placestats

Homeownership rate and Material security score

Where Homeownership rate is higher, Material security score tends to be higher. The link is weak: +0.34 across 3,093 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 40 states, the middle value of the link is +0.38.

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

Material security score

Poverty, child poverty, income inequality, and four basic-needs measures from CDC PLACES: food, housing, utilities and transport. It is flipped so higher means less hardship, which is why it is named for security, not for what it counts.

· derived from 9 measures (ACS, BRFSS, CDC, SAIPE)

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 rateMaterial security score
Highest
Storey County, NV97.4%0.946
Cameron Parish, LA96.9%0.72
Edwards County, TX94.5%-0.949
Elbert County, CO94.4%1.37
Oliver County, ND93.4%1.2
Lowest
Kalawao County, HI0%1.71
Bronx County, NY20.3%-2.12
New York County, NY24.7%-0.481
Aleutians West Census Area, AK29.5%1.14
Kings County, NY29.7%-0.902

By material security score

Homeownership rateMaterial security score
Highest
Morgan County, UT90.2%1.83
Los Alamos County, NM74.5%1.82
Kalawao County, HI0%1.71
Douglas County, CO77.8%1.69
Loudoun County, VA77.9%1.64
Lowest
East Carroll Parish, LA60%-3.95
Holmes County, MS60.6%-3.64
Ziebach County, SD53.9%-3.46
Martin County, KY80.2%-3.33
Sioux County, ND46%-3.24
States

By homeownership rate

Homeownership rateMaterial security score
Highest
West Virginia74.3%-0.691
Maine74%0.594
Michigan72.9%0.229
Vermont72.8%1.07
New Hampshire72.5%1.36
Lowest
District of Columbia41.1%-0.281
New York54.3%-0.294
California55.8%0.0683
Nevada59.3%-0.456
Hawaii62.6%1.14

By material security score

Homeownership rateMaterial security score
Highest
New Hampshire72.5%1.36
Hawaii62.6%1.14
Vermont72.8%1.07
Minnesota72.4%0.969
Utah70.6%0.93
Lowest
Mississippi69.5%-1.25
Texas62.6%-1.03
Louisiana67.3%-1.02
Arkansas66.1%-0.851
Alabama69.9%-0.811
Metro areas

By homeownership rate

Homeownership rateMaterial security score
Highest
Holland, MI86.1%0.833
DeRidder, LA83.2%-0.163
Murrells Inlet, SC83.1%-0.0584
Iron Mountain, MI-WI83%0.745
Picayune, MS82.9%-0.478
Lowest
Los Angeles-Long Beach-Anaheim, CA48.6%0.178
Carbondale, IL49.3%-0.882
Lawrence, KS49.5%0.506
Stillwater, OK50.9%-0.918
Greenville, NC50.9%-0.754

By material security score

Homeownership rateMaterial security score
Highest
Los Alamos, NM74.5%2.07
Heber, UT79.7%1.79
Nantucket, MA76.5%1.7
Appleton, WI73.6%1.55
Manchester-Nashua, NH67.5%1.44
Lowest
Gallup, NM68.8%-4.3
Clarksdale, MS52%-3.97
Cleveland, MS61%-3.8
Greenville, MS56.8%-3.64
Selma, AL61%-3.37
Cities and towns — Material security score is not published for cities and towns

By homeownership rate

Homeownership rateMaterial security score
Highest
East Hills village, NY100%—
White Rock CDP, NM99.6%—
Hooper city, UT99.4%—
Marvin village, NC99.3%—
Todd Creek CDP, CO99.2%—
Lowest
Fort Carson CDP, CO0%—
Air Force Academy CDP, CO0%—
Camp Pendleton Mainside CDP, CA0%—
North Fort Lewis CDP, WA0%—
Lackland AFB CDP, TX0%—
ZIP codes — Material security score is not published for ZIP codes

By homeownership rate

Homeownership rateMaterial security score
Highest
ZIP 29207 (Columbia city, SC)100%—
ZIP 08056 (Gloucester County, NJ)100%—
ZIP 48380 (Oakland County, MI)99.7%—
ZIP 87547 (White Rock CDP, NM)99.6%—
ZIP 06419 (Middlesex County, CT)99.5%—
Lowest
ZIP 01003 (Amherst Town city, MA)0%—
ZIP 99703 (Fairbanks city, AK)0%—
ZIP 99505 (Anchorage municipality, AK)0%—
ZIP 93106 (University of California-Santa Barbara CDP, CA)0%—
ZIP 92136 (San Diego city, CA)0%—

Counties furthest from the pattern

Compared with the straight line that best fits all 3,108 counties: the five with far more material security score than their homeownership rate would suggest, and the five with far less.

Homeownership rateMaterial security score
Above the line
Kalawao County, HI0%1.71
Aleutians West Census Area, AK29.5%1.14
Falls Church city, VA50.2%1.51
Arlington County, VA41.8%1.18
Broomfield County, CO62.6%1.51
Below the line
Martin County, KY80.2%-3.33
East Carroll Parish, LA60%-3.95
Holmes County, MS60.6%-3.64
McKinley County, NM68.8%-3.02
Greene County, AL69.7%-2.98

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+0.3423,093 counties reporting both
After adjusting for density, income and education+0.345the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.335size taken out as well as density
Also comparing only within each state+0.338each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.538the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 287 equally weighted counties
Middle value inside single states+0.381the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting+0.339uses 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 = 2.9e-87. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 5.3e-11. 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 2.2e-86.

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 +0.48 (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

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 Material security score