The Most Livable Counties – A Discretionary Income Analysis

Consumer spending habits and discretionary income determine the quality of life for many Americans. Which counties are experiencing the best?

The Most Livable Counties – A Discretionary Income Analysis

Consumer spending habits and discretionary income determine the quality of life for many Americans. Which counties are experiencing the best?

America is beginning to return to a more normal state of living. Now that the COVID-19 pandemic is seemingly coming to an end, consumer spending is beginning to increase again. We used STI: PopStats™ data to analyze average household incomes and discretionary incomes to determine where the most livable cities/areas across the country are, and to see where spending is likely to increase the most. 

The ranking for the following cities/areas was determined by comparing average discretionary income versus the average household income in a county. The counties with the most discretionary income to spend on goods not considered necessities are ranked higher. With consumer spending ramping back up in America, the areas with more discretionary income will be spending more than others. 

Along with our rankings, we included economic indicators unique to the PopStats product like ‘Gross Domestic Product’ and ‘Mortgage Risk.’ These unique variables give further insight into our clients’ potential customers and their custom customer profiles. Mortgage risk is an interesting variable in that it rates an area on its chances of defaulting on a mortgage from 1 to 5, 5 being most likely and 1 being least likely.

All numbers and figures used in this analysis are sourced from STI: PopStats™. Contact Us to learn more about the 1000’s of variables we update quarterly.

Most Livable Counties in the United States

10. Nassau County – Long Island Area

  • Population: 1,356,138
  • Average HH Income: $157,016
  • Average Discretionary  Income: $65,977
  • GDP per Capita: $111,661
  • Mortgage Risk: 3.2672
  • Average Disposable Income: $107,841

This county is the first county outside of New York City. A theme that you are going to notice through the rest of this analysis is that “most livable cities” are actually areas right outside of thriving metropolitans. These professionals are benefitting from high salaries and then escaping back to more affordable real estate. This combo allows for more discretionary income worth the extra time spent in the car.

9. Philadelphia – Chester County

  • Population: 533,178
  • Average HH Income: $139,215
  • Average Discretionary Income: $66,893
  • GDP per Capita: $91,027
  • Mortgage Risk: 2.7338
  • Average Disposable Income: $99,520

With a population of over 500,000, Chester county hosts several cities that are reaping the benefits of having a manageable drive time to Philadelphia. 

8. The Bay Area

  • Population: 3,816,251
  • Average HH Income: $177,761
  • Average Discretionary Income: $67,454
  • GDP per Capita: $148,538
  • Mortgage Risk: 3.9355
  • Average Disposable Income: $118,504

Several counties in the bay area made the cut. This analysis is comprised of the following counties: Marin, San Mateo, Santa Clara, San Francisco.

It’s not a common thought to think of the bay area as livable with their housing crisis and homeless problem the area faces; however, looking at the data shows that those employed (especially in the booming tech industry) are able to fully utilize everything the area has to offer.  

The bay area has the highest average household income on the list as well as the highest GDP per capita. The affordability of the city plays a heavy role with the fact that this area has the highest difference between income and discretionary income. 

7. Indianapolis – Hamilton County

  • Population: 353,562
  • Average HH Income: $134,750
  • Average Discretionary Income: $68,669
  • GDP per Capita: $81,691
  • Mortgage Risk: 2.7086
  • Average Disposable Income: $97,894

Hamilton County is what you can consider a “healthy economy.” Their economic vitality score places them right on par with the national average. This along with high spending potential make it a solid area to live. 

6. Forsyth County, GA

  • Population: 253,007
  • Average HH Income: $130,218
  • Average Discretionary Income: $69,663
  • GDP per Capita: $81,203
  • Mortgage Risk: 3.0771
  • Average Disposable Income: $100,934

Although this is the most rural county on our list, their incomes and spending power allow them to put up a good fight. Forsyth County has the highest economic vitality index on the list but the lowest GDP per capita. 

5. New Jersey (New York Suburbs)

  • Population: 824,369
  • Average HH Income: $157,070
  • Average Discretionary Income: $69,707
  • GDP per Capita: $106,938
  • Mortgage Risk: 2.8996
  • Average Disposable Income: $108,721

This is another area with several counties making this top cities list. The counties included are Morris and Somerset.

A commute from a Jersey town to the bustling island of Manhattan is a pop culture reference at this point. With their close proximity to high incomes and the availability of more affordable real estate, it’s not hard to believe these counties host some of the most livable cities. 

4. Baltimore – Howard County

  • Population: 330,939
  • Average HH Income: $151,890
  • Average Discretionary Income: $71,558
  • GDP per Capita: $90,908
  • Mortgage Risk: 3.2304
  • Average Disposable Income: $110,736

This county has an advantage that no other county on this list has. This county is sandwiched between two major metropolitan cities (Baltimore being the closest). The residents of this county get to benefit from both Washington D.C. and Baltimore.

3. Washington D.C. – Loudoun and Fairfax County

  • Population: 1,556,521
  • Average HH Income: $164,066
  • Average Discretionary Income: $73,537
  • GDP per Capita: $92,160
  • Mortgage Risk: 3.4726
  • Average Disposable Income: $116,647

The capitol city is hosting quite a few different neighboring counties on this list. Loudoun and Fairfax are benefitting from the city the most. These counties are enjoying healthy economies. The discretionary incomes in these areas are mirroring some people’s entire income. 

2. Denver – Douglas County

  • Population: 367,726
  • Average HH Income: $150,232
  • Average Discretionary Income: $74,097
  • GDP per Capita: $81,981
  • Mortgage Risk :3.4863
  • Average Disposable Income: $110,941

For a city of its size, Denver has a relatively high cost of living. That does come with some great salaries. The neighboring counties, like Douglas, are the ones taking the most advantage of that. 

The most livable county in America:

1. Nashville – Williamson County

  • Population: 250,620
  • Average HH Income: $153,023
  • Average Discretionary Income: $78,515
  • GDP per Capita: $98,173
  • Mortgage Risk: 3.2665
  • Average Disposable Income: $112,789

Austin didn’t make the top 10 in a city list? Not this time. A Nashville county currently holds the rank as the most livable city in America according to our discretionary income data. Between the cost of living and the cost of real estate in Tennessee, residents are able to afford to shop and spend lavishly. 

scatter plot of the top 10 liveable counties

Everyone’s definition of the most livable city/county will be different. Spending on necessities takes a large portion of our annual salaries. The money that is left over is what we can spend on pleasantries and entertainment like vacations, luxury goods, gifts, etc. Having the ability to spend on activities and goods like that are what make cities livable and popular. 

Using variables like discretionary income and comparing them to staple variables like household income and mortgage risk can make for effective customer profiles and city stories. Combining different datasets and cross analyzing data is how you make effective and profitable site-location and related decisions. 

STI: PopStats and STI: Spending Patterns made this analysis possible. Contact us to learn how you can put our data to work for you.

Put Data into Action

Academy Sports + Outdoors

STI: PopStats™ Informs Most Departments at Academy

Academy Sports + Outdoors

STI: PopStats™ Informs Most Departments at Academy

At one point in its long history of providing communities across the country with sports and outdoor equipment, Academy Sports and Outdoors identified a few underperforming stores in its network. Naturally, the retailer began investigating the problem. Critical tools in this research were its three key datasets — STI: PopStats™, STI: LandScape™, and STI: Spending Patterns™.

The research revealed that the problem was not the locations, but the stores’ merchandising strategies. The underperforming stores were not stocked in a locally relevant way to fit their neighborhood demographics. The diagnosis inspired the company to rethink its merchandising strategies and make locally focused changes. As soon as they made changes, sales increased.

This scenario is just one example how the power of geodemographic data can solve problems and help companies make business decisions beyond strictly the real estate department. In fact, Academy has been extending its market research services outside of its real estate department for several years — bringing the power of data to several other departments in the company.

STI Data Informs Business Decision

Gaining critical insight for site selection is the reason why Academy switched to STI data after using another company’s data for several years. “We’d been hearing about PopStats in our industry. When our contract was coming up for renewal with another vendor, we took the opportunity to evaluate PopStats and found that it was superior,” said Rich Babson, Real Estate Research at Academy.

“PopStats was more accurate and more current, which is especially important the further out we get from the decennial U.S. Census. With our previous data product, the further we got from the Census, the bigger the population estimate variances were from reality. But with PopStats we’ve found that the data stays consistent and the error rate stays small. This is important to us because the more accurate the data is, the better informed our decisions are.”

Along with PopStats, Academy also uses LandScape neighborhood segmentation data and Spending Patterns consumer spending data. LandScape has helped Academy identify its ideal neighborhood segments. Spending Patterns informs product demand analysis.

Neighborhood-Specific Merchandising

To better understand its consumers, Academy performed an in-depth study to determine its ideal neighborhood lifestyle segments using LandScape. In particular, it wanted to identify consumers who fit its ideal customer personas, including outdoorsmen, military personnel, soccer moms, and fitness buffs.

Using LandScape’s 72 neighborhood segments, the company looked at neighborhoods where its best- and lowest-performing stores were located. From there, it identified patterns that shaped its understanding of its ideal neighborhood segments.

“One of the main things I like about LandScape is that the segments are the same across the nation versus our former vendor’s product, which was heavily influenced by regional consumer characteristics,” explained Rich.

“With the old system, the consumers in the segments in which we do well — in Texas and Oklahoma, for example — were not home to the same consumers who live in Florida and North Carolina. With LandScape, I get consistent neighborhood segments across the country.”

Academy now uses its LandScape-based neighborhood segmentation insight in all site selection research. What’s more, the merchandising and marketing departments rely on neighborhood segmentation research, as well.

“For sporting goods, it’s critical to know who our customers are beyond the demographic data,” said Rich.

“Trade areas with the same demographic characteristics can be inhabited by people with very different lifestyles. Knowing the exact lifestyles of our ideal customers gives us critical consumer insight. It’s much more profitable for our company to identify areas with large pockets of our ideal customers and, similarly, to avoid areas with the wrong types of consumer lifestyles.”

Along with PopStats and LandScape, the SpendingPatterns data plays an important role in helping Academy determine each store’s ideal merchandising mix. Before opening a new store, the research team creates a demand analysis to assess what products will fit best with the lifestyles of the consumers living in that area.

In this way, the stores minimize some products, such as athletic apparel, and maximize other categories, such as sports team apparel, depending on the SpendingPatterns data. Rich particularly likes that the data is consistent across the country, so they can depend on the insight no matter where in the U.S. they are researching.

Research-Driven Business Decisions

For real estate, the research department uses STI data in a variety of ways including:

  • Creates executive committee presentations for every new store location
  • Conducts property analysis for new locations, including lifestyle segments
  • Conducts existing store analysis to improve performance issues
  • Employs regression analysis to understand which variables impact store performance
  • Creates forecasts using over 300 store-level attributes

Examples of research projects for other departments include these requests:

  • Advertising Department – requested maps of zip codes in trade areas for advertising distribution and distance calculators to brand-name competitors
  • Promotions Department of a Regional Store – requested a map of the shooting schools within a specific proximity to its stores, so the stores could reach out and set up partnerships with them
  • Human Resource Department – requested maps of healthcare facilities located near contracted e-commerce employees to provide them with medical services information to meet labor compliance regulations
  • Merchandise Department – requested analog models to support merchandising decisions for each department within each store

In fact, conducting research for the merchandising department is a major focus of Academy’s trade area research, because there are such big regional differences in its customer bases. For example, some areas are home to hunters or fishermen, while others are team-sport-oriented.

“This analysis has proven to deliver a significant impact on our merchandising,” notes Rich. “Every store in which it’s been executed has experienced significant performance improvements. That’s the decision-making power we’ve come to expect from our powerful suite of STI datasets.”

Want to create reports like Academy?

Contact us to learn howVisit Academy’s Website