1# RECORD 6e83e47fe67686f424f9c68dca8dac1370a653d32# TITLE: An Empirical Analysis of the Relationship Between Shopping Malls and Surrounding Property Prices: Evidence from Xian, China3# YEAR: 2024.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/94/2024ox01414# AUTHORS: Ao Tan5# PAGES: 10 CANDIDATE TABLE PAGES (1-based): [6, 7, 8]6# 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.789===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====1011 12 13An Empirical Analysis of the Relationship Between Shopping 14Malls and Surrounding Property Prices: Evidence from 15Xi’an, China 16Ao Tan1,a,* 171Faculty of Social Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom 18a. at2g20@soton.ac.uk 19*corresponding author 20Abstract: Shopping malls have become integral components of urban areas in China, and 21their influence on the local economy and real estate markets is substantial due to the country's 22rapid urbanisation and economic expansion. This study examines the price premium of 23properties in Weiyang District, Xi'an, China that are affected by their proximity to malls. The 24study analyses a comprehensive dataset of 10,175 transaction prices for second-hand homes 25in 12 administrative zones of Weiyang District from September 2017 to June 2021 based on 26the hedonic price model and ordinary linear regression. The findings indicate that the value 27of properties in proximity to local shopping malls is notably enhanced; however, this positive 28effect fades as the distance from the malls increases. This study addresses a research gap in 29the influence of shopping centres on property prices in the Weiyang District. It offers valuable 30insights for local urban planners, property developers, and investors. These results have the 31potential to enhance the value of property and optimise urban development. The research 32methodology includes statistical methodologies to guarantee the accuracy of the findings and 33stresses the necessity of strategic urban planning to maximise the advantages of commercial 34amenities. To develop a more comprehensive understanding of the dynamics between 35commercial amenities and property values, future research might investigate the longitudinal 36impacts over a longer period and incorporate supplementary variables, such as socio-37economic factors and real estate developments. 38Keywords: Hedonic Price Model, Property Price, Shopping Mall, Price Premium. 391. 40Introduction 41Shopping malls are large, indoor commercial complexes that include a wide range of retail 42establishments, restaurants, entertainment venues, and other services, providing a complete shopping 43experience. Typically, these complex malls are home to a variety of businesses and play a significant 44role in modern urbanization. 45However, nowadays, shopping malls are experiencing severe challenges. Firstly, the widespread 46of e-commerce has led many consumers to opt for online shopping platforms. Furthermore, customers 47now tend towards experiential purchasing rather than traditional conventional retail. Also, the mall 48situation is being worsened by the excessive competition in retail centres [1]. 49Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 50DOI: 10.54254/2754-1169/94/2024OX0141 51© 2024 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 52(https://creativecommons.org/licenses/by/4.0/). 5365 545556===== PAGE 6 (score 29, numeric-paren 1, table-words 4, var-words 13) =====5758 59 60density of malls is low (less than one mall) within a 500m, 800m and 1km radius, suggesting that 61while some dwellings are close to retail areas, others are further away. 62Table 2: Descriptive statistics of second-hand housing transaction data. 63Variable 64Abbreviation 65Mean 66Std. Dev. 67Min 68Total price 69tprice 70132.085 7153.841 7214 73Total number of rooms 74room 753.934 761.138 771 78A79size 80207.683 81316.332 8221.65 83Total floors of the building 84tfloors 8524.791 869.547 871 88Age of building 89bage 909.9 914.294 921 93Orientation of house 94orientation 953.211 961.113 971 98House decoration degree 99decoration 1002.464 101.665 1021 103Floor/ level of the house 104floorno 1053.106 106.799 1071 108Structure of building 109structure 1103.102 111.654 1121 113Subway nearby 114subway 115.153 116.36 1170 118Distance from community to CBD (km) 119cbd2 1203.945 1213.032 122.162 123Distance from community to mall (km) 124dshop 1251.648 1261.068 127.036 128MallsNo around community within 3km 129shop3km 1307.009 1314.683 1320 133MallsNo around community within 500m shop500m 134.260 135.834 1360 137MallsNo around community within 800m shop800m 138.641 1391.568 1400 141MallsNo around community within 1km 142shop1km 143.894 1441.928 1450 146 147Furthermore, the correlation matrix (Table 3) shows correlations between the distance to a shopping 148mall and the CBD and the malls number of shopping centres within 3 km. The strong negative 149correlation between the distance to a mall and the number of malls within 3 km ( -0.619) shows that 150houses closer to a large shopping centre have fewer malls within 3km. This pattern is indicative of 151urban planning strategies. Besides, a relatively positive correlation between distance to a mall and 152distance to the CBD (0.513) shows that houses further away from the CBD are closer to suburban 153malls and serve as local commercial centres. These links show the distribution and planning of 154commercial facilities in different locations. 155Table 3: Correlation matrix of key variables. 156Variables 157tprice 158room 159size 160tfloors 161bage 162subway 163cbd2 164dshop 165shop3km 166tprice 1671.000 168 169 170 171 172 173 174 175 176room 1770.688 1781.000 179 180 181 182 183 184 185 186size 1870.196 1880.208 1891.000 190 191 192 193 194 195 196tfloors 1970.114 198-0.089 199-0.006 2001.000 201 202 203 204 205 206bage 207-0.350 208-0.089 209-0.060 210-0.628 2111.000 212 213 214 215 216subway 2170.008 218-0.000 219-0.003 2200.004 221-0.001 2221.000 223 224 225 226cbd2 2270.044 2280.145 2290.034 2300.016 231-0.269 2320.003 2331.000 234 235 236dshop 237-0.025 2380.105 2390.001 2400.003 241-0.138 242-0.002 2430.513 2441.000 245 246shop3km 247-0.029 248-0.121 249-0.016 250-0.001 2510.239 252-0.002 253-0.723 254-0.619 2551.000 2564.2. Multicollinearity Results (VIF) 257The study utilizes the variance inflation factor (VIF) as shown in Table 4 to identify the presence of 258multicollinearity in the regression analysis. As a result, the study reveals the existence of moderate to 259Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 260DOI: 10.54254/2754-1169/94/2024OX0141 26170 262263264===== PAGE 7 (score 53, numeric-paren 33, table-words 3, var-words 9) =====265266 267 268strong multicollinearity in the distance to the nearest shopping mall and CBD, the number of malls 269within an 800-meter and one-kilometre radius. The existence of multicollinearity is expected because 270of the mutual connection between these variables. For example, properties in proximity to a certain 271shopping mall may also be near other commercial amenities, resulting in a convergence with other 272location-related factors. On the other hand, the variable subway exhibits no substantial 273multicollinearity, which suggests that underground access is separate from other factors in the area. 274Table 4: Variance inflation factor. 275Variable 276VIF 2771/VIF 278lndshop 2794.905 280.204 281lncbd2 2824.494 283.223 284bshop800m 2854.126 286.242 287bshop1km 2883.629 289.276 290shop3km 2913.514 292.285 293bshop500m 2942.63 295.38 296lntfloors 2971.902 298.526 299lnbage 3001.85 301.541 302lnstructure 3031.398 304.715 305lnroom 3061.254 307.797 308lnorientation 3091.154 310.866 311lnsize 3121.138 313.879 314lndecoration 3151.032 316.969 317lnfloorno 3181.002 319.998 320subway 3211.001 322.999 323Mean VIF 3242.335 325. 3264.3. Regression Analysis 327The regression results shown in Table 5 provide important insights into the impact of shopping malls 328on property prices in Weiyang District, Xi'an, China. Three models, specified in column (1) to (3) 329respectively, were used to analyse this relationship, each containing different variables relating to the 330proximity and number of shopping malls. 331Table 5: Regression results. 332Variables 333(1) 334(2) 335(3) 336lnroom 3370.766*** 3380.764*** 3390.765*** 340 341(113.44) 342(113.22) 343(113.23) 344lnsize 3450.0309*** 3460.0308*** 3470.0310*** 348 349(11.21) 350(11.18) 351(11.26) 352lntfloors 3530.124*** 3540.118*** 3550.118*** 356 357(23.33) 358(22.23) 359(22.14) 360lnbage 361-0.191*** 362-0.199*** 363-0.198*** 364 365(-30.31) 366(-31.88) 367(-31.08) 368lnorientation 3690.0421*** 3700.0417*** 3710.0423*** 372 373(8.57) 374(8.47) 375(8.60) 376lndecoration 3770.0800*** 3780.0788*** 3790.0775*** 380 381(12.16) 382(11.99) 383(11.75) 384Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 385DOI: 10.54254/2754-1169/94/2024OX0141 38671 387388389===== PAGE 8 (score 50, numeric-paren 31, table-words 3, var-words 9) =====390391 392 393Table 5: (continued) 394lnfloorno 3950.00440 3960.00482 3970.00556 398 399(0.56) 400(0.61) 401(0.70) 402lnstructure 4030.164*** 4040.161*** 4050.164*** 406 407(14.45) 408(14.14) 409(14.42) 410subway 4110.0124* 4120.0123* 4130.0120* 414 415(2.04) 416(2.04) 417(1.99) 418lncbd2 419-0.0487*** 420-0.0413*** 421-0.0684*** 422 423(-11.28) 424(-8.07) 425(-17.63) 426lndshop 427-0.0239*** 428 429 430 431(-5.63) 432 433 434shop3km 435 4360.00478*** 437 438 439 440(5.98) 441 442bshop500m 443 444 445-0.0393*** 446 447 448 449(-3.86) 450bshop800m 451 452 4530.0553*** 454 455 456 457(5.01) 458bshop1km 459 460 461-0.0334*** 462 463 464 465(-3.74) 466Constant 4673.449*** 4683.443*** 4693.500*** 470 471(89.43) 472(89.06) 473(89.20) 474Adjusted 𝑅2 4750.685 4760.686 4770.685 478Note: t-statistics in parentheses; * p < 0.05, ** p < 0.01, *** p < 0.001. 479 480Model 1 includes the logarithm of the nearest shopping centre and other residential variables. The 481coefficient for distance to a shopping centre means that the property price decreases by 3,154.83 RMB 482for every 1 increase in distance to the nearest shopping centre. The decline in property prices with 483increasing distance illustrates that convenience is a key determinant of property value. Several 484reasons support this claim. For example, properties near retail areas are attractive due to their diversity 485of amenities and services. Shopping centres include restaurants, entertainment, and other amenities 486that improve the lifestyle and raise property values. This result suggests that urban planners should 487consider integrating shopping centres into residential areas to increase property values. Also, 488investors should select properties near shopping areas to enhance their investment potential. 489Model 2 shifts the focus to the number of shopping malls within a 3-kilometre radius rather than 490proximity to the nearest shopping centre. The coefficient for the variable the number of malls within 4913km means each additional shopping centre within this radius increases property prices by 631.37 492RMB. This model highlights the positive cumulative effect of multiple malls on property values. 493Multiple shopping centres increase property prices due to their convenience, appeal to varied 494demographics, economic impact, shopping experience, infrastructure, and community. This result 495suggests that developers should aim to create clusters of malls to maximise the positive impact on 496surrounding properties. Also, investors should consider mixed-use projects that combine residential, 497commercial and retail space. Such projects create a self-sustaining community that increases property 498values and attracts long-term tenants. In summary, clustering shopping centres not only improves the 499shopping experience but also contributes to increased footfall, benefiting both retailers and residents. 500Model 3 introduces binary variables reflecting proximity to shopping malls within 500 metres, 800 501metres and one kilometre. The variable malls within 500 metres indicates that proximity to a shopping 502Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 503DOI: 10.54254/2754-1169/94/2024OX0141 50472 505506507===== PAGE 9 (score 16, numeric-paren 0, table-words 2, var-words 12) =====508509 510 511centre within 500 metres lowers property prices by 5,187.94 RMB, which is likely due to negative 512externalities such as noise and traffic congestion. This suggests that policy makers should take 513measures to mitigate these negative effects, such as better traffic management and noise reduction 514strategies. Conversely, the coefficient of the variable for the number of malls within 800 metres means 515that properties within this radius experience an increase in value of 7,305.30 RMB. This indicates 516that a balanced distance to shopping centres can increase property value, suggesting that urban 517developers should aim for a moderate distance between residential areas and shopping centres. The 518variable for malls within one kilometre has negative coefficient of -0.0334, which correlates with a 519decrease in property values of 4,411.64 RMB, suggesting that careful land use planning is needed to 520balance the benefits of proximity with the potential disadvantages. The mixed effects at different 521distances highlight the complexity of urban living, where benefits such as accessibility need to be 522weighed against potential disruption. 523In addition to proximity to shopping centres, various housing attributes such as the number of 524rooms, size of the house, number of total floors, age of the building, orientation, quality of amenities, 525building structure, proximity to the metro and distance to the CBD also have a notable impact on 526housing prices across all models. These findings indicate that the various characteristics of the 527property also play a crucial role in influencing house values. 528The results of this study provide important insights for urban planners as they suggest that 529strategically located shopping centres can increase property values. However, care must be taken to 530mitigate negative externalities in densely populated urban areas. For property developers, these 531findings can be used to optimise the location of projects and ensure that new residential areas are 532close enough to shopping facilities to maximise property values. Simultaneously, the results suggest 533investors to consider properties that are strategically located near shopping centres as these tend to 534have higher appreciation rates. Investing in residential properties within an 800-metre radius of 535shopping malls can be particularly lucrative due to the observed increase in property values. In 536addition, properties in areas where there are multiple shopping centres within a 3-kilometre radius 537can offer significant returns due to the positive impact on property values. 538In summary, proximity to shopping malls has a significant and variable impact on property prices, 539with the relationship varying according to distance and specific valuation methods. These results 540provide a solid basis for future urban development strategies and underline the importance of 541balanced planning to optimise property values. 5425. 543Conclusion 544This empirical study of housing price premiums in the Weiyang district of Xi'an shows that the 545proximity and density of shopping malls relatively significantly affect surrounding housing prices. 546The results of this study are of great importance for urban planners, property developers and 547policymakers and fill a research gap on the influence of shopping malls on house prices in the 548Weiyang District of Xi'an. Better integration of malls with residential areas and careful determination 549of the optimal proximity of such facilities can drive up property prices and improve the overall quality 550of life in the city. 551However, there are some limitations to this study. Data collection is prohibited beyond June 2021 552due to political restrictions. In addition, this study did not assess the external effects of universities. 553Future research could consider extending the data collection period beyond June 2021 to understand 554recent trends and the impact of the pandemic on property values and urban development. Overcoming 555policy restrictions by working with local authorities to gain access to more comprehensive and up-556to-date data will be crucial. Secondly, assessing the externalities of universities and higher education 557institutions, such as their impact on local housing markets, economic activity and social dynamics, 558will help policymakers integrate educational institutions into urban development plans. 559Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability 560DOI: 10.54254/2754-1169/94/2024OX0141 56173 562