Forecasting 2020 U.S. County and MSA Populations
April 2006
ABSTRACT
Population growth at the county level can be predicted using widely available demographic and
economic data. Past recent growth, the presence of immigrants, the fraction of population older than
25 and younger than 65, low taxes, and good weather are all positively associated with population
growth. Our forecasts reveal that most growth and real estate development will occur in the West,
Sunbelt, and along the Southern I-85 route. However, our model only accounts for 75 percent of the
variance in growth experiences between 1980 and 2000, with the other 25 percent explained by
“surprise” events. Many unexpected places will be winners or losers in the game of future local real
estate development. A companion spreadsheet of our population predictions at the county level
(metropolitan counties) is available in the Working Paper section of the Zell-Lurie Real Estate
Center Website
Peter Linneman
The Wharton School
University of Pennsylvania
Real Estate Department
1470 Steinberg Hall-Dietrich Hall
3620 Locust Walk
Philadelphia, PA 19104-6302
linnemap@wharton.upenn.edu
Albert Saiz
The Wharton School
University of Pennsylvania
Real Estate Department
1466 Steinberg Hall-Dietrich Hall
3620 Locust Walk
Philadelphia, PA 19104-6302
saiz@wharton.upenn.edu
Whether using explicit statistical models, recent information on the evolution of local markets,
conversations with friends, the latest headlines in the local newspaper, or gut feelings, real estate
entrepreneurs are constantly guessing the future demand for their product. Population growth is
associated with increased residential demand, increased demand in the office and distribution
sectors, and more shoppers to patronize local retail. In short, population growth drives real estate
development opportunities.
We examine the key statistical determinants of population growth in U.S. metropolitan
counties, identifying characteristics that are important predictors of subsequent population growth.
From our statistical analysis we gain a better understanding of the conceptual underpinnings of the
population growth across U.S. metropolitan counties during the last 30 years. In addition to learning
what makes cities “tick,” we are also able to make predictions of population growth for all
metropolitan counties in the U.S.
It is perilous to predict the future. However, our model accurately describes the population
growth that took place from 1980 to 2000, and past growth forecasts future growth relatively
accurately. We therefore believe that our estimates for 2000-2020 population growth will prove to be
not too far off the mark. Nevertheless, our statistical work fails to account for about a quarter of all
the variation in county population growth. That is, growth surprises do occur, and in some cases they
matter a lot. In the 1950s, who would have predicted that Benton County, Arkansas, would emerge
as the center of the biggest commercial empire in world history? Spurred by the phenomenal growth
of Wal-Mart, Benton County makes the Census list of top 70 counties by population growth. The
point is that our statistical analysis cannot predict who the next Sam Walton will be, and where he or
she will be based.
2
POPULATION GROWTH 1980-2000
Regression analysis allows us to identify some of the key variables that predict future population
growth. We explore a variety of variables at the county level. Examples include demographic
variables (such as the percentage of individuals older than 65), fiscal variables (such as taxes) and
geographic factors (such as local weather and elevation). These variables have predictive power for
several reasons. First, they capture attributes of an area that cause it to grow economically, and
therefore attract employees. Firm productivity varies across locales for several reasons: the skills and
education of their population; accessibility to markets and transportation nodes; the impact of local
public finances (taxes and expenditures); and agglomeration economies. The latter refers to firms
becoming more productive if they locate closer to similar firms, enabling them to share information,
infrastructures, and a pool of relevant workers, and to reduce the transportation costs of their
common input and output transactions.
Other variables predict how attractive an area is for prospective inhabitants due to local
amenities. Research by Edward Glaeser, Jed Kolko, and Albert Saiz (2001) demonstrates that cities
are becoming as important in terms of consumption as they used to be in terms of traditional
productivity. The capacity to generate and retain amenities adds considerably to the appeal of a city.
Some cities will attract high-income residents with varied shopping experiences, proximity to
attractive activities, good schools, and a strong social milieu that is conductive to both work and
play. The attraction to a city on the basis of its physical and social environment represents a major
paradigm shift; whereas people formerly followed jobs, jobs now also follow workers.
Thanks to information and ethnic networks, people tend to move to areas where they have
social contacts. Thus, metropolitan areas with large immigrant populations, for example, tend to
attract yet more immigrants. In addition, the characteristics of the population of a county can predict
population growth for simple biological reasons: younger populations tend to be more fertile, while
the elderly experience higher mortality rates. Finally, some variables are good predictors of
3
population growth even though they are difficult to measure: a vibrant lifestyle, an openness to
entrepreneurs, a good climate, and so on.
This study focuses on “metropolitan counties” as defined by the Office of Management and
Budget (OMB) in 2000. These are counties that belong to OMB-defined metropolitan areas which
are major population centers. We limit ourselves to the continental U.S., excluding Hawaii, Alaska
and Puerto Rico. The 804 counties that we examine in our analysis represent 76 percent of all U.S.
population in 2000.
The U.S. population has grown by about 10 percent every decade since 1970, and is
predicted to continue doing so through 2020. In the 2000, the population was estimated to be 282
million, and by 2020 it is expected to grow to 336 million. This means that
Population
(Millions of People)
Figure 1. US Population, 1970 - 2020
400
350
300
250
200
150
100
50
0
1970
1980
1990
2000
2010
2020
Year
between 2000 and 2020 the population will increase by a staggering 53.7 million. Where will these
people live? Our statistical model addresses this question by analyzing county population growth
across all metropolitan counties between 1980 and 2000. The focus is on long term urban population
growth.
Whereas most of the previous research on city growth has focused on percentage population
growth, we use a more relevant growth metric that recognizes that in very small counties, growth
rates can be extremely high although the actual number of new people moving into the area is very
4
small. We calculate the share of county population as a percentage of total U.S. population, and use
the change in that share between 1980 and 2000 in our statistical analysis. To the best of our
knowledge, this is the first time this particular variable has been used in the context of long term
growth. Since this measure is relative to the total size of the population, we combine our regression
results with Census projections of future population growth to forecast local growth. Table I shows
the results of a regression analysis of the change in total population share 1980-2000 as a function of
a number of county characteristics in 1980.
The dependent variable in the regressions is multiplied by 10,000 so that our regression
coefficients do not display an inordinate number of decimal positions. Table I presents the results,
including a variable that has high forecasting power: the “population market share capture” of the
county during the period 1970-1980 (growth in the recent past). Thus, we find that recent past
growth forecasts future growth. This regression accounts for 75 percent of the variability in county
growth.
Our model is rich in specification, including 26 local economic, demographic, political,
climatologic, geological, and housing variables. We will focus primarily on describing the impacts
of the variables that are most statistically significant, although we will comment on a few variables
that we expected to be more important. We begin by noting the importance of recent growth. Our
results confirm this result of previous research that used data from different countries, and different
geographic definitions (for example, city level forecasts), population growth definitions, and time
periods. Everyone (including us) finds that even after controlling for a variety of other variables
population growth is extremely persistent; absent other information, the best way to predict a
county’s population growth is to look at how much it grew in the past decade. It appears that the
forces that shape an area’s attractiveness have persistent impacts.
5
TABLE I
US Metropolitan County Growth Model
US Population
Share Change
1980-2000
Share Foreign Born in 1980
% with bachelor's degree or higher in 1980
% with less than a high school diploma in 1980
% White in 1980
% over 65 years old in 1980
% under 25 years old in 1980
Income Tax per capita / Income per capita in 1980
Sales Tax per capita / Income per capita in 1980
Log Population Density in 1980
Log Density Squared in 1980
Presidential Election Vote Over 55% Republican in 1980
Presidential Election Vote Below 45% Republican in 1980
All State Senators Republican in 1980
All State Senators Democrat in 1980
Log Average Precipitation
Log Average Snowfall
Log January Average Temperature
Log Average January Sun Days
Share Housing Older than 30 years
Share Housing Newer than 11 years
=1 if county borders an ocean or a Great Lake
Hills or Mountains in County
Northeast
South
West
US Population Share Change 1970-1980
Constant
12.621*
-0.132
-0.188
0.184
-10.873*
-9.035*
34.189
-23.86*
0.512
-0.050
-0.193
0.317*
-0.407
-0.195
-0.681
-0.238
0.282
1.101*
3.040*
5.206*
-0.684*
-0.079
-0.349
-0.357
0.351*
1.026*
-2.011
Observations
R-squared
805
0.76
Robust standard errors in parentheses
*Significant at 10%
Immigration has become a primary driver of population growth. In the 1960s, most
Americans claimed European or African ancestry, and the number of foreign-born households was
relatively low. Between now and 2050, immigrants and their offspring will account for about half of
the total growth in U.S. population, and Americans of European and African origin will become
primi inter pares in a country of Mexican-Americans, Chinese-Americans, Korean-Americans,
Indian-Americans, Filipino-Americans, and many others.
6
It is obvious that immigration will be a key element of county-level growth, but can we
forecast where immigrants will settle? The answer to the question is a qualified yes. Immigrants tend
to concentrate wherever previous immigrants have settled. Kinship ties, shared language, and the
existence of common amenities and public goods, make “immigrant enclaves” attractive to
subsequent immigrants. Thus, a county’s share of the foreign-born in 1980 was an important
predictor of population growth from 1980-2000. And so it will be in the future.
Previous research by Edward Glaeser and Albert Saiz (2003) has shown that during the last
century local educational achievement has been an important explanatory factor for population
growth in cities. In short, smart cities grow faster. We find the same to be true at the county level.
Specifically, counties with lower shares of high-school dropouts grew more quickly. However,
education is a weaker predictor of county growth than metropolitan growth, especially when one
includes previous growth trends. This means that education has an important long-run impact, but
that short-term changes in education levels are not powerful predictors of short-term changes in
growth patterns. Metropolitan areas with highly educated individuals are more productive, allowing
them to pay higher wages, which attracts population inflows. On the other hand, highly educated
populations are typically more effective in curtailing local residential development at the local level,
and may be a counter influence on population growth.
The age distribution of the population is another predictor of future growth, that is, very
young and very old populations tend to grow more slowly. Specifically, we find that population
growth is negatively related to both the share of people younger than 25, and older than 65,
reflecting the fact that households in their prime earning years are typically older than 25, and
younger than 65. Moreover, areas with a major proportion of older residents are less attractive to
younger generations.
Tax rates are not uniform for different municipalities. We use data from the Census of
Governments on local taxation (municipal and county) to create two measures of fiscal burden,
income taxes and the sales tax. Furthermore, since different individuals typically face different tax
7
rates depending on their location, income, and type of business, we use total tax revenues per capita
divided by income per capita to measure a county’s tax burden. A high degree of taxation may make
a county less attractive to taxpayers and entrepreneurs. On the other hand, higher tax revenues may
be associated with a better public schools and public services. Our statistical analysis reveals that the
local sales tax burden is generally associated with slower population growth. Since all tax measures
are strongly associated, we tentatively conclude that higher taxation discourages local growth. We
suspect, however, that the efficiency of local government in spending sensibly, and government
efficiency in providing key public services are also important. Studying the factors that are
associated with local mismanagement or good government remains a topic for future research.
We find that population density also matters, although in a complex way. Counties with very
low densities tended to grow more slowly. But above a certain threshold, higher density is associated
with slower growth. This threshold population density corresponds with a median county density of
60 persons per square mile. Therefore, density increases growth up to about 60 people per square
mile, after which amenity levels drop and population growth diminishes.
The impact of demography on politics is a hotly debated topic by political scientists and
media pundits. Observations on the growth of “red” states and the demise of “blue” states are
commonplace. If we run our analysis with politics as the only variable, we find that Republicandominated counties (based upon presidential and senatorial election data from early 1980s) do tend
to grow faster. However, this can be explained by other variables. Republican-dominated counties
were already rapidly growing, so it is possible that the new rapidly growing areas are attracting
individuals with a more libertarian or conservative outlook. Moreover, many of the metropolitan
areas in “red” states have geographic attributes that are associated with growth. When we control for
these other factors, we find that political orientation is not strongly associated with growth. There is
a weak link, however, between the 1980 presidential results and subsequent county growth. Almost
half of the counties in our sample of 804 metropolitan counties had between 45 - 55 percent support
for Ronald Reagan. A number of counties were more polarized, with more than a 55 percent share
8
for either Reagan (about 40 percent) or Carter (about 12 percent). These strongly Democratic
counties grew significantly faster during the 1980-2000 period, controlling for a host of other
variables. It is unclear why.
Some of the most powerful predictors of county population growth during our sample years
are weather-related. Briefly put, Americans are rapidly leaving cold, damp, and snowy areas for
sunnier and drier climates. Both a West regional indicator and “good weather” variables are strong
predictors of population growth. All of the weather variables (snowfall, precipitation, temperature,
and sun days) are interrelated, with the number of sun days in January being the variable that comes
out more strongly in our analysis. In short, people are moving to “the bright side.” We speculate that
there may be a geopolitical economic shift from the Atlantic to the Pacific area, motivated by
changing trade links and the emergence of China and India as global powerhouses. The impact of
globalization on population growth remains an understudied topic for future exploration.
The age distribution of the county’s housing stock also has some predictive power,
confirming previous research by Edward Glaeser and Joseph Gyourko (2005). Areas with large
amounts of new housing have three important attributes that favor growth: they are favorably
inclined to development; they have a large recent demand relative to pre-existing housing; and their
housing stock is more in line with modern housing preferences. Interestingly, there is some (weak)
evidence that having a very old housing stock is mildly correlated with relatively faster growth than
would otherwise be the case. The very old housing stock that has survived was generally built for
high-income families, hence are of good quality. Since declining cities such as New Orleans, Detroit,
and Buffalo have massive and valuable housing stocks, reduced housing demand translated into
lower housing prices, and made these cities a bit less unattractive. All things equal, areas with older
housing stocks experienced slower decline than expected.
Counties that are adjacent to the coastlines of the Atlantic, Pacific, and Great Lakes, tend to
grow more slowly than inland counties. Coastal areas in the west and northeast often have restrictive
zoning, which raises prices and discourages growth. However, there appears to be no relationship
9
between the altitude of a county and its growth. This is a somewhat surprising findings, as mountain
areas are generally popular.
A LOOK AT 2020
Combining county characteristics with our statistical growth model and Census projections of total
population in 2020, we obtain county and MSA population forecasts for 2020. Table II details the
counties which are the biggest projected population losers. Also displayed are their MSAs, our
estimate of population losses (expressed in both levels and as a percentage of the 2000 population),
our estimate of population levels in 2020, and previous population gains or losses over the 19802000 period. Due to the fact that we used the change in the shares of the total population, 5 counties
display negative population predictions for 2020, which we replace by zero. Our expectations for
these counties are bleak, notwithstanding the fact that we do not know exactly what number of
people will actually be living there.
Baltimore has the dubious honor of being ranked the biggest loser by 2020. That city (which
is also a county) is forecast to lose about 100,000 residents, or about 15 percent of its year 2000
population. Most other counties that we expect to decline are in the Rustbelt.
Interestingly, 10 percent (5 out of 50) of the bottom counties are in the New Orleans
metropolitan area. (And this is without factoring in the impact of Katrina.) In other words, New
Orleans was the rare case of a Sunbelt area that was losing population like a Rustbelt area.
According to research by Donald Davis and David Weinstein (2002), who used data from the
bombing of Japanese cities during World War II, the impact of major disasters on a city’s population
growth tends to dissipate over time. Remarkably, they found that the cities that lost more population
during the war grew faster after, and their populations after 20 years were at the point that one would
have predicted by looking at pre-War growth trends. Thus, we are very pessimistic about New
Orleans’ growth over the next 20 years, irrespective of what aid flows to this area.
10
Table III displays the “winners” in terms of forecasted county growth. Big counties in major
metropolitan areas tend to dominate. Insofar as the U.S. population is growing, and the share of
population captured by a county is not declining too quickly, big counties are expected to grow
because of general population growth trends. However, Table III also captures the massive expected
growth of relatively new areas, such as Maricopa County, the top county in terms of expected
population growth in 2020. It is apparent that most of the big growth counties are in the West, the
Sunbelt, and the Southern I-85 corridor linking Atlanta with Raleigh, N.C. Our results reveal that
prospective real estate developers had better buy a good pair of sunglasses and some sunblock.
11
TABLE II
Biggest Population Loss Counties (2020 Forecast)
Forecast:
Population in
2020
Loss as
Percentage
of 2000
Population
Population
Loss/Gain
2000-1980
-137,751
Rank
County Name
Metropolitan Area
Population
Loss 20202000
1
Baltimore
Baltimore, MD
-91,607
556,950
-14.1%
2
Oswego
Syracuse, NY
-62,809
59,728
-51.3%
8,762
3
Herkimer
Utica-Rome, NY
-59,174
5,217
-91.9%
-2,272
4
Cayuga
Syracuse, NY
-56,550
25,388
-69.0%
1,986
5
Chautauqua
Jamestown, NY
-49,891
89,698
-35.7%
-7,372
6
Allegheny
Pittsburgh, PA
-48,588
1,231,395
-3.8%
-168,271
7
Cambria
Johnstown, PA
-48,233
103,997
-31.7%
-30,756
8
St. Charles
New Orleans, LA
-48,196
0
-100%
10,713
9
Terrebonne
Houma, LA
-47,720
56,804
-45.7%
9,541
10
St. Bernard
New Orleans, LA
-47,531
19,484
-70.9%
2,620
11
Lafourche
Houma, LA
-41,959
47,996
-46.6%
6,621
12
Erie
Buffalo-Niagara Falls, NY
-40,544
908,823
-4.3%
-64,847
13
St. John the Baptist
New Orleans, LA
-39,592
3,547
-91.8%
10,852
14
Grand Forks
Grand Forks, ND-MN
-38,635
27,233
-58.7%
-403
15
Erie
Erie, PA
-38,486
242,201
-13.7%
644
16
Ashtabula
Cleveland-Lorain-Elyria, OH
-37,191
65,579
-36.2%
-1,249
17
Somerset
Johnstown, PA
-36,894
43,131
-46.1%
-1,261
18
Oneida
Utica-Rome, NY
-36,758
198,450
-15.6%
-18,352
19
Madison
Syracuse, NY
-36,066
33,378
-51.9%
4,187
20
Belmont
Wheeling, WV-OH
-34,829
35,290
-49.7%
-12,345
21
St. Louis
St. Louis, MO-IL
-33,793
312,975
-9.7%
-104,344
22
Orleans
Rochester, NY
-33,150
11,028
-75.0%
5,652
23
Acadia
Lafayette, LA
-32,338
26,489
-55.0%
2,196
24
Mercer
Sharon, PA
-32,073
88,113
-26.7%
-8,019
25
Ouachita
Monroe, LA
-31,782
115,441
-21.6%
7,569
26
Schoharie
Albany-Schenectady-Troy, NY
-30,077
1,516
-95.2%
1,886
27
Webster
Shreveport-Bossier City, LA
-29,886
11,849
-71.6%
-1,923
28
Rapides
Alexandria, LA
-28,461
97,952
-22.5%
-9,009
29
Douglas
Duluth-Superior, MN-WI
-28,419
14,988
-65.5%
-1,156
30
Livingston
Rochester, NY
-27,988
36,395
-43.5%
7,298
31
Plaquemines
New Orleans, LA
-26,746
0
-100%
645
32
Tioga
Binghamton, NY
-26,731
25,021
-51.7%
1,821
33
Ohio
Wheeling, WV-OH
-26,563
20,771
-56.1%
-14,010
34
Columbia
Scranton-Wilkes-Barre-Hazleton, PA
-26,520
37,585
-41.4%
2,036
35
Strafford
Boston-Worcester-Lawrence, MA-NH
-26,439
86,241
-23.5%
26,746
36
Carroll
Canton-Massillon, OH
-26,412
2,467
-91.5%
3,288
37
Chemung
Elmira, NY
-25,628
65,413
-28.1%
-6,402
38
Niagara
Buffalo-Niagara Falls, NY
-24,756
194,829
-11.3%
-7,476
39
Calhoun
Anniston, AL
-24,576
86,764
-22.1%
-8,676
40
Wayne
Rochester, NY
-24,492
69,276
-26.1%
8,996
41
Onondaga
Syracuse, NY
-24,414
434,030
-5.3%
-5,288
42
Morton
Bismarck, ND
-24,052
1,273
-95.0%
99
43
Wayne
Huntington-Ashland, WV-KY-OH
-23,953
18,958
-55.8%
-3,136
44
Polk
Grand Forks, ND-MN
-23,628
7,752
-75.3%
-3,423
45
Genesee
Rochester, NY
-23,423
36,901
-38.8%
828
46
Lawrence
Huntington-Ashland, WV-KY-OH
-21,975
40,308
-35.3%
-1,459
47
Sequoyah
Fort Smith, AR-OK
-21,946
17,114
-56.2%
8,282
48
Broome
Binghamton, NY
-21,465
178,829
-10.7%
-13,414
49
Philadelphia
Philadelphia, PA-NJ
-21,309
1,492,375
-1.4%
-171,750
50
St. James
New Orleans, LA
-21194
0
-100%
-370
12
TABLE III
Biggest Population Gain Counties (2020 Forecast)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
County Name
Maricopa
Los Angeles
Clark
Harris
Orange
Miami-Dade
Riverside
Broward
Dallas
San Diego
Queens
Cook
San Bernardino
Santa Clara
Tarrant
Palm Beach
Gwinnett
Collin
Orange
Travis
King
Kings
Alameda
Hidalgo
Wake
Pima
Bexar
Contra Costa
Mecklenburg
Fulton
Montgomery
Hillsborough
San Francisco
Fresno
Cobb
Sacramento
Denton
Kern
Bronx
San Mateo
El Paso
Salt Lake
Fort Bend
Will
Douglas
Ventura
San Joaquin
Washington
DeKalb
Washoe
Metropolitan Area
Phoenix-Mesa, AZ
Los Angeles-Long Beach, CA
Las Vegas, NV-AZ
Houston, TX
Orange County, CA
Miami, FL
Riverside-San Bernardino, CA
Fort Lauderdale, FL
Dallas, TX
San Diego, CA
New York, NY
Chicago, IL
Riverside-San Bernardino, CA
San Jose, CA
Fort Worth-Arlington, TX
West Palm Beach-Boca Raton, FL
Atlanta, GA
Dallas, TX
Orlando, FL
Austin-San Marcos, TX
Seattle-Bellevue-Everett, WA
New York, NY
Oakland, CA
McAllen-Edinburg-Mission, TX
Raleigh-Durham-Chapel Hill, NC
Tucson, AZ
San Antonio, TX
Oakland, CA
Charlotte-Gastonia-Rock Hill, NC-SC
Atlanta, GA
Washington, DC-MD-VA-WV
Tampa-St. Petersburg-Clearwater, FL
San Francisco, CA
Fresno, CA
Atlanta, GA
Sacramento, CA
Dallas, TX
Bakersfield, CA
New York, NY
San Francisco, CA
El Paso, TX
Salt Lake City-Ogden, UT
Houston, TX
Chicago, IL
Denver, CO
Ventura, CA
Stockton-Lodi, CA
Portland-Vancouver, OR-WA
Atlanta, GA
Reno, NV
Population
Gain
20202000
1,547,026
1,414,155
1,120,793
958,645
853,954
722,061
681,484
677,134
642,464
634,780
610,051
604,597
569,074
497,900
480,496
466,127
443,177
438,551
433,910
424,739
424,664
413,431
412,117
396,458
392,122
380,615
374,932
358,894
349,854
331,074
326,464
321,649
315,630
315,146
307,450
302,821
301,085
297,696
297,217
292,084
285,652
284,412
283,281
279,533
277,818
269,157
268,931
266,919
262,934
259,487
Forecast:
Population
in 2020
4,643,369
11,000,000
2,513,962
4,373,626
3,710,400
2,982,303
2,241,310
2,309,574
2,867,806
3,459,858
2,840,947
5,981,749
2,288,002
2,184,374
1,934,957
1,601,782
1,039,634
938,606
1,336,228
1,244,583
2,163,580
2,879,690
1,862,524
970,381
1,025,270
1,229,259
1,772,749
1,312,457
1,050,071
1,148,117
1,204,245
1,324,731
1,092,232
1,117,160
920,222
1,532,860
739,896
961,370
1,631,465
1,000,422
967,352
1,185,036
642,237
787,760
458,222
1,025,782
837,094
715,361
931,246
600,804
Gain as
Percentage
of 2000
Population
50.0%
14.8%
80.4%
28.1%
29.9%
31.9%
43.7%
41.5%
28.9%
22.5%
27.3%
11.2%
33.1%
29.5%
33.0%
41.0%
74.3%
87.7%
48.1%
51.8%
24.4%
16.8%
28.4%
69.1%
61.9%
44.8%
26.8%
37.6%
50.0%
40.5%
37.2%
32.1%
40.6%
39.3%
50.2%
24.6%
68.6%
44.9%
22.3%
41.2%
41.9%
31.6%
78.9%
55.0%
154.0%
35.6%
47.3%
59.5%
39.3%
76.0%
Population
Gain
20001980
1,575,503
2,038,470
923,984
976,442
908,379
617,110
890,353
606,378
659,694
949,458
336,600
128,274
815,972
384,959
586,130
549,913
427,113
353,516
427,576
397,846
462,391
232,473
340,476
287,383
329,792
312,864
402,676
294,780
293,722
224,253
295,728
351,249
95,772
284,335
312,184
441,764
293,360
257,267
165,845
119,896
197,989
276,617
225,689
183,253
154,785
223,798
217,859
200,680
184,219
145,945
13
Figure 2: Map: Expected Population Growth in Metropolitan Counties 2000-2020
Bellingham, WA
Seattle-Bellevue-Everett, WA
Bremerton, WA
Grand Forks, ND-MN
Spokane, WA
Duluth-Superior, MN-WI
Great Falls, MT
Olympia, WATacoma, WA
Yakima, WA Richland-Kennewick-Pasco, WA
Bismarck, ND
Missoula, MT
Portland-Vancouver, OR-WA
Fargo-Moorhead, ND-MN
Billings, MT
St. Cloud, MN
Bangor, ME
Salem, OR
Corvallis, OR
Rapid City, SD
Eugene-Springfield, OR
Boise City, ID
Casper, WY
Pocatello, ID
Medford-Ashland, OR
Cheyenne, WY
Redding, CA
Salt Lake City-Ogden, UT
Fort Collins-Loveland, CO
Reno, NV
Provo-Orem, UT
Chico-Paradise, CA
Yuba City, CASacramento, CA
Santa Rosa, CAYolo, CA
Vallejo-Fairfield-Napa, CA
Stockton-Lodi, CA
Oakland, CA Modesto, CA
San Jose, CAMerced, CA
Santa Cruz-Watsonville, CAFresno, CA
Greeley, CO
Boulder-Longmont, CO
Denver, CO
Grand Junction, CO
Colorado Springs, CO
Pueblo, CO
Las Vegas, NV-AZ
Flagstaff, AZ-UT
Joplin, MOSpringfield, MO
Enid, OK
Salinas, CAVisalia-Tulare-Porterville, CA
Santa Fe, NM
Albuquerque, NM
Bakersfield, CA
San Luis Obispo-Atascadero-Paso Robles, CA
Clarksville-Hopkinsville, TN-KY
Nashville, TN
Tulsa, OK
Fayetteville-Springdale-Rogers, AR Jonesboro, AR
Norfolk-Virginia Beach-Newport News, VA-NC
Greensboro-Winston-Salem-High Point, NC
Raleigh-Durham-Chapel Hill, NC
Knoxville, TNAsheville, NC
Rocky Mount, NC
Charlotte-Gastonia-Rock Hill, NC-SCGreenville, NC
Jackson, TN
Memphis, TN-AR-MS
Fort Smith, AR-OK
Fayetteville, NC
Greenville-Spartanburg-Anderson, SC
Little Rock-North Little Rock, AR Florence, ALHuntsville, AL
Jacksonville, NC
Decatur, AL
Pine Bluff, AR
Florence, SCWilmington, NC
Gadsden, ALAtlanta, GA Athens, GA
Myrtle
Beach,
SC
Sumter,
SC
Birmingham, ALAnniston, AL
Lubbock, TX
Wichita Falls, TX Sherman-Denison, TX
Columbia, SC
Texarkana, TX-Texarkana AR
Augusta-Aiken, GA-SC Charleston-North Charleston, SC
Tuscaloosa, AL
Shreveport-Bossier City, LA
Columbus, GA-AL
Fort Worth-Arlington, TXLongview-Marshall, TX
Auburn-Opelika, ALMacon, GA
Monroe, LA Jackson, MS
Dallas, TX Tyler, TX
Abilene, TX
Montgomery, AL
Savannah, GA
Odessa-Midland, TX
Albany, GA
Waco, TX
San Angelo, TX
Alexandria, LA
Hattiesburg, MS
Dothan, AL
Killeen-Temple, TX
Mobile, AL Pensacola, FLTallahassee, FL
Bryan-College Station, TX
Jacksonville, FL
Lafayette, LA
Biloxi-Gulfport-Pascagoula, MS Panama City, FL
Lake Charles, LA
Panama City, FL
Austin-San Marcos, TXHouston, TXBeaumont-Port Arthur, TX New Orleans, LA
Gainesville, FL
San Antonio, TX
Houma, LA
Ocala, FLDaytona Beach, FL
Brazoria, TX
Victoria, TX
Melbourne-Titusville-Palm Bay, FL
Melbourne-Titusville-Palm Bay, FL
Corpus Christi, TX
Tampa-St. Petersburg-Clearwater, FLOrlando, FL
Laredo, TX
Lakeland-Winter Haven, FL
Sarasota-Bradenton, FL
Punta Gorda, FLFort Pierce-Port St. Lucie, FL
West Palm Beach-Boca Raton, FL
McAllen-Edinburg-Mission, TX
Naples, FLFort Lauderdale, FL
Brownsville-Harlingen-San Benito, TX
Amarillo, TX
Oklahoma City, OK
Lawton, OK
Ventura, CA Los Angeles-Long Beach, CA
Riverside-San Bernardino, CA
Orange County, CA
Phoenix-Mesa, AZ
San Diego, CA
Yuma, AZ
Tucson, AZ
Las Cruces, NM
El Paso, TX
Population Gain
Burlington, VT
Minneapolis-St. Paul, MN-WIEau Claire, WIWausau, WI
Green Bay, WI
Lewiston-Auburn, ME
Appleton-Oshkosh-Neenah, WI
Rochester, MN
Portland, ME
Saginaw-Bay City-Midland, MI
Sheboygan, WI
Sioux Falls, SD
La Crosse, WI-MN
Syracuse, NY
Milwaukee-Waukesha, WI
Glens Falls, NY
Rochester, NYUtica-Rome, NY
Madison, WI
Flint, MI
Buffalo-Niagara Falls, NY Albany-Schenectady-Troy, NY
Waterloo-Cedar Falls, IA
Janesville-Beloit, WI
Ann Arbor, MIDetroit, MI
Dubuque, IARockford, IL
Sioux City, IA-NE
Pittsfield, MASpringfield, MA
Jamestown, NY
Jackson, MI
Binghamton, NY
Cedar Rapids, IA
Erie, PA
Chicago, IL Benton Harbor, MI
Dutchess County, NYNew London-Norwich, CT
Des
Moines,
IA
Iowa
City,
IA
Newburgh,
NY-PA
Toledo,
OH
Omaha, NE-IA
Gary, IN
Williamsport, PA Newark, NJ Hartford, CTBarnstable-Yarmouth, MA
Davenport-Moline-Rock Island, IA-IL
Akron, OHSharon, PA
Kankakee, IL
Fort Wayne, IN
State College, PA
Nassau-Suffolk, NY
Peoria-Pekin, IL
Lincoln, NE
Lima, OHMansfield, OH
Bloomington-Normal, IL
Muncie, IN
Canton-Massillon, OH Altoona, PA
Reading, PAMonmouth-Ocean, NJ
Champaign-Urbana, IL
Lancaster,
PA
Springfield,
IL
Dayton-Springfield, OH
St. Joseph, MO
Johnstown, PA
York, PA
Decatur, IL
Philadelphia, PA-NJ
Columbus, OH
Pittsburgh, PA
Terre Haute, IN
Baltimore, MD
Hagerstown, MD
Bloomington, IN Parkersburg-Marietta, WV-OH
Dover, DE Atlantic-Cape May, NJ
St.
Louis,
MO-IL
Topeka, KS
Columbia, MO
Huntington-Ashland, WV-KY-OH
Lawrence, KS
Charleston, WV
Washington, DC-MD-VA-WV
Evansville-Henderson, IN-KYLouisville, KY-INLexington, KY
Wichita, KS
Lynchburg, VA
Owensboro, KY
Roanoke, VARichmond-Petersburg, VA
Miami, FL
14
The map (Figure 2) displays the expected population growth for all metropolitan counties.
Since we are measuring overall population growth numbers, rather than percentage growth, the
Northeastern metropolitan counties are shown to expect considerable growth in numbers even if
percentage growth there will be relatively slow. Otherwise, growth will be concentrated in
California, Arizona, New Mexico, Florida, the greater Seattle metropolitan area, Salt Lake City, the
Denver North-South corridor, Texas, the Atlanta-Charlotte-Raleigh corridor, and the ChicagoMadison region.
Lastly, Table IV displays our population growth forecasts for all U.S. metropolitan areas
used in our analysis, based upon our county level forecasts and year 2000 MSA definitions. In this
case, we rank metropolitan areas according to their expected population gains (or losses). A small
number of major metropolitan areas are forecasted to lose population by 2020: New Orleans,
Syracuse, Rochester, Buffalo, Pittsburgh, and Youngstoung-Warren.
The central cities of many other Rustbelt MSAs will continue to lose population. However,
modest gains in their suburbs will offset further population decline from their MSAs.
Notwithstanding mild positive metropolitan area growth, the cities of Cleveland, Philadelphia,
Detroit, Milwaukee, New Haven, and Saint Louis are all expected to lag behind general U.S.
population growth patterns through 2020.
Atlanta, Chicago, Phoenix, New York, Dallas, Houston, Los Angeles, Orlando, and Denver,
are all predicted to experience substantial population inflows. However, if we look at percentage
growth in the biggest metropolitan areas, the forecasts single out Las Vegas, driven by good weather,
gambling, tourism and an easy lifestyle. The group of major metropolitan areas with very high
expected growth rates includes Phoenix, Dallas, Houston, Denver, Orlando, Charlotte, Austin, and
Raleigh-Durham-Chapel Hill, all of them in the Sunbelt.
15
CONCLUSION
Population growth at the county level can be predicted using widely available demographic and
economic data. Past recent growth, the presence of immigrants, the fraction of population older than
25 and younger than 65, low taxes, and good weather are all positively associated with population
growth. Our forecasts reveal that most growth and real estate development will occur in the West,
Sunbelt, and along the Southern I-85 route. However, our model only accounts for 75 percent of the
variance in growth experiences between 1980 and 2000, with the other 25 percent explained by
“surprise” events. Many unexpected places will be winners or losers in the game of future local real
estate development.
[A companion spreadsheet of our population predictions at the county level (metropolitan counties) is
available in the Working Paper section of the Zell-Lurie Real Estate Center Website]
16
TABLE IV
Forecasts for Metropolitan Areas
MSA
Code
MSA Name
5560
8160
8680
6840
3350
3400
3680
9000
7560
1280
2985
6280
3610
960
7000
2240
2360
2975
7610
5200
3880
220
2335
3580
450
7680
8080
1010
2340
1320
280
6020
1480
2180
9320
1900
733
6240
2650
4800
5990
6323
870
8050
4640
New Orleans, LA
Syracuse, NY
Utica-Rome, NY
Rochester, NY
Houma, LA
Huntington-Ashland, WV-KY-OH
Johnstown, PA
Wheeling, WV-OH
Scranton-Wilkes-Barre-Hazleton, PA
Buffalo-Niagara Falls, NY
Grand Forks, ND-MN
Pittsburgh, PA
Jamestown, NY
Binghamton, NY
St. Joseph, MO
Duluth-Superior, MN-WI
Erie, PA
Glens Falls, NY
Sharon, PA
Monroe, LA
Lafayette, LA
Alexandria, LA
Elmira, NY
Jackson, TN
Anniston, AL
Shreveport-Bossier City, LA
Steubenville-Weirton, OH-WV
Bismarck, ND
Enid, OK
Canton-Massillon, OH
Altoona, PA
Parkersburg-Marietta, WV-OH
Charleston, WV
Dothan, AL
Youngstown-Warren, OH
Cumberland, MD-WV
Bangor, ME
Pine Bluff, AR
Florence, AL
Mansfield, OH
Owensboro, KY
Pittsfield, MA
Benton Harbor, MI
State College, PA
Lynchburg, VA
Population Gain
2020-2000
Forecast:
Population in
2020
Gain as
Percentage
of 2000
Population
Population
Gain
2000-1980
-214,098
-179,839
-95,932
-91,710
-89,678
-88,634
-85,127
-73,815
-72,260
-65,300
-62,264
-61,107
-49,891
-48,197
-41,008
-39,580
-38,486
-32,592
-32,073
-31,782
-28,768
-28,461
-25,628
-24,707
-24,576
-23,940
-20,951
-20,688
-18,395
-16,106
-15,994
-15,946
-14,445
-11,659
-10,774
-10,504
-9,402
-7,693
-7,273
-6,860
-3,181
-2,750
-2,459
-1,221
-408
1,122,720
552,524
203,667
1,007,018
104,801
226,593
147,128
79,035
551,352
1,103,652
34,984
2,295,862
89,698
203,849
61,579
204,258
242,201
91,755
88,113
115,441
357,055
97,952
65,413
82,844
86,764
368,342
110,720
74,143
39,277
390,848
113,050
135,126
236,971
126,386
583,313
91,339
135,483
76,526
135,726
168,820
88,423
132,059
160,152
134,758
31,486
-16.02%
-24.56%
-32.02%
-8.35%
-46.11%
-28.12%
-36.65%
-48.29%
-11.59%
-5.59%
-64.03%
-2.59%
-35.74%
-19.12%
-39.97%
-16.23%
-13.71%
-26.21%
-26.69%
-21.59%
-7.46%
-22.51%
-28.15%
-22.97%
-22.07%
-6.10%
-15.91%
-21.82%
-31.90%
-3.96%
-12.39%
-10.56%
-5.75%
-8.45%
-1.81%
-10.31%
-6.49%
-9.13%
-5.09%
-3.90%
-3.47%
-2.04%
-1.51%
-0.90%
-1.28%
28,407
9,647
-20,624
66,504
16,162
-21,159
-32,017
-32,490
-35,478
-72,323
-3,826
-211,721
-7,372
-11,593
839
-22,633
644
14,622
-8,019
7,569
52,901
-9,009
-6,402
19,997
-8,676
14,338
-31,674
14,629
-5,507
2,589
-7,399
-6,776
-18,306
15,067
-49,736
-6,025
7,657
-6,503
7,677
-5,510
5,512
-10,272
-8,677
22,848
2,766
17
840
1350
6660
2030
9140
2880
2290
3605
4243
160
3960
8600
2620
2040
6690
3870
3285
8360
4420
5280
9080
7620
8003
4200
8940
1890
8750
3700
6340
6980
2520
920
6800
1560
860
5523
4320
2190
40
2200
8920
7200
1680
3710
7640
6403
8440
6600
8140
1580
3180
5140
8320
1400
8760
580
Beaumont-Port Arthur, TX
Casper, WY
Rapid City, SD
Decatur, AL
Williamsport, PA
Gadsden, AL
Eau Claire, WI
Jacksonville, NC
Lewiston-Auburn, ME
Albany-Schenectady-Troy, NY
Lake Charles, LA
Tuscaloosa, AL
Flagstaff, AZ-UT
Decatur, IL
Redding, CA
La Crosse, WI-MN
Hattiesburg, MS
Texarkana, TX-Texarkana AR
Longview-Marshall, TX
Muncie, IN
Wichita Falls, TX
Sheboygan, WI
Springfield, MA
Lawton, OK
Wausau, WI
Corvallis, OR
Victoria, TX
Jonesboro, AR
Pocatello, ID
St. Cloud, MN
Fargo-Moorhead, ND-MN
Biloxi-Gulfport-Pascagoula, MS
Roanoke, VA
Chattanooga, TN-GA
Bellingham, WA
New London-Norwich, CT
Lima, OH
Dover, DE
Abilene, TX
Dubuque, IA
Waterloo-Cedar Falls, IA
San Angelo, TX
Cleveland-Lorain-Elyria, OH
Joplin, MO
Sherman-Denison, TX
Portland, ME
Topeka, KS
Racine, WI
Sumter, SC
Cheyenne, WY
Hagerstown, MD
Missoula, MT
Terre Haute, IN
Champaign-Urbana, IL
Vineland-Millville-Bridgeton, NJ
Auburn-Opelika, AL
-392
703
788
1,420
1,433
1,475
1,881
1,881
1,882
2,442
4,423
5,708
5,737
7,833
8,631
8,783
9,167
9,616
10,000
11,638
12,167
13,685
17,322
18,354
18,751
19,450
19,492
20,235
20,306
20,384
20,567
21,056
21,649
21,785
22,245
22,822
24,528
25,697
25,700
26,027
26,620
27,165
27,723
27,996
28,292
28,451
28,967
29,126
29,953
30,914
32,076
32,153
32,857
33,029
33,559
33,587
384,345
67,253
89,559
147,481
121,368
104,775
150,490
152,104
105,747
878,789
187,943
170,773
128,455
122,316
172,457
135,790
121,271
139,348
218,747
130,312
152,500
126,439
626,271
132,918
144,653
97,609
103,509
102,721
95,888
188,460
195,253
385,880
52,215
487,506
189,847
282,282
179,689
152,793
152,162
115,285
154,522
131,107
2,278,531
185,698
139,304
294,410
199,014
218,111
134,694
112,623
164,188
128,235
181,911
212,994
179,933
149,045
-0.10%
1.06%
0.89%
0.97%
1.19%
1.43%
1.27%
1.25%
1.81%
0.28%
2.41%
3.46%
4.67%
6.84%
5.27%
6.92%
8.18%
7.41%
4.79%
9.81%
8.67%
12.14%
2.84%
16.02%
14.89%
24.88%
23.20%
24.53%
26.87%
12.13%
11.77%
5.77%
70.83%
4.68%
13.27%
8.80%
15.81%
20.22%
20.32%
29.16%
20.81%
26.14%
1.23%
17.75%
25.49%
10.70%
17.03%
15.41%
28.60%
37.83%
24.28%
33.46%
22.04%
18.35%
22.93%
29.09%
9,940
-5,973
18,287
25,626
1,664
188
17,204
36,708
4,334
51,127
15,466
27,234
43,101
-16,722
47,236
17,311
21,665
16,353
27,002
-9,720
11,442
11,847
26,257
1,643
14,656
9,688
14,640
19,182
9,932
34,294
36,707
63,706
7,255
46,910
60,380
20,232
308
28,816
14,645
-4,443
-10,111
18,619
-25,649
29,857
20,894
49,563
14,913
16,006
16,005
12,715
19,108
19,967
-6,247
11,085
13,286
38,848
18
2720
3740
3040
1660
8400
1740
6015
3520
6580
743
3850
7800
3800
1880
2281
1020
4600
5910
1260
4150
2980
880
5160
6960
6120
2400
8800
2995
3620
3720
1040
8640
1620
2640
7880
3660
2655
2750
3080
3150
1303
2960
240
760
1150
2920
3500
6680
80
5240
6820
3240
2000
560
120
6895
Fort Smith, AR-OK
Kankakee, IL
Great Falls, MT
Clarksville-Hopkinsville, TN-KY
Toledo, OH
Columbia, MO
Panama City, FL
Jackson, MI
Punta Gorda, FL
Barnstable-Yarmouth, MA
Kokomo, IN
South Bend, IN
Kenosha, WI
Corpus Christi, TX
Dutchess County, NY
Bloomington, IN
Lubbock, TX
Olympia, WA
Bryan-College Station, TX
Lawrence, KS
Goldsboro, NC
Billings, MT
Mobile, AL
Saginaw-Bay City-Midland, MI
Peoria-Pekin, IL
Eugene-Springfield, OR
Waco, TX
Grand Junction, CO
Janesville-Beloit, WI
Kalamazoo-Battle Creek, MI
Bloomington-Normal, IL
Tyler, TX
Chico-Paradise, CA
Flint, MI
Springfield, IL
Johnson City-Kingsport-Bristol, TN-VA
Florence, SC
Fort Walton Beach, FL
Green Bay, WI
Greenville, NC
Burlington, VT
Gary, IN
Allentown-Bethlehem-Easton, PA
Baton Rouge, LA
Bremerton, WA
Galveston-Texas City, TX
Iowa City, IA
Reading, PA
Akron, OH
Montgomery, AL
Rochester, MN
Harrisburg-Lebanon-Carlisle, PA
Dayton-Springfield, OH
Atlantic-Cape May, NJ
Albany, GA
Rocky Mount, NC
34,256
34,494
34,777
35,214
35,767
36,451
36,533
36,797
36,808
38,089
38,952
40,572
41,300
41,498
42,223
42,526
42,590
42,768
43,073
44,253
44,584
45,375
46,107
47,027
48,334
48,683
48,814
49,649
49,866
50,234
50,680
51,100
51,120
51,640
55,160
56,740
59,507
60,088
60,104
60,770
60,991
61,366
61,877
64,219
64,615
66,336
67,275
67,888
69,506
69,693
69,929
73,652
73,749
75,220
75,257
75,297
242,220
138,371
114,960
242,826
654,012
172,276
184,776
195,527
179,076
261,331
140,478
306,433
191,369
422,188
323,059
163,219
285,480
251,132
195,875
144,435
157,914
174,928
587,572
450,122
395,520
372,096
262,815
167,124
202,404
503,553
201,556
226,508
254,890
488,593
256,724
468,693
185,300
231,048
287,361
194,859
260,487
693,187
700,751
668,478
297,141
317,061
178,637
442,355
765,426
403,185
194,773
703,441
1,024,036
430,570
196,045
218,350
16.47%
33.21%
43.37%
16.96%
5.79%
26.84%
24.64%
23.18%
25.87%
17.06%
38.37%
15.26%
27.52%
10.90%
15.03%
35.23%
17.53%
20.53%
28.19%
44.17%
39.34%
35.02%
8.52%
11.67%
13.92%
15.05%
22.81%
42.26%
32.69%
11.08%
33.59%
29.13%
25.09%
11.82%
27.37%
13.77%
47.31%
35.15%
26.45%
45.32%
30.57%
9.71%
9.69%
10.63%
27.79%
26.46%
60.41%
18.13%
9.99%
20.90%
56.01%
11.69%
7.76%
21.17%
62.31%
52.64%
44,880
991
-444
57,025
1,080
35,049
49,925
7,176
82,796
74,395
-2,033
24,509
27,126
52,875
35,499
21,486
30,773
83,039
57,397
32,137
16,016
20,977
96,328
-18,197
-18,716
47,705
42,547
34,679
13,269
31,992
31,522
46,092
58,942
-12,178
13,784
42,787
15,272
60,281
51,522
43,509
44,066
-9,798
87,073
107,056
83,764
53,697
29,213
61,451
35,586
60,187
32,491
72,233
8,136
78,030
7,942
19,590
19
2440
1540
9280
3440
2330
2560
1360
4040
3200
6560
5660
7920
480
1145
3840
8560
320
7760
3920
1960
7460
4000
4360
4890
2900
1800
8480
5800
3560
5330
9200
7840
7520
9260
6080
5790
7720
460
500
3060
2670
2710
4680
2760
7080
4720
1000
3810
9040
4900
1440
8200
9160
7485
3283
6520
Evansville-Henderson, IN-KY
Charlottesville, VA
York, PA
Huntsville, AL
Elkhart-Goshen, IN
Fayetteville, NC
Cedar Rapids, IA
Lansing-East Lansing, MI
Hamilton-Middletown, OH
Pueblo, CO
Newburgh, NY-PA
Springfield, MO
Asheville, NC
Brazoria, TX
Knoxville, TN
Tulsa, OK
Amarillo, TX
Sioux Falls, SD
Lafayette, IN
Davenport-Moline-Rock Island, IA-IL
San Luis Obispo-Atascadero-Paso Robles, CA
Lancaster, PA
Lincoln, NE
Medford-Ashland, OR
Gainesville, FL
Columbus, GA-AL
Trenton, NJ
Odessa-Midland, TX
Jackson, MS
Myrtle Beach, SC
Wilmington, NC
Spokane, WA
Savannah, GA
Yakima, WA
Pensacola, FL
Ocala, FL
Sioux City, IA-NE
Appleton-Oshkosh-Neenah, WI
Athens, GA
Greeley, CO
Fort Collins-Loveland, CO
Fort Pierce-Port St. Lucie, FL
Macon, GA
Fort Wayne, IN
Salem, OR
Madison, WI
Birmingham, AL
Killeen-Temple, TX
Wichita, KS
Melbourne-Titusville-Palm Bay, FL
Charleston-North Charleston, SC
Tacoma, WA
Wilmington-Newark, DE-MD
Santa Cruz-Watsonville, CA
Hartford, CT
Provo-Orem, UT
75,351
75,660
76,931
77,994
78,776
79,882
80,226
80,470
80,717
81,719
82,869
84,046
84,180
89,454
89,876
90,396
90,801
90,809
91,505
95,229
96,291
96,896
97,268
103,137
107,987
108,793
113,600
113,645
114,708
115,446
116,943
118,094
119,243
122,911
125,081
126,184
126,855
128,178
132,142
133,687
134,548
134,951
135,454
136,513
138,556
140,455
143,657
143,878
144,657
145,575
151,196
151,866
156,082
156,829
157,093
160,268
371,650
111,255
459,651
421,489
262,283
382,720
272,448
528,894
414,402
223,557
472,546
410,871
310,850
332,674
779,010
895,332
309,186
264,347
274,816
454,124
343,969
568,550
348,458
284,977
326,282
383,768
465,180
349,877
556,539
313,473
351,356
536,767
412,557
345,620
537,832
386,474
250,982
487,724
286,186
316,851
387,486
455,511
458,665
639,661
486,786
568,854
1,066,520
458,080
690,690
623,407
701,561
855,827
744,400
412,611
1,307,965
531,128
25.43%
212.56%
20.10%
22.71%
42.93%
26.38%
41.74%
17.95%
24.19%
57.61%
21.27%
25.72%
37.14%
36.78%
13.04%
11.23%
41.58%
52.33%
49.92%
26.53%
38.88%
20.54%
38.72%
56.72%
49.47%
39.56%
32.31%
48.11%
25.96%
58.30%
49.89%
28.21%
40.65%
55.19%
30.30%
48.48%
102.20%
35.65%
85.78%
72.99%
53.19%
42.10%
41.91%
27.13%
39.79%
32.79%
15.57%
45.79%
26.49%
30.47%
27.47%
21.57%
26.53%
61.31%
13.65%
43.22%
19,832
17,614
69,121
99,875
46,215
54,942
22,572
28,315
74,153
15,826
110,789
98,242
48,544
72,371
140,732
143,935
43,855
49,967
29,799
-26,173
90,892
108,234
57,591
48,911
66,072
20,348
43,784
35,223
78,649
95,558
94,427
75,734
61,623
49,591
120,921
136,072
6,502
67,908
48,677
59,397
102,847
166,790
49,550
58,955
97,381
104,045
107,179
98,244
101,636
202,168
116,750
215,207
129,200
66,477
97,414
150,967
20
9340
9270
6483
8240
7480
3980
8780
5880
6880
2700
4400
4100
1125
1240
2580
4280
5080
7510
2020
7500
3290
6740
2120
600
4080
4940
5345
1720
3000
4520
7490
5483
7120
1760
5720
4920
9360
5190
3640
5920
5170
6720
440
8120
8735
2320
680
1080
8720
7040
3160
5380
8520
4880
875
6760
Yuba City, CA
Yolo, CA
Providence-Warwick-Pawtucket, RI
Tallahassee, FL
Santa Barbara-Santa Maria-Lompoc, CA
Lakeland-Winter Haven, FL
Visalia-Tulare-Porterville, CA
Oklahoma City, OK
Rockford, IL
Fort Myers-Cape Coral, FL
Little Rock-North Little Rock, AR
Las Cruces, NM
Boulder-Longmont, CO
Brownsville-Harlingen-San Benito, TX
Fayetteville-Springdale-Rogers, AR
Lexington, KY
Milwaukee-Waukesha, WI
Sarasota-Bradenton, FL
Daytona Beach, FL
Santa Rosa, CA
Hickory-Morganton-Lenoir, NC
Richland-Kennewick-Pasco, WA
Des Moines, IA
Augusta-Aiken, GA-SC
Laredo, TX
Merced, CA
Naples, FL
Colorado Springs, CO
Grand Rapids-Muskegon-Holland, MI
Louisville, KY-IN
Santa Fe, NM
New Haven-Bridgeport-Stamford, CT
Salinas, CA
Columbia, SC
Norfolk-Virginia Beach-Newport News, VA-NC
Memphis, TN-AR-MS
Yuma, AZ
Monmouth-Ocean, NJ
Jersey City, NJ
Omaha, NE-IA
Modesto, CA
Reno, NV
Ann Arbor, MI
Stockton-Lodi, CA
Ventura, CA
El Paso, TX
Bakersfield, CA
Boise City, ID
Vallejo-Fairfield-Napa, CA
St. Louis, MO-IL
Greenville-Spartanburg-Anderson, SC
Nassau-Suffolk, NY
Tucson, AZ
McAllen-Edinburg-Mission, TX
Bergen-Passaic, NJ
Richmond-Petersburg, VA
161,321
167,084
167,874
169,427
170,907
173,954
174,369
176,642
177,111
177,526
180,592
180,911
180,944
181,585
182,824
183,128
188,685
197,219
199,448
200,535
204,666
205,560
206,664
213,036
213,157
215,010
217,819
222,520
223,359
223,650
224,125
225,394
228,776
229,297
230,045
230,618
236,192
242,004
242,171
244,940
247,120
259,487
261,703
268,931
269,157
285,652
297,696
317,238
331,254
337,622
366,869
369,709
380,615
396,458
399,718
406,660
300,829
336,850
1,132,863
454,505
570,653
659,358
543,214
1,262,292
549,290
621,315
765,784
355,891
473,917
518,369
496,307
663,890
1,690,777
789,935
694,990
660,873
547,659
398,206
664,268
691,067
407,818
426,643
471,890
742,011
1,315,681
1,251,377
372,235
1,934,985
631,809
767,509
1,013,710
1,369,085
396,899
1,372,732
851,548
963,811
696,910
600,804
843,700
837,094
1,025,782
967,352
961,370
753,305
853,006
2,944,132
1,332,334
3,130,120
1,229,259
970,381
1,776,350
1,077,913
115.64%
98.42%
17.40%
59.43%
42.75%
35.84%
47.27%
16.27%
47.59%
40.00%
30.86%
103.39%
61.76%
53.92%
58.32%
38.09%
12.56%
33.27%
40.25%
43.56%
59.67%
106.70%
45.16%
44.57%
109.50%
101.60%
85.73%
42.83%
20.45%
21.76%
151.32%
13.18%
56.76%
42.60%
29.35%
20.26%
146.97%
21.40%
39.74%
34.07%
54.94%
76.03%
44.97%
47.33%
35.57%
41.90%
44.86%
72.75%
63.49%
12.95%
38.00%
13.39%
44.85%
69.08%
29.04%
60.58%
37,120
55,975
97,869
93,533
99,555
161,366
121,419
219,202
45,928
235,739
109,751
77,968
102,038
124,840
134,430
109,151
105,433
237,992
222,894
158,752
71,925
47,131
89,491
113,691
94,180
76,008
166,567
207,448
249,302
73,783
54,646
138,822
110,627
126,531
295,293
200,074
69,314
278,713
50,785
112,370
181,938
145,945
126,842
217,859
223,798
197,989
257,267
178,033
184,965
191,189
217,831
154,617
312,864
287,383
82,277
256,517
21
5360
7160
3600
1640
200
1840
5015
3760
8960
720
2840
5640
7400
2160
6920
7240
8280
3120
6160
7320
2800
3480
7600
2680
5000
5775
7360
6440
6640
640
5945
1520
1123
5960
5120
2080
6780
4120
8840
4480
3360
1920
6200
1600
5600
520
Nashville, TN
Salt Lake City-Ogden, UT
Jacksonville, FL
Cincinnati, OH-KY-IN
Albuquerque, NM
Columbus, OH
Middlesex-Somerset-Hunterdon, NJ
Kansas City, MO-KS
West Palm Beach-Boca Raton, FL
Baltimore, MD
Fresno, CA
Newark, NJ
San Jose, CA
Detroit, MI
Sacramento, CA
San Antonio, TX
Tampa-St. Petersburg-Clearwater, FL
Greensboro-Winston-Salem-High Point, NC
Philadelphia, PA-NJ
San Diego, CA
Fort Worth-Arlington, TX
Indianapolis, IN
Seattle-Bellevue-Everett, WA
Fort Lauderdale, FL
Miami, FL
Oakland, CA
San Francisco, CA
Portland-Vancouver, OR-WA
Raleigh-Durham-Chapel Hill, NC
Austin-San Marcos, TX
Orange County, CA
Charlotte-Gastonia-Rock Hill, NC-SC
Boston-Worcester-Lawrence-Lowell, MA-NH
Orlando, FL
Minneapolis-St. Paul, MN-WI
Denver, CO
Riverside-San Bernardino, CA
Las Vegas, NV-AZ
Washington, DC-MD-VA-WV
Los Angeles-Long Beach, CA
Houston, TX
Dallas, TX
Phoenix-Mesa, AZ
Chicago, IL
New York, NY
Atlanta, GA
416,861
420,031
421,801
425,905
437,323
447,346
447,355
455,683
466,127
467,127
478,510
491,983
497,900
531,480
538,971
560,660
587,691
600,863
604,153
634,780
652,796
662,227
667,900
677,134
722,061
771,010
776,304
834,721
837,397
843,168
853,954
884,449
927,833
932,809
1,062,726
1,161,122
1,250,558
1,353,348
1,389,616
1,414,155
1,552,407
1,624,924
1,739,038
1,912,411
1,969,280
2,653,713
1,652,993
1,758,405
1,525,501
2,076,104
1,151,941
1,993,370
1,621,940
2,238,064
1,601,782
3,024,426
1,404,155
2,526,593
2,184,374
4,977,649
2,177,470
2,159,863
2,991,828
1,856,583
5,708,962
3,459,858
2,366,071
2,275,157
3,087,827
2,309,574
2,982,303
3,174,980
2,508,855
2,760,577
2,031,703
2,107,774
3,710,400
2,393,562
7,000,417
2,589,134
4,043,656
3,283,739
4,529,312
2,935,680
4,762,682
11,000,000
5,753,543
5,168,165
5,016,813
10,200,000
11,300,000
6,798,925
33.72%
31.38%
38.22%
25.81%
61.20%
28.94%
38.09%
25.57%
41.04%
18.27%
51.69%
24.18%
29.52%
11.95%
32.89%
35.06%
24.44%
47.85%
11.83%
22.47%
38.10%
41.06%
27.60%
41.48%
31.95%
32.07%
44.81%
43.34%
70.12%
66.67%
29.90%
58.61%
15.28%
56.32%
35.65%
54.70%
38.14%
85.53%
41.20%
14.82%
36.95%
45.86%
53.06%
23.06%
21.11%
64.02%
383,221
421,078
377,695
181,274
196,532
328,268
286,127
330,831
549,913
353,914
344,006
71,462
384,959
72,320
645,335
503,432
777,162
302,124
319,909
949,458
713,971
305,763
758,548
606,378
617,110
635,256
240,335
587,047
526,350
675,024
908,379
533,119
725,956
843,100
774,385
683,792
1,706,325
1,047,239
832,437
2,038,470
1,414,017
1,472,984
1,665,593
1,044,858
1,044,813
1,898,202
22
REFERENCES
Davis, Donald R., and David E. Weinstein (2002). “Bones, Bombs, and Break Points: The
Geography of Economic Activity,” American Economic Review 92(5): 1269–1289.
Glaeser, Edward L., and Joseph Gyourko (2005). “Urban Decline and Durable Housing,” Journal of
Political Economy 113: 345-375.
Glaeser, Edward L., Jed Kolko, and Albert Saiz (2001). “Consumer City,” Journal of Economic
Geography 1: 27-50.
Glaeser, Edward L., and Albert Saiz (2003). “The Rise of the Skilled City,” Discussion Paper #2050,
Harvard Institute of Economic Research, Harvard University.
23
County FIPS
48441
39153
39133
13177
13095
36091
36001
36083
36093
36057
36095
35001
35061
35043
22079
42077
42095
42025
42013
48375
48381
26161
26093
26091
1015
55087
55139
55015
37021
37115
13059
13219
13195
13135
13121
13067
13089
13117
13151
13063
13057
13223
13077
13113
13013
County Name
Taylor
Summit
Portage
Lee
Dougherty
Saratoga
Albany
Rensselaer
Schenectady
Montgomery
Schoharie
Bernalillo
Valencia
Sandoval
Rapides
Lehigh
Northampton
Carbon
Blair
Potter
Randall
Washtenaw
Livingston
Lenawee
Calhoun
Outagamie
Winnebago
Calumet
Buncombe
Madison
Clarke
Oconee
Madison
Gwinnett
Fulton
Cobb
DeKalb
Forsyth
Henry
Clayton
Cherokee
Paulding
Coweta
Fayette
Barrow
MSA Name
Abilene, TX (MSA)
Akron, OH (PMSA)
Akron, OH (PMSA)
Albany, GA (MSA)
Albany, GA (MSA)
Albany-Schenectady-Troy, NY (MSA)
Albany-Schenectady-Troy, NY (MSA)
Albany-Schenectady-Troy, NY (MSA)
Albany-Schenectady-Troy, NY (MSA)
Albany-Schenectady-Troy, NY (MSA)
Albany-Schenectady-Troy, NY (MSA)
Albuquerque, NM (MSA)
Albuquerque, NM (MSA)
Albuquerque, NM (MSA)
Alexandria, LA (MSA)
Allentown-Bethlehem-Easton, PA (MSA)
Allentown-Bethlehem-Easton, PA (MSA)
Allentown-Bethlehem-Easton, PA (MSA)
Altoona, PA (MSA)
Amarillo, TX (MSA)
Amarillo, TX (MSA)
Ann Arbor, MI (PMSA)
Ann Arbor, MI (PMSA)
Ann Arbor, MI (PMSA)
Anniston, AL (MSA)
Appleton-Oshkosh-Neenah, WI (MSA)
Appleton-Oshkosh-Neenah, WI (MSA)
Appleton-Oshkosh-Neenah, WI (MSA)
Asheville, NC (MSA)
Asheville, NC (MSA)
Athens, GA (MSA)
Athens, GA (MSA)
Athens, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Gain as
Percentage of
2000
Population Gain Population in
Population Gain 20202000-1980
2000
2000
Forecast: Population in 2020 Population
25,700
152,162
20.3%
14,645
126,462
62,624
606,176
11.5%
19,457
543,552
6,882
159,250
4.5%
16,129
152,368
61,622
86,511
247.6%
13,046
24,889
13,635
109,534
14.2%
-5,104
95,899
34,999
236,450
17.4%
47,413
201,451
13,482
308,059
4.6%
8,404
294,577
4,866
157,473
3.2%
610
152,607
246
146,687
0.2%
-3,467
146,441
-21,073
28,604
-42.4%
-3,719
49,677
-30,077
1,516
-95.2%
1,886
31,593
213,425
770,970
38.3%
135,250
557,545
112,412
178,867
169.2%
5,327
66,455
111,486
202,104
123.0%
55,955
90,618
-28,461
97,952
-22.5%
-9,009
126,413
41,801
354,376
13.4%
39,797
312,575
36,432
303,900
13.6%
41,814
267,468
-16,356
42,474
-27.8%
5,462
58,830
-15,994
113,050
-12.4%
-7,399
129,044
57,655
171,371
50.7%
14,636
113,716
33,146
137,815
31.7%
29,219
104,669
131,777
456,195
40.6%
60,018
324,418
95,069
253,539
60.0%
57,809
158,470
34,858
133,967
35.2%
9,015
99,109
-24,576
86,764
-22.1%
-8,676
111,340
54,423
216,214
33.6%
32,858
161,791
44,912
201,940
28.6%
25,282
157,028
28,842
69,569
70.8%
9,768
40,727
80,248
287,217
38.8%
45,737
206,969
3,932
23,633
20.0%
2,807
19,701
53,135
154,915
52.2%
26,747
101,780
47,667
74,073
180.5%
13,881
26,406
31,341
57,199
121.2%
8,049
25,858
443,177
1,039,634
74.3%
427,113
596,457
331,074
1,148,117
40.5%
224,253
817,043
307,450
920,222
50.2%
312,184
612,772
262,934
931,246
39.3%
184,219
668,312
165,304
265,820
164.5%
72,382
100,516
152,220
273,848
125.2%
85,140
121,628
137,344
375,844
57.6%
87,252
238,500
133,534
277,317
92.9%
91,538
143,783
111,802
194,837
134.6%
56,736
83,035
101,088
191,230
112.1%
50,656
90,142
84,901
177,014
92.2%
62,575
92,113
60,800
107,354
130.6%
25,072
46,554
13297
13097
13217
13247
13015
13227
13045
13255
34001
34009
1081
13073
45003
45037
13245
13189
48453
48491
48021
48209
48055
6029
24027
24005
24003
24025
24013
24035
24510
23019
25001
22033
22063
22005
22121
48245
48199
48361
53073
26021
34003
34031
30111
28047
28059
28045
36007
36107
1117
1073
Walton
Douglas
Newton
Rockdale
Bartow
Pickens
Carroll
Spalding
Atlantic
Cape May
Lee
Columbia
Aiken
Edgefield
Richmond
McDuffie
Travis
Williamson
Bastrop
Hays
Caldwell
Kern
Howard
Baltimore
Anne Arundel
Harford
Carroll
Queen Anne's
Baltimore (Independent City)
Penobscot
Barnstable
East Baton Rouge
Livingston
Ascension
West Baton Rouge
Jefferson
Hardin
Orange
Whatcom
Berrien
Bergen
Passaic
Yellowstone
Harrison
Jackson
Hancock
Broome
Tioga
Shelby
Jefferson
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlanta, GA (MSA)
Atlantic-Cape May, NJ (PMSA)
Atlantic-Cape May, NJ (PMSA)
Auburn-Opelika, AL (MSA)
Augusta-Aiken, GA-SC (MSA)
Augusta-Aiken, GA-SC (MSA)
Augusta-Aiken, GA-SC (MSA)
Augusta-Aiken, GA-SC (MSA)
Augusta-Aiken, GA-SC (MSA)
Austin-San Marcos, TX (MSA)
Austin-San Marcos, TX (MSA)
Austin-San Marcos, TX (MSA)
Austin-San Marcos, TX (MSA)
Austin-San Marcos, TX (MSA)
Bakersfield, CA (MSA)
Baltimore, MD (PMSA)
Baltimore, MD (PMSA)
Baltimore, MD (PMSA)
Baltimore, MD (PMSA)
Baltimore, MD (PMSA)
Baltimore, MD (PMSA)
Baltimore, MD (PMSA)
Bangor, ME (NECMA)
Barnstable-Yarmouth, MA (NECMA)
Baton Rouge, LA (MSA)
Baton Rouge, LA (MSA)
Baton Rouge, LA (MSA)
Baton Rouge, LA (MSA)
Beaumont-Port Arthur, TX (MSA)
Beaumont-Port Arthur, TX (MSA)
Beaumont-Port Arthur, TX (MSA)
Bellingham, WA (MSA)
Benton Harbor, MI (MSA)
Bergen-Passaic, NJ (PMSA)
Bergen-Passaic, NJ (PMSA)
Billings, MT (MSA)
Biloxi-Gulfport-Pascagoula, MS (MSA)
Biloxi-Gulfport-Pascagoula, MS (MSA)
Biloxi-Gulfport-Pascagoula, MS (MSA)
Binghamton, NY (MSA)
Binghamton, NY (MSA)
Birmingham, AL (MSA)
Birmingham, AL (MSA)
59,097
58,985
58,668
51,826
50,389
45,633
29,265
8,225
82,198
-6,977
33,587
76,074
67,878
37,350
34,780
-3,045
424,739
243,679
81,805
62,752
30,194
297,696
168,351
138,606
120,320
64,402
58,544
8,511
-91,607
-9,402
38,089
31,719
24,578
18,391
-10,469
10,356
5,787
-16,536
22,245
-2,459
231,466
168,252
45,375
25,789
5,059
-9,791
-21,465
-26,731
83,802
23,234
120,720
151,693
121,579
122,369
127,092
69,000
117,292
66,700
335,247
95,324
149,045
165,904
210,658
61,949
234,325
18,232
1,244,583
498,647
140,116
161,754
62,676
961,370
417,918
894,523
611,703
283,893
210,156
49,283
556,950
135,483
261,331
444,485
117,149
95,748
11,096
261,996
53,923
68,426
189,847
160,152
1,117,196
659,154
174,928
215,452
136,939
33,489
178,829
25,021
228,326
685,253
95.9%
63.6%
93.3%
73.5%
65.7%
195.3%
33.2%
14.1%
32.5%
-6.8%
29.1%
84.7%
47.5%
151.8%
17.4%
-14.3%
51.8%
95.6%
140.3%
63.4%
93.0%
44.9%
67.5%
18.3%
24.5%
29.3%
38.6%
20.9%
-14.1%
-6.5%
17.1%
7.7%
26.6%
23.8%
-48.5%
4.1%
12.0%
-19.5%
13.3%
-1.5%
26.1%
34.3%
35.0%
13.6%
3.8%
-22.6%
-10.7%
-51.7%
58.0%
3.5%
30,420
37,845
27,937
33,698
35,802
11,666
31,366
10,348
58,483
19,547
38,848
49,361
36,823
7,042
17,777
2,688
397,846
177,338
33,229
57,908
8,703
257,267
129,712
100,039
118,968
73,097
54,759
15,090
-137,751
7,657
74,395
44,483
33,215
26,933
2,425
2,121
7,245
574
60,380
-8,677
39,826
42,451
20,977
31,401
13,733
18,572
-13,414
1,821
77,607
-8,915
61,623
92,708
62,911
70,543
76,703
23,367
88,027
58,475
253,049
102,301
115,458
89,830
142,780
24,599
199,545
21,277
819,844
254,968
58,311
99,001
32,482
663,674
249,567
755,917
491,383
219,491
151,612
40,772
648,557
144,885
223,242
412,766
92,571
77,357
21,565
251,639
48,136
84,962
167,602
162,611
885,730
490,902
129,553
189,663
131,880
43,280
200,294
51,752
144,524
662,019
1115
1009
38015
38059
18105
17113
16001
16027
25017
25025
25009
25027
25021
25005
33011
25023
33015
33017
8013
48039
53035
48061
48041
36063
36029
50007
50013
50011
39151
39019
56025
19113
17019
54079
54039
45035
45019
45015
37119
37179
45091
37025
37109
37071
37159
51065
51079
47065
13047
13295
St. Clair
Blount
Burleigh
Morton
Monroe
McLean
Ada
Canyon
Middlesex
Suffolk
Essex
Worcester
Norfolk
Bristol
Hillsborough
Plymouth
Rockingham
Strafford
Boulder
Brazoria
Kitsap
Cameron
Brazos
Niagara
Erie
Chittenden
Grand Isle
Franklin
Stark
Carroll
Natrona
Linn
Champaign
Putnam
Kanawha
Dorchester
Charleston
Berkeley
Mecklenburg
Union
York
Cabarrus
Lincoln
Gaston
Rowan
Fluvanna
Greene
Hamilton
Catoosa
Walker
Birmingham, AL (MSA)
Birmingham, AL (MSA)
Bismarck, ND (MSA)
Bismarck, ND (MSA)
Bloomington, IN (MSA)
Bloomington-Normal, IL (MSA)
Boise City, ID (MSA)
Boise City, ID (MSA)
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boston-Worcester-Lawrence-Lowell-Brocktn, M
Boulder-Longmont, CO (PMSA)
Brazoria, TX (PMSA)
Bremerton, WA (PMSA)
Brownsville-Harlingen-San Benito, TX (MSA)
Bryan-College Station, TX (MSA)
Buffalo-Niagara Falls, NY (MSA)
Buffalo-Niagara Falls, NY (MSA)
Burlington, VT (NECMA)
Burlington, VT (NECMA)
Burlington, VT (NECMA)
Canton-Massillon, OH (MSA)
Canton-Massillon, OH (MSA)
Casper, WY (MSA)
Cedar Rapids, IA (MSA)
Champaign-Urbana, IL (MSA)
Charleston, WV (MSA)
Charleston, WV (MSA)
Charleston-North Charleston, SC (MSA)
Charleston-North Charleston, SC (MSA)
Charleston-North Charleston, SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlotte-Gastonia-Rock Hill, NC-SC (MSA)
Charlottesville, VA (MSA)
Charlottesville, VA (MSA)
Chattanooga, TN-GA (MSA)
Chattanooga, TN-GA (MSA)
Chattanooga, TN-GA (MSA)
18,539
18,081
3,363
-24,052
42,526
50,680
194,897
122,341
211,526
139,034
127,492
109,305
96,337
86,318
78,617
77,509
28,135
-26,439
180,944
89,454
64,615
181,585
43,073
-24,756
-40,544
34,689
17,085
9,217
10,307
-26,412
703
80,226
33,029
1,403
-15,848
65,286
43,731
42,179
349,854
124,502
105,425
101,448
74,144
66,664
62,413
44,608
31,052
28,334
21,716
1,900
83,650
69,290
72,869
1,273
163,219
201,556
497,889
255,416
1,680,561
829,081
852,925
861,947
747,585
622,337
460,945
551,951
306,845
86,241
473,917
332,674
297,141
518,369
195,875
194,829
908,823
181,655
24,020
54,812
388,382
2,467
67,253
272,448
212,994
53,147
183,824
161,994
354,400
185,167
1,050,071
250,148
271,122
233,662
138,183
257,316
193,061
64,841
46,414
336,314
75,376
63,022
28.5%
35.3%
4.8%
-95.0%
35.2%
33.6%
64.3%
91.9%
14.4%
20.1%
17.6%
14.5%
14.8%
16.1%
20.6%
16.3%
10.1%
-23.5%
61.8%
36.8%
27.8%
53.9%
28.2%
-11.3%
-4.3%
23.6%
246.4%
20.2%
2.7%
-91.5%
1.1%
41.7%
18.4%
2.7%
-7.9%
67.5%
14.1%
29.5%
50.0%
99.1%
63.6%
76.7%
115.8%
35.0%
47.8%
220.5%
202.1%
9.2%
40.5%
3.1%
23,786
14,701
14,530
99
21,486
31,522
128,902
49,131
100,238
38,372
90,192
105,636
44,277
60,700
104,139
68,316
87,340
26,746
102,038
72,371
83,764
124,840
57,397
-7,476
-64,847
31,011
2,293
10,762
-699
3,288
-5,973
22,572
11,085
13,492
-31,798
37,019
32,472
47,259
293,722
54,851
58,353
45,912
21,538
27,568
31,175
9,950
7,664
19,605
16,546
4,555
65,111
51,209
69,506
25,325
120,693
150,876
302,992
133,075
1,469,035
690,047
725,433
752,642
651,248
536,019
382,328
474,442
278,710
112,680
292,973
243,220
232,526
336,784
152,801
219,585
949,367
146,966
6,935
45,595
378,075
28,879
66,550
192,222
179,965
51,744
199,672
96,708
310,669
142,988
700,217
125,646
165,697
132,214
64,039
190,652
130,648
20,233
15,362
307,980
53,660
61,122
13083
47115
56021
17031
17197
17043
17097
17089
17111
17093
17037
17063
6007
39165
21015
39025
21117
18029
39015
21037
21081
21077
18115
39061
21191
47125
21047
39103
39055
39093
39085
39035
39007
8041
29019
45063
45079
13145
13215
1113
13053
39049
39041
39045
39089
39097
39129
48355
48409
41003
Dade
Marion
Laramie
Cook
Will
DuPage
Lake
Kane
McHenry
Kendall
DeKalb
Grundy
Butte
Warren
Boone
Clermont
Kenton
Dearborn
Brown
Campbell
Grant
Gallatin
Ohio
Hamilton
Pendleton
Montgomery
Christian
Medina
Geauga
Lorain
Lake
Cuyahoga
Ashtabula
El Paso
Boone
Lexington
Richland
Harris
Muscogee
Russell
Chattahoochee
Franklin
Delaware
Fairfield
Licking
Madison
Pickaway
Nueces
San Patricio
Benton
Chattanooga, TN-GA (MSA)
Chattanooga, TN-GA (MSA)
Cheyenne, WY (MSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chicago, IL (PMSA)
Chico-Paradise, CA (MSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Cincinnati, OH-KY-IN (PMSA)
Clarksville-Hopkinsville, TN-KY (MSA)
Clarksville-Hopkinsville, TN-KY (MSA)
Cleveland-Lorain-Elyria, OH (PMSA)
Cleveland-Lorain-Elyria, OH (PMSA)
Cleveland-Lorain-Elyria, OH (PMSA)
Cleveland-Lorain-Elyria, OH (PMSA)
Cleveland-Lorain-Elyria, OH (PMSA)
Cleveland-Lorain-Elyria, OH (PMSA)
Colorado Springs, CO (MSA)
Columbia, MO (MSA)
Columbia, SC (MSA)
Columbia, SC (MSA)
Columbus, GA-AL (MSA)
Columbus, GA-AL (MSA)
Columbus, GA-AL (MSA)
Columbus, GA-AL (MSA)
Columbus, OH (MSA)
Columbus, OH (MSA)
Columbus, OH (MSA)
Columbus, OH (MSA)
Columbus, OH (MSA)
Columbus, OH (MSA)
Corpus Christi, TX (MSA)
Corpus Christi, TX (MSA)
Corvallis, OR (MSA)
-9,154
-21,010
30,914
604,597
279,533
253,679
249,616
216,569
172,830
70,886
33,744
30,957
51,120
105,865
81,464
52,168
34,496
34,235
24,189
22,805
18,869
18,711
15,801
13,296
4,006
48,982
-13,768
60,233
4,415
3,709
911
-4,354
-37,191
222,520
36,451
135,240
94,056
49,496
39,412
15,840
4,044
208,772
105,727
46,900
40,176
23,892
21,880
43,341
-1,843
19,450
6,034
6,761
112,623
5,981,749
787,760
1,160,361
898,216
624,211
434,470
126,080
123,048
68,640
254,890
266,608
168,470
230,809
186,134
80,582
66,763
111,449
41,418
26,569
21,443
857,350
18,509
184,412
58,414
212,087
95,650
288,924
228,541
1,387,750
65,579
742,011
172,276
352,096
415,412
73,303
225,891
65,537
19,036
1,280,531
217,447
170,327
186,232
64,114
74,720
356,761
65,427
97,609
-60.3%
-75.7%
37.8%
11.2%
55.0%
28.0%
38.5%
53.1%
66.1%
128.4%
37.8%
82.2%
25.1%
65.9%
93.6%
29.2%
22.7%
73.9%
56.8%
25.7%
83.7%
238.1%
280.1%
1.6%
27.6%
36.2%
-19.1%
39.7%
4.8%
1.3%
0.4%
-0.3%
-36.2%
42.8%
26.8%
62.4%
29.3%
207.9%
21.1%
31.9%
27.0%
19.5%
94.6%
38.0%
27.5%
59.4%
41.4%
13.8%
-2.7%
24.9%
2,895
3,309
12,715
128,274
183,253
245,392
206,168
128,790
113,508
17,850
14,605
7,018
58,942
61,287
40,895
49,609
14,485
11,980
10,592
5,460
9,212
3,018
522
-29,308
3,522
51,764
5,261
38,420
16,673
10,529
14,489
-104,511
-1,249
207,448
35,049
75,697
50,834
8,389
15,921
2,425
-6,387
200,315
57,659
29,276
24,721
7,085
9,212
43,927
8,948
9,688
15,188
27,771
81,709
5,377,152
508,227
906,682
648,600
407,642
261,640
55,194
89,304
37,683
203,770
160,743
87,006
178,641
151,638
46,347
42,574
88,644
22,549
7,858
5,642
844,054
14,503
135,430
72,182
151,854
91,235
285,215
227,630
1,392,104
102,770
519,491
135,825
216,856
321,356
23,807
186,479
49,697
14,992
1,071,759
111,720
123,427
146,056
40,222
52,840
313,420
67,270
78,159
24001
54057
48113
48085
48121
48397
48139
48257
48231
48213
17161
19163
17073
12127
12035
39109
39057
39113
39023
1103
1079
17115
8035
8031
8001
8005
8059
19153
19049
19181
26125
26099
26087
26147
26115
26163
1069
1045
10001
19061
27137
55031
36027
55035
55017
48141
18039
36015
40047
42049
Allegany
Mineral
Dallas
Collin
Denton
Rockwall
Ellis
Kaufman
Hunt
Henderson
Rock Island
Scott
Henry
Volusia
Flagler
Miami
Greene
Montgomery
Clark
Morgan
Lawrence
Macon
Douglas
Denver
Adams
Arapahoe
Jefferson
Polk
Dallas
Warren
Oakland
Macomb
Lapeer
St. Clair
Monroe
Wayne
Houston
Dale
Kent
Dubuque
St. Louis
Douglas
Dutchess
Eau Claire
Chippewa
El Paso
Elkhart
Chemung
Garfield
Erie
Cumberland, MD-WV (MSA)
Cumberland, MD-WV (MSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Dallas, TX (PMSA)
Davenport-Moline-Rock Island, IA-IL (MSA)
Davenport-Moline-Rock Island, IA-IL (MSA)
Davenport-Moline-Rock Island, IA-IL (MSA)
Daytona Beach, FL (MSA)
Daytona Beach, FL (MSA)
Dayton-Springfield, OH (MSA)
Dayton-Springfield, OH (MSA)
Dayton-Springfield, OH (MSA)
Dayton-Springfield, OH (MSA)
Decatur, AL (MSA)
Decatur, AL (MSA)
Decatur, IL (MSA)
Denver, CO (PMSA)
Denver, CO (PMSA)
Denver, CO (PMSA)
Denver, CO (PMSA)
Denver, CO (PMSA)
Des Moines, IA (MSA)
Des Moines, IA (MSA)
Des Moines, IA (MSA)
Detroit, MI (PMSA)
Detroit, MI (PMSA)
Detroit, MI (PMSA)
Detroit, MI (PMSA)
Detroit, MI (PMSA)
Detroit, MI (PMSA)
Dothan, AL (MSA)
Dothan, AL (MSA)
Dover, DE (MSA)
Dubuque, IA (MSA)
Duluth-Superior, MN-WI (MSA)
Duluth-Superior, MN-WI (MSA)
Dutchess County, NY (PMSA)
Eau Claire, WI (MSA)
Eau Claire, WI (MSA)
El Paso, TX (MSA)
Elkhart-Goshen, IN (MSA)
Elmira, NY (MSA)
Enid, OK (MSA)
Erie, PA (MSA)
-726
-9,779
642,464
438,551
301,085
78,393
71,289
55,439
27,992
9,711
46,712
34,095
14,422
133,913
65,535
26,645
25,325
19,341
2,438
12,337
-10,917
7,833
277,818
239,167
238,246
213,649
192,242
122,204
70,531
13,929
245,437
149,273
47,285
37,776
28,524
23,183
-4,434
-7,225
25,697
26,027
-11,161
-28,419
42,223
5,066
-3,185
285,652
78,776
-25,628
-18,395
-38,486
74,080
17,258
2,867,806
938,606
739,896
122,241
183,760
127,585
104,977
83,295
195,828
192,822
65,474
578,879
116,111
125,631
173,540
577,804
147,061
123,537
23,944
122,316
458,222
794,577
605,314
705,172
720,454
497,965
111,584
54,720
1,442,052
940,134
135,600
202,480
175,004
2,082,379
84,495
41,891
152,793
115,285
189,270
14,988
323,059
98,342
52,148
967,352
262,283
65,413
39,277
242,201
-1.0%
-36.2%
28.9%
87.7%
68.6%
178.8%
63.4%
76.8%
36.4%
13.2%
31.3%
21.5%
28.3%
30.1%
129.6%
26.9%
17.1%
3.5%
1.7%
11.1%
-31.3%
6.8%
154.0%
43.1%
64.9%
43.5%
36.4%
32.5%
171.8%
34.1%
20.5%
18.9%
53.5%
22.9%
19.5%
1.1%
-5.0%
-14.7%
20.2%
29.2%
-5.6%
-65.5%
15.0%
5.4%
-5.8%
41.9%
42.9%
-28.1%
-31.9%
-13.7%
-5,778
-247
659,694
353,516
293,360
29,102
52,428
32,865
21,373
30,646
-17,906
-1,401
-6,866
183,522
39,372
8,471
18,397
-13,206
-5,526
20,960
4,666
-16,722
154,785
61,175
119,923
194,222
153,687
72,058
11,556
5,877
186,564
97,453
18,129
25,597
11,747
-267,170
13,949
1,118
28,816
-4,443
-21,477
-1,156
35,499
14,223
2,981
197,989
46,215
-6,402
-5,507
644
74,806
27,037
2,225,342
500,055
438,811
43,848
112,471
72,146
76,984
73,584
149,116
158,727
51,052
444,966
50,576
98,986
148,215
558,463
144,623
111,200
34,861
114,483
180,404
555,410
367,068
491,523
528,212
375,761
41,052
40,791
1,196,615
790,861
88,314
164,704
146,479
2,059,196
88,929
49,116
127,096
89,258
200,431
43,407
280,836
93,276
55,333
681,700
183,507
91,041
57,672
280,687
41039
18163
18173
21101
18129
38017
27027
37051
5007
5143
4005
49025
26049
1077
1033
45041
8069
12011
12071
12111
12085
5131
5033
40135
12091
18003
18033
18183
18069
18179
18001
48439
48251
48367
48221
6019
6039
1055
12001
48167
18089
18127
36115
36113
37191
27119
38035
8077
26081
26139
Lane
Vanderburgh
Warrick
Henderson
Posey
Cass
Clay
Cumberland
Benton
Washington
Coconino
Kane
Genesee
Lauderdale
Colbert
Florence
Larimer
Broward
Lee
St. Lucie
Martin
Sebastian
Crawford
Sequoyah
Okaloosa
Allen
De Kalb
Whitley
Huntington
Wells
Adams
Tarrant
Johnson
Parker
Hood
Fresno
Madera
Etowah
Alachua
Galveston
Lake
Porter
Washington
Warren
Wayne
Polk
Grand Forks
Mesa
Kent
Ottawa
Eugene-Springfield, OR (MSA)
Evansville-Henderson, IN-KY (MSA)
Evansville-Henderson, IN-KY (MSA)
Evansville-Henderson, IN-KY (MSA)
Evansville-Henderson, IN-KY (MSA)
Fargo-Moorhead, ND-MN (MSA)
Fargo-Moorhead, ND-MN (MSA)
Fayetteville, NC (MSA)
Fayetteville-Springdale-Rogers, AR (MSA)
Fayetteville-Springdale-Rogers, AR (MSA)
Flagstaff, AZ-UT (MSA)
Flagstaff, AZ-UT (MSA)
Flint, MI (PMSA)
Florence, AL (MSA)
Florence, AL (MSA)
Florence, SC (MSA)
Fort Collins-Loveland, CO (MSA)
Fort Lauderdale, FL (PMSA)
Fort Myers-Cape Coral, FL (MSA)
Fort Pierce-Port St. Lucie, FL (MSA)
Fort Pierce-Port St. Lucie, FL (MSA)
Fort Smith, AR-OK (MSA)
Fort Smith, AR-OK (MSA)
Fort Smith, AR-OK (MSA)
Fort Walton Beach, FL (MSA)
Fort Wayne, IN (MSA)
Fort Wayne, IN (MSA)
Fort Wayne, IN (MSA)
Fort Wayne, IN (MSA)
Fort Wayne, IN (MSA)
Fort Wayne, IN (MSA)
Fort Worth-Arlington, TX (PMSA)
Fort Worth-Arlington, TX (PMSA)
Fort Worth-Arlington, TX (PMSA)
Fort Worth-Arlington, TX (PMSA)
Fresno, CA (MSA)
Fresno, CA (MSA)
Gadsden, AL (MSA)
Gainesville, FL (MSA)
Galveston-Texas City, TX (PMSA)
Gary, IN (PMSA)
Gary, IN (PMSA)
Glens Falls, NY (MSA)
Glens Falls, NY (MSA)
Goldsboro, NC (MSA)
Grand Forks, ND-MN (MSA)
Grand Forks, ND-MN (MSA)
Grand Junction, CO (MSA)
Grand Rapids-Muskegon-Holland, MI (MSA)
Grand Rapids-Muskegon-Holland, MI (MSA)
48,683
35,357
30,642
5,272
4,081
31,861
-11,294
79,882
98,703
84,121
52,187
-6,078
51,640
-1,003
-6,270
59,507
134,548
677,134
177,526
89,200
45,751
38,115
18,087
-21,946
60,088
75,560
26,807
22,030
13,644
5,065
-6,593
480,496
92,940
57,257
22,103
315,146
163,364
1,475
107,987
66,336
32,723
28,643
-14,282
-18,310
44,584
-23,628
-38,635
49,649
137,978
62,918
372,096
207,203
83,212
50,081
31,154
155,232
40,021
382,720
253,503
242,804
168,827
0
488,593
86,973
48,753
185,300
387,486
2,309,574
621,315
282,659
172,852
153,633
71,473
17,114
231,048
408,233
67,205
52,775
51,742
32,681
27,025
1,934,957
220,974
146,530
63,610
1,117,160
286,996
104,775
326,282
317,061
517,314
175,873
46,721
45,034
157,914
7,752
27,233
167,124
714,198
302,391
15.1%
20.6%
58.3%
11.8%
15.1%
25.8%
-22.0%
26.4%
63.8%
53.0%
44.7%
-100.0%
11.8%
-1.1%
-11.4%
47.3%
53.2%
41.5%
40.0%
46.1%
36.0%
33.0%
33.9%
-56.2%
35.1%
22.7%
66.4%
71.7%
35.8%
18.3%
-19.6%
33.0%
72.6%
64.1%
53.3%
39.3%
132.1%
1.4%
49.5%
26.5%
6.8%
19.5%
-23.4%
-28.9%
39.3%
-75.3%
-58.7%
42.3%
23.9%
26.3%
47,705
4,375
10,955
3,844
658
34,756
1,951
54,942
76,426
58,004
41,061
2,040
-12,178
7,234
443
15,272
102,847
606,378
235,739
104,624
62,166
20,155
16,443
8,282
60,281
38,839
6,884
4,512
2,553
2,203
3,964
586,130
59,869
44,324
23,648
284,335
59,671
188
66,072
53,697
-37,044
27,246
6,232
8,390
16,016
-3,423
-403
34,679
131,034
81,380
323,413
171,846
52,570
44,809
27,073
123,371
51,315
302,838
154,800
158,683
116,640
6,078
436,953
87,976
55,023
125,793
252,938
1,632,440
443,789
193,459
127,101
115,518
53,386
39,060
170,960
332,673
40,398
30,745
38,098
27,616
33,618
1,454,461
128,034
89,273
41,507
802,014
123,632
103,300
218,295
250,725
484,591
147,230
61,003
63,344
113,330
31,380
65,868
117,475
576,220
239,473
26121
26005
30013
8123
55009
37081
37067
37151
37001
37057
37197
37059
37169
37147
45045
45083
45007
45077
45021
24043
39017
42043
42041
42075
42099
9003
9013
9007
28073
28035
37035
37023
37003
37027
22057
22109
48201
48157
48339
48291
48473
48071
54011
21019
21089
21043
39087
54099
1089
1083
Muskegon
Allegan
Cascade
Weld
Brown
Guilford
Forsyth
Randolph
Alamance
Davidson
Yadkin
Davie
Stokes
Pitt
Greenville
Spartanburg
Anderson
Pickens
Cherokee
Washington
Butler
Dauphin
Cumberland
Lebanon
Perry
Hartford
Tolland
Middlesex
Lamar
Forrest
Catawba
Burke
Alexander
Caldwell
Lafourche
Terrebonne
Harris
Fort Bend
Montgomery
Liberty
Waller
Chambers
Cabell
Boyd
Greenup
Carter
Lawrence
Wayne
Madison
Limestone
Grand Rapids-Muskegon-Holland, MI (MSA)
Grand Rapids-Muskegon-Holland, MI (MSA)
Great Falls, MT (MSA)
Greeley, CO (PMSA)
Green Bay, WI (MSA)
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greensboro-Winston-Salem-High Point, NC (M
Greenville, NC (MSA)
Greenville-Spartanburg-Anderson, SC (MSA)
Greenville-Spartanburg-Anderson, SC (MSA)
Greenville-Spartanburg-Anderson, SC (MSA)
Greenville-Spartanburg-Anderson, SC (MSA)
Greenville-Spartanburg-Anderson, SC (MSA)
Hagerstown, MD (PMSA)
Hamilton-Middletown, OH (PMSA)
Harrisburg-Lebanon-Carlisle, PA (MSA)
Harrisburg-Lebanon-Carlisle, PA (MSA)
Harrisburg-Lebanon-Carlisle, PA (MSA)
Harrisburg-Lebanon-Carlisle, PA (MSA)
Hartford, CT (NECMA)
Hartford, CT (NECMA)
Hartford, CT (NECMA)
Hattiesburg, MS (MSA)
Hattiesburg, MS (MSA)
Hickory-Morganton-Lenoir, NC (MSA)
Hickory-Morganton-Lenoir, NC (MSA)
Hickory-Morganton-Lenoir, NC (MSA)
Hickory-Morganton-Lenoir, NC (MSA)
Houma, LA (MSA)
Houma, LA (MSA)
Houston, TX (PMSA)
Houston, TX (PMSA)
Houston, TX (PMSA)
Houston, TX (PMSA)
Houston, TX (PMSA)
Houston, TX (PMSA)
Huntington-Ashland, WV-KY-OH (MSA)
Huntington-Ashland, WV-KY-OH (MSA)
Huntington-Ashland, WV-KY-OH (MSA)
Huntington-Ashland, WV-KY-OH (MSA)
Huntington-Ashland, WV-KY-OH (MSA)
Huntington-Ashland, WV-KY-OH (MSA)
Huntsville, AL (MSA)
Huntsville, AL (MSA)
14,867
7,596
34,777
133,687
60,104
152,800
114,221
77,742
71,093
67,822
47,259
37,611
32,316
60,770
137,030
73,888
71,639
51,924
32,389
32,076
80,717
35,765
29,429
14,282
-5,824
82,220
42,324
32,550
20,046
-10,879
84,562
56,254
33,338
30,512
-41,959
-47,720
958,645
283,281
216,453
43,361
43,043
7,624
-300
-8,919
-16,242
-17,244
-21,975
-23,953
63,028
14,965
185,387
113,705
114,960
316,851
287,361
575,062
421,099
208,817
202,538
215,454
83,769
72,676
77,169
194,859
518,057
328,283
237,962
162,974
85,059
164,188
414,402
287,561
243,397
134,696
37,787
940,530
179,239
188,197
59,387
61,884
227,029
145,509
67,007
108,114
47,996
56,804
4,373,626
642,237
513,984
114,002
75,896
33,798
96,367
40,735
20,597
9,629
40,308
18,958
340,585
80,903
8.7%
7.2%
43.4%
73.0%
26.4%
36.2%
37.2%
59.3%
54.1%
45.9%
129.4%
107.3%
72.0%
45.3%
36.0%
29.0%
43.1%
46.8%
61.5%
24.3%
24.2%
14.2%
13.8%
11.9%
-13.4%
9.6%
30.9%
20.9%
51.0%
-15.0%
59.4%
63.0%
99.0%
39.3%
-46.6%
-45.7%
28.1%
78.9%
72.7%
61.4%
131.0%
29.1%
-0.3%
-18.0%
-44.1%
-64.2%
-35.3%
-55.8%
22.7%
22.7%
12,670
24,218
-444
59,397
51,522
104,341
62,381
39,447
31,913
34,075
7,987
10,354
11,626
43,509
91,833
50,661
32,398
31,337
11,602
19,108
74,153
18,874
33,976
11,575
7,808
49,370
21,685
26,359
15,196
6,469
36,923
16,634
8,603
9,765
6,621
9,541
976,442
225,689
168,377
23,140
12,799
7,570
-10,257
-5,799
-2,266
1,758
-1,459
-3,136
79,925
19,950
170,520
106,109
80,183
183,164
227,257
422,262
306,878
131,075
131,445
147,632
36,510
35,065
44,853
134,089
381,027
254,395
166,323
111,050
52,670
132,112
333,685
251,796
213,968
120,414
43,611
858,310
136,915
155,647
39,341
72,763
142,467
89,255
33,669
77,602
89,955
104,524
3,414,981
358,956
297,531
70,641
32,853
26,174
96,667
49,654
36,839
26,873
62,283
42,911
277,557
65,938
18057
18097
18063
18081
18059
18109
18011
18145
18095
19103
26075
28121
28089
28049
47113
47023
12031
12019
12109
12089
37133
36013
55105
34017
47179
47163
47073
47171
47019
51169
42111
42021
5031
29097
29145
26077
26025
26159
17091
20091
29095
29047
29165
20209
29037
20103
20121
29107
29049
29177
Hamilton
Marion
Hendricks
Johnson
Hancock
Morgan
Boone
Shelby
Madison
Johnson
Jackson
Rankin
Madison
Hinds
Madison
Chester
Duval
Clay
St. Johns
Nassau
Onslow
Chautauqua
Rock
Hudson
Washington
Sullivan
Hawkins
Unicoi
Carter
Scott
Somerset
Cambria
Craighead
Jasper
Newton
Kalamazoo
Calhoun
Van Buren
Kankakee
Johnson
Jackson
Clay
Platte
Wyandotte
Cass
Leavenworth
Miami
Lafayette
Clinton
Ray
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Indianapolis, IN (MSA)
Iowa City, IA (MSA)
Jackson, MI (MSA)
Jackson, MS (MSA)
Jackson, MS (MSA)
Jackson, MS (MSA)
Jackson, TN (MSA)
Jackson, TN (MSA)
Jacksonville, FL (MSA)
Jacksonville, FL (MSA)
Jacksonville, FL (MSA)
Jacksonville, FL (MSA)
Jacksonville, NC (MSA)
Jamestown, NY (MSA)
Janesville-Beloit, WI (MSA)
Jersey City, NJ (PMSA)
Johnson City-Kingsport-Bristol, TN-VA (MSA)
Johnson City-Kingsport-Bristol, TN-VA (MSA)
Johnson City-Kingsport-Bristol, TN-VA (MSA)
Johnson City-Kingsport-Bristol, TN-VA (MSA)
Johnson City-Kingsport-Bristol, TN-VA (MSA)
Johnson City-Kingsport-Bristol, TN-VA (MSA)
Johnstown, PA (MSA)
Johnstown, PA (MSA)
Jonesboro, AR (MSA)
Joplin, MO (MSA)
Joplin, MO (MSA)
Kalamazoo-Battle Creek, MI (MSA)
Kalamazoo-Battle Creek, MI (MSA)
Kalamazoo-Battle Creek, MI (MSA)
Kankakee, IL (PMSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
Kansas City, MO-KS (MSA)
168,978
142,517
84,651
68,745
48,466
44,191
37,163
36,236
31,281
67,275
36,797
66,264
45,492
2,952
-1,258
-15,545
192,699
96,242
94,747
38,112
1,881
-49,891
49,866
242,171
29,181
21,933
5,053
4,933
4,383
-8,743
-36,894
-48,233
20,235
21,045
6,951
33,512
20,184
-3,462
34,494
172,387
81,119
66,098
46,923
37,557
31,672
28,053
10,570
-3,521
-3,798
-11,377
354,358
1,002,902
190,039
184,719
104,119
111,079
83,548
79,836
164,558
178,637
195,527
182,446
120,550
253,543
90,748
0
972,317
237,914
219,205
96,065
152,104
89,698
202,404
851,548
136,647
174,863
58,748
22,583
61,221
14,632
43,131
103,997
102,721
126,009
59,689
272,389
158,277
72,887
138,371
626,901
736,678
250,875
121,130
195,386
114,302
96,964
39,067
29,505
15,252
12,005
91.2%
16.6%
80.3%
59.3%
87.1%
66.1%
80.1%
83.1%
23.5%
60.4%
23.2%
57.0%
60.6%
1.2%
-1.4%
-100.0%
24.7%
67.9%
76.1%
65.8%
1.3%
-35.7%
32.7%
39.7%
27.2%
14.3%
9.4%
27.9%
7.7%
-37.4%
-46.1%
-31.7%
24.5%
20.0%
13.2%
14.0%
14.6%
-4.5%
33.2%
37.9%
12.4%
35.8%
63.2%
23.8%
38.3%
40.7%
37.1%
-10.7%
-19.9%
-48.7%
102,908
94,695
35,430
38,410
11,693
14,714
9,787
3,667
-5,541
29,213
7,176
46,344
33,292
-987
17,173
2,824
206,448
73,856
72,508
24,883
36,708
-7,372
13,269
50,785
18,355
8,633
9,762
1,235
6,438
-1,636
-1,261
-30,756
19,182
17,757
12,100
26,271
-3,608
9,329
991
183,156
26,023
48,208
27,681
-14,446
31,340
13,917
6,837
3,062
3,081
1,972
185,380
860,385
105,388
115,974
55,653
66,888
46,385
43,600
133,277
111,362
158,730
116,182
75,058
250,591
92,006
15,545
779,618
141,671
124,458
57,953
150,223
139,589
152,538
609,377
107,465
152,930
53,695
17,650
56,838
23,375
80,025
152,230
82,486
104,964
52,738
238,877
138,093
76,349
103,877
454,514
655,559
184,777
74,207
157,828
82,630
68,911
28,497
33,026
19,050
23,382
55059
48027
48099
47093
47009
47105
47155
47173
47001
18067
18159
55063
27055
18157
18023
22055
22099
22097
22001
22019
12105
42071
26065
26045
26037
48479
35013
32003
4015
32023
20045
40031
23001
21067
21209
21151
21239
21113
21017
21049
39011
39003
31109
5125
5045
5119
5085
48203
48183
48459
Kenosha
Bell
Coryell
Knox
Blount
Loudon
Sevier
Union
Anderson
Howard
Tipton
La Crosse
Houston
Tippecanoe
Clinton
Lafayette
St. Martin
St. Landry
Acadia
Calcasieu
Polk
Lancaster
Ingham
Eaton
Clinton
Webb
Dona Ana
Clark
Mohave
Nye
Douglas
Comanche
Androscoggin
Fayette
Scott
Madison
Woodford
Jessamine
Bourbon
Clark
Auglaize
Allen
Lancaster
Saline
Faulkner
Pulaski
Lonoke
Harrison
Gregg
Upshur
Kenosha, WI (PMSA)
Killeen-Temple, TX (MSA)
Killeen-Temple, TX (MSA)
Knoxville, TN (MSA)
Knoxville, TN (MSA)
Knoxville, TN (MSA)
Knoxville, TN (MSA)
Knoxville, TN (MSA)
Knoxville, TN (MSA)
Kokomo, IN (MSA)
Kokomo, IN (MSA)
La Crosse, WI-MN (MSA)
La Crosse, WI-MN (MSA)
Lafayette, IN (MSA)
Lafayette, IN (MSA)
Lafayette, LA (MSA)
Lafayette, LA (MSA)
Lafayette, LA (MSA)
Lafayette, LA (MSA)
Lake Charles, LA (MSA)
Lakeland-Winter Haven, FL (MSA)
Lancaster, PA (MSA)
Lansing-East Lansing, MI (MSA)
Lansing-East Lansing, MI (MSA)
Lansing-East Lansing, MI (MSA)
Laredo, TX (MSA)
Las Cruces, NM (MSA)
Las Vegas, NV-AZ (MSA)
Las Vegas, NV-AZ (MSA)
Las Vegas, NV-AZ (MSA)
Lawrence, KS (MSA)
Lawton, OK (MSA)
Lewiston-Auburn, ME (NECMA)
Lexington, KY (MSA)
Lexington, KY (MSA)
Lexington, KY (MSA)
Lexington, KY (MSA)
Lexington, KY (MSA)
Lexington, KY (MSA)
Lexington, KY (MSA)
Lima, OH (MSA)
Lima, OH (MSA)
Lincoln, NE (MSA)
Little Rock-North Little Rock, AR (MSA)
Little Rock-North Little Rock, AR (MSA)
Little Rock-North Little Rock, AR (MSA)
Little Rock-North Little Rock, AR (MSA)
Longview-Marshall, TX (MSA)
Longview-Marshall, TX (MSA)
Longview-Marshall, TX (MSA)
41,300
108,497
35,381
56,621
25,550
17,554
6,190
-6,834
-9,206
27,451
11,502
12,610
-3,827
60,073
31,432
23,485
-5,121
-14,794
-32,338
4,423
173,954
96,896
33,693
28,044
18,733
213,157
180,911
1,120,793
144,908
87,647
44,253
18,354
1,882
89,207
37,209
21,519
16,901
10,906
4,440
2,946
20,470
4,058
97,268
61,798
45,432
43,467
29,895
11,273
2,933
-4,207
191,369
347,494
110,586
439,430
131,794
56,777
77,897
11,033
62,079
112,421
28,058
119,866
15,924
209,414
65,402
214,062
43,541
72,963
26,489
187,943
659,358
568,550
313,243
131,951
83,700
407,818
355,891
2,513,962
301,155
120,563
144,435
132,918
105,747
350,168
70,638
92,790
40,169
50,125
23,812
36,188
67,087
112,602
348,458
145,733
131,824
405,172
83,055
73,353
114,221
31,172
27.5%
45.4%
47.0%
14.8%
24.0%
44.8%
8.6%
-38.3%
-12.9%
32.3%
69.5%
11.8%
-19.4%
40.2%
92.5%
12.3%
-10.5%
-16.9%
-55.0%
2.4%
35.8%
20.5%
12.1%
27.0%
28.8%
109.5%
103.4%
80.4%
92.7%
266.3%
44.2%
16.0%
1.8%
34.2%
111.3%
30.2%
72.6%
27.8%
22.9%
8.9%
43.9%
3.7%
38.7%
73.6%
52.6%
12.0%
56.2%
18.2%
2.6%
-11.9%
27,126
79,980
18,264
61,998
28,298
10,513
30,007
6,098
3,818
-1,804
-229
15,972
1,339
27,383
2,416
39,161
8,179
3,365
2,196
15,466
161,366
108,234
3,943
15,405
8,967
94,180
77,968
923,984
99,824
23,431
32,137
1,643
4,334
56,363
11,595
17,835
5,469
13,032
-3
4,860
3,952
-3,644
57,591
30,580
40,046
20,539
18,586
9,503
10,937
6,562
150,069
238,997
75,205
382,808
106,244
39,223
71,707
17,867
71,285
84,970
16,556
107,256
19,751
149,341
33,970
190,577
48,662
87,757
58,827
183,520
485,404
471,654
279,550
103,907
64,967
194,661
174,980
1,393,169
156,247
32,916
100,182
114,564
103,865
260,961
33,429
71,271
23,268
39,219
19,372
33,242
46,617
108,544
251,190
83,935
86,392
361,705
53,160
62,080
111,288
35,379
6037
21111
18019
21185
18043
21029
18143
18061
48303
51009
13153
13021
13225
13169
13289
55025
39139
39033
48215
41029
12009
47157
28033
47167
47047
5035
6047
12025
34023
34035
34019
55133
55131
55089
55079
27053
27037
27163
27003
27123
27139
27019
27141
27171
27025
55109
27059
55093
30063
1003
Los Angeles
Jefferson
Clark
Oldham
Floyd
Bullitt
Scott
Harrison
Lubbock
Amherst
Houston
Bibb
Peach
Jones
Twiggs
Dane
Richland
Crawford
Hidalgo
Jackson
Brevard
Shelby
DeSoto
Tipton
Fayette
Crittenden
Merced
Miami-Dade
Middlesex
Somerset
Hunterdon
Waukesha
Washington
Ozaukee
Milwaukee
Hennepin
Dakota
Washington
Anoka
Ramsey
Scott
Carver
Sherburne
Wright
Chisago
St. Croix
Isanti
Pierce
Missoula
Baldwin
Los Angeles-Long Beach, CA (PMSA)
Louisville, KY-IN (MSA)
Louisville, KY-IN (MSA)
Louisville, KY-IN (MSA)
Louisville, KY-IN (MSA)
Louisville, KY-IN (MSA)
Louisville, KY-IN (MSA)
Louisville, KY-IN (MSA)
Lubbock, TX (MSA)
Lynchburg, VA (MSA)
Macon, GA (MSA)
Macon, GA (MSA)
Macon, GA (MSA)
Macon, GA (MSA)
Macon, GA (MSA)
Madison, WI (MSA)
Mansfield, OH (MSA)
Mansfield, OH (MSA)
McAllen-Edinburg-Mission, TX (MSA)
Medford-Ashland, OR (MSA)
Melbourne-Titusville-Palm Bay, FL (MSA)
Memphis, TN-AR-MS (MSA)
Memphis, TN-AR-MS (MSA)
Memphis, TN-AR-MS (MSA)
Memphis, TN-AR-MS (MSA)
Memphis, TN-AR-MS (MSA)
Merced, CA (MSA)
Miami, FL (PMSA)
Middlesex-Somerset-Hunterdon, NJ (PMSA)
Middlesex-Somerset-Hunterdon, NJ (PMSA)
Middlesex-Somerset-Hunterdon, NJ (PMSA)
Milwaukee-Waukesha, WI (PMSA)
Milwaukee-Waukesha, WI (PMSA)
Milwaukee-Waukesha, WI (PMSA)
Milwaukee-Waukesha, WI (PMSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Minneapolis-St. Paul, MN-WI (MSA)
Missoula, MT (MSA)
Mobile, AL (MSA)
1,414,155
77,165
34,956
32,014
26,190
25,375
14,722
13,228
42,590
-408
64,029
26,073
21,339
16,716
7,298
140,455
5,008
-11,868
396,458
103,137
145,575
120,989
88,075
27,573
-2,635
-3,383
215,010
722,061
217,676
178,269
51,410
112,029
54,931
12,021
9,704
210,165
157,773
123,704
116,575
101,368
96,592
67,142
54,904
48,956
38,049
27,452
13,930
6,117
32,153
47,930
11,000,000
770,993
131,751
79,038
97,078
87,016
37,771
47,730
285,480
31,486
175,338
179,884
45,145
40,409
17,890
568,854
133,793
35,027
970,381
284,977
623,407
1,019,176
196,677
79,139
26,486
47,608
426,643
2,982,303
970,807
477,129
174,005
474,105
172,895
94,586
949,191
1,327,845
515,585
326,328
416,346
612,723
187,691
138,012
120,199
139,732
79,606
91,111
45,463
43,016
128,235
189,334
14.8%
11.1%
36.1%
68.1%
36.9%
41.2%
63.9%
38.3%
17.5%
-1.3%
57.5%
17.0%
89.6%
70.6%
68.9%
32.8%
3.9%
-25.3%
69.1%
56.7%
30.5%
13.5%
81.1%
53.5%
-9.0%
-6.6%
101.6%
31.9%
28.9%
59.6%
41.9%
30.9%
46.6%
14.6%
1.0%
18.8%
44.1%
61.1%
38.9%
19.8%
106.0%
94.7%
84.1%
53.9%
91.6%
43.1%
44.2%
16.6%
33.5%
33.9%
2,038,470
9,469
7,779
19,163
9,482
18,172
2,620
7,098
30,773
2,766
33,148
3,354
4,817
7,006
1,225
104,045
-2,331
-3,179
287,383
48,911
202,168
121,615
54,598
18,557
3,811
1,493
76,008
617,110
156,014
95,200
34,913
81,988
33,149
15,436
-25,140
173,341
162,275
88,417
102,837
50,383
47,062
33,624
35,155
31,889
15,738
20,137
7,879
5,648
19,967
62,473
9,545,014
693,828
96,795
47,024
70,888
61,641
23,049
34,502
242,890
31,894
111,309
153,811
23,806
23,693
10,592
428,399
128,785
46,895
573,923
181,840
477,832
898,187
108,602
51,566
29,121
50,991
211,633
2,260,242
753,131
298,860
122,594
362,076
117,964
82,565
939,487
1,117,680
357,812
202,624
299,771
511,355
91,099
70,870
65,295
90,776
41,557
63,659
31,533
36,899
96,082
141,404
1097
6099
34025
34029
22073
1051
1101
1001
18035
45051
12021
47149
47037
47187
47165
47189
47147
47021
47043
36103
36059
9001
9009
9011
22103
22051
22071
22093
22075
22095
22087
22089
36081
36047
36005
36061
36085
36119
36087
36079
34027
34039
34013
34041
34037
36071
42103
51550
51810
51800
Mobile
Stanislaus
Monmouth
Ocean
Ouachita
Elmore
Montgomery
Autauga
Delaware
Horry
Collier
Rutherford
Davidson
Williamson
Sumner
Wilson
Robertson
Cheatham
Dickson
Suffolk
Nassau
Fairfield
New Haven
New London
St. Tammany
Jefferson
Orleans
St. James
Plaquemines
St. John the Baptist
St. Bernard
St. Charles
Queens
Kings
Bronx
New York
Richmond
Westchester
Rockland
Putnam
Morris
Union
Essex
Warren
Sussex
Orange
Pike
Chesapeake (Independent City)
Virginia Beach (Independent City)
Suffolk (Independent City)
Mobile, AL (MSA)
Modesto, CA (MSA)
Monmouth-Ocean, NJ (PMSA)
Monmouth-Ocean, NJ (PMSA)
Monroe, LA (MSA)
Montgomery, AL (MSA)
Montgomery, AL (MSA)
Montgomery, AL (MSA)
Muncie, IN (MSA)
Myrtle Beach, SC (MSA)
Naples, FL (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nashville, TN (MSA)
Nassau-Suffolk, NY (PMSA)
Nassau-Suffolk, NY (PMSA)
New Haven-Bridgprt-Stamfrd-Danbry-Wtrbry, C
New Haven-Bridgprt-Stamfrd-Danbry-Wtrbry, C
New London-Norwich, CT (NECMA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New Orleans, LA (MSA)
New York, NY (PMSA)
New York, NY (PMSA)
New York, NY (PMSA)
New York, NY (PMSA)
New York, NY (PMSA)
New York, NY (PMSA)
New York, NY (PMSA)
New York, NY (PMSA)
Newark, NJ (PMSA)
Newark, NJ (PMSA)
Newark, NJ (PMSA)
Newark, NJ (PMSA)
Newark, NJ (PMSA)
Newburgh, NY-PA (PMSA)
Newburgh, NY-PA (PMSA)
Norfolk-Virginia Beach-Newport News, VA-NC
Norfolk-Virginia Beach-Newport News, VA-NC
Norfolk-Virginia Beach-Newport News, VA-NC
-1,823
247,120
138,434
103,571
-31,782
37,967
22,889
8,837
11,638
115,446
217,819
110,256
109,877
89,665
37,590
33,278
23,981
7,870
4,344
205,562
164,147
158,955
66,439
22,822
45,235
-6,804
-18,296
-21,194
-26,746
-39,592
-47,531
-48,196
610,051
413,431
297,217
212,669
149,100
147,890
84,555
54,367
153,386
146,132
113,454
43,049
35,962
63,544
19,325
97,883
61,760
26,207
398,237
696,910
755,560
617,173
115,441
104,222
246,226
52,737
130,312
313,473
471,890
293,680
679,787
217,760
168,722
122,555
78,835
43,978
47,677
1,629,615
1,500,505
1,043,559
891,426
282,282
237,499
447,893
465,271
0
0
3,547
19,484
0
2,840,947
2,879,690
1,631,465
1,752,938
594,506
1,073,816
371,990
150,485
624,756
669,296
905,882
146,023
180,636
406,572
65,974
298,329
488,425
90,452
-0.5%
54.9%
22.4%
20.2%
-21.6%
57.3%
10.2%
20.1%
9.8%
58.3%
85.7%
60.1%
19.3%
70.0%
28.7%
37.3%
43.7%
21.8%
10.0%
14.4%
12.3%
18.0%
8.1%
8.8%
23.5%
-1.5%
-3.8%
-100.0%
-100.0%
-91.8%
-70.9%
-100.0%
27.3%
16.8%
22.3%
13.8%
33.5%
16.0%
29.4%
56.6%
32.5%
27.9%
14.3%
41.8%
24.9%
18.5%
41.4%
48.8%
14.5%
40.8%
33,855
181,938
113,119
165,594
7,569
22,781
25,721
11,685
-9,720
95,558
166,567
98,677
91,568
69,590
44,911
33,071
17,734
14,467
13,203
138,898
15,719
75,901
62,921
20,232
80,281
-1,344
-74,990
-370
645
10,852
2,620
10,713
336,600
232,473
165,845
111,898
92,385
58,999
27,900
18,713
63,169
18,769
-56,948
18,385
28,087
82,516
28,273
85,513
161,844
16,641
400,060
449,790
617,126
513,602
147,223
66,255
223,337
43,900
118,674
198,027
254,071
183,424
569,910
128,095
131,131
89,277
54,854
36,108
43,333
1,424,053
1,336,358
884,604
824,987
259,460
192,264
454,697
483,567
21,194
26,746
43,139
67,015
48,196
2,230,896
2,466,259
1,334,248
1,540,269
445,406
925,926
287,435
96,118
471,370
523,164
792,428
102,974
144,674
343,028
46,649
200,446
426,665
64,245
37053
51093
51115
51073
6001
6013
12083
48135
48329
40109
40027
40017
40087
40083
40125
53067
31055
31153
19155
31025
31177
6059
12095
12097
12117
12069
21059
12005
54107
39167
12113
12033
17179
17143
17203
42091
42029
42017
34005
34015
34007
42045
34033
42101
4013
4021
5069
42019
42051
42129
Currituck
Isle of Wight
Mathews
Gloucester
Alameda
Contra Costa
Marion
Ector
Midland
Oklahoma
Cleveland
Canadian
McClain
Logan
Pottawatomie
Thurston
Douglas
Sarpy
Pottawattamie
Cass
Washington
Orange
Orange
Osceola
Seminole
Lake
Daviess
Bay
Wood
Washington
Santa Rosa
Escambia
Tazewell
Peoria
Woodford
Montgomery
Chester
Bucks
Burlington
Gloucester
Camden
Delaware
Salem
Philadelphia
Maricopa
Pinal
Jefferson
Butler
Fayette
Westmoreland
Norfolk-Virginia Beach-Newport News, VA-NC
Norfolk-Virginia Beach-Newport News, VA-NC
Norfolk-Virginia Beach-Newport News, VA-NC
Norfolk-Virginia Beach-Newport News, VA-NC
Oakland, CA (PMSA)
Oakland, CA (PMSA)
Ocala, FL (MSA)
Odessa-Midland, TX (MSA)
Odessa-Midland, TX (MSA)
Oklahoma City, OK (MSA)
Oklahoma City, OK (MSA)
Oklahoma City, OK (MSA)
Oklahoma City, OK (MSA)
Oklahoma City, OK (MSA)
Oklahoma City, OK (MSA)
Olympia, WA (PMSA)
Omaha, NE-IA (MSA)
Omaha, NE-IA (MSA)
Omaha, NE-IA (MSA)
Omaha, NE-IA (MSA)
Omaha, NE-IA (MSA)
Orange County, CA (PMSA)
Orlando, FL (MSA)
Orlando, FL (MSA)
Orlando, FL (MSA)
Orlando, FL (MSA)
Owensboro, KY (MSA)
Panama City, FL (MSA)
Parkersburg-Marietta, WV-OH (MSA)
Parkersburg-Marietta, WV-OH (MSA)
Pensacola, FL (MSA)
Pensacola, FL (MSA)
Peoria-Pekin, IL (MSA)
Peoria-Pekin, IL (MSA)
Peoria-Pekin, IL (MSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Philadelphia, PA-NJ (PMSA)
Phoenix-Mesa, AZ (MSA)
Phoenix-Mesa, AZ (MSA)
Pine Bluff, AR (MSA)
Pittsburgh, PA (MSA)
Pittsburgh, PA (MSA)
Pittsburgh, PA (MSA)
22,483
15,813
3,994
1,904
412,117
358,894
126,184
60,004
53,642
118,044
53,695
27,256
8,418
-9,602
-21,169
42,768
113,508
62,846
32,452
23,344
12,790
853,954
433,910
197,558
179,452
121,889
-3,181
36,533
-2,339
-13,607
76,584
48,497
22,752
22,635
2,947
150,097
115,493
113,723
93,862
68,544
58,274
41,766
-16,298
-21,309
1,547,026
192,012
-7,693
25,336
-401
-8,249
40,823
45,696
13,199
36,785
1,862,524
1,312,457
386,474
180,707
169,171
779,633
262,057
115,456
36,265
24,350
44,531
251,132
578,014
186,037
120,431
47,742
31,587
3,710,400
1,336,228
371,727
546,462
334,717
88,423
184,776
85,552
49,574
195,059
342,774
151,227
205,843
38,450
901,053
551,241
713,111
518,507
323,851
566,979
593,919
47,925
1,492,375
4,643,369
373,444
76,526
199,906
148,096
361,519
122.6%
52.9%
43.4%
5.5%
28.4%
37.6%
48.5%
49.7%
46.4%
17.8%
25.8%
30.9%
30.2%
-28.3%
-32.2%
20.5%
24.4%
51.0%
36.9%
95.7%
68.0%
29.9%
48.1%
113.4%
48.9%
57.3%
-3.5%
24.6%
-2.7%
-21.5%
64.6%
16.5%
17.7%
12.4%
8.3%
20.0%
26.5%
19.0%
22.1%
26.8%
11.5%
7.6%
-25.4%
-1.4%
50.0%
105.8%
-9.1%
14.5%
-0.3%
-2.2%
7,225
8,264
1,189
14,617
340,476
294,780
136,072
3,651
31,572
89,951
73,860
30,995
7,435
6,916
10,045
83,039
66,930
36,663
1,428
4,095
3,254
908,379
427,576
123,535
185,042
106,947
5,512
49,925
-5,719
-1,057
61,872
59,049
-3,566
-17,237
2,087
106,501
117,728
118,364
61,031
54,800
35,915
-2,256
-424
-171,750
1,575,503
90,090
-6,503
26,471
-10,588
-22,526
18,340
29,883
9,205
34,881
1,450,407
953,563
260,290
120,703
115,529
661,589
208,362
88,200
27,847
33,952
65,700
208,364
464,506
123,191
87,979
24,398
18,797
2,856,446
902,318
174,169
367,010
212,828
91,604
148,243
87,891
63,181
118,474
294,277
128,475
183,208
35,503
750,956
435,748
599,388
424,645
255,307
508,705
552,153
64,223
1,513,684
3,096,343
181,432
84,219
174,570
148,497
369,768
42125
42007
42003
25003
16005
23005
41067
53011
41051
41005
41071
41009
44007
44001
44003
44009
49049
8101
12015
55101
37183
37063
37101
37037
37135
37069
46103
42011
6089
32031
53021
53005
51041
51087
51085
51145
51075
51127
51036
6065
6071
51023
27109
36055
36069
36037
36117
36051
36073
17201
Washington
Beaver
Allegheny
Berkshire
Bannock
Cumberland
Washington
Clark
Multnomah
Clackamas
Yamhill
Columbia
Providence
Bristol
Kent
Washington
Utah
Pueblo
Charlotte
Racine
Wake
Durham
Johnston
Chatham
Orange
Franklin
Pennington
Berks
Shasta
Washoe
Franklin
Benton
Chesterfield
Henrico
Hanover
Powhatan
Goochland
New Kent
Charles City
Riverside
San Bernardino
Botetourt
Olmsted
Monroe
Ontario
Genesee
Wayne
Livingston
Orleans
Winnebago
Pittsburgh, PA (MSA)
Pittsburgh, PA (MSA)
Pittsburgh, PA (MSA)
Pittsfield, MA (NECMA)
Pocatello, ID (MSA)
Portland, ME (NECMA)
Portland-Vancouver, OR-WA (PMSA)
Portland-Vancouver, OR-WA (PMSA)
Portland-Vancouver, OR-WA (PMSA)
Portland-Vancouver, OR-WA (PMSA)
Portland-Vancouver, OR-WA (PMSA)
Portland-Vancouver, OR-WA (PMSA)
Providence-Warwick-Pawtucket, RI (NECMA)
Providence-Warwick-Pawtucket, RI (NECMA)
Providence-Warwick-Pawtucket, RI (NECMA)
Providence-Warwick-Pawtucket, RI (NECMA)
Provo-Orem, UT (MSA)
Pueblo, CO (MSA)
Punta Gorda, FL (MSA)
Racine, WI (PMSA)
Raleigh-Durham-Chapel Hill, NC (MSA)
Raleigh-Durham-Chapel Hill, NC (MSA)
Raleigh-Durham-Chapel Hill, NC (MSA)
Raleigh-Durham-Chapel Hill, NC (MSA)
Raleigh-Durham-Chapel Hill, NC (MSA)
Raleigh-Durham-Chapel Hill, NC (MSA)
Rapid City, SD (MSA)
Reading, PA (MSA)
Redding, CA (MSA)
Reno, NV (MSA)
Richland-Kennewick-Pasco, WA (MSA)
Richland-Kennewick-Pasco, WA (MSA)
Richmond-Petersburg, VA (MSA)
Richmond-Petersburg, VA (MSA)
Richmond-Petersburg, VA (MSA)
Richmond-Petersburg, VA (MSA)
Richmond-Petersburg, VA (MSA)
Richmond-Petersburg, VA (MSA)
Richmond-Petersburg, VA (MSA)
Riverside-San Bernardino, CA (PMSA)
Riverside-San Bernardino, CA (PMSA)
Roanoke, VA (MSA)
Rochester, MN (MSA)
Rochester, NY (MSA)
Rochester, NY (MSA)
Rochester, NY (MSA)
Rochester, NY (MSA)
Rochester, NY (MSA)
Rochester, NY (MSA)
Rockford, IL (MSA)
-9,654
-19,552
-48,588
-2,750
20,306
28,451
266,919
192,723
186,382
134,489
47,808
6,400
83,675
30,248
27,163
26,788
160,268
81,719
36,808
29,126
392,122
129,104
127,335
70,353
69,053
49,429
788
67,888
8,631
259,487
115,505
90,056
103,604
100,560
73,387
71,236
37,092
32,562
-6,929
681,484
569,074
21,649
69,929
26,555
-9,212
-23,423
-24,492
-27,988
-33,150
84,803
193,357
161,589
1,231,395
132,059
95,888
294,410
715,361
540,216
847,774
474,065
133,092
50,070
706,414
80,985
194,663
150,801
531,128
223,557
179,076
218,111
1,025,270
353,647
250,626
120,062
185,066
97,031
89,559
442,355
172,457
600,804
165,060
233,147
364,564
363,833
160,394
93,847
54,029
46,098
0
2,241,310
2,288,002
52,215
194,773
762,232
91,186
36,901
69,276
36,395
11,028
363,748
-4.8%
-10.8%
-3.8%
-2.0%
26.9%
10.7%
59.5%
55.5%
28.2%
39.6%
56.1%
14.7%
13.4%
59.6%
16.2%
21.6%
43.2%
57.6%
25.9%
15.4%
61.9%
57.5%
103.3%
141.5%
59.5%
103.8%
0.9%
18.1%
5.3%
76.0%
233.1%
62.9%
39.7%
38.2%
84.3%
315.1%
219.0%
240.6%
-100.0%
43.7%
33.1%
70.8%
56.0%
3.6%
-9.2%
-38.8%
-26.1%
-43.5%
-75.0%
30.4%
-13,947
-22,860
-168,271
-10,272
9,932
49,563
200,680
154,263
97,786
96,719
29,652
7,947
50,259
3,910
13,281
30,419
150,967
15,826
82,796
16,006
329,792
71,707
52,482
16,160
38,747
17,462
18,287
61,451
47,236
145,945
14,292
32,839
118,185
82,205
36,446
9,559
5,166
4,730
226
890,353
815,972
7,255
32,491
32,374
11,356
828
8,996
7,298
5,652
27,866
203,011
181,141
1,279,982
134,809
75,582
265,959
448,442
347,493
661,392
339,576
85,284
43,670
622,739
50,737
167,500
124,013
370,860
141,838
142,268
188,985
633,148
224,543
123,291
49,709
116,013
47,602
88,771
374,467
163,826
341,317
49,555
143,091
260,960
263,273
87,007
22,611
16,937
13,536
6,929
1,559,826
1,718,928
30,566
124,844
735,677
100,398
60,324
93,768
64,383
44,178
278,945
17007
17141
37127
37065
6067
6061
6017
26145
26111
26017
41047
41053
6053
49035
49011
49057
48451
48029
48187
48091
48493
6073
6075
6081
6041
6085
6079
6083
6087
35049
35028
6097
12081
12115
13103
13051
13029
42079
42131
42069
42037
53033
53061
53029
42085
55117
48181
22015
22017
22119
Boone
Ogle
Nash
Edgecombe
Sacramento
Placer
El Dorado
Saginaw
Midland
Bay
Marion
Polk
Monterey
Salt Lake
Davis
Weber
Tom Green
Bexar
Guadalupe
Comal
Wilson
San Diego
San Francisco
San Mateo
Marin
Santa Clara
San Luis Obispo
Santa Barbara
Santa Cruz
Santa Fe
Los Alamos
Sonoma
Manatee
Sarasota
Effingham
Chatham
Bryan
Luzerne
Wyoming
Lackawanna
Columbia
King
Snohomish
Island
Mercer
Sheboygan
Grayson
Bossier
Caddo
Webster
Rockford, IL (MSA)
Rockford, IL (MSA)
Rocky Mount, NC (MSA)
Rocky Mount, NC (MSA)
Sacramento, CA (PMSA)
Sacramento, CA (PMSA)
Sacramento, CA (PMSA)
Saginaw-Bay City-Midland, MI (MSA)
Saginaw-Bay City-Midland, MI (MSA)
Saginaw-Bay City-Midland, MI (MSA)
Salem, OR (PMSA)
Salem, OR (PMSA)
Salinas, CA (MSA)
Salt Lake City-Ogden, UT (MSA)
Salt Lake City-Ogden, UT (MSA)
Salt Lake City-Ogden, UT (MSA)
San Angelo, TX (MSA)
San Antonio, TX (MSA)
San Antonio, TX (MSA)
San Antonio, TX (MSA)
San Antonio, TX (MSA)
San Diego, CA (MSA)
San Francisco, CA (PMSA)
San Francisco, CA (PMSA)
San Francisco, CA (PMSA)
San Jose, CA (PMSA)
San Luis Obispo-Atascadero-Paso Robles, CA
Santa Barbara-Santa Maria-Lompoc, CA (MSA
Santa Cruz-Watsonville, CA (PMSA)
Santa Fe, NM (MSA)
Santa Fe, NM (MSA)
Santa Rosa, CA (PMSA)
Sarasota-Bradenton, FL (MSA)
Sarasota-Bradenton, FL (MSA)
Savannah, GA (MSA)
Savannah, GA (MSA)
Savannah, GA (MSA)
Scranton-Wilkes-Barre-Hazleton, PA (MSA)
Scranton-Wilkes-Barre-Hazleton, PA (MSA)
Scranton-Wilkes-Barre-Hazleton, PA (MSA)
Scranton-Wilkes-Barre-Hazleton, PA (MSA)
Seattle-Bellevue-Everett, WA (PMSA)
Seattle-Bellevue-Everett, WA (PMSA)
Seattle-Bellevue-Everett, WA (PMSA)
Sharon, PA (MSA)
Sheboygan, WI (MSA)
Sherman-Denison, TX (MSA)
Shreveport-Bossier City, LA (MSA)
Shreveport-Bossier City, LA (MSA)
Shreveport-Bossier City, LA (MSA)
60,279
32,029
42,400
32,897
302,821
158,330
77,820
24,964
20,374
1,690
109,906
28,650
228,776
284,412
70,306
65,312
27,165
374,932
73,332
68,354
44,042
634,780
315,630
292,084
168,590
497,900
96,291
170,907
156,829
152,538
71,587
200,535
100,606
96,613
55,893
33,331
30,019
-13,730
-14,910
-17,099
-26,520
424,664
238,509
4,726
-32,073
13,685
28,292
10,320
-4,373
-29,886
102,348
83,194
130,155
88,195
1,532,860
409,614
234,996
234,912
103,399
111,812
395,487
91,299
631,809
1,185,036
310,632
262,737
131,107
1,772,749
163,217
147,142
76,755
3,459,858
1,092,232
1,000,422
416,201
2,184,374
343,969
570,653
412,611
282,353
89,882
660,873
366,310
423,625
93,695
265,305
53,557
304,849
13,114
195,805
37,585
2,163,580
847,677
76,569
88,113
126,439
139,304
108,935
247,559
11,849
143.3%
62.6%
48.3%
59.5%
24.6%
63.0%
49.5%
11.9%
24.5%
1.5%
38.5%
45.7%
56.8%
31.6%
29.3%
33.1%
26.1%
26.8%
81.6%
86.8%
134.6%
22.5%
40.6%
41.2%
68.1%
29.5%
38.9%
42.8%
61.3%
117.5%
391.3%
43.6%
37.9%
29.5%
147.9%
14.4%
127.5%
-4.3%
-53.2%
-8.0%
-41.4%
24.4%
39.2%
6.6%
-26.7%
12.1%
25.5%
10.5%
-1.7%
-71.6%
13,319
4,743
20,378
-788
441,764
132,952
70,619
-17,425
9,143
-9,915
80,073
17,308
110,627
276,617
92,441
52,020
18,619
402,676
42,793
42,069
15,894
949,458
95,772
119,896
24,667
384,959
90,892
99,555
66,477
53,943
703
158,752
115,442
122,550
19,409
28,933
13,281
-24,056
1,571
-15,029
2,036
462,391
268,623
27,534
-8,019
11,847
20,894
17,330
-1,069
-1,923
42,069
51,165
87,755
55,298
1,230,039
251,284
157,176
209,948
83,025
110,122
285,581
62,649
403,033
900,624
240,325
197,425
103,942
1,397,817
89,885
78,788
32,713
2,825,078
776,602
708,338
247,611
1,686,474
247,678
399,746
255,782
129,815
18,295
460,338
265,704
327,012
37,802
231,974
23,538
318,579
28,024
212,904
64,105
1,738,916
609,168
71,843
120,186
112,754
111,012
98,615
251,932
41,735
31043
19193
46099
46083
18141
53063
17167
17129
25013
25015
29043
29077
29225
27145
27009
29021
29003
29183
29189
29099
17119
17133
17163
17083
29113
29071
17027
29219
29510
42027
54029
54009
39081
6077
45085
36067
36053
36011
36075
53053
12073
12039
12057
12103
12101
12053
18167
18165
18021
5091
Dakota
Woodbury
Minnehaha
Lincoln
St. Joseph
Spokane
Sangamon
Menard
Hampden
Hampshire
Christian
Greene
Webster
Stearns
Benton
Buchanan
Andrew
St. Charles
St. Louis
Jefferson
Madison
Monroe
St. Clair
Jersey
Lincoln
Franklin
Clinton
Warren
St. Louis (Independent City)
Centre
Hancock
Brooke
Jefferson
San Joaquin
Sumter
Onondaga
Madison
Cayuga
Oswego
Pierce
Leon
Gadsden
Hillsborough
Pinellas
Pasco
Hernando
Vigo
Vermillion
Clay
Miller
Sioux City, IA-NE (MSA)
Sioux City, IA-NE (MSA)
Sioux Falls, SD (MSA)
Sioux Falls, SD (MSA)
South Bend, IN (MSA)
Spokane, WA (MSA)
Springfield, IL (MSA)
Springfield, IL (MSA)
Springfield, MA (NECMA)
Springfield, MA (NECMA)
Springfield, MO (MSA)
Springfield, MO (MSA)
Springfield, MO (MSA)
St. Cloud, MN (MSA)
St. Cloud, MN (MSA)
St. Joseph, MO (MSA)
St. Joseph, MO (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
St. Louis, MO-IL (MSA)
State College, PA (MSA)
Steubenville-Weirton, OH-WV (MSA)
Steubenville-Weirton, OH-WV (MSA)
Steubenville-Weirton, OH-WV (MSA)
Stockton-Lodi, CA (MSA)
Sumter, SC (MSA)
Syracuse, NY (MSA)
Syracuse, NY (MSA)
Syracuse, NY (MSA)
Syracuse, NY (MSA)
Tacoma, WA (PMSA)
Tallahassee, FL (MSA)
Tallahassee, FL (MSA)
Tampa-St. Petersburg-Clearwater, FL (MSA)
Tampa-St. Petersburg-Clearwater, FL (MSA)
Tampa-St. Petersburg-Clearwater, FL (MSA)
Tampa-St. Petersburg-Clearwater, FL (MSA)
Terre Haute, IN (MSA)
Terre Haute, IN (MSA)
Terre Haute, IN (MSA)
Texarkana, TX-Texarkana AR (MSA)
76,115
50,740
51,965
38,845
40,572
118,094
45,695
9,465
14,845
2,477
47,996
32,971
3,079
17,641
2,742
-16,049
-16,536
119,526
63,571
53,262
40,149
33,375
19,079
14,039
13,249
11,899
10,960
-7,695
-33,793
-1,221
-186
-4,477
-16,288
268,931
29,953
-24,414
-36,066
-56,550
-62,809
151,866
114,644
54,783
321,649
143,785
97,293
24,965
17,407
9,649
5,801
6,539
96,393
154,589
200,954
63,394
306,433
536,767
234,745
21,979
471,332
154,939
102,947
273,596
34,328
151,235
37,224
70,002
0
405,690
1,079,951
252,056
299,247
61,142
275,337
35,708
52,506
105,962
46,516
17,041
312,975
134,758
32,436
20,894
57,390
837,094
134,694
434,030
33,378
25,388
59,728
855,827
354,669
99,836
1,324,731
1,065,993
444,648
156,457
123,125
26,427
32,359
46,988
375.4%
48.9%
34.9%
158.2%
15.3%
28.2%
24.2%
75.6%
3.3%
1.6%
87.3%
13.7%
9.9%
13.2%
8.0%
-18.7%
-100.0%
41.8%
6.3%
26.8%
15.5%
120.2%
7.4%
64.8%
33.8%
12.7%
30.8%
-31.1%
-9.7%
-0.9%
-0.6%
-17.6%
-22.1%
47.3%
28.6%
-5.3%
-51.9%
-69.0%
-51.3%
21.6%
47.8%
121.6%
32.1%
15.6%
28.0%
19.0%
16.5%
57.5%
21.8%
16.2%
3,646
2,856
39,352
10,615
24,509
75,734
12,988
796
12,877
13,380
32,490
54,969
10,783
25,131
9,163
-1,681
2,520
140,914
41,420
52,121
11,539
7,573
-11,272
1,071
17,028
22,709
2,718
9,712
-104,344
22,848
-8,339
-5,711
-17,624
217,859
16,005
-5,288
4,187
1,986
8,762
215,207
90,233
3,300
351,249
189,341
150,795
85,777
-6,530
-1,474
1,757
2,580
20,278
103,849
148,989
24,549
265,861
418,673
189,050
12,514
456,487
152,462
54,951
240,625
31,249
133,594
34,482
86,051
16,536
286,164
1,016,380
198,794
259,098
27,767
256,258
21,669
39,257
94,063
35,556
24,736
346,768
135,979
32,622
25,371
73,678
568,163
104,741
458,444
69,444
81,938
122,537
703,961
240,025
45,053
1,003,082
922,208
347,355
131,492
105,718
16,778
26,558
40,449
48037
39051
39173
39095
20177
34021
4019
40143
40131
40145
40113
40037
1125
48423
36065
36043
6095
6055
6111
48469
34011
6107
48309
24031
51107
24033
51013
24021
51179
24009
11001
24017
51061
54003
51047
54037
51187
51043
51099
19013
55073
12099
54051
54069
39013
48485
48009
20173
20015
20079
Bowie
Fulton
Wood
Lucas
Shawnee
Mercer
Pima
Tulsa
Rogers
Wagoner
Osage
Creek
Tuscaloosa
Smith
Oneida
Herkimer
Solano
Napa
Ventura
Victoria
Cumberland
Tulare
McLennan
Montgomery
Loudoun
Prince George's
Arlington
Frederick
Stafford
Calvert
District of Columbia
Charles
Fauquier
Berkeley
Culpeper
Jefferson
Warren
Clarke
King George
Black Hawk
Marathon
Palm Beach
Marshall
Ohio
Belmont
Wichita
Archer
Sedgwick
Butler
Harvey
Texarkana, TX-Texarkana AR (MSA)
Toledo, OH (MSA)
Toledo, OH (MSA)
Toledo, OH (MSA)
Topeka, KS (MSA)
Trenton, NJ (PMSA)
Tucson, AZ (MSA)
Tulsa, OK (MSA)
Tulsa, OK (MSA)
Tulsa, OK (MSA)
Tulsa, OK (MSA)
Tulsa, OK (MSA)
Tuscaloosa, AL (MSA)
Tyler, TX (MSA)
Utica-Rome, NY (MSA)
Utica-Rome, NY (MSA)
Vallejo-Fairfield-Napa, CA (PMSA)
Vallejo-Fairfield-Napa, CA (PMSA)
Ventura, CA (PMSA)
Victoria, TX (MSA)
Vineland-Millville-Bridgeton, NJ (PMSA)
Visalia-Tulare-Porterville, CA (MSA)
Waco, TX (MSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Washington, DC-MD-VA-WV (PMSA)
Waterloo-Cedar Falls, IA (MSA)
Wausau, WI (MSA)
West Palm Beach-Boca Raton, FL (MSA)
Wheeling, WV-OH (MSA)
Wheeling, WV-OH (MSA)
Wheeling, WV-OH (MSA)
Wichita Falls, TX (MSA)
Wichita Falls, TX (MSA)
Wichita, KS (MSA)
Wichita, KS (MSA)
Wichita, KS (MSA)
3,077
15,265
10,702
9,800
28,967
113,600
380,615
92,864
9,126
536
-5,170
-6,959
5,708
51,100
-36,758
-59,174
178,982
152,272
269,157
19,492
33,559
174,369
48,814
326,464
230,667
209,605
176,822
102,482
63,448
53,916
43,635
40,613
38,174
32,718
21,578
18,009
16,350
16,174
-1,040
26,620
18,751
466,127
-12,423
-26,563
-34,829
31,990
-8,974
116,876
22,614
5,166
92,360
57,408
131,910
464,693
199,014
465,180
1,229,259
656,606
80,487
58,269
39,377
60,594
170,773
226,508
198,450
5,217
576,148
276,858
1,025,782
103,509
179,933
543,214
262,815
1,204,245
404,607
1,013,254
366,145
299,077
157,067
129,107
615,072
161,894
93,743
109,148
56,070
60,453
48,069
28,883
15,847
154,522
144,653
1,601,782
22,974
20,771
35,290
163,348
0
570,339
82,304
38,046
3.4%
36.2%
8.8%
2.2%
17.0%
32.3%
44.8%
16.5%
12.8%
0.9%
-11.6%
-10.3%
3.5%
29.1%
-15.6%
-91.9%
45.1%
122.2%
35.6%
23.2%
22.9%
47.3%
22.8%
37.2%
132.6%
26.1%
93.4%
52.1%
67.8%
71.7%
7.6%
33.5%
68.7%
42.8%
62.6%
42.4%
51.5%
127.3%
-6.2%
20.8%
14.9%
41.0%
-35.1%
-56.1%
-49.7%
24.4%
-100.0%
25.8%
37.9%
15.7%
13,773
4,366
13,465
-16,751
14,913
43,784
312,864
90,543
24,507
15,587
5,149
8,149
27,234
46,092
-18,352
-2,272
159,710
25,255
223,798
14,640
13,286
121,419
42,547
295,728
116,175
137,280
35,960
80,889
52,907
40,307
-66,847
47,815
19,499
29,435
11,770
12,010
10,463
2,708
6,338
-10,111
14,656
549,913
-6,135
-14,010
-12,345
9,792
1,650
84,759
14,623
2,254
89,283
42,143
121,208
454,893
170,047
351,580
848,644
563,742
71,361
57,733
44,547
67,553
165,065
175,408
235,208
64,391
397,166
124,586
756,625
84,017
146,374
368,845
214,001
877,781
173,940
803,649
189,323
196,595
93,619
75,191
571,437
121,281
55,569
76,430
34,492
42,444
31,719
12,709
16,887
127,902
125,902
1,135,655
35,397
47,334
70,119
131,358
8,974
453,463
59,690
32,880
42081
37129
37019
10003
24015
53077
6113
42133
39155
39029
39099
6101
6115
4027
Lycoming
New Hanover
Brunswick
New Castle
Cecil
Yakima
Yolo
York
Trumbull
Columbiana
Mahoning
Sutter
Yuba
Yuma
Williamsport, PA (MSA)
Wilmington, NC (MSA)
Wilmington, NC (MSA)
Wilmington-Newark, DE-MD (PMSA)
Wilmington-Newark, DE-MD (PMSA)
Yakima, WA (MSA)
Yolo, CA (PMSA)
York, PA (MSA)
Youngstown-Warren, OH (MSA)
Youngstown-Warren, OH (MSA)
Youngstown-Warren, OH (MSA)
Yuba City, CA (MSA)
Yuba City, CA (MSA)
Yuma, AZ (MSA)
1,433
77,168
39,776
124,523
31,559
122,911
167,084
76,931
6,347
-2,063
-15,058
108,149
53,172
236,192
121,368
237,831
113,526
626,373
118,027
345,620
336,850
459,651
231,288
110,039
241,986
187,317
113,512
396,899
1.2%
48.0%
53.9%
24.8%
36.5%
55.2%
98.4%
20.1%
2.8%
-1.8%
-5.9%
136.6%
88.1%
147.0%
1,664
56,751
37,676
103,322
25,878
49,591
55,975
69,121
-16,765
-1,326
-31,645
26,588
10,532
69,314
119,935
160,663
73,750
501,850
86,468
222,709
169,766
382,720
224,941
112,102
257,044
79,168
60,340
160,707