# RECORD f90cf5207d8f2a48c20e426cb00e7a3d92fecdbe
# TITLE: The Capitalization Effect of Natural Amenities on Housing Price in Urban China: New Evidence From Changsha
# YEAR: 2022.0  VENUE: Frontiers in Environmental Science  DOI: 10.3389/fenvs.2022.833831
# AUTHORS: Tuo-Fu Huang; Qing-Yun He; Xiao Ouyang
# PAGES: 16  CANDIDATE TABLE PAGES (1-based): [8, 9]
# ABSTRACT: This study takes Changsha, an emerging mega-city in China, as a sample to analyze the influence of natural amenity (NA) on housing prices during different periods. We divided the NA into four categories according to their characteristics: Green Space (GS), Wetland Park (WP), XiangRiver Scenic Belt (XR), and green ratio within a community. The housing transaction data is used to calculate the accessibility of a settlement to nearest NA via the distance. The capitalization effect of NA is investigated through a geographic weighted regression (GWR), combined with the hedonic theory. Results show that ①the impact and scope of NA on housing prices gradually expanded during years. ②As the key nature landscape of Changsha, the impact coefficient of XR has risen from 2.34% in 2012 to 5.32% in 2020. WP has a remarkable increase for affecting housing prices, rising from 1.24 to 3.65%. Effect of GS keeps in line during years, at 2.34–3.72%. And green ratio’s impact increase from 3.61 to 4.55%. ③The higher capitalization effect of NA undergoes a spatial expansion from the central area to urban fringe over time, which also implies the capitalization of NA is becoming more pronounced. ④Residential communities impacted by NA have a propensity to separate over time. This study provides a different perspective in defining the importance of natural amenities in urban habitat of modern China.


===== PAGE 1 (score 13, numeric-paren 0, table-words 0, var-words 12) =====

The Capitalization Effect of Natural
Amenities on Housing Price in Urban
China: New Evidence From Changsha
Huang Tuofu 1, He Qingyun 1* and Ouyang Xiao 2*
1College of Geographic Science, Hunan Normal University, Changsha, China, 2Hunan Institute of Economic Geography, Hunan
University of Finance and Economics, Changsha, China
This study takes Changsha, an emerging mega-city in China, as a sample to analyze the
inﬂuence of natural amenity (NA) on housing prices during different periods. We divided the
NA into four categories according to their characteristics: Green Space (GS), Wetland Park
(WP), XiangRiver Scenic Belt (XR), and green ratio within a community. The housing
transaction data is used to calculate the accessibility of a settlement to nearest NA via the
distance. The capitalization effect of NA is investigated through a geographic weighted
regression (GWR), combined with the hedonic theory. Results show that ①the impact and
scope of NA on housing prices gradually expanded during years. ②As the key nature
landscape of Changsha, the impact coefﬁcient of XR has risen from 2.34% in 2012 to
5.32% in 2020. WP has a remarkable increase for affecting housing prices, rising from 1.24
to 3.65%. Effect of GS keeps in line during years, at 2.34–3.72%. And green ratio’s impact
increase from 3.61 to 4.55%. ③The higher capitalization effect of NA undergoes a spatial
expansion from the central area to urban fringe over time, which also implies the
capitalization
of
NA
is
becoming more pronounced.
④Residential
communities
impacted by NA have a propensity to separate over time. This study provides a
different perspective in deﬁning the importance of natural amenities in urban habitat of
modern China.
Keywords: natural amenities, hedonic theory, GWR, spatial expansion, Changsha city
INTRODUCTION
China has experienced a world-renowned urbanization process, with the rate growing from 16.22%
in 1978 to 63.89% in 2020, and the Chinese urban landscape has changed tremendously (Yibo et al.,
2020; Alix-Garcia et al., 2014). Rapid urbanization and the associated increase in vehicle trafﬁc have
many negative implications, including increased air and water pollution, noise and congestion.
Physiological ailments such as chronic bronchitis and pneumonia, as well as mental problems as
depression and burnout syndrome, have emerged as a signiﬁcant threat to public health. However,
urban settings also have features that promote health and may assist in attenuating some of the
negative health implications of urban life. Natural amenities (NA) is increasingly being viewed as an
essential settings for health promotion to solve city residential problems (De Vries et al., 2003;
Anderson and West, 2006; Rajapaksa et al., 2017). The past decades, in particular, have seen an
incline in interest for ecological, nature-based health promotion initiatives in both studies and
practices. The new strategy of urban planning is to create, conserve, maintain, and develop NA across
urban areas (Gómez-Baggethun and Barton, 2013; Tuofu et al., 2021).
Edited by:
Bao-Jie He,
Chongqing University, China
Reviewed by:
Junsong Wang,
South China University of Technology,
China
Jun Yang,
Northeastern University, China
*Correspondence:
He Qingyun
heqingyun1848@gmail.com
Ouyang Xiao
xiao.ouyang@foxmail.com
Specialty section:
This article was submitted to
Land Use Dynamics,
a section of the journal
Frontiers in Environmental Science
Received: 12 December 2021
Accepted: 03 February 2022
Published: 28 February 2022
Citation:
Tuofu H, Qingyun H and Xiao O (2022)
The Capitalization Effect of Natural
Amenities on Housing Price in Urban
China: New Evidence From Changsha.
Front. Environ. Sci. 10:833831.
doi: 10.3389/fenvs.2022.833831
Frontiers in Environmental Science | www.frontiersin.org
February 2022 | Volume 10 | Article 833831
1
ORIGINAL RESEARCH
published: 28 February 2022
doi: 10.3389/fenvs.2022.833831


===== PAGE 8 (score 18, numeric-paren 0, table-words 3, var-words 10) =====

studies. We are primarily concerned with the estimated values
and trends of NA variables.
Among the many structural factors inﬂuencing a property’s
pricing, the green ratio has a long-term and growing impact. The
house price can rise by 3.61% in 2012 and 4.38% in 2020 for each
level increase in the green ratio. Moreover, the GWR’s estimate
rate is increased even faster, from 3.04% in 2012 to 4.55% in 2020.
Evidence shows that the requirements for nature amenities within
a community of home buyers in Changsha have increased rapidly
in the last decade.
The inﬂuence of proximity to WP, XR and GS on housing
price have progressively risen, with varying rates from OLS. XR
had the most signiﬁcant effect on housing prices in 2012 of all
NA. Each additional kilometer of distance resulted in a 3.45%
decline in housing values. At the time, wetlands had a coefﬁcient
of 1.17%, while green spaces had a coefﬁcient of 1.98%. In 2016,
XR effect coefﬁcient climbed slightly to 3.65%, but wetlands and
green spaces inﬂuence on values increased more rapidly, by 1.76%
t and 2.67%, respectively. WP have the most excellent effect on
values in 2020, with a coefﬁcient of 3.34%, followed by XR and
GS, with coefﬁcients of 4.08 and 3.28%, respectively. GWR mean
results of wetland, XR and green spaces have the same trend, from
2012 at 1.24, 2.34, and 2.75–3.65, 5.32, and 3.72% in 2020,
respectively. The increased capitalization of the three forms of
NA accessibility also shows the growing awareness of residents
toward
the
natural
environment
and
green
recreational
opportunities in the immediate vicinity. Simultaneously, the
mean GWR values for all three accessibility factors are higher
than OLS coefﬁcients, indicating that the sample variables are
more susceptible to the effect of housing prices.
With respect to other key variables, the capitalization effects
follow a different trend. CBD still determines housing prices
signiﬁcantly, although this inﬂuence is gradually diminishing.
Each kilometer away from the CBD Wuyi square inﬂuenced
6.91% of samples’ housing prices in 2012, compared with 5.82
and 4.61% in 2016 and 2020. In GWR mean view, the results are
at 8.41, 8.15, and 6.26% during the same time. The structure of
Changsha city is rapidly changing, as evidenced by the fact that
housing prices are decreasingly dependent on the accessibility of
the city’s core. A dispersed polycentric pattern has emerged as
signiﬁcant NA around housing and residency are accumulating in
other
locations
outside
the
core
area.
Neighborhood
characteristics such as metro and educational institutions also
have an effect on housing prices. Due to the shortage of
educational
facilities
in
China’s
main
cities,
property
ownership has become a popular market-based method of
acquiring high-quality schools. Accordingly, the impact factor
for quality middle schools in Changsha with GWR has increased
from 3.24 to 5.72%. The metro is a major transportation route in
modern China, often increasing the value of surrounding real
estate. In Changsha, this impact on property prices is from 3.78%
in 2012 to 4.24% in 2020. The rest variable estimates are shown
with Table 4.
It was easily found that statistic test values of GWR were
higher for all 3 years after accounting for the spatial heterogeneity
variables, indicating the advantage of GWR more suitable to deal
with spatial heterogeneity. As shown in Table 5, while the R2
values of all models have fallen over time, indicating that the
variables
impacting
housing
prices
are
becoming
more
complicated. However, comparing the OLS model with the
GWR model, we can see that the ﬁtted value of GWR is much
better than that of OLS, and the residual squares and standard
deviations of GWR are signiﬁcantly lower than those of OLS.
Therefore, the GWR model ﬁtting effect is superior to the general
OLS regression effect, making it a suitable approach for analyzing
the evolution of the spatial structure of residential pricing in
Changsha and the evolution of heterogeneity of crucial factors.
The Spatial Change of Estimates
The spatial distribution of the estimated coefﬁcients for the years
2012, 2016 and 2020 are compared to show the trend of variables.
Since we focus on NA, the variables XR, WP, GS and green ratio
are the main objects to study. Simultaneously, due to the
accessibility to the CBD being the primary factor impacting
the urban structure, we also analyze this variable. To facilitate
comparison, we group the coefﬁcient values into ﬁve average
quantiles, each one covering a 20% interval in order, from the
lowest to the highest.
The distribution of GWR estimate value of CBD variable is
shown in Figure 5. The distance between the house sample and
Changsha’s CBD Wuyi Square has a negative effect on the
housing prices. In terms of the spatial and temporal evolution
of the coefﬁcient values, their spatial distribution changes more
noticeably and exhibits a trend throughout the three cross-
sectional years. In 2012, the CBD variable had a high value in
the north of Changsha’s urban core area, notably along the
XiangRiver’s north coast and in the central section of Kaifu
District, with a coefﬁcient value of lower than −29% per km.
The area of a strong inﬂuence of CBD variable in 2012 is relatively
small, while the impact value is large. In 2016, the high-value area
of CBD expanded into the center of the city, including a part of
Yuhua and Tianxin districts, the northern part of Yuelu district.
Although the area has expanded, the impact values have
decreased, with the value is greater than −20% per km. In
2020, the CBD high-value area was mainly around Wuyi CBD
and Changsha south region, while the northern part of the city,
west of XR, is less severely affected. In general, the high-value
region of the CBD variable expands and then contracts,
progressively shifting the impact area from the north-central
to the southern section of Changsha city. This change indicates
that as the overall size of the city grows and polycentric structure
emerges, the correlation between CBD accessibility, as an
indicator,
and
housing
prices
tends
to
decline.
The
diminishing
inﬂuence
of
the
CBD
on
price
while
the
increasing impact of NA accessibility on price is a sample of
the evolution toward polycentric city structure. It could be
implied that the development of NA is one of the driving
forces in the evolution of the urban spatial structure of Changsha.
The distribution of GWR estimate value of WP is shown in
Figure 6. With the rapid development of wetlands amenities in
Changsha, an increasing number of people are choosing to locate
homes close to them, attracting more housing development with
high-density. Therefore, the capitalization effect of NA on
housing price increases as market develops, and this effect
Frontiers in Environmental Science | www.frontiersin.org
February 2022 | Volume 10 | Article 833831
8
Tuofu et al.
Capitalization Effect of Natural Amenities


===== PAGE 9 (score 56, numeric-paren 30, table-words 4, var-words 3) =====

reﬂects spatial heterogeneity over time. In 2012, the high values
for the variable area of wetlands were mainly in the southern part
of core city, with the high value of the local capitalization
coefﬁcient being between 3 and 7%.In 2016, the range of WP’s
high impacts changed to the east of the city, and the coefﬁcient
topped 12%, higher than in 2012. House prices in Changsha
TABLE 4 | 2012, 2016, 2020 Global regression results and GWR mean results.
Year 2012
Year 2016
Year 2020
Variable
Estimate
t value
Mean
Estimate
t value
Mean
Estimate
t value
Mean
Cons
8.855364
736.1038
8.752163
8.839498
830.0295
8.841037
8.897656
546.6361
8.868051
Plotratio
0.02112*
1.565706
0.032761
0.02256
0.191491
0.01054
−0.02328**
−1.70085
−0.01448
Greenratio
0.03613***
4.05615
0.030435
0.02976***
4.142,814
0.048289
0.04382***
5.786237
0.045568
P.M
0.01167**
1.834622
0.019406
0.02562***
2.349936
0.031475
0.04145***
2.323811
0.044331
B.Age
−0.01512*
−1.54565
−0.047719
−0.01722**
−1.69759
−0.03906
−0.01691***
−3.53801
−0.02237
Subway
0.06076***
4.50837
0.037899
0.05351***
4.256587
0.043836
0.05325***
2.89867
0.042437
QP.school
0.02421*
1.35006
0.007632
0.022502
0.836127
0.022131
0.029394
0.115719
0.015944
QM.school
0.01405**
1.766699
0.03242
0.030255*
1.519839
0.038927
0.05389***
2.380489
0.05728
d.CBD
−0.06916***
−3.71642
-0.08411
−0.05889***
−3.41646
−0.08158
−0.04623***
−3.15669
−0.06261
d.Wetland
−0.01178***
−2.87305
−0.01241
−0.01763***
−2.42941
−0.02419
−0.03341***
−3.11678
−0.03645
d.Xriver
−0.03445***
−2.7655
−0.02342
−0.03656***
−2.4744
−0.02694
−0.04085***
−2.52188
−0.05321
d.Gspace
−0.01986***
−2.22424
−0.02752
−0.02671***
−3.01611
−0.03334
−0.03286***
−2.36141
−0.03727
N
513
679
1,013
TABLE 5 | Comparison test parameters of OLS model and GWR model for 2012, 2016, 2020.
2012
2016
2020
Test model
OLS
GWR
OLS
GWR
OLS
GWR
Classic AIC
219.39489
148.154552
200.650388
181.790936
233.869488
192.747132
AICc
220.627322
185.185672
201.810693
186.910349
235.733249
201.089886
BIC/MDL
297.742219
556.019445
286.429929
361.450754
305.074561
341.377132
CV
0.085675
0.093511
0.079155
0.077744
13.902162
9.855557
R2
0.175308
0.44046
0.192313
0.261499
0.299147
0.430731
Adjusted R2
0.150093
0.299076
0.170151
0.204694
0.266771
0.355767
Residual Ssquares
46.258981
31.385991
50.289644
45.981838
37.734529
30.649917
FIGURE 5 | Spatial and temporal variation of CBD inﬂuence coefﬁcients in 2012, 2016 and 2020.
Frontiers in Environmental Science | www.frontiersin.org
February 2022 | Volume 10 | Article 833831
9
Tuofu et al.
Capitalization Effect of Natural Amenities


===== PAGE 10 (score 10, numeric-paren 0, table-words 0, var-words 10) =====

increased sharply from 2017 to 2020, with the extreme value of
the coefﬁcient exceeding 20% from 2020, much higher than in
2016. During this period, several well-known wetlands in Yuelu
District, such as Meixi Lake and Yanghu Lake, were highly sought
after by homebuyers and became a rallying point for high-priced
houses, so the inﬂuence of blue on surrounding house prices
increased signiﬁcantly. The area of coefﬁcient extremes is
concentrated in Meixi Lake, Yanghu Lake, and the city’s east,
outside the core urban area, which may imply that homebuyers
are increasingly attracted to fringe blue space. Nonetheless, Yuelu
district as a signiﬁcant urban ecological region in Changsha has
been enhanced, affecting housing prices.
XR is the traditional center of Changsha’s natural landscape,
and its accessibility has a signiﬁcant impact on housing prices.
The spatial heterogeneity variation of this effect was found via
GWR during times, as shown by Figure 7. In 2012, the main
impact high value area of the variable radiated is the southern
area of Yuelu District outside the western ring road, with the
highest coefﬁcient value over −20%. During this period, Yanghu
New Town has been gradually developed, and its main high-end
properties are located along XR, which is typical of river-view
buildings. At this time, on the east bank of the river, lots of older
houses with poorer scenic, which are not as sensitive to distance
to this river as high-end river view houses. Only few new
buildings high-rise residences along the riverbank that are
more affected by the landscape of XR in this region. In 2016,
the high value area spread further to the east of the river and south
of the city as it expanded southward with the construction of a
large
number
of
high-rise
residential
buildings.
The
concentration of high-system value properties in the southern
region increased signiﬁcantly, but the impact high-value diminish
to about −15%.
In 2020, the extreme value further decreased to about −10%.
With the rise of a new residential area on the north side of the
west bank of XiangRiver and the development of areas across the
river, the high-value area of the access variables is further
expanded. Overall, the residential neighborhoods whose house
prices are affected by XR accessibility variable are more robust in
the west of the river than in the east, and there is a higher density.
Despite residential samples in the newly developed area being
more affected by XiangRiver, the regression coefﬁcients of the
3 years have decayed, indicating that the real inﬂuence of this
accessibility variable has weakened.
The green ratio is an important NA within a community, and
it has a profound impact on the housing price of the community.
This impact also has very obvious temporal and spatial
differentiation as shown in Figure 8. In 2012, the extreme
impact area of greening rate on house price was mainly
distributed in the core area within the east ring road of the
city as well the south city area, with the high value range between
5 and 6%.Most of the development areas of good quality
properties in this period were located along with XR in the
core of the city, which affected the degree of community
greening.
Simultaneously,
there
is
a
tendency
that
the
surrounding NA on the east ring road is lower than in west,
making property purchasers more sensitive to the NA inside
community. 2016, high-value area expanded to the west bank of
XR and the east part of Changsha, while the impact of the south
city was signiﬁcantly declined. In 2020, the number of properties
covered by the high-value affection in east XR was signiﬁcantly
reduced. The high-value areas are mainly concentrated in several
large residential groups of Meixi, Yanghu Lake and north region,
with a decreasing distribution pattern of coefﬁcient values from
north to south. The average value of the estimated coefﬁcient for
those 3 years has increased gradually, which is maintained
between 6 and 10%.
As shown in Figure 9, the impact of green space on residential
prices is broadly distributed in urban Changsha, and the high
FIGURE 6 | Spatial and temporal variation of WP inﬂuenced coefﬁcients in 2012, 2016 and 2020.
Frontiers in Environmental Science | www.frontiersin.org
February 2022 | Volume 10 | Article 833831
10
Tuofu et al.
Capitalization Effect of Natural Amenities