Trading-Area Analysis. Understanding Retail Trade Analysis . by Al Myles, Economist and Extension Professor Department of Agriculture Economics Mississippi State University December 11, 2008 Presented at Oktibbeha County Leadership Forum.
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Al Myles, Economist and Extension Professor
Department of Agriculture Economics
Mississippi State University
December 11, 2008
Presented at Oktibbeha County Leadership Forum
Is a way to identify market trends within a local community, including the degree of surplus or leakage of dollars within specific retail sectors.
Retail trade is one of the most important indicators of economic activity in a community or county because local citizens spend a large part of their incomes on goods and services.
The measures of retail trade and spending reflect consumers’ preference for the retail mix in the area and show how well the economy is doing overall.
Since retail is one of the major economic forces in the country, local officials often want to know how they compare with their competitors.
Keeping Local Dollars at Home
Sales Tax Collections
Gap Analysis (Potential sales-Actual Sales)
-Defining a town’s trade area is an important first step in developing a strong retail sector.
-This is the foundation of retail market analysis. It helps existing businesses to identify ways to expand their own market.
-Increasing retail sales is one way an area can:
improve employment multipliers of its local industries.
-Whatever the reasons for existing retail sales, city and county leaders can help local businesses to improve these trends.
-To determine the potential for increasing retail sales, one should establish the trade area.
Of these methods, COMMUTING and RETAIL GRAVITY approaches present the least amount of work to implement.
Is the random canvassing of parking lots at major locations in town at different times on different days and over several weeks.
The locations might include
The downtown area,
Major shopping destinations such as shopping malls and centers, Wal-Mart Super Center, Home Depot, Krogers’, and Other popular establishments in town.
One should combined the results of vehicle license plates from the different locations to obtain a composite count of vehicles from surrounding counties and compare them to regional commuting data.
Results from a traffic study will usually reveal
the major towns and counties that comprise the local trade area or market.
Commuting time to work by local residents is another way of delineating a community’s retail trade area.
Converting commuting time to work into spatial distances or miles and plotting these data on a map, provide a visual picture of the geographic size of its trade area.
Another easy way of defining the retail trade area is to use a gravity model. In retail trade analysis, the most popular method is “Reily’s Law of Retail Gravitation.”
Reily’s law is a rule-of-thumb used to ESTIMATE the distance customers will travel to PURCHASE goods and SERVICES after comparing price, quality, and style.
The law assumes that people desire to shop in larger towns, but their desire declines the farther the distance and time they must travel to get there. Thus, LARGER TOWNS DRAW CUSTOMERS FROM FARTHER DISTANCES THAN SMALLER TOWNS.
The maximum distance a customer will travel to shop in a smaller town can be calculated using the following formula.
Once the physical boundaries of the trade area have been identified, one should estimate the total market size.
The total market consists of populations in the host community plus population from surrounding towns in the trade area.
3.14 X (Average Retail Trade Miles)2 X Average County Population Density
Community A’s population = 22,000
Average trade area retail miles = 8.46
Average trade area population density per square mile = 51.45
Number of new customers = (3.14 x ((8.46)^2) x 51.45) =11,563
Total retail customer base = 33,372 (22,000 + 11,563)
Areas with large populations and densities per square mile can distort the actual situation in retail trade analysis.
Reily’s Law is less accurate when involving larger towns.
Answers the basic question: What is the probability that a consumer located in communityi will shop in communityj, given the presence of competing towns? The spatial interaction model takes into account such variables as distance, attractiveness and competition in different sites.
The probability (Pij)1 that a consumer located in communityi will choose to shop in communityj is calculated as:
Aj is a measure of attractiveness of communityj, such as total retail sales, total personal income, or population of area.
Dij is the distance from i to j.
α2 is an attractiveness parameter from empirical observation.
Β3 is the distance decay parameter estimated from empirical observations. Simply, it is a parameter that reflects the propensity to travel by consumers.
n is the total number of communities including the host communityi .
The product derived from dividing by is known as the perceived utility of communityjby a consumer located in communityi.
After defining the trade area, one can ESTIMATE the local sales potential and COMPARE them to actual sales in the area. The following formula can be used to estimate potential retail sales.
-PSij is potential sales for commercial sector j in county i
-Pi is population for county i
-SSPCj is state sales per capita for commercial sector j
-PCIi is per capita income for county i
-PCIs is per capita income for state s
By comparing POTENTIAL with ACTUAL retail sales, one can determine whether the city has room for retail growth.
One should compare retail sales over SEVERALYEARS to determine the LONG-TERM health of retail sectors in the city.
-ARSij (2005 taxable retail sales for Automotive sector in Pristine Co.) = $1,011,060
-ARSsj (annual taxable retail sales for General merchandise sector for USA) = $3,799,963,834
Pprstc (Pristine County population) = 4,896 people
Pu.s (USA population) = 2,412,301 people
Yprstc (Pristine Co. per capita income) = $26,363
Yu.s (USA per capita income) = $35,744
-Derive a value of captured or lost commercial sales for that sector and county
Trade Area Capture (TAC)
Information about the trade area can help one to estimate the ability of community merchants to capture the retail business of people in the area.
Trade Area Capture (TAC)
is an estimate of the number of people who shop in the local area during a certain period.
Knowledge of the trade area is the first step in retail market analysis.
Knowing the trade area, one can determine the size and pulling power of local merchants in the market using a concept call pull factors.
Pull factors are ratios that estimate the proportion of local sales that occurs in a town.
See slide 23
The most common method of calculating pull factors is as follows:
Figure 1. Weighted Average Pull Factors for Mississippi Counties, 2007
Mississippi Total .74
This presentation shows how a few simple techniques can be used to determine the geographic size of a town’s trade area.
A trade area will often extend beyond its own geographic borders.
-The number of customers in a county
-A sector’s pull factor in the region
-Potential sales in an area
In economics, there is a technique called shift-share analysis. Its purpose is to take the change in employment for an area and decompose it into the three sources that caused the change.
The industries are ordered according to how many people they employed in the latest year selected ( 2007) .
During the period 1990 to 2007, employment in Oktibbeha County grew by 2,869 jobs. In terms of employment growth, the most important industry was Professional and Business Services (1,411 jobs). It is followed by Education and Health Services( 1,376 jobs), and leisure and Hospitality ( 1,929 jobs).
Table 1 presents the employment changes for the time period selected in Oktibbeha County, MS. During the period 1990 to 2007, employment in the county grew by 2,869 jobs.
The first source of change is the growth or contraction in the United States economy. This growth rate is listed in Table 2 as the national growth component.
Overall, the national growth component was responsible for a total of 2,540 jobs in Oktibbeha County.An understandable goal of some local leaders is to make their economy more 'recession proof'. Economies with more employment in government, military and education will experience less fluctuation because those sectors are not directly related to the business cycle.
Also, economic sectors that are experiencing more growth will provide larger employment gains to a local economy.
The industrial mix component measures how well an industry has grown, net the effects from the business cycle.Table 2 lists these components for each sector.
If the county's employment were concentrated in these sectors with higher industrial mix components, then the area could expect more employment growth. After adding up across all eleven sectors, it appears that the industrial mix component was responsible for decreasing Oktibbeha County’s employment by -471 jobs.
Thus, the area has a concentration of employment in industries that are decreasing nation-wide, in terms of employment. The majority of these jobs can be attributed to decreases (-997 jobs) in the Manufacturing sector.
The third and final component of shift-share analysis is called the competitive share. It is the remaining employment change that is left over after accounting for the national and industrial mix components.
If a sector's competitive share is positive, then the sector has a local advantage in promoting employment growth.The top three sectors in competitive share were Professional and Business Services, Education and health Services, and leisure and Hospitality. Across all sectors, the competitive share component equaled 800 jobs. This indicates the county is competitive in securing additional employment. A positive competitive share component indicates the county has a productive advantage. This advantage could be due to local firms having superior technology, management, or market access, or the local labor force having higher productivity and/or lower wages.
A negative competitive share component could be caused by local shortcomings in all these areas.By examining the competitive share components for each industry, the development official can easily identify which local industries have a positive competitive share component. This also indicates which industries have competitive advantages over other counties and regions.
Local officials can then devise strategies to improve local conditions faced by particular industries selected for focus. These strategies may include specialized training programs for workers and management, improved access to input and product markets through transportation and telecommunications, or arranged financial alternatives for new machinery and equipment.