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.

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The Highest Home Values in Texas

In a market this hot, which county is the hottest?

The Highest Home Values in Texas

In a market this hot, which county is the hottest?

Being that Texas is as large as it is, the lone star state’s home values can vary vastly across the market. According to PopStats data, the average Texas home value in January 2021 was $280,816. Some counties blow that number out of the water. Let’s take a closer look at the top 10 counties with the highest home values. 

All numbers and figures used were created using STI: PopStats™ data. Contact us to learn about the thousands of other variables we update quarterly. 

Top 10 Counties With the Highest Home Values

10. Bexar County (Primarily San Antonio) 

san antonio river walk

Population: 2,063,682

2021 Average Home Value:  $248,578
2020 Average Home Value:  $238,003

Average Household Income: $82,607

San Antonio is the major city for Bexar County and sits at number 10 on our list for home values. The counties that are listed higher are counties of the 3 other major cities of Texas, and the counties that make up their suburbs. San Antonio just so happens to also rank number 10 in Texas counties for average household income.

9. Harris County (Primarily Houston)

Population: 4,720,553

2021 Average Home Value:  $294,658
2020 Average Home Value:  $285,051

Average Household Income: $96,504

Harris County is a massive county in that it encompasses all of Houston (the 4th most populous city in the United States) as well as a few of Houston’s suburbs including Katy, Baytown, Friendswood, etc. The size of Harris County attributes to its status as the most populated county in the state, as well as the 3rd most populated county in all of the United States. 

8. Dallas County (Primarily Dallas)

Population: 2,617,867

2021 Average Home Value:  $344,135
2020 Average Home Value:  $332,705

Average Household Income: $94,526

Dallas County is self-explanatory in its name. Although Dallas ranks in the top 10 of Texas counties in home values, its suburbs dominate several rankings in this category. 

7. Tarrant County (Primarily Fort Worth)

Population: 2,111,344

2021 Average Home Value:  $300,664
2020 Average Home Value:  $289,162

Average Household Income: $96,056

People living in Dallas County (more specifically Dallas) may be living there for the amenities that larger cities offer like job opportunities, nightlife, entertainment, etc.  Within the same metro, although smaller and not as vast, is another large city that offers similar amenities – Fort Worth. Ranking 7 in our list, people living in Tarrant county can experience similar amenities, as well as higher home values and better household incomes. 

6. Montgomery County (Houston Suburb)

Image Source: zippsliquor.com/break-room/upcoming-summer-events-in-conroe/

Population: 630,248

2021 Average Home Value:  $338,042
2020 Average Home Value:  $327,020

Average Household Income: $117,377

Our first suburb on the list and it’s one that sits right outside of the massive city that is Houston, Texas. This county is bliss suburbia in that it enjoys having close access to city amenities like incomes, jobs, and entertainment, while also maintaining a healthy housing market.

5. Fort Bend County (Houston Suburb)

Image Source: https://www.visitsugarlandtx.com/blog/post/7-ways-to-enjoy-an-awesome-weekend-in-sugar-land/

Population: 839,981

2021 Average Home Value:  $339,863
2020 Average Home Value:  $328,781

Average Household Income: $127,003

Another Houston suburb making the list, but this time in the southwestern part of the Houston metro. Fort Bend County only offers slightly higher home values than Montgomery County but boasts a significant higher population. 

4. Williamson County (Austin Suburb)

Image Source: https://visit.georgetown.org/

Population: 635,242

2021 Average Home Value:  $390,100
2020 Average Home Value:  $365,462

Average Household Income: $110,183

Our first mention of Austin on this list and it’s one of the suburb counties. This county’s home values are rising even faster than Austin’s! But only barely. With a 1 year change of 6.7416% increase over last year, Williamson County is doubling the national average in rising home values. 

So far, we have mentioned most of the major counties in Texas and a few of their suburbs. With only 3 left in our top 10, keep reading to find out which counties have the highest home values in the state of Texas.

3. Denton County (Dallas Suburb)

Population: 924,022

2021 Average Home Value:  $396,122
2020 Average Home Value:  $382,967

Average Household Income: $117,656

Our first Dallas suburb hitting the list enjoying similar benefits as Houston’s suburbs. With an average drive time of 45 minutes to get to downtown Dallas, living in Denton is very advantageous for a variety of different households.

2. Collin County (Dallas Suburb)

Image Source: http://www.discovercollincounty.com/mckinney-texas/

Population: 1,072,393

2021 Average Home Value:  $437,460
2020 Average Home Value:  $422,931

Average Household Income: $117,656

2 of the 3 counties in Texas with the highest home values are both Dallas suburbs. It makes sense with how the DFW metropolitan area offers numerous job and travel options to many industries.  

The Texas county with the highest home values is: 

1. Travis County (Primarily Austin)

Population: 1,329,463

2021 Average Home Value:  $536,117
2020 Average Home Value:  $502,257

Average Household Income: $116,445

Out of every county in Texas, Travis County has the highest home values out of all of them! They aren’t just the highest, they’re all growing at an exceptional rate. The Travis County market is growing 78% faster than the national average. Many factors are contributing to this like a booming tech industry, a large number of entertainment jobs, and they are consistently making it on our annual top growth markets report.

Travis is the only major county to beat its metro counties, and it beats them in both home values and income. Travis County is the county to look at in Texas for growth and innovation. 

Curious about other markets across the country? Contact us now for a free sample!

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A Migrating Population

Using IRS Data to Track Movement Patterns during Covid-19

A Migrating Population

Using IRS Data to Track Movement Patterns

Understanding the ebb and flow of where people move across the country is crucial to making critical site location decisions. That is why we utilized the latest available IRS data to determine where and from people are moving. We are also able to determine average household income from aggregate income data to understand where higher incomes are moving. The need for past migration trends is more important now more than ever, and something we have commonly been asked about in regards to the COVID-19 pandemic.

Here’s an example:

Map of new york outflow migration

Check out the downloads below to view our full library of data visualizations showcasing population migration impacted by COVID-19: 

  • There are 3 sets of downloads. File names are based on FIPS code:
    • Set 1 “OutMigbyState.zip” – includes maps for the 50 states (and DC) and shows the out-migration from the state in question. (51 maps, 48mb) (Download)
    • Set 2 “InMig_County.zip” – has county-level maps visualizing inflow for all counties with at least 100 counties of origin(there are 109 counties that fit that criteria). (108 maps, 72mb) (Download)
    • Set 3 “InMig_OutST_County.zip” shows the same as previous but excludes those counties in the same state. (108 maps, 72mb) (Download)
    • (Download All) (267 maps, 192mb total)
  • Reading the maps:
    • Height represents the number of IRS exemptions with a 99.5 percentile cut off to remove outliers.
    • Color represents income with a limit of $200,000 annual household income. Red represents the destination county.

Interested in other reports? Send us a message and we’ll whip something up for you!

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STI: PopStats™ April & July 2020 COVID-19 Related Impacts

Different data points on how COVID-19 has affected the United States

STI: PopStats™ April & July 2020 COVID-19 Related Impacts

Different data points on how COVID-19 has affected the United States

The COVID-19 Pandemic has had a large effect on the daily lives of Americans, as well as causing substantial disruption to the US economy. Many of our clients, especially those across the retail, grocery, and real-estate investment industries are attempting to quantify those effects on their operations. We hope the following maps help illustrate the demographic and economic factors and their impact on the April & July 2020 PopStats Estimates, July and October Releases. As we discuss these estimates it is important to remember that our estimates are point in time, and the estimates for April and July are April 1st and July 1st estimates, respectively.

Most Immediate Impacts – Economic

–  Unemployment increased nationally by about 1% in April and 7% by July. The following are the Unemployment rates as of the 1st of January, April, and July mapped at a county level.
Unemployment Rate by County as of January 1st 2020, 20% limit
Unemployment Rate by County as of January 1st 2020, 20% limit
Unemployment Rate by County as of April 1st 2020, 20% limit
Unemployment Rate by County as of April 1st 2020, 20% limit
- Unemployment Rate by County as of July 1st 2020, 20% limit
- Unemployment Rate by County as of July 1st 2020, 20% limit
  • GDP started to dip in April but was more clearly evident with a ~10% national drop in July.
Percentage Change in GDP by County from April 1st 2020 to July 1st 2020, limits of -15% to 15%
Percentage Change in GDP by County from April 1st 2020 to July 1st 2020, limits of -15% to 15%
Percentage Change in GDP by County from January 1st 2020 to July 1st 2020, limits of -15% to 15
Percentage Change in GDP by County from January 1st 2020 to July 1st 2020, limits of -15% to 15
  • Economic Vitality – Economic Vitality is a custom Synergos Technologies, Inc. variable set included in STI: PopStats™. It measures the association between the number of workers in individual industries and our own proprietary stock market indices. This comes in two versions – indexed and non-indexed (both with history). The non-indexed version shows a more historic picture of stock market changes for the relevant industry mix in local areas. While the indexed version shows the relative performance of the local area versus the nation. This was one of the first indicators that we directed our clients to review with the April estimate. Given the frequent and up-to-date nature of the stock source data, it immediately displayed the stock market changes that were occurring in early April (right after the crash induced by Covid-19. However, there is no longer the depth of decline that was seen in April because as of July 1st the stock market had started to rebound. A possible issue here is that the stock market rebound may not be equally correlated with consumer confidence (one of the main goals of this measure). It may be helpful to adjust these measures by an estimated overvalue, or an estimate of inflated value if you have been historically using the non-indexed Economic Vitality measures. The indexed version should still show the relative performance of local industry mix.
Economic Vitality (non-indexed) by County from January 1st 2020, limits of 50 to 250
Economic Vitality (non-indexed) by County from January 1st 2020, limits of 50 to 250
Economic Vitality (non-indexed) by County from April 1st 2020, limits of 50 to 250
Economic Vitality (non-indexed) by County from April 1st 2020, limits of 50 to 250
Economic Vitality (non-indexed) by County from July 1st 2020, limits of 50 to 250
Economic Vitality (non-indexed) by County from July 1st 2020, limits of 50 to 250
  • Unaffected field sets: rent, home values, income, enrollment, and labor force. These field sets rely on source data that lags the current period. While they may be affected by current conditions, to what degree cannot be currently determined nor accurately quantified as of this October 2020 release.

Vacation / Transient Population – One of the hardest hit sectors has been hospitality with hotel occupancy plummeting. Transient Population data in PopStats is based upon hotel, RV, and campground estimated occupancy. We did not adjust April, but with this July estimate we felt the data dictated making a few adjustments. We determined state-level adjustments to be made through researching hotel occupancy rates. While this lacks the geographic granularity we typically require, it helps to differentiate the relatively low impacts in a few areas versus the extreme effects, such as in Hawaii.

Here is a Map of July 2019 to July 2020 percentage change in our estimate of transient population. Grey represents NA values that occur from 0 Transient Pop.

Transient Pop Percentage Change by County from July 1st 2019 to July 1st 2020, limits of 0%,-80%
Transient Pop Percentage Change by County from July 1st 2019 to July 1st 2020, limits of 0%,-80%
  • Seasonal Population – No adjustments have been made to seasonal population currently as any change here will take a while to show up in source data. Still, this may be a very good field set to review. People with seasonal homes may have taken up residence in these areas earlier or for longer amounts of time (we’ll touch more on that topic later). An important note about seasonal population is that it is specific to a quarter of occupancy. Therefore, if you want to know the total annual seasonal pop, you would need to total the last 4 quarters. This can be easily done since this field has 8 quarters of history.
  • The following maps illustrate the Seasonal Population by County for July 2020, April 2020, Jan 2020, and Oct 2019:
Seasonal Pop per 100 Population by County from July 1st 2020, limits of 0,-15
Seasonal Pop per 100 Population by County from July 1st 2020, limits of 0,-15
Seasonal Pop per 100 Population by County from April 1st 2020, limits of 0,-15
Seasonal Pop per 100 Population by County from April 1st 2020, limits of 0,-15
Seasonal Pop per 100 Population by County from January 1st 2020, limits of 0,-15
Seasonal Pop per 100 Population by County from January 1st 2020, limits of 0,-15
Seasonal Pop per 100 Population by County from October 1st 2019, limits of 0,-15
Seasonal Pop per 100 Population by County from October 1st 2019, limits of 0,-15

Current Population Change

  • Population Methodology – Our models for population pick up both decline and growth very accurately. This is due to the use of postal data and delivery statistics in our models. We’ll see the change in our estimates as housing units are built and become occupied or vacant.
Population Percentage Change by County from July 1st 2019 to July 1st 2020, limits of -2%,2%
Population Percentage Change by County from July 1st 2019 to July 1st 2020, limits of -2%,2%
  • New York and Seasonal housing shift. One of the most publicized areas of out-migration and the resulting population shift has been New York City. Especially during Coviid-19 when everyone was stuck at home in tiny New York apartments. It has also been one of the areas our customers have most asked about.
Net Migration per 100 Pop for New York City by BG from July 1st 2019 to July 1st 2020, limits 1, -1
Net Migration per 100 Pop for New York City by BG from July 1st 2019 to July 1st 2020, limits 1, -1

Net Migration here is determined by looking at population changes year-over-year and removing birth and death components. While this is a high level of out-migration, New York City has also regularly been experiencing out-migration. This is not as much of an exodus as indicated in many publications. That does not mean that there hasn’t been a large exodus from the area. This exodus has not resulted in, as of yet (July 1st), the equivalent decrease in housing ocupancy. That may sound counter intuitive, and for most parts of the country it would be, but there is a unique situation with a sizeable portion of this most recent out-migration event in New York being  in large part due to people leaving for their seasonal homes.

 

In PopStats, seasonal homes are determined via data from the Census Bureau, and are defined as residences occupied for less than 6 months a year. This is similar to how the IRS, most states and localities determine tax liabilities. We then estimate when those seasonal homes are likely occupied. Concerning the people who left New York, there is a strong chance that they did not sell their homes there or end their leases. Meaning that they are still receiving mail at these addresses.  Should they choose to not return and remain in their seasonal residence for more than 6 months, eventually the proportion of seasonal homes in New York will increase and those elsewhere decrease. At this point that result is uncertain. Some people will choose to return to the city. Some will not.  

 

A similar situation to watch are those that have purchased homes elsewhere (or moved seasonally) that are in the process of selling their residences. It is not overly common for a large amout of people to purchase a house and vacate their current residence before selling for an extended period of time. Likewise, those that have abandoned leases will remain as residents of those leases until they complete the move-out process. These situations will be captured further in our estimates over the next several quarters. Ultimately, many of those that have fled the city will still be counted as current residents. We will montior future quarters to see how this changes over time.

Seasonal Pop per 100 Pop New York City by BG from July 1st 2020, limits of 0,-15
Seasonal Pop per 100 Pop New York City by BG from July 1st 2020, limits of 0,-15

Migration – Origin Destination

  • Related to New York, and more generally out-migration across the US, we have been asked about the destination of those moves. Unfortunately, there is not an accurate, current source that we can recommend here. We are able to recommend a slightly older source. The IRS publishes county level data on origin/destination migration data. We include this data in our LandScape product, and it’s estimated to the block group level for the top 50 counties. We have also previously sold a processed version of this dataset to interested customers.
  • 3D Migration Map – IRS 2017 to 2018 Exemptions – Height represent number from the State of New York to each county (limit of 99.5 percentile), Color depicts percentage of that counties inflow from the State of New York (limit of 20%).  The higher the bar the more people who moved from NY. The more blue the bar the greater proportion that New Yorkers make up of incoming migration.
Covid-19 3D Migration map

In conclusion – There exists no data source with geographic granularity that allows us to say these moves or changes are all attached to Covid-19.  We have a “sense” that people are moving out of New York and that early and extended seasonal migration stays might turn into permanent out-migration.  However, this is not a hard fact as of yet.  The reasoning’s here could also be multifaceted. It could be Covid-19 related, taxes-related, or a combination of these and other reasons.  The ability for many more people to work from home, has no doubt allowed for changes of residency – but are these permanent?  It’s too early to tell, at least quantifiably.  The estimates over time will continue to morph as more data becomes available.  We will continue to publish field comparisons, allowing our customers to better understand demographic changes across the country over time.

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