# RECORD 3b4b454985f862f1a1d6165d8c81255275be3ddb
# TITLE: The effects of park and sea landscape on property value in a tourist city
# YEAR: 2022.0  VENUE: Frontiers in Environmental Science  DOI: 10.3389/fenvs.2022.967094
# AUTHORS: Dequn Shi; Yuhui Zhang; Zeng-Xian Liang
# PAGES: 10  CANDIDATE TABLE PAGES (1-based): [6, 7]
# ABSTRACT: Theorists have traditionally considered the effects of single rather than multiple landscape attributes on property prices, and ignored the interaction between landscape-specific housing attributes. The aim of this paper is to analyze the hedonic attributes of property prices and compare the effects on property prices of two key landscape attributes. Seventeen hedonic attributes are used to build a hedonic-pricing model with five dimensions: structure, neighborhood and community, location, environment, and time. A sample of 688 valid houses within 1,000 m of Xiangshan Park in the city of Zhuhai, China is collected. The selected hedonic attributes are found to have significant effects on property prices, explaining around 91% of the observed variation in price. Proximity to the sea has a greater positive influence on property prices than proximity to parks. In addition, people prefer to buy properties with sea views than to live close to the sea.


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The effects of park and sea
landscape on property value in a
tourist city
Dequn Shi1, Yuhui Zhang2,3 and Zengxian Liang2,3*
1Guangdong Vocational Institute of Public Administration, Guangzhou, China, 2Key Laboratory of the
Sustainable Development of Xinjiang’s Historical and Cultural Tourism, Xinjiang University, Urumqi,
China, 3School of Tourism Management, SunYat-sen University, Guangzhou, China
Theorists have traditionally considered the effects of single rather than multiple
landscape attributes on property prices, and ignored the interaction between
landscape-speciﬁc housing attributes. The aim of this paper is to analyze the
hedonic attributes of property prices and compare the effects on property
prices of two key landscape attributes. Seventeen hedonic attributes are used to
build a hedonic-pricing model with ﬁve dimensions: structure, neighborhood
and community, location, environment, and time. A sample of 688 valid houses
within 1,000 m of Xiangshan Park in the city of Zhuhai, China is collected. The
selected hedonic attributes are found to have signiﬁcant effects on property
prices, explaining around 91% of the observed variation in price. Proximity to the
sea has a greater positive inﬂuence on property prices than proximity to parks. In
addition, people prefer to buy properties with sea views than to live close to
the sea.
KEYWORDS
landscape, property prices, hedonic analysis, urban tourism, Zhuhai city
Introduction
Attracting immigrants as well as temporary visitors has become an important goal of
many tourist-city authorities in the last few decades. The phenomenon of tourists’
residing for relatively long periods and even settling down fully in tourist cities has
expanded from Western countries to developing countries such as China (Liang et al.,
2021). Tourists and residents cohabit in many tourist cities, which creates policy issues
(Rodriguez, 2001). The tourist city of Gold Coast in Australia’s “sun-belt” (Mullins, 1991)
attracts a large number of long-distance immigrants seeking a better climate and lifestyle
(Stimson and Minnery, 1998). The Costa del Sol in Spain, another famous tourist region,
receives a massive volume of foreign tourists and immigrants, especially retired migrants
(Rodriguez, 2001). The ancient town of Dali in southwestern China attracts both domestic
and international immigrants. This new population growth has stimulated a wave of
tourist-related real estate development in the last decade (Wu and Xu, 2010). Most new
immigrants purchase or rent houses close to areas of natural beauty such as beaches,
rivers, lakes and parks, or tourist attractions such as theme parks (Liang and Bao, 2015).
Landscape plays a critical role in the decision to rent or purchase a house in a tourist city
(Pitkanen, 2008).
OPEN ACCESS
EDITED BY
Rita Yi Man Li,
Hong Kong Shue Yan University, Hong
Kong SAR, China
REVIEWED BY
David Brasington,
University of Cincinnati, United States
Mohd Faris Dziauddin,
Sultan Idris University of Education,
Malaysia
*CORRESPONDENCE
Zengxian Liang,
liangzx3@mail.sysu.edu.cn
SPECIALTY SECTION
This article was submitted to
Environmental Economics and
Management,
a section of the journal
Frontiers in Environmental Science
RECEIVED 12 June 2022
ACCEPTED 12 September 2022
PUBLISHED 27 September 2022
CITATION
Shi D, Zhang Y and Liang Z (2022), The
effects of park and sea landscape on
property value in a tourist city.
Front. Environ. Sci. 10:967094.
doi: 10.3389/fenvs.2022.967094
COPYRIGHT
© 2022 Shi, Zhang and Liang. This is an
open-access article distributed under
the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other
forums is permitted, provided the
original author(s) and the copyright
owner(s) are credited and that the
original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution
or reproduction is permitted which does
not comply with these terms.
Frontiers in Environmental Science
frontiersin.org
01
TYPE Original Research
PUBLISHED 27 September 2022
DOI 10.3389/fenvs.2022.967094


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independent variables concerning distance and area were
logarithmic; and Z-scores were used to non dimensionalize
the
ordinal
variables—property-management
fees
and
number of bus lines—and the dummy variables with actual
values. In addition, average values were calculated to replace
missing data. Table 2 shows the key descriptive statistics
obtained from the data processing.
Results
Effects of urban landscape on property
prices
The correlation coefﬁcients obtained for the three key
landscape variables—sea view (SEAVIEW), distance from park
(DPAR) and distance from sea (DSEA)—and the dependent
variable,
property
sale price,
were
examined. The
three
landscape variables were found to be signiﬁcantly correlated
with the dependent variable. Forced entry multiple linear
regressions were conducted using the software package SPSS
20.0. To prevent multicollinearity and determine the effect of
each landscape variable on property sale price, a separate
regression with controlled variables was carried out (see
Model 1, Table 3), followed by a regression for each landscape
variable (see Models 2–4, Table 3). Finally, a separate regression
with controlled variables and all of the landscape variables
(Model 5) was conducted, as shown in Table 3.
The
results
of
regressing
Model
1
indicated
that
12 controlled variables signiﬁcantly inﬂuenced property
prices. LOT, FEE, LFAC, PUBS, MANA, DECORATION,
and POOL had positive and signiﬁcant effects on property
price, while AGE, DEXE, and DCEM had a negative and
signiﬁcant inﬂuence. These results were as expected, and in
line with the ﬁndings of most early studies. However, some
results were inconsistent with our predictions. TRSP and
PARKING had negative rather than positive effects on
property prices, while neither LIFT nor CENV had a
signiﬁcant effect on property prices. Property prices are
generally
assumed
to
increase
with
the
number
of
accessible bus lines and the availability of parking spaces.
However, although public bus services offer convenient
transport
links,
they
also
increase
noise
and
trafﬁc
congestion. As properties near transportation hubs are
subject to noise, air pollution and trafﬁc congestion on a
daily basis, their sale price will be lower (Wu et al., 2008; Wen
et al., 2012). The ﬁeld investigation conducted during this
study
revealed
that
car-parking
spaces
and
residential
properties in the Xiangshan Park area of Zhuhai are sold
separately; as a result, the cost of parking does not contribute
directly to property price. Xiangshan Park is located in the old
city center, where car-parking spaces are always insufﬁcient,
despite the construction of new residential communities in
recent
years.
Most
new
parking
facilities
have
been
constructed in older communities with lower property
prices, where there is more public space and land value is
lower, making parking spaces cheaper. In short, more parking
is available near properties with lower prices. Overall, Model
1 performed well, with an adjusted R2 value of 0.916 (F =
534.331, p = 0.00).
The regression results for Models 2 to 4 indicate the effects
of individual landscape variables, namely DPAR, DSEA, and
SEAVIEW, on property sale price. Model 1 (a separate
regression with controlled variables only) was compared
with Models 1 to 4, and DPAR (Model 2), DSEA (Model
3), and SEAVIEW (Model 4) were found to increase the
original model’s explanatory power by 0.1%, 0.1%, and
0.2%,
respectively.
Model
2
performed
well,
with
an
adjusted R2 value of 0.917 (F = 505.367, p = 0.00). The
regression results indicate that for every 1% increase in
distance from Xiangshan Park, property prices rise by
0.056%. The ﬁnding that proximity to Xiangshan Park
negatively affects property prices was contrary to our
expectation. Model 3 also performed well, with an adjusted
R2 value of 0.917 (F = 508.761, p = 0.00). With the exception of
distance to Martyrs’ Cemetery and access to elevators, all of
the independent variables in Model 3 signiﬁcantly inﬂuenced
the sale price of housing in the area under study. According to
the main regression results, for every 1% increase in DSEA,
property prices dropped by 0.055%. As expected, therefore,
proximity to the seashore had a signiﬁcant and positive
inﬂuence on property prices. Model 4 also performed well,
with an adjusted R2 value of 0.918 (F = 514.323, p = 0.00). All
TABLE 2 Descriptive statistics of variables.
Variable
Mean
Std. Dev
Max
Min
LOT
4.52
0.37
5.48
3.26
FEE
0
1.00
3.34
−2.48
TRSP
0
1.00
3.73
−0.59
LFAC
0
1.00
1.13
−2.32
AGE
0
1.00
2.52
−2.46
PUBS
0.36
0.48
1.00
0
MANA
0
1.00
2.27
−2.53
DECORATION
0
1.00
2.06
−2.65
CENV
0
1.00
1.80
−1.66
DEXE
6.79
0.88
7.55
4.25
DCEM
6.58
0.67
7.50
5.03
PARKING
0.45
0.50
1.00
0
POOL
0.11
0.31
1.00
0
LIFT
0.72
0.45
1.00
0
DPAR
6.45
0.40
7.00
5.43
DSEA
6.44
0.97
7.76
4.65
SEAVIEW
0.16
0.37
1.00
0
SALES
4.82
0.45
6.06
3.56
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the independent variables in Model 4 signiﬁcantly affected
property prices. The main regression results indicated that a
1% increase in the likelihood of a sea view increased property
prices by 0.059%. As expected, therefore, the variable Seaview
was found to have a signiﬁcant positive inﬂuence on property
prices.
Model 5, which included the controlled variables and all
the landscape variables, performed well, with an adjusted
R2 value of 0.918 (F = 484.039, p = 0.00). When all three
landscape
variables
were
considered,
only
DSEA
and
SEAVIEW were found to have a signiﬁcant inﬂuence on
property prices; the effect of DPAR was not signiﬁcant. For
every 1% increase in the likelihood of a sea view, property
prices rose by 0.072%. For every 1% increase in DSEA,
property prices fell by 0.040%. People are most likely to
buy properties near the sea or with sea views. In addition,
comparison of the results for Model 3 and Model 4, with the
same controlled variables, revealed that Model 4 (adjusted
R2 = 0.918, F = 514.323, p = 0.00) had greater explanatory
power than Model 3 (adjusted R2 = 0.917, F = 508.761, p =
0.00). Even in Model 5, the standardized regression coefﬁcient
of SEAVIEW (β = 0.072) was greater than that of DSEA
(β = −0.040), which indicates that sea views have a greater
inﬂuence on property prices than proximity to the sea.
Together, these results conﬁrm that people prefer to own
properties with sea views rather than to live close to the sea.
Property-price premium
In the logarithmic model, the regression coefﬁcients of each
continuous variable corresponded to the price elasticity of
housing characteristics; that is, when other variables remained
constant, a 1% change in a given variable was assumed to result in
a 1% change in housing price. Due to the presence of
discontinuous variables, unstandardized regression coefﬁcients
cannot be used under conditions of semi-elasticity, so anti-
logarithmic regression coefﬁcients were obtained. The results
are presented in Table 4.
The results for the individual landscape variables are as
follows: 1) Proximity to Xiangshan Park had a negative effect
on property values: every 1% increase in distance from the park
led to a 0.056% increase in property prices. 2) Proximity to the
seashore had a positive inﬂuence on property prices: every 1%
increase in distance from the sea decreased property prices by
0.055%. 3) A sea view increased total property prices by 9.308%.
TABLE 3 Results of multiple linear regression analysis.
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
LOT
0.976(65.585)**
0.967(64.224)**
0.953(59.367)**
0.961(63.918)**
0.949(61.395)**
FEE
0.061(6.817)**
0.049(5.086)**
0.056(6.316)**
0.049(5.336)**
0.049(5.035)**
TRSP
−0.047(−8.016)**
−0.044(−7.563)**
−0.048(−8.367)**
−0.045(−7.897)**
−0.047(−8.248)**
LFAC
0.051(7.211)**
0.056(7.744)**
0.057(7.876)**
0.058(8.103)**
0.061(8.657)**
AGE
−0.071(−8.911)**
−0.073(−9.270)**
−0.072(−9.168)**
−0.080(−9.890)**
−0.078(−9.726)**
PUBS
0.075(5.990)**
0.087(6.660)**
0.072(5.746)**
0.086(6.824)**
0.081(5.933)**
MANA
0.035(4.263)**
0.033(4.052)**
0.033(4.061)**
0.036(4.420)**
0.035(4.289)**
DECORATION
0.026(4.941)**
0.027(5.108)**
0.027(5.055)**
0.026(5.002)**
0.026(5.032)**
CENV
−0.008(−1.374)
−0.011(−1.924)
−0.016(−2.598)**
−0.021(−3.297)**
−0.024(−3.741)**
DEXE
−0.033(−3.145)**
−0.033(−3.140)**
−0.053(−4.474)**
−0.028(−2.639)**
−0.041(−3.237)**
DCEM
−0.069(−4.950)**
−0.086(−5.751)**
−0.015(−0.757)
−0.039(−2.503)*
PARKING
−0.060(−4.013)**
−0.058(−3.875)**
−0.051(−3.339)**
−0.068(−4.556)**
−0.059(−3.928)**
POOL
0.070(3.476)**
0.092(4.321)**
0.083(4.117)**
0.081(4.079)**
0.086(4.120)**
LIFT
0.025(1.424)
0.039(2.183)*
0.026(1.541)
0.034(1.966)*
0.033(1.821)
DPAR
0.056(3.027)**
−0.007(0.408)
DSEA
−0.055(−3.656)**
−0.040(−3.085)**
SEAVIEW
0.089(4.500)**
0.072(3.265)**
Conatant
1.065(6.446)**
0.833(4.599)**
1.297(7.386)**
0.869(5.153)**
1.063(4.634)**
F
534.331**
505.367**
508.761**
514.323**
484.039**
R2
0.917
0.919
0.919
0.920
0.920
Adj. R2
0.916
0.917
0.917
0.918
0.918
VIF
1.151–3.593
1.154–4.180
1.152–9.064
1.151–4.472
1.152–6.552
N
688
688
688
688
688
Note: **p ≤0.01, *p ≤0.05.
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4) Proximity to Huafa Exercise Park, one of the three open spaces
in the area under study, also had a signiﬁcant effect on property
prices. Every 1% increase in distance to Huafa Exercise Park led
to a 0.033% drop in property prices. Of all of the landscape
variables, only distance to Xiangshan Park (DPAR) had a
signiﬁcant negative effect on property prices. There are three
reasons for this ﬁnding. First, Xiangshan Park is located in the old
center of Zhuhai, and properties closer to the park are sold at
lower prices. Second, as the park is the area’s main open space, it
is noisy and compromises the privacy of surrounding properties.
Third, a large proportion of the properties sampled in this study
were located between Xiangshan Park and Qinglv Road, as in
Figure 1, so properties closer to the park were farther from the
sea. This also explains a key ﬁnding from Model 5 (Table 3):
when all of the landscape variables were considered in a single
regression, distance to Xiangshan Park (DPAR) had a positive
but insigniﬁcant effect on property price.
Using average housing prices and hedonic attributes as
benchmarks, the marginal prices of the hedonic property
attributes were estimated. As shown in the descriptive statistics in
Table 2, the average logarithmic price was 4.82. As shown in Table 4,
the marginal prices of the logarithmic variables were obtained from
the elastic coefﬁcient, and the marginal prices of the other variables
were derived from the semi-elastic coefﬁcient. As indicated in
Table 4, 1) each additional 100 m from Xiangshan Park
increased
property
prices
by
10,550
yuan
RMB;
2)
each
additional 100 m from the seashore decreased property prices by
7,640 yuan RMB; 3) a sea view increased total property prices by
1,27,280 yuan RMB, which indicates that the landscape variable
Seaview had a signiﬁcant positive inﬂuence on property prices; and
4) each additional 100 m to Huafa Exercise Park decreased property
prices by 3,760 yuan RMB.
Conclusion and discussion
Tourist cities in China today, beneﬁting from several tourist
attractions, generally offer a livable environment and the facilities
required to attract not only temporary visitors, but also new-rich
immigrants from the northern provinces, Macao and Hong
Kong. However, prospective house buyers consider a series of
property-related factors, such as landscape variables, before
making a purchase decision. The aim of this study was to
determine the effects of landscape characteristics on the
market price of residential properties in a mature tourist city.
A larger set of landscape attributes than used in most previous
studies—several of which investigated only one attribute—was
examined. The HPM was used to analyze 688 properties sampled
from 22 residential areas within 1,000 m of Xiangshan Park,
offering valuable insights into the effects of multiple landscape
features on the property-price premium. More speciﬁcally, an
important theoretical question is answered: how much is a
tourist-city immigrant willing to spend on various landscape
attributes of a property? The major ﬁndings are as follows. First,
LOT, FEE, LFAC, PUBS, MANA, DECORATION, and POOL
had signiﬁcant positive effects on property prices, while AGE,
DEXE, and DCEM had signiﬁcant negative effects. Contrary to
expectation, TRSP and PARKING had a negative inﬂuence on
property prices, and neither LIFT nor CENV had a signiﬁcant
effect on property prices. These controlled independent variables
explained 91.6% of the observed variation in property prices.
Second, people were found to prefer properties close to the sea,
especially having a sea view than those close to large parks.
However,
it
remains
unclear
whether
immigrants
purchase properties close to attractive landscape features
primarily to enjoy a good view or to use the available open
space for leisure activities such as exercise, hiking, riding, and
walking. In many early studies, the effects of landscape and
other facilities on property prices were found to vary
substantially according to the usage of these facilities. Some
early evidence was obtained that parks designed for passive
use have a greater positive inﬂuence on property prices than
parks designed for active use (More et al., 1982; Crompton,
2005). Crompton’s review of the literature on the effects of
landscape features on property prices revealed that properties
“abutting or fronting a passive park” are of particular value
(Crompton,
2001a).
A
passive
area
is
generally
an
undeveloped space or environmentally sensitive area in
which minimal human activity occurs. Passive areas do not
usually provide sports infrastructure or other opportunities to
engage in strenuous physical activity, although they may
contain open space. For a property buyer, a passive area is
a quiet landscape that provides a good view rather than an
opportunity for recreation. Therefore, properties with a good
TABLE 4 The price elasticity/semi-elasticity of landscape variables.
Variable
Regression coefﬁcients
Elasticity coefﬁcients (%)
Semi-elasticity
coefﬁcients (%)
Marginal price
DPAR
0.056
0.056
10,550 yuan/100 m
DSEA
−0.055
−0.055
−7,640 yuan/100 m
SEAVIEW
0.089
9.308
1,27,280 yuan
DEXE
−0.033
−0.033
−3,760 yuan/100 m
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10.3389/fenvs.2022.967094