Homeownership rate and Theft rate
Where Homeownership rate is higher, Theft rate tends to be lower. The link is moderate: -0.42 across 2,281 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 36 states, the middle value of the link is -0.63.
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
Theft rate
Thefts in the year, per 100,000 residents, from the FBI. This covers shoplifting, pickpocketing and stealing from cars or bikes, but not the theft of a vehicle itself. It counts only crimes reported to police or found by them, so crimes nobody reports are missing. For a state, the figure is the FBI's estimate, which fills in for police agencies that did not report. For a county, it is only what the agencies reported, and it is shown only where agencies that reported for all 12 months serve at least 90% of the people who live there.
FBI Uniform Crime Reporting program (Crime Data Explorer) · FBI Crime Data Explorer: states, estimated_crimes larceny; counties, Return A master file, actual offenses
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 rate | Theft rate | |
|---|---|---|
| Highest | ||
| Edwards County, TX | 94.5% | 702 |
| Oliver County, ND | 93.4% | 0 |
| Powhatan County, VA | 93.1% | 527 |
| New Kent County, VA | 92.1% | 615 |
| Keweenaw County, MI | 91.7% | 254 |
| Lowest | ||
| Hudson County, NJ | 31.2% | 1,400 |
| Suffolk County, MA | 36.6% | 1,628 |
| Petersburg city, VA | 38.3% | 2,723 |
| Emporia city, VA | 38.4% | 2,828 |
| San Francisco County, CA | 38.5% | 4,195 |
By theft rate
| Homeownership rate | Theft rate | |
|---|---|---|
| Highest | ||
| Norton city, VA | 52.3% | 9,617 |
| San Francisco County, CA | 38.5% | 4,195 |
| Martin County, TX | 66.9% | 3,965 |
| Shelby County, TN | 54.6% | 3,811 |
| Gilpin County, CO | 79.6% | 3,709 |
| Lowest | ||
| Roberts County, TX | 85.5% | 0 |
| Presidio County, TX | 74.4% | 0 |
| Oliver County, ND | 93.4% | 0 |
| Griggs County, ND | 79.4% | 0 |
| Thomas County, NE | 69.8% | 0 |
States
By homeownership rate
| Homeownership rate | Theft rate | |
|---|---|---|
| Highest | ||
| West Virginia | 74.3% | 916 |
| Maine | 74% | 937 |
| Michigan | 72.9% | 1,071 |
| Vermont | 72.8% | 1,582 |
| New Hampshire | 72.5% | 789 |
| Lowest | ||
| District of Columbia | 41.1% | 2,959 |
| New York | 54.3% | 1,469 |
| California | 55.8% | 1,467 |
| Nevada | 59.3% | 1,451 |
| Hawaii | 62.6% | 1,495 |
By theft rate
| Homeownership rate | Theft rate | |
|---|---|---|
| Highest | ||
| District of Columbia | 41.1% | 2,959 |
| Colorado | 66.3% | 1,899 |
| Oregon | 63.4% | 1,844 |
| New Mexico | 69.3% | 1,839 |
| Louisiana | 67.3% | 1,780 |
| Lowest | ||
| Idaho | 72.4% | 599 |
| New Hampshire | 72.5% | 789 |
| Rhode Island | 63.3% | 858 |
| Massachusetts | 62.6% | 866 |
| West Virginia | 74.3% | 916 |
Metro areas
By homeownership rate
| Homeownership rate | Theft rate | |
|---|---|---|
| Highest | ||
| Wildwood-The Villages, FL | 88.1% | 481 |
| Hemlock Farms, PA | 86.2% | 462 |
| Holland, MI | 86.1% | 611 |
| DeRidder, LA | 83.2% | 727 |
| Punta Gorda, FL | 83.2% | 536 |
| Lowest | ||
| Pullman, WA | 46.6% | 757 |
| Los Angeles-Long Beach-Anaheim, CA | 48.6% | 1,550 |
| Carbondale, IL | 49.3% | 1,875 |
| Laramie, WY | 49.4% | 1,165 |
| Lawrence, KS | 49.5% | 714 |
By theft rate
| Homeownership rate | Theft rate | |
|---|---|---|
| Highest | ||
| Memphis, TN-MS-AR | 60.6% | 3,077 |
| Great Falls, MT | 70.2% | 3,039 |
| Wichita, KS | 64.7% | 2,952 |
| Lumberton, NC | 65.6% | 2,805 |
| Cordele, GA | 53.7% | 2,696 |
| Lowest | ||
| Lexington, NE | 66.1% | 0 |
| Columbus, IN | 69.9% | 9.66 |
| Las Vegas, NM | 75.5% | 90.3 |
| Bedford, IN | 80% | 135 |
| Pikeville, KY | 74% | 162 |
Cities and towns — Theft rate is not published for cities and towns
By homeownership rate
| Homeownership rate | Theft rate | |
|---|---|---|
| Highest | ||
| East Hills village, NY | 100% | — |
| White Rock CDP, NM | 99.6% | — |
| Hooper city, UT | 99.4% | — |
| Marvin village, NC | 99.3% | — |
| Todd Creek CDP, CO | 99.2% | — |
| Lowest | ||
| Fort Carson CDP, CO | 0% | — |
| Air Force Academy CDP, CO | 0% | — |
| Camp Pendleton Mainside CDP, CA | 0% | — |
| North Fort Lewis CDP, WA | 0% | — |
| Lackland AFB CDP, TX | 0% | — |
ZIP codes — Theft rate is not published for ZIP codes
By homeownership rate
| Homeownership rate | Theft rate | |
|---|---|---|
| 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 2,288 counties: the five with far more theft rate than their homeownership rate would suggest, and the five with far less.
| Homeownership rate | Theft rate | |
|---|---|---|
| Above the line | ||
| Norton city, VA | 52.3% | 9,617 |
| Gilpin County, CO | 79.6% | 3,709 |
| Martin County, TX | 66.9% | 3,965 |
| Shelby County, TN | 54.6% | 3,811 |
| Cascade County, MT | 70.2% | 3,039 |
| Below the line | ||
| Sioux County, ND | 46% | 27.4 |
| Madison County, ID | 40.2% | 584 |
| Bristol Bay Borough, AK | 51.9% | 113 |
| North Slope Borough, AK | 49.5% | 252 |
| Brooks County, TX | 53.2% | 117 |
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 | -0.583 | 2,281 counties reporting both |
| After adjusting for density, income and education | -0.421 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.401 | size taken out as well as density |
| Also comparing only within each state | -0.324 | 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 | -0.426 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 227 equally weighted counties |
| Middle value inside single states | -0.632 | the link worked out inside each of 36 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.458 | 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 = 1.1e-98. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.9e-19. The site tested 29,403 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.1e-97.
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 +0.21 (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 theft 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 | 2,018 | +0.02 | +0.02 | 0.686 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 1,883 | -0.02 | -0.02 | 0.564 |
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Homeownership rate and Median age — +0.58
- Homeownership rate and Share never married — -0.53
- Homeownership rate and Share aged 65 and over — +0.53
- Homeownership rate and Share foreign-born — -0.46
- Homeownership rate and Share now married — +0.46
- Homeownership rate and Property crime rate — -0.44
- Homeownership rate and Share White, not Hispanic — +0.44
- Homeownership rate and Share in the same house a year ago — +0.42
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 Theft rate