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Small businesses are the backbone of the United States.
In addition to propping up local economies and providing residents with jobs, small businesses add to the sense of community in a town. They become the local gathering spots where locals congregate to discuss any and everything.
Plus, when you buy from a small business, you know you are buying a product that was made with the utmost care and craftsmanship.
However, success can prove difficult to attain when running a small business. Mega-stores, adverse economic conditions, and bad luck can all significantly damage a small business.
LendEDU does not want to see small businesses fail. That is why we try to help them learn about the best small business loan options and why we have created a report that recognizes the 100 best towns in the state of Virginia for small businesses.
Using licensed data, LendEDU’s team looked at over 700 towns and cities in Virginia according to parameters that are crucial for small businesses to flourish.
The following three weighted parameters were used to evaluate each Virginia community:
- Population Score (20%, listed in table as Pop.)
- Income Score (40%, listed in table as Income)
- Expenses Score (40%, listed in table as Expenses)
Each of the three parameters had a number of sub-metrics that were used to give each parameter a total score. The three parameter total scores for each Virginia town were summed together to get the final score. This process (and the sub-metrics) is explained in more detail in the methodology found below.
The 100 Virginia towns and cities you will find below have been recognized by LendEDU as the best places in Old Dominion for starting a successful small business.
Complete Rankings of the Best 100 Towns in Virginia for Small Businesses
LendEDU has licensed all data used in this study from Onboard Informatics. Onboard Informatics’ dataset was used for a variety of statistics, including the most updated population figures. Over 700 Virginia cities and towns were under analysis for this study. A population cutoff was established at 4,000, meaning any Virginia community with a population below 4,000 was eliminated from further evaluation.
The following three parameters and their respective sub-metrics were used to evaluate Virginia towns and cities according to 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 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. Then, 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 added together to get the total parameter score. All three total parameter scores were then summed together to get the final score. A greater final score meant a better ranking on the list and vice versa.
See more of LendEDU’s Research
Author: Mike Brown