placestats

Homeownership rate and Share never married

Where Homeownership rate is higher, Share never married tends to be lower. The link is moderate: -0.53 across 3,194 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 -0.72.

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

Homeownership rate

The percentage of lived-in homes that are owned, not rented. Empty homes are left out of the count entirely.

Census ACS 5-year · ACS table B25003_002, divided by occupied housing units

Share never married

The percentage of people aged 15 and over who have never married. The Census counts from age 15, not 18, so every county's figure includes some teenagers.

Census ACS 5-year · ACS table B12001_003…B12001_012 (2 cells), divided by population aged 15 and over

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 homeownership rate

Homeownership rateShare never married
Highest
Storey County, NV97.4%23.7%
Cameron Parish, LA96.9%23%
Edwards County, TX94.5%30.8%
Elbert County, CO94.4%21.8%
Oliver County, ND93.4%17.5%
Lowest
Kalawao County, HI0%60.5%
Bronx County, NY20.3%49%
New York County, NY24.7%49.9%
Aleutians West Census Area, AK29.5%37.8%
Kings County, NY29.7%44.3%

By share never married

Homeownership rateShare never married
Highest
Lexington city, VA51.7%64.5%
Oglala Lakota County, SD53.5%62.8%
Radford city, VA42.7%62.6%
Kalawao County, HI0%60.5%
Williamsburg city, VA52.6%60.1%
Lowest
Borden County, TX63.6%9.2%
Banner County, NE75.6%10.6%
Hinsdale County, CO79.7%10.6%
Treasure County, MT66.5%12%
Glasscock County, TX66.4%12.7%
States

By homeownership rate

Homeownership rateShare never married
Highest
West Virginia74.3%29%
Maine74%28.9%
Michigan72.9%33.9%
Vermont72.8%33.1%
New Hampshire72.5%30.3%
Lowest
District of Columbia41.1%55.2%
New York54.3%38.6%
California55.8%37.8%
Nevada59.3%34.3%
Hawaii62.6%34.1%

By share never married

Homeownership rateShare never married
Highest
District of Columbia41.1%55.2%
Puerto Rico68.1%41.9%
New York54.3%38.6%
California55.8%37.8%
Rhode Island63.3%37.3%
Lowest
Wyoming71.9%27.6%
Idaho72.4%28.3%
Maine74%28.9%
West Virginia74.3%29%
Arkansas66.1%29.5%
Metro areas

By homeownership rate

Homeownership rateShare never married
Highest
Wildwood-The Villages, FL88.1%13.8%
Hemlock Farms, PA86.2%28.1%
Holland, MI86.1%25.8%
Homosassa Springs, FL84.8%20.7%
DeRidder, LA83.2%27.8%
Lowest
Pullman, WA46.6%52.5%
Los Angeles-Long Beach-Anaheim, CA48.6%40.7%
Carbondale, IL49.3%45.6%
Laramie, WY49.4%47.2%
Lawrence, KS49.5%45.6%

By share never married

Homeownership rateShare never married
Highest
Vermillion, SD51.8%54.9%
Pullman, WA46.6%52.5%
Gallup, NM68.8%52.5%
Ruston, LA53.5%51%
Clarksdale, MS52%49.5%
Lowest
Wildwood-The Villages, FL88.1%13.8%
Crossville, TN80.2%17.3%
Brookings, OR74.8%17.4%
Mountain Home, AR75%18.6%
Granbury, TX81.3%19.5%
Cities and towns

By homeownership rate

Homeownership rateShare never married
Highest
East Hills village, NY100%26%
White Rock CDP, NM99.6%20.9%
Hooper city, UT99.4%27.5%
Marvin village, NC99.3%22.6%
Todd Creek CDP, CO99.2%19.7%
Lowest
Fort Carson CDP, CO0%51.7%
Air Force Academy CDP, CO0%79.5%
Camp Pendleton Mainside CDP, CA0%72.7%
North Fort Lewis CDP, WA0%47.6%
Lackland AFB CDP, TX0%80.4%

By share never married

Homeownership rateShare never married
Highest
Rochester Institute of Technology CDP, NY0%99.3%
California Polytechnic State University CDP, CA61.7%98.8%
University at Buffalo CDP, NY0%98.1%
Rutgers University-Busch Campus CDP, NJ0%96.1%
Storrs CDP, CT29.9%87.8%
Lowest
Desert Palms CDP, CA83.8%3.45%
Longboat Key town, FL93.6%3.52%
Pelican Bay CDP, FL94.6%3.69%
The Villages CDP, FL94.2%4.24%
On Top of the World CDP, FL91.4%4.4%
ZIP codes

By homeownership rate

Homeownership rateShare never married
Highest
ZIP 29207 (Columbia city, SC)100%82.2%
ZIP 08056 (Gloucester County, NJ)100%26.1%
ZIP 48380 (Oakland County, MI)99.7%24.2%
ZIP 87547 (White Rock CDP, NM)99.6%20.9%
ZIP 06419 (Middlesex County, CT)99.5%22.2%
Lowest
ZIP 01003 (Amherst Town city, MA)0%99.5%
ZIP 93106 (University of California-Santa Barbara CDP, CA)0%98.1%
ZIP 92136 (San Diego city, CA)0%60.5%
ZIP 85613 (Sierra Vista city, AZ)0%50.6%
ZIP 80902 (El Paso County, CO)0%12.7%

By share never married

Homeownership rateShare never married
Highest
ZIP 13244 (Syracuse city, NY)0%99.9%
ZIP 33620 (Tampa city, FL)0%99.9%
ZIP 80310 (Boulder city, CO)0%99.9%
ZIP 55455 (Minneapolis city, MN)0%99.6%
ZIP 01003 (Amherst Town city, MA)0%99.5%
Lowest
ZIP 34228 (Longboat Key town, FL)93.6%3.52%
ZIP 32162 (The Villages CDP, FL)93.4%3.98%
ZIP 85375 (Sun City West CDP, AZ)88.2%5.31%
ZIP 32163 (Wildwood city, FL)95.6%5.45%
ZIP 85622 (Pima County, AZ)88.1%6.24%

Counties furthest from the pattern

Compared with the straight line that best fits all 3,213 counties: the five with far more share never married than their homeownership rate would suggest, and the five with far less.

Homeownership rateShare never married
Above the line
Claiborne County, MS75.2%54.4%
Kusilvak Census Area, AK69.4%55.9%
Jefferson County, MS78.8%50.3%
Lexington city, VA51.7%64.5%
Oglala Lakota County, SD53.5%62.8%
Below the line
Borden County, TX63.6%9.2%
Treasure County, MT66.5%12%
Glasscock County, TX66.4%12.7%
Geary County, KS42.8%26.4%
Thomas County, NE69.8%13.3%

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-0.6423,194 counties reporting both
After adjusting for density, income and education-0.533the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.514size taken out as well as density
Also comparing only within each state-0.530each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight-0.642the 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-0.719the link worked out inside each of 41 states with at least 20 counties reporting
Using ranks, after adjusting-0.524uses 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 = 2.0e-233. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 8.2e-16. 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 2.5e-232.

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 +0.33 (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 homeownership rate rose most, did share never marriedmove 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.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic3,211-0.08-0.06—
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years3,210-0.03-0.04—

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

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 Homeownership rate and Share never married