placestats

Homeownership rate and Economic connectedness in high school

Counting counties equally, Homeownership rate and Economic connectedness in high school have a link of -0.17 across 2,729 counties, on a scale from −1 to +1. Giving bigger places more weight asks about the average resident rather than the average place, and it flips the link to +0.35. Neither figure is likely to be chance. So this is a real disagreement about who is being described.

Flips when bigger places count more

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

Economic connectedness in high school

How many of a low-income teenager's high school friends are high-income, compared with an even mix. It is counted in the county of the school, not where the person lives as an adult, so it describes where people grew up.

Social Capital Atlas · Opportunity Insights child_ec_county

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 rateEconomic connectedness in high school
Highest
Cameron Parish, LA96.9%1.02
Elbert County, CO94.4%1.2
Powhatan County, VA93.1%1.21
New Kent County, VA92.1%1.22
Leelanau County, MI90.6%0.877
Lowest
Bronx County, NY20.3%0.647
New York County, NY24.7%0.652
Kings County, NY29.7%0.61
Hudson County, NJ31.2%0.576
Chattahoochee County, GA36%0.833

By economic connectedness in high school

Homeownership rateEconomic connectedness in high school
Highest
Calvert County, MD87.2%1.61
Hunterdon County, NJ84.8%1.58
Howard County, MD71.7%1.56
Loudoun County, VA77.9%1.55
Stafford County, VA80.8%1.53
Lowest
Magoffin County, KY77.5%0.222
Leslie County, KY82.6%0.229
Breathitt County, KY74.3%0.249
Knox County, KY65.6%0.257
McCreary County, KY74.4%0.265
States — Economic connectedness in high school is not published for states

By homeownership rate

Homeownership rateEconomic connectedness in high school
Highest
West Virginia74.3%—
Maine74%—
Michigan72.9%—
Vermont72.8%—
New Hampshire72.5%—
Lowest
District of Columbia41.1%—
New York54.3%—
California55.8%—
Nevada59.3%—
Hawaii62.6%—
Metro areas

By homeownership rate

Homeownership rateEconomic connectedness in high school
Highest
Wildwood-The Villages, FL88.1%0.568
Hemlock Farms, PA86.2%1.07
Holland, MI86.1%0.88
Homosassa Springs, FL84.8%0.557
DeRidder, LA83.2%0.909
Lowest
Pullman, WA46.6%1.33
Los Angeles-Long Beach-Anaheim, CA48.6%0.776
Carbondale, IL49.3%0.875
Laramie, WY49.4%1.24
Lawrence, KS49.5%1.2

By economic connectedness in high school

Homeownership rateEconomic connectedness in high school
Highest
Lexington Park, MD79.1%1.54
Los Alamos, NM74.5%1.41
Williston, ND57.2%1.39
Fairbanks-College, AK61.7%1.38
Provo-Orem-Lehi, UT68.4%1.38
Lowest
Middlesborough, KY61.1%0.283
Pikeville, KY74%0.338
Deming, NM69.2%0.35
Clarksdale, MS52%0.362
Corbin, KY69.9%0.362
Cities and towns — Economic connectedness in high school is not published for cities and towns

By homeownership rate

Homeownership rateEconomic connectedness in high school
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 — Economic connectedness in high school is not published for ZIP codes

By homeownership rate

Homeownership rateEconomic connectedness in high school
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 2,731 counties: the five with far more economic connectedness in high school than their homeownership rate would suggest, and the five with far less.

Homeownership rateEconomic connectedness in high school
Above the line
Calvert County, MD87.2%1.61
Hunterdon County, NJ84.8%1.58
Howard County, MD71.7%1.56
Loudoun County, VA77.9%1.55
Stafford County, VA80.8%1.53
Below the line
Magoffin County, KY77.5%0.222
Leslie County, KY82.6%0.229
Breathitt County, KY74.3%0.249
Knox County, KY65.6%0.257
Holmes County, MS60.6%0.273

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-0.0272,729 counties reporting both
After adjusting for density, income and education-0.173the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.150size taken out as well as density
Also comparing only within each state-0.063each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.348the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 284 equally weighted counties
Middle value inside single states-0.095the link worked out inside each of 39 states with at least 20 counties reporting
Using ranks, after adjusting-0.201uses 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 = 7.4e-20. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.013. 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 2.4e-19.

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 +0.38 (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 Economic connectedness in high school