Forecasting 2020
U.S. County and
MSA Populations
Between 2000 and 2020
W H E T H E R
the U.S. population will
increase 53.7 million.
Where will they live?
PETER
LINNEMAN
ALBERT
52
SAIZ
ZELL/LURIE
REAL
R E LY I N G
U P O N
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 char-
ESTATE
CENTER
acteristics 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
United States.
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 to 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.
P O P U L AT I O N
GROWTH
1980-20 0 0
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
demonstrates that cities are becoming as
important in terms of consumption as they
used to be in terms of traditional produc-
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53
tivity. The capacity to generate and retain
amenities adds considerably to the appeal
of a city. Some cities will attract highincome residents by offering 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 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 that are
major population centers. We limit ourselves to the continental United States,
excluding Hawaii, Alaska and Puerto Rico.
54
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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 (Figure 1). 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
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
of 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 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
Figure 1: U.S. population, 1970 to 2020
400
350
Population
(Millions of People)
300
250
200
150
100
50
0
1970
1980
1990
2000
2010
2020
Year
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 from
1980 to 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 from 1970 to 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.
Immigration has become a primary
driver of population growth. In the
1960s, most Americans claimed
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55
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-
Table I: U.S. metropolitan county growth model
US Population Share Change 1980-2000
Share foreign-born in 1980
12.621*
% with bachelor’s degree or higher in 1980
-0.132
% with less than a high school diploma in 1980
-0.188
% white in 1980
0.184
% over 65 years old in 1980
-10.873*
% under 25 years old in 1980
-9.035*
ncome tax per capita / Income per capita in 1980
34.189
Sales tax per capita / Income per capita in 1980
Log population density in 1980
0.512
Log density squared in 1980
-0.050
Presidential election vote over 55% Republican in 1980
Presidential election vote below 45% Republican in 1980
All state senators Republican in 1980
-0.193
0.317*
-0.407
All state senators Democrat in 1980
-0.195
Log average precipitation
-0.681
Log average snowfall
-0.238
Log January average temperature
0.282
Log average January sun days
1.101*
Share housing older than 30 years
3.040*
Share housing newer than 11 years
5.206*
=1 if county borders an ocean or a Great Lake
-0.684*
Hills or mountains in county
-0.079
Northeast
-0.349
South
-0.357
West
0.351*
U.S. population share change 1970-1980
Constant
805.00
R-squared
0.76
Robust standard errors in parentheses
*Significant at 10%
ZELL/LURIE
1.026*
-2.011
Observations
56
-23.86*
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CENTER
Americans, Chinese-Americans, KoreanAmericans, Indian-Americans, FilipinoAmericans, and many others.
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 to 2000. And so it will
be in the future.
Previous research by Edward Glaeser
and Albert Saiz 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 of 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 the share of people older
than 65, reflecting 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 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 associ-
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57
ated 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. Determining 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 Republican-dominated counties
58
ZELL/LURIE
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CENTER
(based upon presidential and senatorial
election data from early 1980s) do tend
to grow faster. However, this can be
explained by other variables. Republicandominated 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 percent and
55 percent support for Ronald Reagan. A
number of counties were more polarized,
with more than a 55 percent share for
either Reagan (about 40 percent) or
Carter (about 12 percent). These strongly Democratic counties grew significantly
faster between 1980 and 2000, 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 also 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.
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, and 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 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
between the altitude of a county and its
growth. This is a somewhat surprising
finding, 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
that 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 from 1980 to 2000. Because
we used the change in the shares of the
total population, five counties display neg-
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Table II: Largest population loss counties, 2020 forecast
Rank County Name
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
60
Baltimore
Oswego
Herkimer
Cayuga
Chautauqua
Allegheny
Cambria
St. Charles
Terrebonne
St. Bernard
Lafourche
Erie
St. John the Baptist
Grand Forks
Erie
Ashtabula
Somerset
Oneida
Madison
Belmont
St. Louis
Orleans
Acadia
Mercer
Ouachita
Schoharie
Webster
Rapides
Douglas
Livingston
Plaquemines
Tioga
Ohio
Columbia
Strafford
Carroll
Chemung
Niagara
Calhoun
Wayne
Onondaga
Morton
Wayne
Polk
Genesee
Lawrence
Sequoyah
Broome
Philadelphia
St. James
ZELL/LURIE
Metropolitan Area
Population
Loss
2020-2000
Baltimore, Md.
-91,607
Syracuse, N.Y.
-62,809
Utica-Rome, N.Y.
-59,174
Syracuse, N.Y.
-56,550
Jamestown, N.Y.
-49,891
Pittsburgh, Pa.
-48,588
Johnstown, Pa.
-48,233
New Orleans, La.
-48,196
Houma, La.
-47,720
New Orleans, La.
-47,531
Houma, La.
-41,959
Buffalo-Niagara Falls, N.Y.
-40,544
New Orleans, La.
-39,592
Grand Forks, N.D.-Minn.
-38,635
Erie, Pa.
-38,486
Cleveland-Lorain-Elyria, Ohio
-37,191
Johnstown, Pa.
-36,894
Utica-Rome, N.Y.
-36,758
Syracuse, N.Y.
-36,066
Wheeling, W.V.-Ohio
-34,829
St. Louis, Mo.-Ill.
-33,793
Rochester, N.Y.
-33,150
Lafayette, La.
-32,338
Sharon, Pa.
-32,073
Monroe, La.
-31,782
Albany-Schenectady-Troy, N.Y.
-30,077
Shreveport-Bossier City, La.
-29,886
Alexandria, La.
-28,461
Duluth-Superior, Minn.-Wisc.
-28,419
Rochester, N.Y.
-27,988
New Orleans, La.
-26,746
-26,731
Binghamton, N.Y.
Wheeling, W.V.-Ohio
-26,563
Scranton-Wilkes-Barre-Hazleton, Pa. -26,520
Boston-Worcester-Lawrence, Mass.-N.H. -26,439
Canton-Massillon, Ohio
-26,412
Elmira, N.Y.
-25,628
Buffalo-Niagara Falls, N.Y.
-24,756
Anniston, Ala.
-24,576
Rochester, N.Y.
-24,492
-24,414
Syracuse, N.Y.
Bismarck, N.D.
-24,052
Huntington-Ashland, W.V.-Ky.-Ohio -23,953
-23,628
Grand Forks, N.D.-Minn.
Rochester, N.Y.
-23,423
Huntington-Ashland, W.V.-Ky.-Ohio -21,975
Fort Smith, Ark.-Okla.
-21,946
Binghamton, N.Y.
-21,465
Philadelphia, Pa.-N.J.
-21,309
-21,194
New Orleans, La.
REAL
ESTATE
CENTER
Forecast:
Loss as
Population
Population Percentage of Loss/Gain
in 2020 2000 Population 2000-1980
556,950
59,728
5,217
25,388
89,698
1,231,395
103,997
0
56,804
19,484
47,996
908,823
3,547
27,233
242,201
65,579
43,131
198,450
33,378
35,290
312,975
11,028
26,489
88,113
115,441
1,516
11,849
97,952
14,988
36,395
0
25,021
20,771
37,585
86,241
2,467
65,413
194,829
86,764
69,276
434,030
1,273
18,958
7,752
36,901
40,308
17,114
178,829
1,492,375
0
-14.1%
-51.3%
-91.9%
-69.0%
-35.7%
-3.8%
-31.7%
-100%
-45.7%
-70.9%
-46.6%
-4.3%
-91.8%
-58.7%
-13.7%
-36.2%
-46.1%
-15.6%
-51.9%
-49.7%
-9.7%
-75.0%
-55.0%
-26.7%
-21.6%
-95.2%
-71.6%
-22.5%
-65.5%
-43.5%
-100%
-51.7%
-56.1%
-41.4%
-23.5%
-91.5%
-28.1%
-11.3%
-22.1%
-26.1%
-5.3%
-95.0%
-55.8%
-75.3%
-38.8%
-35.3%
-56.2%
-10.7%
-1.4%
-100%
-137,751
8,762
-2,272
1,986
-7,372
-168,271
-30,756
10,713
9,541
2,620
6,621
-64,847
10,852
-403
644
-1,249
-1,261
-18,352
4,187
-12,345
-104,344
5,652
2,196
-8,019
7,569
1,886
-1,923
-9,009
-1,156
7,298
645
1,821
-14,010
2,036
26,746
3,288
-6,402
-7,476
-8,676
8,996
-5,288
99
-3,136
-3,423
828
-1,459
8,282
-13,414
-171,750
-370
Table III: Largest population gain counties, 2020 forecast
Rank County Name
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
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
Population
Gain
2020-2000
Phoenix-Mesa, Ariz.
1,547,026
Los Angeles-Long Beach, Calif.
1,414,155
Las Vegas, Nev.-Ariz.
1,120,793
Houston, Texas
958,645
Orange County, Calif.
853,954
Miami, Fla.
722,061
Riverside-San Bernardino, Calif.
681,484
Fort Lauderdale, Fla.
677,134
Dallas, Texas
642,464
San Diego, Calif.
634,780
New York, N.Y.
610,051
Chicago, Ill.
604,597
Riverside-San Bernardino, Calif.
569,074
San Jose, Calif.
497,900
Fort Worth-Arlington, Texas
480,496
West Palm Beach-Boca Raton, Fla.
466,127
Atlanta, Ga.
443,177
Dallas, Texas
438,551
Orlando, Fla.
433,910
Austin-San Marcos, Texas
424,739
Seattle-Bellevue-Everett, Wash.
424,664
New York, N.Y.
413,431
Oakland, Calif.
412,117
McAllen-Edinburg-Mission, Texas
396,458
Raleigh-Durham-Chapel Hill, N.C.
392,122
Tucson, Ariz.
380,615
San Antonio, Texas
374,932
Oakland, Calif.
358,894
Charlotte-Gastonia-Rock Hill, N.C.-S.C. 349,854
Atlanta, Ga.
331,074
Washington, D.C.-Md.-Va.-W.V.
326,464
Tampa-St. Petersburg-Clearwater, Fla. 321,649
San Francisco, Calif.
315,630
Fresno, Calif.
315,146
Atlanta, Ga.
307,450
Sacramento, Calif.
302,821
Dallas, Texas
301,085
Bakersfield, Calif.
297,696
New York, N.Y.
297,217
San Francisco, Calif.
292,084
El Paso, Texas
285,652
Salt Lake City-Ogden, Utah
284,412
Houston, Texas
283,281
Chicago, Ill.
279,533
Denver, Colo.
277,818
Ventura, Calif.
269,157
Stockton-Lodi, Calif.
268,931
Portland-Vancouver, Ore.-Wash.
266,919
Atlanta, Ga.
262,934
Reno, Nev.
259,487
Forecast:
Gain as
Population
Population Percentage of
Gain
in 2020 2000 Population 2000-1980
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
50.0% 1,575,503
14.8% 2,038,470
80.4% 923,984
28.1%
976,442
29.9% 908,379
31.9%
617,110
43.7% 890,353
41.5% 606,378
28.9% 659,694
22.5% 949,458
27.3% 336,600
11.2%
128,274
33.1% 815,972
29.5% 384,959
33.0% 586,130
41.0% 549,913
74.3%
427,113
87.7% 353,516
48.1%
427,576
51.8%
397,846
24.4% 462,391
16.8% 232,473
28.4%
340,476
69.1%
287,383
61.9% 329,792
44.8% 312,864
26.8%
402,676
37.6% 294,780
50.0% 293,722
40.5% 224,253
37.2% 295,728
32.1% 351,249
40.6%
95,772
39.3% 284,335
50.2% 312,184
24.6%
441,764
68.6% 293,360
44.9%
257,267
22.3% 165,845
41.2%
119,896
41.9%
197,989
31.6%
276,617
78.9% 225,689
55.0% 183,253
154.0% 154,785
35.6% 223,798
47.3%
217,859
59.5% 200,680
39.3%
184,219
76.0% 145,945
REVIEW
61
ative 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 how many 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 (five 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, 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,
Davis and Weinstein found that the cities
that lost more population during the war
grew faster afterwards, 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.
62
ZELL/LURIE
REAL
ESTATE
CENTER
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, Ariz., 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.
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 AtlantaCharlotte-Raleigh corridor, and the
Chicago-Madison region.
Figure 2: Expected population growth in metropolitan counties, 2000-2020
Population Gain
-91607 – 20046
20047 – 66664
66665 – 1547026
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 YoungstownWarren.
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, 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.
REVIEW
63
Table IV: Forecasts for metropolitan areas, 2020
MSA
Code
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
840
64
MSA Name
Population
Gain
2020-2000
New Orleans, La. (MSA)
-214,098
Syracuse, N.Y. (MSA)
-179,839
Utica-Rome, N.Y. (MSA)
-95,932
Rochester, N.Y. (MSA)
-91,710
Houma, La. (MSA)
-89,678
Huntington-Ashland, W.V.-Ky.-Ohio (MSA)
-88,634
Johnstown, Pa. (MSA)
-85,127
Wheeling, W.V.-Ohio (MSA)
-73,815
Scranton-Wilkes-Barre-Hazleton, Pa. (MSA) -72,260
Buffalo-Niagara Falls, N.Y. (MSA)
-65,300
Grand Forks, N.D.-Minn. (MSA)
-62,264
Pittsburgh, Pa. (MSA)
-61,107
Jamestown, N.Y. (MSA)
-49,891
Binghamton, N.Y. (MSA)
-48,197
St. Joseph, Mo. (MSA)
-41,008
Duluth-Superior, Minn.-Wisc. (MSA)
-39,580
Erie, Pa. (MSA)
-38,486
Glens Falls, N.Y. (MSA)
-32,592
Sharon, Pa. (MSA)
-32,073
Monroe, La. (MSA)
-31,782
Lafayette, La. (MSA)
-28,768
Alexandria, La. (MSA)
-28,461
Elmira, N.Y. (MSA)
-25,628
Jackson, Tenn. (MSA)
-24,707
Anniston, Ala. (MSA)
-24,576
Shreveport-Bossier City, La. (MSA)
-23,940
Steubenville-Weirton, Ohio-W.V. (MSA)
-20,951
Bismarck, N.D. (MSA)
-20,688
-18,395
Enid, Okla. (MSA)
Canton-Massillon, Ohio (MSA)
-16,106
-15,994
Altoona, Pa. (MSA)
Parkersburg-Marietta, W.V.-Ohio (MSA)
-15,946
Charleston, W.V. (MSA)
-14,445
Dothan, Ala. (MSA)
-11,659
Youngstown-Warren, Ohio (MSA)
-10,774
Cumberland, Md.-W.V. (MSA)
-10,504
Bangor, Maine (NECMA)
-9,402
Pine Bluff, Ark. (MSA)
-7,693
Florence, Ala. (MSA)
-7,273
Mansfield, Ohio (MSA)
-6,860
Owensboro, Ky. (MSA)
-3,181
Pittsfield, Mass. (NECMA)
-2,750
Benton Harbor, Mich. (MSA)
-2,459
State College, Pa. (MSA)
-1,221
Lynchburg, Va. (MSA)
-408
Beaumont-Port Arthur, Texas (MSA)
-392
ZELL/LURIE
REAL
ESTATE
CENTER
Forecast:
Population
in 2020
Gain as
Percentage of
2000 Population
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
384,345
-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%
-0.10%
Population
Gain
2000-1980
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
9,940
MSA
Code
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
MSA Name
Casper, Wyo. (MSA)
Rapid City, S.D. (MSA)
Decatur, Ala. (MSA)
Williamsport, Pa. (MSA)
Gadsden, Ala. (MSA)
Eau Claire, Wisc. (MSA)
Jacksonville, N.C. (MSA)
Lewiston-Auburn, Maine (NECMA)
Albany-Schenectady-Troy, N.Y. (MSA)
Lake Charles, La. (MSA)
Tuscaloosa, Ala. (MSA)
Flagstaff, Ariz.-Utah (MSA)
Decatur, Ill. (MSA)
Redding, Calif. (MSA)
La Crosse, Wisc.-Minn. (MSA)
Hattiesburg, Miss. (MSA)
Texarkana, Texas-Texarkana Ark. (MSA)
Longview-Marshall, Texas (MSA)
Muncie, Ind. (MSA)
Wichita Falls, Texas (MSA)
Sheboygan, Wisc. (MSA)
Springfield, Mass. (NECMA)
Lawton, Okla. (MSA)
Wausau, Wisc. (MSA)
Corvallis, Ore. (MSA)
Victoria, Texas (MSA)
Jonesboro, Ark. (MSA)
Pocatello, Idaho (MSA)
St. Cloud, Minn. (MSA)
Fargo-Moorhead, N.D.-Minn. (MSA)
Biloxi-Gulfport-Pascagoula, Miss. (MSA)
Roanoke, Va. (MSA)
Chattanooga, Tenn.-Ga. (MSA)
Bellingham, Wash. (MSA)
New London-Norwich, Conn. (NECMA)
Lima, Ohio (MSA)
Dover, Del. (MSA)
Abilene, Texas (MSA)
Dubuque, Iowa (MSA)
Waterloo-Cedar Falls, Iowa (MSA)
San Angelo, Texas (MSA)
Cleveland-Lorain-Elyria, Ohio (PMSA)
Joplin, Mo. (MSA)
Sherman-Denison, Texas (MSA)
Portland, Maine (NECMA)
Topeka, Kan. (MSA)
Population
Gain
2020-2000
Forecast:
Population
in 2020
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
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
Gain as
Percentage of
2000 Population
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%
REVIEW
Population
Gain
2000-1980
-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
65
MSA
Code
6600
8140
1580
3180
5140
8320
1400
8760
580
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
66
MSA Name
Racine, Wisc. (PMSA)
Sumter, S.C. (MSA)
Cheyenne, Wyo. (MSA)
Hagerstown, Md. (PMSA)
Missoula, Mont. (MSA)
Terre Haute, Ind. (MSA)
Champaign-Urbana, Ill. (MSA)
Vineland-Millville-Bridgeton, N.J. (PMSA)
Auburn-Opelika, Ala. (MSA)
Fort Smith, Ark.-Okla. (MSA)
Kankakee, Ill. (PMSA)
Great Falls, Mont. (MSA)
Clarksville-Hopkinsville, Tenn.-Ky. (MSA)
Toledo, Ohio (MSA)
Columbia, Mo. (MSA)
Panama City, Fla. (MSA)
Jackson, Mich. (MSA)
Punta Gorda, Fla. (MSA)
Barnstable-Yarmouth, Mass. (NECMA)
Kokomo, Ind. (MSA)
South Bend, Ind. (MSA)
Kenosha, Wisc. (PMSA)
Corpus Christi, Texas (MSA)
Dutchess County, N.Y. (PMSA)
Bloomington, Ind. (MSA)
Lubbock, Texas (MSA)
Olympia, Wash. (PMSA)
Bryan-College Station, Texas (MSA)
Lawrence, Kan. (MSA)
Goldsboro, N.C. (MSA)
Billings, Mont. (MSA)
Mobile, Ala. (MSA)
Saginaw-Bay City-Midland, Mich. (MSA)
Peoria-Pekin, Ill. (MSA)
Eugene-Springfield, Ore. (MSA)
Waco, Texas (MSA)
Grand Junction, Colo. (MSA)
Janesville-Beloit, Wisc. (MSA)
Kalamazoo-Battle Creek, Mich. (MSA)
Bloomington-Normal, Ill. (MSA)
Tyler,Texas (MSA)
Chico-Paradise, Calif. (MSA)
Flint, Mich. (PMSA)
Springfield, Ill. (MSA)
Johnson City-Kingsport-Bristol,Tenn.-Va. (MSA)
Florence, S.C. (MSA)
ZELL/LURIE
REAL
ESTATE
CENTER
Population
Gain
2020-2000
Forecast:
Population
in 2020
29,126
29,953
30,914
32,076
32,153
32,857
33,029
33,559
33,587
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
218,111
134,694
112,623
164,188
128,235
181,911
212,994
179,933
149,045
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
Gain as
Percentage of
2000 Population
15.41%
28.60%
37.83%
24.28%
33.46%
22.04%
18.35%
22.93%
29.09%
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%
Population
Gain
2000-1980
16,006
16,005
12,715
19,108
19,967
-6,247
11,085
13,286
38,848
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
MSA
Code
2750
3080
3150
1303
2960
240
760
1150
2920
3500
6680
80
5240
6820
3240
2000
560
120
6895
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
MSA Name
Population
Gain
2020-2000
Fort Walton Beach, Fla. (MSA)
60,088
Green Bay, Wisc. (MSA)
60,104
Greenville, N.C. (MSA)
60,770
Burlington, Vt. (NECMA)
60,991
Gary, Ind. (PMSA)
61,366
Allentown-Bethlehem-Easton, Pa. (MSA)
61,877
Baton Rouge, La. (MSA)
64,219
Bremerton, Wash. (PMSA)
64,615
Galveston-Texas City, Texas (PMSA)
66,336
Iowa City, Iowa (MSA)
67,275
Reading, Pa. (MSA)
67,888
Akron, Ohio (PMSA)
69,506
Montgomery, Ala. (MSA)
69,693
Rochester, Minn. (MSA)
69,929
Harrisburg-Lebanon-Carlisle, Pa. (MSA)
73,652
Dayton-Springfield, Ohio (MSA)
73,749
Atlantic-Cape May, N.J. (PMSA)
75,220
Albany, Ga. (MSA)
75,257
Rocky Mount, N.C. (MSA)
75,297
Evansville-Henderson, Ind.-Ky. (MSA)
75,351
Charlottesville, Va. (MSA)
75,660
York, Pa. (MSA)
76,931
Huntsville, Ala. (MSA)
77,994
Elkhart-Goshen, Ind. (MSA)
78,776
Fayetteville, N.C. (MSA)
79,882
Cedar Rapids, Iowa (MSA)
80,226
Lansing-East Lansing, Mich. (MSA)
80,470
Hamilton-Middletown, Ohio (PMSA)
80,717
Pueblo, Colo. (MSA)
81,719
Newburgh, N.Y.-Pa. (PMSA)
82,869
Springfield, Mo. (MSA)
84,046
Asheville, N.C. (MSA)
84,180
Brazoria, Texas (PMSA)
89,454
Knoxville, Tenn. (MSA)
89,876
Tulsa, Okla. (MSA)
90,396
Amarillo, Texas (MSA)
90,801
Sioux Falls, SD (MSA)
90,809
Lafayette, Ind. (MSA)
91,505
Davenport-Moline-Rock Island, Iowa-Ill. (MSA) 95,229
San Luis Obispo-AtascaderoPaso Robles, Calif. (MSA)
96,291
Lancaster, Pa. (MSA)
96,896
Lincoln, Neb. (MSA)
97,268
103,137
Medford-Ashland, Ore. (MSA)
107,987
Gainesville, Fla. (MSA)
Columbus, Ga.-Ala. (MSA)
108,793
Forecast:
Population
in 2020
Gain as
Percentage of
2000 Population
Population
Gain
2000-1980
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
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
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%
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%
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
19,832
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
343,969
568,550
348,458
284,977
326,282
383,768
38.88%
20.54%
38.72%
56.72%
49.47%
39.56%
90,892
108,234
57,591
48,911
66,072
20,348
REVIEW
67
MSA
Code
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
9340
9270
6483
8240
7480
3980
8780
5880
6880
2700
4400
4100
1125
1240
2580
68
MSA Name
Trenton, N.J. (PMSA)
Odessa-Midland, Texas (MSA)
Jackson, Miss. (MSA)
Myrtle Beach, S.C. (MSA)
Wilmington, N.C. (MSA)
Spokane, Wash. (MSA)
Savannah, Ga. (MSA)
Yakima, Wash. (MSA)
Pensacola, Fla. (MSA)
Ocala, Fla. (MSA)
Sioux City, Iowa-Neb. (MSA)
Appleton-Oshkosh-Neenah, Wisc. (MSA)
Athens, Ga. (MSA)
Greeley, Colo. (PMSA)
Fort Collins-Loveland, Colo. (MSA)
Fort Pierce-Port St. Lucie, Fla. (MSA)
Macon, Ga. (MSA)
Fort Wayne, Ind. (MSA)
Salem, Ore. (PMSA)
Madison, Wisc. (MSA)
Birmingham, Ala. (MSA)
Killeen-Temple, Texas (MSA)
Wichita, Kan. (MSA)
Melbourne-Titusville-Palm Bay, Fla. (MSA)
Charleston-North Charleston, S.C. (MSA)
Tacoma, Wash. (PMSA)
Wilmington-Newark, Del.-Md. (PMSA)
Santa Cruz-Watsonville, Calif. (PMSA)
Hartford, Conn. (NECMA)
Provo-Orem, Utah (MSA)
Yuba City, Calif. (MSA)
Yolo, Calif. (PMSA)
Providence-Warwick-Pawtucket, R.I. (NECMA)
Tallahassee, Fla. (MSA)
Santa Barbara-Santa Maria-Lompoc,
Calif. (MSA)
Lakeland-Winter Haven, Fla. (MSA)
Visalia-Tulare-Porterville, Calif. (MSA)
Oklahoma City, Okla. (MSA)
Rockford, Ill. (MSA)
Fort Myers-Cape Coral, Fla. (MSA)
Little Rock-North Little Rock, Ark. (MSA)
Las Cruces, N.M. (MSA)
Boulder-Longmont, Colo. (PMSA)
Brownsville-Harlingen-San Benito,Texas (MSA)
Fayetteville-Springdale-Rogers, Ark. (MSA)
ZELL/LURIE
REAL
ESTATE
CENTER
Population
Gain
2020-2000
Forecast:
Population
in 2020
Gain as
Percentage of
2000 Population
Population
Gain
2000-1980
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
161,321
167,084
167,874
169,427
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
300,829
336,850
1,132,863
454,505
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%
115.64%
98.42%
17.40%
59.43%
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
37,120
55,975
97,869
93,533
170,907
173,954
174,369
176,642
177,111
177,526
180,592
180,911
180,944
181,585
182,824
570,653
659,358
543,214
1,262,292
549,290
621,315
765,784
355,891
473,917
518,369
496,307
42.75%
35.84%
47.27%
16.27%
47.59%
40.00%
30.86%
103.39%
61.76%
53.92%
58.32%
99,555
161,366
121,419
219,202
45,928
235,739
109,751
77,968
102,038
124,840
134,430
MSA
Code
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
5360
7160
3600
MSA Name
Lexington, Ky. (MSA)
Milwaukee-Waukesha, Wisc. (PMSA)
Sarasota-Bradenton, Fla. (MSA)
Daytona Beach, Fla. (MSA)
Santa Rosa, Calif. (PMSA)
Hickory-Morganton-Lenoir, N.C. (MSA)
Richland-Kennewick-Pasco, Wash. (MSA)
Des Moines, Iowa (MSA)
Augusta-Aiken, Ga.-S.C. (MSA)
Laredo, Texas (MSA)
Merced, Calif. (MSA)
Naples, Fla. (MSA)
Colorado Springs, Colo. (MSA)
Grand Rapids-Muskegon-Holland, Mich. (MSA)
Louisville, Ky.-Ind. (MSA)
Santa Fe, N.M. (MSA)
New Haven-Bridgprt-Stamfrd-DanbryWtrbry, Conn. (PMSA)
Salinas, Calif. (MSA)
Columbia, S.C. (MSA)
Norfolk-Virginia Beach-Newport News,
Va.-N.C. (MSA)
Memphis, Tenn.-Ark., Miss. (MSA)
Yuma, Ariz. (MSA)
Monmouth-Ocean, N.J. (PMSA)
Jersey City, N.J. (PMSA)
Omaha, Neb.-Iowa (MSA)
Modesto, Calif. (MSA)
Reno, Nev. (MSA)
Ann Arbor, Mich. (PMSA)
Stockton-Lodi, Calif. (MSA)
Ventura, Calif. (PMSA)
El Paso, Texas (MSA)
Bakersfield, Calif. (MSA)
Boise City, Idaho (MSA)
Vallejo-Fairfield-Napa, Calif. (PMSA)
St. Louis, Mo.-Ill. (MSA)
Greenville-Spartanburg-Anderson, S.C. (MSA)
Nassau-Suffolk, N.Y. (PMSA)
Tucson, Ariz. (MSA)
McAllen-Edinburg-Mission, Texas (MSA)
Bergen-Passaic, N.J. (PMSA)
Richmond-Petersburg, Va. (MSA)
Nashville, Tenn. (MSA)
Salt Lake City-Ogden, Utah (MSA)
Jacksonville, Fla. (MSA)
Population
Gain
2020-2000
Forecast:
Population
in 2020
Gain as
Percentage of
2000 Population
Population
Gain
2000-1980
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
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
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%
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
225,394
228,776
229,297
1,934,985
631,809
767,509
13.18%
56.76%
42.60%
138,822
110,627
126,531
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
416,861
420,031
421,801
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
1,652,993
1,758,405
1,525,501
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%
33.72%
31.38%
38.22%
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
383,221
421,078
377,695
REVIEW
69
MSA
Code
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
70
MSA Name
Population
Gain
2020-2000
Cincinnati, Ohio-Ky.-Ind. (PMSA)
425,905
Albuquerque, N.M. (MSA)
437,323
Columbus, Ohio (MSA)
447,346
Middlesex-Somerset-Hunterdon, N.J. (PMSA)
447,355
Kansas City, Mo.-Kan. (MSA)
455,683
West Palm Beach-Boca Raton, Fla. (MSA)
466,127
Baltimore, Md. (PMSA)
467,127
Fresno, Calif. (MSA)
478,510
Newark, N.J. (PMSA)
491,983
San Jose, Calif. (PMSA)
497,900
Detroit, Mich. (PMSA)
531,480
Sacramento, Calif. (PMSA)
538,971
San Antonio, Texas (MSA)
560,660
Tampa-St. Petersburg-Clearwater, Fla. (MSA)
587,691
Greensboro-Winston-Salem-High Point,
N.C. (MSA)
600,863
Philadelphia, Pa.-N.J. (PMSA)
604,153
634,780
San Diego, Calif. (MSA)
Fort Worth-Arlington, Texas (PMSA)
652,796
Indianapolis, Ind. (MSA)
662,227
Seattle-Bellevue-Everett, Wash. (PMSA)
667,900
Fort Lauderdale, Fla. (PMSA)
677,134
Miami, Fla. (PMSA)
722,061
Oakland, Calif. (PMSA)
771,010
San Francisco, Calif. (PMSA)
776,304
834,721
Portland-Vancouver, Ore.-Wash. (PMSA)
Raleigh-Durham-Chapel Hill, N.C. (MSA)
837,397
Austin-San Marcos, Texas (MSA)
843,168
Orange County, Calif. (PMSA)
853,954
Charlotte-Gastonia-Rock Hill, N.C.-S.C. (MSA) 884,449
Boston-Worcester-Lawrence-LowellBrocktn, Mass.-N.H. (NECMA)
927,833
Orlando, Fla. (MSA)
932,809
Minneapolis-St. Paul, Minn.-Wisc. (MSA) 1,062,726
Denver, Colo. (PMSA)
1,161,122
Riverside-San Bernardino, Calif. (PMSA)
1,250,558
Las Vegas, Nev.-Ariz. (MSA)
1,353,348
Washington, D.C.-Md.-Va.-W.V. (PMSA)
1,389,616
Los Angeles-Long Beach, Calif. (PMSA)
1,414,155
Houston, Texas (PMSA)
1,552,407
Dallas, Texas (PMSA)
1,624,924
Phoenix-Mesa, Ariz. (MSA)
1,739,038
Chicago, Ill. (PMSA)
1,912,411
New York, N.Y. (PMSA)
1,969,280
Atlanta, Ga. (MSA)
2,653,713
ZELL/LURIE
REAL
ESTATE
CENTER
Forecast:
Population
in 2020
Gain as
Percentage of
2000 Population
Population
Gain
2000-1980
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
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%
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
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
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%
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
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
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%
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
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, the 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,
http://realestate.wharton.upenn.edu.
REVIEW
71