1# RECORD f870a2d97f24564c545a70499dbb596627b9f6072# TITLE: The Impact of Shopping Malls on Surrounding Housing Prices: Evidence from Beijing, China3# YEAR: 2025.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/2025.cau271614# AUTHORS: Yi-Xuan He5# PAGES: 8 CANDIDATE TABLE PAGES (1-based): [6]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.789===== PAGE 1 (score 14, numeric-paren 0, table-words 0, var-words 14) =====1011Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets12DOI: 10.54254/2754-1169/2025.CAU2716113© 2025 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.014(https://creativecommons.org/licenses/by/4.0/).153016The Impact of Shopping Malls on Surrounding Housing17Prices: Evidence from Beijing, China18Yixuan He19BDA School of The High School Affiliated to Renmin University of China, Beijing, China20 1353690955@qq.com21Abstract. This study investigates the impact of shopping malls on the price of residential22housing in the neighborhood, focusing on how the distance of shopping malls to residential23housing affects the price of housing. This study uses statistical regression analysis via24STATA 17 data from six selected urban districts and finds that housing prices of25neighborhoods with higher accessibility to shopping malls and convenience stores improve26significantly. The findings reveal that proximity to shopping centers significantly increases27housing values, with homes closer to these centers becoming more expensive, primarily due28to increased accessibility and improved amenities. It was also established that the quality of29school districts has a significant effect on House prices, especially in high-demand income30areas. Subsequently, neighborhoods in top-ranked districts possess higher prices because of31the value that families attribute to educational assets. Further studies should generalize the32research area, use wider sets of control variables, and make use of long-run models to reflect33the dynamic tendencies of housing price changes.34Keywords: Shopping malls, housing prices, school rank, house age, distance351. Introduction36Home buyers are now considering multiple factors when choosing a property, all of which37contribute to a better quality of life. In addition to the type of house and the neighborhood38environment, the availability of neighboring amenities has become one factor. Such are school,39employment opportunities, and healthcare [1]. Accessibility of good schools is vital for families with40young children, while accessibility to a place of work shortens working hours and expenses on41transportation. Also, the accessibility of shopping centers has a great influence on purchase42decisions.43Sources of retailing facilities such as shopping centers are easily accessible, hence, meet the44needs of demand and supply of goods and services; buyers hence prefer to live close to such centers45[2]. This can lead to fluctuating premiums within the housing prices adjacent to the shopping center.46Hence, in China, the new shopping center and residential buildings are being sold coupled together;47hence, buyers are now paying much attention to whether the house they are buying is announced to48be in proximity to a shopping center.49The rapid urbanization and modernization of Beijing and globalization come with new changes in50the business environment. The malls have now become a complex component of the lifestyle in the515253===== PAGE 6 (score 31, numeric-paren 2, table-words 12, var-words 3) =====5455Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets56DOI: 10.54254/2754-1169/2025.CAU27161573558Table 1. Descriptive statistics59Variable60Obs61Mean62Std. Dev.63Min64Max65rank6636673.5681.73269170671 72 7336741.856751.28776.577778 79 8036818.154823.530833841785age86368722.0838811.975894904491Table 1 displays the descriptive statistics of the variable as was used in the analysis. The average92price of housing in the selected neighbourhoods around shopping malls was 81,540 yuan per square93meter but varies across districts. The smallest price noted was 31,333 yuan in Changping District94and the highest 105,333 yuan was in Xicheng District.95The average value of the school rank variable was, therefore, 3.5 which suggests that there are96moderate differences in the quality of education provided in different districts. Houses averaged 2297years in age, but age ranged from 4 to newly constructed to structures 44 years old. These variations98imply that access to malls and higher-ranked schools can be a leading factor in housing prices, and99this will be examined in further analyses.100Table 2. Linear regression of housing price and distance101price102Coef.103St.Err.104t-value105p-value106[95% Conf107Interval]108Sig109lndis110-.331111.092112-3.59113.003114-.529115-.133116***117age118.012119.0051202.30121.037122.001123.023124**125rank126-.341127.045128-7.561290130-.438131-.244132***133Constant13414.66135.82913617.68137013812.88213916.438140***141*** p<.01, ** p<.05, * p<.1142In Table 2, the coefficient of lndis is -0.331, which means that when other variables remain143unchanged, the house price will decrease by 0.331 units on average for each unit increase in144distance. The p-value was 0.003, and the significance level was *** (p < 0.01), indicating that the145result was significant at the significance level of 1%. The T-value of -3.59 further supports the146significant impact of this variable on house prices. There is a significant negative correlation147between distance and house prices, i.e., as distance increases (which may refer to the distance from148the city center or other major facilities), house prices decrease significantly.149Table 3. Linear regression of low-price housing groupings150price151Coef.152St.Err.153t-value154p-value155[95% Conf156Interval]157Sig158lndis159-.112160.05161-2.22162.044163-.22164-.004165**166age1670168.0041690.11170.914171-.008172.009173rank174.098175.0351762.75177.016178.022179.174180**181Constant18212.191183.35518434.37185018611.4318712.952188***189*** p<.01, ** p<.05, * p<.1190distancei191pricei192193194===== PAGE 7 (score 23, numeric-paren 0, table-words 7, var-words 8) =====195196Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets197DOI: 10.54254/2754-1169/2025.CAU2716119836199Table 3 shows the grouping of low housing prices. The negative impact of lndis on housing prices200is weakened, while the positive impact of rank on housing prices is more obvious. The age(age) of201the home has little effect on the low house price group.202In Table 3, the coefficient of lndis is -0.112, indicating that when other variables remain203unchanged, the housing price will decrease by 0.112 units on average with each unit increase in204distance. The P-value was 0.044, and the significance level was ** (p < 0.05), indicating that the205result was significant at the significance level of 5%. The T-value is -2.22, which supports its206negative impact on house prices. It can also be concluded that distance has a negative impact on207housing prices. In summary, whether it is high or low, the distance and housing prices are negative,208so hypothesis 1 is verified.209The regression coefficient for the rank variable is -0.341, which indicates that assuming all other210factors remain constant, housing prices decrease by an average of 0.341 units for every unit increase211in the school rank value. The negative and highly significant coefficient (p < 0.01, marked by ***)212suggests that changes in the school rank have a substantial negative impact on housing prices. This213decline may be attributed to an increase in the school rank value reflecting a decrease in quality—214such as falling scores or ratings—which in turn leads to lower housing prices. Additionally, the T-215value for the school rank variable is -7.56, further demonstrating that it has a very strong negative216impact on housing prices and is one of the most significant variables in the regression model.2175. Conclusion218The paper offers a view of the impact of shopping malls on surrounding housing prices. Two219hypotheses were put forward at the initial stage of the study. According to these two hypotheses, this220paper divided the question into two parts, discussing the impact of the distance between shopping221centres and residential areas on the housing price and the impact of school district ranking on the222housing price. The age of the housing was used as a reference factor and analysed using the STATA22317 regression model. One key finding of this research is that the distance between shopping centres224and residential areas has an impact on housing prices. Additionally, by further examining school225district rankings and the age of the housing, we can enhance the theoretical value and practical226significance of the study. This approach may also help identify deeper factors that influence housing227prices near shopping centres. The analysis affirms the following:228There is also a very close relationship between the analysed zones’ housing prices and the229distance to the nearest shopping malls. The very idea of shopping centres brings more convenience,230better accessibility, and added amenities, which in turn will make other properties right nearby even231more attractive and valuable. Distance to a shopping mall is negatively related closer to a shopping232mall a home has a higher market value as the study showed in high price and low-price groups.233It is also established that the quality of school districts has a significant effect on House Price234especially in the demand-income high areas. Subsequently, neighbourhoods in top-ranked districts235possess higher prices because of the worth that families attribute to educational assets. Impact of236housing age is insignificant, less than proximity and school district rankings has on property values237though the result may be positive or negative. In general, the age is slightly detrimental, but if a238home is surrounded by other homes, then there will always be a demand for a house due to location239in an established district. These results point to accessibility to the shopping malls and quality of240educational resources as providing significant insights into the form and future of urban housing241markets to those stakeholders involved in real estate and urban planning and policy.242