I recently announced a quest to find the most “profitable” places to be self-employed. I am posting my deliberations online in case others might be interested in my thought process and / or findings.
As I started researching various locations, I found that my initial model of “Median Income – Median Rent” was a little too simple to capture the realities of living in one place vs. another. The hard part is estimating how much I’d earn. For instance, some of the wealthiest neighborhoods are sparsely populated, so it’s hard to see how I’d pull in good business there. This has forced me to take a closer look at what I need in a locale. That, in turn, depends very much on the services and clientele that I serve. Before I can get much further, I need to take a closer look at myself and my needs. This analysis will have very different details for each businessperson, but the same principles will apply: Define your market households and find out where they are.
I run a unique shop as a tutorney: part tutor, part attorney. A look at last year’s bookkeeping reports showed me how last year’s revenue was distributed across three categories of service. The breakdown was pretty close to:
- Legal and patent services = 50%
- One-on-one tutoring = 25%
- Group classes = 25%
Before I looked it up, I didn’t even know this basic fact. I’m learning something already! Thus far, I need all three streams of income to make a living.
My Market Households
To complicate matters, these services cater to different demographics.
My core clientele for legal and patent services is small businesses and mid-to-high income individuals, so let’s say the market household is $100,000+ income. Clients call into my law office from a fairly broad region of the Los Angeles metropolitan area, mostly within, say, a 25-mile zone. It wouldn’t make much of a difference where my law office is located within a city. It’s the city that will make the difference. I need to live in a city with a high number or density of $100,000+ households. (I would rather not adjust this for the cost of living across various cities, because it’s a ballpark figure anyway).
The $100,000+ income bracket is also a good measure of my market household for one-on-one tutoring. Most of them are high-school families. This market is much more localized; most of these clients don’t bother driving more than about four miles to my office. To maximize on this market, it’s best to find a neighborhood within a city that has a high market household density. It’s not very important to have super-rich clients. $500,000 households don’t need five times as much tutoring as $100,000 households. It’s the density of “rich enough” households that matters.
Third, I teach group “test prep” classes to college graduates applying to graduate or professional school. In this market, my clients tend to be low-to-mid income. My classes cost about half as much as the big agencies’ (like Kaplan), so I draw the bargain-hunting crowd. That works for me, because I group them together and earn a good hourly rate. Group-class students are less localized than high-school students. They sometimes drive up to 25 miles to get here, which is practical on the weekends. Living near a college would help if I could really tap into it, but that’s hard to do. Most of my students are already done with college. I’ll say my ideal environment for group classes is a metropolitan area with a high density of $50 – 100K households 1 , with nearby colleges being a nice bonus.
It’s probably not possible to find a neighborhood that is optimal for all three of these markets, but I should be able to identify strong combinations. For example, areas with uniform distributions of low-, middle-, and high-income households offer some of all these markets, while also offering low-rent housing. If I had to sacrifice one of these markets for the others to grow, I would give up the one with the lowest hourly rate — high-school tutoring.
Growing your business is not just about finding a bigger market. You also want a big market share. That depends on the number and size of competing businesses. It’s a major factor that I think many entrepreneurs overlook. A friend of mine is one of the most successful immigration lawyers in Phoenix, AZ. She expanded to LA and counted on an easy-breezy doubling of her fortune. What she hadn’t counted on was the dozens of competing immigration lawyers near her LA office. Dozens — not in her neighborhood but in her freaking building. It was a market she was not prepared for. That office was closed within a few years.
My friend’s example shows that competition might not be proportional to city size. In line with that, the large national test-prep chains like Kaplan can’t be in every city. They focus on the very largest college communities in the very largest cities. I currently live between UCLA and USC, but I hardly ever get students from either school. Blueprint and Manhattan Prep occupy their campuses like invading armies. Maybe a school like Cal State Northridge or the University of Minnesota would be slightly less under their domination.
Toward a metric
If I can capture the most important factors, I can construct a sort of “earnings metric” for each city or neighborhood. From the considerations above, those factors would have to include:
- A = Number of $100,000+ households within a 25-mile radius
- B = Number of $100,000+ households within a 4-mile radius
- C = Number of $50,000 – 100,000 households within a 25-mile radius
- D = Number of competing patent lawyers within a 25-mile radius
- E = Number of competing high-school tutors within a 4-mile radius
- F = Number of competing test-prep centers within a 25-mile radius
Then my earnings metric would be something like:
M = 0.5(A/D) + 0.25(B/E) + 0.25(C/F)
When measured against the baseline neighborhood where I live now, M would be a good first guess for an earnings multiplier. That is, if M = 1 for Rancho Park and M = 2 for Austin, then I could reasonably expect to earn twice as much in Austin as in Rancho Park. To be more precise, each ratio will actually be a ratio of ratios: the new neighborhood ratio over my current ratio. But now we’re getting into formulas that are too hard to type in a simple blog post. 😛
The next phase of the analysis will be determining the baseline ratios for my current location: Who lives here, and who’s serving them in competition against me?