Share in the same house a year ago and Arrivals per 100 people staying
Where Share in the same house a year ago is higher, Arrivals per 100 people staying tends to be lower. The link is weak: -0.33 across 3,080 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 -0.33.
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
Share in the same house a year ago
The percentage of residents who lived in the same home a year earlier. The middle county is 88%, so the low end is the interesting one: college towns, military bases and resort areas turn over fastest. Read it as the opposite of moving around, not as good or bad. A place nobody wants to leave and a place nobody can afford to leave look the same here.
Census ACS 5-year · ACS table B07001_017, divided by total population
Arrivals per 100 people staying
Tax filers who moved in, for every hundred who stayed put.
IRS county migration flows · IRS SOI migration, county aggregates
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 share in the same house a year ago
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| Taliaferro County, GA | 99.9% | 5.86 |
| Baker County, GA | 98.5% | 7.76 |
| Sherman County, TX | 97.9% | 3.59 |
| Haakon County, SD | 97.9% | 2.71 |
| Greene County, AL | 97.6% | 4.81 |
| Lowest | ||
| Madison County, ID | 61% | 15 |
| Chattahoochee County, GA | 62.4% | 46.3 |
| Albany County, WY | 67.9% | 10 |
| Clay County, SD | 67.9% | 9.61 |
| Pulaski County, MO | 68.8% | 17.2 |
By arrivals per 100 people staying
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| Chattahoochee County, GA | 62.4% | 46.3 |
| Williamsburg city, VA | 69.9% | 24.5 |
| Geary County, KS | 69% | 24 |
| Liberty County, GA | 77.5% | 21.1 |
| Long County, GA | 82.3% | 21 |
| Lowest | ||
| Sheridan County, MT | 91.1% | 1.33 |
| North Slope Borough, AK | 85.5% | 1.64 |
| Webb County, TX | 88.7% | 1.79 |
| McPherson County, SD | 92.1% | 1.85 |
| Fallon County, MT | 88.9% | 1.86 |
States
By share in the same house a year ago
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| New Jersey | 89.2% | 2.43 |
| New York | 89% | 1.85 |
| West Virginia | 88.2% | 2.83 |
| Rhode Island | 88.1% | 3.3 |
| New Hampshire | 88% | 3.7 |
| Lowest | ||
| District of Columbia | 79.5% | 9.71 |
| North Dakota | 82.6% | 3.86 |
| Colorado | 82.6% | 4.29 |
| Wyoming | 83.4% | 4.8 |
| Idaho | 83.5% | 4.42 |
By arrivals per 100 people staying
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| District of Columbia | 79.5% | 9.71 |
| Alaska | 84.1% | 4.87 |
| Wyoming | 83.4% | 4.8 |
| Nevada | 84.1% | 4.69 |
| Delaware | 88% | 4.66 |
| Lowest | ||
| California | 87.9% | 1.35 |
| Michigan | 87.5% | 1.57 |
| Ohio | 86.6% | 1.72 |
| New York | 89% | 1.85 |
| Illinois | 87.6% | 1.86 |
Metro areas
By share in the same house a year ago
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| Gallup, NM | 93.9% | 3.54 |
| Española, NM | 93.7% | 4.48 |
| Hereford, TX | 93.5% | 4.51 |
| North Wilkesboro, NC | 93.5% | 4.22 |
| Washington, NC | 93.3% | 6.1 |
| Lowest | ||
| Rexburg, ID | 66.2% | 11.5 |
| Laramie, WY | 67.9% | 10 |
| Vermillion, SD | 67.9% | 9.61 |
| Fort Leonard Wood, MO | 68.8% | 17.2 |
| Pullman, WA | 71.6% | 10.5 |
By arrivals per 100 people staying
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| Hinesville, GA | 78.5% | 19 |
| Fort Leonard Wood, MO | 68.8% | 17.2 |
| Jacksonville, NC | 75.7% | 15.5 |
| Ozark, AL | 82.7% | 14.8 |
| Anderson Creek, NC | 84.9% | 14.3 |
| Lowest | ||
| New York-Newark-Jersey City, NY-NJ | 89.6% | 1.71 |
| Laredo, TX | 88.7% | 1.79 |
| Chicago-Naperville-Elgin, IL-IN | 87.8% | 1.91 |
| St. Marys, PA | 90.6% | 2.08 |
| Detroit-Warren-Dearborn, MI | 88.8% | 2.11 |
Cities and towns — Arrivals per 100 people staying is not published for cities and towns
By share in the same house a year ago
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| Hewlett CDP, NY | 98.7% | — |
| Zuni Pueblo CDP, NM | 98.6% | — |
| Sun Village CDP, CA | 98.4% | — |
| Murillo CDP, TX | 98.3% | — |
| East Rancho Dominguez CDP, CA | 98.2% | — |
| Lowest | ||
| Rochester Institute of Technology CDP, NY | 16.7% | — |
| Clemson University CDP, SC | 25.2% | — |
| University of California-Santa Barbara CDP, CA | 26.8% | — |
| Isla Vista CDP, CA | 30.3% | — |
| University of California-Davis CDP, CA | 31.7% | — |
ZIP codes — Arrivals per 100 people staying is not published for ZIP codes
By share in the same house a year ago
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Highest | ||
| ZIP 53033 (Richfield village, WI) | 99% | — |
| ZIP 31028 (Centerville city, GA) | 98.8% | — |
| ZIP 14131 (Niagara County, NY) | 98.7% | — |
| ZIP 51108 (Plymouth County, IA) | 98.1% | — |
| ZIP 93543 (Los Angeles County, CA) | 98.1% | — |
| Lowest | ||
| ZIP 55455 (Minneapolis city, MN) | 11.5% | — |
| ZIP 01003 (Amherst Town city, MA) | 13% | — |
| ZIP 13244 (Syracuse city, NY) | 14.6% | — |
| ZIP 48109 (Ann Arbor city, MI) | 16.6% | — |
| ZIP 43210 (Columbus city, OH) | 18.3% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,096 counties: the five with far more arrivals per 100 people staying than their share in the same house a year ago would suggest, and the five with far less.
| Share in the same house a year ago | Arrivals per 100 people staying | |
|---|---|---|
| Above the line | ||
| Chattahoochee County, GA | 62.4% | 46.3 |
| Williamsburg city, VA | 69.9% | 24.5 |
| Long County, GA | 82.3% | 21 |
| Geary County, KS | 69% | 24 |
| Liberty County, GA | 77.5% | 21.1 |
| Below the line | ||
| De Baca County, NM | 74.4% | 3.26 |
| Jim Hogg County, TX | 80.8% | 2.7 |
| North Slope Borough, AK | 85.5% | 1.64 |
| Garden County, NE | 80.4% | 3.02 |
| Hudspeth County, TX | 75.3% | 4.38 |
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 | -0.370 | 3,080 counties reporting both |
| After adjusting for density, income and education | -0.329 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.359 | size taken out as well as density |
| Also comparing only within each state | -0.312 | 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 | -0.410 | the 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 | -0.326 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.242 | 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 = 1.4e-78. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 4.4e-10. 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 9.5e-78.
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 +0.40 (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 share in the same house a year ago rose most, did arrivals per 100 people stayingmove 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 | — | — | — | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,078 | -0.11 | -0.08 | 0.003 |
Not measurable over the main period, so only the second period is shown. How change is measured.
Related pairs
- Arrivals per 100 people staying and Mutual-friend share — -0.44
- Homeownership rate and Share in the same house a year ago — +0.42
- Median age and Share in the same house a year ago — +0.42
- Share in the same house a year ago and Departures per 100 people staying — -0.40
- Arrivals per 100 people staying and Friending clustering — -0.34
- Share with a bachelor's degree or higher and Share in the same house a year ago — -0.34
- Arrivals per 100 people staying and Mobility score — -0.34
- Business opening rate and Arrivals per 100 people staying — +0.32
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 Share in the same house a year ago and Arrivals per 100 people staying