Store Location in Shopping Centers:
15 Years After
Dr. Charles C. Carter and
Dimitri M. Teddone, MAI
Frank Lloyd Wright: The Elevator & the
Automobile at the Turn of the Century
 With the advent of the elevator, downtown shopping districts
with its department stores dominated the retail shopping
experience near the end of the nineteenth century.
 Electric streetcar lines then connected the downtown retail
district with residential areas like the spokes of a wheel.
 Chicago: Marshall Field’s; Detroit: Hudson’s; New York:
Macy’s; Minneapolis: Dayton’s; Portland: Meier & Frank
 Frank Lloyd Wright (1867-1959) knew what to expect
when he said:
“The city of the future will depend on the race between the car
and the elevator, and anyone who bets of the elevator is crazy.”
Rise of the Shopping Center
 Automobile use expanded during the 1920’s and 1930’s &
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residential uses expanded rapidly into the suburbs.
Levitt-towns first started on Long Island, NY after World
War II & these became the model of residential uses.
Suburban subdivisions consisting of single-story, detached
houses on single lots became the norm spreading out from
metropolitan areas throughout the U.S.
Two- or three-story row houses along boulevards near the
downtown were no longer the custom.
Shopping centers came to revolutionize retail shopping
during the latter half of the 20th century.
Fall of the Downtown Retail District
 Downtown retail districts of the 1950’s were eventually
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drained of retail uses as suburban shopping centers took over
the retail trade.
Early suburban centers were built by department stores, i.e.,
Hudson Co. built Northland Center in Detroit (1954) &
Dayton Co. built Southdale Center in Minneapolis (1956).
Office uses came to dominate the downtown after the 1960’s
Suburban residences met the demand for more living space
after the time to cover distances (using cars) shortened.
ULI’s Shopping Center Development Handbook, 2nd ed. graphs the
increase of US shopping centers from 1950 to 1985.
Growth of Shopping Centers
 From 1950 to 1955 the graph shows a slow increase in the
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number of centers; from 1955 to 1980 the rate grows at 6
times as much.
By 1985 about 50% of all retail sales were made at shopping
centers, not including automobile sales & gasoline.
The rate of retail sales in shopping centers since 1985 has
declined: 1974: 25%, 1985: 50%, 1996: 52%
In 1998 87% of all US shopping centers were less than
200,000 SF. GLA (smaller than regional or super-regional
centers).
In the late 1970’s & early 1980’s regional shopping returned
Shopping Centers Slow
 to the downtown, e.g., Faneuil Hall, Boston, Harborplace,
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Baltimore, & South Street Seaport, New York
By the1990’s shopping center growth had slowed
considerably, evidence that the market had become saturated
Big box or “category killer” retailers such as Lowe’s, Home
Depot, Costco, Best Buy, & K-Mart became popular during
the 1980’s; online retailing has expanded since about 2000
By 2005 construction of new, enclosed shopping centers had
just about stopped.
Today shopping centers are very much with us, and, unlike
the downtown retail district, are not likely to disappear soon.
What Did Shopping Centers have that
the Central Business District Didn’t?
 Studies of microeconomics of shopping centers started in
earnest about 1989 and continues today.
 Research eventually showed that the structure of rent, space,
leases, and store location under a unified plan & management
results in positive demand externalities that a CBD
cannot reproduce.
 What are positive consumer demand externalities? They get
customers to spend their money by providing a place &
circumstances where consumers want to shop.
 Economists call this “internalizing” positive externalities; the
ULI calls this process synergistic & we can leave it at that.
Original Data: 1992-93 Shopping
Centers, 8 Regional & Super-Regional
 Original data was gathered from 9 regional (300K-900K
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GLA) and super-regional (500K-1.5M GLA) shopping
centers scattered throughout the U.S. (avg. 828.6K GLA)
6 were single-level, 2 double-level, & 1 three-level (3-level
mall not used)
anchor stores ranged from 2 to 6 per center (avg. 3.56)
Parking ratios ranged from 5.35 to 5.62 stalls per 1,000 sq ft
of shopping area (standard = 5.5 stalls per 1,000 sq ft)
Years completed ranged from 1952 to 1990, the center built
in 1952 had been completely renovated in 1987 (avg. 16 yrs.)
Vacancies ranged from 4.4% to 0.4%, avg. 3.1%
Original Data: 1992-93 Shopping
Centers, 8 Regional & Super-Regional
 Numbers of non-anchors (including vacancies) ranged from
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117 to 158 (avg. 129)
Of the 1,010 leases 689 were deemed usable (69%)
Leases were unusable:
1) lease either starts or ends in the year sales were reported
(insufficient info. on renewed/rolled over tenants)
2) some tenants did not report sales (i.e., service tenants
w/out overage rent)
3) cinemas & out-pad tenants (the former often operate on a
different schedule & the latter are outside the mall)
Findings in First Study
 1) Highest customer traffic takes place at the mall’s center
(usually at or near the food court), & customer traffic tapers
off with distance from the center.
 2) Store size increases, & rent per SF decreases, w/distance
from the mall’s center (rents per SF ↑ @ +4.0% /100 ft)
 3) Stores of the same type that promote comparison
shopping (e.g., women’s apparel & men’s apparel) will
generally be dispersed as opposed to clustered
 4) Rents decline at different rates with distance from the mall
center, smaller store types paying higher rents/SF at the
center whose rents decline faster than larger store types.
Conclusion: There is an Optimal Tenant
Mix & Optimal Tenant Locations
 Stores locate according to what is termed a bid-rent process
in microeconomics , so that store types position themselves
to receive maximum sales (and pay maximum rents)
 Store location & size are two of the mall characteristics that,
together, go to maximize rents (lease structure does too)
 Assuming leases are properly structured, the retail market is
just-saturated, and zero vacancies, store sizes and locations
within the mall are the remaining variables to obtain optimal
tenant mix.
 These variables are best understood as the result of trial &
error, Adam Smith’s “invisible hand.”
Follow-Up Data: 2006 Shopping
Centers, 8 Regional & Super-Regional
 Follow-up data was gathered from 8 regional (300K-900K GLA)
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& super-regional (500K-1.5M GLA) shopping centers located
along the U.S. Atlantic Coast (avg. 481.5K GLA)
6 were single level & 2 were double level centers
anchor stores ranged from 2 to 4 per center (avg. 3.25)
Years completed ranged from 1961 to 1992, with 2 centers
completely renovated (ones built in 1961 & 1985 in 1993 & 1995,
respectively) (avg. 25.6 yrs.)
Leases were unusable:
1) 3 shopping centers had too many vacancies (46%, 30%, &
20%) for results to be creditable (previous research shows this)
Follow-up Data: 2006 Shopping
Centers, 8 Regional & Super-Regional
 2) For 3 centers site plans were not readily available (from which
to measure distances from stores)
 For the remaining 2, vacancies were 8.6% and 10%, parking
ratios were 5.5 & 6 spaces per 1000 GLA, & the number of usable
non-anchor stores were 50 (w/33K GLA vacant) and 41 (w/47K
GLA vacant)
 Of the 91 leases 54 were deemed usable (59%)
 Comparisons: 481.5K avg. GLA (2006) vs. 828.6K avg. GLA
(1992); 3.25 anchors avg. (2006) vs. 3.56 anchors avg. (1992);
25.6 avg. age (2006) vs. 16 avg. age (1992); 17.2% avg. vacancies
(2006) vs. 3.1% avg. vacancies (1992); number of usable leases 54
(2006) vs. 689 (1992)
How to Get More Observations:
Bootstrapping
 A bootstrapping technique was used to provide more
observations (lease data to use in statistical analysis).
 By this nonparametric method samples from the observations
at hand are made as if these samples were from a population.
 In bootstrapping, sampling with replacement takes place
using the data on hand (the 54 store leases) to create a larger
number of observations from which to run statistical analysis.
 From this method 1,200 non-anchor observations were
produced, i.e., 60 random samples of 20 leases from the 54
store leases = 1,200 lease observations
Store Characteristics for 2006 Follow-Up Data
Variables
Mean
Std. Dev.
Min.
Max.
Square Feet (SF)
3084
2761
590
15,709
Rent per SF
($/SF)
$43.64/SF
$24.92/SF
$4.09/SF
$118.30/SF
Feet from Mall
Center
267.17
130.78
30
540
Variable
Store Type
Observatio
n
Frequency
Mean
Std. Dev.
Min.
Max.
SF
Women’s
Apparel
11.6%
5,215
1,665.82
3,015
7,773
SF
Jewelry
7.5%
1464.5
205.40
1,205
1,673
SF
Food Court
13.3%
784
254.64
590
1,226
TRNT /SF
Women’s
Apparel
$18.17
$13.91
$8.44
41.36
TRNT / SF
Jewelry
$57.29
$17.13
$40.46
$76.13
TRNT / SF
Food Court
$82.07
$21.55
$60.10
$118.30
Findings in Follow-Up Study
 1) Store size increases, & rent per SF decreases, w/distance
from the mall’s center (rents @ +5.3% /100 ft, square
footage +8.26% / 100 ft)
 2) Generally, store types still line up with distance from the
mall’s center (higher rents & smaller stores w/distance) the
same as they did in the earlier study.
 3) Store types have changed significantly from 1992 to 2006,
e.g., fewer men’s shoe stores & more electronics stores,
which makes testing for location effects of comparison store
shopping difficult.
Comparisons:1992 & 2006 Data
Mean
1992 SF
Std. Dev.
Difference
2,417.06 ft
1992 SF Jewelry 1,239.57 ft
679.90 ft
1992 SF Fast Fd
874.69 ft
859.13 ft
1992 SF Wom A
3,855.74 ft
2,305.82 ft
3,084 ft
2,761 ft
+ 27%
2006 SF Jewelry 1,464.5 ft
205.64 ft
+ 18%
2006 SF Fast Fd
784 ft
254.64 ft
- 10%
2006 SF Wom A
5,215 ft
1,665.8 ft
+ 35%
2006 SF
1992 $Sales
$363.72/SF
1992 $Jewelry
$656.37/SF
$367.37/SF
1992 $Fast Fd
$503.18/SF
$268.42/SF
1992 $Wom Ap
$252.01/SF
$130.19/SF
2006 $Sales
w/Inflation
Comparisons: 1992 & 2006 Data
Mean
Std Dev
Difference %
Inflation %
1992 $Rent
$37.75/SF
$21.52/SF
1992 $Jewelry
$63.84/SF
1992 $Fast Fd
$59.05/SF
1992 $Wom Ap
$30.00/SF
2006 $Rent
$43.64/SF
$24.92/SF
+ 15.6%
+43.69% 2.5
2006 $Jewelry
$57.29/SF
$17.13/SF
- 10%
+43.69%
2006 $Fast Fd
$82.07/SF
$21.55/SF
+ 39%
+ 43.69%
2006 $Wom Ap
$18.16/SF
$13.91/SF
- 39%
+43.69%
2006 $Jewelry
2006 $Fast Fd
2006 $Wom Ap
1992 Ft Cent 295.2 ft
1992 Jewelry
281.6 ft
517.8 ft
485.4 ft
Comparisons: 1992 & 2006 Data
1992 Fast Fd ft
Mean
Std Dev
70.5 ft
115.4 ft
1992 Wom Ap ft 312.7 ft
2006 Ft Cent 267.16 ft
2006 Jewelry ft
2006 Fast Fd ft
2006 Wom Ap ft
Difference %
519.8 ft
130.78
- 9%
Observations on Store Types,
1992 vs 2006 Data
1992 Tenants
% of total
2006 Tenants
% of total
Fast Food
11%
Fast Food
12%
Jewelry
6.2%
Jewelry
6.9%
Specialty Food
6.3%
Specialty Food
6.9%
Men’s Apparel
6.3%
Women’s App
18.5%
Women’s App
14%
Family Apparel
7%
Men & Wom Ap
8.6%
Women’s Shoes
5%
Men Wom Shoes 8.6%
Men’s Shoes
6.7%
Home Furn
6.2%
Home Furnish
3.4%
Cards & Gifts
6.5%
Cards & Gifts
5.2%
Leisure & Enter
22%
Leisure & Ent
8.6%
Books
2%
Services
14%
by number of
stores not GLA
Did Rent per Square Feet Keep Up
with Inflation?
 Definitely not, but it would be premature to say exactly why
and how shopping center rents are falling historically.
 The only obvious conclusion is that regional & superregional shopping centers are not doing as well as they had
been to internalize positive demand externalities
compared with other retailing .
 Still I don’t think any other form of retailing surpasses the
shopping center experience (if only we could impose sales
taxes on online shopping).
 Retailing in urban areas is best done using regional & superregional models such as Harborplace, Baltimore.
Future Research
 Similar research should be done using recent data from like
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regional & super-regional shopping centers.
The best follow-up data would be for those 8 centers used in
the 1st study.
The 1992 data was from the best shopping centers then
owned by a state pension fund.
Choice of malls in 1992 by regional managers was meant to
impress the pension fund managers.
The follow-up data in 2006 was most probably from a crosssection of centers that the owners or lenders thought might
be having financial difficulties.
My Address & Phone Number
 Dr. Charles C. Carter
 9034 S.W. 7th Street
 Boca Raton, FL 33433
 954-708-3654
 cccarter2010@yahoo.com