1# RECORD 6c554bbc30f053c6546ae8df71b70b126d3f41782# TITLE: Evaluating the Impact of Urban Blue Space Accessibility on Housing Price: A Spatial Quantile Regression Approach Applied in Changsha, China3# YEAR: 2021.0 VENUE: Frontiers in Environmental Science DOI: 10.3389/fenvs.2021.6966264# AUTHORS: Tuo-Fu Huang; Qing-Yun He; Dongxiao Yang; Xiao Ouyang5# PAGES: 15 CANDIDATE TABLE PAGES (1-based): [8, 10]6# ABSTRACT: Urban nature spaces are increasingly recognized as essential urban features providing crucial amenities to the residents’ health and well-being. While many studies have been conducted focusing on the influence of green spaces on house prices, very few have explored the impact of urban blue spaces. In this study, we analyzed the proximity effects of different types and sizes of urban blue spaces on property value in Changsha metropolis, China, and examined the spatial quantile effect across different housing prices. A two-stage instrumental method (2SLS) hedonic model was employed to evaluate the impact of different types of urban blue space: river (mainstream and tributary), wetland park, and lakes (large, medium, and small). Spatial quantile regression (SQR) was then used to measure the spatial effect of accessibility on various house price ranges. The 2SLS results show that, except for small-sized lakes, proximity to blue spaces significantly increases property value. Analysis of the SQR model reveals that proximity to major blue spaces increases the marginal willingness-to-pay among homebuyers of high-priced properties, while ordinary blue spaces are more attractive to buyers of low- and medium-priced houses. This may be broadly related to the level of education, utility, and sensitivity to ecosystem services across income groups. Based on these findings, we recommend that urban planners adopt different strategies to promote space utilization efficiency. This is one of the few studies that analyze the capitalization of blue space accessibility in house prices across different quantiles. By categorizing blue spaces and employing the SQR model, this study found the effect of blue spaces on housing prices to be heterogeneous, providing new perspectives to the existing literature.789===== PAGE 1 (score 14, numeric-paren 0, table-words 0, var-words 14) =====1011Evaluating the Impact of Urban Blue12Space Accessibility on Housing Price:13A Spatial Quantile Regression14Approach Applied in Changsha, China15Huang Tuofu 1, He Qingyun 1*, Yang Dongxiao 2 and Ouyang Xiao 3161College of Geographic Science, Hunan Normal University, Changsha, China, 2School of Economics and Trade, Hunan17University, Changsha, China, 3Hunan Institute of Economic Geography, Hunan University of Finance and Economics, Changsha,18China19Urban nature spaces are increasingly recognized as essential urban features providing20crucial amenities to the residents’ health and well-being. While many studies have been21conducted focusing on the influence of green spaces on house prices, very few have22explored the impact of urban blue spaces. In this study, we analyzed the proximity effects23of different types and sizes of urban blue spaces on property value in Changsha24metropolis, China, and examined the spatial quantile effect across different housing25prices. A two-stage instrumental method (2SLS) hedonic model was employed to26evaluate the impact of different types of urban blue space: river (mainstream and27tributary), wetland park, and lakes (large, medium, and small). Spatial quantile28regression (SQR) was then used to measure the spatial effect of accessibility on29various house price ranges. The 2SLS results show that, except for small-sized lakes,30proximity to blue spaces significantly increases property value. Analysis of the SQR model31reveals that proximity to major blue spaces increases the marginal willingness-to-pay32among homebuyers of high-priced properties, while ordinary blue spaces are more33attractive to buyers of low- and medium-priced houses. This may be broadly related to34the level of education, utility, and sensitivity to ecosystem services across income groups.35Based on these findings, we recommend that urban planners adopt different strategies to36promote space utilization efficiency. This is one of the few studies that analyze the37capitalization of blue space accessibility in house prices across different quantiles. By38categorizing blue spaces and employing the SQR model, this study found the effect of blue39spaces on housing prices to be heterogeneous, providing new perspectives to the existing40literature.41Keywords: blue space, housing price, spatial quantile regression, accessibility, urban China42INTRODUCTION43In the process of intensified urbanization across modern China, the well-being of residents faces44numerous challenges. Deteriorating ecological environment, unhealthy lifestyle, and highly45competitive employment market (Wu Q. et al., 2020, Wu et al., 2021), have put significant46pressure and risks on the urban population’s health and well-being. In response to these social47Edited by:48Yanfei Li,49Hunan University of Technology and50Business, China51Reviewed by:52Han Li,53University of Miami, United States54He Nie,55National University of Singapore,56Singapore57Chen Ziyue,58Norwegian University of Science and59Technology, Norway60*Correspondence:61He Qingyun62heqingyun1848@gmail.com63Specialty section:64This article was submitted to65Environmental Economics and66Management,67a section of the journal68Frontiers in Environmental Science69Received: 17 April 202170Accepted: 03 May 202171Published: 20 May 202172Citation:73Tuofu H, Qingyun H, Dongxiao Y and74Xiao O (2021) Evaluating the Impact of75Urban Blue Space Accessibility on76Housing Price: A Spatial Quantile77Regression Approach Applied in78Changsha, China.79Front. Environ. Sci. 9:696626.80doi: 10.3389/fenvs.2021.69662681Frontiers in Environmental Science | www.frontiersin.org82May 2021 | Volume 9 | Article 69662683184ORIGINAL RESEARCH85published: 20 May 202186doi: 10.3389/fenvs.2021.696626878889===== PAGE 8 (score 186, numeric-paren 132, table-words 5, var-words 7) =====9091been corrected to a certain extent in the 2sls model and that the92use of instrumental variables in the spatial 2sls model is needed to93circumvent endogeneity.94Based on the regression coefficients of the spatial lag term, we95found that the 30–90 quantile is significant at the 10% level, and96the spatial dependence coefficient increases significantly with the97increase of the quantile (from 0.013 to 0.087). This indicates a98more pronounced spatial dependence in high-priced housing.99The confidence interval above the 50th quantile exceeds the 2SLS100conditional mean of 0.022, indicating that the 2SLS results may101overestimate the spatial dependence of low-priced houses and102underestimate the high-priced houses.103Blue Space of Changsha104The results in Table 4 show that the Xiangjiang River has a105significant impact on property value, with distance negatively106correlated with price. Each kilometer increase in the distance to107Xiangjiang River correlated with a decline of about 1.72% in108property value (i.e., 210 RMB per m2). The capitalization effect109of the Xiangjiang River (less than Yanghu wetland) was not110consistent with its status as the center of the nature landscape in111Changsha, which may due to increased flood risk. In 2017, the112Xiangjiang River experienced flooding, resulting in economic113losses for residents. Recent flood events can significantly alter114people’s perception and behavior towards flood risks (Bin and115Polasky, 2004). With respect to the different housing prices,116proximity to the Xiangjiang only has a significant impact above117the 70th quartile, peaking at 90th (from 2.36% to 3.14%, each118km). This suggests that nearness to the Xiangjiang river affects119mainly120house121prices122for123high-value124properties.125This126heterogeneity in proximity effects may be caused by better127river scenarios and increased safety found mainly in high-128value properties, similar to the conclusions of Wu Q. et al.129(2020).130The Liuyang River is a tributary of the Xiangjiang River,131flowing through the entire eastern part of the city, and is an132TABLE 3 | OLS and QR model results.133Variable134OLS (1)1350.1 (2)1360.2 (3)1370.3 (4)1380.4 (5)1390.5 (6)1400.6 (7)1410.7 (8)1420.8 (9)1430.9 (10)144Floor area145–0.0151460.01261470.00578148–0.0059149–0.0127150–0.0228*151–0.0292**152–0.0239**153–0.0390***154–0.0469***155[0.0107]156[0.0170]157[0.0122]158[0.0128]159[0.0124]160[0.0122]161[0.0123]162[0.0117]163[0.0133]164[0.0160]165Plot ratio166–0.0056**167–0.0042*168–0.0051***169–0.0066***170–0.0072**171–0.0078**172–0.0072*173–0.0013174–0.0035175–0.0056***176[0.0023]177[0.0019]178[0.0019]179[0.0019]180[0.0026]181[0.0028]182[0.0043]183[0.0045]184[0.0042]185[0.0033]186PM1870.0129***1880.0207***1890.0205***1900.0198***1910.0158***1920.0138***1930.0148***1940.00982*1950.005751960.00663197[0.0036]198[0.0069]199[0.0060]200[0.0036]201[0.0027]202[0.0038]203[0.0040]204[0.0050]205[0.0068]206[0.0060]207Building age208–0.0016***209–0.0027***210–0.0026***211–0.0028***212–0.0025***213–0.0022***214–0.0018***215–0.0016***216–0.00080***217–0.00057***218[0.0001]219[0.0002]220[0.0001]221[0.0001]222[0.0001]223[0.0001]224[0.0001]225[0.0001]226[0.0001]227[0.0001]228QMS2290.0834***2300.0495***2310.0510***2320.0644***2330.0750***2340.0739***2350.0781***2360.0827***2370.0845***2380.0979***239[0.0109]240[0.0189]241[0.0167]242[0.0150]243[0.0171]244[0.0171]245[0.0131]246[0.0188]247[0.0175]248[0.0167]249QPS2500.0294**2510.0427**2520.02222530.0235*2540.01772550.01572560.0274***2570.0389***2580.0468***2590.0299*260[0.0118]261[0.0192]262[0.0232]263[0.0164]264[0.0187]265[0.0170]266[0.0167]267[0.0158]268[0.0152]269[0.0174]270Subway2710.0481***2720.0606***2730.0417***2740.0451***2750.0500**2760.0612***2770.0553***2780.0690***2790.0480**2800.00169281[0.0129]282[0.0264]283[0.0211]284[0.0184]285[0.0197]286[0.0226]287[0.0180]288[0.0178]289[0.0216]290[0.0256]291LF2920.0081***2930.0097***2940.0031***2950.0035***2960.0041***2970.0045***2980.0034***2990.0039***3000.0083***3010.0159***302[0.0003]303[0.0005]304[0.0006]305[0.0005]306[0.0004]307[0.0004]308[0.0003]309[0.0003]310[0.0004]311[0.0005]312Dcbd313–0.0625**314–0.103***315–0.0675***316–0.060**317–0.069*318–0.0705**319–0.0615***320–0.0489**321–0.03295322–0.054323[0.0290]324[0.0331]325[0.0395]326[0.0302]327[0.0297]328[0.0333]329[0.0323]330[0.0195]331[0.0505]332[0.0813]333D.Xriver334–0.0168***335–0.0125336–0.00853370.002653380.00066339–0.0064340–0.0087341–0.013*342–0.0252**343–0.0280**344[0.0056]345[0.0111]346[0.0083]347[0.0072]348[0.0061]349[0.0078]350[0.0072]351[0.0071]352[0.0097]353[0.0083]354D.Lriver355–0.0245**356–0.0357357–0.0318**358–0.0315***359–0.0263***360–0.0223***361–0.01385362–0.0075363–0.0025364–0.0171365[0.0120]366[0.0224]367[0.0158]368[0.0119]369[0.0083]370[0.0061]371[0.0371]372[0.0435]373[0.0446]374[0.0537]375D.YHwetland376–0.0251***377–0.00795378–0.0183**379–0.0166*380–0.0235**381–0.0273***382–0.0299***383–0.0312***384–0.0405***385–0.0420***386[0.0049]387[0.0097]388[0.0101]389[0.0092]390[0.0095]391[0.0087]392[0.0086]393[0.0069]394[0.0067]395[0.0084]396D.LL397–0.0173***3980.00134399–0.0156400–0.0164401–0.0205*402–0.0207**403–0.0223**404–0.0243***405–0.0209**406–0.0319***407[0.0066]408[0.0181]409[0.0131]410[0.0121]411[0.0089]412[0.0069]413[0.0052]414[0.0068]415[0.0077]416[0.0105]417D.ML418–0.0143***419–0.0344***420–0.0156421–0.0039422–0.0014423–0.004424–0.0082425–0.0066426–0.0053427–0.0019428[0.0045]429[0.0108]430[0.0098]431[0.0048]432[0.0043]433[0.0045]434[0.0055]435[0.0052]436[0.0048]437[0.0058]438D.SL439–0.0124440–0.02144410.00524442–0.00014430.00444440.00533445–0.0113446–0.0118447–0.0098448–0.0421449[0.0167]450[0.0260]451[0.0288]452[0.0212]453[0.0241]454[0.0216]455[0.0139]456[0.0183]457[0.0293]458[0.0378]459D.YL460–0.0234***461–0.0277***462–0.0323***463–0.0349***464–0.0311***465–0.0281***466–0.0251***467–0.0190**468–0.0182**469–0.0212**470[0.0045]471[0.0068]472[0.0068]473[0.0056]474[0.0037]475[0.0052]476[0.0044]477[0.0078]478[0.0061]479[0.0073]480D.NWG481–0.0124***482–0.0082***483–0.0108***484–0.0861***4850.0089***486–0.0138***487–0.0135***488–0.0131***489–0.0095***490–0.0054491[0.0047]492[0.0021]493[0.0022]494[0.0015]495[0.0022]496[0.0024]497[0.0022]498[0.0026]499[0.0026]500[0.0038]501Cons5029.603***5039.136***5049.503***5059.502***5069.596***5079.734***5089.738***5099.701***5109.836***51110.14***512[0.1021]513[0.2057]514[0.2139]515[0.1399]516[0.1168]517[0.1379]518[0.1226]519[0.1027]520[0.1305]521[0.1737]522Adj.R25230.519524Pseudo-R25250.3215260.3075270.3135280.3465290.3315300.3585310.3295320.3435330.341534***p < 0.01; **p < 0.05; *p < 0.10535Frontiers in Environmental Science | www.frontiersin.org536May 2021 | Volume 9 | Article 6966265378538Tuofu et al.539Evaluate the Impact of Urban Blue540541542===== PAGE 9 (score 14, numeric-paren 0, table-words 2, var-words 9) =====543544important blue space in Changsha. As shown in Table 4,545proximity to the Liuyang River has a significant impact on546housing prices (at the 10% level), with a 2SLS conditional547mean regression coefficient of 2.36% value decline per km.548The SQR results show that only low and medium-priced549houses (i.e., 30th–50th quantile) are affected by proximity to550this river.551Proximity to the Yanghu National Wetland Park was also552found to significantly affect housing prices, similar to the findings553of Mei et al. (2018), with every kilometer distance correlating with554a 2.27% decline in housing prices (equivalent to 277 RMB per555m2). Similar to the effects of the Xiangjiang River, the SQR shows556that the price effect caused by proximity to the Yanghu wetland557increases as the quantile rises and is most pronounced for high-558value properties (From 1.53% to 2.85%). Yanghu National559Wetland Park was found to have a strong effect on housing560prices among the different landscape factors in Changsha and has561an estimated coefficient almost higher than any blue and green562space. This may suggest wetlands are increasingly becoming more563valued in cities.564While urban lake is the most common blue space in the city,565our results found noticeable differences between varying sizes.566The conditional mean coefficient of large lakes (LL) using 2sls was5671.66% value loss per km (i.e., 202 RMB per m2), significant at the5681% level, and is higher than most landscape coefficients. LL has a569significant impact on house prices above the 40th quantile. The570estimated LL coefficients increased significantly, especially for571mid-to-high-priced housing above the 60–70 range (2.31%,5722.35%, respectively), similar to the impact of wetland parks573and the Xiangjiang River landscape. The impact of medium574lakes (ML) was significant at the 10% level, resulting in a5751.45% value decline per km. The ML coefficient value was576smaller than for large lakes and had a nonlinear trend577across quantiles. According to the SQR, ML affected house578prices below the 10th quantile but had no significant impact579on those above the 10th quantile. Small lake (SL) is the580commonest blue space in Changsha city. Although SL has a581negative coefficient, the variable was not statistically significant.582Housing prices seem to be unaffected by small water bodies,583which is in sharp contrast to the impact on price of other kinds of584blue space.585Green Spaces in Changsha586For comparison, we used the non-water body green spaces as the587control variables. As shown in Table 4, the conditional mean loss588of property value for the Yuelu Forest Park was 2.03% per km,589relatively similar to some previous studies of forest parks590(Czembrowski and Kronenberg, 2016). The SQR results show591that proximity to the Yuelu forest does not significantly influence592house prices above the 80th quantile. For the other common593green spaces, their impact was found to have a noticeable steady594trend. The 2SLS results were significant at the 1% level, with a595value loss of 1.21%. Most of the quantile coefficients for non-596water green spaces were higher. These findings are consistent597with existing studies (Liao and Wang, 2012), and the estimated598accessibility coefficient is also comparatively similar.599FIGURE 2 | Coefficients of blue spaces estimated by QR.600Frontiers in Environmental Science | www.frontiersin.org601May 2021 | Volume 9 | Article 6966266029603Tuofu et al.604Evaluate the Impact of Urban Blue605606607===== PAGE 10 (score 190, numeric-paren 129, table-words 6, var-words 10) =====608609Other Key Variables610The study also found that the housing price gradient can diversify611with the distance from the CBD (Wen et al., 2014). By 2SLS, each612kilometer away from the CBD leads to a 5.75% decay of house613prices. The SQR results showed that the price for accessibility to614the CBD decreases for higher quantiles (from 13.65% to 3.81%,615each km). Low- and medium-priced properties were influenced616more strongly by CBD than high-priced houses. Neighborhood617variables, such as proximity to quality primary and middle618education facilities (2.80% and 8.17%, respectively) and nearness619to a subway station (4.72%), were also found to have significant620impact on housing prices. These results suggest education-related621factors are becoming important factors affecting property house622prices in China.623Density Test of Urban Blue Accessibility624As suggested by Coulson and McMillen (2007), the distribution625of house prices varying to the explanatory variables could exam626the heterogeneity across dependent variable quantiles. If a change627in the value of the independent variable (distance) causes a628change in the dependent variable’s distribution, this would629mean that different quantiles would have varying responses to630the independent variable and that heteroscedasticity exists across631different quantiles. To investigate this effect, we drew the kernel632density curve to compare the distribution of prices among633varying accessibility to various urban blue parameters. The634results are presented in Figure 4. For the Xiangjiang River, the635kernel curve changes its kurtosis as the distance increases.636Similarly, the parameters Liuyang river, Yanghu wetland, large637lakes, and middle lakes have a significant change in the638distribution of housing prices when the value of accessibility639independent variable changing. Most have changes in the kurtosis640(right tail of the distribution), with some increase in variance641distribution, coincident with the SQR results. In contrast, the642kernel density curve for small lakes has little distributional643change, which means no heteroscedasticity in this variable.644TABLE 4 | 2Stage Least Squares and SQR model results.645Variable6462SLS (1)6470.1 (2)6480.2 (3)6490.3 (4)6500.4 (5)6510.5 (6)6520.6 (7)6530.7 (8)6540.8 (9)6550.9 (10)656Floor area657–0.01536580.00996590.00319660–0.00587661–0.0131662–0.0224*663–0.0273**664–0.0278**665–0.0436**666–0.0566**667[0.0147]668[0.0138]669[0.0147]670[0.0120]671[0.0094]672[0.0098]673[0.0080]674[0.0125]675[0.0160]676[0.0219]677Plot ratio678–0.00549**679–0.00525**680–0.005**681–0.00677***682–0.00704*683–0.00759684–0.00684685–0.00135686–0.00263687–0.0065688[0.0023]689[0.0021]690[0.0024]691[0.0027]692[0.0023]693[0.0041]694[0.0045]695[0.0048]696[0.0037]697[0.0040]698PM6990.0129***7000.0212***7010.0211***7020.0199***7030.0159***7040.0143***7050.0143***7060.0103**7070.00461708–0.000734709[0.0036]710[0.0050]711[0.0045]712[0.0037]713[0.0044]714[0.0056]715[0.0053]716[0.0045]717[0.0072]718[0.0046]719Building age720–0.0015***721–0.0026***722–0.0024***723–0.0026***724–0.0024***725–0.0021***726–0.0020***727–0.0017***728–0.0009***729–0.0006***730[0.0001]731[0.0002]732[0.0001]733[0.0001]734[0.0001]735[0.0001]736[0.0001]737[0.0001]738[0.0001]739[0.0001]740QMS7410.0817***7420.0623***7430.0478**7440.0653***7450.0705***7460.0669***7470.0743***7480.0797***7490.0773***7500.0981***751[0.0111]752[0.0210]753[0.0145]754[0.0115]755[0.0136]756[0.0099]757[0.0127]758[0.0134]759[0.0124]760[0.0164]761QPS7620.0280**7630.0503***7640.02087650.02447660.01827670.01047680.0236*7690.0348**7700.0462**7710.0342772[0.0119]773[0.0136]774[0.0140]775[0.0149]776[0.0134]777[0.0178]778[0.0138]779[0.0161]780[0.0156]781[0.0306]782Subway7830.0472***7840.0534***7850.0470***7860.0417**7870.0475***7880.0573***7890.0553***7900.0707***7910.0444*7920.0165793[0.0129]794[0.0220]795[0.0178]796[0.0142]797[0.0196]798[0.0216]799[0.0183]800[0.0200]801[0.0205]802[0.0230]803LF8040.0079***8050.0113***8060.0033***8070.0031***8080.0045***8090.0056***8100.0023***8110.0045***8120.0073***8130.0125***814[0.0003]815[0.0006]816[0.0007]817[0.0006]818[0.0005]819[0.0004]820[0.0003]821[0.0003]822[0.0004]823[0.0007]824D.CBD825–0.0575**826–0.1365***827–0.079***828–0.0595***829–0.0672***830–0.0557*831–0.0555**832–0.0381**833–0.00635834–0.0417835[0.0267]836[0.0450]837[0.0271]838[0.0202]839[0.0266]840[0.0291]841[0.0274]842[0.0224]843[0.0507]844[0.0984]845D.Xriver846–0.0172***847–0.0128848–0.007068490.0004850–0.0007851–0.0073852–0.0066853–0.0115854–0.0236**855–0.0314***856[0.0057]857[0.0106]858[0.0084]859[0.0071]860[0.0086]861[0.0091]862[0.0097]863[0.0135]864[0.0132]865[0.0097]866D.Lriver867–0.0236**868–0.0312869–0.0325870–0.0221**871–0.0252***872–0.0214***873–0.0112874–0.0062875–0.0015876–0.0083877[0.0118]878[0.0512]879[0.0529]880[0.0111]881[0.0104]882[0.0081]883[0.0261]884[0.0263]885[0.0363]886[0.0543]887D.YHwetland888–0.0227***889–0.0125890–0.0153*891–0.0171**892–0.0222***893–0.0237***894–0.0235***895–0.0215*896–0.0260**897–0.0285***898[0.0056]899[0.0099]900[0.0081]901[0.0069]902[0.0067]903[0.0071]904[0.0095]905[0.0086]906[0.0071]907[0.0073]908D.LL909–0.0166**910–0.0074911–0.0189912–0.0152913–0.0210**914–0.0179**915–0.0231***916–0.0235***917–0.0195***918–0.0143*919[0.0008]920[0.0011]921[0.0184]922[0.0181]923[0.0103]924[0.0087]925[0.0095]926[0.0093]927[0.0077]928[0.0081]929D.ML930–0.0145*931–0.0332**932–0.0159933–0.0051934–0.0013935–0.0038936–0.0071937–0.0051938–0.0049939–0.0050940[0.0085]941[0.0137]942[0.0122]943[0.0066]944[0.0066]945[0.0047]946[0.0050]947[0.0048]948[0.0055]949[0.0041]950D.SL951–0.0128952–0.02079530.00136954–0.00659550.00368956–0.0008957–0.0107958–0.0137959–0.0218960–0.029961[0.0167]962[0.0267]963[0.0216]964[0.0231]965[0.0220]966[0.0228]967[0.0227]968[0.0284]969[0.0284]970[0.0345]971D.YL972–0.0203***973–0.0424***974–0.0373***975–0.0320***976–0.0285***977–0.0211***978–0.0214***979–0.0176*980–0.0066981–0.00744982[0.0058]983[0.0084]984[0.0085]985[0.0073]986[0.0077]987[0.0072]988[0.0066]989[0.0103]990[0.0118]991[0.0111]992D.NWG993–0.0121***9940.0043995–0.005996–0.0082**997-0.0091**998–0.0155***999–0.0164***1000–0.0183***1001–0.0114***1002–0.009071003[0.0017]1004[0.0032]1005[0.0034]1006[0.0025]1007[0.0014]1008[0.0018]1009[0.0021]1010[0.0022]1011[0.0030]1012[0.0301]1013WlnP10140.0221**1015–0.00281016–0.007210170.0136*10180.0152*10190.0443**10200.0468**10210.0447***10220.0809***10230.0874***1024[0.0121]1025[0.0076]1026[0.0084]1027[0.0081]1028[0.0091]1029[0.0223]1030[0.0232]1031[0.0192]1032[0.0231]1033[0.0157]1034Cons10359.364***103610.18***10379.799***10389.377***10399.444***10409.236***10419.273***10429.214***10438.895***10449.047***1045[0.2961]1046[0.3726]1047[0.4683]1048[0.3900]1049[0.3734]1050[0.3357]1051[0.3232]1052[0.3933]1053[0.6152]1054[0.6469]1055Adj.R210560.5311057Pseudo-R210580.33510590.31110600.32910610.35210620.34110630.36710640.33910650.36110660.3531067***p < 0.01; **p < 0.05; *p < 0.10.1068Frontiers in Environmental Science | www.frontiersin.org1069May 2021 | Volume 9 | Article 6966261070101071Tuofu et al.1072Evaluate the Impact of Urban Blue107310741075===== PAGE 11 (score 0, numeric-paren 0, table-words 0, var-words 0) =====10761077FIGURE 3 | Coefficients of blue spaces estimated by 2SQR.1078FIGURE 4 | Shifts in the distribution of house prices.1079Frontiers in Environmental Science | www.frontiersin.org1080May 2021 | Volume 9 | Article 6966261081111082Tuofu et al.1083Evaluate the Impact of Urban Blue1084