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
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
Regional Analysis Area Analysis Site Analysis Location Analysis
Information Minimizes Risk Risk Criteria
Factors Affecting Interest ina Region or Trade Area Source: Levy & Weitz
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
Decennial Census Data Complete Count Data Items Population Items Housing Items . Relationship to household head Number of units at this address Color or race Telephone Age Private entrance to living quarters Sex Complete kitchen facilities Marital status Rooms Water supply Flush toilet Bathtub or shower Basement Tenure (owner/renter) Property Value Contract rent Vacancy status Months vacant 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 . Source: The Census and You, U.S. Departmentof 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) BPI Salt Lake City .471 San Diego .935 New 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 Firms Urban Decision Systems Map Objects (W3) Microvision Zip Code System Claritas Corporation’s PRIZM database
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
E.g., in Utah Valley (2000): Population = 358,000 Annual per capita retail spending = $10,377 IRS = 358,000 x $10,377 = $ X ??? Issues Affecting Competition:Index of Retail Saturation IRS = area retail spending retail selling space
Utah Valley Retail Selling Space Provo CBD 1,400,000 University Mall 1,260,000 Provo Towne Centre 950,000 Shops at Riverwoods 190,000 Carillon Square 525,000 American Fork CBD 320,000 Parkway Village 443,000 Pason CBD 360,000 Provo Big Lots Strip Mall 100,000 Orem Center Street 260,000 Springville CBD 175,000 Pleasant Grove CBD 120,000 Lehi CBD 100,000 Brigham’s Landing 120,000 East Bay 300,000 Riverside Shopping Center 250,000 Am Fork Shopping Center 115,000 Northgate Shopping Center 110,000 Park Place 220,000 Macey’s Orem 100,000 Timp Plaza 100,000 University Festival Mall 125,000 Expressway Square 85,000 Edgemont Center 40,000 North Park Shopping Center 125,000 TOTAL 8,013,000 IRS = 358,000 x $10,377 8,013,000 = $464 Interpretation? Source: Colliers & Clarke, Provo, 2002; Utah Valley Economic Development, Provo/Orem Chamber of Commerce This number istoo low
Issues Affecting Competition:Market Expansion Potential MEP - indicates an area’s potential for creating new demand MEP = Expected Sales Actual Sales
Store Average White- % of Predominant Level of Location 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 Medium Site B 70,000 Low 80 Gray Power Low Site C 100,000 High 30 Young Literati Low Site D 120,000 High 50 Money & Brains Medium SuccessfulStore 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
A Provo pop’n = 358K B Salt Lake pop’n = 1275K 45 miles 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?
Gravity Models A B C Site Possibility D Competitors How could we use a Gravity modeling approach here?
Sq feet of selling space in j Travel time from i to j Relative Attractive- ness of store j Probability that consumer living in area i will shop at 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 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 Travel time from i to j Relative Attractive- ness of store j Probability that consumer living in area i will shop at 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 S1 P(Cij) = (Ti1) n j=1 Sj (Tij) Gravity Models:Huff’s Intramarket Area Model 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)
Gravity Models:Huff’s Intramarket Area Model UniversityMall ¶ 2 mi RiverwoodsShopping Center Provo TownCentre ¸ · 4 mi 3 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 thus: 10,000 P(Ci1) = 22 = .46 10,000 + 15,000 + 20,000 22 32 42 Interpretation?
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.) Regression Analysis Approaches Can use data from current successful stores to predictthe probability of success for stores were they placedon sites being considered.
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 = 1 + .5X Y = 1 + 1X 5 4 3 2 1 Y-axise.g., sales X-axise.g., population 1 2 3 4 5 6 7 8
Regression Examplefrom Branch-Bank Performance Model RegressionCumulative Variable 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 .72 C: # of competing banks' branches - 8.689 1.37 .79 SD: Savings deposits ($1,000): PP: Purchasing power ($1,000) 0.002 6.25 .57 EL: Employment level 0.038 3.93 .77 RH: Percentage of renter housing -1.197 2.00 .81
Regression Example Annual Sales for 10 Home Improvement Centers 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 Source: John S. Thompson, Site Selection (New York: Lebar-Friedman), p. 133.
10 Home Improvement Centers 800 -- 700 -- 600 -- 500 -- 400 -- 366 -- 300 -- 200 -- 100 -- 10 7 Change in sales = slope = 0.007 Change in population Sales ($000) 3 6 2 1 8 Change in sales 4 9 Change in population 5 90.97 0 10 20 30 40 50 60 70 80 90 Population (000) What is the regression equation?
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?
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 ... Sales forecast will change by $1000 in mean household income _________ 50 cars per minute _________ 1 retailer in the trade area _________
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?
Multiple Regression Procedure • Select a group of existing stores • Identify a dependent variable (e.g., revenues) • Select a set of independent variables logically related to the dependent variable • Collect data on all variables for both existing stores (including revenues) and the proposed sites (no revenues of course) • Enter the data & develop the a regression equation • Use equation to forecast sales or share for the proposed sites • Focus further attention on sites having highest forecasts
Regression Example:Hogi Yogi 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) 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 Source: BYU Site selectionstudy for Hogi Yogi, 1995
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) Actual Sales Predicted Sales Source: BYU Site selectionstudy for Hogi Yogi, 1995