placestats

House price change and Economic connectedness in high school

Counting counties equally, House price change and Economic connectedness in high school have a link of -0.07 across 2,548 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 +0.10. 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

House price change

The percentage change in house prices from the year before, from homes that sold more than once. It only covers homes with a mortgage from Fannie Mae or Freddie Mac. In small counties with few sales, it can jump around.

FHFA House Price Index · FHFA HPI all-transactions annual (developmental county/state), annual change

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 house price change

House price changeEconomic connectedness in high school
Highest
Gonzales County, TX48.10.835
Evans County, GA46.80.767
Greene County, PA390.865
Hamilton County, FL38.30.697
Screven County, GA37.10.816
Lowest
Sargent County, ND-37.81.11
Valley County, NE-17.70.768
Turner County, GA-15.90.836
Pecos County, TX-15.80.846
Walworth County, SD-15.30.686

By economic connectedness in high school

House price changeEconomic connectedness in high school
Highest
Calvert County, MD4.341.61
Hunterdon County, NJ10.11.58
Howard County, MD7.41.56
Loudoun County, VA6.191.55
Stafford County, VA7.641.53
Lowest
Knox County, KY29.20.257
McCreary County, KY8.470.265
Metcalfe County, KY5.140.27
Wayne County, KY11.30.28
Bell County, KY-4.320.283
States — Economic connectedness in high school is not published for states

By house price change

House price changeEconomic connectedness in high school
Highest
Vermont13.2—
Maine11.6—
New Jersey10.7—
Wisconsin10.6—
New Hampshire10.5—
Lowest
District of Columbia-2.84—
Idaho-0.22—
Nevada0.3—
Utah0.64—
Oregon0.78—
Metro areas — House price change is not published for metro areas

By economic connectedness in high school

House price changeEconomic connectedness in high school
Highest
Lexington Park, MD—1.54
Los Alamos, NM—1.41
Williston, ND—1.39
Fairbanks-College, AK—1.38
Provo-Orem-Lehi, UT—1.38
Lowest
Middlesborough, KY—0.283
Pikeville, KY—0.338
Deming, NM—0.35
Clarksdale, MS—0.362
Corbin, KY—0.362

Counties furthest from the pattern

Compared with the straight line that best fits all 2,549 counties: the five with far more economic connectedness in high school than their house price change would suggest, and the five with far less.

House price changeEconomic connectedness in high school
Above the line
Calvert County, MD4.341.61
Hunterdon County, NJ10.11.58
Howard County, MD7.41.56
Loudoun County, VA6.191.55
Stafford County, VA7.641.53
Below the line
Bell County, KY-4.320.283
Metcalfe County, KY5.140.27
Lincoln County, WV-1.520.305
McCreary County, KY8.470.265
Wayne County, KY11.30.28

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-0.1312,548 counties reporting both
After adjusting for density, income and education-0.070the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.055size taken out as well as density
Also comparing only within each state-0.028each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.104the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 280 equally weighted counties
Middle value inside single states-0.114the link worked out inside each of 39 states with at least 20 counties reporting
Using ranks, after adjusting-0.067uses 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 = 0.000. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.010. The site tested 24,090 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 0.001.

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 +0.37 (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 House price change and Economic connectedness in high school