Location II 8.1

Key Issues

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

Location II 8.2

Selecting a Retail Site

The Expansion StageUse real estate firms, local personnel, etc.to identify a large number of possible sites

The Qualitative StageUse checklists or examine performance of analogous

stores to screen possible sites for the best sites

The Quantitative StageUse quantitative modeling to further screen the likelysites by generating forecasted potentials for each site

The Decision StageMake a site selection decision based on the results of

both the quantitative qualitative assessments

Location II 8.3

Location Analysis

AreaAnalysis

Site Analysis

RegionalAnalysis

Location II 8.4

Information Minimizes Risk

Risk Criteria

Location II 8.5

Factors Affecting Interest ina Region or Trade Area

Source: Levy & Weitz

Location II 8.6

Methods: The “Quantitative” Stage

• Measuring DemandMeasuring Demand• Decennial Census of the U.S.Decennial Census of the U.S.• Buying Power Index (BPI)Buying Power Index (BPI)• Private FirmsPrivate Firms

• Issues Affecting CompetitionIssues Affecting Competition• Index of Retail SaturationIndex of Retail Saturation• Market Expansion PotentialMarket Expansion Potential• Private FirmsPrivate Firms

• Measuring Trade AreasMeasuring Trade Areas• Analog (similar store) ApproachAnalog (similar store) Approach• Customer Spotting (trade-area Customer Spotting (trade-area

mapping)mapping)• Gravity ModelsGravity Models

• Reilly’s Law of Retail GravitationReilly’s Law of Retail Gravitation• Converse’s Breaking Point ModelConverse’s Breaking Point Model• Huff’s Model of Intermarket Huff’s Model of Intermarket

AttractionAttraction• Multiple Regression AnalysisMultiple Regression Analysis

Location II 8.7

Decennial Census Data Complete Count Data Items

Population Items Housing Items .

Relationship to household head Number of units at this addressColor or race TelephoneAge Private entrance to living quartersSex Complete kitchen facilitiesMarital status Rooms

Water supplyFlush toiletBathtub or showerBasementTenure (owner/renter)Property ValueContract rentVacancy statusMonths vacant

Source: The Census and You, U.S. Departmentof Commerce, Bureau of the Census.

Typical Sampled Data Items Population ItemsState or country of birthYears of schoolingNumber of childrenEmployment statusHours worked last week, yearLast year in which workedOccupationIncome, by typeCountry of birth & of parentsMother tongueYear moved into this houseMeans of transportation .

Location II 8.8

Measuring Demand

BPI: Buying Power IndexRetail demand in an area as a % of total retail demand in the U.S.

BPI = (.5 x Income) + (.3 x Sales) + (.2 x Population) BPI

Salt Lake City .471San Diego .935New York City 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 FirmsUrban Decision SystemsMap Objects (W3)Microvision Zip Code SystemClaritas Corporation’s PRIZM database

Location II 8.9

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 8.10

Issues Affecting Competition:Index of Retail Saturation

IRS = IRS = area retail spendingarea retail spending retail selling spaceretail selling space

???

E.g., in Utah Valley (2000):Population = 358,000Annual per capita retail spending =

$10,377

IRS = 358,000 x $10,377 = $ X

Location II 8.11

IRS = IRS = 358,000 x $10,377358,000 x $10,377 8,013,0008,013,000

= $464 = $464

Interpretation?Interpretation?

Utah Valley Retail Selling Space

Source: Colliers & Clarke, Provo, 2002;Utah Valley Economic Development,Provo/Orem Chamber of Commerce

Provo CBD 1,400,000University Mall 1,260,000Provo Towne Centre 950,000Shops at Riverwoods 190,000Carillon Square 525,000American Fork CBD 320,000Parkway Village 443,000Pason CBD 360,000Provo Big Lots Strip Mall 100,000Orem Center Street 260,000Springville CBD 175,000Pleasant Grove CBD 120,000Lehi CBD 100,000Brigham’s Landing 120,000East Bay 300,000Riverside Shopping Center 250,000Am Fork Shopping Center 115,000Northgate Shopping Center 110,000Park Place 220,000Macey’s Orem 100,000Timp Plaza 100,000University Festival Mall 125,000Expressway Square 85,000Edgemont Center 40,000North Park Shopping Center 125,000

TOTAL 8,013,000

Provo CBD 1,400,000University Mall 1,260,000Provo Towne Centre 950,000Shops at Riverwoods 190,000Carillon Square 525,000American Fork CBD 320,000Parkway Village 443,000Pason CBD 360,000Provo Big Lots Strip Mall 100,000Orem Center Street 260,000Springville CBD 175,000Pleasant Grove CBD 120,000Lehi CBD 100,000Brigham’s Landing 120,000East Bay 300,000Riverside Shopping Center 250,000Am Fork Shopping Center 115,000Northgate Shopping Center 110,000Park Place 220,000Macey’s Orem 100,000Timp Plaza 100,000University Festival Mall 125,000Expressway Square 85,000Edgemont Center 40,000North Park Shopping Center 125,000

TOTAL 8,013,000This number istoo low

Location II 8.12

Issues Affecting Competition:Market Expansion Potential

MEP - indicates an area’s potential for creating new demand

MEP = Expected Sales Actual Sales

Location II 8.13

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 areas3. Match the characteristics of these stores with potential new store

locations to determine the best site

Store Average White- % of Predominant Level ofLocation Household Collar Residents PRIZM Profile Competition

Income Occup. Age 45+

Optics City $ 99,999 High 38% Blue Blood Est. Low

Site A 60,000 High 25 Young Suburbia MediumSite B 70,000 Low 80 Gray Power LowSite C 100,000 High 30 Young Literati LowSite D 120,000 High 50 Money & Brains Medium

SuccessfulStore

Location II 8.14

Measuring Trade AreasGravity Models

Location II 8.15

Gravity Models:Reilly’s Law of Retail Gravitation

A BAt what point between A & B will shoppers go to A? B?

AProvo

pop’n = 358K

BSalt Lake

pop’n = 1275K45 miles

Breaking PointDist(A) Dist(B)

Location II 8.16

Gravity Models

Site Possibility

How could we use a Gravity modeling approach here?

A

D

B

C

Competitors

Location II 8.17

Gravity Models:Huff’s Intramarket Area Model

Probability thatconsumer living

in area i willshop at store j

Sq feet of sellingspace in j

Travel timefrom i to j

Sq ft of selling spacein other stores

in trade area

Travel time from ito other stores

in trade area

Relative Attractive-ness of store j

Total Attractivenessof other stores

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

Prob(shoppingj) = f(selling spacej, travel timeij)

Location II 8.18

Gravity Models:Huff’s Intramarket Area Model

where:where:

P(CP(Cijij) = probability that consumer C living in area i will visit shopping center j) = probability that consumer C living in area i will visit shopping center j

SSj j = square footage of selling space in j devoted to a particular class of = square footage of selling space in j devoted to a particular class of

merchandisemerchandise

TTij ij = travel time from area i to shopping center j= travel time from area i to shopping center j

= an estimated parameter to reflect the effect of travel time on various kinds of = an estimated parameter to reflect the effect of travel time on various kinds of

shopping trips (larger shopping trips (larger reflects greater weight for travel time)reflects greater weight for travel time)

S1

(Ti1)

Sj

(Tij)

n

j=1

P(Cij) =

Probability thatconsumer living

in area i willshop at store j

Sq feet of sellingspace in j

Travel timefrom i to j

Sq ft of selling spacein other stores

in trade area

Travel time from ito other stores

in trade area

Relative Attractive-ness of store j

Total Attractivenessof other stores

Location II 8.19

3 mi

2 mi

4 mi

where:where:SS11 = 10,000 square feet = 10,000 square feet

SS22 = 15,000 square feet = 15,000 square feet

SS33 = 20,000 square feet = 20,000 square feet

TTi1i1 = 2 miles = 2 miles

TTi2i2 = 3 miles = 3 miles

TTi3i3 = 4 miles = 4 miles

= 2= 2

thus:thus: 10,00010,000P(CP(Ci1i1) = 2) = 222 = .46 = .46

10,00010,000 + + 15,00015,000 + + 20,00020,000 2222 3 32 2 4422

InterpretationInterpretation??

i

UniversityMall

Provo TownCentre

RiverwoodsShopping Center

Gravity Models:Huff’s Intramarket Area Model

Location II 8.20

Regression Analysis Approaches

Can use data from current successful stores to predictthe 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 8.21

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:

5

4

3

2

1

1 2 3 4 5 6 7 8X-axise.g., population

Y-axise.g., sales

Y = 1 + .5XY = 1 + 1X

Location II 8.22

Regression Examplefrom Branch-Bank Performance Model

Regression CumulativeVariable Coefficient t-value R2

CD: Checking deposits ($1,000):

HI: Median household income ($l00) 1.271 3.26 .55 SF: Retail square footage (1,000) 0.237 3.36 .72C: # of competing banks' branches - 8.689 1.37 .79

SD: Savings deposits ($1,000):

PP: Purchasing power ($1,000) 0.002 6.25 .57EL: Employment level 0.038 3.93 .77RH: Percentage of renter housing -1.197 2.00 .81

Location II 8.23

Regression Example

Store Yearly Sales 0 to 3-Mile Radius ($000) 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

Annual Sales for 10 Home Improvement Centers

Source: John S. Thompson, Site Selection (New York: Lebar-Friedman), p. 133.

Location II 8.24

10 Home Improvement Centers

800 --

700 --

600 --

500 --

400 --366 --

300 --

200 --

100 --

0 10 20 30 40 50 60 70 80 90

Sa

les

($00

0)

1

5

9

4

2 8

3 6

710

90.97

Population (000)

Change in sales = slope = 0.007

Change in population

What is the regression equation?

Change in sales

Change in population

Location II 8.25

Critiquing a MultipleRegression 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 8.26

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 siteCR = # competing retailers within a mile of site

For an increase of ... Sales forecast will change by

$1000 in mean household income _________

50 cars per minute _________

1 retailer in the trade area _________

Location II 8.27

Problems with this 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?

Forecasted Annual Sales (in $000) = 200 + 8.4HHI + 1.2TC - 11.3CRwhere, HHI = mean trade-area household income (in $000)

TC = average no. of cars per minute driving past the siteCR = # competing retailers within a mile of site

Location II 8.28

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 8.29

Regression Example:Hogi Yogi

Method:

20 Hogi Yogi stores were initially analyzed by means of 70 variables per store, along with sales data.

Data was examined for (1) the first 4 months of a store’s opening and (2) the first year of sales.

Using stepwise regression, all 70 were examined, to develop two equations:

1. Initial 4-month sales upon opening a new store.

2. Sustained sales during the first year of operation

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

Clustering: The degree to which the store is clustered with other restaurants (0 to 3)

RGI: Secondary data on Restaurant Growth Index (which shows the relationship between restaurant supply & demand, by market, with average = 100

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 8.30

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)

Act

ual S

ales

Predicted Sales

Source: BYU Site selectionstudy for Hogi Yogi, 1995