Spatial Economics Of Shopping Malls. A Configurational Approach in Rent and Tenanting Decision

Research Paper (postgraduate), 2015

61 Pages, Grade: 2.5



List of Figures ... 3

List of Tables ... 4

1.0 Introduction ... 5
1.1 Background of the research ... 5
1.2 Research objective ... 7

2.0 Literature Review. ... 9
2.1 Location and rent decision of stores in a planned shopping centre ... 9
2.2 Human navigation pattern in indoor environment and spatial configuration ... 10

3.0 Methodology ... 14
3.1 Bid-rent analysis ... 14
3.2 Measuring navigation pattern in a shopping mall 17
3.3 Space Syntax analysis ... 19

4.0 Findings and Discussion ... 21
4.1 Establishing relationship between store area, rent per unit area and customer density ... 21
4.1.1 The relationships for different store types ... 25
4.1.2 The logic of tenanting decision ... 35
4.2 Analysing navigational intentions and shopping motivations ... 44
4.2.1 Respondent’s navigation behaviour ... 45
4.2.2 Navigational behaviour and shopper type ... 46
4.3 Syntactical Analysis ... 52
4.3.1 Analysing the syntactic logic behind navigational preferences ... 52

5.0 Conclusion ... 57

List of Figures:

Figure 1‑1: Mall rentals and vacancy situations in major Indian cities (Q1, 2013) (source: Cushman and Wakefield, 2013) ... 5
Figure 1‑2: Demand supply situation of Indian malls (source: JLL, 2014) ... 6
Figure 1‑3: The complexity of the shopping mall design development 7
Figure 2‑1: Relationship between Sales, Pedestrian Movement and Spatial Configuration. Adopted from Kong and Kim, 201250 ... 13
Figure 3‑1: Alonso (1964)23 bid-rent curve ... 15
Figure 3‑2: Separate land use pattern and their bid-rent curve with relationship to a city center ... 16
Figure 3‑3: Scenes of the simulated situations shown to the respondents ... 19
Figure 3‑4: Convex and Axial map (adopted from Hillier et al., 198334) ... 20
Figure 4‑1: Optimal floor area at different customer densities for a particular category of store ... 24
Figure 4‑2: Optimal rent per unit area for different floor areas for a particular category of store ... 25
Figure 4‑3: Relationship between Optimal rent per unit area and optimal area of Store for 5 stores (From Table 1) 27
Figure 4‑4: Relationship between Area of Store and customer density for five store types (From Table 1) 28
Figure 4‑5: Relationship between area of store and customer density at different values of k2 ... 29
Figure 4‑6: Relationship between area of store and customer density at different values of CF ... 30
Figure 4‑7: Relationship between area of store and customer density at different values of k1 ... 31
Figure 4‑8: Relationship between area of store and customer density at different values of k3 ... 32
Figure 4‑9: Relationship between optimal rent per unit area and optimal area for different values of k2 33
Figure 4‑10: Relationship between optimal rent per unit area and optimal area for different values of CM 34
Figure 4‑11: Relationship between optimal rent per unit area and optimal area at different values of CF 35
Figure 4‑12: The relationship between total revenue per store and customer density ... 36
Figure 4‑13: Total revenue per store and customer density at different values of k2 ... 37
Figure 4‑14: Relationship between Total revenue per store and customer density at different values of CM 38
Figure 4‑15: Relationship between Total revenue per store and customer density at different values of CF 39
Figure 4‑16: Relationship between Total revenue per store and customer density at different values of k1 40
Figure 4‑17: Relationship between Total revenue per store and customer density at different values of k3 41
Figure 4‑18: The logic of introducing new store types in a shopping mall 42
Figure 4‑19: Relationship between area of store and customer density for the two store types ... 43
Figure 4‑20: Difference in rents per unit area of the two store types ... 44
Figure 4‑21: Visibility Graph Analysis of a Shopping Mall 53
Figure 4‑22: a) VGA of the layout and (c) isovist from the viewing position of the First option ; (b)VGA of the Layout and (d) isovist from the viewing position of the Second option Figure 3‑3 ... 54
Figure 4‑23: The relative importance of VGA. ... 55
Figure 4‑24: Isovists at different positions along the path for the first situation ... 55
Figure 4‑25: VGA of different mall typologies ... 56
Figure 5‑1: Spatial decision making model of the shopping mall 59

List of Tables:

Table 1: Assumptions of stores (source: CRISIL Research, 201574) ... 26
Table 2: Respondent characteristics ... 45
Table 3: Score for planning intentions for individuals in different categories ... 47
Table 4: Descriptive statistics for k=4 independent treatments ... 48
Table 5: One-way ANOVA of the 4 independent treatments ... 48
Table 6: Tukey HSD test results ... 49
Table 7: Scheffe' test results ... 50
Table 8: Bonferroni and Holm results: all pairs simultaneously compared ... 51
Table 9: Summary of category wise per capita shopping intention ... 52

1.0 Introduction

There are sufficient reasons to believe and ample evidences to support, that, human activity in general and retail consumer behavior in particular, is profoundly influenced by built environment. This influence can be measured and shaped through spatial design interventions; and in-spite of that, space planning remains a less explored area of study in retail management. This has necessitated researches in this particular area. In the shopping center industry, it has been found, that, developers normally follow some rules of thumb when deciding on store locations and rent allocations in their shopping centers. A wrong approach in taking these strategic decisions may lead to economic malfunction of the entire center. In reality, the competition between stores for location within planned shopping malls is of interest to academic researchers in the field of marketing as well as to mall management professionals. A designer’s perspective remains unexplored.

1.1 Background of the research

Two factors contribute significantly as motivators behind the research. One pertains to the performance of the shopping malls and other to the complexity of its design development. For assessing the performance of the shopping malls, the Indian market scenario is considered, where the modern retail format is on the rise. The retail sector in India has witnessed a significant transformation in the past decade and still experiencing a rapid growth. The modern retail market here grows twice as fast as the traditional trade. Expected compound annual growth rate is 21% for modern trade compared to 10% for traditional. But instead of the growth, the shopping malls in India suffer from high mall vacancy rates. The following figures (Figure 1‑1, Figure 1‑2) will illustrate the situation of mall vacancy of few major cities of India.

[Figures are not displayed in this preview. Please click on the cover to take a look inside the book.]

Figure 1‑1: Mall rentals and vacancy situations in major Indian cities (Q1, 2013) (source: Cushman and Wakefield, 2013) 1

The right vertical axis in Figure 1‑1 represents asking rent in US$ /sf/year and the left axis represents mall vacancy rate in percentage of occupation of total available leasable area.

[Figures are not displayed in this preview. Please click on the cover to take a look inside the book.]

Figure 1‑2: Demand supply situation of Indian malls (source: JLL, 2014)2

New completions and absorptions, in terms of Gross Leasable Area (GLA), of Indian shopping malls (left vertical axis, Figure 1‑2) are expressed in Million Square Feet and vacancy rates (right vertical axis) are expressed in percentage of absorption. Vacancy rates in poorly built and operated malls are as high as 20% where relatively better managed malls have vacancy rates of about 10%. The root cause for this problem is poor tenanting decision, faulty anchor placement and irrational rental plan. A recent report in Economic Times (India), (Ghosh, 2015)3 highlights the fact that retailers opt for high performing malls and for that they are ready to pay rent premiums. High performing malls are those which have low vacancy rates. A report from JLLM (2007)4 identified mall management as a growing phenomenon in Indian retailing and listed tenant-mix, zoning and location of anchors as some of the most important functions of mall management. For proper functioning of a mall, therefore, rental, tenanting and location decisions should be taken on a sound scientific basis and not on a rule of thumb approach.

The design development of a shopping mall is complex. Once the strategic decisions like location and format of the shopping centre is finalized, the concept is briefed to the designers for accommodating the functional requirements within a spatial envelope and for making the space aesthetically pleasant. The transformation of the enclosure into an operational shopping space is achieved through the input of other players like retailer, mall management and brand. The process is explained with the diagram shown in Figure 1‑3.

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Figure 1‑3: The complexity of the shopping mall design development

As discussed earlier, problem of mall vacancy is intrinsically spatial, but, the spatial intervention in the development of the shopping space is limited only to the creation of an enclosure for accommodating Gross Leasable Area (GLA). The potential of space design is not fully explored in formulating strategic decisions like tenanting and rent allocations. In practice, these decisions are under the purview of the mall management professionals and the interventions come much after the spatial design is finalized. For addressing rental and space allocation issues, ideally, the spatial design and mall management decisions should complement each other, without keeping the spatial design as an end in itself. This integration of spatial design and strategic decision making will simplify design development and in turn, address mall vacancy issues based on a scientific rationale.

1.2 Research objective

A shopping mall can be defined as a ‘built environment’

“… (That) attempts to simulate the commercial live centre of cities; artificially devised to recreate the same intensity of urban buzz (if not more) removed from the city streets” (Fong, 20035).

A shopping mall, as defined, can be considered as an urban experience in a closed environment. Therefore, movement and way finding logic applied in the urban scale can be implemented for shopping malls too. Store space allocations, store location within a planned shopping centre and even the lease pricing of an individual store have spatial as well as commercial implications. Proper understanding of the store location, store space allocation and lease price discrimination (most prevailing pricing method for stores in a shopping mall is leasing) requires interdisciplinary knowledge of retail management and architecture.

Retail researchers have focused on the movement of customers within shopping centres and considered metric distance as the only spatial aspect influencing rental differences (e.g. Carter & Haloupek, 20026; Carter & Vandell, 20057; Ingene & Ghosh, 19908) and the takeaways have been targeted at retail professionals. No study was conducted with characteristics of spatial configuration as independent variables. Thus, there is a gap between the outcomes of the in-store-movement genre of research, which have not been actualized in spatial configuration terms and the architectural input, which, in-spite of dealing with a design-centric holistic view, remains insufficient: too few studies on space syntax measures as independent variables have been conducted in retail environments. Retail design research has the potential to bridge this gap through an understanding of retail spatial configurations and its strategic implications. As space syntax measures predict movement, (e.g. Hillier et al., 19879; Hillier et al., 199310) and influencing movement is almost all of what mall management aspire towards, space planning can be used as a strategic decision making tool in shopping malls.

The specific objectives of the study are as follows:

- To examine the effect of customer density distribution within a shopping mall in predicting the optimum area and rent of stores and to understand the effect of customer density in tenanting decision making
- To explore the navigational behaviour of individuals in a shopping mall and the role of shopping motivators behind navigational preferences
- To understand the effect of visibility in predicting customer movement within a shopping mall and the role of Visibility Graph Analysis (VGA) in predicting customer density distribution
- To consider spatial factors (metric and non-metric) in rationalizing location and tenanting decision in shopping malls.

2.0 Literature Review

The research literatures can be classified into two distinct approaches. The first focuses on location and rent decisions of stores within a shopping mall, while the second focuses on syntactical values of space in predicting human navigation patterns. The purpose of this study is to integrate these two approaches for obtaining a better understanding of location, rent and tenanting decision in shopping malls.

2.1 Location and rent decision of stores in a planned shopping centre

Studies on retail have paid more emphasis on inter-store externalities than on the ‘spatial logic’ in deciding location and rent within a shopping mall. Since 1990, studies on shopping malls were focused on economic rationale of lease-price discrimination and store space allocation, relying on the concept of inter-store externality (e.g. Benjamin et al., 199211; Brueckner, 199312; Eppli & Shilling, 199513; Pashigan & Gould, 199814). These studies attempted to identify an ‘ultimate tenant mix’ that would have emerged as a useful tool for the mall management. Other researchers have argued that the notion of ultimate tenant mix is a vague one (Carter & Allen, 201215, quoting Marlow, 1992 and Stambaugh, 197816) and there is no magic formula or hard and fast rule for finding the ‘ultimate tenant mix’; some notions are just better than others (Des Rosiers et al., 200917).

In case of homogeneous mall space, the only factor that affects the rent level of stores is location (compared to other stores). So, price discrimination takes place in shopping centre leasing (Benjamin et al., 199211). Space is allocated to a tenant store up to the level where net marginal revenue for adding that space equates with the marginal cost of the space less the externality term (Brueckner, 199312). Rent subsidies are provided to those that produce externality and rent premium are charged from those who ‘free ride’ on them (Pashigan & Gould, 199814). Fixed rent component varies inversely with sales externality while percentage rent component varies positively (Wheaton, 200018).

During the same period, when studies on inter-store externality were gaining popularity, some studies focused on circulation or movement of customers within shopping centres (e.g. Brown, 199119; Fisher & Yezer, 199320; Sim & Way, 198921). They relied on the concept of bid-rent model to explain and analyse the location decision of stores. They concluded that, as a rule, customers prefer shops that are easily accessible compared to those that are not. Customer density, therefore, decreases for shops with less accessibility.

The earlier studies, which considered this movement component in predicting leasing and location decision, relied on central place theory (Christaller,1966)22, the model as proposed by Alonso(1964)23or revised central place theory as adopted by Carter & Allen (2012)15for explaining density distribution. Carter & Vandell (2005)7, in their analysis, considered highest traffic at the central place of the mall with tapering off traffic density as the distance from the centre increased. This assumption is applicable only for a linear symmetric mall configuration with a central entry. This simplistic technique neither takes into account the difference in location decision approach for anchor and non-anchor stores, nor does it provide a logical framework for the tenanting decision. The explanations for difference in customer density distribution were based on the adaptation of central place theory (Christaller, 1966)22 (highest density at the centre with decreasing density when moving away from it) and not on the logic of spatial arrangement. A detailed scientific investigation on the relationship between spatial configuration and indoor navigation pattern is, therefore, important for proper understanding of the spatial influence on human density distribution within it. The knowledge of spatial configuration of a shopping mall then can aid considerably in improving store space allocation and tenanting decision making.

2.2 Human navigation pattern in indoor environment and spatial configuration

Movement is critical in determining the traffic concentration within a built structure. Human concentration, in turn, depends on accessibility of a particular location compared to other locations in the spatial arrangement under consideration. Hillier (1996)24stated that:

“Natural movement is the proportion of movement on each line that is determined by the structure of the urban grid itself rather than by the presence of specific attractors or magnets”.

Space Syntax analysis quantifies this ‘structure’ of the spatial arrangement, therefore, is a determinant for accessibility. The logic of space syntax analysis was adopted from an urban scale and implemented in buildings. This technique can be applied to two dimensional building plans or urban layouts (depending on the nature of enquiry) to quantify the characteristics of the spatial structure or configuration, which is otherwise expressed in qualitative terms, making it difficult to establish correlation with other variables (e.g. movement, interaction). The analysis is done through decomposing the space in smaller spatial units and then establishing connections between these units (convex spaces). Syntactical analysis involves identifying and quantifying the spatial pattern: in terms of ‘connectivity’ and ‘integration’. Studies on human navigation patterns in indoor environments (e.g. Peponis et al., 199025; Haq & Zimring, 200326; Hölscher et al., 201227 ) suggest that human route choices in a built space are influenced by syntactic properties of space.

Kuipers and his associate researchers worked on the framework of navigational paths. Kuipers et al.,(2003)28 found that, the expert navigators quickly identify a set of paths or ‘skeletons’ in the cognitive map when they explore a complex environment and reach the ultimate destination. The computational simulation used by Kuiper et al., (2003)28 illustrated that, the greater the number of ‘links’ of one path compared to the others, the greater is the likelihood of usage of that path. Hillier (1996)24, Turner & Penn (1999)29, Turner et al. (2001)30and Choi (1999)31 confirmed in their respective researches that patterns of visibility and accessibility are stronger predictors of movement than normal metric measures. In the space syntax literature, integration value is believed to be a potential determinant of human concentration and movement in that particular location compared to other spaces within the spatial arrangement. A higher integration value of a particular location signifies that the particular 'street segment’ or location is highly connected with the overall spatial arrangement (building or an urban area). Though, there is no explicit rigorous theoretical background, it relies mostly on the intuitive arguments and the empirical support that has been found (e.g. Enström & Netzell, 200832). Penn (2003)33 suggested that the integration values capture the sense of how people cognitively perceive spatial arrangements.

The relationship between integration value and traffic movement had been investigated widely by Hillier et al. (1983)34, Hillier et al., (1987)9, Hillier et al., (1993)10, Hillier & Hanson (1984)35, Hillier (1988)36, Peponis et al.,(1989)37and Marcus (2000)38. The outcomes of these researches show a high correlation, in many circumstances, mostly with local integration values.

Visibility was incorporated in space syntax analysis by Benedikt (1979)39, initially as analysis of single viewpoints or isovists. Isovists are central to modelling geometrical properties related to mental representations (Meilinger et al., 201240) and aspects concerning geometry and movement (Batty, 200141), in addition to reflecting the local properties of space (e.g. Franz & Weiner, 200542; Stamps, 200543). Visibility analysis (VA) was developed into a proper syntactic analysis tool through the works of Turner and Penn (1999)29 and Turner (2001)44. VGA has been defined as:

“… (A) set of points distributed symmetrically in space between which inter-visibility can be analysed through isovists.”(Abishirini & Koch, 201345).

Hölscher et al., (2012)27 have asserted that VGA acts as a strong predictor in navigation decision making. Other studies have suggested that VGA is best suited in enclosed spaces because of its simplistic approach compared to other methods of Syntactic Analysis (Axial Line and Convex Space) in highlighting location differences (e.g. Fong, 20035).

The Visibility Graph Analysis (VGA) has been used as a tool by various researchers and scholars to study architectural spaces (e.g. Batty et al., 199846; Turner & Penn, 199929; Turner, 200144; Turner et al.,200130; Desyllas & Duxbury, 200147. Turner (2001)48 and Doxa (2001)48applied Visibility Graph Analysis in way-finding. Batty et al., (1998)46, Turner & Penn (1999)29, Turner et al., (2001)30 and Desyllas & Duxbury (2001)47 studied the relationship of Visibility Graph Analysis and pedestrian movement. When using a space, a space with higher visibility enjoys easier accessibility. From a study of a departmental store (Turner & Penn, 1999)29 it is seen that VGA representation yields a higher correlation than axial line method when relationship of Graph measure of Mean Depth and pedestrian movement is compared between the two approaches. Similar study by Desyllas & Duxbury (2001)47 showed that VGA method is better in establishing correlation between movement and visibility in urban pedestrian space, when compared with axial line measures. The best correlation with VGA is r2= 0.625 and the best relationship with axial line isr2= 0.429. The study investigated pedestrian flow on pavement at 84 locations to identify a pattern. Parvin et al., (2007)49 also analysed pedestrian flow and visual accessibility of a public area and found a strong relationship. Kong & Kim (2012)50 suggested a model (Figure 2‑1) to express the relationship between spatial configuration characteristics and sales, relying on the Visibility Graph Analysis of 28 stores in basement floor of a mall in Seol, Korea. It can be deciphered that, spatial configuration has the potential to influence pedestrian movement and sales.

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Figure 2‑1: Relationship between Sales, Pedestrian Movement and Spatial Configuration. Adopted from Kong and Kim, 201250

3.0 Methodology

3.1 Bid-rent analysis

The basic premise of bid-rent theory is to analyse the way spatial arrangement has to do with economics by establishing the relationship between rent of a particular location with the accessibility of the same. It is, thus, the spatial distribution of socio-economic activity and it explains the way commercial and non-commercial properties in an urban area are distributed with respect to spatial accessibility. As shopping malls are considered as urban experience in a closed environment, the methodology can also be applied in understanding the economics of their spatial arrangement.

There is centralization of activities in a city and so should be for shopping malls. In the field of urban economics and urban planning, accessibility modulates and accommodates the generation of ‘movement’. The centrality has been approached in different ways. Hillier (1999)51approached centrality as a spatial process and grid deformation. In the field of economic geography, centrality has been dealt with in terms of attractiveness (e.g. Christaller, 196622; Lösch, 195252; Isard, 195653; Alonso, 196423; Krugman, 199654; Fujita et al., 200155). Centrality can also be described in terms of the concentration of socio-economic activities. The process of concentration is described as agglomeration economics (e.g. Marshall, 189056; Fujita &Thisse, 200857).

The concept of centrality in spatial terms was first addressed by von Thünen (1826)58with the analogy of agricultural development of a city. The subsequent growth of a city is represented by concentric rings from the centre outwards. VonThünen prescribed several important assumptions to his ‘isolated state’ model. The most important assumption of his theory was a flat featureless plane with a single city at the centre (e.g. Dickinson, 196959; O’ Kelly &Bryan, 199660). Concentric rings (Figure 3‑2) illustrate the distance from the city centre. Assumption of a single market place allowed von Thünen to assess the impact of price differential with respect to the distance from the central location. So, rent near the city centre would be higher compared to the locations away from the centre. The rent would decrease with increasing distance from the city centre or Central Business District (CBD). Dickinson (1969)59 added that, in von Thünen’s model, there would be a consequential change in land-use pattern due to the trade-off between rent and transportation cost. The classical approach in the bid-rent analysis ignored cost of transportation and fertility of soil in determining price of land. Von Thünen’s model showed a new way through consideration of transportation cost. The work of von Thünen (1826)58 has profound influence on Alonso’s theory.


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Spatial Economics Of Shopping Malls. A Configurational Approach in Rent and Tenanting Decision
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spatial, economics, shopping, malls, configurational, approach, rent, tenanting, decision
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Sumanta Deb (Author)Dr. Keya Mitra (Author), 2015, Spatial Economics Of Shopping Malls. A Configurational Approach in Rent and Tenanting Decision, Munich, GRIN Verlag,


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