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With May in full bloom, seniors around the country are counting down the few days left until graduation.
In fact, many students have already made the transition from student to alumni this Spring.
Some will go on to graduate school for their masters, law degree, or another professional diploma. Others will be joining the workforce before they even have time to unpack. And, many will use their summer for some well-deserved relaxation and traveling before they begin the job hunt.
One of the biggest questions facing many new graduates will be where they choose to begin their new journeys.
Undoubtedly, a good portion of this group will move back in with their parents in order to save some money before moving out. However, many postgraduates will want to branch out and find their own places to begin their careers. Whether they are forced to relocate because of their job or simply moving to a city near home, the moving process can be a stressful one.
LendEDU has put together a list of the 250 cities in the U.S. that present the ideal conditions for postgraduates looking to find their first homes.
Using licensed data, our list was compiled by looking at a number of criteria important for the postgraduate relocation process.
30,000 cities were evaluated for their conduciveness towards postgraduates on the following six weighted parameters:
- Age Ratio Score (30%, listed in table as “AR,” proportion of population ages 25-34)
- Cost of Living Score (10%, listed in table as “COL,” general living expenses)
- Income Growth Score (20%, listed in table as “IG,” projected five year income growth)
- Entertainment Experience Score (20%, listed in table as “EE,” number of bars, gyms, etc.)
- Commute Score (5%, listed in table as “C,” accessibility to commuters)
- Single Ratio Score (15%, listed in table as “SR,” proportion of single residents)
The cities you will find in the table below accumulated enough points to be named as one of the 250 best cities for postgraduates.
Complete Rankings of the Best 250 Cities for Postgraduates
All data used in this study was under license from Onboard Informatics. Onboard Informatics’ dataset was used for the most recent population figures, location purposes, general cost of living data, forecasted income growth, entertainment data, commuting data, and demographic data. Our original list contained over 30,000 U.S. cities and towns but that list was narrowed down after a population cutoff was established at 15,000. Any city or town with a population below 15,000 was immediately eliminated from further analysis.
Once we had a list of all the towns and cities that met the population requirement, we were able to move forward with our study. To determine an Age Ratio Score for each town and city, the number of people in a given town between the ages of 25 and 34 was divided by that respective town’s total population. The result number provided our Age Ratio Score. The resulting values for each town were ranked against each other on a percent scale from 0 to 100. Each individual percent rank was multiplied by its weight of 30% to produce the value score. The maximum possible value score was thirty points.
To determine a Cost of Living Score for each city and town included in our dataset, we were able to pull a cost of living statistic for every town. Onboard Informatics has the data on cost of living as a stand alone statistic, meaning we did not have to manipulate the statistic at all. The resulting Cost of Living Scores for each town and city were ranked against each other on a percent scale from 0 to 100. Each individual percent rank was multiplied by its weight of 10% to produce the value score. The maximum possible value score was ten points.
Similar to the cost of living statistic, Onboard Informatics’ dataset also provided Income Growth Score, Entertainment Experience Score, and Commute Score as stand alone statistics. The Income Growth Score projected the growth in median incomes in a given town over the next five years. The Entertainment Experience Score was a point total derived from the amount of gyms, parks, theaters, bars, restaurants, stadiums, etc., in a given town.
The Commute Score was calculated by finding the amount of commuting options (busses, trains, subways) in a given town and how easily accessible a town was for commuters. Each of these three parameters were ranked against each other on a percent scale from 0 to 100. Each individual percent rank was multiplied by its weight to produce the value score. The Income Growth Score was weighted at 20% and the maximum possible value score was twenty points. The Entertainment Experience Score was weighted at 20% and the maximum possible value score was twenty points. The Commute Score was weighted at 5% and the maximum possible value score was five points.
The final parameter, Single Ratio Score, was meant to represent the number of singles in a given city proportional to the total population. Using Onboard’s dataset, the number of singles was divided by the total population to get a ratio. The resulting Single Ratio Scores for each town and city were ranked against each other on a percent scale from 0 to 100. Each individual rank was multiplied by its weight of 15% to produce the value score. The maximum possible value was 15 points.
Once each town and city was assigned a weighted score for each parameter, we were able to move ahead and tabulate the final scores for each town. Each parameter for each town was simply summed together to develop the final score. Towns and cities that recorded a greater score total were ranked higher on the list and vice versa. The maximum score was 100.
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Author: Mike Brown