modelling rent performance of shopping center stores in shopping cluster

Chapter 1: Introduction 1.1 Statement of Problem 1.1.1 Research Background “The shopping center has been perhaps the most successful land use, real estate, and retail business concept of the 20th century, and it has become the most powerful and adaptable machine for consumption that the world has ever seen” (Beyard et al., 1999, page 3). The shopping center is a product of extensive decentralization of urban population and retail business driven by the appearance of the automobile as a means for mass transportation. This evolution—the suburbanization of the retail trade and the decline of shopping facilities in the central area—has been evident in Western countries to different extent depending on planning controls. Nevertheless, it rarely occurred in most Asian countries due to relatively poor purchasing power and limited mobility of the mass people (Dale, 1999). However, retail real estate properties and their marketplaces are in a state of constant change. Different types of shopping centers are transforming urban landscapes and the qualitative differences are emerging between areas as consumption centers (Crew and Lowe, 1995). In Western countries, the redevelopment of downtowns becomes popular recently in the urban landscape (Hankins, 2002), while in Asian countries, the dominant competitive position of the central shopping area continues to exist. Downtowns ─ especially main streets in central area of large cities ─ are the setting for a wide variety of individual shops and multi-level shopping center developments (Gray and Melish, 1996), in which many shopping centers tend to cluster and form a new type of retail area. Shopping centers are usually located in close proximity to each other either along a major road or dotted and conveniently linked in a compact space in the central areas of large cities. With prototypes like “Fifth Avenue” in New York or “Orchard Road” in Singapore, a new shopping typology is becoming more prevalent in Asia as “the urban linear mall”. This is also noted in a book of “Harvard Design School Guide to Shopping” (Chung and Leong, 2001). A shopping cluster rescales the plan of the traditional shopping center (indoor shops along a main corridor or path) to one that agglomerates the spaces of shopping centers along a main outdoor corridor in the city. The mechanism of a shopping center cluster is more complicated than that of the stand-alone shopping center as revealed in the recent theoretical shopping center literature (Konishi, 2005; Arakawa, 2006). The cluster of various shopping centers with different owners is full of intense competition yet demands close cooperation among the shopping centers. A shopping cluster’s effectiveness as a catalyst for a broader revival or health growth of retail activity within the cluster depends on whether the retail externality within the shopping area is beneficial to all retail activities (Lorch and Smith, 1993). Here retail externality is referred to as a positive clustering effect of retail activities among shopping centers which is similar to the agglomeration effect of retail activities within a shopping center (Mejia and Eppli, 2003). Motivated by shoppers’ multi-purpose shopping behavior pattern and differences in shoppers’ preferences for shopping centers with diverse major attributes such as size and market-positioning, a variety of shopping centers tend to cluster at a certain urban area as a one-stop shopping area for people from different walks of life. Consequently, at the cluster level, shopping centers can benefit from retail clustering as this increases drawing power for each shopping center. At the same time, each shopping center also produces agglomeration economies at center level which are determined by the shopping center’s attributes and this benefits all in-line stores. In this type of shopping cluster context, besides retail clustering economies and retail agglomeration economies, each shopping center’s association with other shopping centers in terms of the center’s major attributes and space allocation strategies are also important factors influencing retail activities in the shopping cluster and thus economic performance of the shopping center and its stores. 1.1.2 Existing Studies Existing theoretical studies of retail agglomeration and retail externalities provide knowledge related to the mechanism sustaining agglomeration of stores in the shopping center or the cluster of shopping centers. Firstly, theoretical works related to the retail agglomeration phenomenon explain the incentive for stores and centers to agglomerate or cluster. The main incentive is the trade-off between market size effect attributable to the customer’s taste uncertainty and price-cutting effect due to competition in the shopping center or shopping cluster. For example, Fischer and Harrington (1996) observed a general tendency of retail agglomeration patterns based on numerical examples: greater store agglomeration is associated with retail product characterized by greater heterogeneity. Konishi (2005) has constructed a model allowing the co-existence of multiple different shopping centers in both non-overlapped and overlapped market contexts, which respectively provided theoretical explanations of suburban shopping center and shopping cluster phenomena. Secondly, theoretical studies related to two types of retail externalities ─ inter-store externalities and inter-center externalities ─ focus on free-ride phenomenon within the center and among the competing centers. In the midst of numerous studies of inter-store externalities, Brueckner (1993) put forward a comprehensive study of inter-store externalities through an analysis of space allocation strategies decided by shopping center owners based on their estimation of retail externalities produced by each store. In contrast to broadly recognized importance of inter-store externalities, inter-center externalities are relatively unexplored in the literature. Arakawa (2006) has built a theoretical model for understanding inter-center externalities in the shopping cluster, in which shopping centers free-ride on the rivals’ product varieties while consumers search the cluster and buy product varieties of “the total of the centers”. Most existing empirical studies of the shopping center store’s economic performance adopted models consistent with existing shopping center theories and examined major traditional attributes at store and center levels as rent determinants. Tay, et al. (1999), and Des Rosiers, et al. (2005), for example, empirically examined impact on the store rent of a set of attributes such as store’s size, retail product types, floor level, and the center’s size, accessibility, and intangible attributes like image and tenant mix. Furthermore, empirical studies of the shopping cluster (or similar phenomenon) focus on retail distribution, and retail mechanism and dynamics in the shopping cluster. Brown (1987) and Caplin and Leahy (1998), for example, respectively examined spatial structure of the shopping cluster in the city retailing core and explore the forces behind changes happening in the shopping cluster. The studies suggest that, in the shopping cluster, attributes at the store, center and cluster levels, are all relevant to economic performance of the shopping center store. In Singapore, many empirical studies of the shopping center market have been done in several research fields. For example, Sim (1984) and Sim, et al. (2002) examined the evolution of shopping center market. Davies (1994) examined the strategies of foreign retailers and their impact on Singapore retailing market. Ibrahim (2000) evaluated the impact of transport mode/travel attributes on consumers’ shopping center choice. Yeung and Savage (1996), Pow (2002), and Alias (2004), in research fields of urban study or sociology, examined two specific shopping cluster phenomena such as Orchard Road and Marina Center. However, none of the studies focused on economic performance of shopping center stores in shopping clusters. 1.1.3 Research Gap In spite of huge amount of theoretical and empirical shopping center works reported in disciplines as diverse as economics, retail geography, consumer behavior and real estate, there remain two research gaps in the changing shopping center market. First, there is no sound theoretical or conceptual framework for analysis of factors influencing the store’s economic performance in the cluster of shopping centers. Past theoretical studies mostly focus on agglomeration of stores in the shopping center while most empirical retail rent studies concentrate on investigation of factors related to the stand-alone shopping center. Pashigian and Gould (1998) have called for research efforts to explore influence on economic performance of retail establishments from retail externalities in the Central Business Districts in major American cities. This requirement for more research efforts was raised in view of the demand for improvement on the viability of these districts. In view of the increasing popularity of the shopping cluster in the central area of major Asian countries, it is timely to investigate the shopping cluster phenomenon, and particularly, to examine factors influencing the economic performance of the stores in the cluster. Second, in contrast to numerous empirical studies of retail structure (or retail association) and retail dynamics within the city retailing core, few studies have examined the clustering pattern of shopping center stores in the cluster. In view of the context differences between the previous studies (major retail establishments are stand-alone shops) and the present study (major retail establishments in the cluster are shopping centers), this study attempts to derive some factors related to the clustering pattern by examining the retail associations among shopping centers’ stores of different retail product types. 1.1.4 Research Questions With an understanding of the current background and existing research gaps in the shopping center literature, this dissertation seeks to address the following questions: z What empirical evidences can be discerned regarding retail association pattern and thus clustering pattern of shopping center stores in the shopping cluster? z What determinants of the store rent can be found in the shopping cluster? z What forces, at micro urban level, drive the rent dynamics among the shopping center stores in the cluster across certain period of time? 1.2 Research Objectives This dissertation aims to extend the shopping center literature by analyzing the shopping cluster phenomenon and empirically modeling the store rent determinants in the cluster context. z The objectives of this dissertation are described as follows: To examine the retail associations within the shopping center stores in the cluster, by evaluating the shopping centers’ major attributes and space allocation strategies to different retail merchandise categories; z To explore and examine the influence on the store rent (in year 2006) from traditional rent determinants as well as new rent determinants designed in view of the cluster context; z To examine the factors that may explain the dynamic change in historical performance of the store rent in the shopping cluster. To address the above research objectives, this study selects two major shopping clusters, Orchard Road and Marina Center in Singapore, as two cases. Three areas are explored in this study. Firstly, this study investigates the factors related to the clustering pattern of shopping center stores. Secondly, this study models the store rent determinants for the two clusters in year 2006. Thirdly, this study explores factors that may explain the differences among rent growth rates of stores across a period of 11 years (1996-2006). This dissertation is demonstrated in the context of Singapore’s shopping center market because (1) financial, time, and technical constraints limit the expansion of the study area to other shopping clusters in other countries; (2) knowledge and implications obtained by studies on the two major shopping clusters in Singapore can be applicable to other Asian countries since they are recognized as the prototype for the shopping clusters being established in other Asian countries; and (3) reliable current and historical data regarding rent performance of the shopping center stores can be obtained from the Inland Revenue Authority of Singapore (IRAS). 1.3 Organization of Dissertation The study is organized into six chapters. The structure of the dissertation is shown in Figure 1-1. Chapter introduces the research problem in terms of research background, existing research, research gaps, research questions, and objectives as well as organization of the dissertation. Chapter discusses the research context. Definitions of shopping center and shopping cluster are made accordingly. It also presents an overview of the shopping center market in Singapore to serve as local background knowledge for this study. Chapter contains a fundamental discussion of existing theoretical shopping center studies and empirical studies in understanding the store rent determinants. The theoretical shopping center studies provide an understanding of the mechanism in agglomeration of stores in the center and in the cluster of shopping centers. Existing studies of shopping cluster phenomenon are also reviewed. To provide the local research background and knowledge regarding the study area, this chapter reviews existing Singapore shopping center studies. Chapter introduces theoretical framework and research methodology, including the research hypotheses, study area, data collection, and description of database, in the stipulated context of the shopping cluster. Chapter firstly presents the empirical analysis of retail associations within the shopping cluster and explores the factors to explain the clustering pattern within the shopping cluster. Secondly, it presents empirical tests of the research hypotheses regarding the store rent determinants in the two shopping clusters of Singapore, Orchard Road and Marina Center. The empirical store rent analysis puts emphasis on rent determinants designed in view of the shopping cluster context. Finally, it presents empirical tests of the research hypotheses regarding factors that may explain Introduction Research Context: Knowledge of Singapore Shopping Center Literature Review: Understanding Incentives to Retail Agglomeration & Retail Clustering in a Shopping Cluster Theoretical Framework & Research Methodology Empirical Analysis: Clustering Pattern, Store Rent Determinants, and Different Rent Growth Rates of Stores Conclusion Figure 1-1: The Structure of Dissertation 10 5.3.3 One Empirical Estimation Issue: Collinearity Although collinearity does not violate the OLS residual assumptions, it affects the significance of independent variables. Variance Inflation Factor (VIF) measures the increase in the variance of the coefficients in a model due to the correlation between an independent variable and the other independent variables in the model. The maximum admissible VIF value is considered to be 10 (Lardaro, 1993). Therefore, the threshold of 10 is taken in this study. The VIFs estimated in all the empirical models in this study are shown in Table to Table in Appendix F. As Table and Table shows, all VIFs estimated in Model and Model stand below 4. As Table shows, in Model and Models 4a-4f, most VIFs stand below while only a few VIFs are above 5, but below 10. VIFs estimated in Model and Models 6a-6d are presented in Table 4. In Model and Model 6a, all VIFs stand below while in Model 6d, all VIFs stand below 6. However, in Model 6b and Model 6c, all VIFs stand below 10 except for those associated with Clothing_HiMass and RAI, which are slightly above 13.8.5.1 In summary, collinearity remains reasonably well controlled in all the empirical models in this study, which indicates that all the empirical models are stable model with no collinearity between independent variables. This also means that the regression coefficients are estimated with great precision or accuracy. 5.1 The higher VIFs associated with cluster-contextualized indices may be caused by the limited number of clustering shopping centers in the Marina Center, which affect the significance of the related variables. 124 5.4 Longitudinal Analysis: Non-Parametric Tests This study uses stores’ past year rent data to test the eighth and ninth research hypotheses (H8-H9). H8 holds that stores of different RMCs maintain different rent growth rates during the same time span. H9 holds that the rent growth rates are different for stores in different types of shopping centers (H9a), frequently improved and infrequently improved shopping centers (H9b), and shopping centers in different sub-clusters (H9c). This study employs two types of non-parametric statistical tests, either Mann-Whitney test (comparing two unpaired groups) or Kruskal-Wallis test (comparing three or more unpaired groups). Non-parametric statistical tests are employed due to the limited data available since they not require knowledge of how the rent growth rates of the stores are distributed. The tests tend to rely upon the rank of the individual observations rather than their absolute numeric values. The advantages of non-parametric tests lie in that they have less stringent assumptions in contrast to parametric assumptions and the computations are generally easier to be understood. On the other hand, the disadvantages of non-parametric tests will cause the loss of the metric and numerical values of results (only the ranks rather than the numerical values are used) as well as statistical power. However, non-parametric tests are suitable for this study in order to explore factors to explain differences among the rent growth rates of stores when grouped according to RMCs, type and sub-cluster location of the center, and the improvement done to the center. 125 5.4.1 Rent Growth Rate Difference among Stores by RMCs during Same Time Spans The results of testing of the eighth research hypothesis are presented in Table 5-8. Mann-Whitney test (when stores are grouped into C_RMC and M_RMC) and Kruskal-Wallis test (when stores are grouped into five major RMCs) are conducted respectively for stores in selected shopping centers in Orchard Road (73 stores) and Marina Center (56 stores) regarding rent growth rates of stores during 2004-2006.5.2 The results of the two tests (i and ii) are presented in Table 5-8. The results of test i show that the eighth research hypothesis can be confirmed at 8% or less significant level for both Orchard Road and Marina Center. Test ii shows that the eighth research hypothesis can be confirmed at 3% or less significant level for stores in Marina Center while it can be confirmed for stores in Orchard Road only if the significant level is set at 18.5%. As Table 5-8 shows, mean ranks for the stores in the group C_RMC are consistently higher than the stores in the group R_RMC for both clusters while mean ranks for stores in five RMCs groups vary in the two clusters. As the higher rank indicates the better performance, the results indicate that comparison stores perform better than multi-purpose stores during 2004-2006 in Orchard Road (p=0.08) and Marina Center (p=0.002). 5.2 The choice of period between 2004 and 2006 for the test is based on two considerations: First, information related to which RMCs the store belong to is only certain during 2001-2006; second, maximum number of stores for the test can be obtained for both clusters during 2004-2006. 126 Table 5-8 Testing of H8: Differences among Rent Growth Rates of Stores Classified by RMCs Orchard Road Mean Rank N Marina Center Test Statistics U* X2 p N Mean Rank (i)Clothing 31 42.65 22 22.77 Jewelry 18 34.81 11 27.73 Shoes 36.57 20.60 Specialty 13 26.04 14 39.93 Others 39.50 32.00 (ii) C stores 56 39.37 29.21 38.17 23.92 M Stores 17 18 38 6.195 343.5 .185 .077 Test Statistics U* 168.0 X2 p 11.053 .026 .002 5.4.2 Rent Growth Rate Difference among Stores grouped by Centers’ Attributes during Two Time Spans The results of testing of the ninth research hypothesis are presented in Table 5-9 for both Orchard Road and Marina Center. The stores are grouped according to the type, frequent or infrequent improvement on the center, and the sub-cluster location of the center. For this purpose, Orchard Road is divided into three sub-clusters, A, B and C according to the market analysis. Marina Center is also divided into three sub-clusters, A, B and C as well for current years (2004-2006) and divided into two sub-clusters, A and B for the time span (1996-2000) since during this period, data for C sub-cluster is not available.5.3 5.3 In Orchard Road, the section from Lane Crawford/Shaw Center to Bideford Road constitutes A sub-cluster. B sub-cluster covers the stretch from the A sub-cluster segment, to Forum the shopping Mall on one side, and Centrepoint on the other side. Finally, C sub-cluster covers the rest of the area. In Marina Center, A sub-cluster is tentatively proposed to cover Raffles City Shopping Center area, B sub-cluster covers 127 The tests are conducted for two same time spans and also for two clusters. The results all confirm Hypothesis 9(a) (at less than 0.01 significant level) but not unanimously confirm Hypothesis 9(b) and 9(c). As Table 5-9 shows, regarding hypothesis 9(a) relating to shopping center types, mean rank for stores in mass shopping center are significantly higher than for stores in niche shopping center. Mean rank for stores in mass (niche) shopping centers with high quality intangible attributes are significantly higher than for stores in mass (niche) shopping centers with low quality intangible attributes. Regarding hypothesis 9(b) relating to improvement taken by shopping centers, the results for Orchard Road during 1996-2006 confirm the hypothesis 9(b) (p=0.01). The mean rank for stores in shopping centers with frequent improvement is significantly higher than for stores in shopping center with infrequent improvement. However, the results for Marina Center during 1996-2006 not confirm the hypothesis 9(b). The reason may be that Marina Center shopping centers are newer and have less frequent improvement. Regarding hypothesis 9(c) related to sub-clusters, the test results are inconsistent. For stores in Orchard Road during 1996-2000, the results confirm hypothesis 9(c) as expected, mean ranks for sub-clusters A to C decrease while during 2004-2006, mean ranks does not follow the former pattern. On the other hand, for stores in Marina Center during 1996-2000, mean ranks for sub-cluster B is higher than that for sub-cluster A and the results cannot significantly confirm hypothesis 9(c) (p=0.212). During 2004-2006, in which three sub-clusters exist, the results significantly confirm hypothesis 9(c) (p=0.034) with mean ranks decreasing from sub-cluster A to C. Suntec Mall, Marina Square and Millenia Walk and C sub-cluster covers CityLink Mall. 128 Table 5-8 Differences among Rent Growth Rates of Stores Classified by Major Attributes Shopping Center Level Time Span (i) Mass 2004 -2006 33 47.17 40 28.61 14 49.32 18 31.56 19 45.58 22 26.20 26 19 18.92 28.58 33.67 14 18.93 LoMass 10 24.00 LoNiche 12 18.92 24 17.13 21 29.71 27 Niche (ii)HiMass H9b.Improve -ment H9a Shopping Center Types HiNiche H9c: Sub-Clusters N Orchard Road Mean Test Statistics Rank U* X2 LoMass 2004 -2006 LoNiche (iii)Mass Niche 1996 -2006 (iv)HiMass HiNiche (v)Infrequently Improved 1996 -2006 1996 -2006 Frequently Improved (vi) A B 2004 -2006 C (vii) A B C 1996 -2000 324.5 Marina Center U* p X2 p .000 15.40 .002 44 28.43 4.00 34 27.26 16 21.75 39.56 16 34.38 34 33.01 34 28.26 12 42.54 14.17 12 30.08 16 21.75 27 21.30 34 27.26 16.50 141.0 .015 8.499 111.00 5.414 .000 212 .212 .037 .001 2.524 3.0 .283 6.768 212 .034 .212 .067 129 5.5 Summary In this chapter, factors related to the clustering pattern, the store rent determinants and factors related to differences among rent growth rates of stores are explored in the shopping cluster. Three conclusions are derived from these empirical analyses in this chapter. Firstly, regarding the clustering pattern, retail association among RMCs can be represented by the importance of RMCs or chain stores of all RMCs between every two centers while retail spatial structure can be estimated according to the relationship between the importance of RMCs in every two centers and the distance between them; Secondly, this study presents important findings that the rent variations among the stores in the shopping cluster are related to both traditional rent determinants at the store, center and cluster levels and the cluster-contextualized factors. As observed for two shopping clusters of different scales, the relationships between the store rent and most traditional rent determinants at the store and center levels (such as the store size, floor level, RMC interactive with ranking and size of the center, and the center’s age) are found to be consistent with most previous studies. Regarding the relationship between the store rent and the center’s distance to the MRT, it is significantly negative in the context of the relatively small shopping cluster (Marina Center) with a single MRT station. However, it is not significant in the context of big shopping cluster (Orchard Road) with several MRT stations. The reason behind the difference may be that when the shopping cluster is extensively served with public transportation facilities, such as in Orchard Road with three MRT stations, accessibility is a less important factor influencing store rent. Moreover, only in the big shopping cluster context, the relationships can be found between the store rent and traditional rent determinants at the cluster level (such as the center’s distance to the cluster core and 130 linkage with other shopping centers) which are in line with the theoretical expectations in this study. The relationships between the store rent and the cluster-contextualized indices introduced in this study are found to be in line with the theoretical expectations. In the big shopping cluster context, retail externalities (SCI ) captured by the average degree of importance the center put on chain stores of each RMC and retail clustering effect (RCI) captured by the center’s degree of dominance in dealing with each RMC have significant and positive influence on the store rent. In contrast, in the relatively small shopping cluster context, only retail clustering effect (RCI) captured by the center’s degree of dominance in dealing with RMCs have significant and positive influence on the store rent. These findings suggest that besides traditional rent determinants, contextualized factors are important store rent determinants in the shopping cluster, which also depends on the scale of the cluster. Thirdly, some evidence have been obtained to support the hypotheses that differences among rent growth rates of stores are associated in certain patterns with RMCs, type and sub-cluster location of the center, and frequent or infrequent improvement done to the center. In view of only non-parametric tests employed to investigate the differences among rent growth rates of the stores, the results obtained cannot suggest quantitative influence on the rent growth rates of the factors. Nevertheless, these results imply that over time, stores in the shopping center of a bigger size or with frequent improvement or in prime sub-cluster location tend to achieve higher rent level than stores in the shopping center of a smaller size or with less frequent improvement or in a less prime sub-cluster location. 131 Chapter 6: Conclusion 6.1 Summary of Main Findings This study was undertaken with three sets of research objectives, all set in the shopping cluster context. The first objective is to examine factors related to the clustering pattern of stores. The second one is to explore and examine the store rent determinants. The third one is to examine factors related to the clustering pattern of stores. The findings are recapped as below. 6.1.1 First Objective: Factors Related to Clustering Pattern of Stores The first research objective is aimed at exploring factors related to the clustering pattern of stores in the cluster in terms of retail association between stores of each RMC and retail spatial structure. This study finds that retail association among stores of each RMC can be represented by interaction between every two centers in the shopping cluster. It is measured as the relative importance of the corresponding RMC or chain stores of all RMCs in every two centers. In the cluster context, shopping centers emphasize retail association among their stores of each RMC in order to benefit from the clustering effect. Another important finding is that the retail spatial structure of the shopping cluster can be demonstrated by the relationship between the importance the two centers allocate to RMCs and the distance between the two centers. This is possibly due to that stores of various RMCs prefer different distance from their corresponding RMC’s stores in the 132 shopping cluster. Findings from empirical analysis of the clustering pattern of stores in the cluster thus lend support for the hypotheses that there exists retail association among stores of each RMC in two centers; and retail spatial structure of the shopping cluster can be assessed by the relationship between the importance allocated to RMCs by the two centers and the distance between the two centers. 6.1.2 Second Objective: Determinants of the Shopping Center Store Rent in the Cluster Context The second research objective is aimed at exploring and examining the shopping center store rent determinants in the stipulated shopping cluster. The traditional rent determinants and cluster-contextualized factors at the store, center and cluster levels have been identified and their influence on the shopping center store rent have been examined. This study models the influence on the store rent in the shopping cluster from traditional rent determinants and cluster-contextualized factors. The results indicate that most specified traditional rent determinants and cluster-contextualized factors influence the store rent. Regarding traditional rent determinants at the store level, such as the store’s size, floor level, RMC interacting with the center type, and ranking of intangible attributes, significantly influence the store rent. These findings imply that in the cluster context, influence on the store rent from the center’s intangible attributes cannot be ignored. At the same time, most traditional rent determinants at the center level, such as the center’s age, distance to the cluster core, and its linkage with other 133 shopping centers, influence the store rent significantly. Regarding cluster-contextualized factors, firstly, the center’s relative importance of chain stores in each RMC (which estimates the degree of retail externalities in the center relative to all the other centers) significantly influences the store rent. Secondly, the center’s absolute degree of dominance in dealing with each RMC (which captures retail clustering effect) also significantly influences the store rent. These findings thus lend support for the hypotheses that the store rent will be positively influenced by retail externalities generated by the presence of chain retailers. The store rent will be positively influenced by the higher clustering degree of stores in their corresponding RMC. These findings suggest that customers can choose from a wide product variety and retailers have high tendency to agglomerate which will benefit the economic performance of stores in the cluster of shopping centers. 6.1.3 Third Research Objective: Factors to Explain Differences among Growth Rates of Stores’ Rent The third research objective, set in the shopping cluster context, deals with factors relating to the dynamic change of the store rent. It aims to examine possible forces behind differences among growth rates of stores’ rent. Non-parametric tests are employed to identify the differences in growth rates of stores’ rent. The stores are grouped by the store’s RMC, type of center, and sub-cluster. The non-parametric tests show that differences among stores’ rent growth rates are 134 associated with different RMCs, type of the center, sub-cluster location, and frequent improvements undertaken by the center. The results indicate that stores in the bigger shopping center with frequent improvements or in the prime sub-cluster location, tend to perform better than stores in the smaller shopping center with less frequent improvements or in less prime sub-cluster location. 6.2 Contribution and Implications This study extends the shopping center literature by focusing on store rent determinants in shopping clusters, a relatively less explored research topic. Most previous studies concentrated on exploring problems such as retail agglomeration, tenant mix arrangement, and rent setting process within the stand-alone shopping center. This study has contributed insights concerning the mechanisms sustaining the shopping cluster. A major contribution is the theoretical framework for modeling rent performance of the shopping center store in the cluster context. The clustering pattern of shopping center stores is quantitatively identified which expands the current understanding of spatial structure of retail establishments in the cluster as an important retail format. This study has taken a step in the direction of quantitatively identifying the relationship between locations, retail merchandise category, and space allocation strategy to facilitate the understanding of the clustering pattern. Although the same method can be adapted to examine the clustering patterns of shopping centers or other retail establishments at the entire urban level, it is possible that different results may be produced. This may be caused by additional interactions 135 among shopping clusters or shopping areas. The current understanding of retail externalities is also expanded by examining the retail clustering effect in the shopping cluster. Most previous theoretical and empirical shopping center studies have focused on examining inter-store retail externalities within the shopping center. This study broadens and contributes to shopping center literature by empirically exploring evidences relating to retail externalities and retail clustering effect in the shopping cluster. The retail externalities in the cluster are generated mainly by the presence of chain retailers. On the other hand, retail clustering effect is created when the centers emphasize certain retail product types. This study also highlights the influence of retail externalities and retail clustering effect on the store rent in the cluster context. As one of the first attempts to examine influence on the shopping center store rent from the traditional rent determinants and cluster-contextualized factors, this study identifies rent determinants at the store, center, and cluster levels. Six retail indices are designed or employed to quantitatively present the three important aspects relating to a shopping cluster. The three aspects are the clustering pattern in the shopping cluster, retail agglomeration effects within the shopping center, and retail externalities among the shopping centers. Empirical evidences of the influence on the store rent from cluster-contextualized factors lend support to previous theoretical studies and substantiate their theoretical findings. Additionally, this study identifies and defines a collection of factors that influence the growth rates of stores’ rent in the cluster. 136 Implications derived from the findings in this study are important for shopping center owners or developers and urban planners. Firstly, three important implications are significant for shopping center owners. Positive influence on the store rent from high ranked intangible attributes indicates that constant improvements on the center’s intangible attributes can enhance the center’s economic performance for the center. The presence of retail externalities generated by the presence of chain retailers indicates that shopping center owners should balance the allocation of space to chain retailers and independent retailers. The presence of positive retail clustering effect triggered by emphasis on certain retail product types shows that shopping center owners should adjust their space allocation to certain retail product types according to the whole shopping cluster’s retail space allocation pattern to different retail product types. Secondly, there are two important implications for shopping center developers who want to develop new centers in the shopping cluster. The center’s distance to the cluster core, sub-cluster location, and direct building linkage with other centers are critical factors to be considered. Shopping centers with bigger size and frequent improvements tend to perform better than smaller ones and those less frequently improved. Thirdly, two important implications are suggested for urban planners. Urban planners control the entry of new shopping centers, improve the business and physical environment, and invest in the public facilities in the shopping cluster. As the shopping 137 cluster is attractive to shopping center developers, urban planners must control the various aspects of the new shopping centers, such as their size and image. Through allocation of land uses and investment in the public facilities, urban planners can assist with the shopping cluster’s development. While this will enhance the development of the shopping cluster, it may also benefit other land uses near to the shopping cluster. In addition, the six retail indices can be constantly calculated annually and utilized as a tool to trace the dynamics of retail activities. Through greater collaboration, this can facilitate decision making with regard to space allocation or tenant mix. This can also help shopping center investors or owners and retailers for them to maintain an attractive shopping cluster. 6.3 Recommendations Extensions of this study can be considered in three areas. Firstly, in view of the replicable characteristics of this study to other shopping clusters, comparative studies between Singapore shopping clusters and shopping clusters in other developed countries can be carried out based on the theoretical framework developed in this study. Secondly, this study can be extended to include additional variables at sub-cluster level in modeling store rent determinants. These variables can include the space allocation to each RMC in the other retail establishments such as free-standing shop or department stores in the same sub-cluster. 138 Thirdly, this study can also be extended to examine the influence on the old shopping centers’ economic performance from the introduction of new shopping centers in the cluster. Future work might also try to determine whether there exist relationships between clustering degrees of shopping centers in different periods and economic performance of shopping centers. This may help to obtain the optimal scale of the shopping cluster. 139 [...]... Scotts Shopping Center, Bukit Timah, etc 20 2.4 Shopping Center Types and Shopping Center Retailers 2.4.1 Shopping Center Types in Singapore The International Council of Shopping Centers (ICSC) defined eight principal shopping center types (see Appendix A) These definitions serve as guidelines for understanding the major differences among the basic types of shopping centers in the USA in terms of retail... aspects of Singapore’s shopping center market like development of shopping centers, formation of the shopping clusters, operations of existing shopping centers, and challenges faced by the Singapore shopping center market, have been highlighted For a better understanding of micro retail market related to shopping center types and shopping center retailers, detailed illustrations are shown The reasons behind... only one shopping center may exist If the overlapping market size is large and shopping centers are close to each other, two shopping centers can co-exist, since with similar number of shores in each shopping center, neither center can dominate the other in terms of the number of stores 31 The findings of Konishi’s theoretical work can explain the spatial distribution pattern of shopping centers in cities,... discusses types of shopping center and shopping center retailers in Singapore 2.2 Definitions of Shopping Center and Shopping Cluster A shopping center is loosely defined by ICSC (International Council of Shopping Centers, 2004) as: “a group of retail and other commercial establishments that is planned, developed, owned and managed as a single property On-site parking is provided The center' s size and... shopping center and the cluster of shopping centers, with a focus on incentives of stores or centers to concentrate It also presents a survey of empirical literature on determinants of the shopping center store’s economic performance, retail structure and dynamics in the shopping cluster Finally, major studies related to the Singapore shopping center market are discussed 3.2 Theoretical Shopping Center. .. standard shopping center 13 stores are owned by single or several owners; In contrast to the standard shopping center, the strata-title shopping center management can not control the tenant mix and adjust the space allocation decision Therefore, only standard shopping centers in the shopping cluster will be examined in this study In this study, a shopping cluster is defined as an important urban shopping. .. countries, since major aspects of shopping center developments are similar to those of shopping centers in Singapore This study also distinguishes standard shopping center from strata-title shopping centers though they have same configuration These two groups of shopping center differ in ownership and operation strategies: the strata-title shopping center stores are owned by numerous individuals or firms while... willingness-to-pay at each store in the shopping center In the example of the non-overlapped market, different shopping centers are far from each other thus each store’s profit is determined solely by the number of competing stores within the same center In the example of overlapping markets with two (or more) closely located shopping centers, the centers’ difference between numbers of agglomerated stores. .. consisting of various shopping centers in close proximity and conveniently linked for the consumer’s one-stop shopping purpose These shopping centers are either located along a main corridor or in a compact shopping district 2.3 Overview of Shopping Center Market in Singapore 2.3.1 Singapore as a shopping paradise Over the past two to three decades, Singapore has become a popular tourist destination... their intangible attributes like image also varies in shopping centers of each type Therefore, with additional information collection of shopping centers’ intangible attributes, this study will further subdivide two groups of mass and niche shopping centers Given there is only one specialist shopping center in the study area (Park Mall in Orchard Cluster) , this study include Park Mall into niche shopping . overview of the Singapore shopping center market and discusses types of shopping center and shopping center retailers in Singapore. 2.2 Definitions of Shopping Center and Shopping Cluster A shopping. standard shopping centers in the shopping cluster will be examined in this study. In this study, a shopping cluster is defined as an important urban shopping area consisting of various shopping centers. Street. 17 optimizing traffic access) relationships among shopping centers exist in the Orchard Road shopping cluster. Another shopping cluster, Marina Center shopping cluster in the Downtown