Home Value and Homeownership Rates: 

Recession and Post-Recession Comparisons 

From 2007–2009 to 2010–2012 

American Community Survey Briefs
 

By Christine Flanagan and Ellen Wilson 

Issued November 2013 

ACSBR/12-20 

IntroductIon 

This report examines the differences in the median 

home value and homeownership rate across different geographies during and after the recession of 

2007 through 2009. The data used in this report are 

from a 3-year period roughly encompassing part of 

the 2007–2009 recession and the 3 years following 

the recent recession1 (2010 through 2012) collected 

in the American Community Survey (ACS). Using the 

3-year ACS data enables us to look at geographies with 

smaller populations, 20,000 or more, than the geographies available from the 1-year data. This report will 

focus on those changes at the nation, state, county, 

metropolitan area, and place (city) level. 

For the purpose of this report, areas with populations 

of 20,000 to 65,000 will be referred to as smaller cities 

or counties. The 50 least populous of the smaller cities 

or counties will be referred to as the least populous 

cities or counties. 

Since the latest publication of the American 

Community Survey Brief, “Property Value: 2008 and 

2009,” which looked at median home value and the 

percent change in median home value for Metropolitan Statistical Areas, the recession in the United States 

has ended. Subsequently, metropolitan areas saw an 

increase in renter occupied housing units.2 The United 

States experienced a significant decrease in median 

home value (–$17,300) as well as homeownership rate 

(–1.7) from 2007–2009 to 2010–2012. 

1 The recent recession started in December 2007 and ended in June 

2009 according to the National Bureau of Economic Research, Inc., 

1050 Massachusetts Ave., Cambridge, MA, <www.nber.org/cycles 

/cyclesmain.html>. 2 See “Rental Housing Market Condition Measures: A Comparison of 

U.S. Metropolitan Areas From 2009 to 2011” by Christine Flanagan and 

Mary Schwartz. 

U.S. Department of Commerce 

Economics and Statistics Administration 

U.S. CENSUS BUREAU 

census.gov

Home Value: Home value is the owner’s estimate 

of how much the property (house and lot, mobile 

home and lot, or condominium unit) would sell for if 

it were for sale. Median home value means that onehalf of all homes were worth more and one-half were 

worth less. Median home value estimates in this 

report are presented in current dollars. 

Homeownership Rate: The homeownership rate 

is the percent of occupied housing units that are 

owned with a mortgage and those owned free and 

clear. 

Current dollars: Current dollars are the dollar 

values in other time periods that are not converted 

into present-day dollars. They do not take into consideration the effects of inflation, but simply present 

values as they were at the time. In this report, both 

the average over the 3-year period as well as the 

comparison across periods are presented in 

current dollars. 

owner HousIng Market condItIon 

Measures 

Home Value 

As Figure 1 shows, the median home value in current 

dollars, based on decennial census data, rose slowly 

from 1940 to 1970. After that, the median home value 

rose steadily. By 2000, it had increased by more than 

seven times the median home value in 1970. However, the housing bubble was followed by a housing 

crash and the recession. Figure 2, based on ACS data, 

which replaced the decennial long form, shows that 

after the peak of the reported monetary value in 2008, 

the median home value in current dollars declined. 
2 U.S. Census Bureau 

Figure 1. 

Median Home Value in the United States: 1940 to 2010 

(Current dollars. Data based on sample. For information on confidentiality protection, sampling error, nonsampling error, 

and definitions, see www.census.gov/acs/www/) 

Source: U.S. Census Bureau, Census of Population and Housing, decennial volumes. 

0 

50,000 

100,000 

150,000 

200,000 

1940 1950 1960 1970 1980 1990 2000 2010

2,938 7,354 11,900 17,000 

47,200 

79,100 

119,600 

179,900 

By 2009, the median home value 

according to the 2009 American 

Community Survey was $185,200. 

This was the same estimate that 

the 2006 American Community 

Survey reported as the median 

home value in 2006. The median 

home value continued to decline 

from 2009 to 2012. 

states 

The median home value for the 

United States in 2010–2012 was 

$174,600, a $17,300 decline from 

the median home value in 2007– 

2009. Table 1 shows that in 

2010–2012, Hawaii had the highest 

median home value at $503,100, 

followed by the District of 

Columbia at $436,000. California 

came in third place with a median 

home value of $358,800, followed 

by Massachusetts ($328,300), New 

Jersey ($325,800), and Maryland 

($289,300). Rounding out the top 

ten states with the highest median 

home values in 2010–2012 were 

New York ($286,700), Connecticut 

($278,600), Washington 

($256,500), and Rhode Island 

($245,300). West Virginia had the 

lowest median home value at 

$98,300, followed by Mississippi at 

$100,000, Arkansas at $106,900, 

Oklahoma at $112,900, Michigan at 

$119,200, and Kentucky at 

$120,800. All of these states were 

statistically different from each 

other. 

The median home value decreased 

nationally as well as in 28 states 

between 2007–2009 and 2010– 

2012: 7 in the Northeast 

(Connecticut, Maine, 

Massachusetts, New Hampshire, 

New Jersey, New York, and Rhode 

Island), 6 in the South (Delaware, 

District of Columbia, Florida, 

Georgia, Maryland, and Virginia), 

6 in the Midwest (Illinois, Michigan, 

Minnesota, Missouri, Ohio, and 

Wisconsin), and 9 in the West 

(Arizona, California, Colorado, 

Hawaii, Idaho, Nevada, Oregon, 
U.S. Census Bureau 3 

Figure 2. 

Median Home Value in the United States: 2005 to 2011 

(Current dollars. Data based on sample. For information on confidentiality protection, sampling error, nonsampling error, 

and definitions, see www.census.gov/acs/www/) 

Source: ACS 1 Year Estimates 2005 through 2012, ACS 3 Year Estimates, 2005–2007, 2006–2008, 2007–2009, 2008–2010, 2009– 

2011, and 2010–2012. 

150,000 

160,000 

170,000 

180,000 

190,000 

200,000 

3-year using midpoint year 

1-year 

2005 2006 2007 2008 2009 2010 2011 2012

Utah, and Washington). Only one 

state, California, experienced the 

largest decline in median home 

value from 2007–2009 to 2010– 

2012 that exceeded $100,000. 

Nevada, followed by Arizona and 

then Florida, experienced the next 

largest significant declines in 

median home value from 2007– 

2009 to 2010–2012 with declines 

of $99,400, $63,000, and $55,900 

respectively. Of the 19 states that 

experienced statistically significant 

increases in their median home 

value from 2007–2009 to 2010– 

2012, North Dakota experienced 

the highest at $18,200 (Table 1). 

counties 

As shown in Table 2, of the top 

fifty most populous counties, New 

York County, NY had the highest 

median home value at $812,300 

in 2010–2012. Santa Clara County, 

CA had the second highest median 

home value at $634,000, followed 

by Honolulu County, HI ($556,400) 

and Kings County, NY ($556,300), 

which were not significantly different from each other. Westchester 

County, NY ($516,600) and Orange 

County, CA ($509,500) were also 

not significantly different from 

each other and were next in the 

ranking. Rounding out the top nine 

counties with the highest median 

home values were Alameda County, 

CA ($484,200); Fairfax County, VA 

($474,700); and Nassau County, 

NY ($452,200); all of which were 

significantly different from each 

other. Of the top 50 most populous counties in the United States 

in 2010–2012, Wayne County, 

MI had the lowest median home 

value at $83,800. Rounding out 

the bottom five counties with the 

lowest median home values in 

2010–2012 were Allegheny County, 

PA ($121,200); Bexar County, TX 

($123,500); Cuyahoga County, OH 
4 U.S. Census Bureau 

 

Table 1. 

Homeownership rate and Median Property Value by state: 2007–2009 and 2010–2012 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

Estimate Difference 

Margin of 

error (±)1 Estimate 

Margin of 

error (±)1 Estimate 

Margin of 

error (±)1 Estimate 

Margin of 

error (±)1 

United States . . . 66 .41 0 .1 64 .68 0 .1 *–1 .72 191,900 205 174,600 142 *–17,300 

Alabama . . . . . . . . . . . . 70 .33 0 .3 69 .57 0 .3 *–0 .76 118,700 855 123,400 746 *4,700 

Alaska . . . . . . . . . . . . . . 64 .01 0 .7 63 .50 0 .7 –0 .51 232,600 1,996 241,400 2,360 *8,800 

Arizona . . . . . . . . . . . . . 67 .55 0 .3 63 .89 0 .3 *–3 .66 221,100 952 158,100 710 *–63,000 

Arkansas . . . . . . . . . . . . 67 .14 0 .3 66 .95 0 .4 –0 .19 102,900 1,027 106,900 908 *4,000 

California . . . . . . . . . . . . 57 .09 0 .1 54 .91 0 .1 *–2 .18 461,400 1,007 358,800 879 *–102,600 

Colorado . . . . . . . . . . . . 67 .66 0 .4 64 .91 0 .3 *–2 .75 237,800 713 235,000 1,080 *–2,800 

Connecticut . . . . . . . . . . 69 .16 0 .3 67 .63 0 .3 *–1 .53 301,000 1,859 278,600 1,228 *–22,400 

Delaware . . . . . . . . . . . . 73 .07 0 .6 71 .73 0 .6 *–1 .35 246,000 2,306 235,900 1,678 *–10,100 

District of Columbia . . . . 44 .86 0 .7 41 .61 0 .7 *–3 .25 457,100 6,945 436,000 8,360 *–21,100 

Florida . . . . . . . . . . . . . . 69 .27 0 .2 66 .89 0 .2 *–2 .37 210,800 626 154,900 453 *–55,900 

Georgia . . . . . . . . . . . . . 67 .35 0 .3 64 .94 0 .2 *–2 .41 165,100 560 149,300 792 *–15,800 

Hawaii . . . . . . . . . . . . . . 57 .87 0 .6 57 .28 0 .6 –0 .59 543,600 5,606 503,100 5,879 *–40,500 

Idaho . . . . . . . . . . . . . . . 71 .13 0 .5 68 .97 0 .5 *–2 .16 177,400 1,391 160,000 941 *–17,400 

Illinois . . . . . . . . . . . . . . . 68 .84 0 .2 67 .33 0 .2 *–1 .51 207,300 617 179,900 715 *–27,400 

Indiana . . . . . . . . . . . . . . 71 .10 0 .3 69 .91 0 .3 *–1 .19 122,800 471 122,600 478 –200 

Iowa . . . . . . . . . . . . . . . . 72 .78 0 .3 72 .30 0 .3 *–0 .48 120,100 572 124,300 688 *4,200 

Kansas . . . . . . . . . . . . . . 68 .94 0 .4 67 .42 0 .4 *–1 .52 123,600 862 128,500 997 *4,900 

Kentucky . . . . . . . . . . . . 69 .50 0 .3 68 .12 0 .3 *–1 .38 116,800 641 120,800 548 *4,000 

Louisiana . . . . . . . . . . . . 67 .98 0 .3 66 .72 0 .3 *–1 .26 131,800 1,143 138,800 1,140 *7,000 

Maine . . . . . . . . . . . . . . . 72 .77 0 .4 71 .57 0 .5 *–1 .19 178,100 1,776 173,900 1,199 *–4,200 

Maryland . . . . . . . . . . . . 69 .19 0 .3 67 .07 0 .3 *–2 .11 335,100 1,175 289,300 1,168 *–45,800 

Massachusetts . . . . . . . . 64 .68 0 .2 62 .32 0 .2 *–2 .36 352,400 932 328,300 1,184 *–24,100 

Michigan . . . . . . . . . . . . 73 .94 0 .2 71 .88 0 .2 *–2 .06 145,400 379 119,200 401 *–26,200 

Minnesota . . . . . . . . . . . 74 .18 0 .2 72 .39 0 .2 *–1 .79 209,900 557 185,800 692 *–24,100 

Mississippi . . . . . . . . . . . 70 .13 0 .4 69 .23 0 .4 *–0 .91 97,300 888 100,000 1,091 *2,700 

Missouri . . . . . . . . . . . . . 69 .74 0 .2 68 .38 0 .2 *–1 .36 139,700 626 137,100 656 *–2,600 

Montana . . . . . . . . . . . . . 68 .55 0 .6 68 .18 0 .6 –0 .37 174,900 1,682 183,600 2,139 *8,700 

Nebraska . . . . . . . . . . . . 67 .91 0 .4 66 .76 0 .4 *–1 .14 122,600 702 127,800 765 *5,200 

Nevada . . . . . . . . . . . . . 59 .59 0 .4 56 .17 0 .4 *–3 .42 260,700 1,962 161,300 998 *–99,400 

New Hampshire . . . . . . . 72 .83 0 .6 71 .36 0 .5 *–1 .47 257,600 1,878 239,100 1,338 *–18,500 

New Jersey . . . . . . . . . . 66 .75 0 .2 65 .56 0 .2 *–1 .19 361,100 921 325,800 908 *–35,300 

New Mexico . . . . . . . . . . 69 .16 0 .5 68 .07 0 .4 *–1 .09 160,900 1,384 159,300 1,201 –1,600 

New York . . . . . . . . . . . . 55 .39 0 .1 53 .90 0 .1 *–1 .50 310,100 1,639 286,700 1,398 *–23,400 

North Carolina . . . . . . . . 67 .70 0 .2 66 .33 0 .2 *–1 .37 151,800 603 152,800 638 *1,000 

North Dakota . . . . . . . . . 65 .95 0 .6 65 .69 0 .6 –0 .26 112,300 1,509 130,500 1,816 *18,200 

Ohio . . . . . . . . . . . . . . . . 68 .85 0 .2 67 .30 0 .2 *–1 .55 136,900 362 130,600 470 *–6,300 

Oklahoma . . . . . . . . . . . 67 .45 0 .3 67 .07 0 .3 –0 .37 104,900 681 112,900 541 *8,000 

Oregon . . . . . . . . . . . . . . 63 .90 0 .3 61 .65 0 .4 *–2 .26 263,200 1,317 233,900 1,296 *–29,300 

Pennsylvania . . . . . . . . . 70 .94 0 .2 69 .55 0 .2 *–1 .39 161,700 551 164,700 368 *3,000 

Rhode Island . . . . . . . . . 63 .04 0 .7 60 .32 0 .6 *–2 .72 282,300 1,875 245,300 1,637 *–37,000 

South Carolina . . . . . . . . 70 .07 0 .3 68 .78 0 .3 *–1 .29 136,300 797 136,300 951 0 

South Dakota . . . . . . . . . 68 .21 0 .5 68 .02 0 .5 –0 .18 122,500 1,425 131,600 1,445 *9,100 

Tennessee . . . . . . . . . . . 69 .50 0 .3 67 .48 0 .3 *–2 .02 135,400 671 138,400 678 *3,000 

Texas . . . . . . . . . . . . . . . 64 .22 0 .1 63 .01 0 .2 *–1 .21 124,400 355 128,400 482 *4,000 

Utah . . . . . . . . . . . . . . . . 71 .77 0 .4 69 .69 0 .4 *–2 .09 227,400 1,025 209,000 1,134 *–18,400 

Vermont . . . . . . . . . . . . . 71 .68 0 .7 70 .92 0 .7 –0 .77 211,800 1,964 215,700 2,038 *3,900 

Virginia . . . . . . . . . . . . . . 68 .55 0 .2 67 .11 0 .2 *–1 .44 260,100 1,205 243,100 1,042 *–17,000 

Washington . . . . . . . . . . 65 .06 0 .3 62 .80 0 .3 *–2 .25 297,000 1,131 256,500 1,152 *–40,500 

West Virginia . . . . . . . . . 73 .91 0 .4 72 .93 0 .5 *–0 .97 95,400 940 98,300 958 *2,900 

Wisconsin . . . . . . . . . . . 69 .40 0 .2 67 .96 0 .2 *–1 .44 170,800 471 167,200 475 *–3,600 

Wyoming . . . . . . . . . . . . 69 .80 0 .8 69 .98 0 .7 0 .17 181,900 2,636 183,200 2,453 1,300 

* Statistically different from zero at the 90 percent confidence level . 

1

 Data are based on a sample and are subject to sampling variability . A margin of error is a measure of an estimate’s variability . The larger the margin of error 

in relation to the size of the estimate, the less reliable the estimate . When added to and subtracted from the estimate, the margin of error forms the 90 percent 

confidence interval . 

Sources: U .S . Census Bureau, 2007–2009 and 2010–2012 American Community Surveys . 
U.S. Census Bureau 5 

 

 

 

Table 2. 

Homeownership rate and Median Property Value by 50 Most Populous counties: 2007– 

2009 and 2010–20121 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

Estimate Difference 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 

United States . . . . . . . . . . . . . . 66 .41 0 .1 64 .68 0 .1 *–1 .72 191,900 205 174,600 142 *–17,300 

Los Angeles County, California . . . . . . 47 .82 0 .2 46 .42 0 .2 *–1 .40 517,900 2,884 414,100 1,679 *–103,800 

Cook County, Illinois . . . . . . . . . . . . . . 60 .23 0 .3 57 .88 0 .3 *–2 .35 274,400 1,327 227,400 1,201 *–47,000 

Harris County, Texas . . . . . . . . . . . . . . 57 .09 0 .3 56 .30 0 .4 *–0 .79 133,700 820 131,000 942 *–2,700 

Maricopa County, Arizona . . . . . . . . . . 66 .47 0 .3 61 .74 0 .4 *–4 .73 243,400 1,228 167,100 955 *–76,300 

San Diego County, California . . . . . . . . 55 .95 0 .3 53 .58 0 .4 *–2 .37 484,700 2,680 396,500 2,043 *–88,200 

Orange County, California . . . . . . . . . . 60 .87 0 .4 58 .33 0 .4 *–2 .55 608,800 2,583 509,500 3,575 *–99,300 

Miami-Dade County, Florida . . . . . . . . 57 .87 0 .5 55 .71 0 .6 *–2 .16 279,800 1,921 188,400 2,264 *–91,400 

Kings County, New York . . . . . . . . . . . . 30 .37 0 .4 29 .49 0 .3 *–0 .88 580,900 4,034 556,300 4,291 *–24,600 

Dallas County, Texas . . . . . . . . . . . . . . 54 .09 0 .4 52 .52 0 .4 *–1 .57 130,600 1,249 128,000 1,583 *–2,600 

Queens County, New York . . . . . . . . . . 45 .67 0 .5 43 .33 0 .4 *–2 .34 491,100 4,546 447,500 3,089 *–43,600 

Riverside County, California . . . . . . . . 69 .19 0 .5 66 .26 0 .6 *–2 .94 330,100 2,674 223,700 1,873 *–106,400 

San Bernardino County, California . . . 64 .34 0 .6 61 .77 0 .6 *–2 .56 323,800 2,813 211,700 2,879 *–112,100 

Clark County, Nevada . . . . . . . . . . . . . 57 .62 0 .5 53 .79 0 .6 *–3 .83 262,800 2,235 156,500 1,252 *–106,300 

King County, Washington . . . . . . . . . . . 60 .62 0 .4 57 .41 0 .5 *–3 .22 423,600 2,408 369,000 2,547 *–54,600 

Tarrant County, Texas . . . . . . . . . . . . . 62 .77 0 .4 61 .36 0 .5 *–1 .41 135,700 806 135,100 1,107 –600 

Santa Clara County, California . . . . . . 59 .39 0 .6 56 .87 0 .5 *–2 .51 707,900 4,271 634,000 4,342 *–73,900 

Wayne County, Michigan . . . . . . . . . . . 66 .86 0 .5 64 .34 0 .4 *–2 .52 118,100 972 83,800 727 *–34,300 

Broward County, Florida . . . . . . . . . . . 69 .55 0 .5 65 .44 0 .5 *–4 .11 252,800 3,246 170,800 1,521 *–82,000 

Bexar County, Texas . . . . . . . . . . . . . . 61 .64 0 .5 59 .18 0 .6 *–2 .47 117,500 1,110 123,500 1,490 *6,000 

New York County, New York . . . . . . . . . 23 .92 0 .4 22 .03 0 .4 *–1 .89 840,100 15,423 812,300 17,978 *–27,800 

Philadelphia County, Pennsylvania . . . 55 .59 0 .5 53 .42 0 .5 *–2 .17 143,300 2,265 142,300 2,205 –1,000 

Alameda County, California . . . . . . . . . 55 .08 0 .6 52 .81 0 .5 *–2 .27 594,600 4,166 484,200 4,995 *–110,400 

Middlesex County, Massachusetts . . . 64 .29 0 .5 62 .52 0 .5 *–1 .77 419,400 2,814 396,400 2,451 *–23,000 

Suffolk County, New York . . . . . . . . . . . 82 .18 0 .4 78 .89 0 .5 *–3 .29 430,600 2,555 383,000 1,718 *–47,600 

Sacramento County, California . . . . . . 59 .14 0 .6 56 .30 0 .6 *–2 .83 321,100 2,789 224,800 2,298 *–96,300 

Bronx County, New York . . . . . . . . . . . 21 .30 0 .4 19 .02 0 .5 *–2 .28 397,300 7,958 373,500 5,185 *–23,800 

Nassau County, New York . . . . . . . . . . 83 .17 0 .5 80 .37 0 .4 *–2 .80 494,000 2,310 452,200 2,020 *–41,800 

Palm Beach County, Florida . . . . . . . . 72 .73 0 .5 71 .01 0 .5 *–1 .72 263,800 2,556 190,500 2,753 *–73,300 

Cuyahoga County, Ohio . . . . . . . . . . . . 61 .21 0 .5 60 .66 0 .4 –0 .55 136,600 946 125,700 1,080 *–10,900 

Hillsborough County, Florida . . . . . . . . 62 .40 0 .6 59 .84 0 .6 *–2 .57 204,200 2,230 154,900 1,500 *–49,300 

Allegheny County, Pennsylvania . . . . . 66 .33 0 .5 65 .24 0 .4 *–1 .09 116,300 1,092 121,200 1,111 *4,900 

Oakland County, Michigan . . . . . . . . . . 74 .64 0 .5 71 .11 0 .5 *–3 .52 207,100 1,973 165,300 1,021 *–41,800 

Franklin County, Ohio . . . . . . . . . . . . . 57 .55 0 .6 54 .29 0 .5 *–3 .26 157,000 1,143 151,200 1,459 *–5,800 

Orange County, Florida . . . . . . . . . . . . 60 .09 0 .7 57 .32 0 .7 *–2 .77 237,200 2,418 161,000 1,463 *–76,200 

Hennepin County, Minnesota . . . . . . . . 65 .73 0 .5 63 .49 0 .5 *–2 .25 250,900 2,224 228,100 1,570 *–22,800 

Fairfax County, Virginia . . . . . . . . . . . . 71 .54 0 .7 68 .29 0 .7 *–3 .25 510,600 5,122 474,700 3,668 *–35,900 

Contra Costa County, California . . . . . 69 .45 0 .7 65 .08 0 .7 *–4 .38 534,400 6,986 392,900 5,138 *–141,500 

Travis County, Texas . . . . . . . . . . . . . . 52 .89 0 .6 51 .17 0 .8 *–1 .72 206,600 3,169 215,700 3,602 *9,100 

Salt Lake County, Utah . . . . . . . . . . . . 68 .46 0 .7 66 .68 0 .7 *–1 .78 247,500 1,753 228,300 1,734 *–19,200 

St . Louis County, Missouri . . . . . . . . . . 72 .34 0 .6 70 .84 0 .6 *–1 .50 181,600 1,845 174,100 1,475 *–7,500 

Montgomery County, Maryland . . . . . . 70 .11 0 .6 66 .55 0 .6 *–3 .56 484,700 4,101 444,100 4,298 *–40,600 

Pima County, Arizona . . . . . . . . . . . . . 65 .36 0 .6 62 .11 0 .7 *–3 .25 204,000 2,023 164,800 1,712 *–39,200 

Honolulu County, Hawaii . . . . . . . . . . . 55 .79 0 .7 55 .29 0 .6 –0 .49 565,600 5,881 556,400 5,246 *–9,200 

Westchester County, New York . . . . . . 62 .47 0 .6 62 .06 0 .6 –0 .41 562,700 6,781 516,600 6,280 *–46,100 

Milwaukee County, Wisconsin . . . . . . . 53 .56 0 .6 50 .97 0 .6 *–2 .59 168,100 1,040 158,700 1,188 *–9,400 

Fulton County, Georgia . . . . . . . . . . . . 57 .56 0 .8 53 .23 0 .7 *–4 .33 260,400 5,464 235,600 4,822 *–24,800 

Mecklenburg County, North Carolina . . 62 .30 0 .6 59 .89 0 .7 *–2 .41 190,000 2,237 181,600 2,931 *–8,400 

Fresno County, California . . . . . . . . . . 54 .54 0 .8 53 .58 0 .8 –0 .96 261,300 3,908 190,400 3,186 *–70,900 

Shelby County, Tennessee . . . . . . . . . 60 .99 0 .7 58 .62 0 .7 *–2 .37 134,700 2,034 132,500 1,935 –2,200 

Wake County, North Carolina . . . . . . . 66 .53 0 .7 65 .29 0 .8 *–1 .24 226,200 2,443 228,800 2,431 2,600 

* Statistically different from zero at the 90 percent confidence level .
 

1

 Ranked in order of population—highest to lowest .
 

2

 Data are based on a sample and are subject to sampling variability . A margin of error is a measure of an estimate’s variability . The larger the margin of error in 


relation to the size of the estimate, the less reliable the estimate . When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval . 

Sources: U .S . Census Bureau, 2007–2009 and 2010–2012 American Community Surveys . 
6 U.S. Census Bureau 

 

Table 3. 

Homeownership rate and Median Property Value by 50 Least Populous counties: 2007– 

2009 and 2010–20121 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

DifferEstimate enceMargin of 

error (±)2 Estimate 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 

United States . . . . . . . . . . . . 66 .41 0 .1 64 .68 0 .1 *–1 .72 191,900 205 174,600 142 *–17,300 

Covington County, Mississippi . . . . . 83 .44 4 .8 83 .07 3 .9 –0 .36 79,400 6,216 74,100 6,779 –5,300 

Plumas County, California . . . . . . . . 67 .66 5 .8 71 .70 4 .6 4 .03 279,500 25,394 237,100 18,649 *–42,400 

Jackson County, Iowa . . . . . . . . . . . 78 .68 3 .1 73 .82 3 .0 *–4 .86 111,500 6,856 104,800 7,482 –6,700 

Langlade County, Wisconsin . . . . . . 79 .44 3 .0 74 .83 2 .9 *–4 .61 116,300 6,199 112,800 7,034 –3,500 

Elbert County, Georgia . . . . . . . . . . 75 .15 3 .7 66 .47 3 .9 *–8 .68 87,300 12,309 83,000 3,960 –4,300 

Gonzales County, Texas . . . . . . . . . 63 .81 6 .0 72 .28 4 .3 *8 .47 77,600 9,082 82,000 7,433 4,400 

Lampasas County, Texas . . . . . . . . . 74 .92 4 .6 74 .98 3 .9 0 .07 118,400 13,369 125,900 12,071 7,500 

Marion County, Kentucky . . . . . . . . . 79 .12 4 .3 76 .05 3 .9 –3 .07 93,000 8,458 100,900 6,610 7,900 

Minidoka County, Idaho . . . . . . . . . . 78 .31 3 .8 71 .13 4 .5 *–7 .18 99,500 8,397 107,900 6,533 8,400 

Carroll County, Indiana . . . . . . . . . . 77 .21 3 .7 79 .65 3 .2 2 .44 109,000 6,819 105,900 6,306 –3,100 

Breckinridge County, Kentucky . . . . 77 .85 4 .2 82 .41 2 .8 4 .56 81,800 6,689 81,600 5,359 –200 

East Feliciana Parish, Louisiana . . . 84 .15 3 .7 77 .95 3 .2 *–6 .2 98,400 9,605 123,800 20,599 * 25,400 

Allen County, Kentucky . . . . . . . . . . 75 .08 4 .4 73 .93 3 .8 –1 .15 90,800 4,960 91,800 5,164 1,000 

Jones County, Texas . . . . . . . . . . . . 82 .41 3 .6 76 .53 4 .4 *–5 .88 55,700 8,297 65,500 8,171 9,800 

Dodge County, Minnesota . . . . . . . . 86 .78 2 .4 84 .64 2 .4 –2 .14 166,100 5,715 159,700 5,517 –6,400 

Woodward County, Oklahoma . . . . . 69 .55 4 .5 72 .59 4 .3 3 .04 97,000 7,196 105,000 10,023 8,000 

Kent County, Maryland . . . . . . . . . . 72 .63 4 .2 70 .55 4 .0 –2 .08 286,600 25,109 254,000 17,326 *–32,600 

Henry County, Iowa . . . . . . . . . . . . . 71 .10 3 .6 75 .24 3 .4 4 .15 99,100 5,422 100,000 5,452 900 

Washington County, Nebraska . . . . 80 .58 3 .2 81 .69 2 .6 1 .11 168,600 8,741 174,000 9,665 5,400 

DeWitt County, Texas . . . . . . . . . . . . 76 .44 4 .4 73 .85 5 .0 –2 .6 71,000 6,936 79,200 8,591 8,200 

Roosevelt County, New Mexico . . . . 63 .06 4 .4 57 .16 5 .2 –5 .89 87,800 8,060 117,500 8,423 *29,700 

Polk County, North Carolina . . . . . . 77 .04 5 .2 75 .20 4 .4 –1 .84 180,500 25,644 167,000 19,404 –13,500 

Morgan County, Missouri . . . . . . . . . 83 .11 2 .8 82 .73 2 .8 –0 .38 116,200 12,182 114,000 10,058 –2,200 

Hubbard County, Minnesota . . . . . . 83 .96 2 .4 82 .53 2 .0 –1 .43 171,300 6,618 175,000 7,091 3,700 

Buena Vista County, Iowa . . . . . . . . 67 .67 3 .9 71 .35 3 .6 3 .68 85,800 3,938 97,600 5,084 *11,800 

McIntosh County, Oklahoma . . . . . . 76 .54 3 .4 79 .29 3 .4 2 .75 70,200 8,184 82,400 7,251 *12,200 

Lincoln County, New Mexico . . . . . . 77 .81 3 .8 78 .92 3 .6 1 .12 162,200 21,004 157,600 11,495 –4,600 

La Paz County, Arizona . . . . . . . . . . 72 .17 4 .7 75 .21 4 .2 3 .05 97,100 19,165 95,600 13,414 –1,500 

Jackson County, Wisconsin . . . . . . . 76 .23 2 .9 74 .08 2 .5 –2 .15 123,500 4,855 125,000 6,792 1500 

Clay County, Mississippi . . . . . . . . . 70 .62 4 .9 70 .93 3 .9 0 .31 76,100 8,062 80,700 4,959 4,600 

Bandera County, Texas . . . . . . . . . . 83 .56 3 .9 76 .98 4 .0 *–6 .58 142,000 19,031 142,600 23,057 600 

Polk County, Arkansas . . . . . . . . . . . 77 .29 4 .1 75 .94 4 .0 –1 .35 82,700 6,556 85,300 5,919 2,600 

Kewaunee County, Wisconsin . . . . . 81 .05 2 .7 81 .45 2 .9 0 .4 143,500 8,524 146,600 7,000 3,100 

Klickitat County, Washington . . . . . . 69 .64 4 .2 67 .33 4 .4 –2 .31 188,100 13,231 197,900 14,725 9,800 

Butler County, Alabama . . . . . . . . . . 68 .04 4 .3 70 .47 4 .2 2 .43 77,500 9,585 73,900 4,677 –3,600 

Taylor County, Wisconsin . . . . . . . . . 80 .28 2 .4 77 .71 2 .2 –2 .57 121,800 3,643 127,500 5,442 5,700 

Clinton County, Missouri . . . . . . . . . 74 .84 3 .3 72 .19 3 .6 –2 .65 153,000 8,839 143,300 9,387 –9,700 

Martin County, Minnesota . . . . . . . . 76 .21 3 .3 75 .70 2 .5 –0 .51 96,600 4,946 105,800 5,875 *9,200 

Concordia Parish, Louisiana . . . . . . 65 .61 5 .0 63 .30 4 .9 –2 .31 79,400 9,151 78,800 8,530 –600 

Jones County, Iowa . . . . . . . . . . . . . 80 .51 3 .3 80 .33 2 .8 –0 .18 104,200 8,609 124,600 11,517 *20,400 

Marengo County, Alabama . . . . . . . 69 .98 5 .1 70 .10 3 .6 0 .13 79,300 7,936 95,900 11,215 *16,600 

Wayne County, Mississippi . . . . . . . 81 .40 5 .2 83 .45 3 .8 2 .05 57,800 7,015 68,700 7,026 * 10,900 

Franklin Parish, Louisiana . . . . . . . . 71 .94 4 .2 73 .02 3 .8 1 .09 73,600 5,301 77,900 7,836 4,300 

Carroll County, Iowa . . . . . . . . . . . . 72 .37 3 .1 77 .60 3 .1 *5 .24 108,800 5,165 105,400 6,717 –3,400 

Crook County, Oregon . . . . . . . . . . . 75 .18 3 .9 69 .60 3 .8 *–5 .59 237,600 13,443 172,700 18,334 *–64,900 

Colorado County, Texas . . . . . . . . . . 77 .94 3 .8 76 .15 4 .5 –1 .79 84,900 10,731 112,600 15,490 *27,700 

Madison County, North Carolina . . . 78 .71 4 .0 72 .21 4 .6 *–6 .5 173,600 17,812 155,100 11,789 –18,500 

Fulton County, Indiana . . . . . . . . . . . 80 .03 3 .7 73 .44 4 .3 *–6 .6 97,900 5,850 93,300 7,853 –4,600 

Pacific County, Washington . . . . . . . 75 .53 2 .9 72 .73 3 .4 –2 .8 170,100 7,117 158,100 7,493 *–12,000 

Warren County, North Carolina . . . . 77 .30 4 .2 67 .82 4 .3 *–9 .49 93,300 9,947 106,800 9,548 13,500 

* Statistically different from zero at the 90 percent confidence level .
 

1

 Ranked in order of population—lowest to highest .
 

2

 Data are based on a sample and are subject to sampling variability . A margin of error is a measure of an estimate’s variability . The larger the margin of error 


in relation to the size of the estimate, the less reliable the estimate . When added to and subtracted from the estimate, the margin of error forms the 90 percent 

confidence interval . 

Sources: U .S . Census Bureau, 2007–2009 and 2010–2012 American Community Surveys . 
U.S. Census Bureau 7 

($125,700); and Dallas County, TX 

($128,000); all of which were significantly different from each other. 

Worth noting is that out of the top 

50 most populous counties, 43 

counties experienced significant 

declines in their overall median 

home value from 2007–2009 to 

2010–2012. The largest decline 

was in Contra Costa County, CA, 

which declined by $141,500. 

Only Bexar County, TX; Alleghany 

County, PA; and Travis County, TX; 

experienced significant increases 

in their median home value 

from 2007–2009 to 2010–2012 

(Table 2). 

Of the 1,038 smaller counties in 

2010–2012, which include the 50 

least populous counties shown 

in Table 3, 344 (33.1 percent) 

experienced significant changes 

in their median home value from 

2007–2009 to 2010–2012. A little 

over half (183 counties or 53.2 percent) of these counties experienced 

significant declines in their median 

home value, while 46.8 percent 

(161 counties) experienced significant increases in their median 

home value. 

McDowell County, WV had the lowest median home value at $39,900 

of these smaller counties. Five of 

these counties had median home 

values that were over $400,000. 

The highest was Teton County, WY 

at $705,600. Teton County was 

followed by Summit County, UT 

at $488,700, Eagle County, CO 

at $469,800, Fairfax City, VA3 at 

$450,900, and Summit County, 

CO at $ 442,800, none of which 

3 Fairfax city, VA is a county equivalent 

and is included in the discussion of counties. 

were significantly different from 

one another. 

Table 3 shows that of the fifty least 

populous counties in 2010–2012, 

Kent County, MD and Plumas 

County, CA had the highest median 

home values at $254,000 and 

$237,100 respectively and were 

not significantly different from each 

other. Rounding out the bottom 

three counties with the highest 

median value was Klickitat County, 

WA with a median home value of 

$197,900. 

Only 4 of the 50 least populous 

counties experienced significant 

declines in median home value 

from 2007–2009 to 2010–2012. 

They were Crook County, OR 

(–$64,900); Plumas County, CA 

(–$42,400); Kent County, MD 

(–$32,600); and Pacific County, 

WA (–$12,000). Conversely, nine 

counties experienced significant 

increases in median home value 

from 2007–2009 to 2010–2012. 

Metropolitan areas 

Of the top 50 most populous 

metro areas in 2010–2012, Table 4 

shows that the San Jose-SunnyvaleSanta Clara, CA Metro Area had 

the highest median home value 

at $624,800. The San FranciscoOakland-Fremont, CA Metro Area 

had the second highest median 

home value at $568,900. These 

were followed by the Los AngelesLong Beach-Santa Ana, CA Metro 

Area ($442,000) with the third 

highest median home value and the 

New York-Northern New JerseyLong Island, NY-NJ-PA Metro Area 

($412,500) with the fourth highest 

median home value. Rounding out 

the top nine metro areas with the 

highest median home values were 

the San Diego-Carlsbad-San 

Marcos, CA Metro Area ($396,500), 

the Washington-ArlingtonAlexandria, DC-VA-MD-WV Metro 

Area ($369,500), the BostonCambridge-Quincy, MA-NH Metro 

Area ($360,100), the SeattleTacoma-Bellevue, WA Metro Area 

($312,700), and the BaltimoreTowson, MD Metro Area 

($279,300), all significantly different from each other. The 3 of 

the top 50 most populous metro 

areas with the lowest median home 

values in 2010–2012 were the 

Detroit-Warren-Livonia, MI Metro 

Area ($119,900), followed by the 

Buffalo-Niagara Falls, NY Metro 

Area ($121,500), and then the 

Pittsburgh, PA Metro Area 

($124,300) with the third lowest 

median home value, all significantly 

different from each other. 

Of the top 50 most populous metro 

areas, 36 metro areas experienced 

significant declines in median 

home value from 2007–2009 to 

2010–2012. Three of these significant declines amounted to 

over $100,000. These metro areas 

were the Riverside-San BernardinoOntario, CA Metro Area, the Las 

Vegas-Paradise, NV Metro Area, 

and the Los Angeles-Long BeachSanta Ana, CA Metro Area. Only the 

Riverside-San Bernardino-Ontario, 

CA Metro Area and the Los AngelesLong Beach-Santa Ana, CA Metro 

Areas were statistically different 

from each other. Conversely, ten 

metro areas experienced significant 

increases in median home value. 

Places (cities) 

Table 5 shows that the 3 cities of 

the top 50 most populous cities 
8 U.S. Census Bureau 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4. 

Homeownership rate and Median Property Value by 50 Most Populous Metropolitan 

statistical area: 2007–2009 and 2010–20121—Con. 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

Estimate Difference 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 

United States . . . . . . . . . . . . . . . . . . . 66 .41 0 .1 64 .68 0 .1 *–1 .72 191,900 205 174,600 142 *–17,300 

New York-Northern New Jersey-Long Island, 

NY-NJ-PA Metro Area . . . . . . . . . . . . . . . . . 

Los Angeles-Long Beach-Santa Ana, CA 

53 .08 0 .1 51 .59 0 .1 *–1 .49 455,900 1,060 412,500 1,011 *–43,400 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Chicago-Joliet-Naperville, IL-IN-WI Metro 

50 .89 0 .2 49 .23 0 .2 *–1 .66 546,500 2,065 442,000 1,294 *–104,500 

Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Dallas-Fort Worth-Arlington, TX 

67 .69 0 .2 65 .69 0 .2 *–2 .00 258,800 957 221,800 956 *–37,000 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Houston-Sugar Land-Baytown, TX Metro 

62 .46 0 .2 60 .93 0 .2 *–1 .54 148,100 634 149,200 722 *1,100 

Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Philadelphia-Camden-Wilmington, PA-NJ62 .50 0 .3 61 .91 0 .3 *–0 .59 139,400 728 140,700 842 *1,300 

DE-MD Metro Area . . . . . . . . . . . . . . . . . . . 

Washington-Arlington-Alexandria, DC-VA69 .77 0 .3 68 .10 0 .2 *–1 .67 247,700 900 239,700 883 *–8,000 

MD-WV Metro Area . . . . . . . . . . . . . . . . . . . 

Miami-Fort Lauderdale-Pompano Beach, FL 

66 .57 0 .3 63 .67 0 .3 *–2 .90 421,600 1,694 369,500 1,421 *–52,100 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Atlanta-Sandy Springs-Marietta, GA Metro 

65 .52 0 .3 62 .90 0 .3 *–2 .62 266,800 1,366 182,500 1,321 *–84,300 

Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Boston-Cambridge-Quincy, MA-NH Metro 

68 .46 0 .3 65 .06 0 .3 *–3 .40 193,000 818 168,100 904 *–24,900 

Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

San Francisco-Oakland-Fremont, CA 

63 .84 0 .3 61 .51 0 .3 *–2 .33 382,400 1,066 360,100 1,084 *–22,300 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Riverside-San Bernardino-Ontario, CA 

56 .17 0 .4 53 .68 0 .3 *–2 .49 656,300 3,105 568,900 3,547 *–87,400 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 66 .89 0 .4 64 .15 0 .4 *–2 .74 327,200 1,772 218,800 1,726 *–108,400 

Detroit-Warren-Livonia, MI Metro Area . . . . . 72 .98 0 .3 70 .21 0 .3 *–2 .77 158,000 622 119,900 680 *–38,100 

Phoenix-Mesa-Glendale, AZ Metro Area . . . . 67 .26 0 .4 62 .85 0 .4 *–4 .41 237,100 1,140 161,700 907 *–75,400 

Seattle-Tacoma-Bellevue, WA Metro Area . . 

Minneapolis-St . Paul-Bloomington, MN-WI 

62 .62 0 .3 60 .05 0 .3 *–2 .57 371,000 1,361 312,700 1,982 *–58,300 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

San Diego-Carlsbad-San Marcos, CA 

72 .98 0 .3 70 .52 0 .3 *–2 .46 242,300 683 213,900 944 *–28,400 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Tampa-St . Petersburg-Clearwater, FL 

55 .95 0 .3 53 .58 0 .4 *–2 .37 484,700 2,680 396,500 2,043 *–88,200 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 68 .97 0 .4 66 .12 0 .4 *–2 .85 187,200 1,190 141,200 1,242 *–46,000 

St . Louis, MO-IL Metro Area . . . . . . . . . . . . . 71 .95 0 .4 70 .34 0 .4 *–1 .61 160,900 767 157,800 873 *–3,100 

Baltimore-Towson, MD Metro Area . . . . . . . . 68 .09 0 .4 66 .35 0 .4 *–1 .74 307,900 1,913 279,300 1,492 *–28,600 

Denver-Aurora-Broomfield, CO Metro Area . . 66 .72 0 .4 63 .42 0 .4 *–3 .30 246,700 883 245,700 1,206 –1,000 

Pittsburgh, PA Metro Area . . . . . . . . . . . . . . . 

Portland-Vancouver-Hillsboro, OR-WA 

70 .97 0 .4 69 .75 0 .3 *–1 .22 118,100 744 124,300 793 *6,200 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

San Antonio-New Braunfels, TX 

63 .68 0 .4 61 .03 0 .4 *–2 .64 296,900 1,741 260,600 1,722 *–36,300 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Orlando-Kissimmee-Sanford, FL 

64 .82 0 .5 62 .90 0 .5 *–1 .92 123,400 1,091 131,800 1,656 *8,400 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Sacramento—Arden-Arcade—Roseville, CA 

66 .24 0 .5 63 .09 0 .5 *–3 .15 225,200 1,587 155,600 1,204 *–69,600 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Cincinnati-Middletown, OH-KY-IN Metro 

62 .46 0 .5 59 .89 0 .4 *–2 .57 356,000 2,053 261,700 2,192 *–94,300 

Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 .87 0 .5 67 .96 0 .4 *–0 .91 157,500 827 153,100 980 *–4,400 

Cleveland-Elyria-Mentor, OH Metro Area . . . 67 .25 0 .4 66 .41 0 .4 *–0 .85 150,100 1,006 141,700 788 *–8,400 

Kansas City, MO-KS Metro Area . . . . . . . . . . 68 .60 0 .4 67 .10 0 .4 *–1 .50 160,000 831 157,600 849 *–2,400 

Las Vegas-Paradise, NV Metro Area . . . . . . . 

San Jose-Sunnyvale-Santa Clara, CA 

57 .62 0 .5 53 .79 0 .6 *–3 .83 262,800 2,235 156,500 1,252 *–106,300 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 59 .53 0 .5 57 .06 0 .5 *–2 .47 702,700 4,247 624,800 4,296 *–77,900 

Columbus, OH Metro Area . . . . . . . . . . . . . . 

Charlotte-Gastonia-Rock Hill, NC-SC 

64 .28 0 .5 61 .89 0 .4 *–2 .39 164,400 950 160,500 965 *–3,900 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Austin-Round Rock-San Marcos, TX 

67 .31 0 .4 65 .93 0 .5 *–1 .38 173,000 1,150 168,300 1,442 *–4,700 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 59 .30 0 .5 57 .40 0 .6 *–1 .89 184,000 1,847 188,700 1,966 *4,700 

See notes at end of table . 
U.S. Census Bureau 9 

 

 

 

 

 

Table 4. 

Homeownership rate and Median Property Value by 50 Most Populous Metropolitan 

statistical area: 2007–2009 and 2010–20121—Con. 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

Estimate Difference 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 

Indianapolis-Carmel, IN Metro Area . . . . . . . 

Virginia Beach-Norfolk-Newport News, 

67 .94 0 .4 66 .19 0 .5 *–1 .75 144,500 1,007 144,400 1,164 –100 

VA-NC Metro Area . . . . . . . . . . . . . . . . . . . . 

Nashville-Davidson—Murfreesboro— 

63 .87 0 .5 62 .82 0 .5 *–1 .04 247,800 1,645 239,400 1,569 *–8,400 

Franklin, TN Metro Area . . . . . . . . . . . . . . . 

Providence-New Bedford-Fall River, RI-MA 

68 .65 0 .5 65 .83 0 .5 *–2 .82 171,900 1,017 172,100 1,221 200 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

Milwaukee-Waukesha-West Allis, WI 

63 .54 0 .5 61 .06 0 .5 *–2 .48 291,000 1,577 257,700 1,637 *–33,300 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 62 .85 0 .5 61 .09 0 .4 *–1 .76 207,300 1,553 198,200 1,281 *–9,100 

Jacksonville, FL Metro Area . . . . . . . . . . . . . 67 .76 0 .6 66 .68 0 .6 *–1 .07 196,300 1,775 159,500 1,678 *–36,800 

Memphis, TN-MS-AR Metro Area . . . . . . . . . 

Louisville/Jefferson County, KY-IN 

64 .42 0 .6 62 .64 0 .6 *–1 .77 135,300 1,671 134,400 1,804 –900 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 69 .56 0 .5 67 .86 0 .5 *–1 .70 145,100 1,119 147,000 1,166 *1,900 

Oklahoma City, OK Metro Area . . . . . . . . . . . 65 .50 0 .5 65 .21 0 .6 –0 .29 121,700 1,221 130,400 1,051 *8,700 

Richmond, VA Metro Area . . . . . . . . . . . . . . . 

Hartford-West Hartford-East Hartford, CT 

69 .28 0 .6 67 .88 0 .5 *–1 .40 231,800 1,530 216,600 1,443 *–15,200 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 

New Orleans-Metairie-Kenner, LA 

68 .95 0 .5 67 .76 0 .5 *–1 .19 261,500 2,071 250,000 1,727 *–11,500 

Metro Area . . . . . . . . . . . . . . . . . . . . . . . . . . 65 .48 0 .8 62 .11 0 .6 *–3 .37 182,800 1,993 174,100 1,277 *–8,700 

Raleigh-Cary, NC Metro Area . . . . . . . . . . . . 68 .21 0 .7 66 .75 0 .7 *–1 .46 200,400 2,708 205,800 2,902 *5,400 

Salt Lake City, UT Metro Area . . . . . . . . . . . . 69 .09 0 .6 67 .47 0 .6 *–1 .62 246,800 1,780 228,700 1,710 *–18,100 

Buffalo-Niagara Falls, NY Metro Area . . . . . . 67 .13 0 .5 65 .98 0 .5 *–1 .15 113,600 892 121,500 1,186 *7,900 

Birmingham-Hoover, AL Metro Area . . . . . . . 71 .31 0 .6 70 .12 0 .7 *–1 .19 141,700 1,693 144,200 1,636 *2,500 

* Statistically different from zero at the 90 percent confidence level .
 

1

 Ranked in order of population—highest to lowest .
 

2

 Data are based on a sample and are subject to sampling variability . A margin of error is a measure of an estimate’s variability . The larger the margin of error in 


relation to the size of the estimate, the less reliable the estimate . When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval .
 

Sources: U .S . Census Bureau, 2007–2009 and 2010–2012 American Community Surveys .
 

that had the largest median home 

values in 2010–2012 were San 

Francisco City, CA ($737,700), 

followed by San Jose City, CA 

($546,300), and then New York 

City, NY ($490,400). The city that 

had the lowest median home value 

of the 50 most populous cities in 

2010–2012 was Detroit City, MI at 

$48,000. Cleveland City, OH had 

the second lowest median home 

value ($76,700), followed by 

Memphis City, TN ($96,800), and 

San Antonio City, TX ($113,400). 

Thirty-five of the top fifty most 

populous cities experienced significant declines in their median home 

value from 2007–2009 to 2010– 

2012. Seven cities experienced 

significant increases in median 

home value. The cities with the 

highest increase in median home 

values were Austin City, TX with 

a $13,800 increase and Denver 

City, CO with a $9,600 increase, 

which were not significantly different from each other. Additionally, 

Denver City was not significantly 

different from Oklahoma City, OK; 

Tulsa City, OK; El Paso City TX; or 

Wichita City, KS. 

Of the 1,596 smaller cities in 2010– 

2012, which include the 50 least 

populous cities shown on Table 6, 

1,023 (64.1 percent) experienced 

significant changes in their median 

home value from 2007–2009 to 

2010–2012. Of these cities, 90.9 

percent (930 cities) experienced 

significant declines in their median 

home value. Conversely, 93 cities 

(9.1 percent) experienced significant increases in their median 

home value. Sixty-one cities 

were not discussed in this report 

because comparable statistics for 

previous years were not available. 

Table 6 shows that Lake Forest City, 

IL had the highest median home 

value in 2010–2012 of the 50 least 

populous cities at $837,600.4 In 

second place was Naples City, FL at 

$722,100 followed by Syosset CDP, 

NY ($609,700) and La CrescentaMontrose CDP, CA ($608,700), 

which were not significantly different from one another. Rounding out 

the top five was Marblehead CDP, 

4 Six places that would have been 

included in the fifty least populous cities were 

not included because comparable statistics 

for previous years were not comparable. 
10 U.S. Census Bureau 

 

 

 

 

 

 

 

Table 5. 

Homeownership rate and Median Property Value by 50 Most Populous cities: 2007–2009 

and 2010–20121 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

Estimate Difference 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 Estimate 

Margin 

of error 

(±)2 

United States . . . . . . . . . . . . . . . . 66 .41 0 .1 64 .68 0 .1 *–1 .72 191,900 205 174,600 142 *–17,300 

New York city, New York . . . . . . . . . . . . . . 33 .49 0 .2 31 .66 0 .2 *–1 .83 530,800 3,053 490,400 2,263 *–40,400 

Los Angeles city, California . . . . . . . . . . . 38 .53 0 .3 36 .99 0 .3 *–1 .54 561,700 4,260 437,600 2,964 *–124,100 

Chicago city, Illinois . . . . . . . . . . . . . . . . . 47 .86 0 .4 44 .87 0 .4 *–2 .99 277,900 2,512 229,200 2,251 *–48,700 

Houston city, Texas . . . . . . . . . . . . . . . . . 47 .12 0 .4 45 .09 0 .5 *–2 .03 129,200 1,566 123,900 1,110 *–5,300 

Philadelphia city, Pennsylvania . . . . . . . . 55 .59 0 .5 53 .42 0 .5 *–2 .17 143,300 2,265 142,300 2,205 –1,000 

Phoenix city, Arizona . . . . . . . . . . . . . . . . 60 .30 0 .5 54 .77 0 .7 *–5 .53 226,800 1,883 146,400 2,144 *–80,400 

San Antonio city, Texas . . . . . . . . . . . . . . 58 .83 0 .6 55 .31 0 .6 *–3 .52 112,200 1,087 113,400 1,413 1,200 

San Diego city, California . . . . . . . . . . . . . 49 .75 0 .5 47 .94 0 .6 *–1 .82 498,100 4,821 434,500 4,551 *–63,600 

Dallas city, Texas . . . . . . . . . . . . . . . . . . . 45 .83 0 .5 43 .35 0 .6 *–2 .49 131,300 2,854 130,000 3,543 –1,300 

San Jose city, California . . . . . . . . . . . . . 60 .04 0 .7 57 .17 0 .7 *–2 .87 641,900 4,731 546,300 5,251 *–95,600 

Jacksonville city, Florida . . . . . . . . . . . . . 62 .71 0 .7 61 .82 0 .7 –0 .88 177,300 2,212 142,000 2,180 *–35,300 

Indianapolis city (balance), Indiana . . . . . 57 .91 0 .6 54 .94 0 .7 *–2 .97 122,000 1,211 117,200 1,130 *–4,800 

Austin city, Texas . . . . . . . . . . . . . . . . . . . 45 .94 0 .7 44 .44 0 .8 *–1 .50 205,000 3,248 218,800 2,700 *13,800 

San Francisco city, California . . . . . . . . . . 37 .18 0 .7 36 .37 0 .7 –0 .82 796,700 10,106 737,700 10,274 *–59,000 

Columbus city, Ohio . . . . . . . . . . . . . . . . . 49 .89 0 .8 45 .97 0 .6 *–3 .92 141,000 1,351 131,200 1,531 *–9,800 

Fort Worth city, Texas . . . . . . . . . . . . . . . . 59 .90 0 .8 57 .86 0 .9 *–2 .03 120,800 1,401 120,000 1,734 –800 

Charlotte city, North Carolina . . . . . . . . . . 58 .26 0 .7 56 .82 0 .8 *–1 .43 178,700 2,922 170,500 2,168 *–8,200 

Detroit city, Michigan . . . . . . . . . . . . . . . . 54 .61 0 .8 51 .63 0 .8 *–2 .98 81,000 1,102 48,000 1,045 *–33,000 

El Paso city, Texas . . . . . . . . . . . . . . . . . . 61 .43 0 .8 59 .25 1 .0 *–2 .18 111,600 1,412 117,600 1,429 *6,000 

Memphis city, Tennessee . . . . . . . . . . . . . 53 .69 0 .9 50 .49 0 .8 *–3 .20 100,100 1,987 96,800 1,546 *–3,300 

Boston city, Massachusetts . . . . . . . . . . . 36 .97 0 .9 33 .21 0 .7 *–3 .75 394,600 5,293 368,600 3,677 *–26,000 

Seattle city, Washington . . . . . . . . . . . . . . 49 .53 0 .7 46 .13 0 .7 *–3 .40 468,500 4,869 426,600 6,195 *–41,900 

Baltimore city, Maryland . . . . . . . . . . . . . 50 .65 1 .0 47 .15 0 .9 *–3 .50 167,300 2,367 156,800 2,109 *–10,500 

Denver city, Colorado . . . . . . . . . . . . . . . . 53 .39 0 .9 48 .98 0 .8 *–4 .40 240,100 2,629 249,700 4,399 *9,600 

Washington city, District of Columbia . . . 44 .86 0 .7 41 .61 0 .7 *–3 .25 457,100 6,945 436,000 8,360 *–21,100 

Nashville-Davidson metropolitan 

government (balance), Tennessee . . . . 58 .00 0 .9 53 .16 0 .9 *–4 .84 162,500 1,534 164,900 1,834 *2,400 

Louisville/Jefferson County metro 

government (balance), Kentucky . . . . . . 63 .41 0 .8 61 .04 0 .8 *–2 .36 137,800 1,654 139,400 1,581 1,600 

Milwaukee city, Wisconsin . . . . . . . . . . . . 47 .02 0 .7 43 .21 0 .7 *–3 .81 144,800 1,532 128,200 1,834 *–16,600 

Portland city, Oregon . . . . . . . . . . . . . . . . 55 .22 0 .8 52 .50 0 .8 *–2 .72 298,800 3,834 278,000 3,421 *–20,800 

Oklahoma City city, Oklahoma . . . . . . . . . 60 .27 0 .8 59 .45 0 .9 –0 .82 124,000 1,886 132,100 1,716 *8,100 

Las Vegas city, Nevada . . . . . . . . . . . . . . 56 .53 0 .9 53 .29 1 .0 *–3 .24 257,300 4,000 153,700 2,965 *–103,600 

Albuquerque city, New Mexico . . . . . . . . . 60 .78 0 .9 59 .40 0 .9 *–1 .39 191,700 2,404 187,000 2,468 *–4,700 

Tucson city, Arizona . . . . . . . . . . . . . . . . . 53 .66 0 .9 50 .40 1 .0 *–3 .26 175,200 2,248 137,300 2,244 *–37,900 

Fresno city, California . . . . . . . . . . . . . . . 48 .64 1 .1 47 .68 1 .1 –0 .96 249,000 4,410 174,000 3,141 *–75,000 

Sacramento city, California . . . . . . . . . . . 50 .17 1 .0 48 .24 1 .0 *–1 .93 309,100 5,309 218,200 4,882 *–90,900 

Long Beach city, California . . . . . . . . . . . 41 .99 0 .9 40 .11 1 .0 *–1 .87 515,600 10,961 421,300 7,380 *–94,300 

Kansas City city, Missouri . . . . . . . . . . . . 58 .54 0 .9 55 .90 0 .8 *–2 .64 138,200 2,356 135,000 2,384 –3,200 

Mesa city, Arizona . . . . . . . . . . . . . . . . . . 65 .79 1 .0 60 .59 0 .9 *–5 .19 204,800 3,239 140,500 2,774 *–64,300 

Virginia Beach city, Virginia . . . . . . . . . . . 65 .43 1 .0 64 .27 0 .9 –1 .15 282,000 2,836 266,200 3,133 *–15,800 

Atlanta city, Georgia . . . . . . . . . . . . . . . . . 51 .67 1 .0 44 .25 0 .8 *–7 .41 261,500 8,163 204,800 7,946 *–56,700 

Colorado Springs city, Colorado . . . . . . . 62 .86 1 .1 58 .37 0 .9 *–4 .48 215,800 2,827 210,100 3,344 *–5,700 

Omaha city, Nebraska . . . . . . . . . . . . . . . 60 .81 1 .0 57 .75 0 .9 *–3 .06 133,700 1,411 132,300 1,304 –1,400 

Raleigh city, North Carolina . . . . . . . . . . . 54 .96 1 .0 53 .47 1 .0 *–1 .49 209,100 3,972 204,800 5,336 –4,300 

Miami city, Florida . . . . . . . . . . . . . . . . . . 36 .97 1 .0 32 .09 1 .1 *–4 .88 296,400 7,170 202,100 6,895 *–94,300 

Oakland city, California . . . . . . . . . . . . . . 42 .59 1 .0 39 .95 0 .9 *–2 .64 535,400 11,313 420,400 11,251 *–115,000 

Cleveland city, Ohio . . . . . . . . . . . . . . . . . 46 .62 1 .1 44 .52 0 .8 *–2 .10 87,400 967 76,700 977 *–10,700 

Tulsa city, Oklahoma . . . . . . . . . . . . . . . . 54 .10 1 .0 53 .50 0 .9 –0 .59 115,500 2,082 122,600 1,735 *7,100 

Minneapolis city, Minnesota . . . . . . . . . . . 51 .04 0 .8 49 .24 0 .9 *–1 .80 231,000 2,258 205,100 3,302 *–25,900 

Wichita city, Kansas . . . . . . . . . . . . . . . . . 62 .59 0 .9 60 .57 0 .9 *–2 .02 113,900 2,021 117,600 2,395 *3,700 

Arlington city, Texas . . . . . . . . . . . . . . . . . 57 .88 1 .1 57 .50 1 .2 –0 .39 132,600 1,552 128,900 1,939 *–3,700 

* Statistically different from zero at the 90 percent confidence level .
 

1

 Ranked in order of population—highest to lowest .
 

2

 Data are based on a sample and are subject to sampling variability . A margin of error is a measure of an estimate’s variability . The larger the margin of error in 


relation to the size of the estimate, the less reliable the estimate . When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval . 

Sources: U .S . Census Bureau, 2007–2009 and 2010–2012 American Community Surveys . 
U.S. Census Bureau 11 

 

 

 

 

Table 6. 

Homeownership rate and Median Property Value by 50 Least Populous cities: 2007–2009 

and 2010–20121 

Area 

Homeownership rate Median property value (in current dollars) 

2007–2009 2010–2012 

Difference 

2007–2009 2010–2012 

Estimate Difference 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 Estimate 

Margin of 

error (±)2 

United States . . . . . . . . . . . . . . . . . . . . 66 .41 0 .1 64 .68 0 .1 *–1 .72 191,900 205 174,600 142 *–17,300 

Lake Forest city, Illinois . . . . . . . . . . . . . 91 .01 2 .8 88 .90 2 .7 –2 .11 882,700 40,820 837,600 51,962 –45,100 

Norwood city, Ohio . . . . . . . . . . . . . . . . . 53 .55 4 .0 46 .85 4 .2 *–6 .7 130,600 5,571 118,800 8,224 *–11,800 

Syosset CDP, New York . . . . . . . . . . . . . 95 .25 2 .5 91 .81 3 .8 –3 .44 643,500 18,192 609,700 12,455 *–33,800 

Cockeysville CDP, Maryland . . . . . . . . . 35 .66 3 .2 37 .76 3 .5 2 .1 320,500 18,875 329,200 18,560 8,700 

Ronkonkoma CDP, New York . . . . . . . . . 79 .62 4 .2 79 .43 4 .8 –0 .19 403,200 17,147 344,200 9,038 *–59,000 

Pace CDP, Florida . . . . . . . . . . . . . . . . . 68 .42 8 .1 81 .70 4 .4 *13 .28 153,100 15,482 156,800 12,918 3,700 

Saratoga Springs city, Utah . . . . . . . . . . 93 .50 4 .0 80 .33 6 .9 *–13 .17 283,000 11,736 238,100 10,281 *–44,900 

Farmington city, Utah . . . . . . . . . . . . . . . 87 .31 3 .5 83 .57 4 .1 –3 .74 324,500 17,755 269,900 15,723 *–54,600 

Oxford city, Mississippi . . . . . . . . . . . . . 51 .27 5 .7 38 .63 4 .9 *–12 .64 192,300 16,332 243,900 24,851 *51,600 

Ypsilanti city, Michigan . . . . . . . . . . . . . . 40 .11 4 .0 32 .02 3 .1 *–8 .08 157,300 5,876 108,100 10,149 *–49,200 

Woodbridge CDP, New Jersey . . . . . . . . 64 .05 5 .0 61 .56 4 .6 –2 .49 321,600 15,646 273,600 11,021 *–48,000 

Wilsonville city, Oregon . . . . . . . . . . . . . 49 .12 4 .3 48 .01 4 .2 –1 .11 391,600 24,079 336,800 14,947 *–54,800 

Morrisville town, North Carolina . . . . . . 50 .04 4 .9 50 .91 6 .3 0 .87 253,300 28,134 267,700 16,495 14,400 

McKeesport city, Pennsylvania . . . . . . . 55 .52 3 .8 55 .60 3 .1 0 .08 49,000 4,710 47,100 4,292 –1,900 

Canyon Lake CDP, Texas . . . . . . . . . . . . 79 .48 5 .3 82 .37 5 .7 2 .89 141,300 11,719 163,000 15,398 *21,700 

Arvin city, California . . . . . . . . . . . . . . . . 52 .79 7 .0 43 .64 5 .4 *–9 .15 172,300 30,696 94,500 4,859 *–77,800 

Lake Ronkonkoma CDP, New York . . . . 76 .00 4 .6 73 .13 4 .0 –2 .87 391,200 12,875 347,500 10,272 *–43,700 

Lake Zurich village, Illinois . . . . . . . . . . . 89 .17 3 .1 88 .73 3 .4 –0 .44 355,700 8,723 316,300 13,921 *–39,400 

Fort Walton Beach city, Florida . . . . . . . 61 .87 3 .8 62 .21 4 .0 0 .34 180,500 10,632 154,000 10,856 *–26,500 

Gardner city, Kansas . . . . . . . . . . . . . . . 77 .87 5 .1 70 .47 6 .4 –7 .39 165,800 7,913 159,600 5,702 –6,200 

Naples city, Florida . . . . . . . . . . . . . . . . 76 .15 3 .9 79 .23 3 .2 3 .08 838,700 55,583 722,100 61,859 *–116,600 

Pleasant Prairie village, Wisconsin . . . . 79 .93 3 .7 80 .07 3 .8 0 .14 244,400 13,065 216,200 17,384 *–28,200 

Spanish Lake CDP, Missouri . . . . . . . . . 57 .39 5 .2 46 .65 5 .0 *–10 .73 116,000 5,121 107,200 6,818 *–8,800 

Washington city, Utah . . . . . . . . . . . . . . 74 .59 6 .5 69 .69 8 .0 –4 .9 247,900 23,148 221,800 14,087 –26,100 

Pooler city, Georgia . . . . . . . . . . . . . . . . 74 .74 4 .9 67 .15 5 .8 *–7 .59 184,000 10,898 173,700 10,900 –10,300 

Montclair CDP, Virginia . . . . . . . . . . . . . 93 .08 3 .7 88 .47 4 .4 –4 .61 401,600 16,245 338,100 11,127 *–63,500 

Willowbrook CDP, California . . . . . . . . . 48 .83 4 .5 39 .98 6 .3 *–8 .84 348,400 13,697 212,100 24,174 *–136,300 

Oak Ridge CDP, Florida . . . . . . . . . . . . . 36 .88 4 .9 34 .55 5 .0 –2 .33 168,200 14,853 89,000 7,429 *–79,200 

Bellview CDP, Florida . . . . . . . . . . . . . . 78 .31 4 .2 75 .31 4 .4 –3 .00 130,000 8,951 99,100 7,388 *–30,900 

Marblehead CDP, Massachusetts . . . . . 79 .55 3 .0 80 .39 4 .1 0 .84 596,100 27,951 544,400 40,952 *–51,700 

Marina city, California . . . . . . . . . . . . . . 37 .28 3 .5 45 .89 4 .5 *8 .61 546,100 32,489 349,400 14,119 *–196,700 

Ferndale city, Michigan . . . . . . . . . . . . . 73 .09 4 .1 59 .91 3 .9 *–13 .19 128,400 6,620 88,900 3,862 *–39,500 

La Crescenta-Montrose CDP, 

California . . . . . . . . . . . . . . . . . . . . . . . 64 .89 5 .0 65 .44 4 .7 0 .55 732,900 31,175 608,700 26,853 *–124,200 

White Oak CDP, Ohio . . . . . . . . . . . . . . 70 .23 5 .4 73 .47 4 .1 3 .24 145,600 8,642 131,100 6,906 *–14,500 

Camas city, Washington . . . . . . . . . . . . 80 .59 3 .9 78 .73 4 .8 –1 .86 357,800 16,630 286,800 16,464 *–71,000 

Carrboro town, North Carolina . . . . . . . 34 .79 3 .5 33 .05 3 .1 –1 .73 345,400 19,351 329,400 31,325 –16,000 

Union City city, Georgia . . . . . . . . . . . . . 50 .31 3 .8 43 .20 5 .4 *–7 .12 146,700 7,459 102,800 16,153 *–43,900 

Trussville city, Alabama . . . . . . . . . . . . . 92 .75 2 .7 87 .62 4 .1 *–5 .13 234,600 8,087 228,900 10,177 –5,700 

Hammond city, Louisiana . . . . . . . . . . . . 51 .58 5 .0 47 .97 5 .0 –3 .61 151,500 18,560 156,900 22,186 5,400 

Mountlake Terrace city, Washington . . . . 57 .38 4 .9 60 .61 4 .0 3 .22 298,300 11,280 252,100 10,544 *–46,200 

Papillion city, Nebraska . . . . . . . . . . . . . 74 .78 3 .5 69 .21 3 .5 *–5 .57 166,500 4,374 167,200 3,470 700 

St . Andrews CDP, South Carolina . . . . . 34 .14 3 .6 35 .33 3 .6 1 .18 108,100 6,673 108,500 6,505 400 

North Bay Shore CDP, New York . . . . . . 83 .93 6 .4 71 .86 6 .3 *–12 .07 351,000 11,298 281,700 17,058 *–69,300 

Rocky River city, Ohio . . . . . . . . . . . . . . 74 .83 3 .3 75 .62 3 .5 0 .79 210,800 9,462 205,500 12,262 –5,300 

Palm Springs village, Florida . . . . . . . . . 58 .70 5 .4 57 .71 5 .2 –0 .99 178,900 12,146 78,700 10,675 *–100,200 

Hunters Creek CDP, Florida . . . . . . . . . 47 .32 6 .4 52 .50 6 .5 5 .19 331,500 27,013 228,200 24,731 *–103,300 

Bear CDP, Delaware . . . . . . . . . . . . . . . 69 .46 5 .1 68 .33 5 .6 –1 .13 211,800 22,454 182,600 13,178 *–29,200 

Easley city, South Carolina . . . . . . . . . . 68 .30 4 .3 62 .77 4 .1 –5 .53 130,800 7,578 137,800 6,156 7,000 

Murrysville municipality, Pennsylvania . . 93 .21 2 .5 87 .17 3 .8 *–6 .04 206,100 12,170 209,500 11,785 3,400 

Miamisburg city, Ohio . . . . . . . . . . . . . . 75 .11 3 .5 69 .27 4 .2 *–5 .84 153,600 7,457 141,600 5,923 *–12,000 

* Statistically different from zero at the 90 percent confidence level .
 

1

 Ranked in order of population—lowest to highest .
 

2

 Data are based on a sample and are subject to sampling variability . A margin of error is a measure of an estimate’s variability . The larger the margin of error in 


relation to the size of the estimate, the less reliable the estimate . When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval . 

Sources: U .S . Census Bureau, 2007–2009 and 2010–2012 American Community Surveys . 
!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(

!(

!( !(

!( !(

!(

!( !(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(!(!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!( !( !(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!( !(

!( !(

!(!(!(

!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(!(!(

!( !( !(

!(!(

!(!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!( !(!(!(!(!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(!(!( !(

!(

!(

!(!(!(

!(!(

!(!( !( !(

!(

!(!(!(!(

!(!(

!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(

!(!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(!(!(!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !( !(

!(

!( !( !(

!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(!(!(

!(

!(

!( !(

!( !(

!(

!(

!( !(

!( !(

!(

!(

!(

!( !(

!(

!( !( !(

!(

!( !(

!(

!(

!(!(

!(

!(

!(!( !(

!(

!(

!(

!(

!(

!(!(!(!(

!(

!(!(

!( !( !( !( !(

!( !(

!(

!(

!(!( !( !(

!( !(

!(

!(

!(

!(!(

!(

!(

!(

!( !(!( !( !(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!( !(

!(

!(!( !(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!( !(

!(

!(

!( !(

!( !(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!( !( !(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!( !( !(

!( !(

!(!(!(

!(

!(

!(

!(

!( !( !(

!(

!(

!(

!(

!( !(

!(!(!( !(

!( !(

!(

!(

!(!(!( !( !(!(!( !(

!(

!( !(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !(!(

!(

!(

!( !(

!(

!( !( !(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!( !(

!( !(!(

!(

!(

!( !(

!(

!( !(!(

!(

!(

!(

!(!(

!(

!( !( !(

!(

!(

!(

!( !(

!(

!(!(

!(!( !(

!(!(

!( !(!(

!(

!(

!( !(

!(

!(

!( !(

!(

!(!(!(!(

!(!(!(

!(

!( !(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!( !(

!(

!(!(!(

!(

!(

!(!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(!(

!(!(!(

!( !(!( !(!( !(!(

!(

!( !(

!(

!(!(!(!(!( !(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(!(!(!(!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(!( !( !(!(!(

!( !(

!(

!( !(

!(!(

!(

!( !( !(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!( !(

!( !( !(

!(

!( !(

!(

!(!(!(!(

!(!(

!(!(!(!(!(

!(!(!( !(

!(

!( !(!(

!( !(!(

!(

!(

!(

!( !(

!( !(

!( !(

!(

!(!(

!(

!(

!( !(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(

!(

!(

!(

!( !( !(

!(

!(

!(!(!(!(

!(

!(

!(

!(

!( !(

!(!(

!(

!(

!(!(!(!(!( !(

!(

!(

!(!(!(!(!(!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(!(!(

!( !(

!(!(!(

!(

!(

!( !(

!(

!(

!(

!( !( !( !( !(!( !(!(!(

!(

!(

!(!( !(

!(

!(!(!(!(!(

!( !(!( !(

!( !(!(!(!(

!(

!(!( !( !(

!( !(

!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(!(!(!(

!( !(!(

!(

!(

!(

!(!(!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!(

!(

!(!(!(

!( !(

!(!(!(!(!(!(!(

!(!(

!( !(!(

!(

!(!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(!( !(!(!(!(!(

!(

!( !(!(!(

!(

!(!(

!(

!( !( !(

!(!(!(!(

!(

!(!( !(!(!(!(!(

!(

!(

!(!(

!(

!( !( !(

!(

!(!( !( !(

!(

!( !(

!( !(

!(!( !(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!( !(

!( !(

!(

!(!(

!(!(

!(

!(

!(!(!( !(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !( !(

!(

!(

!(

!(

!(

!( !( !( !(!(

!(

!(

!(

!(

!( !(

!(

!(

!( !( !( !(

!(

!(!(!(

!(

!(

!(

!(

!( !(

!( !(

!(

!(

!(

!(

!( !(

!(

!(

!( !( !(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!( !( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!( !(

!( !(

!(

!(!(

!(!(

!(

!(

!(

!(

!(

!( !(!(

!(!(

!(

!(

!( !( !(

!(!(!(

!(!(!(!(!(

!(

!(!(!(!(!(

!(

!(

!(

!(

!(

!(!( !( !(!(

!(!(

!(

!(!(

!(

!( !(

!(

!(!(

!(

!( !(

!(

!(

!( !(

!( !(

!(

!(!(

!(

!(

!(!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !( !(

!(

!(

!( !( !(

!(

!(

!(

!(!( !(!( !(

!(!(

!(!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!( !(

!(

!(!(!(!(

!(

!(!(

!(!( !(

!(!(!(!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(

!(

!(!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!( !(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!(

!( !(

!(

!( !( !( !( !(

!( !(!(

!(!(!(

!(!( !(

!( !(

!(!(!(

!(!(

!(

!(

!(

!(

!( !(

!(!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(!(!(

!(

!(

!( !(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !( !(!(

!(

!(

!(

!( !(

!(

!( !(

!(

!(

!(

!(

!(!( !(

!(!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(!(!(!(!( !( !( !(

!(

!(

!(

!(!(!(!( !(

!(

!(

!(

!( !( !( !( !(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!( !(

!(

!(

!(!(

!(!(!(

!(!(!(

!(!( !(

!(

!(!(

!(

!(

!(!( !(

!(!(

!(!(!( !(

!(

!(

!(!(

!(!(!(!(!(!(

!(!(

!(

!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(!(

!(

!(

!(!( !( !(

!(

!(

!(

!(!(!( !(

!(

!(

!(

!( !(!(

!(

!(!( !(

!(

!(

!(

!(

!(!(

!(

!(!(!(

!(

!(

!( !( !(

!(

!(!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(!(!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(!(!(

!(!(

!( !(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!( !( !(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!( !(

!( !(

!(

!(

!(!(

!(

!(

!(

!( !( !( !(

!(

!(!(!( !(

Source: U.S. Census Bureau,

American Community Survey 2007–2009 and 2010–2012. 

0 500 Miles

0 100 Miles

0 100 Miles

Value in currentdollars by place

-$14,999 to -$1

-$49,999 to -$15,000

-$50,000 or less $0 or more 

Figure 3.

Differences in Median Home Value for Places 

With a 

Population of 20,000 or 

More: 2007–2009 to 2010–2012 

Comparable data

not avaliable

U.S. change in value

is -$17,300 

12 U.S. Census Bureau 
U.S. Census Bureau 13 

Figure 4. 

Homeownership Rate in the United States: 2005 to 2012 

(Percent of occupied housing units that are owner-occupied. Data based on sample. For information on confidentiality 

protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) 

Source: ACS 1 Year Estimates 2005 through 2012, ACS 3 Year Estimates, 2005–2007, 2006–2008, 2007–2009, 2008–2010, 

2009–2011, and 2010–2012. 

62 

63 

64 

65 

66 

67 

68 

3 year using midpoint year 

1 year 

2005 2006 2007 2008 2009 2010 2011 2012

MA at $544,400.5 The city with the 

lowest median home value of those 

cities in 2010–2012 was 

McKeesport City, PA with a median 

home value of $47,100. 

Thirty-three of the fifty least populous cities experienced significant 

declines in median home value 

5 Census Designated Places (CDPs) are the 

statistical counterparts of incorporated places 

and are delineated to provide data for settled 

concentrations of population that are identifiable by name but are not legally incorporated 

under the laws of the state in which they are 

located. The boundaries usually are defined 

in cooperation with local or tribal officials 

and generally updated prior to each decennial 

census. These boundaries, which usually 

coincide with visible features or the boundary 

of an adjacent incorporated place or another 

legal entity boundary, have no legal status, 

nor do these places have officials elected to 

serve traditional municipal functions. CDP 

boundaries may change from one decennial 

census to the next with changes in the settlement pattern; a CDP with the same name as 

in an earlier census does not necessarily have 

the same boundary. CDPs must be contained 

within a single state and may not extend into 

an incorporated place. There are no population size requirements for CDPs. 

from 2007–2009 to 2010–2012. Of 

the 50 cities, one, Marina City, CA 

(–$196,700), had a higher decline 

than any other city with the exception of Naples City, FL (–$116,600). 

For graphical presentation of 

change in median home values for 

places with populations of 20,000 

or more, see Figure 3. 

Homeownership rates 

Historical homeownership rates 

from the decennial census show a 

dramatic upward trend from 1940 

to 1960, after which the percent 

of owners started to increase at a 

slower rate and finally to decrease 

in 2010.6 ACS data show that after 

2006, when the homeownership 

rate was at its highest (not significantly different from 2007), the 

6 See Housing Characteristics: 2010, 2010 

Census Brief; Figure 5. Owner-Occupied Units 

as a Percentage of All Occupied Housing Units 

1890 to 2010, issued October 2011. 

rate dropped continuously through 

2012 (Figure 4). 

states 

The rise and fall of the housing 

market measured in change in 

home value has contributed to a 

decline in homeownership rates 

across the country. As seen on 

Table 1, the United States had 

a homeownership rate of 64.7 

percent in 2010–2012, which was 

significantly lower than the nation’s 

homeownership rate of 66.4 

percent in 2007–2009. Only nine 

states did not show a significant 

decrease in homeownership rate 

between 2007–2009 and 2010– 

2012: Vermont, Hawaii, Alaska, 

Montana, Oklahoma, North Dakota, 

Arkansas, South Dakota, and 

Wyoming. All other states had 

a lower homeownership rate in 

2010–2012 (Table 1). 
14 U.S. Census Bureau 

In 2010–2012, West Virginia had a 

higher homeownership rate than 

any other state (72.9 percent). The 

District of Columbia (41.6 percent), 

New York (53.9 percent), California 

(54.9 percent), Nevada (56.2 

percent), Hawaii (57.3 percent), 

Rhode Island (60.3 percent), 

Oregon (61.7 percent), and 

Massachusetts (62.3 percent) had 

the lowest homeownership rates, 

all of which were statistically different from each other. The U.S. homeownership rate of 64.7 percent 

was not significantly different from 

that of Colorado (64.9 percent), 

which in turn was not significantly 

different from the rate in Georgia 

(64.9 percent). Aside from those 

states, the U.S. rate was different 

from the homeownership rate of all 

other states. 

counties 

Table 2 shows that of the 50 most 

populous counties, 5 experienced 

a higher percentage point difference in homeownership rates 

from 2007–2009 to 2010–2012 

than the change at the national 

level (–1.72 percent). Those counties—Westchester County, NY; 

Honolulu County, HI; Cuyahoga 

County, OH; Harris County, TX; and 

Kings County, NY all experienced 

smaller decreases (–0.41 percent, 

–0.49 percent, –0.55 percent, –0.79 

percent, and –0.88 percent respectively). Conversely, 22 counties 

had larger decreases, ranging from 

–2.35 percent to –4.73 percent, 

than that of the United States 

(–1.72 percent). 

Most of the 50 most populous 

counties in the United States had 

statistically lower homeownership 

rates in 2010–2012 than in 2007– 

2009. Only Cuyahoga County, OH 

(–0.55 percent); Honolulu County, 

HI (–0.49 percent); Westchester 

County, NY (–0.41 percent); and 

Fresno County, CA (–0.96 percent) 

experienced a similar percentage 

of owner-occupied housing units in 

2010–2012 as in 2007–2009. 

In 2010–2012, Nassau (80.4 percent) and Suffolk (78.9 percent) 

counties in New York had higher 

homeownership rates than any 

other of the 50 most populous 

counties in the United States. This 

was followed by Oakland County, 

MI (71.1 percent); Palm Beach 

County, FL (71.0 percent); and St. 

Louis County, MO (70.8 percent); 

which were not significantly different from each other, and then 

Fairfax County, VA (68.3 percent). 

The four lowest were also in New 

York: Bronx County (19.0 percent), 

New York County (22.0 percent), 

Kings County (29.5 percent), 

Queens County (43.3 percent), followed by Los Angeles County, CA 

(46.4 percent), which was in fifth 

place. All of these are significantly 

different from each other. 

The 2007–2009 homeownership 

rates for 81.5 percent of smaller 

counties, which include the 50 least 

populous counties shown in Table 

3, were similar to the rate in 

2010–2012. Of those that were 

significantly different, 135 had a 

lower rate in 2010–2012, while 57 

were higher. Two county equivalents in Virginia, Fredericksburg 

and Petersburg cities, experienced 

the lowest homeownership rate for 

smaller counties in 2010–2012 

(35.2 percent and 44.3 percent, 

respectively). 

Looking at the same data for the 

50 least populous counties in Table 

3, only 13 showed a significant 

change. Of these, only Gonzales 

County, TX (8.5 percent) and Carroll County, IA (5.2 percent), which 

were not statistically different 

from each other, had an increase 

in the percentage point change in 

homeownership rate. Eleven of the 

thirteen counties showed decreases 

from 2007–2009 to 2010–2012: 

Langlade County, WI (–4.6 percent); 

Jackson County, IA (–4.9 percent); 

Crook County, OR (–5.6 percent); 

Jones County, TX (–5.9 percent); 

East Feliciana Parish, LA (–6.2 

percent); Madison County, NC (–6.5 

percent); Bandera County, TX (–6.6 

percent); Fulton County, IN (–6.6 

percent); Minidoka County, ID (–7.2 

percent); Elbert County, GA (–8.7 

percent); and Warren County, NC 

(–9.5 percent), which were all similar to each other. 

Metropolitan areas 

Table 4 shows that 49 of the 50 

metropolitan areas with the largest total population had a statistically lower homeownership rate 

in 2010–2012 than in 2007–2009. 

The homeownership rate for the 

Oklahoma City, OK Metro Area, 

the only metropolitan area whose 

homeownership rate did not 

decrease was similar in 2010–2012 

to that in 2007–2009. 

Of those 50 most populous metropolitan areas, those with the highest homeownership rates were the 

Minneapolis-St. Paul-Bloomington, 

MN-WI Metro Area (70.5 percent); 

the St. Louis, MO-IL Metro Area 

(70.3 percent); the Detroit-WarrenLivonia, MI Metro Area (70.2 percent); and the Birmingham-Hoover, 

AL Metro Area (70.1 percent). 

All were statistically similar but 

higher than all of the other 45 

metro areas, with the exception 

of the Pittsburgh, PA Metro Area. 

The Pittsburgh Metro Area (69.8 

percent) was higher than all the 

remaining metro areas but similar to the Birmingham-Hoover, AL 

Metro Area. 

Places (cities) 

Forty-three of the fifty cities with 

the largest total population had a 

statistically lower homeownership 

rate in 2010–2012 than in 2007– 

2009. As shown on Table 5, the 

remaining seven cities had similar 
U.S. Census Bureau 15 

rates between the two time periods—Virginia Beach city, VA; Fresno 

city, CA; Jacksonville city, FL; San 

Francisco city, CA; Oklahoma City, 

OK; Tulsa city, OK; and Arlington 

city, TX. 

Twenty-three of the fifty most 

populous cities had bigger percentage point decreases than the 

United States (–1.72 percent) ranging from –2.49 percent to –7.41 

percent. Atlanta city, GA had the 

largest percentage point decrease 

(–7.4 percent) of the 50 most populous cities. 

In 2010–2012, Virginia Beach city, 

Virginia, had a higher homeownership rate (64.3 percent) than any 

of the other 49 most populous 

cities. The lowest rate was in New 

York city, NY (31.7 percent), which 

was not significantly different from 

Miami city, FL (32.1 percent), but 

was different from Boston city, MA 

(33.2 percent) and all other remaining cities. Miami was not different 

from Boston or New York, but the 

rates in New York, Miami, and 

Boston were significantly lower 

than those in the 50 remaining 

cities. 

Smaller places showed a slightly 

different story. The majority (73.5 

percent) of those cities showed no 

significant change between 2007– 

2009 and 2010–2012. Of those 

that were significant, 349 cities 

experienced a lower homeownership rate in 2010–2012, 29 cities 

had increases, and for 45 cities, 

comparable statistics were not 

available. 

As Table 6 shows, for the 50 

least populous cities, only 17 

experienced a significant percentage point change in homeownership rates between 2007–2009 

and 2010–2012. Pace CDP, FL and 

Marina city, CA increased by 13.3 

and 8.6 percentage points, respectively, during that time period, 

increases that were higher than 

that of the other 15 cities. Pace 

CDP and Marina city were not statistically different from each other. 

For graphical presentation of the 

differences in homeownership 

rates for places with populations of 

20,000 or more, see Figure 5. 

tHree-Year data 

source and accuracY 

stateMent 

The data presented in this report 

are based on the ACS sample interviewed from January 2010 through 

December 2012 and from January 

2007 through December 2009. The 

estimates based on this sample 

describe the actual average values 

of person, household, and housing 

unit characteristics over this period 

of collection. Sampling error is the 

uncertainty between an estimate 

based on a sample and the corresponding value that would be 

obtained if the estimate were based 

on the entire population (as from 

a census). Measures of sampling 

error are provided in the form of 

margins of error for all estimates 

included in this report. All comparative statements in this report 

have undergone statistical testing, 

and comparisons are significant 

at the 90 percent level unless 

otherwise noted. In addition to 

sampling error, nonsampling error 

may be introduced during any of 

wHat Is tHe aMerIcan 

coMMunItY surVeY? 

The American Community Survey (ACS) is a nationwide survey 

designed to provide communities with reliable and timely 

demographic, social, economic, 

and housing data for the nation, 

states, congressional districts, 

counties, places, and other localities every year. It has an annual 

sample size of about 3.3 million 

addresses across the United 

States and Puerto Rico and 

includes both housing units and 

group quarters (e.g., nursing 

facilities and prisons). The ACS 

is conducted in every county 

throughout the nation, and 

every municipio in Puerto Rico, 

where it is called the Puerto Rico 

Community Survey. Beginning in 

2006, ACS data for 2005 were 

released for geographic areas 

with populations of 65,000 and 

greater. For information on the 

ACS sample design and other 

topics, visit <www.census.gov 

/acs/www>. 

the operations used to collect and 

process survey data such as editing, reviewing, or keying data from 

questionnaires. For more information on sampling and estimation 

methods, confidentiality protection, 

and sampling and nonsampling 

errors, please see the ACS Multiyear 

Accuracy of the Data document 

located at <www.census.gov 

/acs/www/Downloads 

/data_documentation 

/Accuracy/MultiyearACS 

/AccuracyofData2012.pdf>. 
!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(

!(

!( !(

!( !(

!(

!( !(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(!(!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!( !( !(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!( !(

!( !(

!(!(!(

!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(!(!(

!( !( !(

!(!(

!(!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!( !(!(!(!(!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(!(!( !(

!(

!(

!(!(!(

!(!(

!(!( !( !(

!(

!(!(!(!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(

!(!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(!(!(!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !( !(

!(

!( !( !(

!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(!(!(

!(

!(

!( !(

!( !(

!(

!(

!( !(

!( !(

!(

!(

!(

!( !( !(

!( !( !(

!(

!( !(

!(

!(

!(!(

!(

!(

!(!( !(

!(

!(

!(

!(

!(

!(!(!(!(

!(

!(!(

!( !( !( !( !(

!( !(

!(

!(

!(!( !( !(

!( !(

!(

!(

!(

!(!(

!(

!(

!(

!( !(!( !( !(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!( !(

!(

!(!( !(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!( !(

!(

!( !( !(

!( !(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!( !( !(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!( !( !(

!( !(

!(!(!(

!(

!(

!(

!(

!( !( !(

!(

!(

!(

!(

!( !(

!(!(!( !(

!( !(

!(

!(

!(!(!( !( !(!(!( !(

!(

!( !(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !(!(

!(

!(

!( !(

!(

!( !( !(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!( !(

!( !(!(

!(

!(

!( !(

!(

!( !(!(

!(

!(

!(

!(!(

!(

!( !( !(

!(

!(

!(

!( !(

!(

!(!(

!(!( !(

!(!(

!( !(!(

!(

!(

!( !(

!(

!(

!( !(

!(

!(!(!(!(

!(!(!(

!(

!( !(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!( !(

!(

!(!(!(

!(

!(

!(!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(!(

!(!(!(

!( !(!( !(!( !(!(

!(

!( !(

!(

!(!(!(!(!( !(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(!(!(!(!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(!( !( !(!(!(

!( !(

!(

!( !(

!(!(

!(

!( !( !(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!( !(

!( !( !(

!(

!( !(

!(

!(!(

!(!(!(!(

!(!(!(!(!(

!(!(!( !(

!(

!( !(!(

!( !(!(

!(

!(

!(

!( !(

!( !(

!( !(

!(

!(!(

!(

!(

!( !(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(

!(

!(

!(

!( !( !(

!(

!(

!(!(!(!(

!(

!(

!(

!(

!( !(

!(!(

!(

!(

!(!(!(!(!( !(

!(

!(

!(!(!(!(!(!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(!(!(

!( !(

!(!(!(

!(

!(

!( !(

!(

!(

!(

!( !( !( !( !(!( !(!(!(

!(

!(

!(!( !(

!(

!(!(!(!(!(

!( !(!( !(

!( !(!(!(!(

!(

!(!( !( !(

!( !(

!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(!(!(!(

!( !(!(

!(

!(

!(

!(!(!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!(

!(

!(!(!(

!( !(

!(!(!(!(!(!(!(

!(!(

!( !(!(

!(

!(!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(!( !(!(!(!(!(

!(

!( !(!(!(

!(

!(!(

!(

!( !( !(

!(!(!(!(

!(

!(!( !(!(!(!(!(

!(

!(

!(!(

!(

!( !( !(

!(

!(!( !( !(

!(

!( !(

!( !(

!(!( !(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!( !(

!( !(

!(

!(!(

!(!(

!(

!(

!(!(!( !(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !( !(

!(

!(

!(

!(

!(

!( !( !( !(!(

!(

!(

!(

!(

!( !(

!(

!(

!( !( !( !(

!(

!(!(!(

!(

!(

!(

!(

!( !(

!( !(

!(

!(

!(

!(

!( !(

!(

!(

!( !( !(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!( !( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!( !(

!( !(

!(

!(!(

!(!(

!(

!(

!(

!(

!(

!( !(!(

!(!(

!(

!(

!( !( !(

!(!(!(

!(!(!(!(!(

!(

!(!(!(!(!(!(

!(

!(

!(

!(

!(!( !( !(!(

!(!(

!(

!(!(

!(

!( !(

!(

!(!(

!(

!( !(

!(

!(

!( !(

!( !(

!(

!(!(

!(

!(

!(!(

!(

!( !(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!( !( !(

!(

!(

!( !( !(

!(

!(

!(

!(!( !(!( !(

!(!(

!(!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(!(

!( !(

!(

!(!(!(!(

!(

!(!(

!(!( !(

!(!(!(!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(

!(

!(!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!( !(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!(

!( !(

!(

!( !( !( !( !(

!( !(!(

!(!(!(

!(!( !(

!( !(

!(!(!(

!(!(

!(

!(

!(

!(

!( !(

!(!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(!(!(

!(

!(

!( !(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !( !(!(

!(

!(

!(

!( !(

!(

!( !(

!(

!(

!(

!(

!(!( !(

!(!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(!(!(!(!( !( !( !(

!(

!(

!(

!(!(!(!( !(

!(

!(

!(

!( !( !( !( !(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!( !(

!(

!(

!(!(

!(!(!(

!(!(!(

!(!( !(

!(

!(!(

!(

!(

!(!( !(

!(!(

!(!(!( !(

!(

!(

!(!(

!(!(!(!(!(!(

!(!(

!(

!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(!(

!(

!(

!(!( !( !(

!(

!(

!(

!(!(!( !(

!(

!(

!(

!( !(!(

!(

!(!( !(

!(

!(

!(

!(

!(!(

!(

!(!(!(

!(

!(

!( !( !(

!(

!(!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(!(!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(!(!(

!(!(

!( !(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(!( !(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!( !( !(

!(

!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!( !(

!( !(

!(

!(

!(!(

!(

!(

!(

!( !( !( !(

!(

!(!(!( !(

Source: U.S. Census Bureau,

American Community Survey 2007–2009 and 2010–2012. 

0 500 Miles

0 100 Miles

0 100 Miles

Percentage pointsby place

0.0 or more 

-2.9 to -0.1

-4.9 to -3.0

-5.0 or less 

Figure 5.

Percentage Point Differences in Homeownership Ratefor Places 

With a Population of 20,000 or 

More: 2007–2009 to 2010–2012 

Comparable data

not avaliable

U.S. percentage point

difference is -1.72 

16 U.S. Census Bureau