Median age and Vacancy rate
Where Median age is higher, Vacancy rate tends to be higher. The link is moderate: +0.42 across 3,180 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 41 states, the middle value of the link is +0.55.
Holds after adjusting
Every place, plotted
One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.
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What each measure is
Median age
The middle age of the county: half the residents are older and half are younger. The typical county is 41.5, and four in five are between 36 and 49. The extremes come from one big group of residents, such as retirees or students: 68.9 in Loving County, Texas and 68.4 in Sumter County, Florida, against 21.4 in Madison County, Idaho, where a university sets the middle age. Since it is a middle value, county figures cannot be averaged into a state figure.
Census ACS 5-year · ACS table B01002_001
Vacancy rate
The percentage of all homes and apartments that nobody lives in. The middle county is 15%, which surprises people. It counts vacation and seasonal homes, so the top of the list is resort and outdoor-recreation areas, such as Daggett County, Utah at 77%, not abandoned towns. A high number can tell two very different stories, and this measure cannot tell them apart.
Census ACS 5-year · ACS table B25002_003, divided by housing units
Highest and lowest
Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.
Counties
By median age
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Sumter County, FL | 68.4 years | 16.7% |
| Kenedy County, TX | 65.5 years | 75.4% |
| Catron County, NM | 62.6 years | 50.6% |
| Terrell County, TX | 60.9 years | 31.7% |
| Charlotte County, FL | 60 years | 23.9% |
| Lowest | ||
| Madison County, ID | 21.4 years | 13.5% |
| Lexington city, VA | 22.5 years | 22.1% |
| Radford city, VA | 23 years | 14.8% |
| Kusilvak Census Area, AK | 23.6 years | 19.2% |
| Chattahoochee County, GA | 23.9 years | 34.6% |
By vacancy rate
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Daggett County, UT | 45 years | 77.3% |
| Kalawao County, HI | 39.9 years | 76.8% |
| Lake and Peninsula Borough, AK | 33.4 years | 76.2% |
| Kenedy County, TX | 65.5 years | 75.4% |
| Rich County, UT | 37.2 years | 74.6% |
| Lowest | ||
| Somerset County, NJ | 41.9 years | 2.54% |
| Scott County, MN | 37.3 years | 2.6% |
| Kendall County, IL | 36 years | 2.73% |
| Dakota County, MN | 38.4 years | 2.73% |
| Loudoun County, VA | 37.8 years | 2.75% |
States
By median age
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Maine | 44.8 years | 21.1% |
| Puerto Rico | 44.2 years | 21.3% |
| New Hampshire | 43.2 years | 14.4% |
| Vermont | 43 years | 20.1% |
| West Virginia | 42.7 years | 16.1% |
| Lowest | ||
| Utah | 31.7 years | 8.23% |
| District of Columbia | 34.9 years | 9.7% |
| Texas | 35.5 years | 9.62% |
| Alaska | 35.6 years | 18.2% |
| North Dakota | 35.7 years | 13.3% |
By vacancy rate
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Puerto Rico | 44.2 years | 21.3% |
| Maine | 44.8 years | 21.1% |
| Vermont | 43 years | 20.1% |
| Alaska | 35.6 years | 18.2% |
| West Virginia | 42.7 years | 16.1% |
| Lowest | ||
| Washington | 38.2 years | 7.42% |
| Oregon | 40.1 years | 7.46% |
| Connecticut | 41.2 years | 7.54% |
| California | 37.6 years | 7.55% |
| New Jersey | 40.1 years | 7.88% |
Metro areas — Median age is not published for metro areas
By vacancy rate
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Nantucket, MA | — | 59.5% |
| Vineyard Haven, MA | — | 58.4% |
| Ocean Pines, MD | — | 57.9% |
| Breckenridge, CO | — | 57.3% |
| Kill Devil Hills, NC | — | 52.7% |
| Lowest | ||
| Lancaster, PA | — | 3.36% |
| Appleton, WI | — | 3.77% |
| Provo-Orem-Lehi, UT | — | 3.82% |
| Andrews, TX | — | 4.01% |
| Greeley, CO | — | 4.01% |
Cities and towns
By median age
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Leisure World CDP, MD | 76.9 years | 9.76% |
| Desert Palms CDP, CA | 76.2 years | 24.8% |
| Laguna Woods city, CA | 75 years | 12.6% |
| Sun City West CDP, AZ | 74.9 years | 16.7% |
| Pelican Bay CDP, FL | 73.8 years | 44.9% |
| Lowest | ||
| Kiryas Joel village, NY | 15.1 years | 6.96% |
| Monsey CDP, NY | 15.6 years | 7.38% |
| Lakewood CDP, NJ | 17.3 years | 3.89% |
| Clemson University CDP, SC | 19.3 years | 100% |
| University at Buffalo CDP, NY | 19.4 years | 0% |
By vacancy rate
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| Clemson University CDP, SC | 19.3 years | 100% |
| Ocean City town, MD | 56 years | 87% |
| Big Bear Lake city, CA | 45.5 years | 76.7% |
| Orange Beach city, AL | 51.1 years | 73.9% |
| Ocean City city, NJ | 57.3 years | 73.1% |
| Lowest | ||
| Severance town, CO | 33.2 years | 0% |
| Mead town, CO | 38.1 years | 0% |
| South Monrovia Island CDP, CA | 34.4 years | 0% |
| Salida CDP, CA | 33.9 years | 0% |
| Dos Palos city, CA | 36.5 years | 0% |
ZIP codes
By median age
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| ZIP 92637 (Laguna Woods city, CA) | 75 years | 12.6% |
| ZIP 85375 (Sun City West CDP, AZ) | 74.8 years | 16.7% |
| ZIP 32162 (The Villages CDP, FL) | 74.2 years | 16.9% |
| ZIP 85622 (Pima County, AZ) | 73.1 years | 20.4% |
| ZIP 85351 (Sun City CDP, AZ) | 72.6 years | 14.5% |
| Lowest | ||
| ZIP 08701 (Ocean County, NJ) | 18.2 years | 6.9% |
| ZIP 55455 (Minneapolis city, MN) | 18.9 years | 0% |
| ZIP 22807 (Harrisonburg city, VA) | 18.9 years | 0% |
| ZIP 48109 (Ann Arbor city, MI) | 19.1 years | 0% |
| ZIP 79406 (Lubbock city, TX) | 19.2 years | 100% |
By vacancy rate
| Median age | Vacancy rate | |
|---|---|---|
| Highest | ||
| ZIP 29634 (Clemson University CDP, SC) | 19.3 years | 100% |
| ZIP 79406 (Lubbock city, TX) | 19.2 years | 100% |
| ZIP 43210 (Columbus city, OH) | 19.4 years | 84.6% |
| ZIP 21842 (Ocean City town, MD) | 53.7 years | 82.3% |
| ZIP 08008 (Ocean County, NJ) | 62.5 years | 80.1% |
| Lowest | ||
| ZIP 08886 (Warren County, NJ) | 42.8 years | 0% |
| ZIP 08823 (Pleasant Plains CDP, NJ) | 39.8 years | 0% |
| ZIP 08502 (Somerset County, NJ) | 41.8 years | 0% |
| ZIP 07738 (Lincroft CDP, NJ) | 43.4 years | 0% |
| ZIP 07630 (Emerson borough, NJ) | 47.9 years | 0% |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,202 counties: the five with far more vacancy rate than their median age would suggest, and the five with far less.
| Median age | Vacancy rate | |
|---|---|---|
| Above the line | ||
| Lake and Peninsula Borough, AK | 33.4 years | 76.2% |
| Rich County, UT | 37.2 years | 74.6% |
| Kalawao County, HI | 39.9 years | 76.8% |
| Daggett County, UT | 45 years | 77.3% |
| Alpine County, CA | 41.1 years | 70.2% |
| Below the line | ||
| Sumter County, FL | 68.4 years | 16.7% |
| Wahkiakum County, WA | 54.8 years | 8.2% |
| Hunterdon County, NJ | 46.2 years | 3.06% |
| Curry County, OR | 57.2 years | 14% |
| Charles City County, VA | 52.6 years | 9.85% |
Two series, one county
Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.
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Every version of the link
Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.
| Before adjusting for anything | +0.490 | 3,180 counties reporting both |
| After adjusting for density, income and education | +0.422 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.402 | size taken out as well as density |
| Also comparing only within each state | +0.376 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.481 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 292 equally weighted counties |
| Middle value inside single states | +0.550 | the link worked out inside each of 41 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.409 | uses each county’s rank, not its value, so a few extreme counties cannot create the link |
How much to believe it
Could it be chance?
The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 3.0e-137. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.9e-10. The site tested 20,910 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 2.8e-136.
Do nearby places look alike?
Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.30 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.
Did they change together?
The figures above compare counties at one moment. This asks a different question: where median age rose most, did vacancy ratemove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 3,195 | -0.06 | -0.05 | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,189 | -0.12 | -0.10 | — |
Both come from the American Community Survey, which asks a sample of households. Random error in a county's sample pushes both changes at once. So part of any agreement here is that shared error. It is shown, but not counted as a finding. How change is measured.
Related pairs
- Median age and Share aged 65 and over — +0.93
- Median age and Share never married — -0.59
- Homeownership rate and Median age — +0.58
- Median age and Share widowed — +0.53
- Median age and People per tax return — -0.49
- Median age and Per capita income — +0.47
- Median age and Mean income of arriving households — +0.44
- Median age and Returns reporting capital gains — +0.43
Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Median age and Vacancy rate