placestats

Homeownership rate and Longest dry spell

Where Homeownership rate is higher, Longest dry spell tends to be lower. The link is weak: -0.36 across 3,089 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 39 states, the middle value of the link is -0.02.

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

Longest dry spell

The most days in a row in the year without a wet day, meaning a day with a tenth of an inch of rain or more. It counts only inside the calendar year, so a dry spell that runs past New Year's Day is split in two. The rain is the county average of NOAA's daily map of weather station readings, made of squares about 3 miles wide. A state's figure is the average of its counties, each weighted by its land area. Lower 48 states only, from 1951.

NOAA NCEI nClimGrid-Daily · NOAA NCEI nClimGrid-Daily v1.0.0 county averages (prcp/tmax/tmin, scaled), longest_dry_spell; states, counties averaged by land area

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

By longest dry spell

Homeownership rateLongest dry spell
Highest
San Joaquin County, CA61.5%206
Kings County, CA54.8%198
San Luis Obispo County, CA61.9%197
Merced County, CA53.3%184
Contra Costa County, CA67.7%172
Lowest
Wise County, VA74.1%10
Union County, TN80.4%10
Bell County, KY61.1%10
Pike County, KY75.1%11
Perry County, KY72.2%11
States

By homeownership rate

Homeownership rateLongest dry spell
Highest
West Virginia74.3%18.5
Maine74%17.1
Michigan72.9%25.5
Vermont72.8%16.6
New Hampshire72.5%18.4
Lowest
New York54.3%19.3
California55.8%111
Nevada59.3%55.1
Texas62.6%56.8
Massachusetts62.6%23.9

By longest dry spell

Homeownership rateLongest dry spell
Highest
California55.8%111
Arizona67%82.9
New Mexico69.3%71.2
Texas62.6%56.8
Nevada59.3%55.1
Lowest
Vermont72.8%16.6
Maine74%17.1
New Hampshire72.5%18.4
West Virginia74.3%18.5
New York54.3%19.3
Metro areas

By homeownership rate

Homeownership rateLongest dry spell
Highest
Wildwood-The Villages, FL88.1%32
Hemlock Farms, PA86.2%31
Holland, MI86.1%22
Homosassa Springs, FL84.8%32
DeRidder, LA83.2%32
Lowest
Pullman, WA46.6%73
Los Angeles-Long Beach-Anaheim, CA48.6%107
Carbondale, IL49.3%22
Laramie, WY49.4%42
Lawrence, KS49.5%31

By longest dry spell

Homeownership rateLongest dry spell
Highest
Stockton-Lodi, CA61.5%206
Hanford-Corcoran, CA54.8%198
San Luis Obispo-Paso Robles, CA61.9%197
Merced, CA53.3%184
San Francisco-Oakland-Fremont, CA55.4%170
Lowest
Middlesborough, KY61.1%10
Oneonta, NY74.3%12
Beckley, WV75%12
Pikeville, KY74%12.7
Elkins, WV77.1%13
Cities and towns — Longest dry spell is not published for cities and towns

By homeownership rate

Homeownership rateLongest dry spell
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 — Longest dry spell is not published for ZIP codes

By homeownership rate

Homeownership rateLongest dry spell
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 3,105 counties: the five with far more longest dry spell than their homeownership rate would suggest, and the five with far less.

Homeownership rateLongest dry spell
Above the line
San Joaquin County, CA61.5%206
San Luis Obispo County, CA61.9%197
Kings County, CA54.8%198
Merced County, CA53.3%184
Contra Costa County, CA67.7%172
Below the line
Bronx County, NY20.3%22
Norton city, VA52.3%12
Bell County, KY61.1%10
New York County, NY24.7%22
Kings County, NY29.7%22

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-0.1343,089 counties reporting both
After adjusting for density, income and education-0.360the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.330size taken out as well as density
Also comparing only within each state-0.153each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal-0.328compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more)
Giving bigger counties more weight-0.512the 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-0.019the link worked out inside each of 39 states with at least 20 counties reporting
Using ranks, after adjusting-0.270uses 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 = 6.3e-95. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.013. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 6.2e-94.

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 +0.69 (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 longest dry spellmove 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.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic3,104-0.01-0.020.448
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years3,105-0.02-0.010.881

Over the main period, any link between the changes could easily be chance. How change is measured.

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 Longest dry spell