# RECORD a7fe404df2e413395450e192cebe2f68aa921579
# TITLE: IMPACT OF ACCESS TO SUNLIGHT ON RESIDENTIAL PROPERTY VALUES: AN EMPIRICAL ANALYSIS OF THE HOUSING MARKET IN SHANGHAI
# YEAR: 2022.0  VENUE: International Journal of Strategic Property Management  DOI: 10.3846/ijspm.2022.18004
# AUTHORS: Y. Zhong; Jia-Jun Lu; Zhe-Han Li
# PAGES: 12  CANDIDATE TABLE PAGES (1-based): [7, 8, 11, 12]
# 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.


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Copyright © 2022 The Author(s). Published by Vilnius Gediminas Technical University
This 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-
stricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Introduction
Sunlight may be considered the most valuable natural re-
source human beings could employ for free. It is taken 
more as public good than a private or club good in most 
cases since it provides inexhaustible power and forms a 
non-rivalrous situation for the usage (Engerer, 2011). That 
said, it is critical to put an accurate value on the sunshine 
since, in some cases, sunlight is still limited in availabil-
ity. Then, environmental economists should pay more at-
tention to evaluating access to sunlight in some specific 
context. A prominent example is access to sunshine for 
residents living at homes, especially for those in big cit-
ies. In modern urban cities, residents spend more time 
indoors, and having direct access to sunlight is strongly 
preferred. Thus, the attainability of sunlight, which be-
comes an important environmental amenity for citizens 
living in large metropolises, becomes of great worth. This 
goes to the main question this paper plans to answer: how 
much exactly do homeowners living in cities value access 
to sunshine. This paper empirically estimates the price 
premium of the access to sunshine by studying homeown-
ers’ purchase behavior in the housing market of Shanghai 
in China. Specifically, using a hedonic pricing model, new 
empirical evidence of the value of sunshine is provided by 
investigating the relationship between the availability of 
sunshine in apartments and the different expenditures of 
staying in these housing units.
The starting point relates the project to the strand 
of research about the hedonic pricing model. Access to 
sunshine should be considered one of the environmental 
amenities for attractive apartments. Estimating its value 
comes from analyzing the trade-off behavior between the 
availability of sunshine and the higher value it brings. 
Thus, it is proper to put this project into a line of research 
about the evaluation of the willingness to pay (WTP) to 
environmental amenities under the framework of the he-
donic pricing method (Rosen, 1974), which is a promi-
nent existing method applied in valuing the non-market 
good, as the sunlight here in our paper (Ready & Ab-
dalla, 2005; Levinson, 2012). Among them, many studies 
evaluate non-marketed goods in the context of housing 
International Journal of Strategic Property Management
ISSN: 1648-715X / eISSN: 1648-9179
2022 Volume 26 Issue 5: 385–396
https://doi.org/10.3846/ijspm.2022.18004
*Corresponding author. E-mail: lizhehan@zju.edu.cn
IMPACT OF ACCESS TO SUNLIGHT ON RESIDENTIAL  
PROPERTY VALUES: AN EMPIRICAL ANALYSIS OF THE HOUSING 
MARKET IN SHANGHAI
Yi ZHONG1, Jiajun LU2, Zhehan LI3,*
1 College of Economics and Management, China Jiliang University, Hangzhou, China
2 International Business School, Zhejiang University, Haining, China
3 School of Philosophy, Zhejiang University, Hangzhou, China
Received 05 June 2022; accepted 03 November 2022
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 em-
pirically 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 apart-
ments 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.
Keywords: environmental amenity, value of sunshine, hedonic pricing, housing market.


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391
The estimate is smaller than that in Lu (2018) showing 
that a south- facing orientation brings a 14% premium in 
property value. After multiplying the mean unit price of 
46,712 CNY/m2 by 7.2%, the price premium on the sun-
light, on average, becomes 3,363 CNY/m2 (46.32 USD/ft2) 
for homeowners in Shanghai.14
In addition to the view orientation, both PM2.5 and 
dust pollution have a negative impact on house prices. 
Homeowners prefer to live in a high position with a better 
view outside relative to lower places. Based on the non-
linearity of the relative height for the house prices, an-
14	 The exchange rate is $1.00 = ¥6.745 in 07/2022.
other piece of empirical evidence that needs to be pointed 
out is that, when the relevant height of an apartment is 
higher, its value becomes first rises and then drops when 
it reaches the top of the building. It could be attributed 
to the structure of buildings in Shanghai. Most buildings 
in Shanghai are equipped with an elevator, thus making 
it easier for residents living on a higher level. Moreo-
ver, living at the bottom is unattractive since it usually 
is noisier and more humid down below. However, the 
values of apartment located on the top of buildings are 
lower relatively since living too high takes more time to 
get down and thus is less safe in some emergency. Hence, 
there exists a nonlinear relationship between the relative 
height and house prices.
Table 2. The regression results of the baseline model (1)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Dependent variable: the natural log of a house price per square meter, log (Yidct)
South
0.080***
0.066***
0.067***
0.071***
0.077***
0.072***
0.071***
(–0.003)
(–0.003)
(–0.003)
(–0.006)
(–0.009)
(–0.002)
(–0.002)
Rheight
2.771***
2.732***
2.932***
2.551***
2.441***
2.516***
2.519***
(–0.078)
(–0.072)
(–0.073)
(–0.092)
(–0.081)
(–0.057)
(–0.075)
Rheight2
–1.251***
–1.207***
–1.133***
–1.350***
–1.523***
–1.521***
–1.516***
(–0.164)
(–0.181)
(–0.157)
(–0.164)
(–0.167)
(–0.15)
(–0.154)
Age
0.072***
0.067***
0.081***
0.075***
0.049***
0.025***
0.026***
(–0.006)
(–0.006)
(–0.006)
(–0.006)
(–0.006)
(–0.005)
(–0.005)
Area
1.020***
(–0.015)
PM25
–0.023***
–0.023***
–0.108***
–0.109***
–0.112***
(–0.007)
(–0.007)
(–0.008)
(–0.013)
(–0.014)
Dust
–0.354***
–0.353***
–0.332***
–0.321***
–0.319***
(–0.06)
(–0.06)
(–0.068)
(–0.061)
(–0.021)
Pop density
0.910***
0.935***
0.833***
0.831***
(–0.137)
(–0.143)
(–0.138)
(–0.136)
Factory
–0.060**
–0.022
–0.113**
–0.121**
(–0.029)
(–0.021)
(–0.05)
(–0.05)
School
0.754***
1.362***
1.216***
1.218***
(–0.161)
(–0.163)
(–0.156)
(–0.161)
Shopping
0.573***
1.123***
0.234*
0.241*
(–0.139)
(–0.139)
(–0.134)
(–0.122)
Hospital
2.014***
0.284
3.411***
3.419***
(–0.46)
(–0.479)
(–0.452)
(–0.422)
Greenspace
0.004***
0.005**
0.006***
0.006***
(–0.001)
(–0.001)
(–0.002)
(–0.002)
Constant
–1.193***
1.339***
–1.388***
–1.354***
–2.134***
–2.819***
–0.291***
(–0.171)
(–0.206)
(–0.498)
(–0.729)
(–0.194)
(–0.129)
(–0.012)
District FE
Y
Y
N
N
N
N
N
Community FE
N
N
N
Y
Y
Y
Y
Year FE
Y
Y
Y
Y
Y
Y
Y
N
40,448
40,448
40,448
40,448
40,448
40,448
40,448
Adjusted R2
0.239
0.239
0.234
0.372
0.355
0.479
0.421
Note: To control for the potential serial correction with a district, robust standard errors in parentheses are clustered at the district level. For the ro-
bustness 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 
unit price as dependent variable. *** p < 0.01, ** p < 0.05, * p < 0.1.


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392
Y. Zhong et al. Impact of access to sunlight on residential property values: an empirical analysis...
4.2. Extended models
Built upon the baseline model, the hedonic estimation is 
extended by incorporating the interaction terms to allow 
varying marginal effects and other complexities. Table 3 
shows the regression results of the extended model (2).15 
The coefficient on Dust × South is significantly negative. 
Given the positive sign of South, it confirms the previ-
ous presumption that the value from having an advanta-
geous orientation shrinks with dust pollution. Based on 
the empirical results about the interaction term Rheight × 
South, the positive relation between the marginal effect of 
facing south and relative height provides significant evi-
dence that the availability and hedonic value of sunshine 
could be magnified by the height of an apartment. As for 
the interaction between air pollution and relative height, 
the empirical evidence indicates that the height would 
alleviate the negative influence of both PM2.5 and dust 
pollution on the house prices. It implies that, if you stay 
in a very high apartment, you are still able to see miles 
away from where you stand even on heavily polluted days. 
Dust pollution has a less significant influence than PM2.5 
on high apartments. Since the PM2.5 pollution is almost 
invisible by our naked eyes, its impact on house prices 
should be identical across the heights. Due to the negative 
sign of the coefficient on dust pollution in the baseline 
model before, the positive coefficient of the interaction 
term makes the total coefficient less negative, resulting in 
a smaller marginal effect.
Given the ambiguity of the direction of the influence of 
Rheight × Orientation × Airpollution on the house prices, 
two presumptions are provided in the analysis. The first 
presumption is that, as pollution gets more serious, the 
value of access to sunshine declines (Zhang et al., 2020). 
It is proper to take the first two items, Rheighti × Southi, as 
the comprehensive indicator of measuring the availability 
of sunshine. We examine how the comprehensive indicator 
interacts with air pollution and how it changes the house 
price given different levels of air pollution. The other pro-
posed presumption is that residents living in more expen-
sive apartments are supposed to care more about environ-
mental quality and thus be more responsive to air pollution 
(Tu et al., 2020). Figure 3 graphically outlines the spatial 
heterogeneity. Empirically, house prices are higher for those 
being higher and facing south. People living in section A 
would have to pay more to purchase the apartment and are 
more likely to be rich relative to those living in other sec-
tions. As people get wealthier, they are willing to pay more 
to reduce the air pollution (Chay & Greenstone, 2005). 
Therefore, residents in section A would be more concerned 
about air pollution. As a result, the negative marginal effect 
of air pollution would be larger for them, which is reflected 
in the larger price elasticity to air pollution. However, from 
15	 Due to the limited space, only the estimates on variables of 
interest are shown here, and the complete regression results 
of the extended model (2) are presented in Table A2 in Ap-
pendix.
Table 3. The regression results of the extended model (2)
(1)
(2)
(3)
(4)
Dependent variable: house price per square meter, log (Yidct)
Rheight × South
0.327***
0.294***
(0.015)
(0.015)
Rheight × Dust
0.024***
0.021***
(0.008)
(0.009)
Rheight × PM25
0.031***
0.029***
(0.006)
(0.007)
Dust × South
–0.352***
–0.323***
(0.074)
(0.064)
PM25 × South
–0.219***
–0.319***
(0.065)
(0.064)
Rheight × South × 
Dust
–0.006
(0.007)
Rheight × South × 
PM25
–0.004
(0.006)
Community FE
Y
Y
Y
Y
Year FE
Y
Y
Y
Y
N
40,448
40,448
40,448
40,448
Adjusted R2
0.644
0.643
0.641
0.642
Note: To control for the potential serial correction with a district, ro-
bust standard errors in parentheses are clustered at the district level. *** 
p < 0.01, ** p < 0.05, * p < 0.1.
A
C
B
D
North
South
Figure 3. Representation of the orientation and relative height
the empirical results, we cannot actually see any clear and 
significant relationship. It might be the case that two types 
of influence coexist simultaneously and impact the house 
prices in the opposite ways.
Conclusions
This paper documents the relationship between the view 
orientation of dwelling units and their property values 
and empirically estimate the nonmarket value of sun-
light in the Shanghai housing market under a hedonic 
pricing framework. Using the real estate transaction 
data of over 40,000 housing units in 2019–2021, empiri-
cal results suggest that, other factors being equal, home-


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International Journal of Strategic Property Management, 2022, 26(5): 385–396
393
owners in Shanghai, on average, are willing to pay extra 
3,363 CNY/m2 (46.32 USD/ft2) to choose the apartments 
with sufficient sunlight and orientation facing south, 
smaller than that in Lu (2018) showing that a south-facing 
orientation brings a 14% premium in property value. The 
value of sunlight becomes larger with relative height, sug-
gesting that people living in higher apartments are will-
ing to pay more for south-facing apartments rather than 
ones in other orientations. It is also found that the price 
premium of sunlight shrinks with pollution and that dust 
pollution has a less significant influence than PM2.5 on 
higher apartments. Moreover, we find that, according to 
the empirical results of three-variable interactions, people 
living in high apartments facing the south have a slightly 
larger willingness to pay for the sunlight if the air quality 
is improved.
On top of the new findings, there might exist some 
potential for further improvement in this paper. The es-
timated value of sunlight potentially gets entangled with 
the price premium of Geomancy. Admittedly, due to the 
lack of data on real-time apartment-specific sunlight in-
tensity, the premium effect caused by sunlight is insepa-
rable from that of Geomancy. However, as suggested in 
the literature review, these studies have found little, if 
any, solid evidence showing the empirical importance of 
Geomancy compared to another more significant benefit, 
sunlight. This makes the issue much less worrying, and 
the estimates are just slightly upward biased if any. Moreo-
ver, exploring the heterogeneity of sunlight’s value by air 
pollution and relative height could provide the unbiased 
estimated marginal value of sunlight since the value of 
Geomancy is not very likely to vary by them. Thus, we 
could take the estimates of south-facing orientation as the 
upper bound of the estimated value of sunlight.
Sunlight brings us a large number of benefits and im-
proves the quality of our daily lives, and its welfare meas-
urement depends on concrete living conditions. During 
the ongoing COVID-19 pandemic, many residents in 
Shanghai have to stay indoors due to a lockdown and thus 
need sunlight more than ever. Such an unusual scenario 
could raise the value of sunlight and reinforces its impor-
tance for potential homeowners in the housing market. 
Furthermore, real estate developers would also find the 
estimates useful since they need them to help develop the 
best pricing strategy based on the apartment attributes. 
Lastly, understanding the hedonic value of different view 
orientations and their interaction with different environ-
mental and urban amenities is also important for a wide 
range of city planning purposes. This paper sheds light 
on the welfare measurement of sunlight and has profound 
implications for the capitalization of environmental amen-
ities reflected in housing prices.
Acknowledgements
Authors acknowledge the financial support from the Zhe-
jiang Provincial Philosophy and Social Sciences Planning 
Project.
Funding
This work was supported by Zhejiang Provincial Philoso-
phy and Social Sciences Planning Project (23NDJC078YB).
Author contributions
Y. Zhong carried out the conceptualization and methodol-
ogy. J. Lu finished the original draft preparation and writ-
ing, as well as the funding acquisition. Z. Li conducted the 
review and editing of the manuscript. All authors read and 
approved the final manuscript.
Disclosure statement
The authors declare no potential conflicts of interest with 
respect to the research, authorship, and publication of this 
article. This article does not contain any studies involving 
human participants performed by any of the authors.
References
Berger, M. C., Blomquist, G. C., & Peter, K. S. (2008). Compen-
sating differentials in emerging labor and housing markets: 
estimates of quality of life in Russian cities. Journal of Urban 
Economics, 63(1), 25–55. 
https://doi.org/10.1016/j.jue.2007.01.006
Chan, E., Yim Yiu, C., Baldwin, A., & Lee, G. (2009). Value of 
buildings with design features for healthy living: a contingent 
valuation approach. Facilities, 27(5/6), 229–249. 
https://doi.org/10.1108/02632770910944952
Chay, K. Y., & Greenstone, M. (2005). Does air quality matter? 
Evidence from the housing market. Journal of Political Econ-
omy, 113(2), 376–424. https://doi.org/10.1086/427462
Chen, Y., Ebenstein, A., Greenstone, M., & Li, H. (2013). Evi-
dence on the impact of sustained exposure to air pollution 
on life expectancy from China’s Huai River policy. Proceedings 
of the National Academy of Sciences, 110(32), 12936–12941. 
https://doi.org/10.1073/pnas.1300018110
Des Rosiers, F., Bolduc, A., & Thériault, M. (1999). Environment 
and value Does drinking water quality affect house prices? 
Journal of Property Investment & Finance, 17(5), 444–463. 
https://doi.org/10.1108/14635789910294877
Engerer, H. (2011). Security as a public, private or club good: some 
fundamental considerations. Defence and Peace Economics, 
22(2), 135–145. https://doi.org/10.1080/10242694.2011.542333
Faux, J., & Perry, G. M. (1999). Estimating irrigation water value 
using hedonic price analysis: a case study in Malheur County, 
Oregon. Land Economics, 75(3), 440–452. 
https://doi.org/10.2307/3147189
Guntermann, K. L., & Norrbin, S. (1987). Explaining the vari-
ability of apartment rents. Real Estate Economics, 15(4), 321–
340. https://doi.org/10.1111/1540-6229.00435
Guven, C. (2012). Reversing the question: does happiness affect 
consumption and savings behavior? Journal of Economic Psy-
chology, 33(4), 701–717. 
https://doi.org/10.1016/j.joep.2012.01.002
Harrison, D., & Rubinfeld, D. L. (1978). Hedonic housing prices 
and the demand for clean air. Journal of Environmental Eco-
nomics and Management, 5(1), 81–102. 
https://doi.org/10.1016/0095-0696(78)90006-2


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International Journal of Strategic Property Management, 2022, 26(5): 385–396
395
Variable
Definition and explanation
Data source
Air quality
Dust pollution
Weight of dust pollution, in unit of ton per square kilometer
SEPB
PM2.5 pollution
Concentration of PM2.5 pollution, in unit of milligram per cubic meter
SEPB
Weather
Temperature
The city’s average temperature in Fahrenheit on a daily basis
NOAA
Dew point
The city’s average dew point in Fahrenheit on a daily basis
NOAA
Sea level pressure
The city’s average pressure in hPa on a daily basis
NOAA
Humidity
Daily likelihood of precipitation (%)
NOAA
Wind
Daily average wind speed, miles per hour (MPH)
NOAA
Sunny day
The number of sunny days in a given period
NOAA
Urban spatial structure
District
There are a total of 16 districts in the city and my sample data cover 15 of them
SBS
Subdistrict
There are a total of 214 subdistricts in the city
SBS
View
Discrete variable that indicates how many attractions one unit could have the views of
SMTA
Distance
The linear distance in meters between attraction and unit. It becomes average distance 
if it has several views
SMTA
Table A2. The complete regression results of the extended model (2)
(1)
(2)
(3)
(4)
Dependent variable: house price per square meter, log (Yidct)
South
0.067***
0.071***
0.077***
0.072***
(0.003)
(0.006)
(0.009)
(0.002)
Rheight South
0.327***
0.294***
(0.015)
(0.015)
Rheight × Dust
0.024***
0.021***
(0.008)
(0.009)
Rheight × PM25
0.031***
0.029***
(0.006)
(0.007)
Dust × South
–0.352***
–0.323***
(0.074)
(0.064)
PM25 × South
–0.219***
–0.319***
(0.065)
(0.064)
Rheight × South × Dust
–0.006
(0.007)
Rheight × South × PM25
–0.004
(0.006)
Rheight
1.932***
1.552***
1.433***
1.522***
(0.073)
(0.092)
(0.076)
(0.061)
Rheight2
–1.133***
–1.350***
–1.531***
–1.532***
(0.157)
(0.164)
(0.167)
(0.150)
Age
0.081***
0.072***
0.047***
0.026***
(0.006)
(0.006)
(0.006)
(0.005)
PM25
–0.023***
–0.108***
–0.111***
–0.121***
(0.006)
(0.007)
(0.008)
(0.013)
Dust
–0.354***
–0.353***
–0.331***
–0.323***
(0.060)
(0.060)
(0.068)
(0.061)
Pop density
0.807***
0.914***
0.932***
0.831***
(0.124)
(0.137)
(0.143)
(0.138)
End of Table A1


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(1)
(2)
(3)
(4)
Factory
–0.030***
–0.061**
–0.024
–0.121**
(0.009)
(0.029)
(0.021)
(0.050)
School
0.643***
0.751***
1.359***
1.221***
(0.034)
(0.161)
(0.163)
(0.156)
Shopping
0.235***
0.573***
1.132***
0.241*
(0.131)
(0.141)
(0.139)
(0.134)
Hospital
1.743***
2.014***
0.256
3.411***
(0.455)
(0.460)
(0.479)
(0.452)
Greenspace
0.003*
0.004***
0.005**
0.006***
(0.002)
(0.001)
(0.001)
(0.002)
Community FE
Y
Y
Y
Y
Year FE
Y
Y
Y
Y
N
40,448
40,448
40,448
40,448
Adjusted R2
0.644
0.643
0.641
0.642
Note: To control for the potential serial correction with a district, robust standard errors in parentheses are clustered at the district 
level. *** p < 0.01, ** p < 0.05, * p < 0.1.
End of Table A2