence enrollment” policy itself is a demand-side policy as it regu-847late the method for enrollment, and so are most of the policies and measures 848adopted in an attempt to improve China’s educational inequality, such as desig-849nating common school districts for higher-ranking and lower-ranking schools 850