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DOCUMENT DE TRAVAIL 2002-008
SHOPPING CENTER RENTS AND AGGLOMERATION ECONOMIES : PRELIMINARY
FINDINGS FROM EMPIRICAL EVIDENCE François Des Rosiers Marius
Thériault Ünsal Özdilek
Version originale : Original manuscript : Version original :
ISBN – 2-89524-145-7
Série électronique mise à jour : On-line publication updated :
Seria electrónica, puesta al dia
06-2002
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SSHHOOPPPPIINNGG CCEENNTTEERR RREENNTTSS AANNDD
AAGGGGLLOOMMEERRAATTIIOONN EECCOONNOOMMIIEESS::
PPRREELLIIMMIINNAARRYY FFIINNDDIINNGGSS FFRROOMM
EEMMPPIIRRIICCAALL EEVVIIDDEENNCCEE
Paper presented
at the 9th European Real Estate Society Conference, Glasgow,
Scotland, June 4-7, 2002
by
François Des Rosiers, Ph.D., Faculty of Business Administration,
Marius Thériault, Ph.D., Director, Land Planning Research
Center,
and Ünsal Özdilek, Ph.D. Candidate,
Laval University, Quebec City, Canada
ABSTRACT This study is an attempt to model shopping centre rents
and to investigate whether agglomeration economies are a driving
force in the rent determination process. It is part of a research
program based on physical and financial information obtained from
seven super-regional, regional and community shopping centre
managers in Quebec City for the 1998-2000 period. At this point,
some 784 shops are covered in the study with gross base rent,
operating expenses and gross leasable area being available for all
of them. Partial information on lease duration as well as yearly
sales is also available. While still preliminary, findings clearly
suggest that larger centres generate substantial agglomeration
economies for several categories of shops. Retail structure
internal to shopping centres as well as cyclical factors also play
a significant role in the rent determination process.
KEY WORDS:
Shopping Centres, Rents, Agglomeration Economies, Commercial
Mix
____________________________________
1. OBJECTIVE AND CONTEXT OF RESEARCH
This study is an attempt to model shopping centre rents and to
investigate whether
agglomeration economies are a driving force in the rent
determination process. It is part of
a research program based on physical and financial information
obtained from seven super-
regional, regional and community shopping centre managers in
Quebec City for the 1998-
2000 period. At this point, some 784 shops are covered in the
study with gross base rent,
operating expenses and gross leasable area being available for
all of them. Partial
information on store frontage, lease duration as well as yearly
sales is also available. When
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2
completed, the data bank will include a series of shopping
centre amenities and design
features (physical configuration, parking facilities,
landscaping, etc.) as well as
neighbourhood characteristics (household composition and income)
and urban externalities
(accessibility to services, proximity of competitors, etc.) in
order to account for all major
internal and external determinants of shopping centre rents.
2. LITERATURE REVIEW
The academic literature on shopping malls has evolved around
various theories of urban
spatial structure (Hotelling, 1929; Christaller, 1933; Lösh,
1940 and Alonso, 1964) with
strategies relating to mall configuration and store location
within shopping centres
replicating those observed at the urban level (Vandell and Lane,
1987; Pearson, 1991;
Brueckner, 1993; Roulac, 1996; 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 et al., 1996; Des Rosiers
and Thériault, 1994 &
1996; Chinloy and Maribojoc, 1998) and office sector (Rosen,
1984; Hekman, 1985;
Gabriel and Nothaft, 1988; Wheaton and Torto, 1995; Sivitanides,
1997) where the rent
issue has been widely investigated, studies on the dynamics of
commercial rent structuring
remain embryonic because of the confidential nature of the
required information.
The mechanics underlying additional, or overage rents -
expressed as a percentage of yearly
sales over and above a given, pre-negotiated threshold - are
among the issues raised by
authors (Hartzell et al., 1987; Benjamin et al., 1990; Bruecker,
1993; Colwell et al., 1998;
Wheaton, 2000; Chun et al, 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 and independent stores are
regressed against sales,
discount rates, overage rents, lease terms, lease provisions,
etc. Results suggest that while
base rents are lower where higher overage rent rates apply, they
rise with higher sales
thresholds. In another similar study, Sirmans and Guidry (1993)
point out that higher
consumer traffic levels are a prerequisite for the success of a
store. In a totally different
urban context, Tay et al. (1991) investigate the Hong Kong
commercial market. Their data
base includes 405 stores distributed among nine high-rise
shopping centres. In contrast with
the literature, their study namely reveals that rent level is
positively related to the age of a
shopping centre due to both customers’ fidelity - which tends to
grow with time - and
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3
continuous improvements to buildings. It also suggests that
while the unit rent of a store is
positively correlated with the size of a centre, it is inversely
related to its own size. This
latter finding brings us to the core of the current paper, that
is agglomeration economies.
With location theories as the conceptual background (Weber,
1929), sales potential in
shopping centres are looked upon through the concepts of
agglomeration economies and
externalities derived from the presence of anchor tenants (Eaton
and Lipsey, 1983;
Mulligan, 1983; West et al., 1985; Ghosh, 1986; Ingene and
Ghosh, 1990; Fisher and
Yezer, 1993; Eppli and Benjamin, 1993; Mejia and Benjamin, 2002)
as well as from tenant
mix and product diversity (Konrad, 1984; Pashigan and Gould,
1998). Behind the concept
of agglomeration economies lies the reduction of consumer search
and of uncertainty costs.
Such advantages allow major tenants to negotiate lower rents
with shopping centres’
owners (Anderson, 1985), the fact that their departure may cause
rental income to drop by
as much as 25% (Gatzlaff et al., 1994) greatly enhancing their
bargaining power.
According to Nelson (1958) and Eppli and Shilling (1996), the
clustering of similar stores
leads to an increase in their total sales level, thereby
contributing to the success of the
shopping centre.
The image of a shopping centre may also impact upon sales level
(Brown, 1992; Kirkup
and Rafiq, 1994; Anikeeff, 1996). It stems from consumers’
perception of major occupants
(Nevin and Houston, 1980), shopping centre size and
configuration as well as the quality of
goods and services offered. In this respect, image is
increasingly dependent upon fashion
(James et al, 1976; Jain and Etgar, 1976; Mazursky and Jacoby,
1986; Grewal, 1998).
Similarly, it affects tenants in their negotiation for an
optimal location (Mejia et al., 2001).
Finally, accounting for all these features raises the spatial
autocorrelation issue, recently
addressed by Carter and Haloupek (2000) on the grounds of
previous work performed
mainly on the residential market (Griffith, 1987; Pace and
Guilley, 1998; Dubin et al.,
1999).
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3. DATA BANK AND ANALYTICAL APPROACH
3.1 Original Data Bank
As mentioned earlier, this study is based on physical and
financial information obtained for
seven super-regional (2), regional (2) and community (3)
shopping centres in Quebec City.
While financial data apply to the 1998-2000 period, leases were
negotiated over a much
longer period that extends from 1976 to 2000. At this point, 784
stores are accounted for in
the study. Whereas net rent – defined as total yearly rent minus
operating expenses - and
gross leasable area (GLA) is available for all retail units,
partial information only is
available with respect to yearly sales (526 stores), store
frontage (231) and lease duration
(621). Eight retail categories are distinguished in the
analysis, namely jewellery stores (58),
clothing stores (264), shoeshops (57), restaurants (128),
personal services (74) specialty
stores (151), kiosks (36) and anchor stores (16), all of which
totalling nearly 2,4 million
square feet of GLA. As for shopping centre size, it is accounted
for via dummy variables
used in interaction with other descriptors to generate hedonic
coefficients that are specific
to each shopping centre category. Community (COM – 79 stores and
less), regional (REG –
80 to 149 stores) and super-regional (SREG – 150 stores and
over) centres’ rented space
roughly amount to 283 000 sq. ft., 612 000 sq. ft. and 1 489 000
sq. ft., respectively. GLA
distribution by retail category and shopping centre size is
displayed in Exhibit 1 while
Exhibit 2 provides an operational definition for each variable
used in the modelling process.
3.2 Structural and Cyclical Attributes
In addition to basic financial and physical characteristics, a
series of five market structure
and cyclical attributes are developed in order to capture the
effect on rent of both
commercial dynamics within shopping centres and overall economic
situation. These are:
Lease duration (LEASEDURN): It might be expected that risk
reduction deriving
from a longer lease term should result in a rent discount for
the tenant, provided that
regional commercial rents at the time of negotiation are
relatively stable or
declining; otherwise, a longer than average term may penalize
the landlord.
An agglomeration index (AGGLOMINDX): Defined as the ratio of GLA
for a
given retail category within a shopping centre to total GLA in
that centre, this index
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5
measures agglomeration economies resulting from the amalgamation
of a large
number of tenants in a given field of retail activity and,
therefore, from the
increased consumer traffic it implies. It should be expected
that the larger the index,
the higher the rent that the landlord can extract from tenants
in that category.
A concentration index (CONCNINDX): While the landlord can take
advantage of
the abundance of retailers in a given commercial category, he
will in contrast be
prone to grant a rent discount where relatively few tenants are
in control of the field,
since any defection might induce a severe drop in yearly
revenues. The Herfindahl
Index, designed at measuring industrial concentration, is used
here. Since it
accounts for both the number of stores in a retail category and
their relative share of
the total GLA, it mirrors the level of competition in a field
and the bargaining power
of tenants. It can be assumed that the larger the index, the
lower the rent paid.
The GLA open to negotiation (OPENLEASE) in any given year is
also a good
indication of the bargaining power of tenants in a shopping
centre. Indeed, where a
substantial proportion of total GLA is under negotiation at the
same time, the
landlord should be expected to grant rent discounts in order to
avoid massive
departures to competitors. Therefore, the higher the index, the
lower the rent.
Finally, economic cycles (CYCLE91_92) should theoretically
affect the negotiation
process. An analysis of the consumption expenses of households
and retail sales in
the Quebec metropolitan area between 1981 and 2001 (expressed in
1992 constant
dollars) indicates that the 1991-92 period is characterized by a
drop in commercial
activity. Thus, the coefficient of this dummy variable should
display a negative
sign.
3.3 Analytical approach
Two functional forms are used in the study, namely the linear
and log-linear ones; a
logarithmic transformation is also applied to the GLA variable.
Exhibit 3, displaying basic
descriptive statistics for all variables, clearly indicates that
neither NETRENT nor GLA are
normally distributed; Figure 1 provides further evidence of
this. Hence the rationale for
such transformations. While included in the data base and
available for some 526 stores,
unit sales (SALES/SQFT) are not included in the analysis at this
stage of the research. The
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6
final calibration of the equations involves the removal of 15
extreme residuals considered
to be highly detrimental to the model performances; these
represents less than 2% of the
initial data base. Finally, the regression procedure adopted is
a combination of both
standard and stepwise approaches.
4. MAJOR FINDINGS
4.1 The Linear Form – Model A
As can be seen from Exhibit 4, the linear model yields
interesting results in spite of the non-
linearity of the dependent variable. While the explanatory
performance of Model A, which
displays an adjusted R-Square of 0,714 and a F value of 84,2, is
quite fair, its predictive
performance remains rather weak: considering the global average
rent ($64), the relative
SEE stands at 38,4%, which was to be expected in the light of
the highly skewed
distribution of the NETRENT/SQFT variable. Examination of the
regression coefficients
suggests that no inconsistency can be detected with respect to
either their magnitude or
sign, with the exception of the ANCHOR parameter estimate which
is unexpectedly
positively signed whereas it should be negative. Safe for a few
coefficients whose statistical
significance do not meet the 0,10 (CYCLE91_92; SPECIALTY*SREG)
or the 0,05
(SHOESTORE*COM; AGGLOMINDX*REG) threshold, all other descriptors
exhibit
strong t values. Most interestingly, the two structural
attributes indicative of tenants’
bargaining power (CONCNINDX*SREG and OPENLEASE*REG) emerge as
significant
and with the right sign.
Considering that the linear model may not provide the best
indications on the hedonic
shopping centre rent function, let us turn to Model B for a more
reliable interpretation of
the coefficients obtained via the log-linear form.
4.2 The Log-Linear Form – Model B
Regression results for Model B are shown in Exhibit 5. While
overall performances
obtained with the log-linear form are quite similar to those
derived from the linear one1, the
1 It should be kept in mind that performance tests obtained with
a linear form cannot be compared with those derived from a
non-linear function without first applying the test suggested by
Box and Cox (1964). The latter consists in computing the linear
model using the transformed dependent variable Yi / YG , with YG
being the geometric mean of the Y.
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7
statistical significance of parameter estimates is substantially
improved for most
descriptors. Only one coefficient (KIOSK*REG) is not significant
at the 0,05 level while
all coefficient signs – including that of the ANCHOR variable -
are in line with theoretical
expectations. Most important though, four out of five structural
and cyclical variables
emerge as statistically significant, lease duration (LEASEDURN)
alone being rejected from
the model. As for excessive multicollinearity, VIFs suggest that
there is none, the highest
VIF value standing at 2,89, which is well below the critical
threshold of 10. In spite of that,
it is interesting to note that there might be a structural link
between store agglomeration
(AGGLOMINDX*REG) and the clothing retail sector (CLOTHING*REG)
in regional
shopping centres.
In order to make an adequate interpretation of the implicit
rents of commercial attributes
derived from Model B, it is appropriate to keep in mind the
retail structure of shopping
centres. In that respect, Exhibit 1 may prove very helpful.
4.3 Interpreting the coefficients of basic physical and retail
category attributes
As expected, the prominent role of gross leasable area in the
retail rent determination
process clearly emerges from the model, the negative sign of the
Ln_GLA coefficient
indicating that unit rent decreases with store size. More
precisely, increasing average store
size (3 000 sq. ft.) by 10% will result in a 2,4% drop in unit
rent2. As shown by the Beta,
standardized coefficient, frontage is also a major determinant3
and exerts a positive
influence on rent due to the enhanced visibility and
attractiveness it confers to a store.
Thus, raising the mean frontage (34 ft.) by 10% increases unit
rent by roughly 1,5%.
Quite interestingly, the presence of a jewellery impacts
differently on rents depending on
the size category of the shopping centre it belongs to. In
community shopping centres
(JEWELRY*COM), the effect is negative whereas it is positive in
both regional
(JEWELRY*REG) and super-regional (JEWELRY*SREG) structures. A
twofold
explanation can be brought forward: on the one hand, the
relative scarcity of this retail
activity in community centres (6,8% of stores and 1,6% of GLA)
as opposed to regional
2 This marginal impact is obtained by applying mean values to
all model variables and then simulating a 10% increase in GLA, from
3 000 to 3 300 sq. ft. As a result, unit rent drops by 2,4%, from
$38,53 to $37,59. 3 In the current state of the data base, this
attribute is only available for super-regional centres.
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centres where jewellers are in excess (25,7% of stores and 3,0%
of GLA) translates into a
rent discount in the former case and in a location premium in
the latter case. On the other
hand, jewellery stores located in community centres typically
offer cheaper products than
those found in more glamorous, either regional4 or
super-regional ones that also benefit
from agglomeration economies; hence the positive sign of the
latter coefficients. The same
rationale, combining relative scarcity, prestige location and
agglomeration economies,
applies – with even greater relevance – to clothing and shoe
stores located in community
(negative sign) and regional (positive sign) shopping
centres.
Agglomeration economies are clearly at stake when it comes to
interpreting the marginal
contribution of restaurants to unit rents, with regional and
super-regional centres
commanding substantially higher rents than community ones. Rent
patterns differ though
with respect to personal services and specialty stores. In spite
of their relative abundance,
the negative coefficients assigned to the SERVICES*COM and
SPECIALTY*COM
estimates reflect the bargaining power of local retailers that
are at the very core of
community centres’ mission, namely proximity services. In
contrast to all other statistically
significant retail categories, specialty stores located in
super-regional centres
(SPECIALTY*SREG) also command lower unit rents: according to the
“combined” and
“compared” purchase concept, consumers are attracted by
high-tech specialty stores
(computer, audio and video stores) which, for that reason,
benefit from rent discounts;
hence the negative sign attached to the related coefficient.
Kiosks are found only in regional and super-regional shopping
centres. Since they occupy
the central alley of shopping malls where consumer traffic is
optimal, they generate very
high sales per square foot and, consequently, command the
highest unit rents of all retail
categories. Their contribution to the retail rent determination
process is among the
interesting findings of this study. While the KIOSK*REG
coefficient is only significant at
the 0,10 level, the parameter estimate of the KIOSK*SREG
descriptor (Beta coefficient of
0,286) undoubtedly suggests that kiosks located in
super-regional centres substantially
increase their overall profitability; actually, the unit rent
they pay represents a 144%
4 It is worth noting that among the two regional shopping
centres included in the study, one is located in a rather
low-class, popular environment while the other attracts
high-income, most sophisticated customers. Considering that bias,
the magnitude of the regional centres-related coefficients would
project a different picture if the “low-profile” centre were
distinguished from the “high-profile” one.
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9
premium over the base (intercept) rent. Finally, anchor tenants
do benefit, as expected,
from a major rent discount; while it amounts to roughly 24% on
average, it can be much
higher in some cases.
4.4 Interpreting the coefficients of structural and cyclical
descriptors
Central to this study, findings pertaining to agglomeration
economies in shopping centres
corroborate the literature on the subject. They clearly suggest
that while larger size
shopping centres involve greater competition among tenants,
store clustering also generate
substantially higher sales volumes, which translates into higher
rents. When applied to
regional shopping centres (AGGLOMINDX*REG), a 10% increase in
the mean
agglomeration index (0,14) results in a 1,3% rise in unit rent.
In contrast, and according to
theoretical expectations, concentration levels in retail
activity, as measured by the
Herfindahl Index, impact negatively on rents. In as much as
super-regional shopping
centres are concerned (CONCNINDX*SREG), a 10% rise in the
average concentration
index (0,11) translates into a 1,5% drop in rent. Tenants who
negotiate their lease while a
large number of retailers do the same do experience, as
expected, a strengthening of their
bargaining power. In regional shopping centres, every 10%
increase in the proportion of
open leases (OPENLEASE*REG) results in a 0,8% reduction in unit
rent. This raises the
importance of lease renewal strategy for shopping centre owners.
Finally, a recessionary
economic cycle affects retail rents negatively. Over the 1991-92
period, overall shopping
centre rents in the Quebec metropolitan region dropped by
12%.
5. SUMMARY OF FINDINGS AND SUGGESTIONS FOR FURTHER RESEARCH
5.1 Summary of findings
This study is an attempt to model shopping centre rents and to
investigate whether
agglomeration economies are a driving force in the rent
determination process. It is based
on physical and financial information obtained for seven
super-regional, regional and
community shopping centres in Quebec City. While financial data
apply to the 1998-2000
period, leases were negotiated over a longer period that extends
from 1976 to 2000. At this
point, 784 stores are accounted for in the study. In addition to
basic financial and physical
characteristics, a series of five market structure and cyclical
attributes are developed in
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10
order to capture the effect on rent of both commercial dynamics
within shopping centres
and overall economic situation. Main findings can be summarized
as follows:
As expected, the prominent role of gross leasable area in the
retail rent determination process clearly emerges from the model,
the negative sign of the Ln_GLA coefficient indicating that unit
rent decreases with store size;
Frontage is also a major determinant and exerts a positive
influence on rent due to the enhanced visibility and attractiveness
it confers to a store;
Quite interestingly, the presence of a jewellery impacts
differently on rents depending on the size category of the shopping
centre it belongs to. In community shopping centres, the effect is
negative whereas it is positive in both regional and super-regional
structures. A combination of relative scarcity, prestige location
and agglomeration economies provides a sensible explanation to such
findings;
A similar rationale applies – with even greater relevance – to
clothing and shoe stores located in community and regional shopping
centres;
Agglomeration economies are clearly at stake when it comes to
interpreting the marginal contribution of restaurants to unit
rents, with regional and super-regional centres commanding
substantially higher rents than community ones;
Rent patterns differ though with respect to personal services
and specialty stores. In spite of their relative abundance, the
negative contribution of this retail category in community shopping
centres reflects the bargaining power of local retailers that are
at the very core of community centres’ mission, namely proximity
services;
Specialty stores located in super-regional centres also command
lower unit rents due to the attraction they exert on high-tech
goods consumers;
Since they occupy the central alley of shopping malls where
consumer traffic is optimal, kiosks command the highest unit rents
of all retail categories;
Anchor tenants do benefit, as expected, from a major rent
discount which amounts, on average, to 24% of base rent;
Findings clearly suggest that while larger size shopping centres
involve greater competition among tenants, store clustering
generates substantial agglomeration economies that translate into
higher rents;
Concentration levels in retail activity, as measured by the
Herfindahl Index, impact negatively on rents;
The proportion of leasable space under negotiation in a given
year strengthens the bargaining power of tenants, who consequently
benefit from a rent discount;
Finally, a recessionary economic cycle affects retail rents
negatively.
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11
In short, this study corroborates previous research findings
about the critical role played by
anchor tenants in the generation of agglomeration economies as
well as the importance of
tenant mix, product diversity, quality of goods and image in the
retail rent structuring
process. While increased attractiveness has an overall positive
effect on both sales and unit
rents for most stores - namely in the case of kiosks - , they
allow major tenants and other
specialty goods retailers to negotiate lower rents with shopping
centres’ owners. Such
factors however impact differently on rents depending on
shopping centre size and profile.
Finally, retail structure within shopping centres and lease
renewal strategies by owners will
affect tenants’ bargaining power and, therefore, rent
levels.
5.2 Suggestion for further research
While this study is but a preliminary investigation into the
retail rent dynamics, it raises
several interesting issues namely with respect to the
measurement of externalities and
agglomeration, as opposed to dispersion, effects. While shopping
centre configuration and
design deserve further attention, resorting to concepts and
analytical tools found in
geography and spatial economics to measure such phenomena may
lead to major
developments in the field.
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12
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________________________________________________
-
15
Exhi
bit 1
: Str
uctu
ring
Dat
a B
ase
by R
etai
l Cat
egor
y an
d Ty
pe o
f Sho
ppin
g C
entr
e
Nb.
%G
LA (s
f)%
Nb.
%G
LA (s
f)%
JEW
ELR
Y5
6,8%
4 63
71,
6%19
8,6%
18 4
573,
0%C
LOTH
ING
1317
,6%
23 7
148,
4%82
37,1
%21
8 12
835
,6%
SHO
ES2
2,7%
4 99
51,
8%22
10,0
%39
796
6,5%
RES
TAU
RAN
T12
16,2
%12
983
4,6%
3415
,4%
33 6
985,
5%SE
RVI
CES
2027
,0%
41 8
2614
,8%
2210
,0%
34 6
205,
7%SP
ECIA
LTY
1925
,7%
120
397
42,6
%36
16,3
%69
749
11,4
%AN
CH
OR
34,
1%74
148
26,2
%5
2,3%
197
699
32,3
%K
IOSK
00,
0%0
0,0%
10,
5%23
80,
0%To
tal
7410
0,0%
282
700
100,
0%22
110
0,0%
612
385
100,
0%
Nb.
%G
LA (s
f)%
Nb.
%G
LA (s
f)%
JEW
ELR
Y34
7,0%
25 4
221,
7%58
7,4%
48 5
162,
0%C
LOTH
ING
169
34,6
%39
7 03
726
,7%
264
33,7
%63
8 87
926
,8%
SHO
ES33
6,7%
58 3
423,
9%57
7,3%
103
133
4,3%
RES
TAU
RAN
T82
16,8
%56
923
3,8%
128
16,3
%10
3 60
44,
3%SE
RVI
CES
326,
5%38
048
2,6%
749,
4%11
4 49
44,
8%SP
ECIA
LTY
9619
,6%
307
394
20,6
%15
119
,3%
497
540
20,9
%AN
CH
OR
81,
6%60
0 53
940
,3%
162,
0%87
2 38
636
,6%
KIO
SK35
7,2%
5 61
00,
4%36
4,6%
5 84
80,
2%To
tal
489
100,
0%1
489
315
100,
0%78
410
0,0%
2 38
4 40
010
0,0%
SU
PER
_REG
ION
AL (2
)
A
ll C
ateg
orie
s (7
)St
ore
Cat
egor
y
Stor
e C
ateg
ory
CO
MM
UN
ITY
(3)
R
EGIO
NAL
(2)
-
16
Exhibit 2: Operational Definition of Variables
N.B.: M = Metric variable; D = Dummy variable
V A R IA B L E
O PE R A T IO N A L D E FIN IT IO N T Y PE
N E T R E N T /SQ F T N et rent / sq . foot (D ependent
variable); expressed as total gross rent (base + overage) m inus
transferable operating expenses.
M
B asic A ttributes G L A G ross Leasable A rea (sq. feet). M
SA L E S/SQ F T Store yearly realized sales / G LA . M
F R O N TA G E Store frontage (feet) M
JEW E L R Y Jew elry retailer. D
C L O T H IN G C lothing retailer; includes w om en 's, m en 's
and children 's read-to -w ear clothes and accessories.
D
SH O ESTO R E Shoe retailer; includes fam illy shoes, w om en
's, m en 's & boy's shoes, athletic footw ear, unisex / jean
store.
D
R E ST A U R A N T Food services; includes restaurant & fast
food (w ith/out liquor), sandw ich & pizzas stores, candy &
nuts stores.
D
SE R V IC E S Personal and financial services; includes banks
& insurance, m edical & dental offices, beauty salons,
cleaners & dryers, unisex hair, barbershops, travel agents.
D
SP E C IA L TY Specialty and gifts retailer; includes radio,
video & m usic centres, cards & gifts, books, decorative
accessories, eyeglasses-optician stores.
D
A N C H O R A nchor tenant; large, chain stores having betw een
20 000 et 200 000 sq. feet of G LA - U sually seen as providing
both stability and custom er draw ing pow er to the shopping centre
(W al-M art, S im ons, T oys' R ' U S, The Bay, Sears, etc.).
D
K IO SK K iosk store; sm all, light structure w ith open sides,
usually located at a central position of the m all, close to high
pedestrian traffic and having a G LA of betw een 70 and 300 sq.
feet (cellular phone, candy and lottery stores).
D
Structural and cyclical attributes LE A SED U R N Lease duration
(years) M
A G G L O M IN D X A gglom eration index, expressed as the ratio
of G LA for a given retail category w ithin a shopping center to
total G LA in that center.
M
C O N C N IN D X H erfindahl Index, used as a m easure of the
concentration of a retail category w ithin a given shopping center;
it is expressed as the sum of the squared proportion of each
store’s G LA in that category.
M
O P EN L EA SE Leasable area open to negotiation during a given
year, in a given shopping center, as a proportion of total G LA in
that shopping centre; it is used as a m easure of the bargaining
pow er of tenants.
M
C Y C L E91_92 Lease w as negotiated over the 1991-1992
recessionary period. D
-
17
Exhi
bit 3
: Des
crip
tive
Stat
istic
s
VARI
ABLE
SNb
. Com
pute
dTy
peM
ean
Med
ian
Mod
eSt
d. D
ev.
Min
.M
ax.
NETR
ENT/
SQFT
($)
784
M64
5430
460,
430
8G
LA (S
q.ft)
784
M3
041
1 23
32
000
10 8
2922
163
034
SALE
S/SQ
FT1 (
$)52
6M
431
339
214
335
42
627
FRO
NTAG
E*SR
EG2 (F
t.)23
1M
3428
3320
913
6JE
WEL
RY58
D0,
070
1CL
OTH
ING
264
D0,
340
1SH
OES
TORE
57D
0,07
01
REST
AURA
NT12
8D
0,16
01
SERV
ICES
74D
0,09
01
SPEC
IALT
Y15
1D
0,19
01
ANCH
OR
16D
0,02
01
KIO
SK36
D0,
050
1LE
ASED
URN3
(Yea
rs)
621
M10
1010
51
47AG
GLO
MIN
DX78
4M
0,14
0,08
0,22
0,13
0,00
0,86
CONC
NIND
X78
4M
0,11
0,08
0,02
0,13
0,02
1O
PENL
EASE
362
1M
0,08
0,08
0,00
0,06
0,00
0,50
CYCL
E91_
9237
D0,
050
1
1. S
ales
wer
e no
t ava
ilabl
e fo
r one
sup
er-re
gion
al s
hopp
ing
cent
re (2
58 c
ases
mis
sing
).2.
Fro
ntag
e w
as a
vaila
ble
for o
nly
one
supe
r-reg
iona
l sho
ppin
g ce
ntre
(FR
ON
TAG
E*SR
EG, 2
31 c
ases
).3.
Sin
ce y
ear o
f lea
se n
egoc
iatio
n w
as a
vaila
ble
for 6
21 s
tore
s on
ly (1
63 c
ases
mis
sing
), bo
th le
ase
dura
tion
(LEA
SED
UR
N)
an d
pro
porti
on o
f ope
n le
ases
(OPE
NLE
ASE)
wer
e co
mpu
ted
on th
is g
roun
d.
-
18
Fig
ure
1: N
et U
nit R
ent a
nd G
LA D
istr
ibut
ions
NET
REN
T/SQ
FT
300,0
280,0
260,0
240,0
220,0
200,0
180,0
160,0
140,0
120,0
100,0
80,0
60,0
40,0
20,0
0,0NET
REN
T/SQ
FT
Frequency
160
140
120
100 80 60 40 20 0
Std.
Dev
= 4
5,92
M
ean
= 63
,7
N =
784
,00
GLA
1600
00,0
1500
00,0
1400
00,0
1300
00,0
1200
00,0
1100
00,0
1000
00,0
9000
0,0
8000
0,0
7000
0,0
6000
0,0
5000
0,0
4000
0,0
3000
0,0
2000
0,0
1000
0,0
0,0GLA
Frequency
700
600
500
400
300
200
100 0
Std.
Dev
= 1
0829
,43
M
ean
= 30
41,3
N =
784
,00
-
19
Exhibit 4: Model A - Linear Form / N=769 / K=23
Model Summaryb
,850 ,722 ,714 24,60Model1
R R SquareAdjusted R
SquareStd. Error of the
Estimate
Dependent Variable: NETRENT/SQFTb.
ANOVAb
1171410 23 50931 84,162 ,000450838 745 605
1622248 768
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Dependent Variable: NETRENT/SQFTb.
Coefficientsa
157,98 8,44 18,71 ,000-14,88 1,12 -,371 -13,25 ,000 2,10
,32 ,05 ,131 6,03 ,000 1,25-29,40 11,21 -,051 -2,62 ,009
1,0325,64 6,20 ,087 4,13 ,000 1,1826,48 4,70 ,119 5,63 ,000
1,19
-30,21 7,37 -,082 -4,10 ,000 1,0615,34 4,89 ,103 3,14 ,002
2,89
-29,64 17,51 -,033 -1,69 ,091 1,0116,67 5,85 ,061 2,85 ,005
1,21
-22,04 7,72 -,057 -2,85 ,004 1,0735,51 5,11 ,159 6,94 ,000
1,4029,69 3,52 ,195 8,42 ,000 1,44
-25,82 5,98 -,087 -4,32 ,000 1,10-25,64 6,29 -,082 -4,08 ,000
1,0915,07 5,54 ,068 2,72 ,007 1,69-4,65 3,16 -,033 -1,47 ,141
1,3657,34 24,77 ,045 2,32 ,021 1,01
122,24 5,14 ,555 23,76 ,000 1,4622,15 8,67 ,067 2,55 ,011
1,8330,62 16,65 ,058 1,84 ,066 2,63
-60,07 19,51 -,081 -3,08 ,002 1,87
-78,57 25,94 -,080 -3,03 ,003 1,86
-4,48 4,30 -,021 -1,04 ,298 1,05
(Constant)
Ln_GLAFRONTAGE*SREGJEWELRY*COMJEWELRY*REGJEWELRY*SREGCLOTHING*COMCLOTHING*REGSHOESTORE*COMSHOESTORE*REGRESTAURANT*COMRESTAURANT*REGRESTAURANT*SREGSERVICES*COMSPECIALTY*COMSPECIALTY*REGSPECIALTY*SREGKIOSK*REGKIOSK*SREGANCHORAGGLOMINDX*REGCONCNINDX*SREGOPENLEASE*REGCYCLE91_92
B Std. Error
Unstand'zd Coeff.
Beta
Stand'zdCoeff.
t Sig. VIF
CollinearityStatistics
Dependent Variable: NETRENT/SQFTa.
-
20
Exhibit 5: Semi-Log Model / N=769 / K=23
Model Summaryb
,843 ,711 ,702 ,3543Model1
R R SquareAdjusted R
SquareStd. Error of the
Estimate
Dependent Variable: Ln_NETRENT/SQFTb.
ANOVAb
231 23 10,024 79,845 ,00094 745 ,126
324 768
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Dependent Variable: Ln_NETRENT/SQFTb.
Coefficientsa
5,725 ,122 47,07 ,000-,259 ,016 -,457 -16,04 ,000 2,10,006 ,001
,177 8,02 ,000 1,25
-,780 ,161 -,097 -4,83 ,000 1,03,345 ,089 ,083 3,86 ,000
1,18,291 ,068 ,092 4,29 ,000 1,19
-,928 ,106 -,177 -8,74 ,000 1,06,206 ,070 ,098 2,92 ,004
2,89
-1,189 ,252 -,093 -4,71 ,000 1,01,295 ,084 ,076 3,50 ,000
1,21
-,518 ,111 -,095 -4,66 ,000 1,07,429 ,074 ,136 5,82 ,000
1,40,361 ,051 ,168 7,12 ,000 1,44
-,800 ,086 -,191 -9,28 ,000 1,10-,819 ,091 -,186 -9,05 ,000
1,09,191 ,080 ,061 2,40 ,017 1,69
-,114 ,045 -,057 -2,50 ,013 1,36,591 ,357 ,033 1,66 ,098
1,01,891 ,074 ,286 12,02 ,000 1,46
-,269 ,125 -,057 -2,15 ,032 1,83,940 ,240 ,125 3,92 ,000
2,63
-1,350 ,281 -,129 -4,80 ,000 1,87
-1,028 ,374 -,074 -2,75 ,006 1,86
-,129 ,062 -,042 -2,08 ,038 1,05
(Constant)
Ln_GLAFRONTAGE*SREGJEWELRY*COMJEWELRY*REGJEWELRY*SREGCLOTHING*COMCLOTHING*REGSHOESTORE*COMSHOESTORE*REGRESTAURANT*COMRESTAURANT*REGRESTAURANT*SREGSERVICES*COMSPECIALTY*COMSPECIALTY*REGSPECIALTY*SREGKIOSK*REGKIOSK*SREGANCHORAGGLOMINDX*REGCONCNINDX*SREGOPENLEASE*REGCYCLE91_92
BStd.Error
Unstand'zd Coeff.
Beta
Stand'zdCoeff.
t Sig. VIF
CollinearityStatistics
Dependent Variable: Ln_NETRENT/SQFTa.