industries at risk and implications for...

KATE SEABAUGHSr. Research Analyst

[email protected](949) 870-1211

RICK PALACIOS, JR.Director of Research


FEBRUARY 2017

Industries at Risk

JOHN BURNSCEO


Health Care, Technology, and Automotive

and Implications for Housing

See Terms & Conditions of Use and Disclaimers. Distribution to Non-Clients is Prohibited © 2017

Executive Summary

2


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


#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


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


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


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)

7


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

8


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)

9


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


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)

11


-10%

-5%

0%

5%

10%

15%

20%

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

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.


12


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


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.


13


-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

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20

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09

20

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20

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20

14

20

15

20

16

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



14


Health Care Sector Risks

15


10%

11%

12%

13%

14%

15%

16%

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

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04

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05

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06

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07

20

08

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09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

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%

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

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


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


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.


17


-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

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.


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



18


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


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


2,500

2,600

2,700

2,800

2,900

3,000

3,100

3,200

19

99

20

00

20

01

20

02

20

03

20

04

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05

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07

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08

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13

20

14

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


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


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)

23

http://www.bizjournals.com/sanfrancisco/news/2016/09/09/cpmc-hospital-hits-half-way-mark.html

http://www.dmagazine.com/publications/d-ceo/2014/november/dallas-fort-worth-hospital-construction-boom/

http://www.bizjournals.com/tampabay/news/2016/04/04/m-a-low-rates-contribute-to-hospital-construction.html

http://forwardflorida.com/healthcare/floridas-hospital-construction-boom-part-1/

http://www.bizjournals.com/atlanta/news/2016/12/12/piedmont-hospital-gets-approval-for-603-million.html

http://www.wsj.com/articles/new-york-city-hospitals-spend-billions-as-they-expand-and-update-1480639823

http://www.mydaytondailynews.com/business/60m-hospital-expansion-bring-100-jobs-dayton-area/zD8L3fXV4QM7QHepiWgZSN/

http://gazette.com/health-care-building-boom-underway-in-colorado-springs-area/article/1564077

http://www.beckershospitalreview.com/finance/healthcare-construction-booming-across-houston-6-things-to-know.html


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

20

02

20

03

20

04

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16

Annual Medical Costs for Average Family of Four*

24


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


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.”

26

http://www.wsj.com/articles/community-health-systems-retrenches-1476091800

http://www.chron.com/news/houston-texas/article/MD-Anderson-set-to-announce-layoffs-today-10837056.php

https://www.bloomberg.com/news/articles/2017-01-11/drug-stocks-plunge-as-trump-threatens-to-force-price-bidding

http://www.economist.com/news/united-states/21711333-president-elect-formerly-pharma-industrys-preferred-candidate-has-promised-bring?fsrc=scn/tw/te/bl/ed/highpricetagsfornewmedicinesareabouttocomeunderrenewedpressure


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

27


Technology Sector Risks

28


-6%

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

20

07

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08

20

09

20

10

20

11

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.


29


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


Technology Sector Growth (2009–Current): Debt, GDP, Jobs

Current Technology Industry Boom: Corporate Debt +322%, 20 Times More




30


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

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

31


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“

Twitter

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

http://www.reuters.com/article/us-tech-layoffs-idUSKCN10T02R

http://www.usnews.com/news/economy/articles/2016-10-27/are-layoffs-at-twitter-and-alphabet-a-sign-of-a-bursting-bubble

http://money.cnn.com/2016/04/19/technology/intel-layoffs/

http://www.wsj.com/articles/ibm-layoffs-continue-1463784692

http://fortune.com/2016/07/28/microsoft-layoffs-thousands-phone/

http://money.cnn.com/2016/08/17/technology/cisco-layoffs-earnings/

http://money.cnn.com/2016/10/27/technology/twitter-layoffs-earnings/

http://fortune.com/2016/10/18/hpe-layoffs/


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


-2%

-1%

0%

1%

2%

3%

4%

5%

6%

7%

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

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.


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.


34


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


Automotive Sector Risks

36


-20%

-18%

-16%

-14%

-12%

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

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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06

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11

20

12

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14

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20

16

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.


37


153%

49%

24%

-50%

0%

50%

100%

150%

2009 2010 2011 2012 2013 2014 2015 2016


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




38


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)

39


50

100

150

200

250

300

350

20

01

20

02

20

03

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20

16

<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)

40


4

5

6

7

8

9

10

11

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

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20

16

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)

41


-5%

0%

5%

10%

15%

20%

25%

20

00

20

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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.


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



42


“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.

43

http://www.wsj.com/articles/amid-rising-delinquencies-auto-lenders-scalingbackloans-to-subprime-borrowers-1480945500

http://www.nytimes.com/2016/11/30/business/dealbook/as-auto-lending-rises-so-do-delinquencies.html?_r=0

http://www.usatoday.com/story/money/cars/2016/11/27/record-number-car-buyers-upside-down-trade-ins/94506786/


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.

44


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.

45


Methodology

46


• 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

47

http://www.bea.gov/industry/gdpbyind_data.htm

http://www.bls.gov/webapps/legacy/cesbtab1.htm


• 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

48


• 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

49


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)

50


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


Vehicles and parts + Retail Trade: Auto parts,

accessory, and tire stores


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



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


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.





51


Chemicals

• Dow Chemical

• Lyondell

• DuPont

• Praxair

• Monsanto



of Chemical companies

Manufacturing (Nondurable Goods):

Chemicals + Plastics and Rubber

Products


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


*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.




52


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


Group. Group consists of

Lumber and Paper

companies

Mining and Logging: Logging +


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)


*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.




53


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


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 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)


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)


*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.




54


Technology &

Electronics

• Microsoft

• Apple

• Oracle

• Dell

• Cisco

Group Includes:

• Electronics

• Software/Services

• Tech Hardware &

Equipment


Computer and Electronic Products +

Information: Data Processing,

Hosting, and Related 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 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 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





55


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)

56


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

https://www.bls.gov/oes/


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


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)

58


Methodology - Sector Exposure by MSA: Health Care


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


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




59


Methodology - Sector Exposure by MSA: Technology


Quotient*





Portland-Vancouver-Hillsboro, OR-WA 2.8


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



Quotient*

Raleigh, NC 2.1

Madison, WI 2.0


Baltimore-Columbia-Towson, MD 1.8


Provo-Orem, UT 1.7

Oakland-Hayward-Berkeley, CA 1.7

Columbus, OH 1.6






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.


60


Methodology - Sector Exposure by MSA: Technology


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





Warren-Troy-Farmington Hills, MI 1.3


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.


61


Methodology - Sector Exposure by MSA: Automotive


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


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


Fort Worth-Arlington, TX 1.3








62


Methodology - Sector Exposure by MSA: Automotive


Quotient*


Raleigh, NC 1.0












Quotient*
















63


• 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

64

http://www.sifma.org/research/statistics.aspx

https://www.federalreserve.gov/releases/z1/current/

https://data.bls.gov/timeseries/CES0500000001


• 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

65

https://fred.stlouisfed.org/series/A191RL1Q225SBEA


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.

66

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