# RECORD 6e83e47fe67686f424f9c68dca8dac1370a653d3
# TITLE: An Empirical Analysis of the Relationship Between Shopping Malls and Surrounding Property Prices: Evidence from Xian, China
# YEAR: 2024.0  VENUE: Advances in Economics, Management and Political Sciences  DOI: 10.54254/2754-1169/94/2024ox0141
# AUTHORS: Ao Tan
# PAGES: 10  CANDIDATE TABLE PAGES (1-based): [6, 7, 8]
# ABSTRACT: Shopping malls have become integral components of urban areas in China, and their influence on the local economy and real estate markets is substantial due to the country's rapid urbanisation and economic expansion. This study examines the price premium of properties in Weiyang District, Xi'an, China that are affected by their proximity to malls. The study analyses a comprehensive dataset of 10,175 transaction prices for second-hand homes in 12 administrative zones of Weiyang District from September 2017 to June 2021 based on the hedonic price model and ordinary linear regression. The findings indicate that the value of properties in proximity to local shopping malls is notably enhanced; however, this positive effect fades as the distance from the malls increases. This study addresses a research gap in the influence of shopping centres on property prices in the Weiyang District. It offers valuable insights for local urban planners, property developers, and investors. These results have the potential to enhance the value of property and optimise urban development. The research methodology includes statistical methodologies to guarantee the accuracy of the findings and stresses the necessity of strategic urban planning to maximise the advantages of commercial amenities. To develop a more comprehensive understanding of the dynamics between commercial amenities and property values, future research might investigate the longitudinal impacts over a longer period and incorporate supplementary variables, such as socio-economic factors and real estate developments.


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An Empirical Analysis of the Relationship Between Shopping 
Malls and Surrounding Property Prices: Evidence from 
Xi’an, China 
Ao Tan1,a,* 
1Faculty of Social Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom 
a. at2g20@soton.ac.uk 
*corresponding author 
Abstract: Shopping malls have become integral components of urban areas in China, and 
their influence on the local economy and real estate markets is substantial due to the country's 
rapid urbanisation and economic expansion. This study examines the price premium of 
properties in Weiyang District, Xi'an, China that are affected by their proximity to malls. The 
study analyses a comprehensive dataset of 10,175 transaction prices for second-hand homes 
in 12 administrative zones of Weiyang District from September 2017 to June 2021 based on 
the hedonic price model and ordinary linear regression. The findings indicate that the value 
of properties in proximity to local shopping malls is notably enhanced; however, this positive 
effect fades as the distance from the malls increases. This study addresses a research gap in 
the influence of shopping centres on property prices in the Weiyang District. It offers valuable 
insights for local urban planners, property developers, and investors. These results have the 
potential to enhance the value of property and optimise urban development. The research 
methodology includes statistical methodologies to guarantee the accuracy of the findings and 
stresses the necessity of strategic urban planning to maximise the advantages of commercial 
amenities. To develop a more comprehensive understanding of the dynamics between 
commercial amenities and property values, future research might investigate the longitudinal 
impacts over a longer period and incorporate supplementary variables, such as socio-
economic factors and real estate developments. 
Keywords: Hedonic Price Model, Property Price, Shopping Mall, Price Premium. 
1. 
Introduction 
Shopping malls are large, indoor commercial complexes that include a wide range of retail 
establishments, restaurants, entertainment venues, and other services, providing a complete shopping 
experience. Typically, these complex malls are home to a variety of businesses and play a significant 
role in modern urbanization. 
However, nowadays, shopping malls are experiencing severe challenges. Firstly, the widespread 
of e-commerce has led many consumers to opt for online shopping platforms. Furthermore, customers 
now tend towards experiential purchasing rather than traditional conventional retail. Also, the mall 
situation is being worsened by the excessive competition in retail centres [1]. 
Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 
DOI: 10.54254/2754-1169/94/2024OX0141 
© 2024 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 
(https://creativecommons.org/licenses/by/4.0/). 
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density of malls is low (less than one mall) within a 500m, 800m and 1km radius, suggesting that 
while some dwellings are close to retail areas, others are further away. 
Table 2: Descriptive statistics of second-hand housing transaction data. 
Variable 
Abbreviation 
Mean 
Std. Dev. 
Min 
Total price 
tprice 
132.085 
53.841 
14 
Total number of rooms  
room 
3.934 
1.138 
1 
A
size 
207.683 
316.332 
21.65 
Total floors of the building  
tfloors 
24.791 
9.547 
1 
Age of building  
bage 
9.9 
4.294 
1 
Orientation of house  
orientation 
3.211 
1.113 
1 
House decoration degree 
decoration 
2.464 
.665 
1 
Floor/ level of the house  
floorno 
3.106 
.799 
1 
Structure of building 
structure 
3.102 
.654 
1 
Subway nearby  
subway 
.153 
.36 
0 
Distance from community to CBD (km) 
cbd2 
3.945 
3.032 
.162 
Distance from community to mall (km) 
dshop 
1.648 
1.068 
.036 
MallsNo around community within 3km 
shop3km 
7.009 
4.683 
0 
MallsNo around community within 500m shop500m 
.260 
.834 
0 
MallsNo around community within 800m shop800m 
.641   
1.568 
0 
MallsNo around community within 1km 
shop1km 
.894 
1.928 
0 
 
Furthermore, the correlation matrix (Table 3) shows correlations between the distance to a shopping 
mall and the CBD and the malls number of shopping centres within 3 km. The strong negative 
correlation between the distance to a mall and the number of malls within 3 km ( -0.619) shows that 
houses closer to a large shopping centre have fewer malls within 3km. This pattern is indicative of 
urban planning strategies. Besides, a relatively positive correlation between distance to a mall and 
distance to the CBD (0.513) shows that houses further away from the CBD are closer to suburban 
malls and serve as local commercial centres. These links show the distribution and planning of 
commercial facilities in different locations. 
Table 3: Correlation matrix of key variables. 
Variables 
tprice 
room 
size 
tfloors 
bage 
subway 
cbd2 
dshop 
shop3km 
tprice 
1.000 
 
 
 
 
 
 
 
 
room 
0.688 
1.000 
 
 
 
 
 
 
 
size 
0.196 
0.208 
1.000 
 
 
 
 
 
 
tfloors 
0.114 
-0.089 
-0.006 
1.000 
 
 
 
 
 
bage 
-0.350 
-0.089 
-0.060 
-0.628 
1.000 
 
 
 
 
subway 
0.008 
-0.000 
-0.003 
0.004 
-0.001 
1.000 
 
 
 
cbd2 
0.044 
0.145 
0.034 
0.016 
-0.269 
0.003 
1.000 
 
 
dshop 
-0.025 
0.105 
0.001 
0.003 
-0.138 
-0.002 
0.513 
1.000 
 
shop3km 
-0.029 
-0.121 
-0.016 
-0.001 
0.239 
-0.002 
-0.723 
-0.619 
1.000 
4.2. Multicollinearity Results (VIF) 
The study utilizes the variance inflation factor (VIF) as shown in Table 4 to identify the presence of 
multicollinearity in the regression analysis. As a result, the study reveals the existence of moderate to 
Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 
DOI: 10.54254/2754-1169/94/2024OX0141 
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strong multicollinearity in the distance to the nearest shopping mall and CBD, the number of malls 
within an 800-meter and one-kilometre radius. The existence of multicollinearity is expected because 
of the mutual connection between these variables. For example, properties in proximity to a certain 
shopping mall may also be near other commercial amenities, resulting in a convergence with other 
location-related factors. On the other hand, the variable subway exhibits no substantial 
multicollinearity, which suggests that underground access is separate from other factors in the area. 
Table 4: Variance inflation factor. 
Variable 
VIF 
1/VIF 
lndshop 
4.905 
.204 
lncbd2 
4.494 
.223 
bshop800m 
4.126 
.242 
bshop1km 
3.629 
.276 
shop3km 
3.514 
.285 
bshop500m 
2.63 
.38 
lntfloors 
1.902 
.526 
lnbage 
1.85 
.541 
lnstructure 
1.398 
.715 
lnroom 
1.254 
.797 
lnorientation 
1.154 
.866 
lnsize 
1.138 
.879 
lndecoration 
1.032 
.969 
lnfloorno 
1.002 
.998 
subway 
1.001 
.999 
Mean VIF 
2.335 
. 
4.3. Regression Analysis 
The regression results shown in Table 5 provide important insights into the impact of shopping malls 
on property prices in Weiyang District, Xi'an, China. Three models, specified in column (1) to (3) 
respectively, were used to analyse this relationship, each containing different variables relating to the 
proximity and number of shopping malls. 
Table 5: Regression results. 
Variables 
(1) 
(2) 
(3) 
lnroom 
0.766*** 
0.764*** 
0.765*** 
 
(113.44) 
(113.22) 
(113.23) 
lnsize 
0.0309*** 
0.0308*** 
0.0310*** 
 
(11.21) 
(11.18) 
(11.26) 
lntfloors 
0.124*** 
0.118*** 
0.118*** 
 
(23.33) 
(22.23) 
(22.14) 
lnbage 
-0.191*** 
-0.199*** 
-0.198*** 
 
(-30.31) 
(-31.88) 
(-31.08) 
lnorientation 
0.0421*** 
0.0417*** 
0.0423*** 
 
(8.57) 
(8.47) 
(8.60) 
lndecoration 
0.0800*** 
0.0788*** 
0.0775*** 
 
(12.16) 
(11.99) 
(11.75) 
Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 
DOI: 10.54254/2754-1169/94/2024OX0141 
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Table 5: (continued) 
lnfloorno 
0.00440 
0.00482 
0.00556 
 
(0.56) 
(0.61) 
(0.70) 
lnstructure 
0.164*** 
0.161*** 
0.164*** 
 
(14.45) 
(14.14) 
(14.42) 
subway 
0.0124* 
0.0123* 
0.0120* 
 
(2.04) 
(2.04) 
(1.99) 
lncbd2 
-0.0487*** 
-0.0413*** 
-0.0684*** 
 
(-11.28) 
(-8.07) 
(-17.63) 
lndshop 
-0.0239*** 
 
 
 
(-5.63) 
 
 
shop3km 
 
0.00478*** 
 
 
 
(5.98) 
 
bshop500m 
 
 
-0.0393*** 
 
 
 
(-3.86) 
bshop800m 
 
 
0.0553*** 
 
 
 
(5.01) 
bshop1km 
 
 
-0.0334*** 
 
 
 
(-3.74) 
Constant 
3.449*** 
3.443*** 
3.500*** 
 
(89.43) 
(89.06) 
(89.20) 
Adjusted 𝑅2 
0.685 
0.686 
0.685 
Note: t-statistics in parentheses; * p < 0.05, ** p < 0.01, *** p < 0.001. 
 
Model 1 includes the logarithm of the nearest shopping centre and other residential variables. The 
coefficient for distance to a shopping centre means that the property price decreases by 3,154.83 RMB 
for every 1 increase in distance to the nearest shopping centre. The decline in property prices with 
increasing distance illustrates that convenience is a key determinant of property value. Several 
reasons support this claim. For example, properties near retail areas are attractive due to their diversity 
of amenities and services. Shopping centres include restaurants, entertainment, and other amenities 
that improve the lifestyle and raise property values. This result suggests that urban planners should 
consider integrating shopping centres into residential areas to increase property values. Also, 
investors should select properties near shopping areas to enhance their investment potential.  
Model 2 shifts the focus to the number of shopping malls within a 3-kilometre radius rather than 
proximity to the nearest shopping centre. The coefficient for the variable the number of malls within 
3km means each additional shopping centre within this radius increases property prices by 631.37 
RMB. This model highlights the positive cumulative effect of multiple malls on property values. 
Multiple shopping centres increase property prices due to their convenience, appeal to varied 
demographics, economic impact, shopping experience, infrastructure, and community. This result 
suggests that developers should aim to create clusters of malls to maximise the positive impact on 
surrounding properties. Also, investors should consider mixed-use projects that combine residential, 
commercial and retail space. Such projects create a self-sustaining community that increases property 
values and attracts long-term tenants. In summary, clustering shopping centres not only improves the 
shopping experience but also contributes to increased footfall, benefiting both retailers and residents. 
Model 3 introduces binary variables reflecting proximity to shopping malls within 500 metres, 800 
metres and one kilometre. The variable malls within 500 metres indicates that proximity to a shopping 
Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 
DOI: 10.54254/2754-1169/94/2024OX0141 
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centre within 500 metres lowers property prices by 5,187.94 RMB, which is likely due to negative 
externalities such as noise and traffic congestion. This suggests that policy makers should take 
measures to mitigate these negative effects, such as better traffic management and noise reduction 
strategies. Conversely, the coefficient of the variable for the number of malls within 800 metres means 
that properties within this radius experience an increase in value of 7,305.30 RMB. This indicates 
that a balanced distance to shopping centres can increase property value, suggesting that urban 
developers should aim for a moderate distance between residential areas and shopping centres. The 
variable for malls within one kilometre has negative coefficient of -0.0334, which correlates with a 
decrease in property values of 4,411.64 RMB, suggesting that careful land use planning is needed to 
balance the benefits of proximity with the potential disadvantages. The mixed effects at different 
distances highlight the complexity of urban living, where benefits such as accessibility need to be 
weighed against potential disruption. 
In addition to proximity to shopping centres, various housing attributes such as the number of 
rooms, size of the house, number of total floors, age of the building, orientation, quality of amenities, 
building structure, proximity to the metro and distance to the CBD also have a notable impact on 
housing prices across all models. These findings indicate that the various characteristics of the 
property also play a crucial role in influencing house values. 
The results of this study provide important insights for urban planners as they suggest that 
strategically located shopping centres can increase property values. However, care must be taken to 
mitigate negative externalities in densely populated urban areas. For property developers, these 
findings can be used to optimise the location of projects and ensure that new residential areas are 
close enough to shopping facilities to maximise property values. Simultaneously, the results suggest 
investors to consider properties that are strategically located near shopping centres as these tend to 
have higher appreciation rates. Investing in residential properties within an 800-metre radius of 
shopping malls can be particularly lucrative due to the observed increase in property values. In 
addition, properties in areas where there are multiple shopping centres within a 3-kilometre radius 
can offer significant returns due to the positive impact on property values. 
In summary, proximity to shopping malls has a significant and variable impact on property prices, 
with the relationship varying according to distance and specific valuation methods. These results 
provide a solid basis for future urban development strategies and underline the importance of 
balanced planning to optimise property values. 
5. 
Conclusion 
This empirical study of housing price premiums in the Weiyang district of Xi'an shows that the 
proximity and density of shopping malls relatively significantly affect surrounding housing prices. 
The results of this study are of great importance for urban planners, property developers and 
policymakers and fill a research gap on the influence of shopping malls on house prices in the 
Weiyang District of Xi'an. Better integration of malls with residential areas and careful determination 
of the optimal proximity of such facilities can drive up property prices and improve the overall quality 
of life in the city. 
However, there are some limitations to this study. Data collection is prohibited beyond June 2021 
due to political restrictions. In addition, this study did not assess the external effects of universities. 
Future research could consider extending the data collection period beyond June 2021 to understand 
recent trends and the impact of the pandemic on property values and urban development. Overcoming 
policy restrictions by working with local authorities to gain access to more comprehensive and up-
to-date data will be crucial. Secondly, assessing the externalities of universities and higher education 
institutions, such as their impact on local housing markets, economic activity and social dynamics, 
will help policymakers integrate educational institutions into urban development plans. 
Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 
DOI: 10.54254/2754-1169/94/2024OX0141 
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