1# RECORD a7fe404df2e413395450e192cebe2f68aa9215792# TITLE: IMPACT OF ACCESS TO SUNLIGHT ON RESIDENTIAL PROPERTY VALUES: AN EMPIRICAL ANALYSIS OF THE HOUSING MARKET IN SHANGHAI3# YEAR: 2022.0 VENUE: International Journal of Strategic Property Management DOI: 10.3846/ijspm.2022.180044# AUTHORS: Y. Zhong; Jia-Jun Lu; Zhe-Han Li5# PAGES: 12 CANDIDATE TABLE PAGES (1-based): [7, 8, 11, 12]6# ABSTRACT: As an important environmental amenity, sunlight brings us a large number of benefits and improves the quality of our daily lives, and its welfare measurement depends on concrete living conditions. The purpose of this article is to empirically document the non-marketed value of sunlight in light of the view orientation of an apartment in the context of the housing market. Using a hedonic pricing model estimated with the real estate transaction data over 40,000 housing units in 2019–2021 in Shanghai, it is found that: (1) homeowners, on average, are willing to pay an extra 7.2% to choose the apartments with a high level of sunshine (facing south), relative to those with no direct access to sunlight (facing north); (2) the value of sunlight shrinks with pollution and becomes larger if living in a higher apartment; (3) residents living in higher units have a larger willingness to pay for the sunlight and environmental quality improvement. These empirical findings shed light on the welfare measurement of sunlight and have profound implications for the capitalization of environmental amenities reflected in housing prices.789===== PAGE 1 (score 11, numeric-paren 1, table-words 0, var-words 10) =====1011Copyright © 2022 The Author(s). Published by Vilnius Gediminas Technical University12This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unre-13stricted use, distribution, and reproduction in any medium, provided the original author and source are credited.14Introduction15Sunlight may be considered the most valuable natural re-16source human beings could employ for free. It is taken 17more as public good than a private or club good in most 18cases since it provides inexhaustible power and forms a 19non-rivalrous situation for the usage (Engerer, 2011). That 20said, it is critical to put an accurate value on the sunshine 21since, in some cases, sunlight is still limited in availabil-22ity. Then, environmental economists should pay more at-23tention to evaluating access to sunlight in some specific 24context. A prominent example is access to sunshine for 25residents living at homes, especially for those in big cit-26ies. In modern urban cities, residents spend more time 27indoors, and having direct access to sunlight is strongly 28preferred. Thus, the attainability of sunlight, which be-29comes an important environmental amenity for citizens 30living in large metropolises, becomes of great worth. This 31goes to the main question this paper plans to answer: how 32much exactly do homeowners living in cities value access 33to sunshine. This paper empirically estimates the price 34premium of the access to sunshine by studying homeown-35ers’ purchase behavior in the housing market of Shanghai 36in China. Specifically, using a hedonic pricing model, new 37empirical evidence of the value of sunshine is provided by 38investigating the relationship between the availability of 39sunshine in apartments and the different expenditures of 40staying in these housing units.41The starting point relates the project to the strand 42of research about the hedonic pricing model. Access to 43sunshine should be considered one of the environmental 44amenities for attractive apartments. Estimating its value 45comes from analyzing the trade-off behavior between the 46availability of sunshine and the higher value it brings. 47Thus, it is proper to put this project into a line of research 48about the evaluation of the willingness to pay (WTP) to 49environmental amenities under the framework of the he-50donic pricing method (Rosen, 1974), which is a promi-51nent existing method applied in valuing the non-market 52good, as the sunlight here in our paper (Ready & Ab-53dalla, 2005; Levinson, 2012). Among them, many studies 54evaluate non-marketed goods in the context of housing 55International Journal of Strategic Property Management56ISSN: 1648-715X / eISSN: 1648-9179572022 Volume 26 Issue 5: 385–39658https://doi.org/10.3846/ijspm.2022.1800459*Corresponding author. E-mail: lizhehan@zju.edu.cn60IMPACT OF ACCESS TO SUNLIGHT ON RESIDENTIAL 61PROPERTY VALUES: AN EMPIRICAL ANALYSIS OF THE HOUSING 62MARKET IN SHANGHAI63Yi ZHONG1, Jiajun LU2, Zhehan LI3,*641 College of Economics and Management, China Jiliang University, Hangzhou, China652 International Business School, Zhejiang University, Haining, China663 School of Philosophy, Zhejiang University, Hangzhou, China67Received 05 June 2022; accepted 03 November 202268Abstract. As an important environmental amenity, sunlight brings us a large number of benefits and improves the quality 69of our daily lives, and its welfare measurement depends on concrete living conditions. The purpose of this article is to em-70pirically document the non-marketed value of sunlight in light of the view orientation of an apartment in the context of the 71housing market. Using a hedonic pricing model estimated with the real estate transaction data over 40,000 housing units in 722019–2021 in Shanghai, it is found that: (1) homeowners, on average, are willing to pay an extra 7.2% to choose the apart-73ments with a high level of sunshine (facing south), relative to those with no direct access to sunlight (facing north); (2) the 74value of sunlight shrinks with pollution and becomes larger if living in a higher apartment; (3) residents living in higher 75units have a larger willingness to pay for the sunlight and environmental quality improvement. These empirical findings 76shed light on the welfare measurement of sunlight and have profound implications for the capitalization of environmental 77amenities reflected in housing prices.78Keywords: environmental amenity, value of sunshine, hedonic pricing, housing market.798081===== PAGE 7 (score 87, numeric-paren 68, table-words 4, var-words 11) =====8283International Journal of Strategic Property Management, 2022, 26(5): 385–3968439185The estimate is smaller than that in Lu (2018) showing 86that a south- facing orientation brings a 14% premium in 87property value. After multiplying the mean unit price of 8846,712 CNY/m2 by 7.2%, the price premium on the sun-89light, on average, becomes 3,363 CNY/m2 (46.32 USD/ft2) 90for homeowners in Shanghai.1491In addition to the view orientation, both PM2.5 and 92dust pollution have a negative impact on house prices. 93Homeowners prefer to live in a high position with a better 94view outside relative to lower places. Based on the non-95linearity of the relative height for the house prices, an-9614 The exchange rate is $1.00 = ¥6.745 in 07/2022.97other piece of empirical evidence that needs to be pointed 98out is that, when the relevant height of an apartment is 99higher, its value becomes first rises and then drops when 100it reaches the top of the building. It could be attributed 101to the structure of buildings in Shanghai. Most buildings 102in Shanghai are equipped with an elevator, thus making 103it easier for residents living on a higher level. Moreo-104ver, living at the bottom is unattractive since it usually 105is noisier and more humid down below. However, the 106values of apartment located on the top of buildings are 107lower relatively since living too high takes more time to 108get down and thus is less safe in some emergency. Hence, 109there exists a nonlinear relationship between the relative 110height and house prices.111Table 2. The regression results of the baseline model (1)112(1)113(2)114(3)115(4)116(5)117(6)118(7)119Dependent variable: the natural log of a house price per square meter, log (Yidct)120South1210.080***1220.066***1230.067***1240.071***1250.077***1260.072***1270.071***128(–0.003)129(–0.003)130(–0.003)131(–0.006)132(–0.009)133(–0.002)134(–0.002)135Rheight1362.771***1372.732***1382.932***1392.551***1402.441***1412.516***1422.519***143(–0.078)144(–0.072)145(–0.073)146(–0.092)147(–0.081)148(–0.057)149(–0.075)150Rheight2151–1.251***152–1.207***153–1.133***154–1.350***155–1.523***156–1.521***157–1.516***158(–0.164)159(–0.181)160(–0.157)161(–0.164)162(–0.167)163(–0.15)164(–0.154)165Age1660.072***1670.067***1680.081***1690.075***1700.049***1710.025***1720.026***173(–0.006)174(–0.006)175(–0.006)176(–0.006)177(–0.006)178(–0.005)179(–0.005)180Area1811.020***182(–0.015)183PM25184–0.023***185–0.023***186–0.108***187–0.109***188–0.112***189(–0.007)190(–0.007)191(–0.008)192(–0.013)193(–0.014)194Dust195–0.354***196–0.353***197–0.332***198–0.321***199–0.319***200(–0.06)201(–0.06)202(–0.068)203(–0.061)204(–0.021)205Pop density2060.910***2070.935***2080.833***2090.831***210(–0.137)211(–0.143)212(–0.138)213(–0.136)214Factory215–0.060**216–0.022217–0.113**218–0.121**219(–0.029)220(–0.021)221(–0.05)222(–0.05)223School2240.754***2251.362***2261.216***2271.218***228(–0.161)229(–0.163)230(–0.156)231(–0.161)232Shopping2330.573***2341.123***2350.234*2360.241*237(–0.139)238(–0.139)239(–0.134)240(–0.122)241Hospital2422.014***2430.2842443.411***2453.419***246(–0.46)247(–0.479)248(–0.452)249(–0.422)250Greenspace2510.004***2520.005**2530.006***2540.006***255(–0.001)256(–0.001)257(–0.002)258(–0.002)259Constant260–1.193***2611.339***262–1.388***263–1.354***264–2.134***265–2.819***266–0.291***267(–0.171)268(–0.206)269(–0.498)270(–0.729)271(–0.194)272(–0.129)273(–0.012)274District FE275Y276Y277N278N279N280N281N282Community FE283N284N285N286Y287Y288Y289Y290Year FE291Y292Y293Y294Y295Y296Y297Y298N29940,44830040,44830140,44830240,44830340,44830440,44830540,448306Adjusted R23070.2393080.2393090.2343100.3723110.3553120.4793130.421314Note: To control for the potential serial correction with a district, robust standard errors in parentheses are clustered at the district level. For the ro-315bustness check, we put area as one of the control variables in column (7), which shows the similar results as before. Thus, we will mainly focus on the 316unit price as dependent variable. *** p < 0.01, ** p < 0.05, * p < 0.1.317318319===== PAGE 8 (score 49, numeric-paren 22, table-words 9, var-words 9) =====320321392322Y. Zhong et al. Impact of access to sunlight on residential property values: an empirical analysis...3234.2. Extended models324Built upon the baseline model, the hedonic estimation is 325extended by incorporating the interaction terms to allow 326varying marginal effects and other complexities. Table 3 327shows the regression results of the extended model (2).15 328The coefficient on Dust × South is significantly negative. 329Given the positive sign of South, it confirms the previ-330ous presumption that the value from having an advanta-331geous orientation shrinks with dust pollution. Based on 332the empirical results about the interaction term Rheight × 333South, the positive relation between the marginal effect of 334facing south and relative height provides significant evi-335dence that the availability and hedonic value of sunshine 336could be magnified by the height of an apartment. As for 337the interaction between air pollution and relative height, 338the empirical evidence indicates that the height would 339alleviate the negative influence of both PM2.5 and dust 340pollution on the house prices. It implies that, if you stay 341in a very high apartment, you are still able to see miles 342away from where you stand even on heavily polluted days. 343Dust pollution has a less significant influence than PM2.5 344on high apartments. Since the PM2.5 pollution is almost 345invisible by our naked eyes, its impact on house prices 346should be identical across the heights. Due to the negative 347sign of the coefficient on dust pollution in the baseline 348model before, the positive coefficient of the interaction 349term makes the total coefficient less negative, resulting in 350a smaller marginal effect.351Given the ambiguity of the direction of the influence of 352Rheight × Orientation × Airpollution on the house prices, 353two presumptions are provided in the analysis. The first 354presumption is that, as pollution gets more serious, the 355value of access to sunshine declines (Zhang et al., 2020). 356It is proper to take the first two items, Rheighti × Southi, as 357the comprehensive indicator of measuring the availability 358of sunshine. We examine how the comprehensive indicator 359interacts with air pollution and how it changes the house 360price given different levels of air pollution. The other pro-361posed presumption is that residents living in more expen-362sive apartments are supposed to care more about environ-363mental quality and thus be more responsive to air pollution 364(Tu et al., 2020). Figure 3 graphically outlines the spatial 365heterogeneity. Empirically, house prices are higher for those 366being higher and facing south. People living in section A 367would have to pay more to purchase the apartment and are 368more likely to be rich relative to those living in other sec-369tions. As people get wealthier, they are willing to pay more 370to reduce the air pollution (Chay & Greenstone, 2005). 371Therefore, residents in section A would be more concerned 372about air pollution. As a result, the negative marginal effect 373of air pollution would be larger for them, which is reflected 374in the larger price elasticity to air pollution. However, from 37515 Due to the limited space, only the estimates on variables of 376interest are shown here, and the complete regression results 377of the extended model (2) are presented in Table A2 in Ap-378pendix.379Table 3. The regression results of the extended model (2)380(1)381(2)382(3)383(4)384Dependent variable: house price per square meter, log (Yidct)385Rheight × South3860.327***3870.294***388(0.015)389(0.015)390Rheight × Dust3910.024***3920.021***393(0.008)394(0.009)395Rheight × PM253960.031***3970.029***398(0.006)399(0.007)400Dust × South401–0.352***402–0.323***403(0.074)404(0.064)405PM25 × South406–0.219***407–0.319***408(0.065)409(0.064)410Rheight × South × 411Dust412–0.006413(0.007)414Rheight × South × 415PM25416–0.004417(0.006)418Community FE419Y420Y421Y422Y423Year FE424Y425Y426Y427Y428N42940,44843040,44843140,44843240,448433Adjusted R24340.6444350.6434360.6414370.642438Note: To control for the potential serial correction with a district, ro-439bust standard errors in parentheses are clustered at the district level. *** 440p < 0.01, ** p < 0.05, * p < 0.1.441A442C443B444D445North446South447Figure 3. Representation of the orientation and relative height448the empirical results, we cannot actually see any clear and 449significant relationship. It might be the case that two types 450of influence coexist simultaneously and impact the house 451prices in the opposite ways.452Conclusions453This paper documents the relationship between the view 454orientation of dwelling units and their property values 455and empirically estimate the nonmarket value of sun-456light in the Shanghai housing market under a hedonic 457pricing framework. Using the real estate transaction 458data of over 40,000 housing units in 2019–2021, empiri-459cal results suggest that, other factors being equal, home-460461462===== PAGE 9 (score 14, numeric-paren 0, table-words 0, var-words 14) =====463464International Journal of Strategic Property Management, 2022, 26(5): 385–396465393466owners in Shanghai, on average, are willing to pay extra 4673,363 CNY/m2 (46.32 USD/ft2) to choose the apartments 468with sufficient sunlight and orientation facing south, 469smaller than that in Lu (2018) showing that a south-facing 470orientation brings a 14% premium in property value. The 471value of sunlight becomes larger with relative height, sug-472gesting that people living in higher apartments are will-473ing to pay more for south-facing apartments rather than 474ones in other orientations. It is also found that the price 475premium of sunlight shrinks with pollution and that dust 476pollution has a less significant influence than PM2.5 on 477higher apartments. Moreover, we find that, according to 478the empirical results of three-variable interactions, people 479living in high apartments facing the south have a slightly 480larger willingness to pay for the sunlight if the air quality 481is improved.482On top of the new findings, there might exist some 483potential for further improvement in this paper. The es-484timated value of sunlight potentially gets entangled with 485the price premium of Geomancy. Admittedly, due to the 486lack of data on real-time apartment-specific sunlight in-487tensity, the premium effect caused by sunlight is insepa-488rable from that of Geomancy. However, as suggested in 489the literature review, these studies have found little, if 490any, solid evidence showing the empirical importance of 491Geomancy compared to another more significant benefit, 492sunlight. This makes the issue much less worrying, and 493the estimates are just slightly upward biased if any. Moreo-494ver, exploring the heterogeneity of sunlight’s value by air 495pollution and relative height could provide the unbiased 496estimated marginal value of sunlight since the value of 497Geomancy is not very likely to vary by them. Thus, we 498could take the estimates of south-facing orientation as the 499upper bound of the estimated value of sunlight.500Sunlight brings us a large number of benefits and im-501proves the quality of our daily lives, and its welfare meas-502urement depends on concrete living conditions. During 503the ongoing COVID-19 pandemic, many residents in 504Shanghai have to stay indoors due to a lockdown and thus 505need sunlight more than ever. Such an unusual scenario 506could raise the value of sunlight and reinforces its impor-507tance for potential homeowners in the housing market. 508Furthermore, real estate developers would also find the 509estimates useful since they need them to help develop the 510best pricing strategy based on the apartment attributes. 511Lastly, understanding the hedonic value of different view 512orientations and their interaction with different environ-513mental and urban amenities is also important for a wide 514range of city planning purposes. This paper sheds light 515on the welfare measurement of sunlight and has profound 516implications for the capitalization of environmental amen-517ities reflected in housing prices.518Acknowledgements519Authors acknowledge the financial support from the Zhe-520jiang Provincial Philosophy and Social Sciences Planning 521Project.522Funding523This work was supported by Zhejiang Provincial Philoso-524phy and Social Sciences Planning Project (23NDJC078YB).525Author contributions526Y. Zhong carried out the conceptualization and methodol-527ogy. J. Lu finished the original draft preparation and writ-528ing, as well as the funding acquisition. Z. Li conducted the 529review and editing of the manuscript. All authors read and 530approved the final manuscript.531Disclosure statement532The authors declare no potential conflicts of interest with 533respect to the research, authorship, and publication of this 534article. This article does not contain any studies involving 535human participants performed by any of the authors.536References537Berger, M. C., Blomquist, G. C., & Peter, K. S. (2008). Compen-538sating differentials in emerging labor and housing markets: 539estimates of quality of life in Russian cities. Journal of Urban 540Economics, 63(1), 25–55. 541https://doi.org/10.1016/j.jue.2007.01.006542Chan, E., Yim Yiu, C., Baldwin, A., & Lee, G. (2009). Value of 543buildings with design features for healthy living: a contingent 544valuation approach. Facilities, 27(5/6), 229–249. 545https://doi.org/10.1108/02632770910944952546Chay, K. Y., & Greenstone, M. (2005). Does air quality matter? 547Evidence from the housing market. Journal of Political Econ-548omy, 113(2), 376–424. https://doi.org/10.1086/427462549Chen, Y., Ebenstein, A., Greenstone, M., & Li, H. (2013). Evi-550dence on the impact of sustained exposure to air pollution 551on life expectancy from China’s Huai River policy. Proceedings 552of the National Academy of Sciences, 110(32), 12936–12941. 553https://doi.org/10.1073/pnas.1300018110554Des Rosiers, F., Bolduc, A., & Thériault, M. (1999). Environment 555and value Does drinking water quality affect house prices? 556Journal of Property Investment & Finance, 17(5), 444–463. 557https://doi.org/10.1108/14635789910294877558Engerer, H. (2011). Security as a public, private or club good: some 559fundamental considerations. Defence and Peace Economics, 56022(2), 135–145. https://doi.org/10.1080/10242694.2011.542333561Faux, J., & Perry, G. M. (1999). Estimating irrigation water value 562using hedonic price analysis: a case study in Malheur County, 563Oregon. Land Economics, 75(3), 440–452. 564https://doi.org/10.2307/3147189565Guntermann, K. L., & Norrbin, S. (1987). Explaining the vari-566ability of apartment rents. Real Estate Economics, 15(4), 321–567340. https://doi.org/10.1111/1540-6229.00435568Guven, C. (2012). Reversing the question: does happiness affect 569consumption and savings behavior? Journal of Economic Psy-570chology, 33(4), 701–717. 571https://doi.org/10.1016/j.joep.2012.01.002572Harrison, D., & Rubinfeld, D. L. (1978). Hedonic housing prices 573and the demand for clean air. Journal of Environmental Eco-574nomics and Management, 5(1), 81–102. 575https://doi.org/10.1016/0095-0696(78)90006-2576577578===== PAGE 11 (score 90, numeric-paren 78, table-words 3, var-words 6) =====579580International Journal of Strategic Property Management, 2022, 26(5): 385–396581395582Variable583Definition and explanation584Data source585Air quality586Dust pollution587Weight of dust pollution, in unit of ton per square kilometer588SEPB589PM2.5 pollution590Concentration of PM2.5 pollution, in unit of milligram per cubic meter591SEPB592Weather593Temperature594The city’s average temperature in Fahrenheit on a daily basis595NOAA596Dew point597The city’s average dew point in Fahrenheit on a daily basis598NOAA599Sea level pressure600The city’s average pressure in hPa on a daily basis601NOAA602Humidity603Daily likelihood of precipitation (%)604NOAA605Wind606Daily average wind speed, miles per hour (MPH)607NOAA608Sunny day609The number of sunny days in a given period610NOAA611Urban spatial structure612District613There are a total of 16 districts in the city and my sample data cover 15 of them614SBS615Subdistrict616There are a total of 214 subdistricts in the city617SBS618View619Discrete variable that indicates how many attractions one unit could have the views of620SMTA621Distance622The linear distance in meters between attraction and unit. It becomes average distance 623if it has several views624SMTA625Table A2. The complete regression results of the extended model (2)626(1)627(2)628(3)629(4)630Dependent variable: house price per square meter, log (Yidct)631South6320.067***6330.071***6340.077***6350.072***636(0.003)637(0.006)638(0.009)639(0.002)640Rheight South6410.327***6420.294***643(0.015)644(0.015)645Rheight × Dust6460.024***6470.021***648(0.008)649(0.009)650Rheight × PM256510.031***6520.029***653(0.006)654(0.007)655Dust × South656–0.352***657–0.323***658(0.074)659(0.064)660PM25 × South661–0.219***662–0.319***663(0.065)664(0.064)665Rheight × South × Dust666–0.006667(0.007)668Rheight × South × PM25669–0.004670(0.006)671Rheight6721.932***6731.552***6741.433***6751.522***676(0.073)677(0.092)678(0.076)679(0.061)680Rheight2681–1.133***682–1.350***683–1.531***684–1.532***685(0.157)686(0.164)687(0.167)688(0.150)689Age6900.081***6910.072***6920.047***6930.026***694(0.006)695(0.006)696(0.006)697(0.005)698PM25699–0.023***700–0.108***701–0.111***702–0.121***703(0.006)704(0.007)705(0.008)706(0.013)707Dust708–0.354***709–0.353***710–0.331***711–0.323***712(0.060)713(0.060)714(0.068)715(0.061)716Pop density7170.807***7180.914***7190.932***7200.831***721(0.124)722(0.137)723(0.143)724(0.138)725End of Table A1726727728===== PAGE 12 (score 46, numeric-paren 38, table-words 2, var-words 4) =====729730396731Y. Zhong et al. Impact of access to sunlight on residential property values: an empirical analysis...732(1)733(2)734(3)735(4)736Factory737–0.030***738–0.061**739–0.024740–0.121**741(0.009)742(0.029)743(0.021)744(0.050)745School7460.643***7470.751***7481.359***7491.221***750(0.034)751(0.161)752(0.163)753(0.156)754Shopping7550.235***7560.573***7571.132***7580.241*759(0.131)760(0.141)761(0.139)762(0.134)763Hospital7641.743***7652.014***7660.2567673.411***768(0.455)769(0.460)770(0.479)771(0.452)772Greenspace7730.003*7740.004***7750.005**7760.006***777(0.002)778(0.001)779(0.001)780(0.002)781Community FE782Y783Y784Y785Y786Year FE787Y788Y789Y790Y791N79240,44879340,44879440,44879540,448796Adjusted R27970.6447980.6437990.6418000.642801Note: To control for the potential serial correction with a district, robust standard errors in parentheses are clustered at the district 802level. *** p < 0.01, ** p < 0.05, * p < 0.1.803End of Table A2804