Spatial Versus Non-Spatial Determinants of Shopping …muscamags/A-PapersMUSCAMAGS-team/A...between...

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S p a t i a l Ve r s u s N o n - S p a t i a l

D e t e r m i n a n t s o f S h o p p i n g C e n t e r

R e n t s : M o d e l i n g L o c a t i o n a n d

N e i g h b o r h o o d - R e l a t e d F a c t o r s

A u t h o r s Francois Des Rosiers, Mar ius The´ r iaul t and

Laurent Menet r ier

A b s t r a c t This study is an attempt to model the economic trade-offbetween spatial and non-spatial determinants of shopping centerrents while assessing the role of neighborhood and locationattributes in the rent setting process. For that purpose, two space-related indices, namely the Economic Potential Index (EPI) andthe Center Attraction Index (CAI), are designed based on a majororigin-destination phone survey and on financial data obtainedfor eight major shopping centers in Quebec City, Canada. Thedatabase, which is processed through a regional GIS, includes1,007 retail units, representing some 4.4 million square feet ofgross leasable area. While findings confirm that the EPI act as asignificant determinant of shopping center rents, they also bringout the complexity of the relationships between endogenous andexogenous rent determinants.

O b j e c t i v e a n d C o n t e x t o f R e s e a r c h

This study develops a model of the economic trade-off between spatial and non-spatial determinants of shopping centre rents while assessing the contribution ofneighborhood characteristics (household spending power) and center locationattributes (proximity to clientele) to the rent setting process. For that purpose, twospace-related indices are designed, namely the Economic Potential Index (EPI)and the Center Attraction Index (CAI), which are successively added to aregression model of retail unit base rents.

The study is part of a research program based on physical and financialinformation obtained from super-regional, regional and community shoppingcenter managers in Quebec City for the 1998–2000 period. While information wasmade available for only eight shopping centers, all of which have a mallconfiguration,1 these include the largest corporate and institutional retail property

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assets in the region. At this point, some 1,007 shops are included in the database,with base and total rent, gross leasable area (GLA) and retail category beingavailable for roughly 954 of them. Census information on 1996 neighborhoodprofiles (household composition, age and income) as well as regional data onproximity of and accessibility to jobs and services is processed via a regionalGIS. Finally, a 2001 origin–destination (O–D) phone survey provides usefulinformation on daily commuting patterns in the Quebec Metropolitan Area(QMA).

Located 150 miles east of Montreal, Quebec City has a population of roughly560,000 while the QMA totals about 683,000 inhabitants. Apart from itsuniversally prized historical center and old neighborhoods, Quebec City is atypical North American agglomeration characterized by a highly extensivehighway network2 and sprawling residential, retail and industrial developments.The vast majority (73%) of daily trips are car-generated while nearly a third ofall trips are for shopping and leisure purposes. Retail activities in the region areparticularly well developed, with shopping center complexes that rank among thelargest in Eastern Canada. As with most other urban regions throughout Canadaand the United States, ‘‘big boxes’’ and ‘‘category killers’’ are a major concernfor traditional shopping centers whose hegemony is seriously threatened. In thisincreasingly competitive context, retail establishments’ managers need to bettermonitor and understand the shopping patterns of individuals and households.Because it is quite representative of any medium-size North American city interms of both urban form and household travel behavior, and despite some specificfeatures,3 findings about Quebec City’s retail market may be extended to severalother similar agglomerations.

Location has long been known to play a major role in the retail rent settingprocess. In particular, center site selection and retail store development has longbeen driven by primary market data linking income, wealth and location. However,to the best of our knowledge, no record has been found of any research resortingto actual, rather than expected, clienteles in order to assess retail establishments’economic potential and trade area at the entire city level. This specific feature ofthe current research is made possible thanks to the above mentioned O–D phonesurvey, which clearly identifies customers’ home location. This, used incombination with census information on households’ annual income, leads to amore reliable proxy for local sales potential. In this study, both internal property-related characteristics and external location-driven factors are considered in therent modeling process, in line with Mejia and Benjamin’s (2002) suggestions.

� L i t e r a t u r e R e v i e w

The academic literature on shopping malls has evolved around various theories ofurban spatial structure (Hotelling, 1929; Christaller, 1933; Lo¨sh, 1940; andAlonso, 1964) with strategies relating to space configuration and store locationwithin shopping centers replicating those observed at the urban level (Vandell and

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Lane, 1987; Pearson, 1991; Brueckner, 1993; Roulac, 1996; and Brown, 1999).In contrast to what prevails in the residential market (Follain and Malpezzi, 1980;Noland, 1980; Sirmans and Benjamin, 1991; Benjamin and Sirmans, 1994; Jud,Benjamin and Sirmans, 1996; Des Rosiers and The´riault, 1994, 1996; and Chinloyand Maribojoc, 1998) and office sector (Rosen, 1984; Hekman, 1985; Gabriel andNothaft, 1988; Wheaton and Torto, 1995; and Sivitanides, 1997) where the rentissue has been widely investigated, studies on retail rents remain embryonic,mostly because of the confidential nature of the required information.

P e r c e n t a g e R e n t s a n d t h e R i s k - s h a r i n g I s s u e

The mechanics underlying additional, or overage rents, expressed as a percentageof yearly sales over and above a given, pre-negotiated threshold, are among theissues raised by some authors (Hartzell, Shulman and Wurtzebach, 1987;Benjamin, Boyle and Sirmans, 1990; Bruecker, 1993; Colwell and Munneke,1998; Wheaton, 2000; and Chun, Eppli and Shilling, 2001). Benjamin et al. (1990)were the first to apply hedonics to the analysis of commercial rent. In their study,base rents derived from 103 commercial leases pertaining to national, local andindependent stores are regressed against sales, discount rates, overage rents, leaseterms, lease provisions, etc. Results suggest that while base rents are lower wherehigher overage rent rates apply, they rise with higher sales thresholds. In a recentstudy dealing with the effect of firm characteristics on the use of percentage retailleases, Chun, Eppli and Shilling (2003) address the risk-sharing issue betweenowner and tenants by looking at the debt–asset structure of the latter. They findthat lessee firms with high debt-to-asset ratios tend to opt for percentage leaseagreements since percentage lease payments are considered contingent rentals and,as such, are not reported as a future lease liability.

T r a f f i c F l o w a n d C u s t o m e r s ’ F i d e l i t y

In another study, Sirmans and Guidry (1993) point out that higher consumer trafficlevels are a prerequisite for the success of a store. In a totally different urbancontext, Tay, Lau and Leung (1991) investigate the Hong Kong commercialmarket. Their database includes 405 stores distributed among nine high-riseshopping centers. In contrast with the literature, their study namely reveals thatrent level is positively related to the age of a shopping center due to bothcustomers’ fidelity, which tends to grow over time, and continuous improvementsto buildings. It also suggests that while the unit rent of a store is positivelycorrelated with the size of a center, it is inversely related to its own size.

A g g l o m e r a t i o n E c o n o m i e s a n d E x t e r n a l i t i e s

With location theories as the conceptual background (Weber, 1929), sales potentialin shopping centers are looked upon through the concepts of agglomeration

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economies and externalities derived from the presence of anchor tenants (Eatonand Lipsey, 1983; Mulligan, 1983; West, Von Hohenbalken and Kroner, 1985;Ghosh, 1986; Ingene and Ghosh, 1990; Fisher and Yezer, 1993; Eppli andBenjamin, 1993; and Mejia and Benjamin, 2002), as well as from tenant mix andproduct diversity (Konrad, 1982; and Pashigan and Gould, 1998). Behind theconcept of agglomeration economies lies the reduction of consumer search and ofuncertainty costs. Such advantages allow major tenants to negotiate lower rentswith shopping center owners (Anderson, 1985), the fact by which their departuremay cause rental income to drop by as much as 25% (Gatzlaff, Sirmans andDiskin, 1994) greatly enhancing their bargaining power. According to Nelson(1958) and Eppli and Shilling (1996), the clustering of similar stores leads to anincrease in their total sales level, thereby contributing to the success of theshopping center. While preliminary, recent findings by Des Rosiers, The´riault andOzdilek (2002) tend to corroborate the importance of agglomeration economies,store concentration and lease renewal strategies as determinants of commercialrents.

� I m a g e , R e t a i l M i x a n d S p a t i a l A u t o c o r r e l a t i o n

The image of a shopping center may also impact upon sales level (Brown, 1992;Kirkup and Rafiq, 1994; and Anikeeff, 1996). It stems from consumers’ perceptionof major occupants (Nevin and Houstan, 1980), shopping center size andconfiguration, as well as the quality of goods and services offered. In this respect,image is increasingly dependent on fashion (James, Durand and Dreves, 1976;Jain and Etgar, 1976; Mazursky and Jacoby, 1986; and Grewal, Krishnan, Bakerand Borin, 1998). Similarly, it affects tenants in their negotiation for an optimallocation (Mejia, Eppli and Benjamin, 2001). Finally, accounting for all thesefeatures raises the spatial autocorrelation issue, addressed by Carter and Haloupek(2000) on the grounds of previous work performed mainly on the residentialmarket (Griffith, 1987; Pace and Guilley, 1998; and Dubin, Pace and Thibodeau,1999).

As put forth by Mejia and Benjamin (2002), non-spatial factors (e.g., retail imageand mix) are the most relevant determinants of shopping center sales and rents.While a similar conclusion derives from Hardin, Wolverton and Carr’s (2002)study on community centers, the authors insist on the need to include spatialinformation in rental market models. As they note, empirical research on retailactivity remains sporadic. In particular, interactions between retail and consumersubmarkets need be further investigated, which is the purpose of this study.

� D e s c r i p t i o n o f D a t a b a s e a n d o f S p a c e - R e l a t e d I n d i c e s

T h e D a t a b a s e : S t r u c t u r a l a n d F i n a n c i a l F e a t u r e s

This study is based primarily on physical and financial information obtained foreight super-regional (2), regional (2) and community (4) shopping centers in

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Quebec City, Canada.4 Financial data apply to the 1998–2000 period, with leasesextending from 1976 to 2000. The representativeness of our database can beassessed from Exhibit 1, which compares Quebec City’s retail structure (withinshopping centers) with that of the database with respect to the number of shoppingcenters in each category, their overall lot area and the number of shops. Categoriesfor retail establishments are defined according to the International Council ofShopping Centers’ guidelines, namely on the combined basis of overall acreageof centers, their gross leasable area (GLA), as well as the relative importance ofanchor tenants. Community centers used in this study occupy some 7.4 millionsquare feet (170 acres) of land and display a GLA of roughly 780,000 square feet,of which 51% is occupied by anchor tenants. For regional centers, figures amountto 5.1 million square feet (117 acres) of land and 1.5 million square feet of GLA,with an anchor share of 50%.5 Super-regional centers, finally, occupy 5.4 millionsquare feet (123 acres) of land and nearly 2.2 million square feet of GLA; some56% of their leasable area is anchor-occupied.

Considering that data were provided by professional managers on a voluntarybasis, no neighborhood center is included in the analysis while only fourcommunity centers agreed to disclose their financial data. Consequently,community center stores only compose 16% of all shops in the database asopposed to 52% for the City as a whole. In contrast, information on regional andsuper-regional centers was obtained for all establishments.

At this point, complete or near complete structural and financial information isavailable for 1,007 stores, that is, 40.5% of all shopping center retail outlets inQuebec City, out of a potential database of 1,220 cases. Whereas yearly base rentand gross leasable area (GLA) are available for all retail units, partial informationis only available with respect to yearly sales, store frontage and lease terms(beginning and ending date) which, for that reason, are not included in theanalysis. Similarly, available information on vacancy rates for each retailestablishment does not allow a link to rent levels as they apply over the periodof the study (1998–2000) with the economic context that prevailed upon leasenegotiation. Therefore, the impact of vacancies on rents cannot be measuredadequately.

B a s e v e r s u s P e r c e n t a g e R e n t A g r e e m e n t s

Since the primary purpose of this study is to explain how retail space is pricedwith regard to both endogenous (size of premises, type of product and centercategory or image) and exogenous (center location and customers’ profile) factors,only the unit base rent (i.e. the base rent per square foot of GLA) is modeled. Inthat respect, it is worth mentioning here that the kind of retail lease agreementsprevailing in Canada and Quebec does not significantly differ from what is foundin the United States. Although shopping center managers may use different waysof reporting rents, the typical arrangement between a landlord and tenants involvesa base rent, which is also a net rent to the former, as well as a series of transferable

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Exhibi t 1 � Representativeness of Study Sample

Shopping Center CategoryNb. ofCenters %

Lot Area1

(sq. ft.) %Nb. ofShops %

Panel A: Quebec City

Neighborhood Center 37 45.7 4,256,075 14.8 349 14.0

Community Center 40 49.4 13,726,466 47.7 1,290 51.9

Regional Center 2 2.5 5,248,280 18.2 290 11.7

Super-Regional Center 2 2.5 5,553,814 19.3 556 22.4

Total 81 100.0 28,784,635 100.0 2485 100.0

Panel B: Database

Shopping Center CategoryNb. ofCenters %

Lot Area1

(sq. ft.) %Nb. ofShops %

Neighborhood Center 0 0.0 0 0.0 0 0.0

Community Center 4 50.0 7,423,926 41.5 161 16.0

Regional Center 2 25.0 5,082,435 28.4 290 28.8

Super-Regional Center 2 25.0 5,378,314 30.1 556 55.2

Total 8 100.0 17,884,675 100.0 1,007 100.0

Panel C: Database as % of City

Shopping Center CategoryCenters%

Lot Area1

%Shops%

Neighborhood Center 0.0 0.0 0.0

Community Center 10.0 54.1 12.5

Regional Center 100.0 96.8 100.0

Super-Regional Center 100.0 96.8 100.0

Total 9.9 62.1 40.5

Notes: The sources are the Quebec City 1997 assessment roll and corporate data provided byshopping center managers.1. Discrepancies observed between Quebec City and the database with respect to lot areas ofregional and super-regional centers are due to differences in lot size measurement by the twosources used.2. Place de la Cite, a fashion center, is not accounted for in the calculation due to incompleteinformation.

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operating expenses including, but not limited to, Common Area Maintenance(CAM) costs. While transferable from lessor to lessee firms, recoveries are notshared equally by all tenants: due to their bargaining power, anchor stores areonly transferred a small portion of these costs and are often exempt from anycharge. The total rent charged to tenants, also termed ‘‘gross’’ rent by managers,is obtained by adding recoveries to the base rent.

Finally, percentage lease agreements are, as in the U.S., a common feature ofshopping center rent rolls, although such overage clauses may never be, and areoften not, applied. Percentage rents actually take two forms: they may substitutefor base rent wherever yearly sales exceed a given predetermined threshold, inwhich case the net rent charged to the tenant is obtained by multiplying the salesfigure by the percentage rate indicated in the lease agreement. Alternately, thepercentage component may add up to the base rent as an ‘‘excess’’ rent, in whichcase the percentage rate negotiated only applies to the portion of yearly sales inexcess of some predetermined threshold. While landlords and managers tend toadopt a common strategy for all retail assets they hold or manage, the shift fromprivate to institutional ownership that has characterized commercial real estateover the past decade or so in Quebec resulted in all types of lease agreementsbeing eventually found on the same rent roll.6

While information on percentage rent, recoveries and total rent is available formost stores, this study essentially focuses on base rent modeling.

R e t a i l M i x a n d S h o p p i n g C e n t e r I m a g e

Eight main retail categories are distinguished in the analysis, namely jewelry stores(49), clothing stores (302), shoe stores (65), restaurants (109), personal services(80), specialty stores (229), kiosks (86) and anchor stores (34). Together, theselected shopping centers total nearly 4.4 million square feet of GLA, with theoverall vacancy rate standing at 4.6% (53 premises). In order to provide a moreaccurate assessment of the marginal contribution of retail mix to the rent settingprocess, sub-categories have also been defined for clothing stores (i.e., Women,Men, Child and Mixed), food services (Fast-food), personal services (Banking)and specialty stores (Drugstore, Electronic apparel, Optician, Home decorationand Food market). They are used in the analysis in as much as their numberallows it. This said, it was decided that, despite the abundance of stores, clothingsub-categories would not be used—their marginal contribution did notsignificantly differ from one another while adding much complexity to the list ofvariables when used in interaction with shopping center ranking. Finally, whileanchor stores are included in the analysis when considering their impact on theGLA parameter, they are not used as descriptors, the current paper focusing onthe marginal contribution of non-anchor tenants to base rent. In other words,anchor stores are used here as the reference category, or base case, against whichall model parameters are calibrated.

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The retail mix for each category of shopping center is portrayed in Exhibit 2.Total GLA for community, regional and super-regional shopping centers roughlyamounts to 780,000, 1,453,000 and 2,164,000 square feet, respectively. The typeof retail establishment, and therefore the image of a shopping center, is accountedfor via dummy variables used in interaction with retail mix descriptors to generateregression coefficients that are specific to each shopping center category. Whileusing only eight centers in the analysis might be assumed to induce some noisein the models, resorting to interaction variables substantially increases the varianceat stake and the reliability of resulting coefficients. As with neighborhood-derivedindices that are applied uniformly to all stores within a given shopping center, therelatively limited number of retail establishment is a common, and almostunavoidable, limitation of shopping center research which, more often than not,has to deal with even fewer observations. In this case, it should be added that, atthe time the financial data were collected, six independent owners were involvedin the process; moreover, leases were negotiated over a period of more than twentyyears during which time the retail establishments changed hands several times.

As can be seen from Exhibit 2, low-class and high-class regional centers aretreated separately considering their marked differences with respect to location,customer profile and image. While Center 5 (Place Fleur-de-Lys) attracts a vastbut low-income, more local clientele highly concentrated in the Lower City portionof the agglomeration, Center 10 (Place Sainte-Foy) mainly serves fashionable,high-income customers belonging to the Upper City and offers much moresophisticated products. Considering that these two establishments stand at theopposite end of the retail image spectrum, not making such a distinction wouldhave resulted in unreliable marginal prices for space, even where spatial and socio-economic indices are used.

Basic descriptive statistics for base rent and store size (GLA), expressed in theiroriginal form and with a logarithmic transformation, are reported in Exhibit 3. Ascan be seen, original data are markedly skewed to the right and exhibit a strongstandard deviation. Unit base rents display a very wide range, from a minimumof $0.32 to a maximum of $319 per square foot, with mean and median valuesat $45 and $34, respectively. Not surprisingly, the inclusion of both anchor storesand kiosks in the study translates into even more dispersed GLAs, with mean sizeof stores standing at 4,374 square feet while the median value is at 1,275 squarefeet.7 Consequently, a logarithmic transformation on both the Base Rent and GLAvariables is a prerequisite to their inclusion in the modeling process.

M e a s u r i n g E c o n o m i c P o t e n t i a l a n d C e n t e r A t t r a c t i o nF a c t o r s

While modeling shopping center rents using endogenous features of retailestablishments—both structural and financial—have been the object of severalinvestigations since the early 1990s, the analysis of exogenous neighborhood-related attributes remains embryonic. This study takes advantage of a recent O-D

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Exhibi t 2 � Structuring Database by Retail Category and Type of Shopping Center

Store Category

Community (4)

Nb. %GLA(sq. ft.) %

Regional Low-Class (1)

Nb. %GLA(sq. ft.) %

JEWELRY 5 3.1 7,108 0.9 8 4.8 7,052 0.8

CLOTHING 29 18.0 90,049 11.5 44 26.2 127,871 15.2

SHOESHOP 8 5.0 22,461 2.9 14 8.3 26,209 3.1

RESTAURANT 10 6.2 18,210 2.3 25 14.9 33,533 4.0

SERVICES 25 15.5 74,182 9.5 9 5.4 12,633 1.5

SPECIALTY STORE 46 28.6 140,499 18.0 42 25.0 122,729 14.6

KIOSK 7 4.3 1,058 0.1 10 6.0 2,332 0.3

ANCHOR 8 5.0 394,987 50.6 6 3.6 496,329 58.9

VACANT 23 14.3 31,879 4.1 10 6.0 14,644 1.7

Total 161 100.0 780,433 100.0 168 100.0 843,332 100.0

Store Category

Regional High-Class (1)

Nb. %GLA(sq. ft.) %

Super-Regional (2)

Nb. %GLA(sq. ft.) %

JEWELRY 7 5.7 10,217 1.7 29 5.2 26,761 1.2

CLOTHING 51 41.8 137,625 22.6 178 32.0 419,269 19.4

SHOESHOP 11 9.0 28,427 4.7 32 5.8 57,156 2.6

RESTAURANT 10 8.2 11,283 1.9 64 11.5 52,030 2.4

SERVICES 8 6.6 25,221 4.1 38 6.8 62,215 2.9

SPECIALTY STORE 25 20.5 45,704 7.5 116 20.9 289,676 13.4

KIOSK 4 3.3 609 0.1 65 11.7 10,560 0.5

ANCHOR 5 4.1 226,283 37.1 15 2.7 1,215,487 56.2

VACANT 1 0.8 124,126 20.4 19 3.4 30,775 1.4

Total 122 100.0 609,495 100.0 556 100.0 2,163,929 100.0

Store Category

All Categories (8)

Nb. %GLA(sq. ft.) %

JEWELRY 49 4.9 51,138 1.2

CLOTHING 302 30.0 774,814 17.6

SHOESHOP 65 6.5 134,253 3.1

RESTAURANT 109 10.8 115,056 2.6

SERVICES 80 7.9 174,251 4.0

SPECIALTY STORE 229 22.7 598,608 13.6

KIOSK 86 8.5 14,559 0.3

ANCHOR 34 3.4 2,333,086 53.1

VACANT 53 5.3 201,424 4.6

Total 49 4.9 51,138 1.2

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Exhibi t 3 � Descriptive Statistics–Continuous Financial and Structural Variables

BaseRent ($) Ln BaseRent GLA (sq. ft.) Ln GLA

Mean 45.11 3.48 4,374 7.17

Median 34.42 3.54 1,275 7.15

Mode 30.00 3.40 2,000 7.60

Standard Deviation 44.08 0.84 16,228 1.30

Minimum 0.32 �1.14 22 3.09

Maximum 318.60 5.76 187,000 12.14

Note: Data from 41 cases. The filter used on the data bank insures that only non-vacant premisesdisplaying non-zero, positive base rent and GLA values enter the analysis.

survey performed in 2001 on the QMA territory to develop a series of indicesdesigned at modeling the influence of spatial, neighborhood-related factors onretail rent determination. Two indices are developed below, which account for‘‘economic potential’’ (EPI), and ‘‘shopping center attraction’’ (CAI). Beforedefining these indices, more information on the 2001 O-D survey is necessary.Managed jointly by the Ministe`re des Transports du Que´bec and the Re´seau deTransport de la Capitale (RTC), Quebec City’s transit authority is in charge of theprovision of mass transit services on a regional basis.

For the 2001 survey, 68,121 individuals (27,839 households) were interviewed,which represents roughly 9% of the QMA population. Detailed information wascollected on 174,243 daily trips on a normal weekday, with both transportationmode and commuting goals being reported by respondents. Designed with thecooperation of the Center for Research in Regional Planning and Development atLaval University, the survey accounts for trips directed at major stores, non-anchorretail units, grocery stores, leisure facilities and restaurants. Expansion factors,based on type and structure of household as well as on type of trip, are thenapplied to surveyed trips in order to generate a reliable estimation of the totalactual trips in the region. For 2001, 5,575 trips for shopping, leisure and grocerypurposes were surveyed; after expansion, the actual daily trips pertaining to thesegoals were estimated at 74,885.

The fact that the O-D survey links commuter trip patterns to households’ place ofresidence provides a unique opportunity to measure the influence spatial andneighborhood factors exert on shopping center rents. Indeed, it makes it possibleto accurately assess the customer traffic of each shopping center in relation toboth the origin and economic profile of customers. Only 69% of shopping andleisure trips reported during the O-D survey originated from home; the remainingtrips originated from workplaces, schools or other activity places, including othershops. It was therefore necessary to relocate the non-home-based trips considering

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specific home location for each person. In this study, 1996 census data onhousehold profile and income are integrated to a regional GIS (MapInfo software)after information by enumeration area has been reshuffled according to a finerhexagonal grid composed of 6,150 cells.

Economic Potential Index: The first index, the Economic Potential Index (EPI), isused as a proxy for the sales potential of each shopping center and is measuredas the product of the actual number of customers shopping at centeri (Ci) andoriginating from zonej, times the annual personal income for zonej (Rj). TheEPI is then expressed as a percentage of total EPIs computed for all centers.8 Itis worth noting that while personal income is used here as an indication ofpotential spending by individuals and households on a yearly basis (hence thedenomination of the index), customer traffic is based on actual trips, as estimatedthrough the O-D survey. Pinpointing actual rather than expected customers ofmajor retail establishments using observed retail trip patterns is a main feature ofthis research. This innovative, methodological approach to shopping center rentmodeling differs from the one traditionally used for marketing studies wherebyoverall rather than selected population nearby a given retail outlet is used tomeasure economic interaction and trade area.

The EPI, which is assumed to impact positively on rents while capturing the imagecomponent through personal income and potential spending, can be formulatedas:

6150 10

EPI � [C � R ] � 100 EPI , (1)� ��i ij j ij�1 i�1

where:

Cij � Number of customers originating from zonej and shopping at centeri; andRj � Annual personal income for zonej.

Center Attraction Index: The second index, referred to as the Center AttractionIndex (CAI), is a direct adaptation of Reilly’s (1929) model of retail gravitation.It measures the combined influence of shopping center size (Si), based on GLA,and squared distance of customers to centeri from any cellj the accessibility2(D );ij

ratio thereby computed is then weighted by the cell population (Pj) and the CAIexpressed as a percentage of total CAIs.

The CAI, which is also assumed to affect rents positively, can be written as:

6150 102CAI � [(S /D ) � P ] � 100 CAI , (2)� ��i i ij j i

j�1 i�1

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Exhibi t 4 � Economic Potential and Center Attraction Indices

Center Identification and Type EPI (%) CAI (%)

C1—Carrefour Beauport (COM) 7.3 2.7

C2—Carrefour Charlesbourg (COM) 5.4 4.4

C3—Carrefour Neufchatel (COM) 3.5 5.1

C4—Place de la Cite (FASH) 3.9 3.2

C5—Place Fleur-de-Lys (REG-L) 15.8 30.6

C6—Galeries de la Capitale (SREG) 21.8 15.0

C7—Galeries Charlesbourg (COM) 4.6 3.8

C8—Place Laurier (SREG) 22.5 21.7

C9—Place des Quatre Bourgeois (COM) 5.3 3.7

C10—Place Sainte-Foy (REG-H) 10.0 9.8

Total 100.0 100.0

Notes: Spatial indices are expressed as percentages of total; while not accounted for in the rentmodeling process due to incomplete information. Centers 2 (Community) and 4 (Fashion) arenevertheless included for the calculation of the Economic Potential and Center Attraction Indices.

where:

Si � Shopping center size, as measured by GLA;�2Dij Squared distance from any cellj to centeri; and

Pj � Population in cellj.

Exhibit 4 reports the results of the computations. They first confirm the prevalentsales potential of the two super-regional centers (C6 and C8), which are relativelysheltered from excessive competition, and of the two regional centers (C5 andC10), due to their well circumscribed market area and the particular profile oftheir customers. Community centers, as expected, lag far behind. As for therelative attraction of shopping centers, the high CAI figure assigned to the low-class regional center (C5) suggests that it attracts a large clientele from denselypopulated neighborhoods in spite of its rather limited sales potential. Retail marketareas and home location of customers for selected shopping centers can bevisualized in Des Rosiers and The´riault (2003).

� A n a l y t i c a l A p p r o a c h

F u n c t i o n a l F o r m a n d R e s e a r c h H y p o t h e s e s

This paper focuses on modeling the space-related component of the rent settingprocess prevailing in shopping centers, using unit base rent (that is, its logarithmic

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form) as the dependent variable. Considering the statistical distribution of baserents as shown in Exhibit 3, and in line with the current real estate literature onretail modeling, regression models are calibrated using a log-linear functionalform. Similarly, a logarithmic transformation is used on the store size (GLA)variable.

The analytical approach used in this paper rests on three research hypotheses thatare to be empirically tested:

H1. Unit base rents are inversely related to the size of the store: the largerthe store, the lower the unit rent imposed on a tenant;

H2. Base rents are set according to the line of product sold, whose marginalcontribution may vary depending on the image and retail mix of theshopping center; and

H3. Exogenous, both economic and space-related, dimensions, as measuredthrough EPI and CAI, significantly impact rent levels while reflectingthe image factor.

While all three hypotheses raise fundamental issues with respect to retail rentmodeling, this paper focuses on the second and third ones, which tests therespective contribution of non-spatial (H2) as opposed to space-related (H3)determinants. As will be seen, shopping center image can hardly be isolated fromits spatial context.

The general formulation of the hedonic equation underlying the current empiricalinvestigation is expressed as follows:

B B Size B Mix B Potential B Attraction �0 1i 2i 3i 4iBase Rent� e e e e e e , (3)

where ‘‘Size’’ and ‘‘Mix’’ account for the endogenous attributes of shoppingcenters while ‘‘Potential’’ and ‘‘Attraction’’ refer to exogenous dimensions. This,in turn, can be put as:

Ln BaseRent� B � B Size� B Mix � B Potential0 1i 2i 3i

� B Attraction � �. (4)4i

� D a t a S e l e c t i o n , R e g r e s s i o n P r o c e d u r e a n d P r o d u c tC a t e g o r i z a t i o n

The standard multiple linear regression procedure is used throughout, with a filteron the database so as to select only non-vacant premises displaying positive, non-

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zero base rent and GLA values while avoiding any missing values on the retailmix descriptors. After filtering, the sample size stands at 939 or 941 valid cases,depending on the model.9

As mentioned earlier, and with few exceptions, major categories of products, ratherthan sub-categories, are used in the computations to avoid unduly complexinteractive terms where shopping center ranking is accounted for. From a purelystatistical standpoint, this is justified by the fact that increased segmentationreduces the number of cases available for inclusion in the regressions, therebygenerating unreliable coefficients. For that reason, no variable is created unless atleast ten cases are reported. Moreover, in order to facilitate the comparisonbetween models, the ‘‘Clothing’’ category is treated globally, despite the sufficientnumber of stores selling women’s, men’s, children’s and mixed ready-to-wearclothes and accessories. Since the marginal contribution of each sub-category doesnot significantly differ from one another and that model performances are onlyvery slightly improved by including them, this is of no consequence on the studyfindings.

The ‘‘Fast-food’’ sub-category is used instead of the more general ‘‘Food services’’category since the vast majority of restaurants are actually fast-food chains.Banking businesses are also included in the regression equation in addition to thegeneral ‘‘Services’’ category they belong to, considering their particularlysignificant contribution to base rent. In some models (Models 2 and 3), a similartreatment applies to optician stores and food markets, both being considered hereass subdivisions of specialty stores.

Te s t i n g H y p o t h e s e s U s i n g a T h r e e - S t e p A p p r o a c h

In order to test the research hypotheses, a three-step approach is employed. First(Step 1), the natural logarithm of base rent is regressed on major retail categories,excluding any interactions between variables, as well as space-related attributes.The ensuing model (Model 1) serves as a basis to measure the marginalcontribution of both image-driven interactions and space-related attributes. In Step2 (Model 2), interactive terms combining product line with center image, orranking, are substituted for basic retail categories as a means of testing the secondhypothesis. Wherever possible, that is where the number of cases allows it, the‘‘regional high-class’’ and ‘‘regional low-class’’ sub-categories are used instead ofthe more general ‘‘regional’’ category. In Step 3, space-related indices are addedon to either interactive terms (Model 3) or major retail categories (Models 4a to4c) in order to assess the marginal contribution of socio-economic and locationfactors to the retail rent setting process.

The operational definition of model variables is provided in Exhibit 5 while themain regression results are displayed in Exhibits 6 to 9 (Models 1 to 4). Withrespect to variable denominations, it should be added that, where interactive termsare used, full denominations are replaced by a prefix indicating the line of product

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Exhibi t 5 � Operational Definition of Variables

Variable Operational Definition Type

Dependent VariableLn BaseRent Natural logarithm of base rent per sq. foot, as specified in the

lease agreement.M

Size, Retail Mixand ImageAttributesGLA Size of premises, expressed as Gross Leasable Area (square feet). MLn GLA Natural logarithm of Gross Leasable Area MJEWELRY (JEW ) Jewelry store. DCLOTHING (CLO ) Clothing retailer; includes women’s, men’s, children’s and mixed

ready-to-wear clothes and accessories.D

SHOESTORE(SHOE )

Shoe store; includes family shoes, women’s, men’s & boy’s shoes,athletic wear, unisex/ jean store.

D

FASTFOOD (FAST ) Fast-food, a sub-category of food services which includesrestaurant & fast food. (with/out liquor), sandwich & pizzasstores, candy & nuts stores (excluding kiosks).

D

SERVICES (SERV ) Personal and financial services; includes banks & insurance,medical & dental offices, beauty salons, cleaners & dryers, unisexhair, barbershops, travel agents.

D

BANKING Banking, a sub-category of personal services. DSPECIALTY (SPEC ) Specialty and gifts stores; includes radio, video & music centers,

cards & gifts, books, decorative accessories, drugstores, opticianstores.

D

OPTICIAN Optician and spectacle store, a sub-category of specialty store. DFOODMRKT Food market, a sub-category of specialty store.KIOSK (KIOS ) Kiosk store; small, light structure with open sides, usually

occupying the central alley of the building, close to highpedestrian traffic and having a GLA of between 70 and 300square feet, on average (cellular phone, newspaper, candy andlottery stores, other specialty shops, . . .).

D

ANCHOR(used as thereference category)

Anchor tenant; large, chain stores having between 20,000 and200,000 square feet of GLA. Usually seen as providing bothstability and customer drawing power to the shopping center(Wal-Mart, Simons, Toys’ R’ US, The Bay, Sears, . . .).

D

SRG The retail unit is located in a super-regional shopping center. DREG The retail unit is located in a regional shopping center. DRGH The retail unit is located in a high-class, regional shopping center. DRGL The retail unit is located in a low-class, regional shopping center. DCOM The retail unit is located in a community shopping center. D

Space-RelatedIndicesEPI Economic Potential Index MCAI Center Attraction Index M

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Exhibi t 6 � Model 1: Regressing Ln BaseRent on Size and Retail Mix Attributes

Model Summary and ANOVA

Adj. R2 Std. Error of Est.

ANOVA

F df Significance

0.429 0.63298 79.5 9/931 0.000

Coefficients

Variables

Unstand. Coefficients

B Std. Error t Sig.CollinearityVIF

Constant 5.187 0.223 23.2 0.000

Ln GLA �0.283 0.023 �12.4 0.000 2.0

JEWELRY 0.450 0.133 3.4 0.001 2.1

CLOTHING 0.373 0.097 3.9 0.000 4.8

SHOESTORE 0.429 0.120 3.6 0.000 2.2

FASTFOOD 0.491 0.126 3.9 0.000 3.1

SERVICES �0.272 0.126 �2.2 0.031 2.7

BANKING 0.641 0.190 3.4 0.001 1.2

SPECIALTY 0.109 0.101 1.1 0.284 4.4

KIOSK 0.933 0.142 6.6 0.000 3.9

Note: Reference Category: Anchor Stores

(e.g. JEW for jewelry), followed by a suffix referring to the type of establishment(e.g. SRG for super-regional, leadingto JEW SRG for jewelry stores located insuper-regional centers). Again, not all possible combinations are represented sincesome interactions involve too few cases or do not apply at all.

� M a j o r R e g r e s s i o n F i n d i n g s

S t e p 1 : R e g r e s s i n g L n B a s e R e n t o n M a j o r R e t a i lC a t e g o r i e s

With only store size (LnGLA) and major retail categories included in theequation, Model 1 (Exhibit 6) explains 42.9% of rent variations among stores anddisplays anF value of 79.5; the predictive performance, as shown by the SEE, is

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Exhibi t 7 � Model 2: Interacting Retail Mix with Center Image Attributes



ANOVA

F df Significance

0.559 0.55666 48.6 25/915 0.000

Coefficients

Variables

Unstd. Coeff.


Constant 4.957 0.168 29.5 0.000

Ln GLA �0.265 0.018 �14.3 0.000 1.7

JEW SRG 0.786 0.126 6.3 0.000 1.4

JEW REG 0.498 0.160 3.1 0.002 1.2

CLO SRG 0.574 0.078 7.3 0.000 2.9

CLO RGH 0.781 0.102 7.7 0.000 1.6

CLO RGL 0.290 0.107 2.7 0.007 1.5

CLO COM �0.552 0.126 �4.4 0.000 1.3

SHOE SRG 0.613 0.119 5.1 0.000 1.4

SHOE RGH 0.929 0.180 5.2 0.000 1.1

SHOE RGL 0.348 0.163 2.1 0.033 1.2

SHOE COM �0.071 0.207 �0.3 0.732 1.1

FAST SRG 0.732 0.107 6.8 0.000 1.9

FAST REG 0.577 0.135 4.3 0.000 1.4

SERV SRG �0.030 0.122 �0.2 0.808 1.7

SERV REG 0.275 0.158 1.7 0.083 1.3

SERV COM �0.621 0.139 �4.5 0.000 1.4

BANKING 0.586 0.167 3.5 0.000 1.2

SPEC SRG 0.298 0.086 3.5 0.001 2.4

SPEC RGH 0.565 0.135 4.2 0.000 1.4

SPEC RGL 0.210 0.111 1.9 0.058 1.6

SPEC COM �0.461 0.110 �4.2 0.000 1.7

OPTICIAN 0.308 0.160 1.9 0.055 1.1

FOODMRKT 0.237 0.115 2.1 0.039 1.2

KIOS SRG 1.154 0.114 10.2 0.000 2.5

KIOS REG 1.037 0.173 6.0 0.000 1.3


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Exhibi t 8 � Model 3: Combining Interactions and Space-related Indices



ANOVA

F df Significance

0.583 0.53640 49.6 27/911 0.000

Coefficients

Variables

Unstd. Coeff.


Constant 4.550 0.180 25.2 0.000

Ln GLA �0.257 0.018 �14.0 0.000 1.8

JEW SRG 0.356 0.149 2.4 0.017 2.2

JEW REG 0.797 0.160 5.0 0.000 1.3

CLO SRG 0.142 0.113 1.3 0.208 6.4

CLO RGH 0.837 0.101 8.3 0.000 1.7

CLO RGL 0.790 0.129 6.1 0.000 2.4

CLO COM �0.467 0.133 �3.5 0.000 1.6

SHOE SRG 0.188 0.142 1.3 0.188 2.2

SHOE RGH 0.986 0.175 5.6 0.000 1.2

SHOE RGL 0.851 0.176 4.8 0.000 1.5

SHOE COM 0.040 0.208 0.2 0.849 1.2

FAST SRG 0.325 0.135 2.4 0.017 3.2

FAST REG 0.983 0.144 6.8 0.000 1.8

SERV SRG �0.412 0.145 �2.8 0.005 2.5

SERV REG 0.588 0.159 3.7 0.000 1.4

SERV COM �0.464 0.146 �3.2 0.002 1.7

BANKING 0.571 0.161 3.5 0.000 1.2

SPEC SRG �0.118 0.117 �1.0 0.315 4.8

SPEC RGH 0.629 0.132 4.8 0.000 1.4

SPEC RGL 0.716 0.133 5.4 0.000 2.4

SPEC COM �0.361 0.120 �3.0 0.003 2.2

OPTICIAN 0.298 0.154 1.9 0.053 1.1

FOODMRKT 0.225 0.111 2.0 0.043 1.2

KIOS SRG 0.721 0.143 5.0 0.000 4.3

KIOS REG 1.430 0.177 8.1 0.000 1.5

EPI 0.069 0.010 6.6 0.000 14.4

CAI �0.041 0.005 �7.9 0.000 6.1


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Exhibi t 9 � Models 4a to 4c: Major Product Categories and Space-Related Indices

Model Summaries and ANOVA

Model 4aMajor ProductCategories � EPI

Model 4bMajor ProductCategories � CAI

Model 4cMajor ProductCategories � EPI & CAI

Adj. R2 0.474 0.429 0.476

Std. Error of Est. 0.6030 0.6279 0.6018

F (Sig.) 85.4 (0.000) 71.6 (0.000) 78.4 (0.000)

N of Cases 939 939 939

Coefficients

Variables

Model 4aMajor Product Categories � EPI

Unstd. Coeff. t VIF

Model 4bMajor Product Categories � CAI

Unstd. Coeff. t VIF

Model 4cMajor Product Categories � EPI & CAI

Unstd. Coeff. Std. Coeff. t VIF

Constant 4.672 21.3 5.074 22.7 4.669 21.3

Ln GLA �0.273 �12.5 2.0 �0.281 �12.4 2.0 �0.269 �0.418 �12.4 2.0

JEWELRY 0.365 2.9 2.1 0.403 3.0 2.1 0.371 0.099 2.9 2.1

CLOTHING 0.280 3.0 4.9 0.326 3.4 4.8 0.282 0.158 3.0 4.9

SHOESTORE 0.367 3.2 2.2 0.378 3.2 2.2 0.378 0.116 3.3 2.2

FASTFOOD 0.384 3.2 3.1 0.427 3.4 3.2 0.400 0.139 3.3 3.2

SERVICES �0.277 �2.3 2.7 �0.300 �2.4 2.7 �0.273 �0.089 �2.3 2.8

BANKING 0.629 3.5 1.2 0.644 3.4 1.2 0.621 0.091 3.4 1.2

SPECIALTY 0.056 0.6 4.4 0.065 0.6 4.4 0.063 0.032 0.6 4.4

KIOSK 0.808 5.9 3.9 0.885 6.3 3.9 0.813 0.281 6.0 3.9

EPI 0.030 9.5 1.0 0.035 0.267 9.1 1.5

CAI 0.009 3.5 1.0 �0.006 �0.064 �2.2 1.5

Note: The reference category is Anchor Stores.

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rather weak (0.633). Considering that neither vacancies nor lease-specificattributes are accounted for in the study, these are reasonable performances;nevertheless, there is still room for improvement. With respect to individualcoefficients, the strongt value and negative sign assigned to the size variable arein line with theoretical expectations and confirm the first research hypothesis (H1):the larger the GLA, the lower the base rent charged to the tenant. Since both thedependent and size variables are expressed in their logarithmic form, the GLAparameter may be interpreted as an elasticity coefficient; it suggests that a 10%increase in the size of a shop results in a 2.8% drop in the unit base rent.

All remaining coefficients except two emerge as being significant at the 1% levelor below, and with consistent signs. While theSPECIALTY (specialty stores)parameter is not statistically significant and that personal services (SERVICES), acategory encompassing a large array of heterogeneous businesses, display anegative coefficient (only significant at 0.031), findings suggest that jewelry stores,clothing stores, shoe stores, fast-food businesses and banking activities all largelycontribute to the profitability of shopping centers and largely compensate for thelow level of rent usually charged to anchor tenants (serving as the reference).Most important though, kiosks, which usually occupy a strategic, most centralposition in the building and are exposed to high pedestrian traffic, display thehighest marginal contribution to base rent. Finally, multicollinearity remainsreasonably well controlled, as shown by the VIF values that all stand below thethreshold of 5.

S t e p 2 : C o m b i n i n g P r o d u c t L i n e w i t h C e n t e r I m a g e

Model 2 (Exhibit 7) is built on the hypothesis that the marginal contribution of agiven product line to base rent will vary depending on the image ranking of theshopping center (H2). On such grounds, and conditional to some qualifications,10

it should be expected that rents charged in super-regional establishments prevailover those found in regional ones; these, in turn, should normally prevail overrents charged for community center space.

Substituting interactive, image-specific variables for basic retail categories yieldsan improved rent model, with the adjustedR2 now standing at .559 while the SEEhas dropped to 0.557. Multicollinearity is also substantially reduced (maximumVIF is less than 3). While the GLA coefficient is only slightly affected (�0.265vs. �0.283 in Model 1), most interactive variables emerge with a high statisticalsignificance, except forSHOE COM and SERV SRG, which are not significantat the 10% level. Their coefficients also exhibit consistent signs, with storeslocated in community centers (clothing stores, personal services and specialtystores) generating negative marginal contributions to rent, even when comparedto anchor stores.11 Considering that the vocation of community centers is to fulfillthe basic, daily needs of local populations, this should not be a surprise: lookingback at Exhibit 2, it can be seen that while personal services and specialty stores

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account, overall, for 4.0% and 13.6% of total GLA, they occupy respectively 9.5%and 18.0% of community centers’ leasable area.

Finally, it is worth noting that regression coefficients relative to super-regionalcenters tend to be larger in magnitude than those obtained for regional ones, whichcorroborates previous findings regarding the existence of substantial agglomerationeconomies in larger commercial complexes (Eppli and Shilling, 1996; Mejia andBenjamin 2002; and Des Rosiers, The´riault and Ozdilek, 2002). However, storeslocated in the regional ‘‘high-class’’ establishment run at a premium compared toeven higher-level centers, which mirrors the highly sophisticated features of bothits product lines and customers.

Results derived from the use of interactive variables tend to confirm our secondresearch hypothesis with regard to the heterogeneous impact various lines ofproducts have on rents, depending on the center image (Grewal, Krishnan, Bakerand Borin 1998; and Mejia, Eppli and Benjamin, 2001), and to corroborate Hardin,Wolverton and Carr’s (2002) findings about the existence of distinct retailsubmarkets.

S t e p 3 : A d d i n g o n S p a c e - r e l a t e d I n d i c e s

With Model 3 (Exhibit 8), space-related indices are added to the previous model,which raises the adjustedR2 by 2% to .583 and lowers the SEE to 0.536. As canbe seen, both the EPI and the CAI emerge as highly significant, although the latteris, quite unexpectedly, signed negatively. This counterintuitive result may belinked to the strong collinearity that obviously undermines the reliability andstability of several regression coefficients in the equation. As shown by the VIFvalues, parameters pertaining to super-regional centers are particularly affected,which suggests that space-related indices, especially the EPI, may capture asignificant portion of the image component also accounted for by interactive terms.

While reducing the number of interactions actually lessens the problem, space-related indices still display high levels of collinearity, which makes theirinterpretation rather risky. As a solution, the EPI and CAI are, alternately first(Models 4a and 4b), and then jointly (Model 4c), introduced into the basic, un-segmented model to allow sorting out of endogenous from exogenouscontributions to base rents (Exhibit 9). As can be seen, model performances donot match those obtained with interactive terms while the CAI parameter remainsunstable with respect to both magnitude and sign. In contrast, the EPI is shownto significantly contribute to the shaping of shopping center rents: with astandardized regression coefficient of 0.267, the EPI comes third in terms ofrelative contribution to base rent, next to store size and theKIOSK category. Thus,every point increase in the EPI raises base rent by a proportion of 3.5%. Findingsalso suggest that it tends to prevail over the CAI when used in combination withit.

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On such grounds, we may conclude that our third research hypothesis (H3),namely that space-related factors significantly impact rent levels while capturingthe image factor, is confirmed, at least with regard to the EPI.

� C o n c l u s i o n

This study is an attempt to model the economic trade-off between spatial andnon-spatial determinants of shopping center rents while assessing the role ofneighborhood and location attributes in the rent setting process. It is basedprimarily on physical and financial data obtained for eight super-regional, regionaland community shopping centers in Quebec City during the 1998–2000 period.A 1,007 (941 after filtering) retail unit database, representing some 4.4 millionsquare feet of gross leasable area, is used to model unit base rent.

The study also benefits from a 2001 origin-destination phone survey that providesinformation on some 174,000 daily trips in the Quebec metropolitan area. Twospace-related indices are designed, namely the EPI and the CAI. Following a three-step approach, base rents are first regressed on store size (GLA) and main retailcategories. Interactive variables combining product line with shopping centerranking, or image, are then tested with conclusive results. Finally, space-relatedindices are included in the equation, with and without interactive terms. Thefindings suggest that the EPI significantly contributes to the shaping of shoppingcenter rents, every point increase in the EPI raising base rent by a proportion of3.5%.

The main contribution of this paper is to highlight the complex interactionsbetween, on the one hand, endogenous determinants of shopping center rents(agglomeration economies, retail mix and concentration, image and interiordesign, bargaining power, etc.) and, on the other hand, exogenous, space-relatedfactors, a research area that deserves further investigation. In that perspective,resorting to transportation-designed decision tools such as O-D surveys might leadshopping center managers to gain a better insight into the shopping behavior andshopping trip patterns of households.

This study also suggests that an index based on observed shopping trips andhousehold income, namely the EPI, provides a better understanding of marketpotential and its impact on commercial rents, compared to a more classical index(here, the CAI) based on a gravity-type model. Moreover, in Model 4c, both theEPI and the CAI were found to exert a significant impact on rents while avoidingmulticollinearity. However, the negative impact of the CAI may be specific toQuebec City where higher population densities are associated with lower incomes.At least, its inverted coefficient sign should be interpreted as a marginal effectwhen economic market potential is accounted for using the EPI. This deservesfurther investigation and comparison with other cities. Finally, we also argue thatshopping center image (sophistication of product lines) is intertwined with homelocation and income of actual customers, providing justification for using O-D

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surveys to improve assessing the impact of macro-spatial (city-wide) factors oncommercial rents.

� E n d n o t e s1 According to the ICSC, ‘‘malls typically are enclosed, with a climate-controlled walkway

between two facing strips of stores,’’ (Source: ICSC Shopping Center Definitions,International Council of Shopping Centers, New York).

2 Quebec City’s highway network has 21.7 km of highways per 100,000 inhabitants; thisis about three times the ratio observed in Toronto and Montreal.

3 Quebec City’s major territorial feature remains the physical and socio-economic splitbetween the upper and lower neighborhoods, separated by a cliff.

4 While data have actually been provided for ten shopping centers, full financialinformation remains incomplete for two of them, which belong to the community andfashion center categories; consequently, these are not accounted for in the current study.

5 Such a figure underestimates the actual share of ‘‘active’’ major tenants in regionalcenters. Indeed, over the 1998–2000 period, one of the two regional centers (Place Ste-Foy) displayed an abnormally, although temporary, high vacancy rate after Eaton Canadawent into bankruptcy.

6 As an example, Place Laurier, the largest super-regional shopping center in the QMA,which was acquired a few years ago by the SITQ, the real estate arm of the powerfulpublic pension fund Caisse de de´pot et placement du Que´bec (CDPQ), uses all threetypes of lease agreements, namely base, percentage and excess rents. In contrast, itsdirect competitor, Les Galeries de la Capitale, property of Les De´veloppements IbervilleInc., a private family-run business owning some 100 shopping centers throughout theprovince, resorts to nothing but base rent.

7 The smallest store, St-Cinnamon, is only 22 sq. ft., while Sears occupies 187,000 sq.ft.

8 While only eight shopping centers are used in this study, indices are computed for allten centers for which full or partial information is available.

9 Variations in the number of cases are due to the deletion procedure whereby only caseswith full information are included in the analysis.

10 While consistent with rational expectations, such a hypothesis needs to be qualified inlight of other factors that affect the bargaining power of landlords and tenants at thetime of leasing, such as the search for the right balance between supply and demandfor a given product line, as well as the specific vocation of a retail establishment inreference to its neighborhood and clientele.

11 The majority (26) of the 34 anchor stores referred to in this study are found in regionaland super-regional malls. While most are charged relatively low unit base rents, thesemay, on average, exceed what is charged to some community center stores, whichexplains the negative contribution the latter are assigned.

� R e f e r e n c e s

Alonso, W.,Location and Land Use. Toward a General Theory of Land Rent, Cambridge,MA: Harvard University Press, 1964.

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Anderson, P. M., Association of Shopping Center Anchors with Performance of Non-Anchor Specialty Chain’s Stores,Journal of Retailing, 1985, 61, 61–74.

Anikeeff, M., Shopping Center Tenant Selection and Mix: a Review, in J. Benjamin (ed.),Research Issues in Real Estate, Megatrends in Retail Real Estate, Vol. 3, Norwell: KluwerAcademic Publishers, 1996.

Benjamin, J. D., G. W. Boyle and C. F. Sirmans, Retail Leasing: Determinants of ShoppingCenter Rents,Journal of the American Real Estate and Urban Economics Association,1990, 18:3, 302–12.

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This research was funded by the Canadian SSHRC (Social Science and HumanitiesResearch Council) through Major Collaborative Research Initiative and TeamResearch grants. Authors are grateful to anonymous reviewers for various suggestionsaimed at improving this paper’s quality and readability.

Francois Des Rosiers, Laval University, Quebec City, Canada G1K 7P4 or [email protected].

Marius Theriault, Laval University, Quebec City, Canada G1K 7P4 or [email protected].

Laurent Menetrier, Laval University, Quebec City, Canada G1K 7P4 or [email protected].

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