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Personal Finance Reports

The Best 100 Towns in Alabama for Starting A Small Business

A community is not a true community without at least a few small businesses.

Small businesses can become staples, or even institutions, in a town if the conditions are so that a mom and pop shop can flourish.

There is nothing quite like walking into a local store and having the attendant say, “What are we having today? The usual?” Or, something as simple as being on a first-name basis with an employee can truly brighten your day.

Moments like these are usually only reserved for small businesses. How often can you walk into a Wal-Mart and have a personal connection with a worker?

Small businesses not only provide that personal connection, but they also provide that personal touch to their products that makes what they are selling truly special.

Here at LendEDU, we love small businesses and try to support them as much as possible. In that spirit, we have created a study that recognizes the 100 best towns in the state of Alabama for small businesses.

Our report used licensed data to evaluate roughly 500 Alabama communities according to a few parameters deemed important for the success of small businesses.

The following three parameters were used to rank each Alabama town and city:

  1. Population Score (20%, listed in table as Population)
  2. Income Score (40%, listed in table as Income)
  3. Expenses Score (40%, listed in table as Expenses)

All of the parameters contained a few sub-metrics that were used to give each parameter a total score. Then, each Alabama town’s three parameter scores were added together for the final score. This process is explained in more detail in the methodology at the bottom of this page.

The 100 Alabama communities that recorded the highest scores were featured in this report as the 100 best places in the state to start a small business.

Complete Rankings of the Best 100 Towns in Alabama for Small Businesses

Methodology

All data used in this report has been licensed by LendEDU from Onboard Informatics. Onboard Informatics’ dataset was used for a multitude of statistics, including the most recent population figures. In total, nearly 500 Alabama towns and cities were evaluated over the course of this study. An arbitrary population cutoff was set at 3,500; any town or city with a population below 3,500 was taken out of consideration.

The following three parameters and their respective sub-metrics were used to evaluate Alabama communities based on their conduciveness towards small businesses.

1. Population Score (Weight – 20%, Maximum Point Total – 20 points)

  • Daytime Population Score – The difference in the normal population and the population that is present during standard working day hours (Weight – 10%, Maximum Point Total – 10 points)
  • Population Growth Score – Forecasted population growth over the next five years (Weight – 10%, Maximum Point Total – 10 points)

2. Income Score (Weight – 40%, Maximum Point Total – 40 points)

  • Disposable Income Score – The average disposable income available to residents (Weight – 20%, Maximum Point Total – 20 points)
  • Income Growth Score – Forecasted income growth over the next five years (Weight – 20%, Maximum Point Total – 20 points)

3. Expenses Score (Weight – 40%, Maximum Point Total – 40 points)

  • Property Tax Score – Property tax rates (Weight – 8%, Maximum Point Total – 8 points)
  • Sales Tax Score – Sales tax rate (Weight – 8%, Maximum Point Total – 8 points)
  • Utilities Score – Average cost of utilities (Weight – 8%, Maximum Point Total – 8 points)
  • Burglary Score – Rate of burglaries compared to the national average (Weight- 8%, Maximum Point Total – 8 points)
  • Property Crime Score – Rate of property crimes compared to the national average (Weight – 8%, Maximum Point Total – 8 points)

For each sub-metric category, the respective town’s score in that sub-metric category was ranked against all the other respective town’s sub-metric scores in the same category on a percent scale from 0 to 100. After, each individual percent rank was multiplied by its weight to produce the point total. All of a given town’s sub-metric scores in a specific parameter were summed together to get the total parameter score. All three total parameter scores were then summed together to get the final score. Towns and cities with a greater final score were ranked higher on the list and vice versa. 

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