Where Will US Population Growth Occur? A Glimpse at

Where Will U.S.
Population Growth Occur?
A Glimpse at 2020 and 2030
By Dr. Peter Linneman, NAI Global Chief Economist
Linneman Associates believes that the confluence of strengthening
macroeconomic and real estate fundamentals has created select
investment opportunities in the U.S. commercial real estate sector.
The delayed recovery to date has created a prolonged opportunistic
investment period for well-capitalized investors. However, given
varying stages of recovery by region and asset quality, investors
must be highly selective.
Where Will U.S. Population Growth
Occur? A Glimpse at 2020 and 2030
By Dr. Peter Linneman,
NAI Global Chief Economist
Impact on Population Share Change 1990-2010 (Basis Point)
Population change 1980-1990
+100bp
0.03
Population share change 1980-1990
+100bp
46.95
Share foreign-born in 1990
+100bp
0.26
% with bachelor's degree or higher in 1990
+100bp
0.09
% with less than a high school diploma in 1990
+100bp
-0.13
% white in 1990
+100bp
-0.05
% over 65 years old in 1990
+100bp
-0.22
% under 25 years old in 1990
+100bp
-0.32
Income tax per capita / Income per capita in 1990
+100bp
-0.72
Sales tax per capita / Income per capita in 1990
+100bp
0.33
Presidential election vote over 55% Republican in 1992
If Yes
0.67
Presidential election vote below 45% Republican in 1992
If Yes
2.82
All state senators Republican in 1990
If Yes
0.83
All state senators Democrat in 1990
If Yes
0.43
Average precipitation
+1%
-0.03
January average temperature
+1%
0.02
average January sun days
+1%
-0.02
Share housing older than 30 years in 1990
+100bp
-0.19
Share housing newer than 11 years in 1990
+100bp
-0.04
County borders an ocean or a Great Lake
If Yes
-1.95
Hills or mountains in county
If Yes
-0.28
Northeast
If Yes
0.96
South
If Yes
-1.07
West
If Yes
-5.96
Observations
1159.00
Adj R-squared
0.71
Source: Linneman Associates
Despite being 5 full years into economic recovery, we
believe that we are still only in the 3rd or 4th inning
of the growth cycle. It is important to note that the
2008-2009 recession was far deeper than all other
post-World War II recessions, with a loss of 8.7 million
jobs or 6.2% of the U.S. job base. In comparison,
the “super recessions” of 1973-1975 and 1980-1982
only saw job losses of 1.3 million (1.6%) and 2 million
(2.2%), respectively, and recovered all lost jobs within
9-10 months. This time, it took 5 full years from the
official end of the recession to recover all of the 8.7
million jobs lost. Thus, we are back to where we
started at the beginning of the recession at year-end
2007, but the population has grown by 16 million. As
a result, the U.S. still has tremendous growth potential
due to pent-up demand associated with housing,
automobiles, and other durable goods - this is in
addition to new growth going forward.
In 2006, Peter Linneman and Albert Saiz co-authored
a study in which they forecasted U.S. population
by county and metropolitan statistical area (MSA)
through 2020. With the release of 2010 Census data,
Linneman Associates has since updated and extended
population forecasts through 2030. Our analysis
employs historical population data for 1,161 counties
and 381 metropolitan statistical are as (MSAs). MSA
definitions are those published in February 2013 by
the Census Bureau.
Our revised 2020 population forecasts are based
on changes in population and other variables from
2000 to 2010, while the 2030 forecasts are based on
changes between 1990 and 2010. MSA population as
a percent of the total U.S. population is the dependent
variable in our analysis, while local factors such as
the share of foreign-born residents, educational
attainment, race and age share percentages, taxes
Large cities tend to be attractive
because of their concentrations of
businesses, culture, entertainment,
transportation infrastructure,
education, etc.
per capita, voting trends, political party
representation, weather characteristics, age of
housing stock, proximity to an ocean or Great
Lake, and geographic region are independent
variables.
We do not directly forecast U.S. population
growth, but rather overlay our estimates
of local population share of the total U.S.
population with the U.S. Census forecasts
of total U.S. population in 2020 and 2030
to obtain local population estimates. County
forecasts are then aggregated at the MSA
level. Between 2000 and 2013, the Houston,
Dallas, and Atlanta MSAs registered the
greatest absolute population growth.
We find that the single most important factor
in determining future population growth is
past growth. In fact, past growth accounts for
69% of the forces driving the 2030 forecasts.
Many factors create a county’s attractiveness,
but even if underlying conditions change
noticeably, reputation and growth may lag.
Historical MSA Population Growth (2000-2013)
Largest Absolute Growth
Metropolitan Statistical Area
Houston-Sugar Land-Baytown, TX
Dallas-Fort Worth-Arlington, TX
Atlanta-Sandy Springs-Roswell, GA
Phoenix-Mesa-Scottsdale, AZ
Riverside-San Bernardino-Ontario, CA
Washington-Arlington-Alexandria, DC-VA-MD-WV
New York-Newark-Jersey City, NY-NJ-PA
Miami-Fort Lauderdale-Pompano Beach, FL
Los Angeles-Long Beach-Santa Ana, CA
Las Vegas-Henderson-Paradise, NV
Austin-Round Rock-San Marcos, TX
Orlando-Kissimmee-Sanford, FL
Charlotte-Concord-Gastonia, NC-SC
Seattle-Tacoma-Bellevue, WA
San Antonio-New Braunfels, TX
Denver-Aurora-Lakewood, CO
Tampa-St. Petersburg-Clearwater, FL
Chicago-Naperville-Elgin, IL-IN-WI
Minneapolis-St. Paul-Bloomington, MN-WI
Sacramento--Arden-Arcade--Roseville, CA
Raleigh-Cary, NC
San Diego-Carlsbad-San Marcos, CA
San Francisco-Oakland-Hayward, CA
Portland-Vancouver-Hillsboro, OR-WA
Nashville-Davidson--Murfreesboro--Franklin, TN
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Indianapolis-Carmel-Anderson, IN
Boston-Cambridge-Quincy, MA-NH
Columbus, OH
Jacksonville, FL
McAllen-Edinburg-Mission, TX
Kansas City, MO-KS
Oklahoma City, OK
Cape Coral-Fort Myers, FL
Baltimore-Columbia-Towson, MD
Bakersfield-Delano, CA
Salt Lake City, UT
Richmond, VA
Provo-Orem, UT
Boise City-Nampa, ID
Largest Percent Growth
Change in
Population
1,619,997
1,606,787
1,259,504
1,146,886
1,126,057
1,112,431
1,004,983
820,627
765,804
652,103
633,288
623,285
617,986
566,227
565,847
539,720
474,572
438,973
427,228
418,913
417,445
397,419
392,536
386,673
376,625
347,531
295,499
292,955
292,053
271,874
246,533
243,219
224,256
220,227
217,744
202,479
201,361
190,081
185,465
185,448
Metropolitan Statistical Area
CAGR (%)
The Villages, FL
5.5
St. George, UT
3.9
Raleigh-Cary, NC
3.3
Austin-Round Rock-San Marcos, TX
3.2
Myrtle Beach-Conway-North Myrtle Beach, SC-NC 3.2
Cape Coral-Fort Myers, FL
3.2
Provo-Orem, UT
3.1
Greeley, CO
3.1
Las Vegas-Henderson-Paradise, NV
3.0
Bend-Redmond, OR
2.8
McAllen-Edinburg-Mission, TX
2.8
2.7
Fayetteville-Springdale-Rogers, AR-MO
Kennewick-Pasco-Richland, WA
2.7
Hilton Head Island-Bluffton-Beaufort, SC
2.6
Boise City-Nampa, ID
2.6
Daphne-Fairhope-Foley, AL
2.6
Orlando-Kissimmee-Sanford, FL
2.5
Port St. Lucie, FL
2.5
2.4
Charlotte-Concord-Gastonia, NC-SC
Laredo, TX
2.4
Phoenix-Mesa-Scottsdale, AZ
2.4
Naples-Immokalee-Marco Island, FL
2.3
Gainesville, GA
2.3
Riverside-San Bernardino-Ontario, CA
2.3
Houston-Sugar Land-Baytown, TX
2.3
Dover, DE
2.3
Wilmington, NC
2.2
San Antonio-New Braunfels, TX
2.2
Coeur d'Alene, ID
2.2
Auburn-Opelika, AL
2.1
Idaho Falls, ID
2.1
Lake Havasu City-Kingman, AZ
2.1
Dallas-Fort Worth-Arlington, TX
2.1
2.1
Bakersfield-Delano, CA
Ocala, FL
2.1
Greenville, NC
2.1
Sioux Falls, SD
2.0
Midland, TX
2.0
Charleston-North Charleston-Summerville, SC
2.0
Atlanta-Sandy Springs-Roswell, GA
2.0
Source: U.S. Census Bureau, Linneman Associates
CAGR = Compounded annual growth rate
Agglomeration economies occur as firms
cluster in a location and share a large pool
of input resources, resulting in an increased
efficiency, greater innovation, and declining
costs. Large cities tend to be attractive because of their
concentrations of businesses, culture, entertainment,
transportation infrastructure, education, etc.
Additional factors that influence population growth are
diversity, education levels, race, age, taxes, weather
characteristics, age of housing stock, geographic
location, regulations, and politics. Our research reveals
that more diverse local economies experience greater
growth, because it will be less likely for the area to be
calcified by the social and political control of a single
industry constituency. This is exemplified by Houston,
which has boomed as it transformed from a pure play oil
city to a more diversified economy, while New Orleans
remained tied to the oil industry and stagnated. Locating
in areas where young workers want to live has become
increasingly important to innovation and growth. Our
research underscores that areas with highly educated
workers grow more rapidly, while those with high levels
of unemployment and high school dropout
rates grow more slowly.
Our research also reveals that immigrants,
a great source of growth, tend to move
to where immigrants already represent a
large portion of the population. Additionally,
education and age distribution are important
indicators. Areas with prosperous industries
offer more and better job opportunities, and
therefore attract those with higher education
attainment. Fertility rates are high and
mortality rates are low for the 25-65-yearold population, hence the importance
of this age cohort in population growth.
People also clearly prefer to live near better
schools. This factor, however, is becoming
somewhat less important as the proportion
of childless households grows. This may
bode well for central city areas but should
not mask the fact that good schools remain
an important growth determinant.
MSA Population Forecasts 2013-2030
Absolute Growth
Metropolitan Statistical Area
Atlanta-Sandy Springs-Roswell, GA
Washington-Arlington-Alexandria, DC-VA-MD-WV
Dallas-Fort Worth-Arlington, TX
New York-Newark-Jersey City, NY-NJ-PA
Houston-Sugar Land-Baytown, TX
Chicago-Naperville-Elgin, IL-IN-WI
Riverside-San Bernardino-Ontario, CA
Minneapolis-St. Paul-Bloomington, MN-WI
Richmond, VA
San Antonio-New Braunfels, TX
Phoenix-Mesa-Scottsdale, AZ
Charlotte-Concord-Gastonia, NC-SC
Nashville-Davidson--Murfreesboro--Franklin, TN
Denver-Aurora-Lakewood, CO
Virginia Beach-Norfolk-Newport News, VA-NC
Austin-Round Rock-San Marcos, TX
Indianapolis-Carmel-Anderson, IN
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Kansas City, MO-KS
Orlando-Kissimmee-Sanford, FL
St. Louis, MO-IL
Las Vegas-Henderson-Paradise, NV
Cincinnati, OH-KY-IN
Columbus, OH
Louisville-Jefferson County, KY-IN
Sacramento--Arden-Arcade--Roseville, CA
Memphis, TN-MS-AR
Portland-Vancouver-Hillsboro, OR-WA
Miami-Fort Lauderdale-Pompano Beach, FL
Baton Rouge, LA
Raleigh-Cary, NC
Tampa-St. Petersburg-Clearwater, FL
Seattle-Tacoma-Bellevue, WA
Oklahoma City, OK
Jacksonville, FL
Boise City-Nampa, ID
Baltimore-Columbia-Towson, MD
Albuquerque, NM
San Francisco-Oakland-Hayward, CA
Columbia, SC
Percent Growth
Change in
Population
2,517,921
2,097,882
1,719,465
1,541,427
1,275,241
897,409
871,873
862,641
852,159
819,441
807,050
797,544
784,256
762,012
749,353
671,833
618,639
618,415
615,817
605,308
593,308
562,488
543,422
539,662
510,400
484,669
467,341
458,430
457,228
446,268
412,394
411,025
399,936
394,100
394,058
392,550
389,665
389,228
378,597
374,822
Metropolitan Statistical Area
Walla Walla, WA
Hinesville-Fort Stewart, GA
Carson City, NV
Valdosta, GA
Rapid City, SD
Charlottesville, VA
Lewiston, ID-WA
Brunswick, GA
Albany, GA
Wenatchee-East Wenatchee, WA
Athens-Clarke County, GA
Winchester, VA-WV
Bismarck, ND
Macon, GA
Idaho Falls, ID
Pine Bluff, AR
Kennewick-Pasco-Richland, WA
Grand Island, NE
Hattiesburg, MS
The Villages, FL
Amarillo, TX
Warner Robins, GA
Jefferson City, MO
Victoria, TX
St. George, UT
Hilton Head Island-Bluffton-Beaufort, SC
Dalton, GA
College Station-Bryan, TX
New Bern, NC
Manhattan, KS
El Centro, CA
Ithaca, NY
Burlington-South Burlington, VT
Bend-Redmond, OR
Yuma, AZ
Wichita Falls, TX
Parkersburg-Vienna, WV
Bowling Green, KY
Sioux Falls, SD
Morristown, TN
CAGR (%)
6.3
5.7
5.4
5.2
5.2
5.1
5.0
5.0
4.7
4.5
4.4
4.2
4.0
4.0
3.9
3.7
3.7
3.7
3.7
3.7
3.6
3.6
3.5
3.5
3.4
3.4
3.4
3.3
3.3
3.3
3.2
3.2
3.2
3.2
3.2
3.2
3.2
3.2
3.2
3.1
To assess the impact of local taxes, we
examined income and sales tax data
Source: U.S. Census Bureau, Linneman Associates
CAGR = Compounded annual growth rate
from the Annual Surveys of State & Local
Government Finance. We found that higher
tax rates reduce a county’s attractiveness
to residents and businesses, all else being
always leads to less growth unless this spending is on
equal. However, higher taxes may reflect better public
highways. However, politicians refuse to grasp the simple
services, like schools, public transportation, police
message that high taxes are the death knell for long-term
service, and emergency response systems. We find that
growth.
the local income tax has a negative impact on population
Not surprisingly, weather and geographic characteristics
growth, but only at 75% confidence level. The effect of
are strong predictors of population growth. Americans,
the local sales tax is even weaker. Our research shows
both retirees and workers, have clearly demonstrated a
that, in today’s world, both firms and individuals are
preference for warm, dry locations over the last 35 years
too competitive and mobile to be captured by high tax
by moving en masse to the Sun Belt. A large stock of
communities. In fact, more local government spending
Not surprisingly, weather and
geographic characteristics are
strong predictors of population
growth.
MSA Population Forecasts 2013-2030
Absolute Growth
Percent Growth
Change in
Metropolitan Statistical Area
Population
Metropolitan Statistical Area
CAGR (%)
-0.8
Urban Honolulu, HI
-30,222
Grand Forks, ND-MN
Anchorage, AK
Grand Forks, ND-MN
-15,321
-12,523
Anchorage, AK
Kahului-Wailuku-Lahaina, HI
-0.2
-0.2
Kahului-Wailuku-Lahaina, HI
-5,368
Urban Honolulu, HI
-0.2
Fairbanks, AK
Muncie, IN
Springfield, OH
Williamsport, PA
-2,855
738
1,429
4,244
Fairbanks, AK
Muncie, IN
Springfield, OH
Los Angeles-Long Beach-Santa Ana, CA
-0.2
0.0
0.1
0.1
Danville, IL
Johnstown, PA
6,634
7,523
Buffalo-Cheektowaga-Niagara Falls, NY
Williamsport, PA
0.1
0.2
Lima, OH
Decatur, IL
9,437
9,968
Detroit-Warren-Dearborn, MI
Pittsburgh, PA
0.2
0.3
Bay City, MI
Cumberland, MD-WV
10,923
11,525
Johnstown, PA
San Diego-Carlsbad-San Marcos, CA
0.3
0.3
Waterloo-Cedar Falls, IA
Longview, WA
Great Falls, MT
Lewiston-Auburn, ME
11,643
12,788
13,301
13,658
Bridgeport-Stamford-Norwalk, CT
Mobile, AL
Dayton, OH
Cleveland-Elyria-Mentor, OH
Bangor, ME
Mansfield, OH
14,162
14,625
Boston-Cambridge-Quincy, MA-NH
Waterloo-Cedar Falls, IA
0.3
0.4
0.4
0.4
0.4
0.4
Altoona, PA
Elmira, NY
15,529
17,533
New Haven-Milford, CT
New York-Newark-Jersey City, NY-NJ-PA
0.4
0.4
Niles-Benton Harbor, MI
Kokomo, IN
17,627
18,435
Miami-Fort Lauderdale-Pompano Beach, FL
Danville, IL
0.4
0.5
Gadsden, AL
Battle Creek, MI
Weirton-Steubenville, WV-OH
20,135
20,335
20,405
San Francisco-Oakland-Hayward, CA
Toledo, OH
Lima, OH
0.5
0.5
0.5
Dubuque, IA
Beckley, WV
20,645
21,188
Canton-Massillon, OH
Utica-Rome, NY
0.5
0.5
Sheboygan, WI
Pittsfield, MA
21,885
22,115
Decatur, IL
Scranton--Wilkes-Barre--Hazleton, PA
0.5
0.5
Racine, WI
Michigan City-La Porte, IN
23,208
23,512
Erie, PA
Bangor, ME
0.5
0.5
Buffalo-Cheektowaga-Niagara Falls, NY
Ocean City, NJ
24,405
24,539
Chicago-Naperville-Elgin, IL-IN-WI
Youngstown-Warren-Boardman, OH-PA
0.5
0.5
Lebanon, PA
Erie, PA
Mobile, AL
Wausau, WI
25,186
25,759
25,870
26,036
San Jose-Sunnyvale-Santa Clara, CA
Santa Rosa-Petaluma, CA
Duluth, MN-WI
Flint, MI
0.5
0.5
0.5
0.6
Albany, OR
26,614
Bay City, MI
0.6
Source: U.S. Census Bureau, Linneman Associates
CAGR = Compounded annual growth rate
old housing negatively affects population share change.
A high percentage of old housing may indicate relatively
less business activity and development in recent years
as well as lower quality housing. In contrast, areas with
a good transportation infrastructure, newer housing,
and more open space tend to attract growth. The
major problem faced by many central cities is that their
housing stocks are not what people desire today. . In
contrast to our original research, countries that border
Atlantic, Pacific Ocean, and Great Lakes
experience slower population growth on
average compared to inland counties
after controlling for other factors. This
phenomenon most likely reflects greater
regulatory opposition to growth in coastal
areas which has resulted in high home
prices, muting the latent desire to live
in these areas. That is, if approvals for
the necessary housing and workspace
to accommodate growth are difficult,
expensive, and uncertain, latent growth
is diverted elsewhere. For example, there
is little doubt that a beautiful city such as
San Francisco would exhibit lower growth
than expected over the past 25 if not for
stringent regulations, since its location
has highly desirable attributes such as fair
weather, lively culture, and strong economy.
Political affiliations generally do not impact
population growth. However, our research
reveals that a key to growth is a “competitive”
political environment. Areas dominated by a
single party (either Democrats or Republicans)
tend to grow more slowly than communities
where political control is tenuous. Thus,
political competition, similar to economic
competition, fuels growth.
”Wild cards” for growth are unforeseen factors such
as the Cuban immigration wave or the presence of an
extraordinarily successful entrepreneur who generates
massive job opportunities, such as Bill Gates in Seattle.
What will be the “wild cards” over the next 20 years?
No one can know, but our research indicates that they
will explain as much as a third of the variation in growth
across U.S.
Percent
Total Population Growth Per Year
1.0
1.0
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.93 0.93
0.97 0.96 0.95
0.88
0.83
0.73 0.74 0.72
0.70
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Household Formations Per Year
3,000
2,389
Thousands
2,500
2,000
1,627
1,343
1,500
1,041
1,000
722
1,157
772
398
500
0
1,375
357
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Estimated Land Absorption Through 2030
Estimated population growth
42,000,000
People per U.S. Household
2.6
Estimated household growth
16,153,846
City
HH/Acre
Est. Required Acres
Philadelphia
6.8
2,388,228
Miami
6.6
2,454,874
Los Angeles
4.4
3,677,212
Atlanta
2.1
7,690,142
Phoenix
1.6
10,348,856
Looking Forward. Over the past 20 years through
2013, the U.S. population has grown by about 56
million people and 26 million households. While the
U.S. population growth rate has slowed from 1% to
about 0.75% per year, consistently positive growth
is a major distinguishing feature of the U.S. economy
relative to other developed economies. In 2013, the
total U.S. population was estimated at 316 million, while
the Census Bureau projects populations of 334 million
and 358 million for 2020 and 2030, respectively, for
compounded annual growth rates of 0.78 and 0.74%. To
put these figures in perspective, over the next 17 years,
the U.S. will add the combined population of the Berlin,
London, Paris, Madrid, and Beijing, or nearly 18.6 million
households. It is this growth that fuels U.S. real
estate development.
As illustrated in Figure [2020 and 2030 Population –
Table 4], the projected population increase will require
significant real estate development. For example, if this
new population lives with the density of the Philadelphia
MSA, this population growth of 42 million will require
nearly 2.4 million acres of new development. We
estimate that of this required land, more than 60% will
be for residential use, with the remainder for office, retail,
industrial, hotel, institutional and infrastructure, and open
space.
Figure [2020 and 2030 Population-Table 2] reveals that
Atlanta, Washington, D.C., and Dallas, MSAs lead the
pack in our 2030 population growth forecasts (versus
2013) on an absolute level, while Walla Walla, WA;
Hinesville, GA; and Carson City, WV show the strongest
percentage growth through 2030. Combined, the top
10 percentage growth markets are expected to add
only 1.5 million new people through 2030, while the top
10 absolute growth markets will increase in population
by 13.5 million on an aggregate basis through 2030.
In the end, future growth will be
driven by a community’s openness
to change and competitive political
environment.
Of those, Richmond (68.4%), Atlanta (45.6%), and San
Antonio (36%) have the highest projected percentage
growth over the 17-year period. The aggregate sample
population (212.6 million) in our statistical analysis
represents about 67.3% of the total 2013 population
of 316.1 million. We project that the sample urban
population will experience a net increase of 61.5 million
people, or about 29%, by 2030.
Conclusion. Real median household income is
estimated at just under $53,000 in May 2014. If it grows
by 2% per year through 2030, it will be roughly 40%
higher – rising to roughly $74,000. This real income
increase means greater demand for amenities, such as
gadgets, travel, and cosmetic surgery. It also means that
the outdated housing stock will have an even harder time
competing for these households. Similarly, if real wealth
grows by approximately 40%, to over $940,000 per
household by 2030, individuals will own homes and retire
far sooner and more comfortably than ever before. With
their unprecedented wealth, boomers will desire easyto-navigate, warm, and safe communities with access to
the best medical facilities. Ultimately, higher real incomes
and real wealth will increase the latent demand to live in
the most desirable areas.
Aging Echo workers will increasingly work in the
healthcare and service sectors and will choose to live in
places that are attractive. As they delay marriages and
parenthood longer than any previous generation, they
will desire urban, social environments. Firms will produce
where the talent desires to live. This is often confused
with areas with universities. While great universities are
obviously very important, the best young talents tend to
migrate to wherever there are the best job networks and
“excitement” after graduation. Many firms in industries
such as healthcare and airlines will expand to the Sun
Belt to be near their customer base.
In the end, future growth will be driven by a community’s
openness to change and a competitive political
environment. Those areas that have grown over the past
20 years (controlling for the factors we have discussed
above) will continue to grow because of their openness
to growth.
Despite surpassing its previous high, the U.S. economy
continues to be far below its long-term trend, with real
GDP nearly $2.3 trillion (13.5% of GDP) below trend.
This gap represents a loss of $7,140 per capita, and
surpasses the GDPs of Russia, Italy, and Brazil, and
approaches the GDPs of the U.K. and France. That is,
the gap is now greater than all but the top six largest
economies in the world. And while real GDP and real
GDP per capita are now at their highest levels in history,
they remain about a standard deviation below trend.
With pent-up demand on top of new population growth,
the potential to close the GDP gap is concentrated in the
housing, automobile, and durable goods sectors. This
will translate into demand for both residential (seniors,
student, multifamily, and single family)and commercial
real estate (retail, industrial, office, lodging, and selfstorage).
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