Vacancy rate and Water area
Where Vacancy rate is higher, Water area tends to be higher. The link is weak: +0.34 across 3,029 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 39 states, the middle value of the link is +0.13.
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
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
Water area
The percentage of the county's total area that is water: lakes, rivers, bays and coastal sea. A county with a big lake or a long coast scores high. About two in three counties are under 2%.
USDA ERS Natural Amenities Scale · USDA ERS natural amenities scale, percent water area
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 vacancy rate
| Vacancy rate | Water area | |
|---|---|---|
| Highest | ||
| Daggett County, UT | 77.3% | 3.42 |
| Kenedy County, TX | 75.4% | 25.1 |
| Rich County, UT | 74.6% | 5.32 |
| Hamilton County, NY | 73.3% | 4.82 |
| Forest County, PA | 72.1% | 0.76 |
| Lowest | ||
| Somerset County, NJ | 2.54% | 0.11 |
| Scott County, MN | 2.6% | 3.19 |
| Kendall County, IL | 2.73% | 0.63 |
| Dakota County, MN | 2.73% | 2.84 |
| Loudoun County, VA | 2.75% | 0.23 |
By water area
| Vacancy rate | Water area | |
|---|---|---|
| Highest | ||
| Dukes County, MA | 58.4% | 75 |
| Nantucket County, MA | 59.5% | 75 |
| Alger County, MI | 43.8% | 75 |
| Keweenaw County, MI | 52.4% | 75 |
| Leelanau County, MI | 39.6% | 75 |
| Lowest | ||
| Cheyenne County, CO | 22.2% | 0 |
| Highland County, VA | 45.5% | 0 |
| Floyd County, VA | 16.3% | 0 |
| Craig County, VA | 26.9% | 0 |
| Buchanan County, VA | 28.9% | 0 |
States — Water area is not published for states
By vacancy rate
| Vacancy rate | Water area | |
|---|---|---|
| Highest | ||
| Puerto Rico | 21.3% | — |
| Maine | 21.1% | — |
| Vermont | 20.1% | — |
| Alaska | 18.2% | — |
| West Virginia | 16.1% | — |
| Lowest | ||
| Washington | 7.42% | — |
| Oregon | 7.46% | — |
| Connecticut | 7.54% | — |
| California | 7.55% | — |
| New Jersey | 7.88% | — |
Metro areas — Water area is not published for metro areas
By vacancy rate
| Vacancy rate | Water area | |
|---|---|---|
| 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 — Water area is not published for cities and towns
By vacancy rate
| Vacancy rate | Water area | |
|---|---|---|
| Highest | ||
| Clemson University CDP, SC | 100% | — |
| Ocean City town, MD | 87% | — |
| Big Bear Lake city, CA | 76.7% | — |
| Orange Beach city, AL | 73.9% | — |
| Ocean City city, NJ | 73.1% | — |
| Lowest | ||
| Severance town, CO | 0% | — |
| Mead town, CO | 0% | — |
| South Monrovia Island CDP, CA | 0% | — |
| Salida CDP, CA | 0% | — |
| Dos Palos city, CA | 0% | — |
ZIP codes — Water area is not published for ZIP codes
By vacancy rate
| Vacancy rate | Water area | |
|---|---|---|
| Highest | ||
| ZIP 29634 (Clemson University CDP, SC) | 100% | — |
| ZIP 79406 (Lubbock city, TX) | 100% | — |
| ZIP 43210 (Columbus city, OH) | 84.6% | — |
| ZIP 21842 (Ocean City town, MD) | 82.3% | — |
| ZIP 08008 (Ocean County, NJ) | 80.1% | — |
| Lowest | ||
| ZIP 08886 (Warren County, NJ) | 0% | — |
| ZIP 08823 (Pleasant Plains CDP, NJ) | 0% | — |
| ZIP 08502 (Somerset County, NJ) | 0% | — |
| ZIP 07738 (Lincroft CDP, NJ) | 0% | — |
| ZIP 07630 (Emerson borough, NJ) | 0% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,047 counties: the five with far more water area than their vacancy rate would suggest, and the five with far less.
| Vacancy rate | Water area | |
|---|---|---|
| Above the line | ||
| Ozaukee County, WI | 4.18% | 75 |
| Lake County, OH | 5.25% | 75 |
| Milwaukee County, WI | 8.21% | 75 |
| San Francisco County, CA | 11.9% | 75 |
| St. Bernard Parish, LA | 12.2% | 74.1 |
| Below the line | ||
| Forest County, PA | 72.1% | 0.76 |
| Alpine County, CA | 70.2% | 0.61 |
| Mineral County, CO | 65.3% | 0.23 |
| Hinsdale County, CO | 65.8% | 0.49 |
| Valley County, ID | 70% | 1.49 |
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 | +0.145 | 3,029 counties reporting both |
| After adjusting for density, income and education | +0.342 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.336 | size taken out as well as density |
| Also comparing only within each state | +0.273 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Also holding winter sunshine equal | +0.342 | compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more) |
| Giving bigger counties more weight | +0.340 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 276 equally weighted counties |
| Middle value inside single states | +0.134 | the link worked out inside each of 39 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.353 | 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 = 8.6e-84. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.4e-5. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 7.8e-83.
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 +0.42 (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?
Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.
Related pairs
- Median age and Vacancy rate — +0.42
- Vacancy rate and Mean income of arriving households — +0.41
- Share aged 65 and over and Vacancy rate — +0.38
- Median household income and Vacancy rate — -0.35
- Water area and Expected yearly hurricane loss — +0.31
- Vacancy rate and Medicare Advantage share — +0.13
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 Vacancy rate and Water area