In business, we talk a lot about “the market”. Identification of customers, spending, demand and supply within an area is so important that it has its own discipline within schools of business called Marketing. But, when actually trying to apply market principles to demand and supply it is tempting to simply alter our market parameters to give us the answers we seek. Either artificially inflating or deflating a market geographic region can substantially change population, income, categorical supply and/or demand.
We have presented “ring studies” in the past that simply look at a static ring around a set point in a community that represents a certain arbitrary distance. For example: we can look at everything included in a ten, twenty-five or fifty mile ring of Emporia fairly easily. But, are these arbitrary distances representative of our actual market? Simple rings don’t account for things like drive times, other markets that overlap our own and the concept of “gravity” within markets (the larger the community, the greater distance they can pull customers from
Without a solid idea of our market geography, we can’t determine our sustainable consumer needs or market opportunities for potential entrepreneurs with any degree of accuracy. But, accurate market modeling takes a tremendous amount of time, access to quality data and expertise in the application of market modeling. A few months ago Dr. Rob Catlett, an Economics Professor from Emporia State University introduced Emporia Main Street to an intern named Nizomiddin Kuchkarov. Nizomiddin is an exceptionally bright student from Uzbekistan, and part of his educational directives from his home nation required him to participate in an internship. With guidance from Dr. Catlett, we developed the parameters of a special project to determine the actual geographic region that represented Emporia’s market, how Emporia’s market interacted with surrounding markets and what the revised data meant for our community in terms of supply and demand for area products and services.
This is what Nizomiddin found:
First, some background. The term “market” can be used to identify different elements of a geographic area as it pertains to potential consumers. For our purposes, the term market identifies the geographic area in which typical Emporia consumers exist. We understand that other markets exist within proximity to Emporia. We have the Kansas City market, the Manhattan market, the Lawrence market, the Topeka market, the Wichita market, Salina, Hutchinson and so on.
Each market has “gravity”. Nizomiddin stated: “Gravity models are mathematical formulations that are used to analyze and forecast spatial interaction patterns. Gravity models define a trade area based on its attractiveness relative to other trade areas.” To explain this concept in simpler terms, think of two cities that each had 25,000 people in them that were 50 miles apart with city “a” 50 miles north of city “b”. In the middle of those cities exists a highway that runs on a parallel axis running east and west between “a” and “b”. A gravity model suggests that residents north of the highway would travel to city “a” and people south of the highway would travel to city “b” because each city had equal gravity. Now, let us suppose that city “a” had 50,000 people and city “b” had 25,000 people. In that scenario, city “a” would have twice the gravity (population is one of the factors in “attractiveness” because it leads to increased buying power). In this scenario, city “a” would pull consumers from south of the imaginary highway and encroach upon the city “b” trade area. The increase in population for city a, when coupled with its increased buying power creates more business opportunities and thus provides market trade area residents more choices for businesses that can be sustained within the larger market.
The distance people will travel to an existing market (without considering pressures applied by other markets
) is identified in a formula known as “Reilly’s Law
“. You can see the actual formula for Reilly’s Law below:
(where dxj is the break point between customers who will go to one city (i) and customers who will go to the other (j). The break point maximum is the distance from the smaller city. dij is the total distance between two cities; Pi and Pj are size variables of cities i and j respectively).
While Reilly’s Law gives us a good approximation of distance break points for singular markets, it does have some limitations. Highways, rivers and other geographic barriers have an impact on market size by making the geographic region easier or harder to traverse. So, instead of distance traveled in a point to point line, newer models consider the actual minutes traveled. In addition, city pull factors are a representation of market strength within an area and generally can be utilized as a multiplier in conjunction with population to gain a more realistic perspective on geographic market size. Finally, we understand that markets interact with a variety of other markets. In the Emporia area, we are surrounded by much larger markets and exist in an area that doesn’t contain a lot of dense population points immediately adjacent to Emporia.
To compensate for variables listed above, Nizomiddin added additional qualifiers via data sets obtained through Esri, Neilsen Claritas, Info USA, the US Census, the Kansas Department of Revenue and the Kansas Small Business Development Center at ESU. The formula below describes the more complete gravity model:
Where B= break point- the distance from the smaller city in a comparison to the trade area boundary, Dij is the distance from city i to j. P is the population of the city and C is the City Trade Pull Factor. In this scenario, city “i” is assumed larger.
When we look at a static geographic market trade area that doesn’t compensate for the interaction of other markets upon the Emporia market, our geographic area is quite pronounced. The average drive time that we pull from is approximately 41 minutes of drive time. The simple geographic model is represented in the graphic below:
Notice within the gravity based market trade area that the highway system extends the geographic boundary of the market.
We know that Emporia is surrounded by some much larger cities. Nizomiddin calculated market gravity of those areas using the most conservative data available. For example, Manhattan is a metroplex that includes Junction City and Fort Riley, but only Manhattan was considered in the gravity model. Likewise, Kansas City is a metroplex that contains several cities including communities across the state line, but only Kansas City Kansas was used in the gravity model. Pittsburg competes with Joplin, MO, but again this scenario did not study the interaction between Kansas communities and communities outside the state of Kansas. Similarly, Coffeyville competes with several similarly sized communities in their area and communities in Oklahoma, but those markets were not quantified within this exercise. This is important, because other communities, even with conservative data used, intersect our market trade area.
Wichita, for example, includes Emporia within their gravity model of a trade area. Thus, if a business exists in Wichita, they may not want to create another store in Emporia because they may think they are competing with themselves within the same market area. The issue becomes more pronounced as we look at a series of other communities that surround east-central Kansas.
Even with the Emporia market as the top layer of the imposed map, you can clearly see how Kansas City, Lawrence, Topeka, Manhattan, Salina and Wichita markets overlap the Emporia market. Remember the initial gravity model cited that indicated that the larger community typically pushes back the market region of the smaller community? That basic market truth adjusts the geographic boundaries of our traditional gravity market to look more like this:
By looking at our actual market trade area, and voids that exist within the nexus of larger trade areas, we can determine what our actual market opportunities are and how we should move forward positioning Emporia for the coming decades. A quick glance at a trade area intersection map makes it abundantly clear that we cannot exercise the traditional market power enjoyed by many of a our large regional competitors, but we can take steps to fill our existing market gaps with destination businesses: businesses that can pull from beyond traditional market boundaries because you can’t find the exact business in other surrounding areas. Only through the utilization of unique businesses, events and attractions can we hope to consistently and sustainably build our local economy.
Emporia has had differing views about the size of our market for as long as I can remember. Although we have several destination businesses that can routinely pull consumers from outside of our market due to their unique products or services, the vast majority of our consumers come from the geographic region in the map posted above. The intersection of several different, much larger, markets within our traditional trade area region should give us a clear direction in how to develop as a community. We cannot simply “outsize” communities that have a distinctive population advantage, but we can use our relative size to support unique businesses as destinations that also support our local population.
Only by using our market data to make decisions based on what our market is rather than what we wish it would be will we be able to make the smart decisions that will improve our market so that our population and incomes will increase. This is the way to building a better Emporia. The alternative is a frustrating scenario that requires us trying to sell what we aren’t to the outside world. That never turns out well.
If you would like complete maps and data used in the creation of the information listed within this article, please contact Emporia Main Street. If you are looking for a bright young employee that can create economic modeling for your community or business, we hope you consider contacting Emporia Main Street to gain the contact information for Nizomiddin Kuchkarov.