# RECORD f870a2d97f24564c545a70499dbb596627b9f607
# TITLE: The Impact of Shopping Malls on Surrounding Housing Prices: Evidence from Beijing, China
# YEAR: 2025.0  VENUE: Advances in Economics, Management and Political Sciences  DOI: 10.54254/2754-1169/2025.cau27161
# AUTHORS: Yi-Xuan He
# PAGES: 8  CANDIDATE TABLE PAGES (1-based): [6]
# ABSTRACT: This study investigates the impact of shopping malls on the price of residential housing in the neighborhood, focusing on how the distance of shopping malls to residential housing affects the price of housing. This study uses statistical regression analysis via STATA 17 data from six selected urban districts and finds that housing prices of neighborhoods with higher accessibility to shopping malls and convenience stores improve significantly. The findings reveal that proximity to shopping centers significantly increases housing values, with homes closer to these centers becoming more expensive, primarily due to increased accessibility and improved amenities. It was also established that the quality of school districts has a significant effect on House prices, especially in high-demand income areas. Subsequently, neighborhoods in top-ranked districts possess higher prices because of the value that families attribute to educational assets. Further studies should generalize the research area, use wider sets of control variables, and make use of long-run models to reflect the dynamic tendencies of housing price changes.


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Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets
DOI: 10.54254/2754-1169/2025.CAU27161
© 2025 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/).
30
The Impact of Shopping Malls on Surrounding Housing
Prices: Evidence from Beijing, China
Yixuan He
BDA School of The High School Affiliated to Renmin University of China, Beijing, China
 1353690955@qq.com
Abstract. This study investigates the impact of shopping malls on the price of residential
housing in the neighborhood, focusing on how the distance of shopping malls to residential
housing affects the price of housing. This study uses statistical regression analysis via
STATA 17 data from six selected urban districts and finds that housing prices of
neighborhoods with higher accessibility to shopping malls and convenience stores improve
significantly. The findings reveal that proximity to shopping centers significantly increases
housing values, with homes closer to these centers becoming more expensive, primarily due
to increased accessibility and improved amenities. It was also established that the quality of
school districts has a significant effect on House prices, especially in high-demand income
areas. Subsequently, neighborhoods in top-ranked districts possess higher prices because of
the value that families attribute to educational assets. Further studies should generalize the
research area, use wider sets of control variables, and make use of long-run models to reflect
the dynamic tendencies of housing price changes.
Keywords: Shopping malls, housing prices, school rank, house age, distance
1. Introduction
Home buyers are now considering multiple factors when choosing a property, all of which
contribute to a better quality of life. In addition to the type of house and the neighborhood
environment, the availability of neighboring amenities has become one factor. Such are school,
employment opportunities, and healthcare [1]. Accessibility of good schools is vital for families with
young children, while accessibility to a place of work shortens working hours and expenses on
transportation. Also, the accessibility of shopping centers has a great influence on purchase
decisions.
Sources of retailing facilities such as shopping centers are easily accessible, hence, meet the
needs of demand and supply of goods and services; buyers hence prefer to live close to such centers
[2]. This can lead to fluctuating premiums within the housing prices adjacent to the shopping center.
Hence, in China, the new shopping center and residential buildings are being sold coupled together;
hence, buyers are now paying much attention to whether the house they are buying is announced to
be in proximity to a shopping center.
The rapid urbanization and modernization of Beijing and globalization come with new changes in
the business environment. The malls have now become a complex component of the lifestyle in the


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Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets
DOI: 10.54254/2754-1169/2025.CAU27161
35
Table 1. Descriptive statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
rank
36
3.5
1.732
1
6
 
 
36
1.856
1.287
.5
7
 
 
36
8.154
3.530
3
17
age
36
22.083
11.975
4
44
Table 1 displays the descriptive statistics of the variable as was used in the analysis. The average
price of housing in the selected neighbourhoods around shopping malls was 81,540 yuan per square
meter but varies across districts. The smallest price noted was 31,333 yuan in Changping District
and the highest 105,333 yuan was in Xicheng District.
The average value of the school rank variable was, therefore, 3.5 which suggests that there are
moderate differences in the quality of education provided in different districts. Houses averaged 22
years in age, but age ranged from 4 to newly constructed to structures 44 years old. These variations
imply that access to malls and higher-ranked schools can be a leading factor in housing prices, and
this will be examined in further analyses.
Table 2. Linear regression of housing price and distance
price
Coef.
St.Err.
t-value
p-value
[95% Conf
Interval]
Sig
lndis
-.331
.092
-3.59
.003
-.529
-.133
***
age
.012
.005
2.30
.037
.001
.023
**
rank
-.341
.045
-7.56
0
-.438
-.244
***
Constant
14.66
.829
17.68
0
12.882
16.438
***
*** p<.01, ** p<.05, * p<.1
In Table 2, the coefficient of lndis is -0.331, which means that when other variables remain
unchanged, the house price will decrease by 0.331 units on average for each unit increase in
distance. The p-value was 0.003, and the significance level was *** (p < 0.01), indicating that the
result was significant at the significance level of 1%. The T-value of -3.59 further supports the
significant impact of this variable on house prices. There is a significant negative correlation
between distance and house prices, i.e., as distance increases (which may refer to the distance from
the city center or other major facilities), house prices decrease significantly.
Table 3. Linear regression of low-price housing groupings
price
Coef.
St.Err.
t-value
p-value
[95% Conf
Interval]
Sig
lndis
-.112
.05
-2.22
.044
-.22
-.004
**
age
0
.004
0.11
.914
-.008
.009
rank
.098
.035
2.75
.016
.022
.174
**
Constant
12.191
.355
34.37
0
11.43
12.952
***
*** p<.01, ** p<.05, * p<.1
distancei
pricei


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Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets
DOI: 10.54254/2754-1169/2025.CAU27161
36
Table 3 shows the grouping of low housing prices. The negative impact of lndis on housing prices
is weakened, while the positive impact of rank on housing prices is more obvious. The age(age) of
the home has little effect on the low house price group.
In Table 3, the coefficient of lndis is -0.112, indicating that when other variables remain
unchanged, the housing price will decrease by 0.112 units on average with each unit increase in
distance. The P-value was 0.044, and the significance level was ** (p < 0.05), indicating that the
result was significant at the significance level of 5%. The T-value is -2.22, which supports its
negative impact on house prices. It can also be concluded that distance has a negative impact on
housing prices. In summary, whether it is high or low, the distance and housing prices are negative,
so hypothesis 1 is verified.
The regression coefficient for the rank variable is -0.341, which indicates that assuming all other
factors remain constant, housing prices decrease by an average of 0.341 units for every unit increase
in the school rank value. The negative and highly significant coefficient (p < 0.01, marked by ***)
suggests that changes in the school rank have a substantial negative impact on housing prices. This
decline may be attributed to an increase in the school rank value reflecting a decrease in quality—
such as falling scores or ratings—which in turn leads to lower housing prices. Additionally, the T-
value for the school rank variable is -7.56, further demonstrating that it has a very strong negative
impact on housing prices and is one of the most significant variables in the regression model.
5. Conclusion
The paper offers a view of the impact of shopping malls on surrounding housing prices. Two
hypotheses were put forward at the initial stage of the study. According to these two hypotheses, this
paper divided the question into two parts, discussing the impact of the distance between shopping
centres and residential areas on the housing price and the impact of school district ranking on the
housing price. The age of the housing was used as a reference factor and analysed using the STATA
17 regression model. One key finding of this research is that the distance between shopping centres
and residential areas has an impact on housing prices. Additionally, by further examining school
district rankings and the age of the housing, we can enhance the theoretical value and practical
significance of the study. This approach may also help identify deeper factors that influence housing
prices near shopping centres. The analysis affirms the following:
There is also a very close relationship between the analysed zones’ housing prices and the
distance to the nearest shopping malls. The very idea of shopping centres brings more convenience,
better accessibility, and added amenities, which in turn will make other properties right nearby even
more attractive and valuable. Distance to a shopping mall is negatively related closer to a shopping
mall a home has a higher market value as the study showed in high price and low-price groups.
It is also established that the quality of school districts has a significant effect on House Price
especially in the demand-income high areas. Subsequently, neighbourhoods in top-ranked districts
possess higher prices because of the worth that families attribute to educational assets. Impact of
housing age is insignificant, less than proximity and school district rankings has on property values
though the result may be positive or negative. In general, the age is slightly detrimental, but if a
home is surrounded by other homes, then there will always be a demand for a house due to location
in an established district. These results point to accessibility to the shopping malls and quality of
educational resources as providing significant insights into the form and future of urban housing
markets to those stakeholders involved in real estate and urban planning and policy.