Key Issues
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Location II 8.1
Elements of risk
Quantitative methods of location analysis
Buying Power Index
Index of Retail Saturation
Market expansion potential
Gravity models: Reilly, Huff
Regression as used in trade area analysis
Selecting a Retail Site
The Expansion Stage
Use real estate firms, local personnel, etc.
to identify a large number of possible sites
The Qualitative Stage
Use checklists or examine performance of analogous
stores to screen possible sites for the best sites
The Quantitative Stage
Use quantitative modeling to further screen the likely
sites by generating forecasted potentials for each site
The Decision Stage
Make a site selection decision based on the results of
both the quantitative qualitative assessments
Location II 8.2
Location Analysis
Regional
Analysis
Area
Analysis
Site Analysis
Location II 8.3
Information Minimizes Risk
Risk Criteria
Location II 8.4
Factors Affecting Interest in
a Region or Trade Area
Source: Levy & Weitz
Location II 8.5
Methods: The “Quantitative” Stage
• Measuring Demand
• Decennial Census of the U.S.
• Buying Power Index (BPI)
• Private Firms
• Issues Affecting Competition
• Index of Retail Saturation
• Market Expansion Potential
• Private Firms
• Measuring Trade Areas
• Analog (similar store) Approach
• Customer Spotting (trade-area mapping)
• Gravity Models
• Reilly’s Law of Retail Gravitation
• Converse’s Breaking Point Model
• Huff’s Model of Intermarket Attraction
• Multiple Regression Analysis
Location II 8.6
Decennial Census Data
Complete Count Data Items
Location II 8.7
Population Items
Housing Items
Relationship to household head
Color or race
Age
Sex
Marital status
Typical Sampled Data Items
Population Items
State or country of birth
Years of schooling
Number of children
Employment status
Hours worked last week, year
Last year in which worked
Occupation
Income, by type
Country of birth & of parents
Mother tongue
Year moved into this house
Means of transportation .
Number of units at this address
Telephone
Private entrance to living quarters
Complete kitchen facilities
Rooms
Water supply
Flush toilet
Bathtub or shower
Basement
Tenure (owner/renter)
Property Value
Contract rent
Vacancy status
Months vacant
.
Source: The Census and You, U.S. Department
of Commerce, Bureau of the Census.
Measuring Demand
BPI: Buying Power Index
Retail demand in an area as a % of total retail demand in the U.S.
BPI = (.5 x Income) + (.3 x Sales) + (.2 x Population)
Salt Lake City
San Diego
New York City
BPI
.471
.935
3.345
Decennial Census of the U.S.
SMSA: Standard Metropolitan Statistical Area
“a central city of 50,000+ population, the counties in which it is located,
and other contiguous metropolitan counties that are economically and
socially integrated with the central city”
Private Firms
Urban Decision Systems
Map Objects (W3)
Microvision Zip Code System
Claritas Corporation’s PRIZM database
Location II 8.8
Issues Affecting Competition:
Index of Retail Saturation
Shows the relationship between demand and supply
for a store type -- the average retail spending per
square foot of selling space
IRS = population of area x per capita retail spending
retail selling space (in sq. feet)
Or just …
IRS = area retail spending
retail selling space
Location II 8.9
Issues Affecting Competition:
Index of Retail Saturation
IRS = area retail spending
retail selling space
E.g., in Utah Valley (2000):
Population = 358,000
Annual per capita retail spending =
$10,377
IRS = 358,000 x $10,377 = $
X
Location II 8.10
???
Utah Valley Retail Selling Space
Provo CBD
University Mall
Provo Towne Centre
Shops at Riverwoods
Carillon Square
American Fork CBD
Parkway Village
Pason CBD
Provo Big Lots Strip Mall
Orem Center Street
Springville CBD
Pleasant Grove CBD
Lehi CBD
Brigham’s Landing
East Bay
Riverside Shopping Center
Am Fork Shopping Center
Northgate Shopping Center
Park Place
Macey’s Orem
Timp Plaza
University Festival Mall
Expressway Square
Edgemont Center
North Park Shopping Center
TOTAL
Location II 8.11
1,400,000
1,260,000
950,000
190,000
525,000
320,000
443,000
360,000
100,000
260,000
175,000
120,000
100,000
120,000
300,000
250,000
115,000
110,000
220,000
100,000
100,000
125,000
85,000
40,000
125,000
8,013,000
IRS = 358,000 x $10,377
8,013,000
= $464
Interpretation?
This number is
too low
Source: Colliers & Clarke, Provo, 2002;
Utah Valley Economic Development,
Provo/Orem Chamber of Commerce
Issues Affecting Competition:
Market Expansion Potential
MEP - indicates an area’s potential for creating new
demand
MEP = Expected Sales
Actual Sales
Location II 8.12
Measuring Trade Areas:
Analog Approach
Information about customers, competition, and sales of current similar
stores are used to predict sales of a new store or center.
Steps:
1. Determine trade area for successful stores (customer spotting)
2. Characterize their primary, secondary, fringe trade areas
3. Match the characteristics of these stores with potential new store
locations to determine the best site
Store
Location
Successful
Store
Location II 8.13
Average
Household
Income
WhiteCollar
Occup.
% of
Residents
Age 45+
Predominant
PRIZM Profile
Level of
Competition
Optics City $ 99,999
High
38%
Blue Blood Est.
Low
Site A
Site B
Site C
Site D
High
Low
High
High
25
80
30
50
Young Suburbia
Gray Power
Young Literati
Money & Brains
Medium
Low
Low
Medium
60,000
70,000
100,000
120,000
Measuring Trade Areas
Gravity Models
Location II 8.14
Gravity Models:
Reilly’s Law of Retail Gravitation
Breaking Point
Dist(B)
Dist(A)
A
B
At what point between A & B will shoppers go to A? B?
A
Provo
pop’n = 358K
Location II 8.15
45 miles
B
Salt Lake
pop’n = 1275K
Gravity Models
A
B
C
Site Possibility
D
Competitors
How could we use a Gravity modeling approach here?
Location II 8.16
Gravity Models:
Huff’s Intramarket Area Model
Prob(shoppingj) = f(selling spacej, travel timeij)
Based on the premise that the probability that a given
customer will shop in a particular store
or shopping center becomes larger as the size
of store or center grows and distance or
travel time from customer shrinks
Sq feet of selling
space in j
Probability that
consumer living
in area i will
shop at store j
Travel time
from i to j
Relative Attractiveness of store j
Sq ft of selling space
in other stores
in trade area
Total Attractiveness
of other stores
Travel time from i
to other stores
in trade area
Location II 8.17
Gravity Models:
Huff’s Intramarket Area Model
Sq feet of selling
space in j
S1
P(Cij) =
(Ti1) 
n

j=1
Sj
(Tij) 
Probability that
consumer living
in area i will
shop at store j
Travel time
from i to j
Relative Attractiveness of store j
Sq ft of selling space
in other stores
in trade area
Total Attractiveness
of other stores
Travel time from i
to other stores
in trade area
where:
P(Cij) = probability that consumer C living in area i will visit shopping center j
Sj = square footage of selling space in j devoted to a particular class of
merchandise
Tij = travel time from area i to shopping center j
 = an estimated parameter to reflect the effect of travel time on various kinds of
shopping trips (larger reflects greater weight for travel time)
Location II 8.18
Gravity Models:
Huff’s Intramarket Area Model
University
Mall

2 mi
Riverwoods
Shopping Center

3 mi
Location II 8.19
4 mi
i
where:
S1 = 10,000 square feet
S2 = 15,000 square feet
S3 = 20,000 square feet
Ti1 = 2 miles
Ti2 = 3 miles
Ti3 = 4 miles
= 2
Provo Town
Centre

thus:
10,000
P(Ci1) =
22
= .46
10,000 + 15,000 + 20,000
22
32
42
Interpretation?
Regression Analysis Approaches
Can use data from current successful stores to predict
the probability of success for stores were they placed
on sites being considered.
E.g.,
predicted sales on site A =
f(visibility of store from street,
no. of competitors in trade area,
population density in trade area,
average age in trade area,
average income in trade area,
number cars per hour past site,
etc.)
Location II 8.20
Regression Fundamentals
 R2 = variance in Y explained by X
 Equation for a line?
 Y = a + bX … a? b?
 a = intercept … point where line crosses Y axis
 b = slope … Y / X
 Interpret:
Y-axis
e.g., sales
Y = 1 + .5X
Y = 1 + 1X
5
4
3
2
1
X-axis
1
Location II 8.21
2
3
4
5
6
7
8
e.g., population
Regression Example
from Branch-Bank Performance Model
Variable
Regression
Coefficient
t-value
Cumulative
R2
1.271
0.237
- 8.689
3.26
3.36
1.37
.55
.72
.79
0.002
0.038
-1.197
6.25
3.93
2.00
.57
.77
.81
CD: Checking deposits ($1,000):
HI: Median household income ($l00)
SF: Retail square footage (1,000)
C: # of competing banks' branches
SD: Savings deposits ($1,000):
PP: Purchasing power ($1,000)
EL: Employment level
RH: Percentage of renter housing
Location II 8.22
Regression Example
Annual Sales for 10 Home Improvement Centers
Store
Yearly Sales
($000)
0 to 3-Mile Radius
Population
1
$ 402
54,000
2
367
29,500
3
429
49,000
4
252
22,400
5
185
18,600
6
505
61,100
7
510
49,000
8
330
33,200
9
210
26,400
10
655
83,200
Source: John S. Thompson, Site Selection (New
York: Lebar-Friedman), p. 133.
Location II 8.23
10 Home Improvement Centers
800 -700 --
Sales ($000)
600 --
10
7
500 --
3
400 -366 --
2
4
300 --
9
200 -100 --
8
6
Change in sales
= slope = 0.007
Change in population
1
Change in
sales
Change in
population
5
90.97
0
10 20 30 40 50 60 70 80 90
Population (000)
What is the regression equation?
Location II 8.24
Critiquing a Multiple
Regression Equation
 Is the equation complete on all important
variables?
 Do the signs of the independent variables
make sense?
 Is there logical reason to expect that the
performance of a store is related to the
independent variables?
 Are the regression forecasts kept within the
range of the input data?
Location II 8.25
Interpretation?
Suppose Media Play has developed a regression equation to identify
attractive retail trade areas and sites, based on a study of 100 of its stores
having from $450,000 to $1 million in sales.
Forecasted Annual Sales (in $000) = 200 + 8.4HHI + 1.2TC - 11.3CR
where,
HHI = mean trade-area household income (in $000)
TC = average no. of cars per minute driving past the site
CR = # competing retailers within a mile of site
For an increase of ...
$1000 in mean household income
50 cars per minute
1 retailer in the trade area
Location II 8.26
Sales forecast will change by
_________
_________
_________
Problems with this Equation?
Forecasted Annual Sales (in $000) = 200 + 8.4HHI + 1.2TC - 11.3CR
where, HHI = mean trade-area household income (in $000)
TC = average no. of cars per minute driving past the site
CR = # competing retailers within a mile of site
 Is the equation complete on all important variables?
 Do the signs of the independent variables make sense?
 Is there logical reason to expect that the performance of a
store is related to the independent variables?
 Are the regression forecasts kept within the range of the
input data?
Location II 8.27
Multiple Regression Procedure
1. Select a group of existing stores
2. Identify a dependent variable (e.g., revenues)
3. Select a set of independent variables logically related to the
dependent variable
4. Collect data on all variables for both existing stores (including
revenues) and the proposed sites (no revenues of course)
5. Enter the data & develop the a regression equation
6. Use equation to forecast sales or share for the proposed sites
7. Focus further attention on sites having highest forecasts
Location II 8.28
Regression Example:
Hogi Yogi
Method:
20 Hogi Yogi stores were
initially analyzed by means
of 70 variables per store,
along with sales data.
No. competitors: No. of frozen yogurt competitors
within 1-mile radius
Visibility: A subjective 1-7 index rating based on how
visible the store is within the shopping center or on
the street
Data was examined for (1)
the first 4 months of a
store’s opening and (2) the
first year of sales.
Clustering: The degree to which the store is
clustered with other restaurants (0 to 3)
Using stepwise regression,
all 70 were examined, to
develop two equations:
RGI: Secondary data on Restaurant Growth Index
(which shows the relationship between restaurant
supply & demand, by market, with average = 100
1. Initial 4-month sales
upon opening a new
store.
2. Sustained sales
during the first year
of operation
University: Whether there is a university within 1
mile (0=no, 1=yes)
Local owner: Whether the owner lives near the
restaurant (0=no, 1=yes)
Source: BYU Site selection
study for Hogi Yogi, 1995
Location II 8.29
Regression Example:
Hogi Yogi
$ Sales during the
first four months =
-$262984 +
No. competitors  (-$31330) +
Visibility  ($13297) +
Clustering  ($27566) +
3-mile/capita income  ($6.69)+
Size in sq ft  ($56.7) +
RGI  ($1382) +
University  ($27521) +
Local owner  ($21422)
Predicted Sales
Source: BYU Site selection
study for Hogi Yogi, 1995
Location II 8.30