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a portrait of retail market demand:
Greater Paseo Trade Area Study
summer 2012 | urban planning + policy
abstract
This paper develops a method for projecting retail demand using demographic
characteristics from the Decennial Census, Consumer Expenditure Survey Tables,
County Business Patterns, Zip Code Business Patterns and Illinois Department of
Revenue sales tax data. In contrast to the data commercially available from vendors like Claritas and ESRI, the Greater Paseo Trade Area Study also disaggregated
retail demand across the major races and Hispanic origins that compose the
trade area geography. This method reveals commonalities between segments,
and points the way toward businesses that could thrive in spaces of universally
unmet need. It is roughly divided in half between explaining and contextualizing
the results of the study and developing a detailed methodology.
Elizabeth Scot t
o f f e r s t h i s p r o j e c t s u b m i t t e d i n pa r t i a l f u l f i l m e n t
o f t h e r e q u i r e m e n t s f o r t h e d e g r ee o f
Master
of
Urban Planning
and
P o l i c y ( MUPP )
specializing in economic de velopm ent
w i t h a ss i s ta n c e f r o m a d v i s o r
Summer, 2012
Janet Smith
table of contents
briefing on greater paseo trade area study results
......... 1
introduction:
the purpose of the study
methodology:
how to understand gap and surplus estimates
......... 2
intelligence on segments . . . . . . . . . 4
demand estimates . . . . . . . . . 9
gap and surplus calculations . . . . . . . . . 11
focus on restaurants . . . . . . . . . 13
focus on footwear . . . . . . . . . 14
focus on fees and admissions . . . . . . . . .15
conclusion . . . . . . . . . 15
full methodology
inroduction: overview of the methodology . . . . . . . . . 17
geography: the puerto rican influence area / great paseo trade area . . . . . . . . . 18
demand estimates: determining and tabulating sectors inside the trade area . . . . . . . . . 22
annual sales estimates by retail type: overcoming government silos . . . . . . . . . 27
actual sales estimates: applying state-level sales estimates to local establishment . . . . . . . . . 32
gap estimates: bringing it all together . . . . . . . . . 34
conclusion:
final thoughts
+ caveats . . . . . . . . . 35
appendix a . . . . . . . . . 36
appendix b . . . . . . . . . 37
appendix c . . . . . . . . . 39
introduction: the purpose of the study
The Greater Paseo Trade Area Study was initiated to complement other research conducted by the author
regarding the status of the Puerto Rican community in the Chicago Metro Area. For this reason, the geography of
interest is 2010 Census tracts on the Northwest side of the City of Chicago that are composed of greater than 10%
Puerto Ricans. These neighborhoods, once solidly majority Puerto Rican, have been transforming since the peak of
the Puerto Rican population in the 1980s. Today, Puerto Ricans comprise 11% - 38% of the population in this geography, referred to as the Puerto Rican Influence Area (or PRIA)1. Acknowledging that addressing poverty involves
creating wealth, the Greater Paseo Trade Area Study attempts to show where there might be opportunities for entrepreneurs to open new businesses in the PRIA. Additionally, it disaggregates retail market demand estimates by race
and Hispanic Origin in a way that is not generally available from the commercial market data vendors such as Claritas
or ESRI.
There are reasons beyond wanting to look at retail demand across race and origin to eschew data from na-
tional clearing houses. Among these reasons, which include often-prohibitive cost for small organizations, there are
three structural problems with these data that prejudice them against inner city markets. First, companies like Nielsen
PRIZM (Claritas) and ESRI favor average income estimates over density of income estimates. The value judgment that
underlies this reporting decision elevates sprawled suburban locations over dense urban ones. Though suburban
households regularly have substantially higher average incomes than urban households, urban spaces often have
greater purchasing power per square mile, or income density2.
Second, commercial data vendors tend to base their products on infrequent public counts like the Decennial
Census and then manipulate data for the whole country in proprietary, obscure ways to “keep them up to date.” This
“30,000 feet” perspective makes it difficult to account for local factors or take advantage of local data that can often
enhance the appeal of urban markets. A perfect example of this tendency to ignore local information is commercial
firms’ use of crime indices, which “use a demographic-based model that estimates crime risk based on historic correlation between types of crimes and the demographics of people residing in the areas where crimes are committed3”,
rather than actual crime counts to report conclusions on neighborhood safety. These kinds of static assumptions
1
See complete methodology for a full treatment of the relevant geographies.
2
Weissbourd, Robert (1999) “The Market Potential of Inner-City Neighborhoods: Filling the Information Gap (Attracting Business Investment to Neighborhood Markets)” available online at http://www.brookings.edu/~/media/research/files/reports/1999/3/communitydevelopment%20weissbourd/weissbourd (accessed 7/12); Weissbourd demonstrates that Austin (a poor
neighborhood in Chicago) has higher income density than Kenilworth (an affluent Chicago suburb).
3
Pawasarat, J and Quinn, L (2001) “Exposing Urban Legends: The Real Purchasing Power of Central City Neighborhoods,”
The Brookings Institution Center on Urban and Metropolitan Policy, available online at http://www4.uwm.edu/eti/pdf/ExposingUrbanLegends.pdf (accessed 7/12)
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about neighborhood quality do not account for the complex, dynamic nature of cities and further render national
data vendors unreliable reporters of business opportunities in cities.
Finally, this tendency of national data vendors to gloss over differences and eschew local knowledge also manifests itself in the over-simplified, sometimes offensive language they use to describe many inner city market segments. For instance, the language in Claritas/Nielsen PRIZM’s “You Are Where You Live” free data product gives names
to the market segments in the PRIA like “Big City Blues.” Big City Blues,
with a population that’s more than 45 percent Latino, [this segment] has one of the highest concentrations of Hispanic-Americans in the nation. … Concentrated in a few major metros, these younger singles and single-parent families face enormous challenges: low incomes, uncertain jobs and modest educations4.
Topping off these otherwise overtly racial descriptions, households in Big City Blues typically read “Ser Padres” and
watch “El Gordo Y La Flaca.” On the other hand, in PRIA there are also “Young Digerati” communities, which are “affluent, highly educated, and ethnically mixed, are typically filled with trendy apartments and condos, fitness clubs and
clothing boutiques, casual restaurants and all types of bars—from juice to coffee to microbrews.” Young Digerati read
“the Economist” and watch “IFC5.”
Overtly racialized descriptions such as these are less than helpful in today’s increasingly integrated and diverse
city6. When now-President of the Congress for New Urbanism John Norquist was mayor of Milwaukee, he railed
against Claritas’ description of neighborhoods in inner-Milwaukee, saying, “We’re not asking them to guild the lily, but
they’re spraying DDT on the lily. It’s incredible7.”
In an effort to paint a new kind of picture of the consumer characteristics of the households in the PRIA, the
Greater Paseo Trade Study attempts to overcome these limited descriptions of urban demand in favor of more nuanced picture that can help start a conversation about the future of this unique and dynamic Chicago area.
brief description of the methodology:
how to understand gap and surplus estimates
In order to estimate retail demand in the Puerto Rican Influence Area by segments, public data from the US
4
Nielsen PRIZM (Claritas) (2012) “My Best Segments lookup,” online at http://www.claritas.com/MyBestSegments/ (accessed 7/12); see Appendix A for a description of market segments Claritas reports for the PRIA.
5
Ibid.
6
Frey, R. (2010) “Race and Ethnicity” in “State of Metropolitain America: On the Front Lines of Demographic Transformation,” available online at http://www.brookings.edu/about/programs/metro/stateofmetroamerica (accessed 7/12).
7
Borowski, G. and Gertzen, J. (2001) “Market Research Image of Milwaukee Called Racist” in the Milwaukee Journal Sentinal (6/14/01), retrievable through news.google.com, (accessed 7/12).
trade area study
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Bureau of Labor Statistics, US Census Bureau and Illinois Department of Revenue were downloaded and analyzed. The
analysis proceeded on the idea that disaggregating total retail demand estimates by race and Hispanic origin might
shed some light on areas of mutual unmet demand across the five segments that compose the total population:
Puerto Ricans, Non-Hispanic whites, African Americans, Mexicans and all others8.
These segments were examined across four indicators: housing tenure, age, household size and race / Hispanic origin. These characteristics were then used to estimate how much each segment spends per year in thirteen categories: grocery stores, restaurants, liquor stores, health and personal care, men’s clothes, women’s clothes, children
and family clothes, shoes, audio/visual equipment, recreation fees, pets and hobbies, sports, and books and magazines (section “Demand Estimates”, below). These demand estimates were then compared with estimated annual sales
of actual businesses inside the PRIA for each category.
Annual sales inside the PRIA were calculated by multiplying the number of businesses in each category—e.g.,
restaurants—by the average sales that type of business had in Illinois in 2011. Finally, the demand in dollars displayed
by each segment in the PRIA was compared to the sales the local businesses would have if they all made sales equal
to the Illinois average for their type of business. This process of estimation boils down to a simple formula:
Local Sales – Local Demand = either a negative number (a Gap) or a positive number (a Surplus)
A Gap suggests that there may be some leakage in the local economy. “Leakage” refers to instances where
consumers spend their money outside their home area, profiting business owners in other communities. People
choose to shop outside their communities for myriad, sometimes obscure reasons. There are, however, a few common reasons consumers tend to shop outside their home area, including:
-
There is no equivalent retail destination in the consumer’s home area.
-
There is an outside shop that is more convenient to the consumer’s commute.
-
Other shops have more prestige or brand presence.
-
The consumer’s home area has streetscaping or crime problems, making shopping unpleasant.
-
The consumer is seeking out a business with which they have ethnic, religious or cultural affinity.
8
“All others” were calculated by subtracting (Puerto Ricans + Non-Hispanic whites + African Americans + Mexicans) from
the total population. This segment includes multi-racial individuals as well as Asians, Native Americans and people of Hispanic
Origin other than Puerto Ricans and Mexicans. It was beyond the scope of the Study to treat each segment separately. For more
information, see the complete methodology section.
trade area study
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Whatever the reason, when the available funds of local area residents are spent outside that area—when they leak
out—and are not replaced by outside shoppers traveling to the district, the profitability of the local commercial
districts suffer. These districts, in turn, are less able to expand their businesses or to hire more employees. Additionally, new businesses may be dissuaded from locating in the community due to the perception of a weak market. This
scenario reinforces a cycle of disinvestment that can reverberate through all the dimensions of a community.
The bright side of a Sales Gap, on the other hand, is that it may also indicate an opening for this leaked capital
to be collected up by a new local business. Since so much of the success of businesses—particularly small retailers
and restaurateurs—is wrapped up in tapping the right market, the presence of a Sales Gap in the right neighborhood
at the right time can signal opportunity for entrepreneurs. When new businesses locate in a trade area and capture
otherwise-leaked sales, the positive spillover effects multiply through the neighborhood: employees in the commercial district patronize each other’s shops, local consumers have less reason to spend their money elsewhere, and there
is one more busy shop contributing to a lively commercial space.
In contrast to a Sales Gap, a Surplus suggests one of two things. First, a Surplus (where the sales of local busi-
nesses exceed the demand displayed by local residents) can indicate that local businesses are importing customers
from other areas. A large number of extra-local customers traveling to shop in a trade area often results from a cluster
of specialty businesses that creates a destination district. A destination district might be a cluster of ethnic restaurants
and shops—such as a Chinatown—or a strip of car dealerships, like the ones often seen on the major arterials of inner
ring suburbs.
Second, a Surplus can also indicate that a trade area is oversaturated with certain retail types. Oversaturation,
which suggests that customers are spending significantly more than their average demand in local establishments,
can signal that either a neighborhood is going through structural changes causing the retail profile to lag changes in
neighborhood composition, or that there is some error in the market demand model. Since market demand estimates
rely heavily on national spending patterns and state-wide average sales, the reality in a specific trade area can sometimes deviate strongly from average numbers. In the case of a calculated Surplus, a market demand study should lead
to more information-gathering to determine whether surplus figures are accurate on the ground. If the figures are
accurate, more demographic data on customers must be collected to reveal whether the Surplus results from destination districts or local oversaturation.
results from the greater paseo trade area study: intelligence on segments
One of the most helpful ancillary findings of the Greater Paseo Trade Area Study was more fully articulated
demographic profiles for the five segments that compose the total population in the PRIA. Since market demand is
trade area study
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projected through composition of demographic characteristics, it is helpful to briefly examine these characteristics
before touching on the demand estimates based on them.
Tenure of Households
in PRIA
Home-owners
with mortgage
Puerto Ricans
Renters
5,073
788
12,268
10,422
4,185
17,142
African Americans
1,942
272
6,760
Mexicans
9,834
1,090
15,402
all others
4,242
553
6,085
Non-Hispanic whites
Home-owner s
without mortgage
source: 2010 Census
PRIA: Tenure Proﬁles, 2010
Puerto Ricans
Non-Hispanic whites
African Americans
Mexicans
all others
-
5,000
10,000 15,000 20,000 25,000 30,000 35,000
Home Owner with a Mortgage
Home Owner Owning Free + Clear
Renter
There are 96,058 households in PRIA. Of these households, 33% own with a mortgage, 7% own free and clear,
and 60% rent. While Non-Hispanic whites are the largest segment when the main groups are disaggregated (33%);
when Puerto Ricans (19%) and Mexicans (27%) are combined into a Latino group, they comprise an even larger segment (46%). The failure of any one segment to reach a majority (greater than 50%) goes to the dynamic, multicultural
nature of the Trade Area.
Among the segments, Non-Hispanic white owner households are the largest segment (46%), with all others
(44%) and Mexicans (41%) following close behind. Few African American households are owned in PRIA (25%), along
with about a third of Puerto Rican households (32%). From a market demand perspective, these differences are important because, on average, rental households spend about a third less across all categories than owner households.
This spending disparity seems to go to differences in life stage and accumulated wealth. For instance, renters and
owners are at parity in annual spending on shoes, but renters spend 72% less on sporting goods, boats and camera
trade area study
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equipment9.
Under 25
years
Age of Households in PRIA
Puerto Ricans
25-34
years
35-44
years
45-54
years
55-64
years
65 years
and older
679
3,077
3,806
4,083
3,464
3,020
1,805
9,337
5,682
4,722
4,617
5,586
476
1,860
1,850
2,077
1,545
1,166
Mexicans
1,449
6,650
7,567
5,747
3,205
1,708
all others
523
2,736
2,481
2,198
1,598
1,344
Non-Hispanic whites
African Americans
source: 2010 Census
PRIA: Household Age Profiles, 2010
Puerto Ricans
Non‐Hispanic whites
African Americans
Mexicans
all others
‐
5,000 10,000 15,000 20,000 25,000 30,000 under 25 years
25‐34 years
35‐44 years
45‐54 years
55‐64 years
65 years and older
35,000 Householders under 35 make up the largest portion of households in the PRIA (30%). Householders between
35 and 44 make up the next highest share (22%), followed by the 45 to 54 bracket (20%). Of the under 35 group, the
supermajority (67%) are Non-Hispanic white (39%) or Mexican (28%). Among both Puerto Ricans and African Americans, the most prevalent age group is householders between 45 and 54 (23%, respectively). Among Mexicans, householders are most often between 35 and 44 (29%), compared with Non-Hispanic white and “other” householders, who
are most often between 25 and 34 (29% and 25%).
9
2010 Consumer Expenditure Survey; for more information, see the complete methodology.
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Average Annual Spending by Life
Stage
Young Adult
(under 35)
Middle Age
(35 to 64)
Senior
(65 and
over)
food at home
2,768
4,102
2,950
food away from home
2,315
2,825
1,608
alcohol
440
438
295
health + personal care
635
1,281
1,480
men's clothes
272
347
188
women's clothes
589
614
384
children's clothes
327
297
86
footwear
309
355
149
fees + admissions
348
725
384
av equipment + music
780
1,055
787
pets + toys + hobbies
356
719
513
other entertainment + sports
252
445
206
50
103
141
books + magazines
source: author’s calculation of Consumer Expenditure Survey Tables
In terms of market demand, the age of the householder is extremely significant. Spending patterns reflect life
stage differences, such as household size and spending on children or medical care. For instance, middle aged householders (35 to 64) spend more, on average, than young adults (under 35) and seniors (65 and over) in almost every
category. Exceptions include spending on alcohol (greater for young adults) and on health and personal care products (greater for seniors.)
Similarly, young adults typically spend almost the same amount of money annually on food to prepare at
home ($2,768) as they spend on eating out ($2,315). In contrast, middle aged people spend about 30% less on eating
out ($2,825) than they do going to the grocery store ($4,102). Seniors spend even less in restaurants ($1,608)—about
half as much as they spend on groceries ($2,950)10.
Since the PRIA has a large proportion of households headed by people less than 35, the market demand profile will skew slightly toward the lower end of spending, except in areas where young adults typically splurge.
10
For more information on 2010 Consumer Expenditure Survey Tables and how they are used in projecting market demand, see the complete methodology.
trade area study
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One
person
Puerto Ricans
Two
persons
Three
persons
Four
persons
Five or
more
persons
4,155
4,460
3,518
2,932
3,064
11,672
11,548
4,796
2,345
1,388
African Americans
2,332
2,206
1,603
1,243
1,590
Mexicans
2,378
3,723
4,208
5,239
10,778
all others
2,041
2,622
2,048
1,775
2,394
Non-Hispanic whites
Source: 2010 Census
PRIA: Households by Household Size
Puerto Ricans
Non‐Hispanic whites
African Americans
Mexicans
all others
‐
One person
5,000 Two persons
10,000 15,000 Three persons
20,000 25,000 Four persons
30,000 35,000 Five or more persons
Household size is another dimension in which the PRIA shows noteworthy variation. An overwhelming major-
ity (73%) of Non-Hispanic white households are composed of 1 or 2 people. About half of Puerto Rican (48%) and
African American (51%) households are also composed of 1 or 2 people. In contrast, few Mexicans households have
only 1 or 2 residents (23%); most have 5 or more (40%).
Consequently, about half of all households in the PRIA are composed of 1 or 2 people (49%). In fact, the large
numbers of Non-Hispanic white households composed of 1 or 2 people (23,220) make up about a quarter (24%) of all
households in the area. Household composition functions similarly to age group in terms of market demand. Households made up or 1 or 2 people spend less in almost every category than those made up of 4 or more people. As with
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young adults, people in 1 or 2 person households tend to spend more on alcohol (126% as much as households of 4 or
more), and more proportionally on eating out. They also spend more on books and magazines than any other group.
In contrast, households of 4 or more spend 859% more annually on children’s clothes than households of 1 or 211.
For these reasons, the household composition of the PRIA—heavily skewed toward small or large house-
holds—is a major factor in both intra- and inter-segment demand.
results from the greater paseo trade area study: demand estimates
All the preceding characteristics, when used to project demand for retail goods and services, ultimately paint
a fairly similar picture of demands across all segments. As might be expected due to their large share of sub-35 and 1
to 2 person households, Non-Hispanic whites spend the least in every category but alcohol, books and magazines and
audio/visual equipment. In contrast, due to their large household size, Mexicans are projected to spend the most in
key categories for families: food from the grocery store, clothing and shoes. Based on this model, African Americans in
the PRIA spend slightly more than Non-Hispanic whites in every category except alcohol and books and magazines.
ANNUAL DEMAND PER HOUSEHOLD ESTIMATES BASED ON ASSORTED CHARACTERISTICS
Number of Households
food at home
food away from home
alcohol
men's clothes
women's clothes
Average Puerto Rican Demand
18,129
$ 4,312
$ 2,495
$ 366
$ 1,064
$ 304
$ 555
Average White Demand
31,749
$ 3,565
$ 2,422
$ 374
$ 1,057
$ 296
$ 537
8,974
$ 3,702
$ 2,470
$ 363
$ 1,035
$ 302
$ 548
Average Mexican Demand
Average African American Demand
26,326
$ 3,993
$ 2,655
$ 366
$ 1,064
$ 317
$ 581
Average Additional Demand 10,880
$ 3,735
$ 2,606
$ 425
$ 1,163
$ 316
$ 574
ANNUAL DEMAND PER HOUSEHOLD ESTIMATES BASED ON ASSORTED CHARACTERISTICS, continued
children + family clothes
footwear
fees + admissions
a/v equipment + music
books + magazines
$ 327
$ 358
$ 504
$ 903
$ 514
$ 301
$ 77
Average White Demand
$ 294
$ 337
$ 477
$ 892
$ 504
$ 287
$ 79
$ 326
$ 357
$ 488
$ 891
$ 499
$ 293
$ 74
Average Mexican Demand $ 387
$ 389
$ 554
$ 931
$ 539
$ 341
$ 74
Average Additional Demand $ 304
$ 315
$ 615
$ 969
$ 623
$ 385
$ 98
Generally displaying moderate demand, Puerto Ricans spend more, on average, than Non-Hispanic whites and
Africans Americans, but less than Mexicans—except in food to prepare at home, where they outspend all groups by
10% to 20%. Finally, the “all other” segment is projected to spend more annually in almost every category, especially
entertainment categories. These numbers should be taken with a grain of salt due to the comparatively higher levels
of intra-segment differentiation in this group. 11
Author’s calculation of 2010 Consumer Expenditure Survey Tables; see complete methodology for more information.
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PRIA: Annual Demand Estimates Based on Selected Characteristics Across All Segments
$400,000,000
$350,000,000
$300,000,000
$250,000,000
$200,000,000
$150,000,000
$100,000,000
$50,000,000
$0
food at home
food away from home
alcohol
health + personal men's clothes
care
TENURE DEMAND ESTIMATE
AGE DEMAND ESTIMATE
women's clothes children + family clothes
footwear
CONSUMER UNIT SIZE DEMAND ESTIMATE
fees + admissions av equipment + music
books + magazines
RACE AND HISPANIC ORIGIN DEMAND ESTIMATE
PRIA: Average Annual Demand for Retail Types Based on Segment Characteristics
$400,000,000 $350,000,000 $300,000,000 $250,000,000 $200,000,000 $150,000,000 $100,000,000 $50,000,000 $‐
When all of these demand estimates are multiplied across the number of households that compose each seg-
ment, they give evidence of tremendous spending power in the PRIA. The chart below compares the total amount
of demand estimates based on demographic characteristics, e.g., housing tenure characteristics projected demand
multiplied by number of households in each category across all five segments. As would be expected due to the large
number of households under 35, the demand exhibited in PRIA based on age projects lower total spending on food
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at home, but higher in alcohol and other non-children discretionary spending like event tickets and health memberships. These numbers are in contrast to demand estimates reflecting the large share renters comprise of the PRIA’s
housing mix. In this way, the demographic composition of PRIA contributes to a nuanced average annual demand
estimate. When averaged, these estimates suggest that all of PRIA annually demands:
Total Average Demand in PRIA based on Segment Characteristics for All Segments
Estimated Annual Spending
food at home
$353,755,548
food away from home
$222,936,942
alcohol
$38,908,895
$94,679,930
men's clothes
$29,991,084
women's clothes
$50,647,512
$30,960,781
footwear
$34,886,559
fees + admissions
$50,035,679
$83,080,906
$47,555,847
$27,510,685
books + magazines
$6,747,120
results from the greater paseo trade area study: gap and surplus calculations
Finally, when these calculations are compared to the annual sales inside the Trade Area12, it is possible to de-
termine whether the retail demands of all segments within PRIA are being met in their local area. The Greater Paseo
Trade Area Study indicates a Sales Surplus in six categories (light blue): food for preparation at home, restaurants,
alcohol, children/family clothes, and a/v equipment and recorded music. The Trade Area Study shows a Sales Gap in
seven categories (dark blue): men’s clothes; women’s clothes; footwear; fees and admissions; pets, toys and hobbies;
other entertainment and sports; and books and magazines. Since these estimates are built around national and statelevel data, in addition to local demographic characteristics, Gap or Surplus calculations were also completed based
on more modest deflated Trade Area Sales figures. Even if the business in the Trade area only make 80% of the 2011
Illinois average sales, the same Sales Surpluses and Gaps are still indicated for the PRIA13.
12
Annual sales were calculated by multiplying the number of establishments within the Trade Area by the average annual
sales of businesses of that type in Illinois in 2011. For more information, see the complete methodology.
13
LISC recommends deflating sales by 20% if dealing with mid- to low-market retailers. See complete methodology for a
discussion.
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INFERRED AVERAGE TRADE AREA Consumer Expenditure Survey AVERAGE DEMAND ANNUAL SALES PER ESTIMATED TOTAL SALES GAP (‐) or Retail Types
ESTIMATE
RETAIL TYPE (IL)
ANNUAL SALES
SURPLUS (+)
food at home
DEFLATED 20% total trade area sales
SALES GAP (‐) or SURPLUS (+)
353,755,548 5,739,029 1,814,949,155 1,461,193,607 1,451,959,324
1,098,203,776
food away from home
222,936,942 434,449 347,146,015 124,209,073 277,716,812
54,779,870
alcohol
38,908,895 947,970 93,287,658 54,378,763 74,630,126
35,721,232
94,679,930 2,499,282 400,395,801 305,715,871 320,316,641
225,636,711
men's clothes
29,991,084 602,646 11,261,654
‐18,729,430 9,009,323
‐20,981,761
women's clothes
50,647,512 381,102 24,960,558
‐25,686,955 19,968,446
‐30,679,066
30,960,781 1,844,140 128,880,582
97,919,801 103,104,466
72,143,684
footwear
34,886,559 428,468 23,067,034
‐11,819,525 18,453,627
‐16,432,931
fees + admissions
50,035,679 96,448 9,909,557
‐40,126,122 7,927,645
‐42,108,034
83,080,906 1,048,297 113,122,813
30,041,907 90,498,250
7,417,345
47,555,847 681,758 11,821,549
‐35,734,298 9,457,239
‐38,098,608
27,510,685 746,643 22,542,751
‐4,967,933 18,034,201
‐9,476,484
books + magazines
6,747,120 469,546 6,224,688
‐522,432 4,979,750
‐1,767,370
While these Gap estimates can suggest sectors that are likely to support new local businesses, it is unlikely
that any local trade area will ever recapture 100% leaked demand. Assuming it is possible for a new local business
making average annual sales to recapture 75% of the leaked sales in the deflated scenario, PRIA may be able to support a number of new businesses.
Consumer Expenditure Survey Retail Types
men's clothes
women's clothes
footwear
fees + admissions
books + magazines
75% recapture of No. of Stores that leakage of deflated could be supported by Gap estimate
the estimated Gap
‐15,736,321
‐23,009,300
‐12,324,699
‐31,581,025
‐28,573,956
‐7,107,363
‐1,325,527
26
60
29
327
42
10
3
However, these estimates rely heavily on a number of assumptions14, and should be treated only as prelimi-
nary figures that might indicate demand profiles. In order to illustrate next steps, and the kinds of wrinkles that impact these demand estimates, it will be helpful to look more closely at the estimates for three retail types: food away
from home, footwear and fees and admissions.
14
See complete methodology for further discussion of underlying assumptions, e.g., using national or state data to project
local demand.
trade area study
| 12
results from the greater paseo trade area study: focus on restaurants
In many ways, the wellbeing of restaurants is central to any thriving commercial district. Many consumers
come specifically to eat and incidentally do some postprandial shopping. The opposite is also often true—consumers
come to shop and then grab a bite—creating a certain symbiosis between food venders and commercial space. In
the case of these demand estimates, the “food away from home” category includes full-service restaurants, fast casual
restaurants, buffets, cafeterias and snack shops, as well as non-alcoholic drink bars, including juice bars, bubble tea
counters and coffee shops.
Since all these different types of businesses must be lumped together to perform demand estimates under
this methodology, it is not surprising that the Greater Paseo Trade Area Study indicates a huge Sales Surplus in this
category. One reason for this is that the Trade Area is home to a slightly higher proportion of fast casual restaurants
(47%) than the State of Illinois (45%), on which the sales estimates are based. Another is that average restaurant sales
in the PRIA—home to many small, family-owned, full-service restaurants—is likely lower than a state-wide average
that includes numerous extremely high-end places15.
A third reason the Trade Area may be displaying a Sales Surplus in the food away from home category is that
there are several clusters of ethnic restaurants that may be acting as destination districts. First, and most prominent,
are the Puerto Rican restaurants on Division Street between Western and California. Second, numerous Cuban restaurants populate the streets around Milwaukee Avenue from Western to Central Park. Finally, the popularity of Northwest Chicago as a destination for all types of Latino cuisine is highlighted by the upcoming “Taste of Latin America
Food, Wine and Art Festival” on Armitage Ave between Kedzie and Pulaski16.
In order to determine whether the Trade Area is generating a true Sales Surplus due to the presence of destination dining districts attracting guests from outside the PRIA, restaurant owners, chambers of commerce and local
neighborhood associations should ban together to gather more information about people who come to shop or
dine inside their portion of the Trade Area. Restaurant owners of all types would benefit from using a single loyalty
program, such as Chicago-startup “Belly Card” to track the zip code origin of customers17. The Belly Card and others like it offer modest rewards to consumers for shopping frequently in exchange for demographic information. By
sharing this information with the local chamber of commerce or other economic development corporation, it will be
15
This would be a case in which median sales figures would be highly preferable to average sales figures. However, those
data are not available from public sources.
16
This event will feature many Latin cuisines, ranging from South America, to Central America and the Caribbean. More
information available online at http://sponsorchicago.com/Taste-Latin-Am-Fest/index.html, (accessed 7/12).
17
For more information on the Belly Card, see http://bellycard.com/.
trade area study
| 13
possible to determine how much businesses the restaurant district is importing—and from where. Armed with this
knowledge, the restaurant district will be better able to improve its brand and target marketing where it will have the
most impact. These steps should improve profitability for existing restaurants and potentially pave the way for new
complementary businesses to open.
results from the greater paseo trade area study: focus on footwear
As of 2009, there were only 54 shoe stores in the PRIA, serving 293,290 people18. If all sales were kept within
the Trade Area (0% leakage), that would work out to 5,431 persons per shoe store, per year. Assuming each person
buys three pairs of shoes per year at a retail location, which would be over 21,000 pairs of shoes for each store to stock
annually, assuming a third of the shoes demanded must be kept on hand as additional sizes or options19. An average
DSW, which is on the huge side of shoe retailers, stocks only 27,000 shoes per year20. Since DSW exceeds the square
footage of the average American shoe store by several thousand square feet21, it is reasonable to assume that most
shoe stores are significantly smaller than DSW. By the same logic, it is reasonable to assume—though by no means
is necessarily proven—that current shoe retailers in the PRIA are not able to carry enough shoes to meet the total
demand.
These kinds of “back of the envelope” calculations help gage the reasonableness of Sales Gap estimates.
Combined with intelligence about the demographic characteristics of the area—facts like 40% of Mexican households
have five more people—these reasonability tests suggest that there is indeed unmet demand for shoe stores in the
Trade Area. Relevant further information to be gathered would include profiles of the existing shoe stores—for instance, whether they sell children’s shoes or work boots—as well as more detailed information on school enrollments
to dictate appropriate sizes and quantities of children’s shoes. Since the Gap in this category is large, there is likely opportunity for new business in this sector if the prospective owner were able to isolate the correct location and product
alignments.
18
Author’s calculation - 2010 Census and 2009 Zip Code Business Patterns; for more information, see full methodology.
19
Author’s calculation: 293,290 people / 54 existing shoe stores = 5,431 persons per store
Three pairs of shoes each year per person: 5,431 * 3 = 16,294
Demanded shoes + an additional 1/3 that amount of stock = 16,294*1.33 = 21,671
20
ZoomInfo corporate profile of DSW, available online at http://www.zoominfo.com/company/DSW+Inc-45190302 (accessed 7/12)
21
RetailSails 2011 profiles, available online at http://retailsails.files.wordpress.com/2011/09/rs_spsf.pdf
(accessed 7/12)
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| 14
results from the greater paseo trade area study: focus on fees and admissions
Similar in many ways to the food away from home category, fees and admissions estimates include many
disparate types of establishments and activities. This category covers tickets to see sporting events, movies, concerts
and plays. It also covers fees and memberships dues for sports and health clubs, country clubs, golf courses and private swimming pools. In addition, it also includes membership dues and fees for other social, recreation and fraternal
activities—for instance, membership to an Elk’s Club, or fees to enter a charity race. Finally, it also covers movie rentals, fees for lessons and recreation spending on trips22.
This is a category where it is very difficult to count all the establishments that might operate on these types of
expenditures. For this reason, it follows that the enormous Sales Gap estimated in this category is likely overinflated,
i.e., the demand estimates are substantially greater than the sales estimates because too few establishments were
counted in the Trade Area in this category23. Next steps for refining demand for tickets and admissions would be to
generate an inventory of formal and informal organizations in the trade area that fit within the category, segmented
by major types. These segments might look like the following: tickets to music and dancing, tickets to dance and
theater, spending on movies, memberships and fees for sports or health related activities, and memberships to other
organizations. By disaggregating this huge group, it may be possible to paint a more realistic picture of the supply
and demand for fees and admissions, particularly fo brick-and-mortar establishments like health clubs. Due to the size
of the current Sales Gap estimate it is highly likely that a gap will still exist even after the number of establishments is
refined.
results from the greater paseo trade area study: conclusion
In the end, the Greater Paseo Trade Area Study reveals a dynamic cluster of neighborhoods that are peopled
by a diverse group of households covering many races and origins, tenure profiles, and stages in life. Despite that
these differences can sometimes make it seem like there are many different communities operating in the same space,
demand profiles suggest that there may be some overlap in the goods and services that all segments demand, but
currently are forced to travel elsewhere to obtain. Moving forward, community leaders and chambers of commerce
should ban together to explore avenues for decreasing retail leakage, enriching neighborhoods and serving the substantial existing untapped purchasing power.
22
See complete methodology, section “Overcoming Government Silos” for a full explanation of the difficulties around Consumer Expenditure Survey types.
23
See complete methodology for a discussion of the misalignment between demand for retail goods and the categorization of establishments.
trade area study
| 15
Methodology
INRODUCTION: overview of the methodology
Data available from national marketing clearinghouses such as Claritas and ESRI are both costly and fraught with
structural problems that tend to under-report the economic power of inner city areas. Through use of public data,
however, it is possible to construct these calculations at a fraction of the cost with a great deal more transparency. The
following data sources can be combined to estimate demand for a number of retail and service sectors:
-
2010 US Census Decennial Census tract-level counts (Census)
-
2009 Zip Code Business Patterns (09 ZBP)
-
2010 County Business Patterns (10 CBP)
-
2010 Bureau of Labor Statistics’ Consumer Expenditure Survey Tables (CEX)
-
2011 Illinois State Department of Revenue Sales Tax Reporting (IDOR sales tax)
Using these data, the Greater Paseo Trade Area Study relied on the following conceptual scheme:
“Potential Demand”:
estimated retail demand by segment
(Census x CEX)
MINUS
“Actual Sales”
estimated annual sales per business
((IDOR annual sales per retail type / 10 CBP establishment counts)
x 09 ZBP establishment counts)
EQUALS
Underserved / Saturated Demand for Retail Types
Despite a fairly simple and straightforward conceptual basis, in actuality, there are a number of significant difficulties with operationalizing this formula. The balance of this methodology expands on the process the Greater Paseo
Trade Study adopted.
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| 17
GEOGRAPHY: the puerto rican influence area / great paseo trade area
Since this Trade Area Study was part of a larger research project detailing the status of Puerto Ricans in Chicagoland,
the geography of interest was defined by a number of data concerns not directly related to issues of market estimation. Along a number of indicators, particularly those related to home values and mortgage lending, disaggregated
information about Puerto Ricans is not available. In order to overcome this obstacle, I isolated those 2010 Census
tracts where Puerto Ricans made up more that 10% of the population in households (figure 1.) At the heart of this
area are a number of tracts where Puerto Ricans make up 21% - 38% of the population.
ILW
AU
K
NORTH
DIVISION
PULASKI
Puerto Rican
Concentration
ARMITAGE
KEDZIE
CICERO
CENTRAL PARK
FULLERTON
EE
0% - 2%
WESTERN
M
KOSTNER
CENTRAL
GRAND
EL
ST
ON
BELMONT
DIVERSEY
Chicago
Puerto
Rican
Influence
Area,
2010
CALIFORNIA
LARAMIE
Figure 1: Project Geography, Chicago Puerto Rican Influence Area
3% - 5%
6% - 10%
CHICAGO
11% - 20%
21% - 38%

Humboldt Park
0 0.25 0.5
1
1.5
source: 2010 Census
prepared by Elizabeth Scott, 4/12
2
Miles
Puerto Rican Influence Area:
Census Tracts >10% Puerto Rican
Influence Area Streets
This geography of high Puerto Rican concentration—covering parts of West Town, north east Humboldt
Park, Logan Square, Hermosa and Belmont-Cragin—more accurately reflects the locus of today’s Puerto Rican and
Latino communities than the 1920s University of Chicago School of Sociology-designated 77 Chicago Community
Areas. While the unchanging boundaries of the Community Areas make them a particularly convenient geography
for comparing longitudinal data, they are not always still relevant demarcaters of dynamic communities. By creating a
unique geography that reflects the current location of the Puerto Rican and Latino community in Northwest Chicago,
I avoided lumping the Puerto Rican Humboldt Park—which is largely composed of Hermosa (Community Area 20),
west West Town (Community Area 24) and north east Humboldt Park (Community Area 23)—with West Humboldt
trade area study
| 18
Park (also Community Area 23), which is predominantly African American (figure 3, figure 4). West Humboldt and Austin are substantially similar—in terms of racial composition and housing conditions—as are East Humboldt and west
West Town. Thus the Puerto Rican Influence Area (PRIA) geography overcomes conflating two very different communities, while also acknowledging that there is always a transition-zone where proximate communities overlap.
In order to gain an accurate-as-possible picture of the latent market demand in the PRIA, I drew a 1-mile buffer around the PRIA boundary, positing that consumers could reasonably be expected to travel an additional eight
city blocks as the crow flies to shop for retail goods and services (figure 5). I used this larger geography to count
number of establishments I later compared to the demand exhibited by residents inside the PRIA (see sections below,
“Demand Estimates” and “Actual Sales Estimates” for more information.) Here is one of the most critical shortcomings
of this method, and indeed any like it: it artificially binds consumer geographies. Of course, in reality, people travel
to a variety of locations to conduct their shopping for as many different reasons as there are consumers and retail
choices. However, one must close the study geography somewhere to create estimates that can reveal something
about latent consumer demand. Otherwise a matryoshka problem results: larger and larger geographies are chosen,
like nesting dolls, until analysis cannot reveal anything about local differences, except in comparison of city to city or
state to state.
In addition, to limit the complexity of the study insofar as possible, I did not include those portions of the
Greater Paseo Trade Area buffer that overlap into suburban Cook County. Instead, I treat the City of Chicago boundary
as impermeable—though, of course, in reality, this is far from the truth.
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| 19
FIgure 3: Latino Concentration in Northwest Chicago Compared with the PRIA
Latino Concentration,
Northwest Chicago, 2010
27% - 46%
0 0.5 1
2
3
4
Miles
source: 2010 Census
0% - 11%
12% - 26%

Chicago Boundary
47% - 70%
Chicago Community Areas
Humboldt Park
71% - 99%
Chicago Suburban Municipalities
Puerto Rican Influence Area
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| 20
Figure 4: African American Concentration in Northwest Chicago compared with the PRIA

African American Concentration,
Northwest Chicago, 2010
0 0.5 1
2
3
4
Miles
source: 2010 Census
0% - 8%
9% - 24%
25% - 48%
Chicago Boundary
49% - 78%
Chicago Community Areas
Humboldt Park
79% - 99%
Chicago Suburban Municipalities
Puerto Rican Influence Area
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| 21
Figure 5: Study Area, Greater Paseo Trade Area Buffer in Comparison with the PRIA
DEMAND ESTIMATES: determining and tabulating sectors inside the trade area
As does Claritas and ESRI, I used The Bureau of Labor Statistics (BLS) annual Consumer Expenditure Survey
(CEX) (available online at http://www.bls.gov/cex/) to estimate consumer demand in the PRIA. According to BLS,
“the Consumer Expenditure Survey program consists of two surveys, the Quarterly Interview Survey and the Diary
Survey, that provide information on the buying habits of American consumers, including data on their expenditures,
income, and consumer unit (families and single consumers) characteristics1.” The Consumer Expenditure Survey is also
the basis for revisions to the Consumer Price Index, which is widely used to estimate inflation. However, what makes
the CEX special and useful for this research is that it relates consumer behavior to a range of characteristics available
at detailed geometries from the US Census Bureau.
Whereas the CEX is almost always applied to the total population inside a geography, I applied it instead to
five segments that compose the total population of the PRIA. These segments included:
1
Bureau of Labor Statistics (2012), “CE Overview,” available online at http://www.bls.gov/cex/ (accessed 7/12).
trade area study
| 22
PRIA segments
total population
1. Puerto Rican
2. white (non-Hispanic)
3. African American
4. Mexican
5. all others
population
293,290
53,220
66,494
26,234
111,895
35,447
households average HH size
96,058
3.05
18,129
2.94
31,749
2.09
8,974
2.92
26,326
4.25
10,880
3.26
There were a couple of reasons to project demand by segments rather than by total population. First, data
available from commercial venders is often not projected in this way, making it a worthwhile academic undertaking.
Second, there are some ongoing conversations on the Northwest side of Chicago between and amongst stakeholders
about what constitutes “the community,” and how (and for whom) development should take place. Estimating retail
demand across race/origin segments is an attempt to show whether there are any mutual instances of unmet demand
that could serve as a starting place for dialogue and cross-segment buy-in, as well as economic development in general.
Taking this route forecloses the use of a number of CEX tables, since one must rely only on characteristics
reported at the 100% level from the Decennial Census. Although it is possible to get tract-level data about disaggregated Hispanic Origins from the American Community Survey (ACS), a significant amount of it is suppressed for Puerto
Ricans. Additionally, the Census Bureau does not recommend comparing the Decennial Census to the ACS, because
the former is a count at one point of time, whereas the latter is an estimate from rolling survey collection2. Considering these limitations, I downloaded tract-level census data from American FactFinder for Puerto Ricans (401), Non-Hispanic whites (451), African Americans (0043), Mexicans (402), and Total Population (001). The former four were used to
project segment demand, whereas Total Population was used to calculate what remained after the primary segments,
“all others”:
Total Population – (Puerto Ricans + Non-Hispanic whites + African Americans + Mexicans) = “all others”
As a quality check, I mapped the percentage that the Puerto Rican, Non-Hispanic white, African American and
Mexican populations take up by census tract of the PRIA overall. These four segments are dominant, comprising a
minimum of 75% of the households, but often much more (figure 6.)
2
For more guidance, see Appendix 4 of the ACS General Handbook, available online at http://www.census.gov/acs/www/
Downloads/handbooks/ACSGeneralHandbook.pdf
3
Looking back, it likely would have been better to track Non-Hispanic African Americans (454); however over 90% of the
African Americans tracked in PRIA were incidentally Non-Hispanic.
trade area study
| 23
Figure 6: Percent Representation of Primary Segments in the PRIA
Across these Primary Segments and “all others,” I calculated tenure, age ranges, household sizes and race or
Hispanic origin for each census tract. I then used ArcGIS to select those tracts which had their centroid in the PRIA.
After exporting the relevant tracts, I was able to tabulate households across four characteristics for each segment. It is
important to note that all CEX estimates refer to households, where the characteristic of the householder determines
that of the whole household. This householder is called the “reference person,” and their household (whether family
or non-family) constitutes a “consumer unit4.” The following mixes of consumer units make up the study segments in
PRIA:
Consumer Units in
PRIA by Tenure
Puerto Ricans
Non-Hispanic whites
Homeowner with
mortgage
Homeowner
without
mortgage
Renter
5,073
788
12,268
10,422
4,185
17,142
African Americans
1,942
272
6,760
Mexicans
9,834
1,090
15,402
all others
4,242
553
6,085
source: author’s calculation of 2010 Census SF2 100% files
4
BLS (2012) “CE FAQs,” available online at http://www.bls.gov/cex/csxfaqs.htm (accessed 7/12)
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| 24
Consumer Units in
PRIA based on AGE
Puerto Ricans
Non-Hispanic whites
African Americans
Under 25
years
65 years
and older
25-34 years
35-44 years
45-54 years
55-64 years
679
3077
3806
4083
3464
3020
1805
9337
5682
4722
4617
5586
476
1860
1850
2077
1545
1166
Mexicans
1449
6650
7567
5747
3205
1708
all others
523
2736
2481
2198
1598
1344
Consumer Units in PRIA
based on HH SIZE
One person
Puerto Ricans
Two persons
Three persons
Five or more
persons
Four persons
4,155
4,460
3,518
2,932
3,064
11,672
11,548
4,796
2,345
1,388
African Americans
2,332
2,206
1,603
1,243
1,590
Mexicans
2,378
3,723
4,208
5,239
10,778
all others
2,041
2,622
2,048
1,775
2,394
Non-Hispanic whites
Consumer Units in PRIA based on
RACE or ORIGIN
Puerto Ricans
Hispanic or Latino
White and all other
races (not AA or
Asian) - Not Hispanic or Latino
Black or AfricanAmerican
18,129
0
0
Non-Hispanic whites
0
31,749
0
African Americans
0
0
8,974
Mexicans
26,326
0
0
all others
0
10,880
0
Once these consumer units were tabulated, they were multiplied by the CEX tables to produce demand estimates for PRIA. The CEX market baskets were reduced from the full Survey5 for purposes of clarity and efficiency. The
baskets were chosen based on the availability of establishment counts and sales tax data—the “Actual Sales” portion
of the Sales Gap equation—and are, in part, the subject of the next section “Annual Sales Estimates by Retail Type.” A
sample calculation, to calculate the Puerto Rican demand for food away from home (restaurants) based on the tenure
table, would be:
Puerto Rican Homeowner Households with mortgage (5,073) x
CEX estimate for Homeowner with mortgage annual spending on food away from home ($3,135)
Portion of Gross PRIA Puerto Rican Demand for food away from home generated by homeowner characteristics
($
5
40,934,047 annually)
Find Current Expenditure Tables online at http://www.bls.gov/cex/tables.htm
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| 25
Spending per Consumer Unit
CEX Market Baskets based on TENURE
food at home
food away from home
alcohol
men's clothes
women's clothes
children's clothes
footwear
fees + admissions
books + magazines
Home‐owner Home‐owner with without mortgage
mortgage
Renter
$4,215
$3,531
$2,902
$3,135
$2,184
$1,900
$519
$305
$338
$1,417
$1,472
$696
$375
$236
$255
$700
$490
$423
$337
$173
$246
$340
$242
$291
$881
$532
$260
$1,166
$926
$717
$829
$672
$280
$546
$383
$132
$122
$129
$52
source: Bureau of Labor Statistics Consumer Expenditure Survey Tables, 2010
CEX Market Baskets based on AGE
food at home
food away from home
alcohol
men's clothes
women's clothes
children's clothes
footwear
fees + admissions
books + magazines
Under 25 years
25‐34 years
35‐44 years
45‐54 years
55‐64 years
65 years and older
$2,197
$3,338
$4,255
$4,369
$3,681
$2,950
$1,876
$2,753
$3,227
$2,861
$2,387
$1,608
$406
$473
$497
$414
$402
$295
$481
$788
$1,131
$1,307
$1,406
$1,480
$219
$325
$320
$390
$331
$188
$628
$550
$555
$722
$564
$384
$229
$424
$493
$254
$143
$86
$305
$313
$414
$360
$292
$149
$235
$460
$849
$780
$545
$384
$595
$965
$1,078
$1,025
$1,061
$787
$232
$480
$716
$736
$705
$513
$158
$346
$414
$548
$372
$206
$39
$61
$80
$104
$126
$141
CEX Market Baskets based on HOUSEHOLD SIZE
food at home
food away from home
alcohol
men's clothes
women's clothes
children's clothes
One person
Two persons Three persons Four persons
Five or more persons
$1,877
$3,480
$4,431
$5,219
$5,746
$1,573
$2,478
$2,866
$3,559
$3,338
$322
$545
$388
$441
$248
$777
$1,413
$1,370
$1,336
$1,185
$174
$339
$375
$377
$363
$288
$599
$732
$804
$662
$42
$118
$392
$610
$764
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footwear
fees + admissions
books + magazines
$151
$273
$337
$449
$584
$264
$576
$624
$1,056
$808
$661
$1,055
$1,056
$1,172
$1,041
$352
$664
$800
$712
$722
$164
$412
$331
$456
$705
$81
$128
$100
$98
$68
CEX Market Baskets Based on RACE and ORIGIN food at home
food away from home
alcohol
men's clothes
women's clothes
children's clothes
footwear
fees + admissions
books + magazines
White and all other races Black or Hispanic or (not AA or African‐
Latino
Asian) ‐ Not American
Hispanic or L ti
$4,012
$3,651
$3,075
$2,474
$2,635
$1,721
$260
$470
$203
$917
$1,282
$863
$298
$321
$202
$550
$578
$466
$413
$249
$257
$476
$273
$323
$332
$683
$195
$802
$998
$841
$343
$714
$192
$167
$433
$125
$37
$119
$41
Please see Appendix B, “CEX Demand Estimates Based on Segment Characteristics” for tables describing all these market baskets multiplied through segment characteristics.
ANNUAL SALES ESTIMATES BY RETAIL TYPE: overcoming government silos
To counterpose the estimated demand side of the sales gap equation, one must also develop estimates of
actual retail sales in the Trade Area. The data for these calculations are much more difficult to manipulate. These
problems mainly come down to anachronisms and differences in workflow between three government agencies: BLS’s
Consumer Expenditure Survey, the Census Bureau’s Zip Code Business Patterns, and (in this case) the Illinois Department of Revenue. To understand the inherent difficulties in combining these data products, it is helpful to briefly
review their histories.
The Consumer Expenditure Survey was first conducted in the late 1880s, and then sporadically until the 1940s.
Later, from the 1940s to the 1980s, it was conducted about every 10 years. In 1980, BLS began conducting the survey
every year in order to provide more timely and consistent update to the Consumer Price Index. The new annual CEX
they developed for launch in 1980 was largely based on the 1972-73 Survey6. For this reason, the CEX is divided into—
by today’s standards—some somewhat anachronistic categories. For instance, the Entertainment primary section is
6
BLS (2010) “CE Turns Thirty,” available online at http://www.bls.gov/cex/ceturnsthirty.htm; BLS (2011) “Consumer Expenditure Survey CNSTAT Panel Briefing,” available online at http://www.bls.gov/cex/redpanl1_ryan.pdf
trade area study
| 27
divided into:
Fees and admissions includes fees for participant sports; admissions to sporting events, movies, concerts, and plays; health, swimming, tennis and
country club memberships; fees for other social, recreational, and fraternal organizations; recreational lessons or instruction; rental of movies, and
recreation expenses on trips.
Television, radio, and sound equipment includes television sets, video recorders, video cassettes, tapes, discs, disc players, video game hardware, video
game cartridges, cable TV, radios, phonographs, tape recorders and players, sound components, records, compact discs, and tapes (including records,
compact discs, and tapes purchased through mail order clubs), musical instruments, and rental and repair of TV and sound equipment.
Pets, toys, hobbies, and playground equipment includes pets, pet food, pet services, veterinary expenses, etc.; toys, games, hobbies, and tricycles; and
playground equipment.
Other entertainment equipment and services includes indoor exercise equipment, athletic shoes, bicycles, trailers, purchase and rental of motorized
campers and other recreational vehicles, camping equipment, hunting and fishing equipment, sports equipment (winter, water, and other), boats, boat
motors and boat trailers, rental of boats, landing and docking fees, rental and repair of sports equipment, photographic equipment and supplies (film
and film processing), photographer fees, repair and rental of photo equipment, fireworks, and pinball and electronic video games7.
Were someone to create new expenditure categories to reflect the reality of today’s retail landscape, it seems likely
that these categories would be bundled differently. For instance, perhaps audio/visual equipment and video games
would no longer be tabulated with recorded music and musical instruments. With the exception of general goods
merchants, it is no longer common for these items to be sold together. Nonetheless, these categories—inherited from
the 1970s—are the basis for today’s CEX, and sales data must be compiled to match them.
The most complete and timely source for the first half of this sales estimate, establishment count by zip code,
is available from the Census Bureau through its Zip Code Business Patterns (ZBP) data product. ZBP is a more detailed
version of County Business Patterns (CBP). According to the Census Bureau,
CBP is an annual series that provides sub-national economic data by industry. This series includes the number
of establishments, employment during the week of March 12, first quarter payroll, and annual payroll. This
data is useful for studying the economic activity of small areas; analyzing economic changes over time; and as
a benchmark for other statistical series, surveys, and databases between economic censuses8.
ZBP is available shortly after CBP, providing establishment counts at the zip code level by highly detailed 6-digit North
American Industry Classification System (NAICS) codes. NAICS codes are product-based, i.e., classify industries based
on what they produce, as opposed to what demand they serve. NAICS was conceived as a complete taxonomy and
is the most current system for counting industries in Canada, the US and Mexico. In 1997, NAICS replaced Standard
Industrial Classification (SIC) codes, which were developed in the 1930s and revised in an ad-hoc manner until 1987.
SIC codes do not rely on a complete conceptual framework; some codes describe products, while others describe demand9. Although CBP/ZBP have been collected since 1964, the Census Bureau updated them to NAICS in 1997 along
with the rest of their data products.
There would be few problems using CEX and ZBP together under the NAICS system alone. However, the State
of Illinois Department of Revenue (IDOR)—which provides the final element in the sales estimates, total sales by segment—still uses the SIC system. A snapshot of the raw data looks like the table below, where “SIC TOTALS” are the total
7
BLS (2012) “CE Glossary,” available online at http://www.bls.gov/cex/csxgloss.htm (accessed 7/2012)
8
Census Bureau (2012) “County Business Patterns Overview,” available online at http://www.census.gov/econ/cbp/overview.htm (accessed 7/12)
9
Census Bureau (2012) “Development of NAICS,” available online at http://www.census.gov/epcd/www/naicsdev.htm
trade area study
| 28
amount of tax the state has collected from all establishments in that category in Illinois for the tax year. I used the
“STATE” portion of the tax to calculate gross sales per SIC type because the rate charged is consistent across the state,
unlike many of the other taxes imposed by local taxing bodies.
ILLINOIS DEPARTMENT OF REVENUE
SIC REPORTING SYSTEM
SALES TAX FOR ANNUAL 2011
SEQUENCED BY STANDARD INDUSTRIAL CLASSIFICATION CODE
SIC
CODE
NO. OF
TRANS
SIC TOTALS STATE
MT
5131 PIECE GOODS AND NOTIONS
418
3,290,490
1,809,775
279,038
5136 MEN’S AND BOY’S CLOTHING
240
2,836,717
1,407,161
280,550
5137 WOMEN’S AND CHILDREN’S CLOTHING
606
4,744,397
2,036,845
395,909
5139 FOOTWEAR
505
8,903,736
4,488,387
876,692
5141 GROCERIES, GENERAL LINE
649
7,376,690
2,391,731
1,005,522
DESCRIPTION
In 2011, the Illinois State sales tax was 6.25% for retail goods, and 1% for “qualifying food, drugs, and medical appliances,” defined by the State as
•
food that has not been prepared for immediate consumption, such as most food sold at grocery stores, excluding hot foods,
alcoholic beverages, candy, and soft drinks;
•
prescription medicines and nonprescription items claimed to have medicinal value, such as aspirin, cough medicine, and
medicated hand lotion, excluding grooming and hygiene products; and
•
prescription and nonprescription medical appliances that directly replace a malfunctioning part of the human body, such as
corrective eyewear, contact lenses, prostheses, insulin syringes, and dentures10.
With these rates, it is possible to calculate gross sales per SIC for the tax year. However, since IDOR is unwilling or
10
IDOR (2012) “Sales and Use Taxes,” available online at http://tax.illinois.gov/Businesses/TaxInformation/Sales/rot.htm (retrieved 7/12)
trade area study
| 29
unble to provide establishment counts by SIC11, it is necessary to rely entirely on ZBP to calculate average annual sales
per retail type.
The process of lining up the CEX market baskets with both NAICSs and SICs is tricky and involves a number of
choices based on professional judgment. I used both the 1987 SIC to 2002 NAICS crosswalk available from the Census
Bureau12, and the detail descriptions on NAICS.com to build a concordance. It is necessary to look up the detail descriptions because many SIC codes refer to wholesaling in a less than obvious manner. For instance, 5141 – Groceries,
General Line, in the table above, refers to wholesaling of non-food items for grocery stores, such as the Osco portion
of the Jewel-Osco stores prevalent in the Midwest. Since these are not retail sales, but business to business sales, their
sales and establishment counts could not be included in our estimates. In general, the idea is to isolate those businesses that are customer-serving and pay a retail sales tax, so as to not artificially inflate or deflate sales estimates by
overcounting establishments (deflate) or overcounting sales (inflate). Here is the scheme I developed and relied upon:
CEX Market Basket
description of category
NAICS
SIC
food at home
supermarket, market
44511
5411
convenience store
44512
5411
specialty food stores (e.g., butcher,
baker, cheese)
4452
5421, 5431, 5441, 5451, 5461,
5499
food away from home
full-service restaurant
72210
5812
fast-casual restaurant
72211
5812
buffets + cafeterias
72212
5812
snacks + nonalcoholic drinks
72213
5812
Alcohol
liquor store
4453
5181, 5182, 5921
drugs
44611
5912
medical supplies
44613, 44619
5995, 5999
personal care products and services
44612
5999
men’s clothes
men’s clothes
44811
5611, 5136
women’s clothes
women’s clothes
44812
5621
children’s clothes
children’s clothing
44813
5641
family clothing
44814
5651
11
Author’s correspondence with IDOR, 7/12:
“Ms. Scott,
In response to your inquiry, the SIC Report contains the total amount of sales reported on form ST-1, Sales and Use Tax Return, by taxpayers registered to report sales made
in Illinois. For transactions that are exempt from sales tax, refer to the Illinois Department of Revenue Regulations Section 130.120, Nontaxable Transactions, located on our
website at www.tax.illinois.gov. The number of establishments by SIC is currently not available.
“No. of Trans” refers to original returns processed, assessment payments processed and adjustments/amended returns processed for a taxpayer in their SIC code category.
If you have any questions, please contact us at the address and telephone number listed below. ANALYSIS & DISTRIBUTION SECTION
LOCAL TAX ALLOCATION DIVISION 3-500
ILLINOIS DEPARTMENT OF REVENUE
101 WEST JEFFERSON STREET
SPRINGFIELD, IL 62702”
12
All Census Bureau industry classification crosswalks are available online at http://www.census.gov/eos/www/naics/concordances/concordances.html
trade area study
| 30
Footwear
footwear
fees + admissions
fees + admissions for events, concerts,
movies + plays
44821
5661
7111, 7112, 71131, 7131
7999, 7922, 7996, 7993, 7933,
7911, 7948, 7993
health + rec memberships
7139
7992, 7997, 7941, 7991
organizational memberships
7139
7997
recreational lessons
7139
7999
movie rentals
45122
7841
instruments
45114
5736
av/tv sales
443112
5731
recorded music
451220
5735
video games
443120
5734
pet stuff
45391
5999
hobbies, toys and games
45112, 45113
5092, 5945, 5949
sporting goods
45111
5941, 5091
camera + film
423410
5946
boats
441222
5551
books + magazines
books
451211
5942
magazines + periodicals
451212
5994
Source: author’s calculations, 2010 CEX, 2010 CBP, 2011 IDOR sales tax figures
Although it would not be possible to report all of the decisions that went into constructing this table, three
important ones stand out. First, I purposefully used data that refer to different years. The 2010 CEX is the newest
available, as are establishment counts for Illinois from the 2010 CBP. Although it might have been preferable to use
2010 sales tax figures to compare them, I choose to compare 2010 establishments to 2011 taxes because 2011 taxes
are more likely to reflect a stronger (and currently more accurate) retail climate, but the number of establishments
is not likely to have changed significantly in the intervening year. Second, I was forced to include convenience store
counts in the grocery establishment category, despite a large body of academic work suggesting that counting convenience store with grocery stores obscures important findings on food security13. Convenience stores were included
because, under the SIC system, grocery stores and conveniences stores fall under the same category (5411), i.e., their
sales are inextricably combined in the State sales tax reporting. Finally, the 4-digit SICs from IDOR included type 5999,
miscellaneous retail sales in several categories. The 1987 5999 included copious random businesses, including gravestone carvers, tropical fish merchants and heraldic insignia painters. Relevant to the CEX baskets, 5999 also includes
all retail pet sales, personal care and medical devices. In order to overcome problem of this huge miscellaneous
category, I decided to collect establishment counts by NAICS, but to substitute national annual average sales from
industry trade publications where necessary.
After developing a concordance, I divided the average annual sales imputed from SIC sales tax data by the
aggregated the number of Illinois establishments in each NAICS category. This number is an Illinois-wide average
annual sales by CEX retail category for 2011. Please see Appendix C “Inferred Average Annual Sales per CEX Basket” for
detailed figures. The summary table is below.
13
See, for instance, Mari Gallagher on Chicago (page 13): http://www.marigallagher.com/site_media/dynamic/project_files/
Chicago_Food_Desert_Report.pdf (accessed 7/12)
trade area study
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CEX Market Baskets
food at home
SUM IL establishments by
CEX baskets
SUM INFERRED
ANNUAL SALES
PER CEX BASKET
INFERRED AVERAGE
ANNUAL SALES PER
CEX BASKET
4,649
26,680,746,384
5,739,029
20,139
8,749,377,318
434,449
alcohol
1,297
1,229,516,819
947,970
3,589
8,969,923,870
2,499,282
303
182,601,645
602,646
women’s clothes
1,382
526,683,593
381,102
1,246
2,297,798,230
1,844,140
footwear
1,076
461,031,495
428,468
fees + admissions
3,416
329,467,038
96,448
2,004
2,100,787,618
1,048,297
881
3,037,446,343
681,758
880
657,046,249
746,643
books + magazines
400
187,818,491
469,546
food away from home
men’s clothes
Source: author’s calculations, 2010 CEX, 2010 CBP, 2011 IDOR sales tax figures
ACTUAL SALES ESTIMATES: applying state-level sales estimates to local establishment
After establishing average annual sales by CEX type, the last step in estimating “actual sales” in the Greater
Paseo Trade Area was to generate establishment counts and multiple them by average annual sales figures. I downloaded 2009 ZBP for Chicago and winnowed out establishment counts based on the CEX/NAICS/SIC concordance. I
then mapped them with ArcGIS and took an area-weighted average of the establishments in the Trade Area.
An area-weighted average assumes that the establishments are distributed evenly across each zip code, which
is—of course—not an accurate reflection of the true-life clustering of commercial corridors. However, it was beyond
the scope of my project to catalog and geo-code businesses, which might have produced more accurate establishtrade area study
| 32
ment counts14. Because the Greater Paseo Trade Area (the 1-mile buffer around PRIA) includes only portions of a number of zip codes, an area-weighted average allowed me to aggregate only the proportion of establishments in that zip
code that matched the proportion of the zip code occupied by the Trade Area. Put another way, if the total alcohol
establishments reported by ZBP for 60641 was 10, but the Trade Area only took up area A, one should calculate an
area-weighted average, where
establishments (10) x (area A / area A + area B + area C) = area-weighted establishments (~6).
Although not always a huge difference-maker, taking area-weighted averages can prevent egregious overcounts,
especially in instances where the portion of a zip code occupied by the Trade Area are very small.
Market
Baskets
60612
60613
60614
60618
60622
60624
60625
60630
60634
60639
60641
60644
60646
60647
60651
60657
60707
CEX
food at
home
7
1
7
50
23
10
8
7
23
39
31
10
0
52
30
4
14
14
2
42
128
112
12
23
23
58
81
32
12
0
129
37
40
51
food away
from home
alcohol
2
0
1
16
6
7
2
3
4
14
7
3
0
15
8
3
8
2
0
6
21
14
9
4
5
9
20
15
2
0
24
15
4
11
men’s
clothes
-
-
1
1
5
7
-
-
1
-
2
0
-
2
-
1
-
women’s
clothes
0
0
5
6
12
3
1
1
6
8
1
-
0
14
2
2
5
children’s
clothes
-
0
3
8
12
5
1
-
3
13
2
1
0
12
2
3
4
footwear
0
0
2
5
8
9
1
0
1
10
5
0
-
8
3
1
1
fees + admissions
3
1
6
29
14
1
2
3
5
5
2
1
0
14
3
8
6
av equipment +
music
1
0
2
17
10
4
2
3
5
22
9
2
0
16
4
3
6
pets + toys +
hobbies
0
0
1
3
3
-
0
1
1
-
-
-
-
4
-
2
2
other entertainment +
sports
-
0
2
5
4
1
0
0
2
-
2
-
0
7
-
2
5
books +
magazines
1
-
0
3
1
-
1
0
2
-
2
-
-
2
-
1
-
The area-weighted establishment counts I used for the Greater Paseo Trade Area are detailed below. Cells with a zero
rather than a dash are those which had at least one establishment in the zip code, but a portion of the Trade Area
insufficient to reach over.49.
Source: author’s calculations, 09 ZBP
14
However, the additional complexity presented by attempting to find and categorize local businesses by NAICS codes
also opens the door for extremely large errors.
trade area study
| 33
These establishment counts are then multiplied by the annual retail sales estimates developed in the preceding section to reach an estimate of actual retail sales by CEX categories inside the Greater Paseo Trade Area for 2011.
CEX Market Baskets
AREAWEIGHTED
AVERAGE
PASEO ESTABLISHMENTS
INFERRED AVERAGE ANNUAL SALES PER CEX BASKET
TOTAL TRADE AREA
SALES
food at home
316
$
5,739,029
$
1,814,949,155
food away from home
799
$
434,449
$
347,146,015
98
$
947,970
$
alcohol
160
93,287,658
2,499,282
400,395,801
men’s clothes
19
$
602,646
$
11,261,654
women’s clothes
65
$
381,102
$
24,960,558
70
$
1,844,140
$
128,880,582
footwear
54
$
428,468
$
23,067,034
fees + admissions
103
$
96,448
$
9,909,557
108
$
1,048,297
$
113,122,813
17
$
681,758
$
11,821,549
30
$
746,643
$
22,542,751
books + magazines
13
$
469,546
$
6,224,688
source: author’s calculation, 2011 IDOR sales tax reports, 2010 CEX, 10 CBP, 09 ZBP
GAP ESTIMATES: bringing it all together
After finding, cleaning and analyzing all these data, it is finally possible to produce a sales gap estimate. I
looked both at total trade area sales and sales deflated 20%, based on advice from LISC15.
RACE AND
HISPANIC
ORIGIN
DEMAND
ESTIMATE
TENURE
DEMAND
ESTIMATE
AGE
DEMAND
ESTIMATE
CONSUMER
UNIT SIZE
DEMAND
ESTIMATE
food at home
365,403,484
342,504,271
365,380,299
341,734,136
1,814,949,155
SURPLUS
1,451,959,324
SURPLUS
food away
from home
223,384,947
228,232,868
229,399,581
210,730,372
347,146,015
SURPLUS
277,716,812
SURPLUS
alcohol
37,944,153
48,834,195
46,710,951
22,146,280
93,287,658
SURPLUS
74,630,126
SURPLUS
94,922,329
100,191,772
105,497,393
78,108,226
400,395,801
SURPLUS
320,316,641
SURPLUS
men’s clothes
28,145,478
32,920,605
33,515,209
25,383,044
11,261,654
GAP
9,009,323
GAP
women’s
clothes
49,823,131
51,825,823
54,093,195
46,847,900
24,960,558
GAP
19,968,446
GAP
25,995,127
29,748,650
32,920,834
35,178,514
128,880,582
SURPLUS
103,104,466
SURPLUS
CEX Market
Baskets
DEFLATED 20%
total trade
area sales
total trade area
sales
15
LISC produce a guide to help community development organizations do commercial strip development. Their advice
in this case pertains to fact checking results from Claritas and ESRI. Deflating sales 20% accounts for the impact of very large or
very high-end stores on annual sales figures. See LISC Center for Commercial Revitalization “Commercial Revitalization Planning
Guide,” available online at http://www.metroedge.org/uploads/metroedge/documents/6100_file_commercial_revitalization.pdf
trade area study
| 34
footwear
29,159,503
33,753,519
36,088,484
40,544,728
23,067,034
GAP
18,453,627
GAP
fees + admissions
46,418,189
61,715,434
63,729,997
28,279,096
9,909,557
GAP
7,927,645
GAP
av equipment
+ music
84,462,515
89,532,583
90,015,768
68,312,756
113,122,813
SURPLUS
90,498,250
Near SURPLUS
pets + toys +
hobbies
46,896,973
55,908,675
58,201,686
29,216,054
11,821,549
GAP
9,457,239
GAP
other entertainment +
sports
27,454,926
33,018,917
35,344,170
14,224,726
22,542,751
Near GAP
18,034,201
GAP
books + magazines
7,731,302
7,925,517
8,180,075
3,151,586
6,224,688
GAP
4,979,750
GAP
source: author’s calculation, 2011 IDOR sales tax reports, 2010 CEX, 10 CBP, 09 ZBP
conclusion: final thoughts + caveats
It is indeed possible to use free public data to construct an approximation of the tables available for purchase
from companies like Claritas and ESRI. It is a worthwhile endeavor in that it illuminates estimates that are otherwise
something of a black box. It would likely be most accurate to use these estimates as ranges, and as a jumping off
point for other kinds of research, particularly customer surveys.
Finally, some important caveats apply to this study:
• Artificially bounded trade areas are not a true-to-life reflection of the way people live and shop. Instead, trade
area market studies offer a constructed view into what people may do, at least in some ways, some of the time.
• The CEX tables refer to products consumers buy, but CBP/ZBP refer to products businesses produce. Just
because a business’ primary function is listed as one thing does not mean that a customer could not purchase
something from outside of that category there. For instance, sales of a t-shirt at a restaurant would be counted as restaurant sales, but reported in CEX expenditures as apparel. Subsequently, there is not a 1-to-1 match
between demand and production.
• The characteristics of households available from the SF2 100% Census files may not be the most probative for
Puerto Ricans, Non-Hispanic whites, African Americans and Mexicans. Educational attainment and income
would have been much better to include; however, it was not possible under this framework.
• Area-weighted averages helped me approach a more-accurate aggregation of establishment counts from
Zip Code Business Patterns. Assuming an even distribution of retail firms is, however, a theoretically flawed
shortcut.
• The complexity of building a CEX/NAICS/SIC concordance presents many potential pitfalls. Similarly, using
data from different years compounds that complexity. Occam’s Razor suggests there may be problems with
proceeding in this manner.
In the end, the Greater Paseo Trade Study was an exercise in triangulation, adjusting slowly and incrementally
to reach a set of potentially significant results. In future, planners and policy analysts should pressure government
statistical agencies and state departments of revenue to modernize and harmonize their data collections and dissemination methods so that this type of information will be easier for citizens to distill from public data sources.
trade area study
| 35
trade area study
| 36
Watch Premio Juventud Go to movies Read Ser Padres Watch El Gordo Y La Flaca Toyota Yaris entertainment reading tv drive Volkswagen GLI Read Seventeen Shop at The Gap Shop at CVS Pharmacy Buy Spanish/Latin music $34,876 An immigrant gateway community, Multi‐Culti Mosaic is the urban home for a mixed populace of Hispanic, Asian, and African‐American singles and families. With nearly a quarter of the residents foreign born, this segment is a mecca for first‐
generation Americans who are striving to improve their lower‐middle‐
class status. With a population that's more than 45 percent Latino, Big City Blues has one of the highest concentrations of Hispanic‐Americans in the nation. But it's also the multi‐ethnic address for low‐
income Asian and African‐American households occupying older inner‐city apartments. Concentrated in a handful of major metros, these younger singles and single‐parent families face enormous challenges: low incomes, uncertain jobs, and modest educations. More than 15 percent haven't have less than a 9th grade education $31,429 5
multi‐culti mosaic 5 big city blues shop median income times cited (out of 6) PRIZM name 5
Volkswagen Rabbit Watch soccer on TV Audi A3 Watch IFC Read The Economist Go water skiing Rent/buy foreign videos $85,599 Shop at Express Read Details 3
Young Digerati are
tech‐savvy and live in fashionable neighborhoods on the urban fringe. Affluent, highly educated, and ethnically mixed, Young Digerati communities are typically filled with trendy apartments and condos, fitness clubs and clothing boutiques, casual restaurants and all types of bars‐‐from juice to coffee to microbrew. young digerati Order from expedia.com $54,098 A collection of mobile urbanites, Bohemian Mix represents the nation's most liberal lifestyles. Its residents are an ethnically diverse, progressive mix of young singles, couples, and families ranging from students to professionals. In their funky row houses and apartments, Bohemian Mixers are the early adopters who are quick to check out the latest movie, nightclub, laptop, and microbrew. bohemian mix 3
Nissan Pathfinder Watch BET Read Ebony In‐home cosmetics purchase Domestic travel by bus $24,378 The most economically challenged urban segment, Low‐Rise Living is known as a transient world for middle age, ethnically diverse singles and single parents. Home values are low‐‐
about half the national average‐‐
and even then less than a quarter of residents can afford to own real estate. Typically, the commercial base of Mom‐and‐Pop stores is struggling and in need of a renaissance. low‐rise living 3
Lexus IS Watch TeleFutura Buy motivational tapes Read Black Enterprise Shop at Old Navy $55,270 American Dreams is a living example of how ethnically diverse the nation has become: just under half the residents are Hispanic, Asian, or African‐American. In these multilingual neighborhoods‐‐one in three speaks a language other than English‐‐middle‐aged immigrants and their children live in upper‐middle‐class comfort. american dreams 1
Volkswagen GTI Watch Tyra Read Latina Play soccer Shop at Banana Republic $35,535 Concentrated in the nation's port cities, Urban Achievers is often the first stop for up‐and‐coming immigrants from Asia, South America, and Europe. These young singles, couples, and families are typically college‐educated and ethnically diverse: about a third are foreign‐born, and even more speak a language other than English. urban achievers Appendix A: Claritas PRIZM “You Are Where You Live” segments for the PRIA 1 Lexus LX Watch BBC America Read Harper's Bazaar Buy classical music Shop at Costco $56,581 Educated, upper‐
midscale, and ethnically diverse, The Cosmopolitans are urbane couples in America's fast‐growing cities. Concentrated in a handful of metros‐‐
such as Las Vegas, Miami, and Albuquerque‐‐these households feature older, empty‐nesting homeowners. A vibrant social scene surrounds their older homes and apartments, and residents love the nightlife and enjoy leisure‐intensive lifestyles. the cosmopolitans trade area study
| 37
Averrage Additional Demand (all others)
CEX DEMAND ESTIMATES BASED ON ASSORTED CHARACTERISTICS
Average White (non‐
Hispanic) Demand
19,285,398
5,516,060
10,054,745
5,922,261
6,482,133
footwear
fees + admissions
13,248,154
16,709,155
19,639,040
8,740,232
14,584,145
fees + admissions
3,613,206
5,447,939
5,483,904
2,979,368
4,381,104
fees + admissions
15,865,122
17,887,662
16,323,584
10,540,668
15,154,259
9,142,158
fees + admissions
23,519,018
26,072,007
27,303,277
21,113,452
24,501,939
7,363,164
8,758,232
8,673,536
7,197,148
7,998,020
28,318,176
30,344,245
29,156,156
25,462,698
28,320,319
15,440,962
17,713,297
17,792,691
14,539,458
16,371,602
4,691,038
6,895,820
7,180,510
3,027,543
5,448,728
other entertainment books + + sports
magazines
2,056,828
623,532
3,437,800
855,108
3,509,671
861,494
1,498,658
332,038
2,625,739 668,043
books + magazines
2,702,733
2,954,968
3,227,370
1,174,713
2,514,946
books + magazines
1,358,494
1,805,574
1,754,923
670,773
1,397,441
16,371,602
9,320,646
5,448,728
other pets + toys + entertainment books + hobbies
+ sports
magazines
13,197,426
7,819,898
2141262
16,311,661
10,356,044
2309867
18,187,412
13,304,190
2336288
9,029,818
4,396,442
974062
14,181,579 8,969,144 1,940,370
3,685,502
5,543,887
5,601,044
3,078,082
4,477,129
other pets + toys + entertainment hobbies
+ sports
16,251,918
9,556,011
18,564,827
11,324,036
18,284,992
10,307,320
10,889,907
5,302,083
15,997,911
9,122,363
8,170,093
11,356,052
11,538,192
6,218,247
9,320,646
1,397,441
books + magazines
105,119,281 69,884,201 9,640,950 28,021,334 8,345,586 15,283,655 10,182,490 10,244,030 14,584,145 24,501,939 14,181,579 8,969,144 1,940,370
40,638,158 28,351,524 4,621,567 12,658,820 3,438,022 6,246,735 3,311,577 3,431,874 6,692,405 10,540,497 6,773,816 4,191,121 1,061,924
26,326
10,880
33,218,731 22,169,175 3,261,006 9,288,960 2,708,095 4,918,404 2,922,203 3,202,998 4,381,104 7,998,020 4,477,129 2,625,739 668,043
6,629,074
women's clothes
footwear
8,089,322
8,915,405
11,440,216
12,531,176
10,244,030
footwear
2,693,264
2,865,854
2,981,248
4,271,624
3,202,998
footwear
9,544,572
9,705,752
8,394,825
15,112,524
10,689,418
footwear
5,485,504
5,677,228
6,136,395
8,629,404
6,482,133
fees + admissions
8,078,209
11,038,579
11,433,016
6,018,828
9,142,158
8,974
45,237,759
alcohol
men's clothes
7,291,520
8,946,893
13,618,908
10,872,638
10,182,490
2,364,470
2,658,463
2,959,618
3,706,262
2,922,203
8,453,151
9,513,474
6,223,802
13,112,337
9,325,691
4,863,853
5,128,651
6,209,262
7,487,277
5,922,261
113,200,799 76,892,409 11,864,359 33,572,547 9,382,117 17,058,365 9,325,691 10,689,418 15,154,259 28,320,319 15,997,911 9,122,363 2,514,946
78,176,557
food at home
food away from home
women's clothes
13,932,946
15,379,983
17,342,389
14,479,300
15,283,655
women's clothes
4,352,160
5,167,396
5,218,358
4,935,700
4,918,404
women's clothes
16,597,116
17,580,696
16,593,696
17,461,950
17,058,365
women's clothes
9,126,584
10,292,394
10,829,052
9,970,950
10,054,745
31,749
18,129
26,259,050
29,040,845
32,644,499
24,140,942
28,021,334
CEX DEMAND ESTIMATES BASED ON MEXICAN food away from CHARACTERISTICS
food at home
home
alcohol
Tenure
89,995,704
62,473,950
10,642,172
Age
99,523,586
72,283,449
11,666,071
Household Size
125,337,923
79,648,881
9,410,798
Race + Hispanic Origin
105,619,912
65,130,524
6,844,760
average
105,119,281 69,884,201 9,640,950
men's clothes
7,872,500
8,523,310
9,141,386
7,845,148
8,345,586
men's clothes
2,516,242
2,841,377
2,800,508
2,674,252
2,708,095
7,857,158
10,399,575
10,669,950
8,229,158
9,288,960
men's clothes
5,216,683
5,673,360
5,771,756
5,402,442
5,516,060
men's clothes
9,267,120
9,668,035
9,132,109
9,461,202
9,382,117
alcohol
7,019,811
7,596,467
7,186,478
4,713,540
6,629,074
16,886,905
21,732,326
21,898,067
16,624,293
19,285,398
32,859,126
35,582,539
36,734,688
29,113,833
33,572,547
food at home
100,700,906
67,455,930
71,815,845
72,733,548
78,176,557
food away from home
40,934,047
46,832,938
48,332,903
44,851,146
45,237,759
CEX DEMAND ESTIMATES BASED ON WHITE (NON‐
HISPANIC) food away from CHARACTERISTICS
food at home
home
alcohol
Tenure
108,452,049
74,382,810
12,479,439
Age
113,413,696
80,939,464
13,431,997
Household Size
103,560,463
73,700,335
13,291,261
127,376,988
78,547,026
8,254,740
76,892,409
11,864,359
average
113,200,799
CEX DEMAND ESTIMATES BASED ON AFRICAN AMERICAN food away from CHARACTERISTICS
food at home
home
alcohol
Tenure
28,763,482
19,526,218
3,375,738
Age
33,327,460
23,488,646
3,817,424
Household Size
34,780,294
23,460,159
3,517,621
36,003,688
22,201,676
2,333,240
average
33,218,731 22,169,175 3,261,006
CEX DEMAND ESTIMATES BASED ON PUERTO RICAN CHARACTERISTICS
Tenure
Age
Household Size
average
Appendix B: CEX Demand Estimates Based on Segment Characteristics
trade area study
| 38
Per Household Average Annual Demand by Race + Origin DEFLATED 20%
Hispanic) Demand
Per Household Average Annual Demand by Race + Origin
Hispanic) Demand
358
504
903
514
301
77
1,996
1,938
1,976
2,124
2,085
3,450
2,852
2,961
3,194
2,988
31,749
8,974
26,326
10,880
food at home
food away from home
18,129
340
293
291
299
293
alcohol
931
852
828
846
851
253
254
241
236
243
men's clothes
459
464
438
430
444
women's clothes
243
309
261
235
261
252
311
286
269
286
footwear
492
443
391
382
403
775
745
713
714
722
av equipment + fees + admissions
music
498
431
399
403
411
308
273
234
230
240
other pets + toys + entertainment + sports
hobbies
78
59
60
63
62
books + magazines
3,735 2,606 425 1,163 316 574 304 315 615 969 623 385 98
327
books + magazines
10,880
555
other pets + toys + entertainment + sports
hobbies
3,993 2,655 366 1,064 317 581 387 389 554 931 539 341 74
304
footwear
av equipment + fees + admissions
music
26,326
1,064
3,702 2,470 363 1,035 302 548 326 357 488 891 499 293 74
366
women's clothes
8,974
2,495
alcohol
men's clothes
3,565 2,422 374 1,057 296 537 294 337 477 892 504 287 79
4,312
food at home
food away from home
31,749
18,129
trade area study
| 39
books + magazines
fees + admissions
footwear
children's clothes
women's clothes
men's clothes
alcohol
food away from home
CEX Category
food at home
description of category
supermarket, market
convenience stores
meat + fish markets
vegetable markets
candy, nut + confectioners
creameries
bakeries
other specialty food
full‐service restaurant
fast‐casual restaurant
buffets + cafeterias
snacks + nonalcoholic drinks
liquor store
liquor store
liquor store
drugs
eye glasses / contacts
other medical suppies
personal care products
men's clothes
men's clothes
women's clothes
women's clothes
children's clothing
family clothing
footwear
footwear
performing arts companies
spectator sports
promotors with facilities
amusement parks + arcades
other amusement and recreation
organizational memberships
recreational lessons
movie rentals
instruments
av/tv sales
recorded music
video games
pet stuff
sewing
sporting goods
sporting goods
camera + film
boats
books
magazines + periodicals
NAICS
44511
44512
44521, 44522
44523
445292
n/a
445291
445299
72210
72211
72212
72213
4453
"
"
44611
44613
44619
44612
44811
"
44812
"
44813
44814
44821
"
7111
7112
71131
7131
7139
"
"
"
"
"
"
45114
443112
451220
443120
45391
45112
"
45113
45111
"
44313
441222
451211
451212
43
113
332
68
166
724
186
1312
109
397
353
362
1,246 297
949
1076
"
387
185
107
108
2629
400 880 881 2,004 3,416 1,076 303 1,382 1382
3,589 1,297 20,139 1687
579
724
599
303
193
227
8980
9069
192
1898
1297
IL establishments SUM by NAICS from establishments CBP 2010
by CEX baskets 2821
4,649 824
253
95
236
Appendix C: Inferred Average Annual Sales per CEX Basket
SIC
5411
"
5421
5431
5441
5451
5461
5499
5812
"
"
"
5181
5182
5921
5912
5995
5999
"
5611
5136
5621
5137
5641
5651
5661
5139
7922
7996
7993
7933
7911
7992
7997
7941
7991
7999
7841
5736
5731
5735
5734
5999
5092
5945
5949
5941
5091
5946
5551
5942
5994
MEN'S AND BOYS' CLOTHING STORES
MEN'S AND BOY'S CLOTHING
WOMEN'S CLOTHING STORES
WOMEN'S AND CHILDREN'S CLOTHING
CHILDREN'S AND INFANTS' WEAR STORES
FAMILY CLOTHING STORES
SHOE STORES
FOOTWEAR
THEATRICAL PRODUCERS AND SERVICES
AMUSEMENT PARKS
COIN‐OPERATED AMUSEMENT DEVICES
BOWLING CENTERS
DANCE STUDIOS, SCHOOLS, AND HALLS
PUBLIC GOLF COURSES
MEMBERSHIP SPORTS AND RECREATION CLUBS
SPORTS CLUBS, MANAGERS, AND PROMOTERS
PHYSICAL FITNESS FACILITIES
AMUSEMENT AND RECREATION, NEC
VIDEO TAPE RENTAL
MUSICAL INSTRUMENT STORES
RADIO, TELEVISION, AND ELECTRONIC STORES
RECORD AND PRERECORDED TAPE STORES
COMPUTER AND SOFTWARE STORES
INTERPOLATED PET STORE SALES TOYS AND HOBBY GOODS AND SUPPLIES
HOBBY, TOY, AND GAME SHOPS
SEWING, NEEDLEWORK, AND PIECE GOODS
SPORTING GOODS AND BICYCLE SHOPS
SPORTING AND RECREATION GOODS
CAMERA AND PHOTOGRAPHIC SUPPLY STORES
BOAT DEALERS
BOOK STORES
NEWS DEALERS AND NEWSSTANDS
BEER AND ALE
WINE AND DISTILLED BEVERAGES
LIQUOR STORES
DRUG STORES AND PROPRIETARY STORES
OPTICAL GOODS STORES
MISCELLANEOUS RETAIL STORES, NEC
MEAT AND FISH MARKETS
FRUIT AND VEGETABLE MARKETS
CANDY, NUT, AND CONFECTIONERY STORES
DAIRY PRODUCTS STORES
RETAIL BAKERIES
MISCELLANEOUS FOOD STORES
EATING PLACES
DESCRIPTION
GROCERY STORES
10,005,442
1,407,161
30,880,879
2,036,845
6,835,729
136,776,661
24,326,081
4,488,387
1,568,920
587,958
430,098
2,877,173
173,382
1,698,012
7,881,278
274,253
964,112
2,696,951
1,439,553
4,210,025
25,427,001
1,677,022
99,985,179
$763,700 / store*
490,986
15,138,517
5,060,680
34,510,905
1,581,228
1,032,679
3,940,579
10,195,120
1,543,536
32,729
2,125,165
74,686,907
62,227,057
408,147
169,150,213
1,034,418
6,499,156
3,230,502
1,168,833
10,824,608
13,293,491
546,836,082
IDR reported STATE TAX LEVIED, 2011 230,756,456
0.0625
0.0625
0.0625
0.06
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.0625
0.01
0.01
0.0625
0.01
0.01
0.01
0.01
0.01
0.01
0.0625
160,087,073
22,514,572
494,094,069
32,589,524
109,371,658
2,188,426,572
389,217,298
71,814,197
25,102,721
9,407,320
6,881,569
46,034,770
2,774,113
27,168,196
126,100,449
4,388,044
15,425,798
43,151,218
23,032,840
67,360,393
406,832,021
26,832,348
1,599,762,856
269,586,100
7,855,772
242,216,279
80,970,884
552,174,482
25,299,649
16,522,859
63,049,259
163,121,919
24,696,572
523,662
34,002,647
1,194,990,510
6,222,705,717
40,814,746
2,706,403,407
103,441,822
649,915,550
323,050,174
116,883,311
1,082,460,848
1,329,349,129
8,749,377,318
INFERRED ANNUAL STATE TAX SALES BY RETAIL RATE
SEGMENT 0.01 23,075,645,550
187,818,491
657,046,249
600,629,036
2,100,787,618
329,467,038
461,031,495
2,297,798,230
526,683,593
182,601,645
8,969,923,870
1,229,516,819
8,749,377,318
469,546
746,643
681,758
1,048,297
96,448
428,468
1,844,140
381,102
602,646
2,499,282
947,970
434,449
SUM INFERRED INFERRED AVERAGE ANNUAL SALES ANNUAL SALES PER PER CEX BASKET CEX BASKET
26,680,746,384 5,739,029
for questions or spreadsheets,
elizabeth scott
please contact:
elizabethduhringscott@gmail.com
photo credits:
cover page and this page : Zol87 via Flickr
methodology cover page: TheeErin via Flickr
trade area study
| 40

Poker Bedrageri Svenska Spel

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