industries at risk and implications for...
TRANSCRIPT
KATE SEABAUGHSr. Research Analyst
[email protected](949) 870-1211
RICK PALACIOS, JR.Director of Research
[email protected](949) 870-1244
FEBRUARY 2017
Industries at Risk
JOHN BURNSCEO
[email protected](949) 870-1210
Health Care, Technology, and Automotive
and Implications for Housing
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Executive Summary
2
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Sector booms and busts have historically been driven by speculation and over borrowing, often
triggering regional or even national recessions. Textbook examples include the 2014 Energy and 2008
Financial sector collapse. In both of these instances, fallacies such as perpetual $100+ oil and ever
rising home prices drove rampant speculation, overinvestment, and unsustainable debt buildup.
A similar pattern of unsustainable growth has driven rapid expansion within three industries since the
end of the Great Recession: Health Care, Technology, and Automotive. The risk of a correction within
each of these three industries has grown substantially, with Health Care posing the biggest systemic
recession risk to the US economy. Health Care jobs account for 16% of jobs nationally, thus a correction
to the industry will likely cause a slowdown for the national economy. Several large housing markets
have an even bigger concentration of jobs tied to the Health Care industry and will be disproportionately
hit by a Health Care slowdown, including: Philadelphia, Boston, New York, and Nashville.
Technology and Automotive industry corrections will likely spur more localized economic contractions in
the major housing markets shown below:
• Major housing markets impacted by Technology sector correction: Bay Area, Seattle, Portland,
Austin, Boston, Denver, and Raleigh
• Major housing markets impacted by Automotive sector correction: Detroit, Nashville, Louisville,
Greenville, SC, and Huntsville, AL
Unsustainable Industry Growth Fueled by Debt Will Likely Trigger Next
Recession; Health Care, Technology, and Auto Sectors at Highest Risk
3
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#1 Industry risk: Health Care. The sector’s corporate debt has increased
308% since 2009—more than 10x GDP and job growth during the same
period. Risk of an industry slowdown is systemic, as Health Care
employment accounts for nearly one in every six private sector jobs
compared to one in ten back in 1990. An aging population supports growth,
but not at the breakneck pace seen in recent years.
#2 Industry risk: Technology. The sector’s corporate debt has increased
332% since 2009—more than 12x GDP and job growth during the same
period. Venture capital invested in the sector has hit a recent peak and is
already pulling back. Companies are staffing up in the belief that capital will
continue flowing in perpetuity. Local economies closely tied to tech such as
the Bay Area have already started to pull back, which we expect to
accelerate over the coming years.
#3 Industry risk: Automotive. Corporate debt growth has been limited since
2009, largely due to the auto bailout during the recent recession. Our concern
with Automotive is consumer debt, which has skyrocketed in recent years.
Subprime lending has shot up, a trend that can only last so long. Auto sales hit
an all-time high in 2016, and we believe lax underwriting has primarily driven
this peak. Delinquency rates are now rising with auto sales plateauing. These
headwinds will curb growth within the industry going forward.
Industries at Highest Risk of Boom/Bust Correction: Health Care,
Technology, and Automotive
4
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Debt Growth Relative to Job/Economic Growth Is Higher than Average—
Indicating Higher Risk; Growing Too Fast Often Leads to Recessions
Comparing annual corporate debt growth to private job/GDP growth is a good way to determine if the
private sector is growing too fast. Since 2009, debt growth has outpaced job growth by 7.1x (versus a 4.8x
historical average). Companies grew their debt by 2.1x GDP growth (versus the 1.3x norm).
7.1
2.1
4.8
1.3
0
2
4
6
8
Debt-to-Jobs Debt-to-GDP
Current cycle ratio (since 2009) Historical ratio (since 1950)
Corporate Debt Growth Ratios vs. Historical Average
Note: Corporate debt growth uses outstanding debt based on SIFMA. GDP and jobs data includes only private sector (no government). Please see appendix for details on
methodology. This calculation takes the average YOY growth of corporate debt divided by average YOY job growth or GDP growth. Debt is denominated in USD.
Sources: BLS; BEA; SIFMA; John Burns Real Estate Consulting, LLC (Data: Dec-16, Pub: Feb-17)
5
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Corporate Debt Levels Growing Well in Excess of Industry Job and
GDP Growth—a Typical Signal of Speculation and Overinvestment
Note: Please see appendix for details on methodology. We did not focus on Chemicals, Services, Energy, or Capital Goods sectors (even though debt growth is high) for a
variety of reasons. Chemicals is very tied to Energy, which is already in a deleveraging state. Services is defined too broadly. Capital Goods is very dependent on defense
and government spending, which we do not analyze in this study. For sectors where GDP or job growth was negative, the ratio is #N/A. Services in the above grouping is
not the same as BLS definition for Professional and Business Services.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch; BEA; BLS (Data: Dec-16, Pub: Feb-17)
• Tech sector debt has grown 20
times more than job growth and
12 times more than industry
GDP growth since 2009. These
multiples exceed past boom/bust
industry cycles within the finance
and energy sectors.
• Health Care sector debt has
grown 17 times more than job
growth and 10 times GDP
growth, also exceeding multiples
of prior finance and energy
sector boom/bust cycles. Health
Care has not experienced a
major downturn in over 25 years.
• Automotive sector debt growth
has been minimal compared to
Technology and Health Care.
Our concern is not corporate
debt, but rather consumer debt
(namely subprime), which we
document later.
6
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Corporate Debt Levels for Tech and Health Care Industries Have
Grown 300%+ since 2009, Topping All Other Industries
332%
308%
-50%
0%
50%
100%
150%
200%
250%
300%
350%
2009 2010 2011 2012 2013 2014 2015 2016
Technology & Electronics = 332%
Health Care = 308%
Chemicals = 222%
Services = 213%
Energy = 178%
Capital Goods = 159%
Automotive = 153%
Corporate Debt Growth since 2009
We analyze 20 sectors that make up the BofA
Corporate Bond Index.* In this graph, we
show the top 7 by debt growth since 2009.
Auto industry corporate debt has steadily increased in recent years, following the government bailout early
in the recovery. Chemical and energy sector debt growth has tapered off due to falling commodity prices.
*Services does not align with BLS definition of Professional and Business Services. Please see appendix for details on methodology. Debt is denominated in USD.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch (Data: Dec-16, Pub: Feb-17)
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1. 1929–33 (43 mos.): Consumers
borrow to buy stocks
2. 1957–58 (8 mos.): Consumers
amass credit card debts
3. 1980–82 (22 mos.*)**: Bad bank
loans to developers and Latin
America; oil price increase
4. 1990–91 (8 mos.*): Junk bonds for
Leveraged Buy Outs; real estate
speculation fueled by S&L lending;
Japan
5. 2000–01 (8 mos.): Tech stock
speculation
6. 2007-09 (18 mos.*): Housing
speculation fueled by subprime
1. 1937–38 (13 mos.): Post-New Deal
2. 1945 (8 mos.): End of WWII
3. 1948–49 (11 mos.): Post-WWII
4. 1953–54 (10 mos.): Post-Korean War
5. 1969–70 (11 mos.): First Vietnam War
spending cutback
1973–75 (16 mos.*): Removal of
gold standard, oil price increase
*Global recessions as defined by the International Monetary Fund.
**We grouped the double-dip recessions.
Note: We have excluded the small recession in the 1960s from our analysis. Also, our research does not
capture every cause of past recessions.
Sources: National Bureau of Economic Research; John Burns Real Estate Consulting, LLC (Pub: Feb-17)
Speculative BubblesUsually fueled by debt
Government Spending CutsUsually after running up
big deficits / debts
Other
Speculative Investing—Often Fueled by Debt—Has Preceded 11 of the
Last 12 Recessions; We Believe Debt Will Spark Next Downturn
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We Forecast Current Cycle Will Extend 2+ Years to Become Longest Recovery
on Record; Overborrowing/Increasing Risk Will Lead to Sector Downturns
7.7
10.0
8.8
7.8
6.0
5.0
3.53.3
3.0
2.0
10.5
2009 1991 1961 1983 2002 1975 1950 1954 1971 19580.0
2.0
4.0
6.0
8.0
10.0
12.0
Starting Year of Economic Recovery
Average recovery = 5.7 years
The current recovery is in the 8th year of expansion. A slowdown in 2020 assumes 2.8 more years of recovery.
Historical Length of US Economic Recovery
Length of expansion cycle in years
Sources: National Bureau of Economic Research; John Burns Real Estate Consulting, LLC (Data: Feb-17, Pub: Feb-17)
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Despite the Long Length of Recovery, GDP Growth Is Well below
Average—Supporting Our View That This Cycle Will Extend Further
Note: We show real GDP here, as inflation varies greatly over the last 60+ years. Please see appendix for details on methodology.
Sources: National Bureau of Economic Research; BEA, John Burns Real Estate Consulting, LLC (Data: 3Q16, Pub: Feb-17)
52%
42%
38%
29%
22%
17% 17% 16%14%
11%
1961 1991 1983 1950 1975 2002 2009 1971 1954 19580%
10%
20%
30%
40%
50%
60%
Starting Year of Economic Recovery
Average real GDP growth in recovery = 26%
Real GDP Growth in US Economic Recovery
10
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Case Study #1 - Financial Sector Boom/Bust Cycle (2000–2007): Corporate Debt +166%,
13 Times More than Industry Job Growth and 3 Times More than Industry GDP Growth
166%
54%
13%
0%
25%
50%
75%
100%
125%
150%
175%
200%
225%
250%
275%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Debt growth = 166% GDP growth = 54% Job growth = 13%
Financial Sector Growth (2000–2010): Debt, GDP, Jobs
Note: GDP data at sector level is annual and only goes through 2015. Growth calculations from 2000 to 2007. Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch, BEA, BLS (Data: Dec-16, Pub: Feb-17)
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-10%
-5%
0%
5%
10%
15%
20%
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High Yield Financial Sector Borrowing Spread
Interest rates that corporations must pay for debt represent a good proxy for industry risk. Starting in summer 2008, the borrowing cost spread for Financial sector companies vs. the high yield market jumped from 0% to nearly 20%. The historical spread average is -1%. A negative spread means the sector is priced as less risky than the high yield market overall.
Financial Sector Corporate Borrowing Costs versus the Total Market Jumped
as Industry Boom Turned to Bust in 2008
Note: The borrowing spread is the difference in Effective Yield of the BofA Merrill Lynch High Yield Banking Sector Index and the BofA High Yield Index. We use the High
Yield Index (instead of Investment Grade) because these are the riskiest companies (most likely to default on debt). Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch (Data: Dec-16, Pub: Feb-17)
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Case Study #2 - Energy Sector Boom/Bust Cycle (2010–2015): Corporate
Debt +113%, +1x to 3x More than Industry Job Growth and Industry GDP Growth
113%
80%
45%
0%
20%
40%
60%
80%
100%
120%
2010 2011 2012 2013 2014 2015 2016
Debt growth = 113% GDP growth = 80% Job growth = 45%
Energy Sector Growth (2010–Current): Debt, GDP, Jobs
Note: GDP data at sector level is annual and only goes through 2015. Growth calculations from 2010 to 2015. Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch, BEA, BLS (Data: Dec-16, Pub: Feb-17)
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-8%
-6%
-4%
-2%
0%
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4%
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Interest rates that corporations must pay for debt represent a good proxy for industry risk. In late 2014, the borrowing spread for energy companies versus the high yield market jumped from 0% to 9%. The historical spread average is 0%.
High Yield Energy Sector Borrowing Spread
A negative spread means the sector is priced as less risky than the high yield market overall.
Energy Sector Corporate Borrowing Costs versus the Total Market
Jumped as Industry Boom Turned to Bust in Late 2014
Note: The borrowing spread is the difference in Effective Yield of the BofA Merrill Lynch High Yield Energy Sector Index and the BofA High Yield Index. We use the High
Yield Index (instead of Investment Grade) because these are the riskiest companies (most likely to default on debt). Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch (Data: Dec-16, Pub: Feb-17)
14
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Health Care Sector Risks
15
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10%
11%
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15%
16%
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Health Care represents 16% of private sector jobs, up from 10% back in 1990.
Health Care Share of Private Jobs
1%
2%
3%
4%
5%
6%
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Health Care Sector Employment
YOY growth
The Health Care job growth rate is slowing down.
Health care facilities have a large multiplier effect on local economies (nurses/doctors all eat, live nearby,
and shop local retail). Municipalities have been eager to invest in health care facilities because of the
assumed jobs/tax dollars. Overenthusiasm/investment has accelerated the industry’s growth.
Health Care Industry Employment Up 113% since 1990: Nonstop
Growth Cannot Continue Indefinitely, Regardless of Demographics
Note: We define Health Care jobs as BLS NAICS code: Health Care and Social Assistance. Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; BLS (Data: Dec-16, Pub: Feb-17)
16
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Since 2009, Health Care corporations have added debt at a rate that far outpaces industry job and GDP
growth, eclipsing the recent Financial/Energy sector booms.
Current Health Care Industry Boom: Corporate Debt +308%, 17 Times More
than Industry Job Growth and 10 Times More than Industry GDP Growth
308%
30%
18%
0%
50%
100%
150%
200%
250%
300%
350%
2009 2010 2011 2012 2013 2014 2015 2016
Debt growth = 308% GDP growth = 30% Job growth = 18%
Health Care Sector Growth (2009–Current): Debt, GDP, Jobs
Note: GDP data at sector level is annual and only goes through 2015. Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch, BEA, BLS (Data: Dec-16, Pub: Feb-17)
17
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-8%
-7%
-6%
-5%
-4%
-3%
-2%
-1%
0%
1%
2%
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High Yield Health Care Sector Borrowing Spread
Interest rates that corporations must pay for debt represent a good proxy for industry risk. The Health Care borrowing spread versus the high yield market is already moving higher—a sign of increasing risk.
A negative spread means the sector is priced as less risky than the high yield market overall.
Health Care Sector Corporate Borrowing Costs Have Moved Higher
Recently versus the Total Market
As the Health Care industry pulls back from several years of stellar growth we anticipate corporate
borrowing costs will trend higher.
Note: The borrowing spread is the difference in Effective Yield of the BofA Merrill Lynch High Yield Health Care Sector Index and the BofA High Yield Index. We use the High
Yield Index (instead of Investment Grade) because these are the riskiest companies (most likely to default on debt). Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch (Data: Dec-16, Pub: Feb-17)
18
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M
10M
20M
30M
40M
50M
60M
70M
National 65+ Population
Medical spending increases significantly with age, as people 65+ account for roughly 40% of total personal health spending.
Graying of America Partially Explains Health Care Boom—65+ Population
Up +41% since 2000; We Forecast 29% Population Gains through 2025
Sources: Journal of American Medical Association; John Burns Real Estate Consulting, LLC; US Census Bureau Population Estimates (Data: Dec-16, Pub: Feb-17)
19
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Demographic Tailwinds Do Not Justify Skyrocketing Debt Growth—Health
Care Sector Corporate Debt Per 65+ Person Up 1,376% since 2000
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch; US Census Bureau Population Estimates (Data: Dec-16, Pub: Feb-17)
$1K
$13K
$0K
$2K
$4K
$6K
$8K
$10K
$12K
$14K
2000 2016
Health Care Corporate Debt per Person 65 Years or Older
In 2000, Health Care corporate debt per 65+ person was less than $1K. Now, debt per 65+ person is over
$13K. Favorable demographic fundamentals are causing companies to unsustainably ramp up debt.
20
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2,500
2,600
2,700
2,800
2,900
3,000
3,100
3,200
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The number of hospitals has increased 26% since 1999. Health systems include large companies like Community Health Systems (CHS), Hospital Corporation of America (HCA), and Tenet Healthcare, which have expanded rapidly in the last 15 years.
Number of Hospitals in Health Systems*
1999–2014
Health Care Industry Debt Binge Has Fueled Rapid Hospital Expansions
*Hospital systems defined by AHA as hospitals that are part of a corporate body that may own and/or manage heath provider facilities or health-related facilities.
Sources: Analysis of American Hospitals Association Annual Survey Data for Community Hospitals; John Burns Real Estate Consulting, LLC (Data: 2014, Pub: Feb-17)
21
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52
72
93107
8899 102
80
125
160
242
293
175
265
0
50
100
150
200
250
300
2009 2010 2011 2012 2013 2014 2015
Number of deals (96% growth*) Number of hospitals (231% growth*)
Announced Hospital Mergers and Acquisitions
2009–2015
Since 2009, Hospitals Have Expanded Rapidly Via Debt-Fueled M&A
*Growth between 2009 and 2015
Sources: Irving Levin Associates, Inc. The Health Care Services Acquisition Report, Twenty-Second Edition; John Burns Real Estate Consulting, LLC (Data: 2016, Pub: Feb-17)
22
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Hospital Construction Booms in Most Major Cities Is Clear Signal of
Overinvestment in the Health Care Industry
A Google search of “hospital construction boom” reveals cities across the US undergoing local health
care infrastructure booms.
“In San Francisco,
new CPMC hospital
hits half-way mark
on $2.1 billion
construction project”
article
“Why Dallas Is
Building So Many
Hospitals”
article
“M&A, low rates
contribute to hospital
construction boom in
Tampa Bay”
article
“Florida’s Hospital
Construction Boom”
article
“The $603 million project will
be the largest health-care
project yet in Georgia.”
article
“New York City Hospitals
Spend Billions as They
Expand and Update”
article
“$60M hospital expansion
to bring 100 jobs to
Dayton area”article
“Health-care building
boom underway in
Colorado Springs area”
article
“Healthcare construction
booming across Houston:
6 things to know”
article
Sources: John Burns Real Estate Consulting, LLC; San Francisco Business Times; The Gazette; dmagazine.com; Becker Hospital Review; forwardflorida.com; Tampa Bay
Business Journal; Atlanta Business Chronicle; Wall Street Journal; myDaytonDailyNews.com (Pub: Feb-17)
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Medical costs for a family of four in an employer-sponsored PPO plan increased 180% since 2002!
Health Care Costs Rising at Unsustainable Clip for Consumers; Drug
Companies and Insurance Companies Facing Pushback on Prices
*Includes employee and employer contributions and health expenses. Milliman Medical Index is an actuarial analysis of projected total health care cost for a hypothetical family of
four covered by an employer-sponsored preferred provider organization (PPO) plan. The MMI only includes health care costs, not plan administrative expenses or profit.
Sources: Milliman Medical Index; John Burns Real Estate Consulting, LLC (Data: 2016, Pub: Feb-17)
$9K
$26K
$5K
$10K
$15K
$20K
$25K
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Annual Medical Costs for Average Family of Four*
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Prescription Drugs and Administrative Costs Lead Annual Gains in
Health Expenditures; Consumers Pushing Back
12.2% 12.1%
4.8% 4.6%4.1%
3.8% 3.8% 3.6%
2.8%
0%
2%
4%
6%
8%
10%
12%
14%
PresciptionDrugs
Admin. & NetCost of
Private HealthInsurance
Home HealthCare
PhysicianServices
Hospital Care Other* OtherProfessional**
Nursing HomeCare
Other MedicalDurables andNon-durables
All Health Services and Supplies = 5.5% gain
Percentage Change in National Expenditures for Health Services and Supplies by Category
2013–2014
*Other includes government public health activities and other personal health care; **Other Professional includes dental and other non-physician professional services.
Sources: Centers for Medicare & Medicaid Services, Office of the Actuary; John Burns Real Estate Consulting, LLC (Data: Dec-15, Pub: Feb-17)
25
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Starting to See Early Signs of Health Care Industry Scaling Back after
Years of Rapid Expansion and Renewed Political Pressure
Sources: John Burns Real Estate Consulting, LLC; Wall Street Journal; The Economist; Houston Chronicle; Bloomberg.com (Pub: Feb-17)
Community Health Systems Retrenches - Hospital
operator forced to sell some hospitals after long buying
spree” W A L L S T R E E T J O U R N A L , 1 0 / 1 6
• After years of acquiring hospitals and increasing debt [current
debt to equity of 10x], one of the largest hospital operators
announced it would be selling several of its 158 hospitals
in order to pay down debt.
MD Anderson cutting staff by 1,000
workers via layoff, retirement
H O U S T O N C H R O N I C L E , 0 1 / 1 7
MD Anderson Cancer Center,
Houston's second-largest employer,
is eliminating about 1,000 jobs as the
elite medical institution continues to
wrestle with losses that exceeded
$100 million last quarter.
“
High price tags for medicines are “
• Salary expenses are growing faster than revenues…physician
recruitment grew too many, too fast according to the CFO.
“
“
about to come under renewed
pressure T H E E C O N O M I S T , 1 2 / 1 6
The president-elect, the pharma
industry’s preferred candidate, has
promised to bring prices down.
Drug stocks plunge as Trump threatens to force
price bidding B L O O M B E R G , 0 1 / 1 7
“Trump said he’d force the industry to bid for
government business…aligning him with congressional
Democrats and against the drug-manufacturing lobby.”
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Health Care Dependent Markets: Nationally, Health Care Makes Up 16% of
Jobs! Philadelphia’s Job Exposure Is 1.5 Times US Average (24% of Jobs!)
Nashville1.1x
Tampa
1.3x
Jacksonville
Cleveland
1.5x
Philadelphia
1.4x
Boston /
New York
1.1x
1.1x
West Palm
Beach
1.1x
1.2x
San Antonio
1.2x
Minneapolis
1.1x
San
Francisco
Note: Exposure numbers are a sector aggregation of BLS location quotient numbers. The location quotient represents the ratio of an occupation’s share of employment in a
given area to that occupation’s share of employment in the US as a whole. Please see appendix for details on methodology and for a more complete list of MSAs.
Sources: John Burns Real Estate Consulting, LLC; BLS (Data: May-16, Pub: Feb-17)
Health Care Job Exposure vs. National Average
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Technology Sector Risks
28
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-6%
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20
12
20
13
20
14
20
15
20
16
Technology Sector Employment
YOY growth
The Technology job growth rate has peaked and is trending lower.
2.8M
3.4M
2.7M
2.8M
2.9M
3.0M
3.1M
3.2M
3.3M
3.4M
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
Technology Sector Employment
Technology Sector Employment Up 21% since Bottoming in Late 2009;
Job Growth Already Slowing
Note: We define Technology jobs as BLS NAICS codes: Computer and Electronic Products, Data Processing & Hosting, Computer Systems Design and Related Services
Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; BLS (Data: Dec-16, Pub: Feb-17)
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Since 2009, technology corporations have added debt at a rate that far outpaces industry job and GDP
growth, eclipsing the recent Financial/Energy sector booms.
332%
28%16%
-50%
0%
50%
100%
150%
200%
250%
300%
350%
2009 2010 2011 2012 2013 2014 2015 2016
Debt growth = 332% GDP growth = 28% Job growth = 16%
Technology Sector Growth (2009–Current): Debt, GDP, Jobs
Current Technology Industry Boom: Corporate Debt +322%, 20 Times More
than Industry Job Growth and 12 Times More than Industry GDP Growth
Note: GDP data at sector level is annual and only goes through 2015. Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch, BEA, BLS (Data: Dec-16, Pub: Feb-17)
30
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Silicon Valley Venture Capital Investment Hit a Recent Peak and Is
Already Pulling Back
Sources: John Burns Real Estate Consulting, LLC; PwC/CBInsights MoneyTree™ data explorer (Data: 4Q16, Pub: Feb-17)
60B
35B
0
10
20
30
40
50
60
70
19
96
19
97
19
98
19
99
20
00
20
01
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20
12
20
13
20
14
20
15
20
16
Venture capital represents primary funding source for most technology start ups; early indicator of up/downtrends in the industry.
Silicon Valley: Total Venture Capital Dollars Invested
TTM $ billions
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Technology Industry Cutting Jobs after Years of Rapid Expansion
Sources: John Burns Real Estate Consulting, LLC; Reuters; US News, cnn.com; fortune.com (Pub: Feb-17)
Challenger, Gray & Christmas estimates the industry plans to slash ~60K jobs
this year, second only to the energy industry.
Are layoffs at Twitter and Alphabet a sign of a bursting bubble?
– U S N E W S 1 0 / 1 6“
announced it is cutting
9% of staff.
O C T 2 0 1 6
HP Inc.
plans to cut 3K–4K workers
over the course of the next
three years.
O C T 2 0 1 6
Cisco
announced it would cut
+5,500 positions.
A U G 2 0 1 6
IBM
IBM layoffs continue.
Analysts estimate total layoffs
could impact more than 14K jobs.
M AY 2 0 1 6
MicrosoftIn July, the company said it would
eliminate 2,850 positions after announcing plans to drop a
separate 1,850 workers in May.
J U LY 2 0 1 6
Intel
In April, the computer
company said it would cut
12K workers.
A P R I L 2 0 1 6
money.cnn.com/2016/04/19
http://fortune.com/2016/07/28/micr
http://money.cnn.com/2016/08/17/
earnings/
http://money.cnn.com/2016/10/27/
32
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Business Environment for US-Based Private Technology Companies
Has Started to Deteriorate since 2015
Note: The Bloomberg U.S. Startups Barometer measures both the occurrence and level of historical and recent venture activity for US-based startups excluding
biotechnology. Each of the input factors is normalized for its historical volatility and then the normalized factors are combined in equal proportions to form a normalized index.
We take the average of weekly values to get monthly values and then run a three month moving average to smooth the graph.
Sources: John Burns Real Estate Consulting, LLC; Bloomberg LLC (Data: Jan-17, Pub: Feb-17)
0
100
200
300
400
500
600
700
800
900
1000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Bloomberg US Tech Startups Index
3-month average
The index is a gauge of startup activity that equally considers capital raised, deal count, first financings, and exit count for US-based startups excluding biotechnology. A higher index number indicates more startup activity and financing.
33
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-2%
-1%
0%
1%
2%
3%
4%
5%
6%
7%
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20
16
High Yield Technology Sector Borrowing Spread
Interest rates that corporations must pay for debt represent a good proxy for industry risk. The Technology borrowing spread versus the high yield market is below its historical average of 1%—a sign lenders may be overly-complacent.
A negative spread means the sector is priced as less risky than the high yield market overall.
Technology Sector Corporate Borrowing Costs versus the Total Market
Remain Low by Historical Standards
As venture capital raising slows and the tech sector pulls back from several years of stellar growth we
anticipate corporate borrowing costs will trend higher.
Note: The borrowing spread is the difference in Effective Yield of the BofA Merrill Lynch High Yield Technology Sector Index and the BofA High Yield Index. We use the
High Yield Index (instead of Investment Grade) because these are the riskiest companies (most likely to default on debt). Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch (Data: Dec-16, Pub: Feb-17)
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Tech-Dependent Housing Markets: Nationally, Tech Makes Up 3% of Jobs;
San Jose’s Job Exposure Is 7.4 Times US Average (22% of Jobs!)
Portland
San Jose
Boston
2.5x
Austin
Seattle
San
Francisco
1.7x
Denver
Note: Exposure numbers are a sector aggregation of BLS location quotient numbers. The location quotient represents the ratio of an occupation’s share of employment in
a given area to that occupation’s share of employment in the US as a whole. Please see appendix for details on methodology and for a more complete list of MSAs.
Sources: John Burns Real Estate Consulting, LLC; BLS (Data: May-16, Pub: Feb-17)
2.1x
Raleigh
2.1x1.7x
East Bay
1.6x
Minneapolis
2.8x
3.4x
3.1x
7.4x
Tech Sector Job Exposure vs. National Average
35
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Automotive Sector Risks
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-20%
-18%
-16%
-14%
-12%
-10%
-8%
-6%
-4%
-2%
0%
2%
4%
6%
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14
20
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20
16
Automotive Sector Employment
YOY growth
The Automotive job growth rate is trending lower.
2.2M
2.3M
2.4M
2.5M
2.6M
2.7M
2.8M
2.9M
3.0M
3.1M
20
05
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Automotive Sector Employment
Automotive Industry Employment Up 31% since Bottoming in Mid-2009;
Job Growth Is Already Slowing
Note: We define Automotive jobs as BLS NAICS codes: Manufacturing: Motor Vehicles and parts and Retail Trade: Auto parts, accessory, and tire stores. Please see
appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; BLS (Data: Dec-16, Pub: Feb-17)
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153%
49%
24%
-50%
0%
50%
100%
150%
2009 2010 2011 2012 2013 2014 2015 2016
Debt growth = 153% GDP growth = 49% Job growth = 24%
Automotive Sector Growth (2009–Current): Debt, GDP, Jobs
In addition to corporate debt, our primary concern is ballooning consumer auto debt and lax underwriting.
As shown on the following slide, consumer auto loans now exceed $1.1 trillion, roughly six times
outstanding corporate auto debt—which we believe is an unsustainable level.
Current Automotive Industry Boom: Corporate Debt +153%; 6 Times More
than Industry Job Growth and 3 Times More than Industry GDP Growth
Note: GDP data at sector level is annual and only goes through 2015. Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch, BEA, BLS (Data: Dec-16, Pub: Feb-17)
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0.7T
0.8T
0.9T
1.0T
1.1T
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Auto Loans Owned and Securitized
Trillions USD (NSA)
Other than student debt, auto debt has far outpaced other types of consumer debt growth since 2010.
Consumer Auto Loans Have Shot Up 58% since 2010 Bottom and
Now Exceed $1.1 Trillion Outstanding
Note: Includes motor vehicle loans owned and securitized by depository institutions, finance companies, credit unions, and nonfinancial business. Includes leases and loans
for passenger cars and other vehicles such as minivans, vans, sport-utility vehicles, pickup trucks, and similar light trucks for personal use. Loans for boats, motorcycles
and recreational vehicles are not included.
Sources: Federal Reserve, John Burns Real Estate Consulting, LLC (Data: 3Q16, Pub: Feb-17)
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50
100
150
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350
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<620 620–659 660–719 720–759 760+
Total Auto Loans Outstanding by Credit Score*
$ billions
Subprime auto loans are now well above prior peak levels.
Post Recession Auto Demand Fueled by Rapid Growth in Subprime
Loans Outstanding (Credit Score < 620); +58% since 2011 Bottom
*Credit score is Equifax Riskscore 3.0; Total auto loans are broken up between auto finance companies and banks/credit unions. Auto finance companies make up ~74% of
subprime loans outstanding. Auto finance companies also historically have much higher levels of delinquency.
Sources: New York Fed Consumer Credit Panel / Equifax; John Burns Real Estate Consulting, LLC (Data: 3Q16, Pub: Feb-17)
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4
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Cars Trucks
Total Auto Sales
Millions
Note: Trucks include cross-overs, SUVs, pickups, and vans.
Trucks have driven sales growth, while car sales have peaked.
Low interest rates, aggressive lending tactics, and extended lease terms of 5+ years drove the recovery in
auto sales. According to Edmunds.com, 32% (a record percent) of all trade-ins toward the purchase of a
new car were underwater through the 3Q16. The average negative equity balance is ~$5K (also a record).
Loose Lending Has Helped Fuel Record Auto Sales; Early Signs of
Industry Pulling Back
Note: Shown figures are a TTM average of seasonally adjusted number.
Sources: Ward Auto; John Burns Real Estate Consulting, LLC; Edmunds.com (Data: Dec-16, Pub: Feb-17)
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-5%
0%
5%
10%
15%
20%
25%
20
00
20
01
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02
20
03
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16
Interest rates that corporations must pay for debt represent a good proxy for industry risk. The Auto sector borrowing spread versus the high yield market is below its historical average of 0%—a sign lenders may be overly-complacent.
A negative spread means the sector is priced as less risky than the high yield market overall.
High Yield Automotive Sector Borrowing Spread
As auto sales slow and credit inevitably tightens for consumers (namely subprime), we anticipate corporate
borrowing costs will trend higher. We view self-driving cars and the rise of ride-sharing as an additional
long term risk for auto companies—further dragging on growth prospects.
Automotive Sector Corporate Borrowing Costs versus the Total Market
Remain Low by Historical Standards; Hit +20% during Great Recession
Note: The borrowing spread is the difference in Effective Yield of the BofA Merrill Lynch High Yield Automotive Sector Index and the BofA High Yield Index. We use the High
Yield Index (instead of Investment Grade) because these are the riskiest companies (most likely to default on debt). Please see appendix for details on methodology.
Sources: John Burns Real Estate Consulting, LLC; Bank of America Merrill Lynch (Data: Dec-16, Pub: Feb-17)
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“As Auto Lending Rises,
So Do Delinquencies”
- N Y T I M E S , 1 1 / 1 6
“Amid Rising Delinquencies,
Auto Lenders Scaling Back
Loans to Subprime Borrowers”
- W S J , 1 2 / 1 6
“Record Number of Car
Buyers ‘Upside Down’ on
Trade-Ins”
- U S A T O D AY, 1 1 / 1 6
1. Longer loan terms has
consumers trading in cars
worth less than the loans.
Aggressive Auto Lending Terms Attracting Media and Regulator
Attention; Delinquencies Rising as Subprime Lending Increases
Sources: John Burns Real Estate Consulting, LLC; Wall Street Journal; New York Times; USA Today (Pub: Feb-17)
2. +60-day delinquency on
subprime loans rose to 5%,
nearing 2008/09 levels.
1. 31% of new auto loans in
3Q16 had repayment periods
of 73-84 months (Experian).
1. Auto dealer qualifies buyer
on Social Security, receiving
food stamps, and living in
subsidized housing for $20K car
loan.
2. Economists fear that if
economy falters, many
consumers will lose cars given
increases in subprime loans
outstanding.
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Underwriting Terms on Subprime Auto Lending Exhibit Alarming
Characteristics—Reminiscent of Housing Bubble
Sources: John Burns Real Estate Consulting, LLC; Kroll Bond Rating Agency (Data: Dec-16, Pub: Feb-17)
• High APRs. Annual percentage rates (consumer borrowing costs) are between 16% and 23%! These high-risk
borrowers have very high interest rates, increasing the likelihood of default.
• Very low and no FICO. Between 15% and 29% of loans have no FICO.
• High LTV = negative equity. Borrowers are underwater on their loans.
• Extended terms. Lenders are able to keep the monthly payment down by extending the term.
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Auto-Dependent Housing Markets: Nationally, Auto Makes Up 2% of
Jobs; Detroit’s Job Exposure Is 2.9x US Average (6% of jobs)*
Note: Exposure numbers are a sector aggregation of BLS location quotient numbers. The location quotient represents the ratio of an occupation’s share of employment
in a given area to that occupation’s share of employment in the US as a whole. Please see appendix for details on methodology and for a more complete list of MSAs.
Sources: John Burns Real Estate Consulting, LLC; BLS; Center for Automotive Research (Data: May-16, Pub: Feb-17)
Nashville
Huntsville
Detroit
Greenville
2.1x
Louisville
2.1x
2.2x
1.9x
Ogden, UT
2.9x
4.1x
1.3x
Fort Worth
1.3x
Portland
Automotive Job Exposure vs. National Average
*Note: The Automotive industry has a very large multiplier effect, at an estimated 6x multiplier: every
auto job supports 6 additional jobs. Given this, we think our job figures are most likely understated.
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Methodology
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• We used three main data sets to analyze sectors of the economy: US Gross Domestic Product
(GDP), US Payroll, and US dollar denominated corporate debt data. All the data sets primarily focus on the private sector—private GDP/job/corporate debt growth.
• We pull GDP data through the Bureau of Economic Analysis (BEA)
http://www.bea.gov/industry/gdpbyind_data.htm. We use the Value Added by Industry (nominal) data.
• We pull US Payroll data through the Bureau of Labor Statistics (BLS)
http://www.bls.gov/webapps/legacy/cesbtab1.htm. We use seasonally adjusted monthly national
numbers from the Employment Statistics survey.
• We source US Dollar Corporate Debt data from Bank of America Merrill Lynch (BofA) US Corporate
Debt Indices, pulled through Bloomberg. We use US Dollar (USD) Face Value (Par Value) of the
indices for sector debt outstanding. The Face Value of an index is equal to the sum of the face values
of its constituent securities converted into the base currency (USD), where constituents’ face value is
equal to the total amount outstanding of the bond issue. We track both the High Yield (HY) and the
Investment Grade (IG) Indices. For the purposes of this study, we summed the face value of the
sector indices for both HY and IG to get an aggregate look at debt outstanding by sector. We use the
BofA sector breakouts and definitions to line up the BLS and BEA data for GDP and Jobs as best as
we could with the BofA sector groupings. See Sector Grouping slides below for more details.
• See the next page for a definition of the IG Index (similar description exists for HY) and an idea of
what type of bonds are included in our index dataset.
Methodology - Data Sets
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• The BofA Merrill Lynch US Corporate Investment Grade Index tracks the performance of US dollar
denominated investment grade corporate debt publicly issued in the US domestic market. Qualifying
securities must have an investment grade rating (based on an average of Moody’s, S&P and Fitch), at
least 18 months to final maturity at the time of issuance, at least one year remaining term to final
maturity as of the rebalancing date, a fixed coupon schedule, and a minimum amount outstanding of
$250 million.
• Callable perpetual securities are included, provided they are at least one year from the first call date.
Fixed to floating rate securities are included, provided they are callable within the fixed-rate period and
are at least one year from the last call prior to the date the bond transitions from a fixed to a floating
rate security.
• Index constituents are capitalization-weighted based on their current amount outstanding times the
market price plus accrued interest. Accrued interest is calculated assuming next-day settlement. Cash
flows from bond payments that are received during the month are retained in the index until the end of
the month and then are removed as part of the rebalancing.
• The borrowing rate we show throughout the report is Effective Yield of the BofA Merrill Lynch High
Yield Index for each respective sector. We use the High Yield Index (instead of Investment Grade)
because these are the most risky companies and the most likely to default on their debt. Effective
Yield as defined per Bloomberg is the average option-adjusted yield of constituents weighted by
market value. For the sector-borrowing spreads, we subtract the sector-effective yield from the Bank
of America High Yield Index (proxy for whole high yield market).
Methodology - Corporate Debt Data
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• The IG and HY indices we use are a good measure of corporate debt outstanding by sector in the US,
but it is important to remember there are some caveats. Two of the most important qualifications are
that these indices include USD denominated debt, with a maturity of +18 months.
• Thus, there are some foreign issuers that have US Dollar debt included in this analysis. To insure that
foreign issuers did not heavily skew the index, we checked to see if there are operations or
headquarters for each issuer in the US. Most (+90%) of the issuers had operations or headquarters in
the US, so we feel comfortable that the data is representative of US companies operating in the US—
creating jobs and stimulating local economies.
• The indices do not capture short term debt (<18 months until maturity) or private debt (i.e., term loans
from syndications or banks). These can make up large components of company financing, but data for
these debt types are not broken out by sector or readily available.
• As shown on the prior page, there are many qualifiers for what is included in the indices and tracked
over time. While these indices do not make up the entire universe of corporate bonds or borrowing in
the United States, we believe it is a good, measurable proxy for US corporate debt by sector.
• On the following pages, we show the breakdown of the components that make up our sectors. Using
BofA sectors as the benchmark, we grouped BLS & BEA data to line up with BofA groupings. The point
of this is to compare economic contribution (GDP), jobs, and debt by sector to attempt to observe
when debt is growing too fast relative to economic output (jobs and GDP). The groupings are not
perfect but are a good proxy. We will go into the caveats to the grouping on the following slides.
Methodology - Corporate Debt Data and Sector Grouping
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Methodology - Sector Grouping
Automotive
• Ford
• GM
• Toyota
• Daimler
• Honda
Group includes:
• Auto Loans
• Auto Parts &
Equipment
• Auto Makers
Manufacturing (Durable Goods):
Motor Vehicles and parts + Retail
Trade: Auto parts, accessory, and
tire stores
Manufacturing (Durable Goods): Motor
vehicles, bodies and trailers, and parts + Retail
Trade: Motor vehicle and parts dealers
Using the Automotive sector as an example, we outline how we group the sectors:
• Top issuers. This column shows the largest issuers as of January 2017 BofA Index constituents, as
measured by aggregate USD Face Value (outstanding debt). We provide this to give readers a sense
of the types of companies in the sector. For Automotive, the largest corporate debt issuers are some of
the large car manufacturers.
• Bank of America (BofA) Industry Index constituents. Includes sub groups or super groups of the
Bank of America sector to give readers a sense of types of companies within the grouping. For Auto,
the debt data includes auto loan, auto parts & equipment, and auto maker companies.
• Jobs. This column includes the components of the BLS data that we use to group jobs data to align
with the BofA data. This is denominated in terms of the number of people employed. In this example,
we sum the number of people employed in manufacturing vehicles and parts and retail auto dealers
employees to get total “Auto payroll.”
• GDP. This column includes components of the BEA data that we group to align the GDP dollar
amounts with the BofA sectors.
Sector Top IssuersBank of
America IndexJobs GDP
Sources: BofA Merrill Lynch, BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
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Air
Transportation
• American Airlines
• Southwest
• Virgin Air
• United
• Allegiant Travel
Part of the
"Transportation“ Group.
Transportation and Warehousing: Air
Transportation
Transportation and Warehousing: Air
Transportation
Automotive
• Ford
• GM
• Toyota
• Daimler
• Honda
Group includes:
• Auto Loans
• Auto Parts &
Equipment
• Auto Makers
Manufacturing (Durable Goods): Motor
Vehicles and parts + Retail Trade: Auto parts,
accessory, and tire stores
Manufacturing (Durable Goods): Motor
vehicles, bodies and trailers, and parts +
Retail Trade: Motor vehicle and parts
dealers
Banking &
Financial
Services*
• JPMorgan
• Bank of America
• Goldman Sachs
• Wells Fargo
• Morgan Stanley
Group includes:
• Banking
• Brokerage
• Lease Financing
• Investments & Misc.
Financial Services
Financial Activities: Finance and Insurance
subtracting out "Insurance carriers and related
activities” = Monetary Authorities (Central
Bank) + Credit Intermediation & Related
Activities + Securities, Commodity Contracts,
Investments, and Funds and Trusts
Finance, insurance, real estate, rental,
and leasing (Finance and Insurance Sub
Group): Federal Reserve Banks, Credit
Intermediation, and Related Activities +
Securities, Commodities Contracts, and
Investments + Funds, Trusts, and other
Financial Vehicles
Building &
Construction
• Lennar
• Pulte
• D.R. Horton
• CalAtlantic
• KB Home
Part of "Basic Industry“
Group. Group consists
of Home Builders
Construction: Construction of Buildings
(Residential Buildings) + Residential Specialty
Trade Contractors + Heavy and Civil
Engineering Construction + Professional and
Business Services: Architectural and
Engineering Services
Real Estate and Rental and Leasing:
(Real Estate Sub Group) Housing +
Construction
Building
Materials
• HD Supply
• Building Materials Corp
• MASCO
• CRH America
• Owens Corning
Part of "Basic Industry“
Group. Group consists
of Building Products
companies
Manufacturing (Durable Goods): Wood
Products + Nonmetallic Mineral Products +
Furniture and Related Products
Manufacturing (Durable Goods): Wood
Products + Nonmetallic Mineral Products
+ Furniture and Related Products
Capital Goods
• General Electric
• Caterpillar
• John Deere
• United Technologies
• Lockheed Martin
Group Includes:
• Aero/Defense
• Capital Goods
• Machinery
• Packaging
Manufacturing (Durable Goods): Machinery +
Electrical Equipment and Appliances +
(Transportation Equipment – Motor Vehicles
and Parts) + Misc. Durable Goods
Manufacturing
Manufacturing (Durable Goods):
Machinery + Electrical Equipment,
Appliances, and Components + Other
Transportation Equipment + Misc.
Manufacturing
*Banking and Financial Services are two separate groups within the BofA codes, but we combine for ease of analysis.
Sources: BofA Merrill Lynch, BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
Methodology - Sector Grouping
Sector Top IssuersBank of
America IndexJobs GDP
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Chemicals
• Dow Chemical
• Lyondell
• DuPont
• Praxair
• Monsanto
Part of "Basic Industry“
Group. Group consists
of Chemical companies
Manufacturing (Nondurable Goods):
Chemicals + Plastics and Rubber
Products
Manufacturing (Nondurable Goods):
Chemical Products + Plastics and Rubber
Products
(Commercial)
Real Estate*
• Simon Property
Group
• HCP
• Boston Property
• Health Care REIT
• Ventas Realty
Group includes:
• Housing Association
• Real Estate Dev &
Management
• REITs
Financial Activities: Real Estate and
Rental and Leasing (Real Estate) +
Construction: Non-residential specialty
trade contractor + Non-residential
building construction
Real Estate and Rental and Leasing: (Real
Estate Sub Group) Other Real Estate
Consumer Goods
& Retail**
• Anheuser-Busch
• Walmart
• CVS Health
• PepsiCo
• Kraft Heinz
Group includes:
• Consumer Goods
Beverage, Food -
Wholesale, Personal
& Household
Products, Tobacco
• Retail
Department,
Discount, Food &
Drug Stores,
Restaurants,
Specialty Stores
Manufacturing (Nondurable Goods): Food
Manufacturing + Textile Mills + Textile
Product Mills + Apparel + Misc.
Nondurable goods + Wholesale Trade +
Retail Trade: All Retail Trade except Auto
(Furniture Stores + Electronics &
Appliance Stores + Building Materials and
Garden Supply Stores + Food and
Beverage Stores + Health and Personal
Care Stores + Gasoline Stations +
Clothing and Accessories Stores +
Sporting Goods, Hobby, Books, Music
Stores + Retailers (General Merc, Misc.,
Nonstore) + Leisure and Hospitality: Food
Services and Drinking Places
Manufacturing (Nondurable Goods): Food
and Beverage and Tobacco Products + Textile
Mills and Textile Product Mills + Apparel and
Leather and Allied Products + Wholesale
Trade + Retail Trade: All Retail Trade except
Auto = Food and Beverage Stores + General
Merchandise Stores +
Other Retail + Arts, Entertainment, Recreation,
Accommodation, and Food Services: Food
Services and Drinking Places
Methodology - Sector Grouping
*The (Commercial) Real Estate group jobs and GDP do not include “Rental and Leasing” BLS/BEA data (it is included in “Services” sector) as it is a more broad definition of
leasing. It includes any intangible asset. These jobs and GDP dollars are included within “Services” as this sector includes companies like Hertz and United Rental. For
analysis, we combine the (Commercial) Real Estate sector with the Building & Construction sector as the “Real Estate” sector grouping. We feel this better represents the
sector as a whole. There is a lot of jobs/GDP overlap between the groups, so we felt grouping the two together for analysis was more appropriate.
**Consumer Goods and Retail are two separate Bank of America subgroups. We combined them because it best captures businesses related to consumer spending. For
Jobs/GDP, this sector also includes Wholesale Trade (intermediate step before reaching consumer). We also include Food Services (Restaurants) in this grouping.
Sources: BofA Merrill Lynch, BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
Sector Top IssuersBank of
America IndexJobs GDP
52
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Energy*
• Petroleos Mexicanos
• Shell
• Kinder Morgan
• Chevron
• BP Capital Markets
PLC
Group includes:
• Energy E&P
• Gas Distribution
• Integrated Energy
• Oil Field Equipment &
Services
• Oil Refining & Marketing
Mining and Logging: Oil and Gas
Extraction + Pro-rata share* of Support
Activities for Mining + Transportation and
Warehousing: Pipeline Transportation +
Manufacturing (Nondurable Goods): Pro-
rata share* of Petroleum and Coal
Products
Mining: Oil and Gas Extraction + Pro-rata
share* of Support Activities for Mining +
Transportation and Warehousing: Pipeline
Transportation + Manufacturing (Nondurable
Goods): Pro-rata share* of Petroleum and
Coal Products
Forestry / Paper**
• International Paper
• Georgia-Pacific
• Weyerhaeuser
• Inversiones CMPC
• Rock-Tenn
Part of "Basic Industry“
Group. Group consists of
Lumber and Paper
companies
Mining and Logging: Logging +
Manufacturing (Nondurable Goods):
Paper and Paper Products + Printing and
Related Support Services
Agriculture, Forestry, Fishing, and Hunting:
Forestry, Fishing, related activities +
Manufacturing (Nondurable Goods): Paper
and Paper Products + Printing and Related
Support Services
Health Care
• Abbvie
• Allergen
• Gilead Sciences
• UnitedHealthcare
• Pfizer
Group includes:
• Health Facilities
• Health Services
• Managed Care
• Medical Products
• Pharmaceuticals
Education and Health Services: Health
Care and Social Assistance
Education and Health Services: Health Care
and Social Assistance
Insurance
• Met Life
• Berkshire Hathaway
• AIG
• Prudential
• Chubb Corp
Group includes:
• Insurance Brokerage
• Life Insurance
• Monoline Insurance
• Multi-Line Insurance
• P&C
• Reinsurance
Financial Activities: Finance and
Insurance sub category "Insurance
Carriers and Related Activities"
Finance, insurance, real estate, rental, and
leasing: (Finance and Insurance Sub Group)
Insurance Carriers and Related Activities
Leisure
• MGM
• Marriott
• Scientific Games
• International Game
Technology
• Wynn Las Vegas
Group includes:
• Gaming
• Hotels
• Recreation & Travel
• Theaters & Entertainment
Leisure and Hospitality: Arts,
entertainment, and recreation +
Accommodations + Transportation and
Warehousing: Scenic and Sightseeing
Transportation
Arts, Entertainment, Recreation,
Accommodation, and Food Services: Arts,
Entertainment, and Recreation +
Accommodations (Entire group with Food
Services and Drinking Places Stripped out)
Methodology - Sector Grouping
*Jobs and GDP data for certain Energy & Mining categories are reported together (ie.”Support Activities for Oil/Gas”). To break up, we calculate the share of each as a
percentage of Total Mining and use this percentage to calculate the share of merged categories.
**Forestry / Paper GDP includes “fishing” and “related activities” because there is not a breakout for only “forestry.” This sector is a proxy for paper production and lumber.
Sources: BofA Merrill Lynch, BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
Sector Top IssuersBank of
America IndexJobs GDP
53
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Media
• Comcast
• Charter
Communications
• TimeWarner Cable
• 21 Century Fox
• Disney
Group Includes:
• Advertising
• Cable/Satellite TV
• Media – Diversified
• Media Content
• Print & Publishing
Information: Publishing Industries,
except Internet + Motion Picture and
Sound Recording Industries +
Broadcasting, except Internet + Other
Information Services
Information: Publishing Industries, except
Internet (Includes Software) + Motion Picture
and Sound Recording Industries +
(Broadcasting and Telecommunications
subtracting out Telecommunications as this
sector has its own breakout)
Metals / Mining /
Steel*
• Freeport-McMoran
• BHP Billiton
• Glencore
• Rio Tinto
• Codelco
Part of "Basic Industry“
Group. Sum of “Metals /
Mining Excluding Steel”
+ “Steel Producers /
Products.”
Mining and Logging: Mining, except Oil
and Gas + Pro-rata share* of Support
Activities for Mining + Manufacturing
(Durable Goods): Primary Metals +
Fabricated Metal Products +
Manufacturing (Nondurable Goods): Pro-
rata share* of Petroleum and Coal
Products
Mining: Mining, except Oil and Gas + Pro-rata
share* of Support Activities for Mining +
Manufacturing (Durable Goods): Primary Metals
+ Fabricated Metal Products + Manufacturing
(Nondurable Goods): Pro-rata share* of
Petroleum and Coal Products
Rail
Transportation**
• Burlington Northern
• Union Pacific
• Norfolk Southern
• CSX Corp
• Canadian National
Railroad
Part of the
"Transportation“ Group.
Transportation and Warehousing: Rail
Transportation
Transportation and Warehousing: Rail
Transportation
Services***
• Enterprise Holdings
• Republic Services
• Waste Management
• United Rentals
• ADT Corp
Group Includes:
• Environmental
• Support-Services
Professional and Business Services:
Administrative and Waste Services +
Financial Activities: Real Estate and
Rental and Leasing (Rental and Leasing)
+ (Lessors of nonfinancial intangible
assets)
Professional and Business Services:
Administrative and Waste Services + Finance,
insurance, real estate, rental, and leasing: Real
Estate and Rental and Leasing (Rental and
Leasing Services and Lessors of Intangible
Assets)
Methodology - Sector Grouping
*Jobs and GDP data for certain Energy & Mining categories are reported together (ie.”Support Activities for Oil/Gas”). To break up, we calculate the share of each as a
percentage of Total Mining and use this percentage to calculate the share of merged categories.
**Rail Transportation is displayed as its own grouping in the debt/jobs/GDP growth analysis because this sector has its own breakout for both HY and IG bond indices.
Transportation Ex Rail includes Air Transportation, Transport Infrastructure/Services, and Trucking & Delivery sectors. These are grouped together for the analysis.
***Services is defined very broadly by Bank of America and is not the same as BLS/BEA Professional and Business Services code. It includes securities like University bonds
(Cornell, CalTech), but also bonds for waste management companies and other miscellaneous services like ADP, Expedia, and HR Block.
Sources: BofA Merrill Lynch, BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
Sector Top IssuersBank of
America IndexJobs GDP
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Technology &
Electronics
• Microsoft
• Apple
• Oracle
• Dell
• Cisco
Group Includes:
• Electronics
• Software/Services
• Tech Hardware &
Equipment
Manufacturing (Durable Goods):
Computer and Electronic Products +
Information: Data Processing,
Hosting, and Related Services +
Professional and Business Services:
Computer Systems Design and
Related Services
Manufacturing (Durable Goods): Computer
and Electronic Products + Information:
Data Processing, Hosting, and Related
Services + Professional and Business
Services: Computer Systems Design and
Related Services + Misc. Professional,
Scientific, and Technical Services
Telecommunications
• AT&T
• Verizon
• Sprint
• T-Mobile
• Frontier
Communications
Group Includes:
• Satellite
• Wireless
• Wireline Integrated &
Services
Information: Telecommunications
There is not a breakout within the GDP
data of Telecom so we proxy by taking the
annual average % share of Telecom jobs
within Information to calculate: % share *
Information: Broadcasting and
Telecommunications.
Transport
Infrastructure /
Services
• DP World
• Sydney Airport
• AP Moeller
• XPO Logistics
• Asciano Limited
Part of the
"Transportation“ Group.
Transportation and Warehousing:
Water Transportation + Transit and
Ground Passenger Transportation +
Support activities for Transportation +
Couriers and Messengers
Transportation and Warehousing: Water
Transportation + Transit and Ground
Passenger Transportation + Other
Transportation and Support activities
Trucking & Delivery
• Fedex
• UPS
• Penske
• Ryder Systems
• JB Hunt
Transport
Part of the
"Transportation“ Group.
Transportation and Warehousing:
Truck Transportation + Warehousing
and Storage
Transportation and Warehousing: Truck
Transportation + Warehousing and
Storage
Utility
• Duke Energy
• Exelon
• Southern Power
• MidAmerican
Energy
• Dominion
Resources
Group Includes:
• Electric-Distr/Trans
• Electric-Generation
• Electric-Integrated
• Non-Electric Utilities
Utilities Utilities
Methodology - Sector Grouping
Sources: BofA Merrill Lynch, BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
Sector Top IssuersBank of
America IndexJobs GDP
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Methodology - Job and GDP Sectors Excluded from Analysis
BLS Super Sector Sector Reason Excluded
Professional and Business Services
Legal Services
Spread out across industries. Each sector has
many of these jobs attached to them, and there is
no appropriate way to break them out. “Legal
Services” & “Management and Technical
Consulting Services” were also excluded from
GDP groupings.
Accounting and Bookkeeping
Management of Companies
Management and Technical Consulting Services
Other Professional and Technical Services
Scientific Research and Development Services
Too broad. These job codes are not a good proxy
for any particular sector.Specialty Design Services
Advertising and Related Services
Other Services
Other Services
Too broad. These job codes are not a good proxy
for any particular sector.
“Other Services” was also excluded from GDP
groupings.
Repair and Maintenance
Personal and Laundry
Membership Associations and Organizations
Education and Health Services Educational ServicesThese jobs are not a good proxy for any sector.
Also excluded from GDP data.
The above BLS NAICS job codes are not included in our analysis. “Agriculture, forestry, fishing, and hunting: Farms” GDP was excluded. Agriculture jobs were also
excluded from the job analysis and the Location Quotient analysis. Government jobs are not included in any of this analysis. We focus on the private sector.
Sources: BLS, BEA, John Burns Real Estate Consulting, LLC (Pub: Feb-17)
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Methodology - Sector Exposure by MSA
• A location quotient is the concentration of an occupation or sector in a MSA divided by the average concentration of that occupation or sector nationwide. For example, an occupation that makes up 10%
of employment in a MSA compared with 2% of US employment would have a location quotient of 5 for the MSA. This is how we roll out our sector thesis to the MSA level. See page 58 for a table with location quotients by MSA.
• The MSA job data, while similar, is not the exact same data set as the national jobs figures. The data source is the Occupational Employment Statistics (OES): May 2015 Data (released March 2016), from the BLS (https://www.bls.gov/oes/). Specifically, we used the “Metropolitan and nonmetropolitan”
data for job classification by MSA, and the “National industry-specific and by ownership” data
for our job concentration by sector analysis. This data includes employment counts for 800
occupations, and uses 3 years of semiannual data to reduce sampling estimates. We like this data
source because it is more complete than alternatives and segments on Metropolitan Divisions.
• To bring our national sector analysis down to the MSA level, we applied the same basic principal of classifying job codes to match Bank of America sectors. We classified all job codes, and analyzed the concentration of job codes within the BLS sector codes to aid our classification. Job codes can be spread out among several sectors. For example, Administrative Assistants are spread across sectors pretty evenly, while Dental Assistants are not. We made our best judgement on where the job code should fall based on these concentrations, to align most closely with our BofA sector definitions.
• The BLS provides the location quotient and total employment for each job code. Once we segmented the job codes, we aggregated all the sector jobs via a weighted average for each MSA based on total employment to get a sector location quotient for each MSA. These are the numbers on the maps.
57
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Methodology - Sector Exposure by MSA: Health Care
Metropolitan Statistical Area (MSA)Location
Quotient*
McAllen-Edinburg-Mission, TX 3.5
Rochester, MN 2.8
Durham-Chapel Hill, NC 2.1
Jackson, MS 1.8
Philadelphia, PA 1.5
Boston-Cambridge-Newton, MA 1.4
Winston-Salem, NC 1.4
New York-Jersey City-White Plains, NY-NJ 1.4
Spokane-Spokane Valley, WA 1.4
Augusta-Richmond County, GA-SC 1.3
Little Rock-North Little Rock-Conway, AR 1.3
Tucson, AZ 1.3
Metropolitan Statistical Area (MSA)Location
Quotient*
Cleveland-Elyria, OH 1.3
Milwaukee-Waukesha-West Allis, WI 1.3
Tacoma-Lakewood, WA 1.3
El Paso, TX 1.3
Birmingham-Hoover, AL 1.3
Albuquerque, NM 1.3
Wilmington, NC 1.3
Lincoln, NE 1.3
San Antonio-New Braunfels, TX 1.2
Minneapolis-St. Paul-Bloomington, MN-WI 1.2
Tampa-St. Petersburg-Clearwater, FL 1.1
Jacksonville, FL 1.1
*Refer to page 57 for a definition of location quotient.
The list for sector exposure by MSA shows MSAs with SF permits +1K, employment +200K, and/or deemed a top market by JBREC.
Source: BLS, John Burns Real Estate Consulting, LLC (Data: May-16, Pub: Feb-17)
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Methodology - Sector Exposure by MSA: Health Care
Metropolitan Statistical Area (MSA)Location
Quotient*
San Francisco-Redwood City-S. San Fran, CA 1.1
West Palm Beach-Boca Raton-Delray Beach, FL 1.1
Nashville-Davidson--Murfreesboro--Franklin, TN 1.1
Miami-Miami Beach-Kendall, FL 1.0
San Diego-Carlsbad, CA 1.0
Phoenix-Mesa-Scottsdale, AZ 1.0
Sacramento--Roseville--Arden-Arcade, CA 1.0
Chicago-Naperville-Arlington Heights, IL 1.0
Dallas-Plano-Irving, TX 0.9
Denver-Aurora-Lakewood, CO 0.9
Charlotte-Concord-Gastonia, NC-SC 0.9
Salt Lake City, UT 0.9
Metropolitan Statistical Area (MSA)Location
Quotient*
Riverside-San Bernardino-Ontario, CA 0.9
Raleigh, NC 0.9
Los Angeles-Long Beach-Glendale, CA 0.9
Houston-The Woodlands-Sugar Land, TX 0.9
Orlando-Kissimmee-Sanford, FL 0.9
Seattle-Bellevue-Everett, WA 0.9
Anaheim-Santa Ana-Irvine, CA 0.9
Atlanta-Sandy Springs-Roswell, GA 0.9
Austin-Round Rock, TX 0.9
San Jose-Sunnyvale-Santa Clara, CA 0.8
Las Vegas-Henderson-Paradise, NV 0.8
Washington-Arlington-Alexandria, DC-VA-MD-WV 0.8
*Refer to page 57 for a definition of location quotient.
The list for sector exposure by MSA shows MSAs with SF permits +1K, employment +200K, and/or deemed a top market by JBREC.
Source: BLS, John Burns Real Estate Consulting, LLC (Data: May-16, Pub: Feb-17)
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Methodology - Sector Exposure by MSA: Technology
Metropolitan Statistical Area (MSA)Location
Quotient*
San Jose-Sunnyvale-Santa Clara, CA 7.4
Seattle-Bellevue-Everett, WA 3.4
San Francisco-Redwood City-S. San Fran, CA 3.1
Washington-Arlington-Alexandria, DC-VA-MD-WV 3.0
Portland-Vancouver-Hillsboro, OR-WA 2.8
Austin-Round Rock, TX 2.5
Crestview-Fort Walton Beach-Destin, FL 2.4
Little Rock-North Little Rock-Conway, AR 2.3
Durham-Chapel Hill, NC 2.2
Palm Bay-Melbourne-Titusville, FL 2.2
Colorado Springs, CO 2.1
Boston-Cambridge-Newton, MA 2.1
Metropolitan Statistical Area (MSA)Location
Quotient*
Raleigh, NC 2.1
Madison, WI 2.0
Dallas-Plano-Irving, TX 1.8
Baltimore-Columbia-Towson, MD 1.8
Denver-Aurora-Lakewood, CO 1.7
Provo-Orem, UT 1.7
Oakland-Hayward-Berkeley, CA 1.7
Columbus, OH 1.6
Minneapolis-St. Paul-Bloomington, MN-WI 1.6
Sacramento--Roseville--Arden-Arcade, CA 1.5
Atlanta-Sandy Springs-Roswell, GA 1.5
San Diego-Carlsbad, CA 1.5
*Refer to page 57 for a definition of location quotient.
The list for sector exposure by MSA shows MSAs with SF permits +1K, employment +200K, and/or deemed a Top Market by JBREC. We believe some Aerospace
and Defense type “tech” jobs are showing up in some of these figures, given jobs overlap. We are focusing more on consumer tech for our analysis.
Source: BLS, John Burns Real Estate Consulting, LLC (Data: May-16, Pub: Feb-17)
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Methodology - Sector Exposure by MSA: Technology
Metropolitan Statistical Area (MSA)Location
Quotient*
Harrisburg-Carlisle, PA 1.5
Albany-Schenectady-Troy, NY 1.5
Ogden-Clearfield, UT 1.5
Newark, NJ-PA 1.5
Omaha-Council Bluffs, NE-IA 1.4
Kansas City, MO-KS 1.4
Rochester, NY 1.4
Charlotte-Concord-Gastonia, NC-SC 1.4
Phoenix-Mesa-Scottsdale, AZ 1.4
Salt Lake City, UT 1.3
Anaheim-Santa Ana-Irvine, CA 1.3
Warren-Troy-Farmington Hills, MI 1.3
Metropolitan Statistical Area (MSA)Location
Quotient*
Chicago-Naperville-Arlington Heights, IL 1.2
Tampa-St. Petersburg-Clearwater, FL 1.2
Houston-The Woodlands-Sugar Land, TX 1.1
San Antonio-New Braunfels, TX 1.1
Philadelphia, PA 1.0
Jacksonville, FL 1.0
West Palm Beach-Boca Raton-Delray Beach, FL 0.9
Orlando-Kissimmee-Sanford, FL 0.9
Los Angeles-Long Beach-Glendale, CA 0.9
Miami-Miami Beach-Kendall, FL 0.7
Las Vegas-Henderson-Paradise, NV 0.6
Riverside-San Bernardino-Ontario, CA 0.4
*Refer to page 57 for a definition of location quotient.
The list for sector exposure by MSA shows MSAs with SF permits +1K, employment +200K, and/or deemed a Top Market by JBREC. We believe some Aerospace
and Defense type “tech” jobs are showing up in some of these figures, given jobs overlap. We are focusing more on consumer tech for our analysis.
Source: BLS, John Burns Real Estate Consulting, LLC (Data: May-16, Pub: Feb-17)
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Methodology - Sector Exposure by MSA: Automotive
Metropolitan Statistical Area (MSA)Location
Quotient*
Greenville-Anderson-Mauldin, SC 4.1
Detroit-Dearborn-Livonia, MI 2.9
Grand Rapids-Wyoming, MI 2.8
Fort Wayne, IN 2.6
Louisville/Jefferson County, KY-IN 2.2
Huntsville, AL 2.1
Nashville-Davidson--Murfreesboro--Franklin, TN 2.1
Ogden-Clearfield, UT 1.9
Chattanooga, TN-GA 1.9
Lexington-Fayette, KY 1.9
Warren-Troy-Farmington Hills, MI 1.8
Tulsa, OK 1.6
Metropolitan Statistical Area (MSA)Location
Quotient*
Knoxville, TN 1.5
North Port-Sarasota-Bradenton, FL 1.4
Columbia, SC 1.4
Indianapolis-Carmel-Anderson, IN 1.4
Milwaukee-Waukesha-West Allis, WI 1.4
Portland-Vancouver-Hillsboro, OR-WA 1.3
Charlotte-Concord-Gastonia, NC-SC 1.3
Fort Worth-Arlington, TX 1.3
Riverside-San Bernardino-Ontario, CA 1.2
Anaheim-Santa Ana-Irvine, CA 1.1
Jacksonville, FL 1.1
Salt Lake City, UT 1.0
*Refer to page 57 for a definition of location quotient.
The list for sector exposure by MSA shows MSAs with SF permits +1K, employment +200K, and/or deemed a top market by JBREC.
Source: BLS, John Burns Real Estate Consulting, LLC (Data: May-16, Pub: Feb-17)
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Methodology - Sector Exposure by MSA: Automotive
Metropolitan Statistical Area (MSA)Location
Quotient*
Atlanta-Sandy Springs-Roswell, GA 1.0
Raleigh, NC 1.0
Tampa-St. Petersburg-Clearwater, FL 1.0
San Antonio-New Braunfels, TX 1.0
Orlando-Kissimmee-Sanford, FL 1.0
West Palm Beach-Boca Raton-Delray Beach, FL 1.0
Miami-Miami Beach-Kendall, FL 1.0
Houston-The Woodlands-Sugar Land, TX 0.9
Phoenix-Mesa-Scottsdale, AZ 0.9
Sacramento--Roseville--Arden-Arcade, CA 0.9
Dallas-Plano-Irving, TX 0.9
Minneapolis-St. Paul-Bloomington, MN-WI 0.9
Metropolitan Statistical Area (MSA)Location
Quotient*
Chicago-Naperville-Arlington Heights, IL 0.9
San Diego-Carlsbad, CA 0.9
Los Angeles-Long Beach-Glendale, CA 0.8
Denver-Aurora-Lakewood, CO 0.8
Seattle-Bellevue-Everett, WA 0.8
Las Vegas-Henderson-Paradise, NV 0.8
Austin-Round Rock, TX 0.8
Washington-Arlington-Alexandria, DC-VA-MD-WV 0.7
Philadelphia, PA 0.7
San Jose-Sunnyvale-Santa Clara, CA 0.6
Boston-Cambridge-Newton, MA 0.6
San Francisco-Redwood City-S. San Fran, CA 0.5
*Refer to page 57 for a definition of location quotient.
The list for sector exposure by MSA shows MSAs with SF permits +1K, employment +200K, and/or deemed a top market by JBREC.
Source: BLS, John Burns Real Estate Consulting, LLC (Data: May-16, Pub: Feb-17)
63
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• Comparative sector analysis. For all the comparative analysis, GDP data goes through year-end 2015. The sector breakout for GDP is only available annually. Jobs and debt data goes through December 2016. The 2009–current growth rates start at 1/1/09 for jobs and debt and 12/31/2008 for GDP, as these are end-of-year values.
• Case studies. For the case studies (Energy and Financials), we define the boom/bust cycle based on job growth and the start of the jobs decline. The Financial job growth cycle is calculated from 1/1/2000 to 11/1/2006, while debt calculations take growth from 1/1/2000 to 1/1/2007, and GDP gains are from 12/31/1999 to 12/31/2006. The Energy job growth cycle and corresponding debt/GDP calculations are from 12/31/09 to 12/31/14.
• National-level debt-to-GDP and debt-to-jobs ratios. For the national debt ratios (page 5), we use SIFMA data (http://www.sifma.org/research/statistics.aspx) for the outstanding debt data. SIFMA outstanding corporate debt data only goes back to 1980, but we were able to construct the time series back to 1950 using their methodology (Summing components of the Federal Reserve Flow of Funds
Z.1 Statistical Release for Dec 8, 2016 https://www.federalreserve.gov/releases/z1/current/). We summed Nonfinancial Corporate Business Bonds (liability) + Domestic Financial Sectors; Corporate and Foreign Bonds (liability) + Issuers of Asset Backed Securities (Corporate and Foreign Bonds; liabilities). We then calculated the average YOY growth from 1Q1950 to 3Q2016 and from 1Q2009 to 3Q2016. Jobs data is from the BLS (https://data.bls.gov/timeseries/CES0500000001). We take the average YOY growth in private employees since 1/1/1950 and since 1/1/2009. GDP data uses the same source as the industry analysis. We calculate average YOY growth rate in private sector GDP. We then take the average YOY growth rates for debt, GDP, and jobs and calculate the debt-to-GDP growth rate ratio and debt-to-job growth rate ratio to analyze this cycle vs. historical average.
Methodology - Calculation Detail
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• Sector-level debt-to-GDP and debt-to-jobs ratios. Debt-to-GDP and debt-to-jobs ratios (page 6) take 1/1/2009 to 12/31/2016 Bank of America sector debt growth divided by the job growth over that period, and GDP growth over that period (12/31/2008 to 12/31/2015 as GDP data is annual and as of year-end). The purpose of this calculation is to highlight historical boom/busts (Energy and Financials) and compare current cycle sector growth rates and ratios to these prior cycles.
• Real GDP growth rate during expansions. Using the National Bureau of Economic Research
(NBER) dates for the expansion start and end for various US recessions, we calculated the associated
real GDP growth over that time period (page 10). The GDP data is a quarter-over-quarter seasonally
adjusted annualized rate (chained 2009 dollars
https://fred.stlouisfed.org/series/A191RL1Q225SBEA). We assigned a quarter to each start and end date of a NBER recession and calculated the total growth for the associated expansions. There are instances where an expansion starts mid-quarter, so our calculation captures months that may not be technically in the expansion. We used real GDP (as opposed to nominal), as inflation-regimes were vastly different in the last 60+ years. We felt an inflation-adjusted growth number was more representative. Throughout the rest of this analysis we use nominal GDP figures.
Methodology - Calculation Detail
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Company Debt
Microsoft $56B
Apple $55B
Oracle $47B
Dell $31B
Cisco $28B
IBM $22B
Intel $17B
Visa $14B
Hewlett-Packard $12B
First Data $10B
Top 10 Total $291B
Top 10 % Sector 60%
Sources: BofA Merrill Lynch, John Burns Real Estate Consulting, LLC (Data: Jan-17, Pub: Feb-17)
Methodology - Top Companies’ Share of Sector Debt
Company Debt
Ford $38B
General Motors $31B
Toyota $20B
DaimlerChrysler $19B
American Honda $12B
Hyundai Americas $7B
BMW $6B
Volkswagen $5B
Nissan Motor $4B
Harley-Davidson $4B
Top 10 Total $145B
Top 10 % Sector 79%
Company Debt
AbbVie $31B
Actavis $29B
Gilead Sciences $26B
UnitedHealth $26B
Pfizer $26B
Amgen $25B
Medtronic $25B
HCA $23B
Abbott Labs $21B
Aetna $19B
Top 10 Total $252B
Top 10 % Sector 38%
Technology Automotive Health Care
• We wanted to understand the magnitude of corporate debt issued by large, well established firms vs. small firms to better
understand the debt risk profile. See results in the tables below for the industries in focus.
• The Auto sector’s top 10 largest corporate debt issuers make up 79% of total sector’s debt. This is not surprising given the
industry had a wave of consolidation and is dominated by a few large companies.
• We were also concerned that IPOs accounted for a large chunk of new debt, particularly in the Tech sector. We checked if
companies in the Tech index filed for IPO in the last 10 years (a conservative timeframe). Less than 15% of these companies
fell into this “recent IPO” category, so we do not believe IPO activity is skewing this debt data.
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