September 2015 Report - UCLA Anderson Forecast · 2016-10-14 · September 2015 Report THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA. FORECASTS: 2015 3. rd. ... Patricia

September 2015 Report

THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA

FORECASTS: 2015 3rd Quarter 2017 4th Quarter

64rd Year

UCLA Anderson Forecast

Director:Edward E. LeamerProfessor of Global Economics and Management and Chauncey J. Medberry Chair in Management

The UCLA Anderson Forecast Staff:Jerry Nickelsburg, Senior Economist, Adjunct Professor of Economics, UCLA Anderson School David Shulman, Senior EconomistWilliam Yu, EconomistPatricia Nomura, Economic Research and Managing EditorEydie Grossman, Director of Business Development George Lee, Publications and Marketing Manager

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This forecast was prepared based upon assumptions reflecting the Project’s judgements as of the date it bears. Actual results could vary materially from the forecast. Neither the UCLA Anderson Forecast nor The Regents of the University of California shall be held responsible as a consequence of any such variance. Unless approved by the UCLA Anderson Forecast, the publication or distribution of this forecast and the preparation, publication or distribution of any excerpts from this forecast are prohibited.

Published quarterly by the UCLA Anderson Forecast, a unit of UCLA Anderson School of Management.

Copyright 2015 by the Regents of the University of California.

The Quarterly Forecast:

“Housing is Back”

Upcoming Events:

Winter Quarterly Conference December 2015Spring Quarterly Conference March 2016Orange County Economic Outlook for 2015 April 2016Summer Conference June 2016Fall Quarterly Conference September 2016

September 2015 Report

THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA

Nation California

Is the Expansion Old? 11Can it Withstand a Rate Increase? Edward Leamer

Housing is BACK 21David Shulman

Homeownership Decline - A Bump in 27the Road for the Housing Market? Joel Singer

Charts 29Recent Evidence

Charts 34Forecast

Tables 43Short-Term

Tables 47Detailed

California Housing - 59Will it Ever Be Affordable? Jerry Nickelsburg

China Syndrome and Its Impact on 67Los Angeles’ Economy and Housing Market William Yu

Charts 79Recent Evidence

Charts 84Forecast

Tables 91Summary

Tables 95Detailed

SEPTEMBER 2015 REPORT

THE UCLA ANDERSON FORECAST FOR THE NATION

Is the Expansion Old? Can It Withstand a Rate Increase?

Housing is BACK

UCLA Anderson Forecast, September 2015 Nation–11

IS THE EXPANSION OLD? CAN IT WITHSTAND A RATE INCREASE?

Is the Expansion Old? Can it Withstand a Rate Increase?Edward LeamerDirector, UCLA Anderson ForecastSeptember 2015

Two items will surely change in the coming year, and each raises risks for the economy.

The current expansion which began in 2009Q3 has already exceeded the lifetimes of most previous expansions and in the next year will get a year older, unless there is a recession. Is the inevitable next recession coming due, or is there a different clock for measuring the age of an expan-sion? Answer: the clock is different.

Since its birth, this expansion has been on life-support, courtesy of the Fed’s rock-bottom interest rates. Always before, this dosage level has been used only for emergency neo-natal care. With no experience to rely on, neither the Fed doctors nor the rest of us really know what will happen when the medicine is withdrawn from this aging patient. Are the zero Fed interest rates the economic equivalent of Medicare for a 90 year old?

But don’t worry. We think things are okay, the expan-sion is quite likely to continue for at least a couple of more years, and will suffer only minor withdrawal problems as the Fed hikes rates. If you are in the auto sector, you have a bit more to worry about.

We are forecasting GDP growth in the next couple of years in the 2.5 to 3% range. The unemployment rate drifts down a bit, but discouraged workers returning to the labor market tends to hold the unemployment rate up. Pay-rolls grow at a better-than-normal rate slightly above 200 thousand a month. Notably, we see inflation ticking up a full percentage point to 3% for the Consumer Price Index in 2016. This stimulates an increase in interest rates at all maturities, including the Fed funds rate, which moves all the way to 3 % by the end of 2017.

12–Nation UCLA Anderson Forecast, September 2015


It’s an old, but still vigorous expansion

The United States has experienced eleven recessions since 1948 and eleven subsequent expansions. In August of 2015, we are in the 25th quarter of the most recent expansion, one quarter longer than the Bush W expansion that began in 2001 and ended in 2007. The current expansion has been exceeded in length by only the three longest expansions: the Bush/Clinton expansion of the 1990s, the Reagan ex-pansion of the 1980s and the Kennedy/Johnson expansion of the 1960s. Based on that history, you might think there is an 8/10 chance of the end of this expansion soon, maybe because of an economic heart attack when the Fed finally starts increasing interest rates later this year. However, the growth of GDP this time has been quite tepid, and if one measures the age of the expansion by the amount of GDP growth, the 13% increase chalked up so far exceeds total growth in only two of the other ten, the short-lived expan-sions that began in 1958Q2 and 1980Q3, so maybe the probability of a recession soon is more like 2/10, not 8/10.

This is an important issue for running your business and for managing your personal portfolio. During expan-sions, debt-to-income ratios typically rise as businesses use debt to finance new ventures and consumers use debt to finance new homes and new cars. During these expansions, the problems that might be associated with rising debt to income ratios are masked by rising asset prices, which make what are actually troubled debt contracts self-collateralizing. But a recession brings what is often a painful comeuppance, when falling incomes and falling revenues make debt service more difficult and when falling asset prices make it hard to reduce debt by selling assets. That’s when delinquencies, defaults and bankruptcies become common.

The preventive medicine for these catastrophic conse-quences of too much debt are to draw down the debt as the expansion ages, and to put less weight on equities in your personal portfolio and more on bonds. Early in an expansion is when leverage is the right choice: issue debt and acquire hard assets. Favor equities over bonds. Late in an expan-





sion, leverage becomes much more dangerous and should be limited. If the next downturn is imminent, action would be required immediately.

But don’t be alarmed. The aging of this expansion is proceeding at a very leisurely pace and the “hardening of the arteries” that usually occurs during expansions simply isn’t present yet. This expansion seems destined to continue for at least a couple more years, and probably more. This is quite evident in the figure below which plots the employment to population ratio over the eleven expansions. Typically, tightening labor markets during expansions is symptom-ized by an increase in the fraction employed by a couple of percentage points, but this time there has been only very modest gain in employment to population and only over the last year. The employment to population ratio fell by about 5% but we have clawed back 2%. The remaining decline in the overall employment to population ratio by 3% from its prerecession levels suggests that there are still a lot of good apples at bargain prices in that barrel of workers. Moreover, the critical housing and automobile sectors are not yet in an overbuilt status, as discussed below.

Incidentally, the male employment to population ratio fell by 6 percent and is still 5% below its prerecession level. The corresponding numbers for females are 3.5 and 3. This relatively bad news for the males contributes to the long-term convergence of male and female employment rates.

The rate risk is modest

We have had seven years of zero short-term interest rates, and a rate increase courtesy of the Fed is imminent, or perhaps has already occurred when you read this. An increase in short-term rates affects Main Street primarily through housing and automobiles. But these sectors are not poised to collapse. A long-overdue increase in short rates can have a huge effect on the psyche of Wall Street investors. Lately, Wall Street gamblers have been reading reports of a debt default on Mars, and rushing to sell their stocks before that news affects other investors. So expect more nervous-ness and volatility and overreaction to a long-expected small rate increase, which will create another buying opportunity for bold investors, like you and me.





Keep in mind that the global bond market, not the Fed, sets the long-term interest rates, and the U.S. ten-year Trea-sury rate has been stuck a little above two percent for several years, symptomatic of a sluggish global economy with little inflation. In that kind of environment, a Fed Fund rate in excess of one percent would be a cause of concern, since the banking sector relies on a steep yield curve to support intermediation profits earned by taking short-term deposits at low rates and making long-term loans at high rates.

Main Street doesn’t have to worry much about the

pending Fed decision on interest rates. Traditionally, a Fed

increase in interest rates, when the expansion is old and frag-ile, kills off an overbuilt housing market, but, as illustrated in the figure below, housing starts remain at recession levels. Underbuilding of places to live for the last half-decade and the return in the future to a more normal rate of household formation are likely to allow many years of growth ahead in this critical sector. But, incidentally, the weakness is all in single-family homes; multi-family building has recovered nicely and is at a normal rate. Seven years of underbuilding has created incipient pent-up demand.





The other interest-sensitive sector is automobiles. This time the subprime loans have been mostly in automo-biles and hardly at all in homes. This is a sector in which a rise in short-term interest rates probably will matter. In automobiles illustrated below, the pace of production has re-turned to the highest levels, 17 million units per year, which proved to be unsustainably high when it occurred from 2001 to 2006. But right now we are replacing depreciated and aged automobiles with new ones, and the fleet is still too old to worry about overbuilding. If we have another couple of years of 17 million new automobiles , then we will need to really worry about this sector. But a Fed rate increase could have a large but temporary effect on automobiles, since it would reduce the number of potential buyers who could qualify for the subprime loans. In other words, there is a risk of a replay of 2007 when the subprime loans in housing collapsed, but this time in automobiles, not housing, which will make the effect much more localized and not great enough to cause a recession.

On a very positive note, keep in mind that the real rate of interest, the nominal rate minus the inflation rate, is at or close to zero, and destined to stay that way, regardless of what the Fed does. The real rate of interest is the price of durability, and in the choice between cheap short-lived items (personal or business) versus more expensive longer-lived items, lean much more heavily toward the long-lived ones. That could give you the highest rate of return right now. In addition, a low real rate of interest should be accompanied by high asset prices, thus high p/e ratios for equities, high prices for homes and farms and businesses. These asset markets are still in the price-discovery mode, trying to figure out exactly how long the low real rates of interest will last, and what that means for asset prices, but there remains a very large disconnect between the low rates of interest in the bond markets, and the relatively moderate asset prices relative to income flows. That gap will be closed with a combination of higher interest rates and higher asset prices. In the meantime, leverage is not a dirty word.



The forecast calls for mild conditions going forward

We see a healthy economy during the next two years with only a small chance of a recession and a small chance of a surge in growth. Our forecast for GDP growth is in the 2% to 3% range, better next year than the year after. This comes with an improving labor market, declining unemploy-ment rate and a rising employment to population ratio. Yield on bonds are driven upward by a rise of inflation by about 1 percentage point.

Interest Rates

Inflation

The Unemployment Rate

GDP Growth

Employment to Population Ratio

2017201520132011200920072005

6%

4%

2%

0%

-2%

-4%

-6%

-8%

-10%

(Percent Change, SAAR)

2017201520132011200920072005

10%

9%

8%

7%

6%

5%

4%

(Percent)

2017201520132011200920072005

62

61

60

59

58

57

56

(Percent)

2017201520132011200920072005

6%

4%

2%

0%

-2%

(Percent Change Year Ago)

Headline Core

2017201520132011200920072005

6%

5%

4%

3%

2%

1%

0%

-1%

(Rates)

Fed Funds 10-Yr. T-bonds


HOUSING IS BACK

Housing is BACKDavid ShulmanSenior Economist, UCLA Anderson ForecastSeptembert 2015

After a long, hard slog, housing starts (both single- and multi-family) are poised to approach the long-term average (1959-2014) of just under 1.5 million units in 2016. (See Figure 1) Specifically we are forecasting housing starts of 1.14 million units this year and 1.42 million units and 1.44 million units in 2016 and 2017, respectively. This level of activity is well above 1.00 million units recorded in 2014 and the 2009 low of 0.55 million units. Remember that the level of activity we forecast is far from the mid-2000s boom level of above two million units a year. We would also note that with the shift to multi-family starts, the per-unit GDP “bang for the buck” has declined, but that factor has been partially offset by increased emphasis on higher-end housing in the new construction market.

Our forecast is underpinned by continued growth in real GDP that will likely run at a 3% rate in 2016, continued jobs gains in excess of 200,000 a month for most of the fore-cast period, relatively low mortgage rates--at least through 2016 and household formations in excess of one million a year in 2016 and 2017. (See Figures 2, 3, 4 and 5) To dig into the weeds, our estimates for household formation is derived from the Current Population Survey which when compared to the Housing Vacancy Survey seem conservative. Further, the improving labor market will act as an ongoing stimulus to household formations.

Although low mortgage rates have been with us for years, what is important is that credit standards have eased

Figure 1 Housing Starts, 2000Q1 -2017Q4F

Sources: U.S. Department of Commerce and UCLA Anderson Forecast

201720152013201120092007200520032001

2500

2000

1500

1000

500

0

(Thousands of Units, SAAR)

with respect to FICO scores and down payment requirements have been reduced. To be sure we are not going back to the “wild west” lending standards of 2005, but compared to 2010, and yes early 2014, mortgage credit conditions have decidedly eased. Moreover, we do not believe that higher mortgage rates will meaningfully cut into housing activity until 2017 as a rise in rates will initially hasten buyers into the market out of fear that rates will go much higher. Time will tell whether or not this assumption is too heroic.


HOUSING IS BACK

Figure 3 Nonfarm Employment, 2005Q1 -2017Q4F, SAAR

Sources: U.S. Department of Labor and UCLA Anderson Forecast

2017201520132011200920072005

150

145

140

135

130

125

(Millions)

Figure 4 30-Year Conventional Mortgage Rate, 2005Q1 – 2017Q4

Source: Freddie Mac and UCLA Anderson Forecast

2017201520132011200920072005

7%

6%

5%

4%

3%

(Percent)

Figure 5 Household Formations, 2010-2017F

Sources: U.S. Bureau of the Census and UCLA Anderson Forecast

201720152013201120092007200520032001

2.0

1.5

1.0

0.5

0.0

-0.5

(Annual Data, in Millions)

Figure 2 Real GDP Growth, 2005Q1 – 2017Q4F


2017201520132011200920072005

6%

4%

2%

0%

-2%

-4%

-6%

-8%

-10%

(Percent Change, SAAR)


HOUSING IS BACK

Figure 6 S&P/Case-Shiller Home Price Index, 20 City Composite, 2000 - June 2015, 2000=100

Sources: Standard and Poor's via FRED

201720152013201120092007200520032001

7.5

7.0

6.5

6.0

5.5

5.0

4.5

4.0

3.5

(Annual Data, in Millions)

Figure 7 Existing Home Sales, 2000 - 2017F

Sources: National Association of Realtors and UCLA Anderson Forecast

The rebound in housing construction is being con-firmed by rising home prices with the widely reported Case-Shiller Index up 5% year-over-year and up 30% since the low in 2012. (See Figure 6) Similarly, existing home sales are forecast to be 5.3 million units this year up from the 4.1 million unit low in 2008. (See Figure 7) We forecast that existing home sales will reach 5.5 million units in 2016 and modestly decline to 5.3 million units in 2017.

Interestingly, the housing recovery is occurring under the backdrop of an unprecedented decline in home ownership. Specifically, the home ownership rate has declined from 69% in 2005 to the current 63.5%, which is roughly where it was in 1989. (See Figure 8) The decline in the home ownership rate is attributable to the after effects of the housing crash of 2006-2010 which scared off would be homeowners, tighter mortgage requirements,


HOUSING IS BACK

Figure 9 Student Loan Debt, 2006Q1-2015Q2F, $Billions

Sources: FRED

figure 8 Homeownership Rate, 1965 - 2015Q2, NSA

Sources: U.S. Department of Commerce and WSJ.com


HOUSING IS BACK

sluggish income growth, a shift in consumer preferences to urban versus suburban lifestyles, and the rapid growth in student loans which now exceed $1.2 trillion (See Figure 9) In fact, the biggest drop in homeownership has taken place in 25-34 year old cohort where the rate dropped 5 full percentage points from 1993 -2014.1 We believe that this declining trend has about run its course and will soon begin reversing. In support of this notion we note that the recent decline in life events associated with home ownership such as marriage and childbirth have ebbed and are now in the process of reversal.

The Boom in Multi-Family and Rentals

The flip-side of the decline in the homeownership rate is a rise in renting which has triggered a boom in multi-family housing starts (See Figure 10). Multi-family housing starts which bottomed in 2009 at 112,000 units will exceed 400,000 units this year and average 460,000 units over the next two years. The boom is underpinned by rents increasing at a 3.5% a year rate in the official data, but according to the publicly traded apartment real estate investment trusts, rents are increasing on the order of 4.5-5.0%. (See Figure 11) As we have noted before, the official data tends to lag the actual market place because of the prevalence of rent controlled jurisdictions in the official sample. Simply put, rents in con-

Figure 11 Consumer Price Index, Rent of Primary Residence, January 2000 - July 2015, Percent Change Year-Over-Year

Source: U.S Bureau of Labor Statitics via FRED

Figure 10 Multi-Family Housing Starts, 2000Q1 – 2017Q4F


201720152013201120092007200520032001

500

400

300

200

100

0

(Thousands of Units, Annual Data)

trolled jurisdictions aren’t typically marked to market until a vacancy occurs. The primary reason that rental increases have been sustainable is a very low 4% national (based on 79 cities) apartment vacancy rate, roughly half of what it was a few years ago. (See Figure 12)

Moreover, this cycle has given rise to nationally ori-ented single-family rental businesses funded by institutional

1. “The State of the Nation’s Housing 2015,” Joint Center on Housing Studies, Harvard University.


HOUSING IS BACK

investors and public offerings of shares. This business is the creature of the huge amount of bank foreclosed property that came on the market in the aftermath of the financial crisis enabling the bulk buying of single-family homes. Thus far, single-family rentals have captured an unprecedented half of the total rental market over the past few years and the public companies have been reporting rental growth on the order of 4% a year. In fact we are now witnessing the pur-chase of new single-family homes for the rental market by investment institutions and the development of homes for rent by traditional home-builders. This consumer preference for single-family rentals is one of the reasons we believe that the American dream of at least living in a single-family home is far from dead and ultimately many of those rental units will turn into owner occupied housing.

The trends outlined above have not gone unnoticed by the investment community as torrents of cash has flowed into the sector driving up apartment values and spurring new construction. In a yield constrained world, the cash flows associated with apartment ownership have looked increas-ingly attractive to institutional and retail investors alike and that has driven initial yields down to below 5% and to below 4% in the more favored markets. Just to note, initial yields on apartment projects were close to 8% at the height of the financial crisis.

Figure 12 Apartment Vacancy Rate, 1980 - March 2015

Source: REIS and calculatedriskblog.com

However, because we expect interest rates to rise over the next few years, the decline in homeownership rate to level off and high new construction levels to negatively impact vacancy rates, the apartment boom is likely to show real signs of strain by late next year.

More importantly, with rents rising faster than in-comes, affordability will soon become a binding constraint on rents. For example, from 2004-2014, the percentage of households paying more than 30% of their income rent increased from 40% to 46%.2 With developers building for the top of the market, meaning high income renters, they may not yet to be cognizant of this trend, but they will soon find out that the high-end apartment market might not be as deep as they think.

Conclusion

Yes, housing is back. It will not be a rerun of the 2005 boom, but starts will soon approach 1.5 million units a year. The multi-family apartment boom will continue throughout 2016 as developers race to keep up with demand for urban infill housing. Nevertheless, housing activity will begin to gradually fade in 2017 as mortgage rates rise and apartment vacancies increase.

2. Ibid.

UCLA Anderson Forecast, September 2015 Nation-27

GUEST CONTRIBUTOR: REAL ESTATE

The national homeownership rate has followed the trajectory of a bumpy rollercoaster with a series of up-and-down movements in the past 20 years, with the end of 2014 marking a period of notable decline. This downward trend has fueled speculation about the future of homeownership in the United States following risks exposed during the re-cession and foreclosure crisis, as the U.S. homeownership rate fell to the lowest level in more than two decades in the fourth quarter of 2014.

In looking at the history of the national homeowner-ship rate, it rose steadily through the late 1960s and 1970s, from 63 to 65.6 percent, before declining slightly in the early 1980s. To address a decade of stagnation, national leaders pushed forward efforts to expand homeownership in the mid-1990s, which led the rate to rise rapidly from 1994 to 2004, from 64 percent to a record high of 69 percent. However, the recent national homeownership rate has declined almost fully back to its 1994 level.

While the decline has provoked worry about home-ownership access, many experts believe the fall in the home-ownership rate actually is at the tail-end of its decline and that advantageous conditions are percolating. For example, mortgage delinquency and foreclosure rates have greatly

decreased, wage growth is expected to follow a period of strong job growth, and there are signs that mortgage credit conditions are improving.

Rising prices and a tight supply of lower-end listings have put homes out of reach for many younger, entry-level buyers. For example, the rate of homeownership is high-est for householders who are 65 and older and lowest for householders under 35.

According to analysis by the Harvard Joint Center for Housing Studies, the homeownership rate for young adults ages 25 to 34, which rose from 45 percent in the mid-1990s to a high of 50 percent in 2004, fell to 40 percent as of last year, representing the largest percentage decline in home-ownership of any age group over the last 10 years.

As a way to counter the observed socio-demographic changes, which are deleterious to the housing market, it is likely that greater effort will have to go toward encouraging favorable measures. For instance, more accessible mortgage terms, affordable housing costs, and income growth are steps that may have greater momentum in shaping future outcomes than socio-demographic characteristics – especially when it comes to the long-term prospects of the homeownership rate.

Homeownership Decline - A Bump in the Road for the Housing Market? Joel Singer Chief Executive Officer, California Association of RealtorsGuest ContributorSeptember 2015

Joel Singer is chief executive officer of the CALIFORNIA ASSOCIATION OF REALTORS®. Singer has held the Association's top staff position since November 1989 after serving as C.A.R.'s chief economist and heading the Association's public affairs department. Singer was instrumental in developing Real Estate Business Services Inc. (REBS), C.A.R.'s for-profit subsidiary, and serves as its president. He also is president and chief executive officer of zipLogixTM. Singer joined C.A.R. in 1978.



Charts

CHARTS – RECENT EVIDENCE


15141312111009080706050403

10

5

0

-5

-10

(% Change Year Ago)

Price InflationConsumer vs. Producers' Price Index

Jan. 2003 to Aug. 2015

Consumer Prices Producer Prices-Fin. Goods 151413121110090807060504030201

65432

10

-1

(Percent)

Interest Rates3-Mo. T-Bills vs. Long Gov't Bond Yields

Jan. 2001 to Aug. 2015

3-MonthLong Gov'ts

1514131211100908070605040302

14

12

10

8

6

4

2

(Mil. Units)

Automobile SalesJan. 2002 to Aug. 2015

CarsTrucks

1514131211100908070605040302

130

120

110

100

90

80

(Index 2004=100)

Composite Indexes of Economic IndicatorsJan. 2002 to Aug. 2015

LeadingCoincident



15141312111009080706050403

144000142000140000138000136000134000132000130000128000

(Thous.)

Total Nonfarm EmploymentJan. 2003 to Aug. 2015

151413121110090807060504

140

120

100

80

60

40

20

($/Barrel)

Crude Oil PriceWest Texas IntermediateJan. 2004 to Aug. 2015

15141312111009080706050403

10.0

9.1

8.1

7.2

6.3

5.4

4.4

3.5

(Percent)

Rate of UnemploymentJan. 2003 to Aug. 2015

15141312111009080706050403

400

350

300

250

200

150

100

(Bil. $)

Retail SalesJan. 2003 to Aug. 2015



15141312111009080706050403

2.5

2.0

1.5

1.0

0.5

0.0

(Mil. Units)

Housing StartsJan. 2003 to Aug. 2015

15141312111009080706050403

1400

1200

1000

800

600

400

200

(Thous.)

Single-Family New Home SalesJan. 2003 to Aug. 2015

15141312111009080706050403

0.950.900.850.800.750.700.650.60

130

120110

100

90

80

70

(Deutschmark/$) (Yen/$)

Japanese and European Exchange Rates

Jan. 2003 to Aug. 2015

Euro/U.S. $ (Left) Yen/U.S. $ (Right)15141312111009080706050403

76543210

-1

(Index Jan.'90 = 1.00)

U.S., Japanese and GermanStock Markets

Jan. 2003 to Aug. 2015

U.S. Japan Germany

CHARTS – FORECAST


2017201320092005200119971993

8

6

4

2

0

-2

-4

(4-Qtr. % Ch.)Real Disposable Income and Consumption

Consumption Disposable Income2017201520132011200920072005200320011999

10

8

6

4

2

0

(3-Yr. % Ch.)

Consumer Expenditures on Medical Services:Quantity % + Price % = Expenditure %

Quantity Price

2017201420112008200520021999199619931990

15

10

5

0

-5

(4-Qtr. % Ch.)Real Export and Import Growth

Exports Imports201720142011200820052002199919961993

6

5

4

3

2

1

0

(5-Yr. % Ch.)

Real GDP GrowthDeveloped World vs. U.S.

U.S. Developed World

CHARTS – FORECAST


2017201420112008200520021999199619931990

6

4

2

0

-2

-4

-6

(4-Qtr. % Ch.)Real GDP Growth

2017201420112008200520021999199619931990

18000

16000

14000

12000

10000

8000

(Bil. 2009 $)

Actual Real GDPVs. Potential Real GDP

Actual Real GDP Potential Real GDP

201720122007200219971992198719821977

10

8

6

4

2

0

(Percent)

Defense SpendingAs A Share of GDP

20172015201320112009200720052003200119991997

875421

-1-3-4

(% Ch. 12-Qtr. Mov. Avg.)

Real Purchases of Goods and Servicesby the Federal Government

CHARTS – FORECAST


20172014201120082005200219991996

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

(% of Real GDP)

Change in Real Business Inventories(3-yr. Moving Average)

20172014201120082005200219991996

30

20

10

0

-10

-20

(3-yr. % Ch.)

Real Investment-Equipment & SoftwareInfo. Processing Equip. vs. Other Equip.

Total Less Info. Equip. Information Processing Equip.

2017201420112008200520021999

14.514.013.513.012.512.011.511.0

50

48

46

44

42

40

38

(Percent) (Percent)

Nonres. Fixed Investment Share of Real GDP Vs.Equip. & Software Share of Bus. Fixed Invest.

Nonres. Fixed Investment ShareEquip. & Software Share/Nonres.Fixed

20172014201120082005200219991996

10

5

0

-5

-10

-15

(3-Yr. % Ch.)

Real Investment in Nonresidential StructuresTotal vs. Commercial Bldgs.

Total Commercial Bldgs.

CHARTS – FORECAST


2017201320092005200119971993

15141312111098

8

6

4

2

0

-2

(Invest. Share %) (4-Qtr. % Ch.)

Nonresidential Fixed Investment Share of Real GDPVs. Capital Stock Growth

Nonres. Fixed Investment Share Capital Stock Growth20172013200920052001199719931989

900

800

700

600

500

400

300

2.5

2.0

1.5

1.0

0.5

0.0

(Bil. 2009 $) (Mil. Units)

Real Investment in Residential StructuresVs. New Housing Starts

Real Investment (Left) Housing Starts (Rt.)

2017201220072002199719921987198219771972

3.02.52.01.51.00.50.0

-0.5

(10-Yr. % Ch.)

Real Hourly Wage CompensationVs. Productivity in Nonfarm Sector

Real Wage Productivity 20172013200920052001199719931989

2

0

-2

-4

-6

-8

-10

(Percent of GDP)Federal Surplus or Deficit

CHARTS – FORECAST


20172013200920052001199719931989

6

5

4

3

2

1

0

-1

(Percent of GDP)

Consumer Price Index Inflation

201720122007200219971992198719821977

100

80

60

40

20

0

(2009$/barrel)

Real Refiner's Cost of Crude Oil

20172013200920052001199719931989

1.61.41.21.00.80.60.40.20.0

(Indexed: 2005 = 1.00)

Real and Nominal Exchange RateIndustrial Countries Trade Weighted Average

Nominal Exchange Rate Real Exchange Rate201720102003199619891982197519681961

15

11

7

2

-2

(Percent)Treasury Yields Vs. CPI Inflation

Inflation 30-Year Bonds 90-Day Bills

CHARTS – FORECAST


201720102003199619891982197519681961

109876543

9590858075706560

(%) (100% - Capacity Util.)

Unemployment and Capacity Utilization Mfg.Postwar Business Cycles

Unemployment Rate Capacity Util. Mfg. Rate 2017201420112008200520021999199619931990

12

11

10

9

8

7

(Percent of GDP)Federal Transfers to Persons

20172013200920052001199719931989

3.5

3.0

2.5

2.0

1.5

(Percent of GDP)

Federal Transfers to PersonsFor Health Insurance

201720132009200520011997199319891985

2.5

2.0

1.5

1.0

0.5

0.0

18161412108642

(Mil. Units) (Percent)

U.S. Housing StartsVs. Mortgage Rate

Housing Starts Mortgage Rate

CHARTS – FORECAST


20172013200920052001199719931989

20

15

10

5

0

(Mil. Units)

U.S. Retail Sales ofAutomobiles and Light Trucks

Automobiles Light Trucks 20172013200920052001199719931989

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

(Percent of National Income)

Federal Net Interest Payments onNational Debt



Tables

FORECAST TABLES - SUMMARY


Table 1. Summary of the UCLA Anderson Forecast for the Nation 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Monetary Aggregates and GDP (% Ch.)Money Supply (M1) 0.2 -0.2 4.5 14.2 6.4 15.4 15.0 10.1 10.3 6.4 -3.0 -4.9Money Supply (M2) 5.3 6.2 6.8 8.1 2.5 7.3 8.6 6.7 6.2 5.3 2.8 2.4GDP Price Index 3.1 2.7 1.9 0.8 1.2 2.1 1.8 1.6 1.6 1.1 2.1 2.4Real GDP 2.7 1.8 -0.3 -2.8 2.5 1.6 2.2 1.5 2.4 2.3 3.0 2.7

Interest Rates (%) on:Federal Funds 5.0 5.0 1.9 0.2 0.2 0.1 0.1 0.1 0.1 0.1 1.0 2.690-day Treasury Bills 4.7 4.4 1.4 0.2 0.1 0.1 0.1 0.1 0.0 0.1 1.0 2.510-year Treasury Bonds 4.8 4.6 3.7 3.3 3.2 2.8 1.8 2.4 2.5 2.2 3.2 3.930-year Treasury Bonds 4.9 4.8 4.3 4.1 4.3 3.9 2.9 3.4 3.3 2.9 3.8 4.3Moody’s Corporate Aaa Bonds 5.6 5.6 5.6 5.3 4.9 4.6 3.7 4.2 4.2 4.0 4.8 5.530-yr Bond Less Inflation 2.2 2.3 1.2 4.1 2.6 1.5 1.0 2.1 1.9 2.7 1.9 1.7

Federal Fiscal PolicyDefense Purchases (% Ch.) Current $ 5.6 5.7 11.1 4.5 5.6 0.5 -2.3 -6.1 -2.5 -1.2 2.9 5.0 Constant $ 2.0 2.5 7.5 5.4 3.2 -2.3 -3.4 -6.7 -3.8 -1.2 1.6 2.7Other Expenditures (% Ch.) Transfers to Persons 6.6 6.4 12.9 13.0 8.9 -0.3 -1.1 2.0 4.2 5.0 4.9 5.3 Grants to S&L Gov’t -0.7 5.3 3.4 23.5 10.3 -6.5 -6.0 1.4 9.9 6.8 5.7 5.9

Billions of Current Dollars, Unified Budget Basis, Fiscal YearReceipts 2406.7 2567.7 2523.6 2104.4 2161.7 2302.5 2449.1 2774.0 3020.4 3246.6 3377.2 3553.0Outlays 2654.9 2729.2 2978.4 3520.1 3455.9 3599.3 3538.3 3454.2 3503.7 3695.1 3892.7 4105.1Surplus or Deficit (-) -248.2 -161.5 -454.8 -1415.7 -1294.2 -1296.8 -1089.2 -680.2 -483.4 -448.5 -515.6 -552.1

As Shares of GDP (%), NIPA BasisRevenues 18.3 18.4 17.5 15.5 16.3 16.6 16.7 18.9 18.8 19.1 19.2 19.1Expenditures 20.0 20.3 21.8 24.2 25.2 24.6 23.5 22.7 22.5 22.4 22.2 22.2 Defense Purchases 4.6 4.7 5.1 5.5 5.6 5.4 5.1 4.6 4.3 4.1 4.0 4.0 Transfers to Persons 11.4 11.6 12.9 14.9 15.6 15.0 14.2 14.1 14.1 14.3 14.3 14.3Surplus or Deficit (-) -1.6 -1.8 -4.3 -8.7 -8.9 -8.0 -6.7 -3.8 -3.6 -3.3 -3.0 -3.1

Details of Real GDP (% Ch.)Real GDP 2.7 1.8 -0.3 -2.8 2.5 1.6 2.2 1.5 2.4 2.3 3.0 2.7Final Sales 2.6 2.0 0.2 -2.0 1.1 1.7 2.1 1.5 2.4 2.2 3.3 2.8Consumption 3.0 2.2 -0.3 -1.6 1.9 2.3 1.5 1.7 2.7 3.1 3.2 2.8Nonres. Fixed Investment 7.1 5.9 -0.7 -15.6 2.5 7.7 9.0 3.0 6.2 3.1 6.3 5.3 Equipment 8.6 3.2 -6.9 -22.9 15.9 13.6 10.8 3.2 5.8 2.9 7.1 5.6 Intellectual Property 4.5 4.8 3.0 -1.4 1.9 3.5 3.9 3.8 5.2 6.6 5.9 3.9 Structures 7.2 12.7 6.1 -18.9 -16.4 2.3 12.9 1.6 8.1 -1.0 5.1 6.6Residential Construction -7.7 -19.0 -24.3 -21.4 -2.7 0.5 13.8 9.6 1.7 9.1 13.2 3.4Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.4 2.8 3.4 1.5 4.2 4.9Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.2 1.1 3.8 5.8 5.8 5.2Federal Purchases 2.5 1.7 6.8 5.7 4.3 -2.7 -1.9 -5.7 -2.4 -0.5 0.7 1.4State & Local Purchases 0.9 1.5 0.3 1.6 -2.7 -3.3 -1.9 -1.0 0.6 1.0 1.3 1.4

Billions of 2009 DollarsReal GDP 14613.8 14873.8 14830.4 14418.8 14783.8 15020.6 15354.6 15583.3 15961.7 16326.4 16819.0 17274.7Final Sales 14542.2 14838.2 14864.1 14566.3 14725.6 14983.0 15300.0 15521.9 15893.6 16236.0 16772.5 17237.9Inventory Change 71.6 35.6 -33.7 -147.6 58.2 37.6 54.7 61.4 68.0 90.4 46.4 36.9



Table 2. Summary of the UCLA Anderson Forecast for the Nation 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Industrial Production and Resource UtilizationIndustrial Prod. (% Ch.) 2.2 2.5 -3.4 -11.3 5.6 3.0 2.8 1.9 3.7 1.4 2.2 3.4Capacity Util. Manuf. (%) 78.6 78.8 74.8 65.7 70.9 73.7 74.5 74.1 75.3 75.8 76.0 75.4Real Bus. Investment as % of Real GDP 18.2 17.5 16.4 14.0 13.9 14.6 15.6 16.1 16.5 16.8 17.6 18.0Nonfarm Employment (mil.) 136.4 137.9 137.2 131.2 130.3 131.8 134.1 136.4 139.0 142.0 144.5 146.6Unemployment Rate (%) 4.6 4.6 5.8 9.3 9.6 8.9 8.1 7.4 6.2 5.3 4.9 4.8

Inflation (% Ch.)Consumer Price Index 3.2 2.9 3.8 -0.3 1.6 3.1 2.1 1.5 1.6 0.0 2.2 3.2 Total less Food & Energy 2.5 2.3 2.3 1.7 1.0 1.7 2.1 1.8 1.7 1.8 2.3 2.7Consumption Chain Index 2.7 2.5 3.1 -0.1 1.7 2.5 1.9 1.4 1.4 0.3 1.8 2.6GDP Chain Index 3.1 2.7 1.9 0.8 1.2 2.1 1.8 1.6 1.6 1.1 2.1 2.4Producers Price Index 4.7 4.8 9.8 -8.7 6.8 8.8 0.5 0.6 0.9 -7.6 1.3 3.9

Factors Related to Inflation (% Ch.)Nonfarm Business Sector Total Compensation 3.9 4.3 2.7 1.1 1.9 2.2 2.7 1.1 2.7 2.1 3.5 4.0 Productivity 0.9 1.6 0.8 3.2 3.3 0.2 0.9 -0.0 0.7 0.2 1.0 1.7 Unit Labor Costs 3.0 2.7 2.0 -2.0 -1.3 2.1 1.7 1.1 2.0 1.9 2.5 2.3Farm Price Index -1.2 22.5 12.4 -16.5 12.2 23.6 3.2 1.4 1.1 -12.6 -2.7 -0.9Crude Oil Price ($/bbl) 66.1 72.3 99.6 61.7 79.4 95.1 94.2 98.0 93.0 48.5 56.0 72.7New Home Price ($1000) 243.1 243.7 230.4 214.5 221.2 224.3 242.1 265.1 283.8 295.6 300.5 308.0

Income, Consumption and Saving (% Ch.)Disposable Income 6.8 4.7 4.6 -0.5 2.7 5.0 5.1 -0.1 4.2 3.7 5.0 6.0Real Disposable Income 4.0 2.1 1.5 -0.4 1.0 2.5 3.1 -1.4 2.7 3.4 3.1 3.3Real Consumption 3.0 2.2 -0.3 -1.6 1.9 2.3 1.5 1.7 2.7 3.1 3.2 2.8Savings Rate (%) 3.3 3.0 4.9 6.1 5.6 6.1 7.6 4.8 4.8 5.0 5.0 5.5

Housing and Automobiles--millions of unitsHousing Starts 1.812 1.342 0.900 0.554 0.586 0.612 0.784 0.928 1.001 1.142 1.420 1.443Auto & Light Truck Sales 16.5 16.1 13.2 10.4 11.6 12.7 14.4 15.5 16.4 17.2 17.6 17.7

Corporate ProfitsBillions of Dollars Before Taxes 1851.4 1748.4 1382.5 1472.6 1840.7 1806.8 2130.8 2161.7 2207.8 2404.0 2585.1 2552.6 After Taxes 1378.1 1302.9 1073.3 1203.1 1470.2 1427.7 1683.2 1692.8 1693.9 1854.6 2004.5 1978.1Percent Change Before Taxes 12.0 -5.6 -20.9 6.5 25.0 -1.8 17.9 1.4 2.1 8.9 7.5 -1.3 After Taxes 11.1 -5.5 -17.6 12.1 22.2 -2.9 17.9 0.6 0.1 9.5 8.1 -1.3

International Trade FactorsNominalU.S. Dollar--% change Industrial Countries -1.5 -5.6 -4.5 4.3 -3.0 -5.9 3.7 3.3 3.3 15.4 -0.2 -3.8 Developing Countries -2.5 -3.8 -2.6 7.2 -4.1 -3.5 2.0 -0.4 3.0 7.7 0.7 -1.5 Exports 12.8 12.8 10.7 -13.8 16.7 13.7 4.4 3.0 3.5 -2.8 5.6 7.3 Imports 10.7 6.0 7.6 -22.7 19.3 13.6 2.9 0.3 3.6 -2.7 4.5 8.9 Net Exports (bil. $) -771 -719 -723 -395 -513 -580 -566 -508 -530 -519 -518 -602RealU.S. Dollar--% change Industrial Countries -2.4 -6.4 -5.3 7.8 -0.5 -7.9 3.8 4.6 4.3 18.3 0.0 -4.1 Developing Countries -5.1 -7.4 -9.5 6.3 -5.2 -8.2 -0.5 -1.2 2.2 9.2 1.3 -2.5 Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.4 2.8 3.4 1.5 4.2 4.9 Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.2 1.1 3.8 5.8 5.8 5.2 Net Exports (bil. ‘09$) -794 -713 -558 -395 -459 -459 -447 -417 -443 -557 -622 -661

FORECAST TABLES - QUARTERLY SUMMARY


Table 3. Quarterly Summary of the UCLA National Anderson Forecast for the Nation 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3 2016:4 2017:1 2017:2 2017:3 2017:4

Monetary Aggregates and GDP (% Ch.)Money Supply (M1) 3.2 0.6 -2.6 -3.8 -5.1 -4.8 -4.7 -4.8 -4.9 -5.6 -5.0Money Supply (M2) 5.0 2.2 2.4 3.0 2.9 2.8 2.8 2.5 2.1 1.8 1.8GDP Price Index 2.0 1.7 1.5 2.4 2.4 2.2 2.4 2.5 2.3 2.4 2.3Real GDP 2.3 2.3 3.2 3.1 3.2 3.3 3.0 2.6 2.4 2.2 2.3

Interest Rates (%) on:Federal Funds 0.1 0.1 0.2 0.5 0.8 1.1 1.4 1.8 2.4 3.0 3.390-day Treasury Bills 0.0 0.1 0.2 0.5 0.8 1.1 1.4 1.8 2.4 3.0 3.110-year Treasury Bonds 2.2 2.2 2.6 2.8 3.1 3.3 3.5 3.8 4.0 4.0 3.830-year Treasury Bonds 2.9 3.0 3.4 3.5 3.7 3.9 4.0 4.2 4.4 4.4 4.2Moody’s Corporate Aaa Bonds 3.9 4.1 4.4 4.5 4.7 4.9 5.1 5.4 5.6 5.7 5.530-yr Bond Less Inflation 0.7 2.2 2.6 1.4 1.3 1.4 1.3 1.6 1.7 1.7 1.6

Federal Fiscal PolicyDefense Purchases (% Ch.) Current $ -1.0 2.5 -0.6 5.1 3.2 5.2 4.2 7.1 4.5 4.2 3.5 Constant $ -1.5 3.1 -0.9 2.2 1.7 3.5 2.4 3.2 2.6 2.4 1.8Other Expenditures (% Ch.) Transfers to Persons 0.4 6.1 3.3 7.9 4.2 4.0 3.9 9.8 3.5 4.1 3.9 Grants to S&L Gov’t -3.9 9.2 4.2 5.5 7.9 7.4 5.0 5.7 6.3 5.2 5.2

Billions of Current Dollars, Unified Budget Basis, NSAReceipts 1027.1 799.7 779.6 736.2 1015.1 846.3 820.3 782.2 1061.2 889.3 852.0Outlays 901.0 934.8 958.4 995.5 963.0 975.9 1006.4 1049.4 1017.7 1031.6 1064.1Surplus or Deficit (-) 126.1 -135.1 -178.7 -259.3 52.1 -129.6 -186.1 -267.1 43.5 -142.3 -212.1

As Shares of GDP (%), NIPA BasisRevenues 19.2 19.1 19.1 19.2 19.2 19.2 19.2 19.2 19.2 19.1 19.0Expenditures 22.5 22.4 22.3 22.3 22.3 22.2 22.1 22.3 22.3 22.2 22.2 Defense Purchases 4.1 4.1 4.1 4.1 4.0 4.0 4.0 4.0 4.0 4.0 4.0 Transfers to Persons 14.2 14.3 14.3 14.3 14.3 14.2 14.2 14.3 14.3 14.3 14.2Surplus or Deficit (-) -3.3 -3.3 -3.1 -3.2 -3.1 -3.0 -2.9 -3.1 -3.1 -3.1 -3.2

Details of Real GDP (% Ch.)Real GDP 2.3 2.3 3.2 3.1 3.2 3.3 3.0 2.6 2.4 2.2 2.3Final Sales 2.4 3.3 3.4 3.6 3.3 3.2 2.9 2.7 2.7 2.3 2.3Consumption 2.9 3.5 3.4 3.1 3.4 3.3 2.5 2.6 2.8 2.6 2.6Nonres. Fixed Investment -0.6 7.4 7.4 7.5 5.5 5.6 6.5 6.0 4.3 3.8 4.2 Equipment -4.1 9.4 9.0 8.1 7.1 6.3 6.3 6.4 4.5 3.7 4.7 Intellectual Property 5.5 7.4 6.6 6.3 5.1 4.8 4.3 4.1 3.4 3.1 3.0 Structures -1.6 3.4 5.2 8.2 2.9 5.2 9.9 7.9 5.4 5.0 5.0Residential Construction 6.6 11.4 15.6 19.0 12.3 8.2 7.4 2.9 0.2 -2.5 -2.4Exports 5.3 0.8 3.1 4.8 5.3 5.6 4.9 5.0 4.2 4.6 4.5Imports 3.5 5.1 5.4 5.8 6.5 7.0 5.2 5.6 3.8 4.1 4.4Federal Purchases -1.1 1.5 -0.8 1.0 0.7 1.9 1.2 1.7 1.3 1.1 0.8State & Local Purchases 2.0 1.9 1.3 0.8 1.3 1.1 1.5 1.6 1.5 1.5 1.4

Billions of 2009 DollarsReal GDP 16270.4 16364.8 16493.0 16619.9 16753.0 16888.9 17014.1 17124.3 17228.1 17323.6 17423.0Final Sales 16160.4 16291.2 16427.8 16574.2 16711.2 16842.2 16962.6 17077.6 17191.5 17291.5 17390.9Inventory Change 110.0 73.6 65.2 45.7 41.7 46.7 51.5 46.7 36.6 32.1 32.1

FORECAST TABLES - QUARTERLY SUMMARY


Table 4. Quarterly Summary of The UCLA National Anderson Forecast for the Nation 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3 2016:4 2017:1 2017:2 2017:3 2017:4

Industrial Production and Resource UtilizationProduction--% change -1.7 -0.5 2.1 1.9 3.8 4.2 3.7 2.9 3.3 3.3 3.2Capacity Util. Manuf. (%) 75.9 75.7 75.8 75.8 75.9 76.1 76.1 75.9 75.5 75.3 75.1Real Bus. Investment as % of Real GDP 16.6 16.9 17.1 17.4 17.5 17.7 17.8 17.9 18.0 18.0 18.0Nonfarm Employment (mil.) 141.6 142.4 142.9 143.5 144.2 144.8 145.4 145.9 146.5 146.8 147.1Unemployment Rate (%) 5.4 5.2 5.1 5.0 4.9 4.8 4.8 4.8 4.7 4.8 4.9

Inflation--% changeConsumer Price Index 3.0 0.7 0.6 2.8 2.7 3.1 3.3 3.2 3.2 3.3 3.2 Total less Food & Energy 2.5 1.7 2.2 2.3 2.6 2.6 2.7 2.8 2.8 2.8 2.7Consumption Deflator 2.2 0.8 0.8 2.2 2.4 2.5 2.7 2.6 2.7 2.7 2.6GDP Deflator 2.0 1.7 1.5 2.4 2.4 2.2 2.4 2.5 2.3 2.4 2.3Producers Price Index -0.8 -8.1 -0.1 3.6 4.3 4.4 3.8 3.7 3.9 4.5 3.2

Factors Related to Inflation--%changeNonfarm Business Sector Total Compensation 1.8 2.6 3.1 3.9 4.1 4.0 3.8 4.1 4.2 4.1 4.1 Productivity 1.3 0.4 0.4 0.7 1.4 1.7 1.7 1.7 1.6 1.7 2.1 Unit Labor Costs 0.5 2.2 2.6 3.2 2.6 2.3 2.0 2.4 2.6 2.3 1.9Farm Price Index -6.8 -8.5 -3.8 1.0 -2.2 -1.7 -1.4 -1.2 -0.6 0.2 1.4Crude Oil Price ($/bbl) 58.0 42.9 44.5 48.4 53.2 58.3 64.0 67.9 71.1 74.3 77.3New Home Price ($1000) 284.8 299.0 305.5 296.4 303.6 301.8 300.5 306.6 312.7 306.3 306.3

Income, Consumption and Saving--%changeDisposable Income 3.7 5.9 3.3 5.6 5.1 6.1 5.3 6.9 6.0 6.1 5.8Real Disposable Income 1.5 5.0 2.5 3.3 2.7 3.5 2.5 4.1 3.3 3.3 3.1Real Consumption 2.9 3.5 3.4 3.1 3.4 3.3 2.5 2.6 2.8 2.6 2.6Savings Rate (%) 4.8 5.2 5.0 5.0 4.9 4.9 5.0 5.3 5.4 5.6 5.8

Housing and Automobiles--millions of unitsHousing Starts 1.144 1.173 1.272 1.354 1.393 1.455 1.476 1.475 1.455 1.431 1.413Auto and Light Truck Sales 17.1 17.5 17.5 17.5 17.6 17.7 17.6 17.5 17.7 17.7 17.7

Corporate ProfitsBillions of Dollars Before Taxes 2454.8 2413.0 2496.1 2502.3 2586.9 2607.5 2643.5 2589.0 2575.7 2538.1 2507.6 After Taxes 1891.6 1860.2 1932.0 1940.7 2005.4 2021.0 2050.9 2002.0 1995.1 1968.6 1946.7Percent Change Before Taxes 41.1 -6.6 14.5 1.0 14.2 3.2 5.6 -8.0 -2.0 -5.7 -4.7 After Taxes 41.4 -6.5 16.4 1.8 14.0 3.1 6.1 -9.2 -1.4 -5.2 -4.4

International TradeNominalU.S. Dollar--% change Industrial Countries 2.4 4.4 3.6 -0.9 -3.9 -3.2 -3.4 -3.6 -4.2 -4.7 -4.3 Developing Countries 0.8 5.6 0.4 0.6 0.1 -1.6 -0.9 -1.7 -3.5 -0.8 0.3 Exports--% change 4.2 -1.6 3.6 7.6 8.3 8.1 7.5 7.7 6.5 6.8 6.5 Imports--% change -1.0 -3.4 1.6 5.6 8.4 10.1 9.1 9.6 8.0 8.0 8.1 Net Exports (bil. $) -521.5 -506.6 -497.0 -493.1 -503.6 -526.5 -547.4 -571.6 -592.0 -611.0 -633.3RealU.S. Dollar--% change Industrial Countries 6.4 7.0 4.8 -1.7 -4.9 -3.8 -3.6 -3.4 -4.3 -5.1 -4.8 Developing Countries 3.2 7.6 1.8 0.8 -0.2 -1.9 -1.4 -2.7 -4.8 -2.6 -1.6 Exports--% change 5.3 0.8 3.1 4.8 5.3 5.6 4.9 5.0 4.2 4.6 4.5 Imports--% change 3.5 5.1 5.4 5.8 6.5 7.0 5.2 5.6 3.8 4.1 4.4 Net Exports (bil. ‘09$) -536.3 -565.3 -585.1 -598.6 -614.6 -632.6 -642.4 -654.4 -658.3 -661.8 -668.2

FORECAST TABLES - DETAILED


Table 5. Part A. Gross Domestic Product 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsGross Domestic Product 13855.9 14477.6 14718.6 14418.7 14964.4 15517.9 16155.3 16663.2 17348.1 17934.4 18863.4 19832.3Personal ConsumptionExpenditures 9304.0 9750.5 10013.6 9847.0 10202.2 10689.3 11050.6 11392.3 11865.9 12266.5 12898.7 13600.6 Durable Goods 1156.1 1184.6 1102.3 1023.3 1070.7 1125.3 1191.9 1237.8 1280.2 1331.2 1406.6 1476.7 Autos and Parts 394.9 400.6 339.6 317.1 342.0 363.5 395.8 416.7 440.2 465.5 499.7 529.5 Nondurable Goods 2079.7 2176.9 2273.4 2175.1 2292.1 2471.1 2547.2 2598.9 2668.2 2641.2 2766.9 2927.4 Services 6068.2 6388.9 6637.9 6648.5 6839.4 7092.8 7311.5 7555.5 7917.5 8294.2 8725.2 9196.6Gross Private DomesticInvestment 2680.7 2643.7 2424.8 1878.1 2100.8 2239.9 2511.7 2665.0 2860.0 3014.0 3217.3 3436.2 Residential 837.4 688.7 515.9 392.3 381.1 386.0 442.3 508.9 549.2 607.8 699.2 745.8 Nonres. Structures 415.6 496.9 552.4 438.2 362.0 381.6 448.0 462.1 507.0 499.2 535.1 593.2 Equipment 856.1 885.8 825.1 644.3 731.8 838.2 937.9 972.3 1036.7 1071.4 1147.9 1229.9 Intellectual Property 504.6 538.0 563.4 550.9 564.4 592.2 621.8 649.9 689.9 734.6 784.0 825.6 Change In Inv. 67.0 34.5 -32.0 -147.6 61.5 41.8 61.8 71.8 77.1 101.1 51.3 41.7

Net Exports -771.0 -718.6 -723.1 -395.5 -512.7 -580.0 -565.7 -508.4 -530.0 -519.2 -517.6 -602.0Exports 1476.3 1664.6 1841.9 1587.7 1852.3 2106.4 2198.2 2263.3 2341.9 2275.5 2404.0 2580.0Imports 2247.3 2383.2 2565.0 1983.2 2365.0 2686.4 2763.8 2771.7 2871.9 2794.7 2921.6 3181.9

Government Purchases 2642.2 2801.9 3003.2 3089.1 3174.0 3168.7 3158.6 3114.3 3152.1 3173.0 3265.0 3397.4 Federal 1002.0 1049.8 1155.6 1217.7 1303.9 1303.5 1292.5 1230.7 1219.9 1219.6 1244.9 1289.0 Defense 642.4 678.7 754.1 788.3 832.8 837.0 817.8 767.7 748.2 739.6 761.1 798.9 Other 359.6 371.1 401.5 429.4 471.1 466.5 474.7 463.0 471.6 480.0 483.8 490.1 State and Local 1640.2 1752.2 1847.6 1871.4 1870.2 1865.3 1866.0 1883.6 1932.3 1953.5 2020.0 2108.4

Billions of 2009 DollarsGross Domestic Product 14613.8 14873.8 14830.4 14418.8 14783.8 15020.6 15354.6 15583.3 15961.7 16326.4 16819.0 17274.7Personal ConsumptionExpenditures 9821.7 10041.6 10007.2 9847.0 10036.3 10263.5 10413.2 10590.4 10875.7 11212.7 11576.5 11895.8 Durable Goods 1091.5 1141.7 1083.2 1023.3 1085.7 1151.5 1236.2 1307.6 1384.1 1466.5 1568.6 1660.9 Autos & Parts 385.1 392.8 340.8 317.1 323.4 333.8 359.1 375.8 396.7 418.1 445.9 467.3 Nondurable Goods 2202.2 2239.3 2214.7 2175.1 2223.5 2263.2 2277.5 2319.8 2367.8 2429.5 2504.5 2563.7 Services 6526.6 6656.4 6708.6 6648.5 6727.6 6851.4 6908.1 6977.0 7144.6 7345.2 7544.1 7724.4Gross Private DomesticInvestment 2730.0 2644.1 2396.0 1878.1 2120.4 2230.4 2465.7 2577.3 2717.7 2852.4 3017.8 3152.0 Residential 806.6 654.8 497.7 392.3 382.4 384.5 436.5 478.0 486.4 530.6 600.1 620.3 Nonres. Structures 451.5 509.0 540.2 438.2 366.3 374.7 423.1 429.7 464.6 460.2 483.8 515.6 Equipment 870.8 898.3 836.1 644.3 746.7 847.9 939.2 969.5 1026.2 1055.4 1130.3 1193.7 Intellectual Property 517.5 542.4 558.8 550.9 561.3 581.3 603.8 626.9 659.5 703.1 744.4 773.8 Change In Inv. 71.6 35.6 -33.7 -147.6 58.2 37.6 54.7 61.4 68.0 90.4 46.4 36.9

Net Exports -794.3 -712.6 -557.8 -395.4 -458.8 -459.4 -447.1 -417.5 -442.5 -557.0 -622.1 -660.7Exports 1506.8 1646.4 1740.8 1587.7 1776.6 1898.3 1963.2 2018.1 2086.4 2118.1 2207.0 2314.4Imports 2301.0 2359.0 2298.6 1983.2 2235.4 2357.7 2410.2 2435.6 2528.9 2675.1 2829.0 2975.0

Government Purchases 2869.3 2914.4 2994.8 3089.1 3091.4 2997.4 2941.6 2854.9 2838.3 2849.6 2878.9 2919.8 Federal 1060.9 1078.7 1152.3 1217.7 1270.7 1236.4 1213.5 1144.1 1116.3 1110.6 1118.1 1133.4 Defense 678.8 695.6 748.1 788.3 813.5 795.0 768.2 716.6 689.1 680.6 691.2 709.9 Other 382.1 383.1 404.2 429.4 457.1 441.4 445.3 427.5 427.0 429.6 426.7 423.6 State and Local 1808.9 1836.2 1842.5 1871.4 1820.8 1761.0 1728.1 1710.2 1720.8 1737.6 1759.3 1784.8



Table 5. Part B. Gross Domestic Product 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Annual Rates of Change of Current Dollar GDP Components (%)Gross Domestic Product 5.8 4.5 1.7 -2.0 3.8 3.7 4.1 3.1 4.1 3.4 5.2 5.1Personal ConsumptionExpenditures 5.8 4.8 2.7 -1.7 3.6 4.8 3.4 3.1 4.2 3.4 5.2 5.4 Durable Goods 2.6 2.5 -7.0 -7.2 4.6 5.1 5.9 3.9 3.4 4.0 5.7 5.0 Autos and Parts -3.7 1.4 -15.2 -6.6 7.9 6.3 8.9 5.3 5.6 5.8 7.3 6.0 Nondurable Goods 6.5 4.7 4.4 -4.3 5.4 7.8 3.1 2.0 2.7 -1.0 4.8 5.8 Services 6.2 5.3 3.9 0.2 2.9 3.7 3.1 3.3 4.8 4.8 5.2 5.4Gross Private DomesticInvestment 6.1 -1.4 -8.3 -22.5 11.9 6.6 12.1 6.1 7.3 5.4 6.7 6.8 Residential -2.2 -17.8 -25.1 -24.0 -2.9 1.3 14.6 15.1 7.9 10.7 15.0 6.7 Nonres. Structures 20.2 19.6 11.2 -20.7 -17.4 5.4 17.4 3.1 9.7 -1.5 7.2 10.9 Equipment 8.3 3.5 -6.8 -21.9 13.6 14.5 11.9 3.7 6.6 3.3 7.1 7.1 Intellectual Property 6.2 6.6 4.7 -2.2 2.5 4.9 5.0 4.5 6.2 6.5 6.7 5.3

Exports 12.8 12.8 10.7 -13.8 16.7 13.7 4.4 3.0 3.5 -2.8 5.6 7.3Imports 10.7 6.0 7.6 -22.7 19.3 13.6 2.9 0.3 3.6 -2.7 4.5 8.9

Government Purchases 6.0 6.0 7.2 2.9 2.7 -0.2 -0.3 -1.4 1.2 0.7 2.9 4.1 Federal 5.9 4.8 10.1 5.4 7.1 -0.0 -0.8 -4.8 -0.9 -0.0 2.1 3.5 Defense 5.6 5.7 11.1 4.5 5.6 0.5 -2.3 -6.1 -2.5 -1.2 2.9 5.0 Other 6.4 3.2 8.2 7.0 9.7 -1.0 1.8 -2.5 1.9 1.8 0.8 1.3 State and Local 6.0 6.8 5.4 1.3 -0.1 -0.3 0.0 0.9 2.6 1.1 3.4 4.4

Annual Rates of Change of Constant Dollar GDP Components (%)Gross Domestic Product 2.7 1.8 -0.3 -2.8 2.5 1.6 2.2 1.5 2.4 2.3 3.0 2.7Personal ConsumptionExpenditures 3.0 2.2 -0.3 -1.6 1.9 2.3 1.5 1.7 2.7 3.1 3.2 2.8 Durable Goods 4.3 4.6 -5.1 -5.5 6.1 6.1 7.4 5.8 5.9 6.0 7.0 5.9 Autos & Parts -3.7 2.0 -13.2 -7.0 2.0 3.2 7.6 4.6 5.6 5.4 6.7 4.8 Nondurable Goods 3.3 1.7 -1.1 -1.8 2.2 1.8 0.6 1.9 2.1 2.6 3.1 2.4 Services 2.7 2.0 0.8 -0.9 1.2 1.8 0.8 1.0 2.4 2.8 2.7 2.4Gross Private DomesticInvestment 2.1 -3.1 -9.4 -21.6 12.9 5.2 10.6 4.5 5.4 5.0 5.8 4.4 Residential -7.6 -18.8 -24.0 -21.2 -2.5 0.5 13.5 9.5 1.8 9.1 13.1 3.4 Nonres. Structures 7.2 12.7 6.1 -18.9 -16.4 2.3 12.9 1.6 8.1 -1.0 5.1 6.6 Equipment 8.6 3.2 -6.9 -22.9 15.9 13.6 10.8 3.2 5.8 2.9 7.1 5.6 Intellectual Property 4.5 4.8 3.0 -1.4 1.9 3.5 3.9 3.8 5.2 6.6 5.9 3.9

Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.4 2.8 3.4 1.5 4.2 4.9Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.2 1.1 3.8 5.8 5.8 5.2

Government Purchases 1.5 1.6 2.8 3.1 0.1 -3.0 -1.9 -2.9 -0.6 0.4 1.0 1.4 Federal 2.5 1.7 6.8 5.7 4.3 -2.7 -1.9 -5.7 -2.4 -0.5 0.7 1.4 Defense 2.0 2.5 7.5 5.4 3.2 -2.3 -3.4 -6.7 -3.8 -1.2 1.6 2.7 Other 3.5 0.3 5.5 6.2 6.5 -3.4 0.9 -4.0 -0.1 0.6 -0.7 -0.7 State and Local 0.9 1.5 0.3 1.6 -2.7 -3.3 -1.9 -1.0 0.6 1.0 1.3 1.4



Table 6. Employment 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Employment (Millions)Total 144.4 146.1 145.4 139.9 139.1 139.9 142.5 143.9 146.3 149.1 151.8 154.0 Nonagricultural 136.4 137.9 137.2 131.2 130.3 131.8 134.1 136.4 139.0 142.0 144.5 146.6 Natural Res. & Mining 0.7 0.7 0.8 0.7 0.7 0.8 0.8 0.9 0.9 0.8 0.8 0.8 Construction 7.7 7.6 7.2 6.0 5.5 5.5 5.6 5.9 6.1 6.4 6.8 7.2 Manufacturing 14.2 13.9 13.4 11.8 11.5 11.7 11.9 12.0 12.2 12.3 12.5 12.7 Trans. Warehous. Util 5.0 5.1 5.1 4.8 4.7 4.9 5.0 5.0 5.2 5.3 5.5 5.6 Trade 21.3 21.5 21.2 20.1 19.9 20.2 20.5 20.8 21.2 21.6 21.8 21.8 Financial Activities 8.4 8.3 8.2 7.8 7.7 7.7 7.8 7.9 8.0 8.1 8.1 8.0 Information 3.0 3.0 3.0 2.8 2.7 2.7 2.7 2.7 2.7 2.8 2.8 2.8 Professional & Busi. 17.6 17.9 17.7 16.6 16.7 17.3 17.9 18.5 19.1 19.8 20.7 21.5 Education & Health 18.1 18.6 19.2 19.5 19.9 20.2 20.7 21.1 21.5 22.0 22.5 22.8 Leisure & Hospitality 13.1 13.4 13.4 13.1 13.0 13.4 13.8 14.3 14.7 15.1 15.3 15.4 Other Services 5.4 5.5 5.5 5.4 5.3 5.4 5.4 5.5 5.6 5.6 5.6 5.6 Government 22.0 22.2 22.5 22.6 22.5 22.1 21.9 21.8 21.9 21.9 22.1 22.4 Federal 2.7 2.7 2.8 2.8 3.0 2.9 2.8 2.8 2.7 2.7 2.7 2.7 State & Local 19.2 19.5 19.7 19.7 19.5 19.2 19.1 19.1 19.1 19.2 19.3 19.7

Population and Labor Force (Millions)Population aged 16+ 234.2 237.0 239.6 242.2 244.7 247.1 249.5 251.9 254.2 256.6 259.3 261.9Labor Force 151.4 153.1 154.3 154.2 153.9 153.6 155.0 155.4 155.9 157.5 159.7 161.9Unemployment (%) 4.6 4.6 5.8 9.3 9.6 8.9 8.1 7.4 6.2 5.3 4.9 4.8

Table 7. Personal Income and Its Disposition 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsPersonal Income 11394.0 12000.2 12502.2 12094.8 12477.1 13254.5 13915.1 14068.4 14694.2 15325.8 16107.0 17069.7Wages & Salaries 6057.4 6395.2 6531.9 6251.4 6377.5 6633.2 6930.3 7114.4 7477.8 7786.8 8233.9 8670.0Other Labor Income 997.6 1041.4 1075.1 1077.5 1114.6 1142.0 1165.3 1197.8 1224.0 1263.0 1315.0 1380.1Nonfarm Income 1017.7 941.1 979.5 937.6 986.7 1068.1 1179.8 1196.3 1268.5 1332.3 1431.0 1502.4Farm Income 36.0 38.1 47.0 35.5 46.0 75.6 61.6 88.8 78.1 63.6 60.5 59.3Rental Income 207.5 189.4 262.1 333.7 402.8 485.3 525.3 563.4 610.8 657.9 693.2 724.9Dividends 723.7 816.6 805.5 553.8 544.6 682.3 835.0 789.1 815.5 879.7 899.7 969.3Interest Income 1214.8 1350.1 1361.6 1264.3 1195.1 1231.6 1288.8 1271.4 1302.0 1308.6 1345.9 1526.4Transfer Payments 1614.6 1728.1 1956.6 2147.5 2324.7 2360.5 2366.4 2426.7 2529.2 2667.9 2798.2 2944.7Personal Contributions For Social Insurance 475.2 499.7 516.9 506.3 514.7 423.9 437.2 579.4 611.8 634.0 670.5 707.4

Personal Tax and Nontax Payments 1357.1 1493.2 1507.8 1152.3 1239.3 1453.2 1511.4 1672.8 1780.2 1938.4 2047.9 2162.0Disposable Income 10036.9 10507.0 10994.4 10942.5 11237.9 11801.4 12403.7 12395.6 12913.9 13387.4 14059.1 14907.7Consumption 9304.0 9750.5 10013.6 9847.0 10202.2 10689.3 11050.6 11392.3 11865.9 12266.5 12898.7 13600.6Interest 275.1 305.9 289.6 274.0 250.8 241.4 240.7 244.2 254.2 268.5 277.5 284.2Transfers To Foreigners 49.7 59.8 71.7 70.7 71.0 75.1 74.7 76.6 78.3 80.5 85.1 90.6Personal Saving 331.4 309.8 536.7 667.4 630.0 710.1 946.7 589.9 620.2 673.9 696.4 827.6

Personal Saving Rate(%) 3.3 3.0 4.9 6.1 5.6 6.1 7.6 4.8 4.8 5.0 5.0 5.5



Table 8. Personal Consumption Expenditures By Major Types 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsPersonal Consumption 9304.0 9750.5 10013.6 9847.0 10202.2 10689.3 11050.6 11392.3 11865.9 12266.5 12898.7 13600.6 Durable Goods 1156.1 1184.6 1102.3 1023.3 1070.7 1125.3 1191.9 1237.8 1280.2 1331.2 1406.6 1476.7 Autos and Parts 394.9 400.6 339.6 317.1 342.0 363.5 395.8 416.7 440.2 465.5 499.7 529.5 Nondurable Goods 2079.7 2176.9 2273.4 2175.1 2292.1 2471.1 2547.2 2598.9 2668.2 2641.2 2766.9 2927.4 Services 6068.2 6388.9 6637.9 6648.5 6839.4 7092.8 7311.5 7555.5 7917.5 8294.2 8725.2 9196.6

Billions of 2009 DollarsPersonal Consumption 9821.7 10041.6 10007.2 9847.0 10036.3 10263.5 10413.2 10590.4 10875.7 11212.7 11576.5 11895.8 Durable Goods 1091.5 1141.7 1083.2 1023.3 1085.7 1151.5 1236.2 1307.6 1384.1 1466.5 1568.6 1660.9 Autos and Parts 385.1 392.8 340.8 317.1 323.4 333.8 359.1 375.8 396.7 418.1 445.9 467.3 Nondurable Goods 2202.2 2239.3 2214.7 2175.1 2223.5 2263.2 2277.5 2319.8 2367.8 2429.5 2504.5 2563.7 Services 6526.6 6656.4 6708.6 6648.5 6727.6 6851.4 6908.1 6977.0 7144.6 7345.2 7544.1 7724.4

Annual Rates of Real GrowthPersonal Consumption 3.0 2.2 -0.3 -1.6 1.9 2.3 1.5 1.7 2.7 3.1 3.2 2.8 Durable Goods 4.3 4.6 -5.1 -5.5 6.1 6.1 7.4 5.8 5.9 6.0 7.0 5.9 Autos and Parts -3.7 2.0 -13.2 -7.0 2.0 3.2 7.6 4.6 5.6 5.4 6.7 4.8 Furniture 5.1 0.8 -4.6 -8.7 7.0 5.8 4.4 5.4 6.5 5.4 5.4 4.0 Other Durables 7.2 4.7 -3.3 -5.0 4.2 5.5 3.7 3.4 3.4 3.4 3.5 2.7 Nondurable Goods 3.3 1.7 -1.1 -1.8 2.2 1.8 0.6 1.9 2.1 2.6 3.1 2.4 Food and Beverages 3.1 1.3 -1.2 -1.5 2.1 1.1 0.1 1.0 0.5 0.4 1.8 1.3 Gasoline and Oil 0.4 -0.3 -3.9 -0.8 -0.1 -2.0 -0.9 1.5 0.5 4.0 2.9 -0.3 Fuel -6.6 1.1 -11.3 15.0 -7.9 -12.4 -10.7 5.2 3.6 2.7 0.9 -0.4 Clothing and Shoes 3.5 2.0 -0.5 -4.9 5.3 3.9 1.1 1.4 1.4 3.6 3.3 2.8 Other Nondurables 4.9 2.7 0.4 -1.7 2.3 3.6 2.0 2.9 4.3 3.7 4.2 3.9 Services 2.7 2.0 0.8 -0.9 1.2 1.8 0.8 1.0 2.4 2.8 2.7 2.4 Housing 2.7 0.9 1.5 1.3 1.1 1.8 0.6 0.3 1.4 1.0 1.3 1.6 Transportation Serv. 0.2 1.0 -5.2 -9.8 -0.9 2.4 1.7 3.2 4.9 4.4 2.9 2.3 Health Care 2.3 2.5 2.3 1.8 1.3 2.5 2.2 1.0 2.7 4.7 3.6 3.5 Recreational Service 3.5 3.9 -0.8 -3.3 1.3 2.3 2.0 1.8 2.9 2.7 3.6 2.9 Food Svcs. Accom. 3.2 1.3 -1.0 -4.1 1.5 2.6 2.6 1.6 3.0 5.1 4.5 3.3 Financial Services 2.3 3.1 -0.7 -2.5 2.1 1.8 -5.5 1.1 1.2 1.9 1.8 1.5 Other Services 2.6 2.3 -0.7 -2.2 0.2 1.3 1.7 0.4 3.7 3.2 3.9 2.3

Table 9. Residential Construction and Housing Starts 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Housing Starts (Millions of Units)Housing Starts 1.812 1.342 0.900 0.554 0.586 0.612 0.784 0.928 1.001 1.142 1.420 1.443 Single-family 1.474 1.036 0.616 0.442 0.471 0.434 0.537 0.620 0.647 0.736 0.951 0.994 Multi-family 0.338 0.306 0.284 0.112 0.114 0.178 0.247 0.308 0.354 0.406 0.469 0.449

Residential Construction Expenditures (Billions of Dollars)Current Dollars 837.4 688.7 515.9 392.3 381.1 386.0 442.3 508.9 549.2 607.8 699.2 745.82009 Dollars 806.6 654.8 497.7 392.3 382.4 384.5 436.5 478.0 486.4 530.6 600.1 620.3 % Change -7.6 -18.8 -24.0 -21.2 -2.5 0.5 13.5 9.5 1.8 9.1 13.1 3.4

Related ConceptsTreas. Bill Rate 4.73 4.35 1.37 0.15 0.14 0.05 0.09 0.06 0.03 0.09 0.97 2.55Conventional 30-year Mortgage Rate 6.41 6.34 6.04 5.04 4.69 4.46 3.66 3.98 4.17 3.94 4.87 5.79Median Sales Price of New Homes (Thous $) 243.1 243.7 230.4 214.5 221.2 224.3 242.1 265.1 283.8 295.6 300.5 308.0Real Disp. Income 10036.9 10507.0 10994.4 10942.5 11237.9 11801.4 12403.7 12395.6 12913.9 13387.4 14059.1 14907.7 % Change 4.0 2.1 1.5 -0.4 1.0 2.5 3.1 -1.4 2.7 3.4 3.1 3.3



Table 10. Nonresidential Fixed Investment and Inventories 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsNonres. Fixed Investment 1776.3 1920.6 1941.0 1633.4 1658.2 1812.1 2007.7 2084.3 2233.7 2305.2 2466.9 2648.7 Equipment 856.1 885.8 825.1 644.3 731.8 838.2 937.9 972.3 1036.7 1071.4 1147.9 1229.9 Intellectual Property 504.6 538.0 563.4 550.9 564.4 592.2 621.8 649.9 689.9 734.6 784.0 825.6 Nonresidential Structures 415.6 496.9 552.4 438.2 362.0 381.6 448.0 462.1 507.0 499.2 535.1 593.2 Buildings 244.8 293.9 317.5 249.1 173.7 170.2 191.6 203.8 232.5 287.4 325.1 355.3 Commercial 128.4 150.7 148.9 95.4 64.7 66.8 75.6 84.2 103.1 118.9 138.8 156.7 Industrial 32.3 40.2 52.8 56.3 39.8 39.0 45.8 48.8 56.1 86.4 93.0 87.0 Other Buildings 84.2 103.0 115.8 97.4 69.2 64.5 70.2 70.7 73.3 82.2 93.3 111.6 Utilities 63.6 89.6 104.6 104.3 93.3 90.7 112.2 108.9 117.1 105.6 110.5 117.5 Mining Exploration 96.0 102.2 117.0 75.0 86.2 112.3 134.1 137.7 144.4 93.7 86.0 103.3 Other 11.1 11.2 13.3 9.9 8.9 8.4 10.1 11.7 13.0 12.4 13.5 17.1

Billions of 2009 DollarsNonres. Fixed Investment 1839.6 1948.4 1934.5 1633.5 1673.8 1802.3 1964.2 2023.8 2148.3 2215.9 2355.1 2479.8 Equipment 870.8 898.3 836.1 644.3 746.7 847.9 939.2 969.5 1026.2 1055.4 1130.3 1193.7 Intellectual Property 517.5 542.4 558.8 550.9 561.3 581.3 603.8 626.9 659.5 703.1 744.4 773.8 Nonresidential Structures 451.5 509.0 540.2 438.2 366.3 374.7 423.1 429.7 464.6 460.2 483.8 515.6 Buildings 268.7 305.2 317.9 249.1 179.3 172.3 188.8 196.1 216.5 262.9 288.6 304.0 Commercial 144.3 159.9 151.7 95.4 66.6 67.3 73.9 80.6 96.1 109.1 125.2 137.4 Industrial 36.5 43.1 53.8 56.3 40.8 39.1 44.9 46.8 52.0 78.2 79.3 69.8 Other Buildings 88.5 102.6 112.8 97.4 71.9 65.9 70.0 68.6 68.2 75.4 84.3 97.9 Utilities 70.0 94.3 103.6 104.3 89.8 82.8 99.1 95.1 101.0 90.0 91.0 91.9 Mining Exploration 99.5 97.9 105.0 75.0 87.8 110.9 123.8 126.7 135.0 94.7 90.8 106.0 Other 10.8 10.6 12.6 9.9 9.2 8.6 10.1 11.3 11.9 11.0 11.4 13.5

Percent Change in Real Nonresidential Fixed InvestmentNonres. Fixed Investment 7.1 5.9 -0.7 -15.6 2.5 7.7 9.0 3.0 6.2 3.1 6.3 5.3 Equipment 8.6 3.2 -6.9 -22.9 15.9 13.6 10.8 3.2 5.8 2.9 7.1 5.6 Intellectual Property 4.5 4.8 3.0 -1.4 1.9 3.5 3.9 3.8 5.2 6.6 5.9 3.9 Nonresidential Structures 7.2 12.7 6.1 -18.9 -16.4 2.3 12.9 1.6 8.1 -1.0 5.1 6.6 Buildings 7.2 13.6 4.2 -21.7 -28.0 -3.9 9.6 3.8 10.4 21.4 9.8 5.3 Commercial 4.9 10.8 -5.2 -37.1 -30.2 0.9 9.8 9.1 19.3 13.5 14.8 9.7 Industrial 6.6 18.2 24.8 4.6 -27.5 -4.2 14.8 4.2 11.2 50.3 1.5 -11.9 Other Buildings 11.0 16.0 9.9 -13.7 -26.2 -8.3 6.2 -2.0 -0.6 10.5 11.7 16.1 Utilities 7.9 34.6 9.9 0.7 -13.9 -7.8 19.8 -4.1 6.2 -10.9 1.2 1.0 Mining Exploration 8.0 -1.6 7.3 -28.6 17.1 26.4 11.7 2.3 6.5 -29.8 -4.1 16.8 Other 0.8 -1.4 18.0 -21.3 -7.4 -5.9 17.4 11.4 5.6 -7.3 3.2 18.5

Related ConceptsAnnual Growth-Price Deflator For: Producers Dur. Equip. -0.3 0.3 0.1 1.3 -2.0 0.9 1.0 0.4 0.7 0.5 0.0 1.4 Structures 12.2 6.1 4.8 -2.2 -1.2 3.0 4.0 1.6 1.5 -0.6 1.9 4.0Moody’s AAA Rate(%) 5.6 5.6 5.6 5.3 4.9 4.6 3.7 4.2 4.2 4.0 4.8 5.5Capacity Utilization in Manufacturing(%) 78.6 78.8 74.8 65.7 70.9 73.7 74.5 74.1 75.3 75.8 76.0 75.4Final Sales(Bil. 2009 $) 14542.2 14838.2 14864.1 14566.3 14725.6 14983.0 15300.0 15521.9 15893.6 16236.0 16772.5 17237.9

Change in Business InventoriesCurrent Dollars 67.0 34.5 -32.0 -147.6 61.5 41.8 61.8 71.8 77.1 101.1 51.3 41.72009 Dollars 71.6 35.6 -33.7 -147.6 58.2 37.6 54.7 61.4 68.0 90.4 46.4 36.9



Table 11. Federal Government Receipts and Expenditures 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current Dollars Unified Budget Basis, Fiscal YearReceipts 2406.7 2567.7 2523.6 2104.4 2161.7 2302.5 2449.1 2774.0 3020.4 3246.6 3377.2 3553.0Outlays 2654.9 2729.2 2978.4 3520.1 3455.9 3599.3 3538.3 3454.2 3503.7 3695.1 3892.7 4105.1Surplus or Deficit (-) -248.2 -161.5 -454.8 -1415.7 -1294.2 -1296.8 -1089.2 -680.2 -483.4 -448.5 -515.6 -552.1 National Income & Products Accounts Basis, Calendar YearCurrent Receipts 2537.8 2667.2 2579.5 2238.4 2443.3 2574.1 2699.1 3141.3 3265.2 3429.8 3620.6 3794.0 Current Tax Receipts 1563.4 1642.4 1520.7 1171.1 1352.7 1553.8 1661.2 1825.0 1974.4 2141.2 2265.0 2364.3 Personal Current Taxes 1054.6 1169.7 1174.3 864.5 941.6 1129.1 1164.7 1300.6 1396.9 1528.6 1615.8 1705.9 Taxes - Corporate Income 395.0 362.8 233.6 200.4 298.7 299.4 363.1 379.3 417.9 446.5 473.3 468.3 Taxes - Production/Imports 99.2 94.6 94.0 91.4 96.8 108.6 115.1 125.8 137.8 142.9 152.5 165.9 Contributions for Soc. Ins. 905.7 947.3 974.4 950.8 970.9 904.0 938.2 1093.4 1145.2 1184.6 1252.8 1321.8 Income Receipts on Assets 28.9 33.4 33.9 48.5 54.6 56.4 52.6 163.2 74.8 47.3 46.9 47.7 Current Transfer Receipts 37.9 42.1 49.7 67.2 68.2 67.1 56.1 71.1 80.6 64.5 62.2 65.0 Surplus of Gov’t. Enterprises 1.8 2.0 0.8 0.7 -3.1 -7.1 -8.9 -11.3 -9.7 -7.8 -6.4 -4.8

Current Expenditures 2764.8 2932.8 3213.5 3487.2 3772.0 3818.2 3789.1 3782.2 3896.7 4013.5 4192.2 4411.4 Consumption Expenditures 763.9 798.3 879.8 933.7 1003.9 1006.1 1007.8 961.3 955.3 956.7 977.3 1013.2 Defense 500.3 526.1 582.8 613.3 653.2 662.3 653.9 614.4 599.8 595.0 610.7 640.3 Nondefense 263.6 272.3 297.0 320.4 350.7 343.8 353.9 346.9 355.5 361.7 366.6 372.9 Transfer Payments 1577.4 1678.8 1896.1 2142.9 2333.2 2327.0 2300.8 2346.0 2444.0 2565.6 2691.0 2833.5 Government Social Benefits 1189.2 1264.2 1464.6 1616.2 1757.9 1779.9 1783.6 1823.2 1877.3 1964.9 2058.7 2166.2 To the Rest of the World 12.5 13.3 15.5 16.0 16.5 17.1 18.0 18.9 19.5 20.0 20.4 21.1 Grants-in-Aid To S&L Governments 340.8 359.0 371.0 458.1 505.3 472.5 444.0 450.1 494.8 528.7 559.1 592.3 To the Rest of the World 35.0 42.3 45.1 52.7 53.5 57.6 55.3 53.9 52.3 51.9 52.7 53.9 Interest Payments 372.4 408.2 388.0 353.6 380.6 425.7 422.9 416.1 440.1 433.3 464.2 503.7 Subsidies 51.1 47.5 49.6 56.9 54.3 59.5 57.6 58.9 57.4 57.9 59.8 61.1

Surplus or Deficit (-) -227.0 -265.6 -634.0 -1248.8 -1328.7 -1244.2 -1090.1 -641.0 -631.5 -583.7 -571.6 -617.4

Table 12. State and Local Government Receipts and Expenditures 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsReceipts 1254.5 1321.3 1328.9 1268.1 1305.7 1368.3 1416.1 1479.8 1517.5 1572.5 1646.9 1731.2 As Share of GDP 9.1 9.1 9.0 8.8 8.7 8.8 8.8 8.9 8.7 8.8 8.7 8.7Personal Tax and Nontax Receipts 302.5 323.5 333.5 287.8 297.6 324.1 346.7 372.2 383.3 409.8 432.0 456.2Corporate Profits 59.2 57.9 47.4 45.6 47.7 50.2 52.5 55.5 58.3 65.1 68.3 65.9Indirect Business Tax and Nontax Accruals 892.7 940.0 947.9 934.8 960.4 994.0 1016.9 1052.2 1075.9 1097.6 1146.6 1209.1Contributions For Social Insurance 21.5 18.9 18.7 18.6 18.2 18.2 18.1 18.6 18.9 18.8 19.4 20.4Federal Grants-In-Aid 340.8 359.0 371.0 458.1 505.3 472.5 444.0 450.1 494.8 528.7 559.1 592.3

Expenditures 1850.3 1973.3 2074.1 2191.2 2235.9 2246.4 2277.9 2323.6 2392.7 2459.8 2543.5 2653.6 As Share of GDP 13.4 13.6 14.1 15.2 14.9 14.5 14.1 13.9 13.8 13.7 13.5 13.4Purchases 1640.2 1752.2 1847.6 1871.4 1870.2 1865.3 1866.0 1883.6 1932.3 1953.5 2020.0 2108.4Transfer Payments 403.9 433.3 455.4 492.6 523.8 530.4 540.0 562.3 609.9 659.9 694.1 731.7Interest Received 25.4 17.3 36.0 114.3 123.0 125.9 141.4 142.1 122.6 127.6 123.3 119.4Net Subsidies 11.5 25.6 25.0 22.8 21.4 17.9 10.8 7.9 9.1 8.5 6.7 4.7Dividends Received 2.1 2.2 2.6 2.2 2.3 2.7 3.3 3.7 3.8 4.3 4.2 4.2Net Wage Accruals

Surplus Or Deficit -39.4 -72.7 -165.1 -271.9 -237.3 -215.9 -220.8 -187.1 -167.7 -144.9 -115.3 -98.2



Table 13. U.S. Exports and Imports of Goods and Services 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current Dollars Net Exports-Goods & Serv. -771.0 -718.6 -723.1 -395.5 -512.7 -580.0 -565.7 -508.4 -530.0 -519.2 -517.6 -602.0 Current Account Balance -806.7 -718.6 -690.8 -384.0 -442.0 -460.4 -449.7 -376.8 -389.5 -431.1 -371.0 -433.5 Merchandise Balance -850.1 -837.3 -850.6 -525.2 -670.2 -777.9 -779.8 -741.0 -770.5 -765.7 -777.0 -868.9

Exports-Goods & Services 1476.3 1664.6 1841.9 1587.7 1852.3 2106.4 2198.2 2263.3 2341.9 2275.5 2404.0 2580.0 Merchandise 1049.6 1166.4 1298.8 1065.1 1279.6 1466.9 1526.0 1560.9 1618.0 1524.9 1603.7 1718.9 Food, Feeds & Beverages 66.0 84.3 108.3 93.9 107.7 126.2 133.0 136.2 143.8 127.3 133.1 140.0 Industrial Supplies 279.1 316.3 386.9 293.5 388.6 485.3 483.2 492.3 500.0 438.0 482.1 547.5 Motor Vehicles & Parts 107.3 121.3 121.5 81.7 112.0 133.0 146.2 152.7 159.7 152.8 170.5 186.0 Capital Goods, Ex. MVP 339.5 360.0 383.7 316.7 375.9 413.8 433.1 429.5 438.2 420.4 427.1 453.1 Computer Equipment 47.6 45.5 43.9 37.7 43.8 48.5 49.2 48.1 48.8 46.2 48.4 53.0 Other 291.9 314.5 339.8 279.0 332.1 365.4 383.9 381.4 389.5 374.2 378.7 400.0 Consumer Goods, Ex. MVP 129.0 145.9 161.2 149.3 164.9 174.7 181.0 188.4 198.3 197.8 200.1 197.9 Other 64.2 65.7 63.3 55.2 58.6 53.4 55.1 56.9 64.9 70.6 70.6 70.3 Services 426.7 498.2 543.1 522.6 572.7 639.5 672.2 702.3 723.9 750.6 800.3 861.1

Imports-Goods & Services 2247.3 2383.2 2565.0 1983.2 2365.0 2686.4 2763.8 2771.7 2871.9 2794.7 2921.6 3181.9 Merchandise 1899.7 2003.8 2149.4 1590.3 1949.8 2244.7 2305.8 2301.9 2388.5 2290.6 2380.8 2587.8 Foods, Feeds & Beverage 76.1 83.0 90.4 82.9 92.5 108.3 111.1 116.0 126.7 128.9 125.3 131.0 Petroleum & Products 316.7 346.7 476.1 267.7 353.6 462.1 434.3 387.8 350.9 181.7 210.4 269.1 Indus Supplies Ex. Petr 293.5 297.9 318.7 196.6 249.4 292.7 288.9 291.2 314.2 298.7 304.1 321.4 Motor Vehicles & Parts 256.0 258.5 233.2 159.2 225.6 255.2 298.5 309.6 328.5 351.0 359.5 372.5 Capital Goods, Ex. MVP 394.1 414.6 423.2 343.4 419.1 477.9 511.6 510.9 542.6 554.3 576.2 622.9 Computer Equipment 101.6 105.5 101.2 94.2 117.3 119.7 122.3 121.2 121.7 119.1 121.3 126.1 Other 292.5 309.2 322.0 249.2 301.9 358.2 389.4 389.7 420.9 435.3 454.9 496.8 Consumer Goods, Ex. MVP 447.6 479.8 485.7 429.9 485.1 515.9 518.8 534.0 559.4 601.6 623.8 673.7 Other 87.2 88.8 86.5 80.0 93.1 97.1 102.4 105.5 111.3 119.8 126.7 139.7 Services 347.6 379.4 415.6 392.9 415.2 441.6 458.0 469.8 483.4 504.1 540.9 594.2

Billions of 2009 Dollars Net Exports-Goods & Serv. -794.3 -712.6 -557.8 -395.4 -458.8 -459.4 -447.1 -417.5 -442.5 -557.0 -622.1 -660.7 Exports-Goods & Services 1506.8 1646.4 1740.8 1587.7 1776.6 1898.3 1963.2 2018.1 2086.4 2118.1 2207.0 2314.4 Imports-Goods & Services 2301.0 2359.0 2298.6 1983.2 2235.4 2357.7 2410.2 2435.6 2528.9 2675.1 2829.0 2975.0

Exports and Imports -- % ChangeCurrent Dollars Exports 12.8 12.8 10.7 -13.8 16.7 13.7 4.4 3.0 3.5 -2.8 5.6 7.3 Imports 10.7 6.0 7.6 -22.7 19.3 13.6 2.9 0.3 3.6 -2.7 4.5 8.9Constant Dollars Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.4 2.8 3.4 1.5 4.2 4.9 Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.2 1.1 3.8 5.8 5.8 5.2

Production Indicators - % ChangeU.S. Industrial Production 2.2 2.5 -3.4 -11.3 5.6 3.0 2.8 1.9 3.7 1.4 2.2 3.4Real GDP -- Industrial Countries 2.8 2.6 0.7 -3.4 2.9 2.2 1.2 1.4 1.8 1.7 2.1 2.1Real GDP -- Developing Countries 6.7 6.6 3.8 0.0 7.4 5.4 3.9 3.7 3.3 3.2 3.8 4.1

Price IndicatorsPrice Deflators (% Ch) Exports 3.4 3.2 4.6 -5.5 4.3 6.4 0.9 0.2 0.1 -4.3 1.4 2.3 Imports 4.1 3.4 10.5 -10.4 5.8 7.7 0.6 -0.8 -0.2 -8.0 -1.2 3.6

Crude Oil Prices ($/barrel) 66.1 72.3 99.6 61.7 79.4 95.1 94.2 98.0 93.0 48.5 56.0 72.7Real U.S. Dollar Ex. Rate-Indust. Countries 1.05 0.98 0.93 1.00 0.99 0.92 0.95 1.00 1.04 1.23 1.23 1.18 %Change -2.4 -6.4 -5.3 7.8 -0.5 -7.9 3.8 4.6 4.3 18.3 0.0 -4.1 Ex. Rate-Dev. Countries 1.12 1.04 0.94 1.00 0.95 0.87 0.87 0.86 0.87 0.95 0.97 0.94 %Change -5.1 -7.4 -9.5 6.3 -5.2 -8.2 -0.5 -1.2 2.2 9.2 1.3 -2.5



Table 14. Price Indexes for GDP and Other Inflation Indicators (Percent Change) 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Implicit Price DeflatorsGDP 3.1 2.7 1.9 0.8 1.2 2.1 1.8 1.6 1.6 1.1 2.1 2.4

Consumption 2.7 2.5 3.1 -0.1 1.7 2.5 1.9 1.4 1.4 0.3 1.8 2.6 Durables -1.6 -2.0 -1.9 -1.7 -1.4 -0.9 -1.3 -1.8 -2.3 -1.9 -1.2 -0.9 Motor Vehicles 0.1 -0.6 -2.3 0.3 5.7 3.0 1.2 0.6 0.1 0.3 0.6 1.1 Furniture -0.5 -0.8 -0.7 -0.4 -4.2 -1.6 -0.3 -2.0 -3.5 -1.7 -0.4 -0.5 Other Durables 1.5 2.6 3.3 1.1 0.4 3.2 0.6 -0.2 -1.6 -2.2 0.2 1.4

Nondurables 3.1 2.9 5.6 -2.6 3.1 5.9 2.4 0.2 0.6 -3.5 1.6 3.4 Food 1.7 3.9 6.1 1.2 0.3 4.0 2.3 1.0 1.9 1.1 2.4 2.5 Clothing & Shoes -0.4 -0.9 -0.8 0.9 -0.7 1.8 3.4 1.0 0.3 -1.2 0.1 0.6 Gasoline 12.9 8.3 18.0 -27.2 18.1 26.4 3.4 -2.6 -3.6 -28.2 1.7 12.8 Fuel 13.7 6.9 35.6 -31.5 17.0 27.2 1.3 -1.2 -0.1 -27.9 4.6 13.4 Motor Vehicle Fuel 12.8 8.4 16.6 -26.8 18.2 26.3 3.6 -2.7 -3.8 -28.2 1.5 12.8

Services 3.4 3.2 3.1 1.1 1.7 1.8 2.2 2.3 2.3 1.9 2.4 2.9 Housing 3.5 3.6 2.7 1.8 0.1 1.3 2.1 2.5 2.7 3.0 3.0 3.2 Utilities 8.0 3.1 7.8 -2.2 1.3 1.7 -0.2 3.2 4.2 -0.5 -0.2 3.2 Electricity 12.1 3.9 6.4 3.0 0.2 1.7 -0.0 2.1 3.6 0.6 -1.2 3.2 Natural Gas 2.4 -1.2 13.8 -21.9 -2.0 -3.0 -9.9 4.7 7.1 -11.8 -3.3 2.6 Water & Sanit. 4.9 5.1 5.9 6.1 6.3 5.2 5.6 4.5 3.7 4.5 3.5 3.5 Health Care 3.0 3.7 2.7 2.7 2.5 1.8 1.8 1.5 1.1 0.6 1.6 2.2 Transportation 4.1 2.3 5.3 3.1 2.0 2.7 1.9 1.3 1.2 0.5 2.3 2.8 Recreation 3.4 2.8 3.1 1.2 1.1 1.7 2.7 1.7 1.9 1.9 2.8 2.9 Food & Accomm. 3.4 3.9 3.9 2.2 1.3 2.5 2.8 2.1 2.6 2.6 2.5 2.9 Financial & Insur. 2.7 2.9 1.1 -4.4 4.0 2.4 4.9 5.0 4.1 3.1 3.7 4.3 Other Services 4.0 3.1 4.6 2.8 3.0 2.5 2.5 2.6 2.2 1.9 2.6 3.1

Investment Deflators: Nonresidential 2.9 2.1 1.8 -0.3 -0.9 1.5 1.7 0.8 1.0 0.1 0.7 2.0 Structures 12.2 6.1 4.8 -2.2 -1.2 3.0 4.0 1.6 1.5 -0.6 1.9 4.0 Equipment -0.3 0.3 0.1 1.3 -2.0 0.9 1.0 0.4 0.7 0.5 0.0 1.4 Intellectual Prop. 1.6 1.7 1.7 -0.8 0.5 1.3 1.1 0.7 0.9 -0.1 0.8 1.3 Residential 5.8 1.3 -1.5 -3.5 -0.4 0.8 0.9 5.1 6.1 1.5 1.7 3.2

Government Purchases 4.4 4.4 4.3 -0.3 2.7 3.0 1.6 1.6 1.8 0.3 1.8 2.6 Federal 3.3 3.0 3.0 -0.3 2.6 2.7 1.0 1.0 1.6 0.5 1.4 2.1 State & Local 5.0 5.2 5.1 -0.3 2.7 3.1 1.9 2.0 1.9 0.1 2.1 2.9

Exports 3.4 3.2 4.6 -5.5 4.3 6.4 0.9 0.2 0.1 -4.3 1.4 2.3Imports 4.1 3.4 10.5 -10.4 5.8 7.7 0.6 -0.8 -0.2 -8.0 -1.2 3.6

Other Inflation Related IndicatorsConsumer Price Index All Urban 3.2 2.9 3.8 -0.3 1.6 3.1 2.1 1.5 1.6 0.0 2.2 3.2Producers Price Index 4.7 4.8 9.8 -8.7 6.8 8.8 0.5 0.6 0.9 -7.6 1.3 3.9

Nonfarm Sector IndicatorsTotal Compensation 3.9 4.3 2.7 1.1 1.9 2.2 2.7 1.1 2.7 2.1 3.5 4.0Productivity 0.9 1.6 0.8 3.2 3.3 0.2 0.9 -0.0 0.7 0.2 1.0 1.7Unit Labor Costs 3.0 2.7 2.0 -2.0 -1.3 2.1 1.7 1.1 2.0 1.9 2.5 2.3

Crude Oil Prices (dollars/barrel)West Texas Intermediate 66.10 72.28 99.61 61.69 79.41 95.07 94.21 97.96 92.97 48.50 55.99 72.66



Table 15. Producers Price Indexes 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Annual Percent ChangeAll Commodities 4.7 4.8 9.8 -8.7 6.8 8.8 0.5 0.6 0.9 -7.6 1.3 3.9Industrial Commodities 5.4 3.8 9.8 -9.0 7.0 8.0 0.0 0.4 0.6 -8.0 1.5 4.5Textiles & Apparel 1.4 1.0 2.4 0.5 1.7 7.6 0.3 0.8 1.5 -1.4 0.9 1.6Fuels 6.6 6.6 20.5 -25.8 17.1 16.0 -1.8 -0.2 -1.0 -25.4 3.4 11.8Chemicals 7.2 4.4 14.3 -6.5 7.5 11.5 0.5 0.9 0.6 -6.1 2.4 4.6Rubber & Plastics 6.9 0.8 7.0 -0.4 3.3 7.1 2.3 1.1 0.6 -2.0 0.3 2.6Lumber & Wood -1.1 -1.0 -0.6 -4.4 5.4 1.1 3.5 6.5 4.3 -1.2 1.6 1.4Pulp & Paper 3.6 3.4 4.6 -0.5 5.0 3.5 -0.4 1.9 0.7 -0.8 0.3 2.5Metals & Products 12.9 6.5 10.1 -12.2 11.1 8.8 -2.7 -2.9 0.7 -5.8 -1.6 1.8Equipment 2.0 0.9 1.9 1.2 -0.1 1.3 1.1 0.7 0.8 0.4 0.2 1.4Trans. Equipment 1.1 1.6 2.3 2.3 0.7 1.7 2.2 1.2 1.4 1.5 1.1 1.7

Farm -1.2 22.5 12.4 -16.5 12.2 23.6 3.2 1.4 1.1 -12.6 -2.7 -0.9Processed Foods & Feeds 0.4 7.3 9.3 -2.4 3.4 8.4 3.9 1.5 3.9 -2.5 1.7 1.1

By Stage of ProcessingCrude Materials 1.4 12.2 21.5 -30.5 21.2 17.5 -3.2 2.1 1.1 -22.9 2.3 6.0Intermediate Materials 6.4 4.0 10.3 -8.2 6.4 8.9 0.5 0.0 0.5 -7.4 0.1 3.0Finished Goods 2.9 3.9 6.4 -2.6 4.2 6.0 1.9 1.2 1.9 -3.8 1.5 3.5Consumers 3.4 4.5 7.4 -3.8 5.5 7.5 2.0 1.4 2.1 -5.4 1.7 4.0Producers 1.5 1.9 2.9 1.8 0.4 1.5 1.9 0.9 1.4 1.2 0.8 1.8

Table 16. Money, Interest Rates and Corporate Profits 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of DollarsMoney Supply (M1) 1374.8 1372.6 1434.4 1637.7 1742.2 2010.0 2311.9 2545.2 2807.2 2987.0 2896.0 2752.9Money Supply (M2) 6844.5 7266.3 7762.5 8389.5 8599.1 9227.9 10017.8 10690.8 11348.4 11951.7 12292.1 12588.6

Percent ChangeMoney Supply (M1) 0.2 -0.2 4.5 14.2 6.4 15.4 15.0 10.1 10.3 6.4 -3.0 -4.9Money Supply (M2) 5.3 6.2 6.8 8.1 2.5 7.3 8.6 6.7 6.2 5.3 2.8 2.4

Interest Rates (Percent) Short-term Rates 3-Month Treas. Bills 4.73 4.35 1.37 0.15 0.14 0.05 0.09 0.06 0.03 0.09 0.97 2.55 Prime Bank Loans 7.96 8.05 5.09 3.25 3.25 3.25 3.25 3.25 3.25 3.23 3.95 5.61

U.S. Government Bond Yields 5 Year Maturity 4.75 4.43 2.80 2.19 1.93 1.52 0.76 1.17 1.64 1.59 2.39 3.46 10 Year Maturity 4.79 4.63 3.67 3.26 3.21 2.79 1.80 2.35 2.54 2.24 3.18 3.90 30 Year Maturity 4.87 4.84 4.28 4.07 4.25 3.91 2.92 3.45 3.34 2.95 3.77 4.29

State and Local Governments Bond Yields Domestic Municipal Bonds 4.41 4.39 4.85 4.62 4.29 4.51 3.73 4.26 4.25 3.82 4.82 5.40

Corporate Bond Yields Moodys AAA Corp. Bonds 5.59 5.56 5.63 5.31 4.94 4.64 3.67 4.24 4.16 3.98 4.80 5.54

Conventional Mortgage Rate 6.41 6.34 6.04 5.04 4.69 4.46 3.66 3.98 4.17 3.94 4.87 5.79

Corporate Profits (Billions of Dollars)Profits Before Taxes 1851.43 1748.43 1382.45 1472.58 1840.68 1806.80 2130.82 2161.65 2207.77 2404.03 2585.06 2552.61Inventory Valuation Adj. -35.68 -39.50 -36.95 6.68 -41.03 -68.30 -14.20 3.23 -2.98 38.95 -29.18 -38.76Profits After Taxes 1378.08 1302.88 1073.33 1203.13 1470.15 1427.70 1683.18 1692.75 1693.93 1854.57 2004.49 1978.12


THE UCLA ANDERSON FORECAST FOR CALIFORNIA

California Housing - Will it Ever Be Affordable?

China Syndrome and Its Impact on Los Angeles’ Economy and Housing Market

UCLA Anderson Forecast, September 2015 California–59

CALIFORNIA HOUSING - WILL IT EVER BE AFFORDABLE?

California Housing - Will it Ever Be Affordable?Jerry NickelsburgSenior Economist, UCLA Anderson ForecastAdjunct Professor of Economics, UCLA Anderson SchoolSeptember 2015

California housing is so unaffordable that everyone is leaving the Golden State. Well, at least part of this is true. California housing compared to the rest of the country is less affordable in the sense that more of personal income is required to purchase the same level of housing services here than in Oklahoma or Georgia or for that matter, most anywhere else in the U.S. But, if everyone were leaving, housing prices would be going down, not up. The elevated values of homes is a function of both demand and supply. Since supply is not shrinking, the increases in prices at a rate faster than the U.S. is evidence of increased, not decreased demand. In this essay we explore the question of affordable housing to ascertain if current prices are sustainable and are expected to increase, decrease or stay the same over the forecast horizon of 2015 to 2017? At the end of the essay are a few comments about affordable housing policy and the transformation of work in California.

To begin let’s consider the relationship between af-fordability and urban success. The five least affordable cities in the world are Hong Kong, Vancouver, Sydney, San Francisco, and San Jose.t Also among the least afford-able in the U.S. are San Diego, Los Angeles, New York City, Miami, and Seattle. Among the most affordable are

Youngstown, Detroit, Grand Rapids, Toledo, Syracuse, and Scranton/Wilkes Barre.2 What jumps out from these lists is that the cities in the unaffordable list are successful and desirable cities and those most affordable are declining manufacturing cities. This is not a definitive statement as many desirable cities such as Pittsburgh and Nashville are much more affordable than those on the “unaffordable list” above. Nevertheless, it is suggestive of where to look in considering the question at hand.

Affordability – Demand and Supply

For economists’ affordability is not a well defined term. If someone purchased a good or service, then for that person it was affordable. They may have given up some-thing else to purchase it, and most assuredly would have liked to pay less, but they did in fact afford it. However, affordability ought not to be trivialized by such semantics. In a state such as California, housing is clearly expensive relative to the rest of the country3 and this has implications for economic growth and the socio-economic structure of the state.4 Thus, it is important to consider housing afford-ability, what it means, and the extent to which policy can influence it.

1 11th Annual Demographia International Housing Affordability Survey 2015, Urban Expansion Project, Stern School of Business, New York University, Dr. Shlomo Angel Director. http://www.demographia.com/dhi.pdf

2 America’s Most and Least Affordable Housing Markets, Bloomberg Business, 2015. http://www.bloomberg.com/ss/08/08/0829_affordable_metros/3 The cost of living includes many other expenses as well. However the cost of housing dominates and puts California in the top 4 states by most

measures. See http://www.usatoday.com/story/money/personalfinance/2014/09/13/cheat-sheet-most-expensive-states/15455129/ for example.4 “California’s High Housing Costs: Causes and Consequences,” California Legislative Analysts Office, http://lao.ca.gov/Publications/Detail/3214

60–California UCLA Anderson Forecast, September 2015


To understand housing affordability we need a defini-tion. The most common one is the extent to which a house-hold earning the median income can purchase the median priced house. The “can purchase” part of the definition is key. This is often taken to mean the cost of servicing a mortgage or paying rent for the median priced house is a set fraction of household income (usually 25% to 30%).

The definition does not account for the fact that the median household does not purchase the median house. Since the lowest quintile of the income distribution is rarely in the market for home purchases, the median household is purchasing a home at a price less than the median home price. Thus both the distribution of income among house-holds and the character of the housing stock matter a great deal. If one were to assume the distribution of income within large cities (MSA) is similar across cities, then using the standard definition makes some sense. It is beyond the scope of this essay to define a new metric for affordability and the standard metrics are taken with this assumption in mind.

To understand housing affordability and the ability to influence it, we need to explore the factors determining the demand and supply of housing. On the supply side are building costs, the availability of land (e.g. constraints imposed by extensive water bodies) and building restric-tions. The latter include the ability of nearby homeowners to block building projects through protracted litigation. It is easy to attribute these challenges to new home construction to NIMBY attitudes, however studies of zoning indicate that the restrictions are much more complex and involve environmental, governance, socio-economic, transportation and asset value considerations.5

While building costs may make a difference, it is dif-ficult to pin down regional variations. In California, sheer walls are required as earthquake mitigation but homes need not be able to withstand the bitter cold and blizzards of the Upper Midwest. While this aspect of home affordability could matter (a study by the Anderson Forecast came to the opposite conclusion)6 it is only land availability and building restrictions that are considered here.

On the demand side, there are two important factors, income and location. An increase in jobs leads to more household formation and more demand for housing. The higher demand will, for any given supply of housing, induce a higher market price. Location matters both within and between cities. The within cities demand should average out when examining average home prices or affordability indices for the entire city. The between cities location ele-ment involves what are commonly called city amenities.

Amenities cover a wide range of benefits that cities provide.7 These can be broadly divided into natural and cultural amenities. Natural amenities are those that come with the geography and include mountains, water bodies, and climate. These can be positive (think California) or negative (think North Dakota). It is usually assumed that all other things equal, households would prefer living in an area with an abundance of positive amenities. This is not to slam the Roughrider State, but one has to like the beauty of the high plains, northern lights and the badlands quite a bit to pay the price of severe winters and very hot summers.

Cultural amenities are those that come from the in-dividuals living in the city. Examples of these are quality restaurants, theater, civic engagement opportunities, man-made parks and a music scene. A city with an abundance of cultural amenities is, all other things equal, more attractive and therefore generates a higher demand for housing than one without. There is some disagreement as to the causal relationship between cultural amenities and the demand for housing among economists.8 Do other factors such as natural amenities and existent knowledge communities, both of which generate demand for housing, also generate a demand for cultural amenities? or would a core of cultural amenities then create a demand for housing independent of the former? That is, could Peoria become San Francisco? In this essay we are going to ignore the role of cultural ameni-ties in determining the demand for housing and focus only on the natural amenities that were in place before the city, and on job creation as the first step in household formation.

5 see for example William Fischel, “Zoning Rules: The Economics of Land Use Regulation,” Lincoln Institute of Land Policy, 2015.6 Jerry Nickelsburg and William Yu, “An Analysis of the Relationship Between Costs and Home Prices for Metropolitan Areas in California and the

United States,” Study Commissioned by Pacific Gas & Electric Company, February, 2015.7 Edward Glaeser “The Triumph of the City: How our greatest invention makes us richer, smarter, greener, healthier, and happier,” Macmillan,

2011.8 Richard Florida writes in “The Rise of The Creative Class, Revisited” Basic Books, New York, 2011, that these cultural amenities are the key to

turning around declining cities. Enrico Moretti in his “The New Geography of Jobs,” Houghton, Mifflin Harcourt, Boston 2012, argues that these cultural amenities are derivative of agglomeration of knowledge communities and that initial endowments of these are important.



To get at the concept of natural amenities, the USDA index of amenities is employed. This is an index that in-corporates the degree to which the land mass differs from a plain, the amount of water surrounding and within the city, winter temperature, winter sun, summer temperature, and summer humidity.9 The index is calculated for each county in the lower 48 states. When an MSA incorporates more than one county, the index for the county that includes the core city was used as the index for the city.

The National Association of Realtors (NAR) calcu-lates the housing affordability index employed herein.10 This index uses the median home price and the median household income to compute affordability. Assuming a 20% down payment and the current national average interest rate on a 30-year mortgage, the ratio of mortgage payment to income is calculated. An index of 100 is assigned to the ratio for which the mortgage payment represents 25% of household income. Lower index number indicate less affordable and higher more affordable cities.

Some Correlations

The first relationship to explore is that between these two indexes for MSA in the U.S. The affordability index for four of the larger California MSAs were not calculated by NAR but were generated by The Anderson Forecast. Most, but not all of the 100 largest MSAs in the U.S. were in the index. Those non-California cities not included in NAR’s calculations were not included in the analysis presented here.

The correlation is presented in Chart 1. While not a perfect relationship, there is a strong correlation between the two. The level of natural amenities predicts much of the affordability of housing. From the chart it is clear that the more amenity rich a city is, the less affordable it is. Notable in the chart is the fact that the California cities are not anomalies but lie close to the correlation line. One has to be a bit careful with the interpretation because the natural amenities index incorporates more than one force acting on housing demand and housing supply.

First, topography matters. It is more difficult to ex-pand the housing stock in a city constrained by water (e.g. San Francisco and Seattle) than one with ample room to expand (e.g. Dallas and Columbus). This natural restriction in supply will lead to higher prices for housing.

Second, amenities are valuable. People like to live in physically beautiful areas and in areas with mild weather and an abundance of outdoor activities. Consequently, the amenity index should predict a higher level of demand due to this household preference and consequently a higher price for housing.

Third, amenities can be perishable. As more people move in congestion and pollution tend to diminish the quality of the amenity. Thus local citizens enact build-ing restrictions to prevent overcrowding in amenity rich areas. One is reminded of the slogan “Don’t Californicate Colorado” used in the 1972 referendum campaign to limit the expected population sprawl induced by the upcoming Winter Olympic Games and the lyrics from John Denver in the same year “Why they try to tear the mountains down to bring in a couple more, more people, more scars upon the land.”11 The meaning is clearly anti-growth. To some, this is xenophobic and elitist and to others, it is environmental and amenity protection. But in either case, building restric-tions have been put in place to protect amenities and these restrictions limited the expansion of the stock of housing. Such limitations make the amenities relatively more scarce and therefore increase their value as reflected in the price of housing.

Which of the three, if any, dominates in Chart 1 is not evident from the data. It is most likely that all three are at play. To try to separate out the restrictions which are related to amenities and those that are put in place for other reasons we employ the Wharton Residential Land Use Regulatory In-dex (WRLURI).12 This index incorporates fifteen measures of regulatory injection into the building cycle including the existence of strong local pressure groups, ease of litigation, zoning and state involvement in local building decisions.

9 http://www.ers.usda.gov/data-products/natural-amenities-scale.aspx 10 http://www.realtor.org/topics/housing-affordability-index/methodology 11 For a discussion of the tension between slow growth and development advocates in the amenity rich west see Earl Pomeroy, “The American Far

West In The 20th Century,” Yale University, 2008. John Denver, “Rocky Mountain High,” RCA, 1972.12 Joseph Gyourko, Albert Saiz, and Anita A. Summers, “A New Measure of the Local Regulatory Market For Housing Markets: The Wharton

Residential Regulatory Land Use Index,” Journal of Urban Studies Vol. 45:3, pp:663-729, 2008.



Chart 2 shows the correlation between the unexplained portion of affordability (that which natural amenities did not capture) and the WRLURI. The correlation is statistically significant and not surprisingly, a higher level of regulation for reasons other than natural amenity protection results in a less affordable city.13 The index is not available for all of the MSAs studied here and therefore the number of observa-tions in the chart is smaller than that for Chart 1. There is a WRLURI index for each state. When the state level index is used for those cities for which no MSA level index exists the same results (though weaker) obtain.

The third partial result of this analysis relates to job gains and affordability. The job gain data is from June 2013 to June 2014.14 This time frame is convenient, but somewhat arbitrary. Nevertheless, it captures the employment basis for household formation. The relation between the percent-age increase in employment and the portion of affordability not explained by natural amenities is shown in Chart 3. As expected, a higher rate of job gain is correlated with less af-fordable housing. The reasoning is that job gain is directly related to household formation, either by moving the kids out of the house, splitting up shared living arrangements or migration, and this increase in household formation increases the demand for housing. Given a constrained supply of housing, prices increase and affordability declines.

Putting the three elements together in a multi-variate regression we obtain the following estimated relationship:

Affordability Index = 215 - 11.7 x Natural Amenities Index – 20.6 x WRLURI

-78.3 x Percentage Employment Growth 2004 to 2014

The Natural Amenities Index, the percentage em-ployment growth and the WRLURI were all statistically significant in this analysis. For the Natural Amenities Index,

an increase of 10% results in a decline in affordability of 1%. Another way to interpret this is if the natural amenities could be reduced by 10% (say by John Denver’s scar upon the land) affordability would only improve by 1%. This is because 90% of the amenities remain and they induce ad-ditional residents to take up the additional housing.

For the WRLURI a 10% increase in building restric-tions barely moves the needle. That is not to say that building restrictions do not matter, they clearly do. However, they are much more highly correlated with natural amenities than with other factors. Thus, the restrictions not associated with natural amenities (about 60% of the WRLURI index) need to be relatively stringent to have a significant bite on affordability.

With respect to the percentage increase in non-farm payroll employment, an annual increase in employment to be 1% higher than the average for a decade is predicted to reduce affordability by 4.1%. Though a spurt in employ-ment growth in any one year is not significant, the impact of sustained employment growth is informative for predicting California’s housing premium.

Some Observations – The Forecast and Policy

Given that the body politic in California does not seem inclined to make significant changes to zoning nor to CEQA, indeed the opposite might be the case, employment growth is the only variable in the equation liable to change over the forecast horizon. The California forecast for September 2015 is for employment growth in the 2.0-2.2% range. To be sure, new housing is being built. However, the analysis here and the results of the Allen Matkins / UCLA Anderson Fore-cast survey of multi-family home builders, suggests that will not be sufficient to meet the new demand. Consequently, one ought to expect housing to become increasingly less affordable in the Golden State over the coming two years.

13 Matthew Kahn documents that both topography and political philosophy result in less building in California cities. Cities with a more liberal political bent tend to be more restrictive, perhaps in part to protect natural amenities and perhaps to increase property values and retain the character of the city.

Matthew Kahn, “Do Liberal Cities Limit New Housing Development: Evidence from California,” Journal of Urban Economics, Vol. 60:2, pp:223-228.

14 Data is from CPS reported by the Bureau of Labor Statistics http://www.bls.gov/news.release/archives/metro_08272014.htm



There are two other implications to the notion that affordable housing, at least as commonly measured, is not coming to California any time soon, if ever. The first is af-fordable housing policy. The economics are clear, when af-fordable housing is provided, say by requiring developers to have a fixed percentage of their new units “affordable,” then the demand for that housing will be in excess of the supply. The policy itself recognizes that building constraints be they natural or regulatory will not permit a sufficient number of new homes to be built to satisfy the demand at affordable levels. This being the case, affordable housing policy needs to be explicit about who the housing is for. For example, one might advocate affordable housing so that teachers in public schools can purchase housing that would otherwise be difficult for them to acquire.

Whatever the non-market allocation of affordable housing is, it ought to serve some public goal. This is be-cause the requirement to build affordable housing will reduce the number of homes sold in free markets (the affordable units being allocated in other ways) and therefore the price of free market housing will increase over that which it would otherwise have been. So policy is explicitly choosing who will get affordable housing and who will be squeezed out by increased prices for the balance of the housing stock.

One might respond by saying “just build more hous-ing.” That would help, but would require a changes in zoning, CEQA, and perhaps building regulations and codes. Certainly some of this is happening, particularly along mass transit corridors, but to make a significant impact the changes would have to be quite dramatic. Realistically, this is not going to happen in the coming few years.

The second long-run implication has been studied by urban economists and goes under the rubric of “super-star cities.15 As natural amenities (and for that matter cultural amenities) become more scarce they become more expen-sive. Those that can afford them are those with skills that generate high value added output. This engenders a shift in employment from medium to high value added firms. For example, software and video game developers replacing auto and airplane manufacturers. The former move into Califor-nia and the latter move out. This is a trend we have been seeing in California for some time. Given the argument in this essay that California housing will become less, not more, affordable, one can expect this transformation of the nature of work in the Golden State to continue into the future.16

15 Joseph Gyourko, Christopher Mayer and Todd Sinai, “Superstar Cities,” American Economic Journal: Economic Policy, Vol. 5:4, pp:167-199, 2013.

Adam Zeidel, “The Case for Demand Side Subsidies in Superstar Cities,” The Urban Lawyer, Vol. 42:1 pp:135-169, 2010. Waldfogel, Joel. "The median voter and the median consumer: Local private goods and population composition." Journal of Urban Economics 63,

no. 2 (2008): 567-582.

16 While the “Superstar Cities” theory remains the subject of debate, (see the earlier references to Moretti, Glasaer, and Florida), the data are very suggestive of a sorting implied by these theories in California cities. For example, see Jerry Nickelsburg, “Comparative Advantage and Job Formation in California and Texas,” California Journal of Politics and Policy, Vol. 3 (1) No. 25, 2011. For an analysis of the sorting of work by the value added of employees.

Employment data by sector which reflect this sorting can be found at EDD.CA.gov.



Chart 1

YOU=Youngstown, OH, TOL=Toledo, OH, NYC=New York City, NY, BOS=Boston, MASV=Silicon Valley, FRS=Fresno, CA. Red diamonds are California Cities

SV

BOS

NYC

TOLYOU

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-4 -2 0 2 4 6 8 10 12

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Natural Amenity Index

Affordability & Amenities (US MSA's, 2014)

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Affordability vs Building Restrictions(Adjusted for Natural Amenities, U.S. MSA’s 2014)

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Building Restriction Index (higher numbers = more restrictive)

Chart 2

Building Restriction Index 2014, Red diamonds = California Cities, Affordability adjustment = portion of affordability not correlated with natural amenities



Chart 3

Job Growth = % Change Non-Farm Payroll Employment, Red diamonds = California Cities, Affordability adjustment = portion of affordability not correlated with natural amenities

-150.

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-40.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

Affordability vs Job Growth (Adjusted for Natural Amenities, U.S. MSA's 2014)

Affo

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% increase in Employment 2004-2014


CHINA SYNDROME AND ITS IMAPACT ON LOS ANGELES' ECONOMY AND HOUSING MARKET

China Syndrome and Its Impact on Los Angeles’ Economy and Housing Market William YuEconomist, UCLA Anderson ForecastSeptember 2015

In June 2014, Anderson Forecast published an article entitled “From China to California: Trans-Pacific Investment in Real Estate,” in which we suggested that smart money in China should invest in real estate in Los Angeles, California, and the U.S. to diversify portfolios in the face of China’s rising risks. In retrospect, that prediction looks pretty good. The question now becomes “Is this strategy still valid in the near future?” Our answer is yes.

China’s economy is in deep trouble now, as reflected by various dismal economic indicators, such as manufactur-ing, trade, prices, and sales data, as well as causing a 20 to 40% global stocks sell-off since June. Anderson Forecast is not surprised at all. In December 2011, we published an article entitled “Understand the Risks of China’s Economy,” in which we pointed out that China’s export- and investment-driven model was highly imbalanced and unsustainable. In September 2012, we published an article entitled “The End of China’s Economic Marvel,” in which we predicted

that China’s GDP growth would face a downturn sooner or later, followed by moderate growth at best in the medium run, while most institutional forecasts at that time were still predicting 8-9% growth for the next three decades.

This report will try to answer three questions: (1) What are the current state and prospects of China’s economy? (2) How will China’s current turmoil affect Los Angeles’ economy and its housing market? and (3) Is Los Angeles in another housing bubble?

We will briefly describe what happened in China’s economy. Its housing market, stock market, and real economy all convulsed with bubble and burst cycles one after the other. On top of this turbulence, China’s pegging its currency—Yuan or RMB (Renminbi, meaning people’s money)—with the U.S. dollar further exacerbates its boom-bust business cycle and complicates its economic prospects, which will be discussed in detail.


CHINA SYNDROME AND ITS IMPACT ON LOS ANGELES' ECONOMY AND HOUSING MARKET

China’s Housing Market

Figure 1 shows the nominal home price index (2006:Q1=100) for eight major cities in China, controlling for quality difference over time. From 2006:Q1, the begin-ning period of the data available, to 2015:Q2, China’s home price index composite for these eight cities has risen by 227% during this nine-plus year period. Note that China’s home prices had already increased more than 30% for several years before 2006. To give you a comparison, the Case-Shiller home price composite index of ten cities in the U.S. rose 190% from 1997:Q1 to 2006:Q2, the peak of the housing bubble, in a similar nine-plus year period.

During this period, L.A.’s home price has risen by 272%, San Francisco’s by 217%, New York’s by 170%. From 2006:Q1 to 2015:Q2, Beijing’s home price has risen by 339%, Shanghai’s by 338%, Shenzhen’s by 348%. If you agree that the U.S. housing bubble was serious, then you would agree that China’s is even more dangerous.

Figure 1 China’s Eight Cities Home Price Index

Source: Center for Urban Development and Land Policy, Peking University-Lincoln Institute; Hang Lung Center for Real Estate, Tsinghua University

0

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2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Beijin g S hangh aiT ian jin S henzhenC hengd u Da lianW u ha n XianC hin a 8 C ity Comp osite

In fact, we saw China’s housing market start to sputter in early 2014. Although the eight city composite index is relatively flat over the past year, mostly due to the continued rise of prices in Shanghai and Shenzhen, the overall hous-ing market (2 and 3-tier cities) in China did not do well. According to the latest data from the National Bureau of Statistics of China, 67 out of 70 cities’ home sale prices have declined from July 2014 to July 2015. In Figure 1, we see China’s housing market slump was propped up by government stimulus funds and favorable policies in 2009 and 2012. 2015 is the third time the government is stepping in to prevent a further housing crisis. The fundamental di-lemma in China’s housing market is that if home prices go down, it will bring down the whole economy directly; on the contrary, if home prices go up, expensive homes will become more unaffordable and cause economic structures to be even more unbalanced and unsustainable. Either way is bad for China. In a nutshell, we suggest China’s housing bubble has busted and the bear market will likely last several years (or even a decade).



China’s Stock Market

Lately, the world was stunned by the swirl of China’s stock market boom and bust and the jaw-dropping gov-ernmental interventions. For instance, the Shanghai stock market composite index rose from 2,200 in August 2014 to 5,166 on June 12, 2015, an increase of 134% within 10 months, and then crashed to 3,000 (- 40% slump) over the next three months. As we mentioned in the previous report, China’s stock market is a dog-eat-dog market. Its extreme risk might be okay for the players like the Wolf of Wall Street, hedge funds, and gamblers. However, it is not an appropriate market for players like mom and pop investors, mutual funds, and pension funds.

In fact, we can see many boom-and-bust cycles in the recent Shanghai stock exchange history as shown in Figure 2. It crashed in 1992, 1994, 2008, and 2015. And it also went through two 5-year bear years: from 2001 to 2005 and again from 2010 to 2014. Again, we are not surprised at the roller

coaster pattern of China’s stock markets. In 2014, Beijing relaxed margin trading and other requirements and regula-tion in order to beef up the stock market for two reasons: (1) Amid the real estate bubble bust, China wanted to cre-ate another asset boom to pull up the weak economy; and (2) With rising stock prices, China hoped that state-owned enterprises could issue new shares to tap the equity fund to improve their high-debt balance sheet. The wild market did not follow what Beijing initially orchestrated.

Since November 2014, with the same goals of luring in foreign capital, Beijing also opened up China’s domestic stock markets for international investors through the Shang-hai-Hong Kong Stock Connect program. The international investment is capped by a quota of $48 billion a year. As of now, the quota is less than half used. It is hard to imagine that there will be further foreign capital coming to China after this summer’s fiasco. We believe that investing in the U.S. stock market is a wiser decision based on the historical record, after adjusting for its risks.

Figure 2 Shanghai Stock Exchange Composite Index, 1991 to 2015

Source: Yahoo Finance

10,000

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10092 94 96 98 00 02 04 06 08 10 12 14



Figure 3 displays the S&P Stock Price Index for the U.S. from 1991 to 2015. If investors hold their investment for 25 years, its annual compound return is 7.2%. That is not bad for a 3% growth economy. For China’s 10% growth economy during the same 25-year period, if investors hold their investment for 25 years, Shanghai stock market’s an-nual compound return is 13.6%. Although China’s stock market has a higher return in the past quarter century than the U.S.’s, we need to understand it also comes with higher risk.

In finance and portfolio management, we can use the so called,Sharpe Ratio to calculate a risk-adjusted return for an investment portfolio. The Sharpe Ratio is calculated by the following simple method:

The higher the Sharpe Ratio, the better risk-adjusted stock returns you get. That said, after considering your investment risk, you get a better investment result when you have a higher Sharpe Ratio. We calculate two returns and their volatilities for both the S&P 500 and the Shanghai Composite Index: (1) monthly one-year returns from 1992 to 2015; and (2) monthly five-year returns from 1996 to 2015. We use the discount rates in China and the U.S. as their corresponding risk-free rates. The average Sharpe Ratios are shown in Table 1.

Figure 3 S&P 500 Stock Index, 1991 to 2015

Source: Yahoo Finance

4,000

2,000

1,600

1,2001,000

800

600

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20092 94 96 98 00 02 04 06 08 10 12 14

Table 1 Average Sharpe Ratios (Average Risk-Adjusted Return) for the U.S. and China

U.S. S&P 500 Stock Index 1.04 2.04

-0.19 1.13

One-Year Return(1992 to 2015)

Five-Year Return(1992 to 2015)

China Shanghai Stock Exchange Composite Index

Average Stock Returns - Risk Free Rates

Volatility (Standard Deviation) of Stock Returns



The results in Table 1 demonstrate that investing in the U.S., whether in the medium=run (one-year) or in the long-run (five-year), will produce a much higher Sharpe Ratio, risk-adjusted returns, than investing in China’s stock market. In other words, China’s stock returns are not worth investing in, considering their high risks.

China’s Real Economy

The deflating Chinese real estate market is hurting China’s real economy because the real estate market has been the growth driver since the global financial crisis in 2008 when the exports engine was cooling down. The stock market roller coaster only makes things worse. We have heard of China’s economic “slowdown” for 4 years. Lately, Beijing trimmed its 2014 GDP growth estimate from 7.4% to 7.3%. And its GDP growth target is 7% in 2015. Really? Is China still running a 7% growth economy? If so, how could other major trading partners exporting to China have experienced miserable outputs and exports growth this year?

Appropriately, fewer and fewer people believe China’s official numbers.1 Some say that China’s true GDP growth should be 5% now, some suggest that it could be 3%, some estimate 1%, and some think China’s economy is actually in recession.

Due to China’s untrustworthy official GDP numbers, the market has turned to private-sector measurements to better understand the current state of the economy. The most popular one is Caixin/Markit China Purchasing Managers’ Index (PMI). According to its latest manufacturing PMI, where above 50 indicates expansion and below 50 indicates contraction, the number is 47.3 in August, down from 47.8 in July, and the PMI has been below 50 for six successive months.

While the manufacturing sector (34% share of GDP in 2014) is the growth driver in China’s export-driven economy, the steady rising share of the service sector (43% of GDP in 2014) has become more important in understanding China’s economy. The Caixin China Services PMI is 51.5 in August, down from 53.8 in July, but still in positive territory. How-ever, on the whole, with the Caixin Composite Index at 48.8 in August 2015, China’s economy could be contracting now.

We can also understand China’s economy with our own numbers. One fairly simple way to measure their economic growth is basing it on the trade data. Let’s call it trade-based GDP. The reason we use this is because in-ternational trade data (exports and imports) should match up in both countries because one country’s exports are the other country’s imports. Therefore, China is less likely to cook this kind of data. The rationale and assumption of this simple method is twofold: (1) When a country’s exports increase, it directly contributes to GDP growth. (2) When a country’s imports increase, it deducts GDP growth directly but it also reflects growth of domestic consumption and investment. Note that this measurement is far from perfect2 but we are sure it will shed more light on the situation than China’s official numbers.

The first step to calculate the trade-based real GDP is to simply combine a country’s nominal imports and ex-ports, for both goods and services, and then adjust them by the average import and export price deflators. We therefore get the real GDP level. The second step is to calculate the simple growth rate of this trade-based GDP and then stan-dardize the trade GDP growth average to the official real GDP growth average. It is interesting to see that for both the U.S. and China the standardized ratios are approximately the same: 0.5.

1 For example, see “What Do U.S. Economists Think of Official China Statistics? “Only a Fool Would Believe Them,” Wall Street Journal, September 11, 2015. http://blogs.wsj.com/economics/2015/09/11/what-do-u-s-economists-think-of-official-china-statistics-only-a-fool-would-believe-them/

2 It is likely that Chinese buy a lot more domestically produced goods than Americans. Therefore, the trade-based GDP and true GDP growth in China might not be as closely related as those in the U.S.



Figure 4 shows the annual U.S. official real GDP growth and our trade-based real GDP growth. We can see that these two series fluctuated closely in terms of business cycle dynamics from 1981 to 2014. There are three times that trade GDP growth was negative: 1982, 2001, and 2009, which identify three out of four U.S. recessions.

Figure 5 shows the official annual China real GDP growth and trade-based real GDP growth estimate. First of all, we can see that our trade-based GDP growth is much more volatile than Beijing’s official numbers. In particular, in 1998 and in 2009, the trade GDP growth rates were nega-tive. It means that China was in recession during these two periods, even though the official numbers still presented above 7% GDP growth.

Secondly, we can see our trade-based GDP growth has been systematically below China’s official number since 2005. For the period of 2005 to 2008, it is possible that China understated its price levels. (Note that we use the U.S. exports and imports deflator for calculating China’s trade GDP.) If you understate your price measurement, you will overstate your real GDP estimate. From 2011 to 2014, it is likely that our trade GDP number does not capture some economic activities that were entirely based in China, for example, inefficient production by using domestic sources of steel and concrete to build ghost cities. Nevertheless, our trade based GDP indicates that China’s true GDP growth has likely been significantly lower than its official numbers since 2011.

In our previous report, based on China’s highly unbal-anced growth model in which investment accounts for 45% of GDP while consumption accounts for only 39% of GDP (2014), we predicted two trajectories for China over the next decade. The first was an optimistic one in which China will grow at 5% in the medium-run after recovering from the recession. The second was a pessimistic one in which China will grow at 2 to 3%, similar to the “lost decades” in Japan over the past 25 years.

The key difference of these two scenarios is the con-sumption growth. If the real consumption growth can stay at 7% to 9%, then China could have a more moderate growth and develop a more balanced economic structure in the next two decades. If not, then China will be more likely to have lower GDP growth and roll into the so-called middle-income trap. The path for state-capitalist China to restructure its economy to be more market-driven and consumption-based is in the right direction but bumpy.

Chinese Currency

As shown in Figure 6, from August 10 to August 12, 2015, China let its currency RMB depreciate from 6.2 to 6.4 against the U.S, dollar, about 3%, by announcing a widened daily trading band of the RMB. Although the official goal of the move is to liberalize the tight control of the RMB, the abrupt depreciation sent shock waves all over the world. Why is it causing so much turbulence globally? Because it sends the belated message that we put forth in our 2012 article: China’s economic marvel has ended. The dream that the world can rely on high growth in China to revive busi-nesses and economies in the aftermath of the global financial crisis cannot continue anymore.

When the country’s economy is strong, its currency tends to be strong through trade and capital market flows. When the country’s economy is weak, so is its currency. Over the past two years, the U.S. economy has been recovering at a robust pace. That is why we see a stronger and stronger dollar. On the other hand, China’s economy is slowing down significantly in many areas, as we said earlier. Thus, the Chinese RMB is supposed to be weaker, other things being equal. However, People’s Bank of China heavily manages (manipulates) its exchange rate at a narrowed range to main-tain stability. For many years the RMB was undervalued, but it has actually been overvalued in the past two years compared to its market equilibrium.



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Figure 4 U.S. Official Real GDP Growth and Trade-Based Real GDP Growth

Source: Oxford Economics

Figure 5 China’s Official Real GDP Growth and Trade-Based Real GDP Growth

Source: Oxford Economics

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confidence, to reduce the continued capital outflow, and to calm the stock market panic.

2) Depreciate gradually (15% probability): If capital flight is not shrinking and if the economy is still contracting, China might want to devalue its RMB in order to boost their exports engine to stimulate the economy. With its mounting trade surplus with the U.S. (it will surpass $350 billion in 2015), the depreciation will cause in-ternational criticism even in the name of making the RMB exchange rate more flexible. The problem of this incremental depreciation is that the expectation will cause further capital flight. This is opposite to what happened in the past, when the expectation of steady appreciation of the yuan, attracted a lot of capital inflow in order to earn the capital gain.

3) Depreciate significantly, instantly (10% probability): China might depreciate, say 20%, overnight unexpect-edly and hold it there. By doing so, it will eliminate the problems of expectation and capital flight and most importantly, will reinvigorate its export power and economy.3 But it could also start a currency war all over the world, especially in Asia. It might create another financial crisis due to uncertainty and rising trade tensions with the U.S. and Europe.

In fact, we can see the RMB has become weaker since January 2014. But it was not clear until the surprising depreciation in August that it was a turnaround of an RMB appreciation trend since 2005. Now, all investors realize the RMB is trending weaker. The desperate move also means that the Chinese economy is running way below what gov-ernment numbers suggest.

The question is: Now what? What will happen for the RMB exchange rate over the next year or two? Here we sum-marize the possibilities with their corresponding likelihoods:

1) Hold still (70% probability): Since the surprising move in August, investors have lost confidence that the RMB will be able to keep its value against the dollar. To prevent further depreciation loss and market turmoil, China’s capital outflow was exacerbated in August. According to the latest news, $97 billion flew out of China in that single month. China saw capital flight of $461 billion in 2014 and $252 billion in 2015Q1. It is certain that capital flight will reach a new high this year. Nevertheless, People’s Bank of China has more than $3.5 trillion in foreign reserves, which will help maintain its exchange rate in order to regain investors’

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Figure 6 Chinese Yuan (Renminbi, RMB) Exchange Rate, Yuan per USD

Source: Federal Reserve Economic Data

3 For example, see Barry Eichengreen’s “China’s Forex Follies,” Project Syndicate, September 11, 2015. http://www.project-syndicate.org/commentary/china-forex-follies-by-barry-eichengreen-2015-09



Will China’s Syndrome Affect Los Angeles’ Economy and Housing Market?

Will China’s slowdown/recession affect Los Angeles’ economy? The short answer is yes; however, it will not be a significant effect. There are negative impacts in some respects, but there is a positive side as well. As shown in Figure 7, L.A. seaports exports have been weak since the beginning of 2014. In fact, if we look at the past, L.A. exports with China as the major destination country remained flat from 2011 to 2013.

Figure 8 displays the departure and arrival of air cargos through Los Angeles Airport (LAX). Both were declining over the past several months. Two main reasons are: (1) China’s slowdown/recession, and (2) Strong dollar. These same reasons could also explain the recent drop of LAX passengers in Figure 9 reflecting the cooling down of the Southern California tourism boom over the past several years.

4) Appreciate (5% probability): China might appreciate its currency in order to repair the damage from the August depreciation. In terms of slowing down capital flight, this could be a strong medicine. However, doing so will hurt exports growth, so,therefore, we think this is the least likely scenario.

China’s strictly controlled exchange rate helped in building up its export empire and the world factory by reduc-ing the exchange rate risks for exporters and importers. The downside of this stable currency policy is aggravating the volatility of its business cycle as shown in Figure 5. With China’s new normal, there will be negative consequences to China whatever the future trend of the RMB is.

In summary, China’s economic growth over the next decade will be mostly likely between 2% and 5%. And it would be naive to believe China’s official data saying that there were no recessions in the past, there is not one in the present, or there won’t be one in the future.

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Figure 7 Monthly Los Angeles Seaport Traffic, Ports of Los Angeles and Long Beach Combined (Thousand TEUs), January 1995 to August 2015

Sources: Ports of Los Angeles and Long BeachNote: A “TEU” is a “twenty-foot equivalent,” a standard shipping container



Figure 8 Los Angeles Airport (LAX) Air Cargo Freights, January 2004 to July 2015

Source: LA World Airports

Figure 9 Los Angeles Airport (LAX) Passenger Traffic, January 2004 to July 2015

Source: LA World Airports, for scheduled carriers only

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Other than international trade and tourism, we don’t see much more negative influence on the L.A. economy from China’s flux. In addition, we suggest that the current Chinese turmoil might further propel investment in the L.A. real estate market. Why? With the dismal outlook and uncer-tainties in China, contrasted with the promising and stable outlook in the U.S., it is wise to reallocate money from China to the U.S. Even with the negative wealth effect generated from China’s deflating real estate, tumbling stock markets, and 3% currency devaluation, wealthy Chinese individuals still have sufficient equity to make a move. Therefore, we predict smart money in China will continue to invest in the U.S. as long as China does not impose much more serious capital controls.

Is Los Angeles in Another Housing Bubble?4

Speaking of foreigners investing in the U.S. real estate, we remember what happened with Japanese investors in California in the late 1980s. It did not end well. Therefore, before investing, Chinese and any other interested investors must ask an important question in the face of rising home

prices in L.A., “Is Los Angeles in another housing bubble waiting to burst?” My answer is NO, at least not now or next year.

Using the all-transactions house price index from the Federal Housing Finance Agency, I examined the housing price history in Los Angeles County, adjusted for inflation, from 1975 to date, 1975 being the first year data was avail-able. Along with some short-term fluctuations, we can see four major housing price cycles in L.A. since 1975:

1) Bull market (1975Q1-1980Q3): real home price in-creased by 69% over 23 quarters. Bear market (1980Q4 to 1984Q2): real price decreased by 9% for 15 quarters.

2) Bull market (1984Q3-1989Q4): up 67% for 22 quar-ters. Bear market (1990Q1-1997Q2): down 37% for 30 quarters.

3) Bull market (1997Q3-2006Q4): up 166% for 38 quar-ters. Bear market (2007Q1 -2012Q2): down 43% for 22 quarters.

4) Bull market (2012Q3-2015Q1): so far the price is up 27% for 11 quarters.

Figure 10 FHFA Real Median Home Price for Los Angeles County in 2015 (adjusted for CPI inflation)

Sources: Federal Housing Finance Agency, Bureau of Labor Statistics, and Zillow

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(2015 $)

4 The revised edition of this section is published as an op-ed in Los Angeles Times on August 20, 2015. http://www.latimes.com/opinion/op-ed/la-oe-0818-yu-la-housing-bubble-20150819-story.html



which is way below the 2006 bubble level. It’s still higher than any time before 2003, though. Why? My hypothesis is that Los Angeles, similar to New York (ratio: Manhattan 25, Brooklyn 23), San Francisco (ratio: 21), London and others, has become a so-called superstar city in the 21st century. Its houses not only serve as residences for local workers but also serve as global investment targets for second homes or wealth accumulation because of its superior size, amenities, weather, geography, and diversity. This might explain why the ratio is higher now than in the pre-bubble period in L.A. and means the higher ratio does not necessarily indicate a bubble.

In fact, all signs indicate Los Angeles is not in a housing bubble. The current rise in home prices seems to be driven by rising effective demand and limited supply, not by speculation. Therefore, the housing bubble burst we experienced several years ago is unlikely to haunt us this year or next, and the smart money will continue to invest here.

Conclusions The take away points of this report are:

• China’s economy was, is, and will be more volatile than what Beijing’s official numbers suggest. China’s economy, housing market, stock market, and currency are all in trouble.

• Even though China has tried to contain its imploding crises one after another, the economy has come to a crossroads of restructuring. If it transitions smoothly to a service and consumption based economy, its medium growth rate could reach 5%. If not, its outlook will be more dismal: 2-3% growth, with an economy trapped in the lower middle income level.

• Considering the risks in China’s economy, housing market, stock market, and exchange rate, portfolios investment or FDI in the U.S. would produce more risk-adjusted return than in China.

• China’s turmoil might reduce the growth of Los Ange-les’ trade and tourism but Chinese investment in L.A. real estate will persist. L.A.’s housing market, despite becoming more expensive and unaffordable, is not in a bubble. Its housing prices are highly unlikely to bust this year or next.

Can these past housing price cycles in L.A. help us predict the future? To some degree, yes. Unlike stock market prices, home price dynamics tend to be more persistent due to various features such as transaction costs and are there-fore somewhat predictable in the context of interacting with macroeconomic and local factors. If history is any guide, the L.A. housing price cycle seems to be about 12 years on average, of which 7 years is spent in the bull market with at least 65% real price appreciation, and 5 years is spent in the bear market. We are 3 years into the housing recovery that started in 2012, with 27% appreciation so far. On aver-age, there will be 4 more years or 35% more price growth before we reach the turning point. Of course, it is possible the bear market could come earlier or later than 4 years, but it is quite unlikely to happen this year or next.

How can I be so sure? Often, during a bubble-making period, we see an accelerating rate of home price apprecia-tion, as in 1988-89 and 2004-06. In the past two years, we haven’t seen that kind of rapid appreciation in L.A. Another way to understand housing price cycles is by looking at building permit numbers, the direct indicator of home sup-ply. For example, L.A. housing permit units peaked in 1977, 1988 (50,500 units), and 2004 (26,900 units), one to three years ahead of the real housing price peaks in 1980, 1989, and 2006. L.A. housing permit units bottomed in 1982, 1993 (7,300 units), and 2009 (5,700 units), which were also a few years before the real housing price troughs in 1984, 1997, and 2012. If more houses are being built, indicating rising demand, the price goes up. If fewer buildings are planned, it is because of expected decreasing demand, and the price will fall. Over the past three years, we have seen L.A. building permits increase from 11,200 units in 2012 to 18,200 units in 2014. The 2015 number will most likely be higher than 2014. Therefore, we can predict the next home price peak is at least 2 years away.

Another measure of L.A. housing value is a simple price-to-rent ratio. The ratio is calculated by taking the median home price over the annual median rent in L.A. If the ratio is high, meaning that home price is beyond its fundamental value of rent/cash inflow, then it is more likely to be a bubble. Again, let’s look at history. Two previous peaks were in December 1989 with a ratio of 14.8 and in February 2006 with a ratio of 24.4. According to Zillow, the current price-to-rent ratio in L.A. was 17.1 in May 2015,



Charts



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California Employment (6-mo. moving avg.)Jan. 2003 to Aug. 2015 (Thous.)

Wage & Salary Emp. (Left) HH Survey Emp. (Right) 1513110907050301999795

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8

6

4

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California Unemployment RateJan. 1995 to Aug. 2015

1413121110090807060504030201

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Taxable Sales in California2001:1Q to 2014:1Q

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100

50

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Indexed 1985 = 100

Source: Conference Board

Indexes of Consumer Attitudes--Pacific AreaJan. 2002 to Aug. 2015

Consumer Confidence Present Expectations



151413121110090807060504

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California New Car RegistrationsJan. 2004 to June 2015

(3-mo. moving avg.)

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400

300

200

100

0

(Thous.)

New One-Family Houses SoldWestern Region

Jan. 2003 to July 2015

(3-mo. moving average)

151311090705030199979593918987

600

500

400

300

200

100

(Thous. $)

Source: California Association of Realtors

California Existing-Home Prices1987:Q1 to 2015Q2

151413121110090807060504

700

600

500

400

300

200

(Thous.)

Source: California Association of Realtors

California Existing-Home SalesJan. 2004 to July 2015



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Building Permit ValuationsTotal Nonresidential


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New Residential Units ThroughCalifornia Building Permits


Single-Unit Multi-Unit

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California Construction EmploymentJan. 2003 to Aug. 2015

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12000

(Thous.) (Thous.)

California Employment by SectorJan. 2003 to Aug. 2015

Goods Producing (Left) Services (Right)

CHARTS – FORECAST


201720112005199919931987198119751969

15

10

5

0

-5

-10

(% Change Year Ago)

Real Personal IncomeCalifornia versus U.S.

California U.S.201720112005199919931987198119751969

86420

-2-4-6-8

(% Change Year Ago)

Nonfarm EmploymentCalifornia versus U.S.

California U.S.

2017201120051999199319871981197519691963

14

12

10

8

6

4

2

(Percent)

Rates of UnemploymentCalifornia versus U.S.

California U.S.2017201120051999199319871981197519691963

15

10

5

0

-5

-10

(3-Yr. % Ch.)

California Employment versusReal Personal Income

Nonfarm Emp. Real Personal Income

CHARTS – FORECAST


2017201120051999199319871981197519691963

20

15

10

5

0

-5

(4-Qtr Percent Change)California Consumer Price Inflation

California U.S.

2017201220072002199719921987

24000220002000018000160001400012000100008000

(Thous)

California Nonfarm EmploymentHistory & Forecast

Vs. 2.3% Trend from 1990:3

6.5 Million Jobs Below Trendby Year 2017

History & Forecast 2.3% Trend Line201720112005199919931987198119751969

13

12

11

10

9

8

(Percent)

California Share of U.S.Employment and Population

Emp Population

201720112005199919931987198119751969

20

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(% Change Year Ago)Real California Taxable Sales

CHARTS – FORECAST


2017201120051999199319871981197519691963

500400300200100

0-100-200-300

(Thous.)

California Net Natural Increase andNet Inmigration

Immigration Natural Increase

20172011200519991993198719811975

52

50

48

46

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42

40

(Percent)

Gross Labor Force Participation RateLabor Force/Total Population

California U.S.2017201120051999199319871981197519691963

40

35

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20

15

(Ca. Mil.; U.S. 10 Mil.)Population of California vs. U.S.

California U.S.

201720112005199919931987198119751969

3.0

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1.0

0.5

0.0

(4-Qtr Percent Change)Growth in Population

CHARTS – FORECAST


20172011200519991993198719811975

400

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100

0

(Thous. Units)

New Residential Units ThroughCalifornia Building Permits

Single-Unit Multi-Unit

2017201220072002199719921987

1000

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400

(Thous.)

California Employmentin Construction

201720122007200219971992198719821977

40

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5

(Bil. 2009 $)

Real Value of NonresidentialConstruction in California

201720112005199919931987198119751969

350

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(Thous. $)U.S. Median Price of Single-Family Homes

CHARTS – FORECAST


201720152013201120092007200520032001

1900

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1400

1300

1200

(Thous.)

California Employmentin Manufacturing

201720152013201120092007200520032001

2500

2400

2300

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2100

(Thous.)

California Employmentin Trade

201720152013201120092007200520032001

600

550

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450

400

(Thous.)

California Employmentin Information

201720152013201120092007200520032001

2600

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1400

(Thous.)

California Employmentin Education and Health Services

CHARTS – FORECAST


201720152013201120092007200520032001

2300

2250

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2150

2100

2050

(Thous.)

California Employmentin State and Local Government

201720152013201120092007200520032001

300

290

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270

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250

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230

(Thous.)

California Employmentin Federal Government

201720152013201120092007200520032001

2800

2600

2400

2200

2000

1800

(Thous.)

California Employmentin Professional & Business Services

201720152013201120092007200520032001

950

900

850

800

750

700

(Thous.)

California Employmentin Financial Activities



Tables



Table 1. Summary of the UCLA Forecast for California 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Personal Income, Taxable Sales, and Price Inflation (%Change)Personal Income (Bil.$) 1564.3 1596.2 1537.1 1578.6 1685.6 1805.2 1856.6 1944.1 2041.5 2161.3 2296.9 Calif. (% Ch) 4.3 2.0 -3.7 2.7 6.8 7.1 2.8 4.7 5.0 5.9 6.3 U.S.(% Ch) 5.3 4.2 -3.3 3.2 6.2 5.0 1.1 4.4 4.3 5.1 6.0Pers. Income (Bil. 2009$) 1604.6 1595.4 1537.1 1558.6 1631.6 1709.8 1734.5 1787.8 1857.8 1928.4 1994.5 Calif. (% Ch) 1.4 -0.6 -3.7 1.4 4.7 4.8 1.4 3.1 3.9 3.8 3.4 U.S. (% Ch) 2.7 1.1 -3.2 1.5 3.7 3.0 -0.3 3.0 4.0 3.2 3.3Taxable Sales (Bil.$) 561.3 532.4 456.6 477.0 520.2 558.1 586.6 612.2 640.1 669.0 697.2 (% Ch) 0.3 -5.2 -14.2 4.5 9.1 7.3 5.1 4.4 4.6 4.5 4.2 (Bil. 2009$) 575.8 532.1 456.6 471.0 503.5 528.6 548.0 563.0 582.5 597.0 605.4 (% Ch) -2.5 -7.6 -14.2 3.2 6.9 5.0 3.7 2.7 3.5 2.5 1.4Consumer Prices (% Ch) 3.3 3.4 -0.3 1.3 2.6 2.2 1.5 1.8 1.3 2.6 3.2

Employment and Labor Force (Household Survey, % Change)Employment 0.9 -0.5 -4.0 -0.5 1.0 2.2 2.5 2.2 2.7 2.2 1.4Labor Force 1.4 1.7 0.1 0.5 0.3 0.7 0.8 0.9 1.2 1.1 1.0Unemployment Rate (%) 5.4 7.5 11.2 12.1 11.6 10.2 8.8 7.5 6.2 5.2 4.8 U.S. 4.6 5.8 9.3 9.6 8.9 8.1 7.4 6.2 5.3 4.9 4.8 Total Nonfarm Nonfarm Employment (Payroll Survey, % Change) Calif. 0.8 -1.1 -5.7 -1.1 1.0 2.4 3.2 3.0 2.9 2.2 1.6 U.S. 1.1 -0.6 -4.3 -0.7 1.2 1.7 1.7 1.9 2.1 1.8 1.4Construction -4.4 -11.7 -20.9 -10.2 0.2 5.1 8.0 6.0 6.7 3.1 1.3Manufacturing -1.7 -2.6 -10.0 -3.1 0.5 0.4 0.1 1.1 0.6 1.4 1.1 Nondurable Goods -1.1 -2.0 -8.1 -2.5 -0.4 0.3 0.4 0.5 -0.7 1.4 1.1 Durable Goods -2.0 -3.0 -11.2 -3.4 1.0 0.4 -0.1 1.4 1.4 1.4 1.2Trans. Warehousing & Util 2.3 -0.5 -6.0 -1.8 1.7 2.7 3.2 3.9 3.4 1.9 1.4Trade 1.1 -2.5 -7.5 -0.3 2.0 1.9 2.0 2.5 2.4 1.5 1.2Information 1.1 1.1 -7.3 -2.8 0.4 1.0 3.1 2.1 3.5 2.8 2.1Financial Activities -3.4 -6.1 -7.0 -2.9 0.2 1.5 1.2 0.2 1.6 1.3 1.3Professional Busi. Serv. 1.1 -1.2 -7.9 0.6 2.8 5.0 4.4 3.9 5.3 4.3 2.8Edu. & Health Serv. 3.8 4.0 2.7 0.6 1.4 4.2 7.0 3.8 2.9 2.5 1.6Leisure & Hospitality 2.7 0.8 -4.4 -0.1 2.3 4.1 4.9 4.8 3.5 2.3 1.8Other Services 1.0 -0.2 -4.9 -0.3 1.8 2.2 2.4 4.5 1.9 1.9 1.8Federal Gov’t -0.6 0.5 1.1 6.8 -4.9 -1.8 -2.0 -1.3 0.2 0.9 0.8State & Local Gov’t 2.0 1.0 -1.9 -2.2 -1.4 -1.1 0.1 1.9 1.3 0.9 0.6

Nonfarm Employment (Payroll Survey, Thous.)Total Nonfarm 15414 15246 14378 14215 14364 14712 15183 15645 16100 16455 16713Construction 893 788 624 560 561 590 637 675 721 743 752Manufacturing 1465 1427 1284 1244 1250 1255 1256 1270 1277 1295 1310 Nondurable Goods 536 526 483 471 469 471 473 475 472 479 484 Durable Goods 929 901 801 773 781 784 783 794 805 817 826Trans. Warehousing & Util 508 505 475 466 474 487 503 522 540 550 558Trade 2405 2345 2168 2162 2204 2247 2291 2349 2405 2441 2470Information 471 476 441 429 431 435 449 458 474 487 497Financial Activities 897 842 783 760 762 773 783 784 797 807 817Professional Busi. Serv. 2268 2241 2064 2077 2135 2242 2341 2433 2562 2672 2747Edu. & Health Serv. 1913 1990 2044 2056 2084 2172 2325 2414 2485 2547 2586Leisure & Hospitality 1560 1573 1503 1502 1536 1599 1676 1757 1819 1860 1895Other Services 512.1 511.3 486.2 485.0 493.7 504.7 516.6 539.7 550.0 560.6 570.5Federal Gov’t 247.1 248.4 251.2 268.3 255.2 250.5 245.5 242.2 242.8 244.9 246.8State & Local Gov’t 2247.9 2271.0 2228.2 2179.8 2149.4 2125.6 2128.7 2168.7 2197.9 2217.8 2231.9

Population and MigrationNet Inmigration(Thous) -24 -25 -89 -51 -11 39 45 92 95 129 146Population (Thous) 36551 36859 37100 37334 37583 37869 38163 38489 38821 39170 39538 (% Ch) 0.8 0.8 0.7 0.6 0.7 0.8 0.8 0.9 0.9 0.9 0.9

Construction ActivityResidential Building Permits (Thous. Un.) 106.5 60.8 33.2 43.0 44.9 56.7 77.9 82.3 105.8 121.1 125.4Nonres.Permits (Mil.’09$) 23172 18818 10895 11301 12810 13826 20297 21260 21283 22085 23262



Table 2. Quarterly Summary of the UCLA Forecast for California 2015:1 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3 2016:4 2017:1 2017:2 2017:3 2017:4

Personal Income, Taxable Sales, and Price Inflation (%Change)Personal Income (Bil.$) 2001.8 2027.2 2056.5 2080.6 2111.9 2144.6 2177.5 2211.2 2248.4 2281.5 2312.9 2344.9 Calif.(% Ch) 4.5 5.2 5.9 4.8 6.2 6.3 6.3 6.3 6.9 6.0 5.6 5.7 U.S. (% Ch) 3.2 3.9 5.9 3.3 5.9 5.1 5.7 5.5 6.8 6.2 5.9 5.4Pers. Income (Bil. 2009$) 1835.0 1843.2 1867.4 1885.4 1903.0 1919.6 1937.3 1953.8 1972.6 1987.7 2001.2 2016.5 Calif.(% Ch) 4.8 1.8 5.4 3.9 3.8 3.5 3.7 3.5 3.9 3.1 2.7 3.1 U.S. (% Ch) 5.2 1.7 5.1 2.5 3.6 2.7 3.2 2.7 4.0 3.4 3.1 2.8Taxable Sales (Bil. $) 629.6 636.2 643.7 650.9 658.1 665.6 672.6 679.9 686.8 693.4 700.7 707.9 (% Ch) 4.3 4.3 4.8 4.6 4.5 4.6 4.3 4.4 4.1 3.9 4.2 4.2 (Bil. 2009$) 577.1 578.4 584.5 589.9 593.0 595.7 598.4 600.8 602.5 604.1 606.2 608.8 (%Ch) 4.5 0.9 4.3 3.7 2.1 1.9 1.8 1.6 1.2 1.1 1.4 1.7Consumer Prices (% Ch) -0.7 4.2 1.4 1.3 2.9 3.0 3.2 3.3 3.1 3.2 3.2 3.0

Employment and Labor Force (Household Survey, % Change)Employment 2.8 3.2 2.4 2.4 2.1 2.1 1.9 1.7 1.4 1.3 0.9 0.9Labor Force 0.8 1.5 1.0 1.0 1.2 1.0 1.0 1.1 1.0 1.1 1.1 1.0Unemployment Rate (%) 6.7 6.3 6.0 5.7 5.5 5.3 5.1 4.9 4.8 4.8 4.8 4.8 U.S. 5.6 5.4 5.2 5.1 5.0 4.9 4.8 4.8 4.8 4.7 4.8 4.9 Total Nonfarm Nonfarm Employment (Payroll Survey, % Change) Calif. 3.4 2.5 2.3 2.4 2.2 2.2 2.0 1.8 1.6 1.5 1.0 0.9 U.S. 2.2 1.7 2.1 1.6 1.7 1.8 1.8 1.7 1.4 1.4 0.9 0.7Construction 9.6 6.9 4.4 4.4 2.2 2.1 1.8 1.5 1.1 1.0 1.0 0.8Manufacturing 0.2 0.9 1.6 1.4 1.5 1.4 1.3 1.5 1.0 1.0 0.8 1.1 Nondurable Goods -0.0 0.5 1.8 1.6 1.3 1.7 1.0 0.9 0.9 0.9 1.4 2.2 Durable Goods 0.3 1.2 1.5 1.3 1.6 1.2 1.4 1.8 1.1 1.1 0.5 0.5Trans. Warehousing & Util. 2.4 0.1 2.2 2.4 2.2 2.1 1.4 1.3 1.4 1.5 1.5 1.3Trade 2.8 2.6 1.1 1.1 1.5 1.7 1.6 1.3 1.3 1.1 0.7 0.6Information 4.6 3.8 2.4 3.2 2.7 3.0 2.6 2.6 2.1 2.1 1.1 1.1Financial Activities 4.9 -2.0 1.1 1.3 1.7 1.7 1.6 1.5 1.1 1.1 1.1 1.0Professional Busi. Serv. 6.2 6.5 4.0 4.1 4.4 4.3 4.1 3.3 2.8 2.5 1.4 1.3Edu. & Health Serv. 2.6 3.8 2.5 2.8 2.4 2.3 2.0 1.8 1.7 1.5 0.7 0.7Leisure & Hospitality 5.6 0.8 2.9 2.9 2.1 2.1 2.2 2.1 1.9 1.7 1.4 1.2Other Services 2.9 -2.9 2.3 2.7 2.3 2.3 1.6 1.7 1.8 1.8 1.7 1.8Federal Gov’t -1.6 2.1 0.7 0.8 0.8 0.8 0.7 0.8 0.9 1.0 0.5 -0.1State and Local Gov’t 0.2 0.8 1.1 1.3 0.9 0.8 0.6 0.6 0.7 0.7 0.5 0.5

Nonfarm Employment (Payroll Survey, Thous.)Total Nonfarm 15957 16055 16145 16242 16330 16419 16499 16572 16638 16698 16739 16777Construction 706 718 726 734 738 741 745 747 750 751 753 755Manufacturing 1271 1274 1280 1284 1289 1293 1297 1302 1306 1309 1311 1315 Nondurable Goods 470 471 473 475 476 478 480 481 482 483 485 487 Durable Goods 801 804 807 809 812 815 818 821 824 826 827 828Trans. Warehousing & Util. 537 538 541 544 547 549 551 553 555 557 559 561Trade 2388 2403 2410 2417 2426 2436 2446 2454 2462 2469 2473 2477Information 468 472 475 479 482 486 489 492 495 497 498 500Financial Activities 798 794 796 799 802 806 809 812 814 816 819 821Professional Busi. Serv. 2513 2553 2578 2604 2632 2660 2686 2708 2727 2744 2754 2763Edu. & Health Serv. 2456.2 2478.9 2494.4 2511.8 2526.5 2541.0 2553.6 2565.1 2576.0 2585.5 2589.9 2594.3Leisure & Hospitality 1806.2 1809.7 1822.7 1835.8 1845.5 1855.3 1865.6 1875.5 1884.2 1892.3 1898.7 1904.2Other Services 550.5 546.5 549.6 553.3 556.5 559.7 562.0 564.3 566.8 569.2 571.7 574.2Federal Gov’t 242 243 243 244 244 245 245 246 246 247 247 247State and Local Gov’t 2190 2194 2200 2207 2212 2217 2220 2223 2227 2231 2234 2236

Population and MigrationNet Inmigration(Thous) 81.8 91.8 99.5 108.1 117.6 125.9 133.9 137.2 140.5 144.6 148.4 151.5Population (Thous) 38695 38778 38863 38949 39036 39124 39214 39305 39397 39490 39584 39680 (% Ch) 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0 1.0

Construction ActivityResidential Building Permits (Thous. Units) 106.9 101.5 106.0 108.8 114.3 121.3 122.8 126.3 125.4 127.6 124.7 124.0Nonres.Permits (Mil. ‘09$) 21638 21128 21163 21203 21379 21683 22311 22968 23235 23298 23303 23212



Table 3. Personal Income, Taxable Sales, Construction and Population in California 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Aggregates (Bil $)Personal Income 1564.3 1596.2 1537.1 1578.6 1685.6 1805.2 1856.6 1944.1 2041.5 2161.3 2296.9Disposable Income 1348.9 1395.3 1375.7 1405.7 1483.3 1586.5 1604.8 1679.0 1754.7 1848.2 1959.5 (Bil 2009$)Personal Income 1604.6 1595.4 1537.1 1558.6 1631.6 1709.8 1734.5 1787.8 1857.8 1928.4 1994.5Disposable Income 1383.7 1394.5 1375.8 1387.9 1435.7 1502.6 1499.3 1544.0 1595.6 1649.0 1701.5 (Nominal %Ch)Personal Income 4.3 2.0 -3.7 2.7 6.8 7.1 2.8 4.7 5.0 5.9 6.3Disposable Income 3.6 3.4 -1.4 2.2 5.5 7.0 1.2 4.6 4.5 5.3 6.0 (Real %Ch)Personal Income 1.4 -0.6 -3.7 1.4 4.7 4.8 1.4 3.1 3.9 3.8 3.4Disposable Income 0.7 0.8 -1.3 0.9 3.4 4.7 -0.2 3.0 3.3 3.3 3.2

Components of Personal Income (Bil $)Personal Income 1564.3 1596.2 1537.1 1578.6 1685.6 1805.2 1856.6 1944.1 2041.5 2161.3 2296.9 Wages & Salaries 834.4 842.9 799.5 814.5 848.7 900.5 934.7 988.5 1042.8 1099.8 1160.9 Other Labor Income 200.6 204.8 197.1 203.3 218.9 219.2 227.9 238.9 251.0 269.2 292.8 Farm 7.5 5.2 5.7 6.3 10.6 10.1 9.8 8.8 10.6 20.2 29.7 Other Income 452.6 458.0 415.9 415.4 460.5 525.0 543.5 564.7 583.9 612.4 649.2 Transfer Payments 192.1 210.3 239.9 261.0 261.8 269.7 281.2 292.8 313.3 332.9 348.9 Social Insurance 122.7 124.9 121.0 121.9 114.5 119.0 140.1 149.2 159.6 172.8 184.1

Taxable Sales NominalLevel (Bil $) 561.3 532.4 456.6 477.0 520.2 558.1 586.6 612.2 640.1 669.0 697.2 %Ch 0.3 -5.2 -14.2 4.5 9.1 7.3 5.1 4.4 4.6 4.5 4.2 RealLevel (Bil. 2009$) 575.8 532.1 456.6 471.0 503.5 528.6 548.0 563.0 582.5 597.0 605.4 %Ch -2.5 -7.6 -14.2 3.2 6.9 5.0 3.7 2.7 3.5 2.5 1.4

New Automobile Sales (Mil Un.)New Registrations 1.68 1.34 0.99 1.11 1.21 1.52 1.68 1.80 1.93 1.97 1.98U.S. Sales 16.09 13.19 10.40 11.55 12.73 14.44 15.53 16.44 17.18 17.58 17.65

Construction Activity Residential Building Permits (Thous.)Total 106.5 60.8 33.2 43.0 44.9 56.7 77.9 82.3 105.8 121.1 125.4 Single-Family 66.2 31.6 24.0 25.0 22.2 27.3 36.3 37.3 47.9 58.7 61.0 Multi-family 40.3 29.2 9.2 18.0 22.6 29.4 41.6 44.9 57.9 62.4 64.5 Nonresidential Permit ValuationNominal (Mil. $) 22622.2 19188.4 10899.0 11171.1 13054.0 14641.6 21830.0 23200.6 23084.5 24427.2 26760.0 %Ch 7.0 -15.2 -43.2 2.5 16.9 12.2 49.1 6.3 -0.5 5.8 9.6Real (Mil. 2009$) 23172.0 18817.6 10895.4 11301.3 12809.8 13826.4 20296.9 21260.1 21282.9 22085.4 23262.3 %Ch 0.9 -18.8 -42.1 3.7 13.3 7.9 46.8 4.7 0.1 3.8 5.3

Population (Thous.)Net Inmigration -24.2 -25.3 -88.8 -51.2 -11.1 39.0 45.0 92.0 95.3 128.6 146.2Net Natural Increase 329.9 328.9 309.8 283.0 272.0 258.0 252.0 243.0 240.9 227.2 229.0Population 36550.6 36859.2 37099.8 37333.5 37582.9 37869.3 38163.2 38488.5 38821.2 39169.6 39537.7

REGIONAL MODELING GROUP

UCLA Anderson Forecast, September 2015 Regional Modeling Group–97

The Los Angeles Department of Water and Power (DWP), established at the beginning of the century is the largest municipally-owned utility in the nation. It exists under and by virtue of the Charter of the City of Los Angeles enacted in 1925.

With a work force in excess of 9,000, the DWP provides water and electricity to some 3.5 million residents and businesses in a 464-square-mile area.

DWP’s operations are financed solely by the sale of water and electric services. Capital funds are raised through the sale of bonds. No tax support is received.

A five-member Board of Water and Power Commissioners establishes policy for the DWP. The Board members are appointed by the Mayor and confirmed by the City Council for five-year terms.

REGIONAL MODELING GROUP

98–Regional Modeling Group UCLA Anderson Forecast, September 2015

The Los Angeles County Metropolitan Transportation Authority (Metro) is unique among the nation’s transportation agencies. It serves as transportation planner and coordinator, designer, builder and operator for one of the country’s largest, most populous counties. More than 9 million people – one-third of California’s residents – live, work, and play within its 1,433-square-mile service area.

Besides operating over 2,000 coaches in the Metro Bus fleet, Metro also designed, built and now operates over 73 miles of Metro Rail service. The Metro Rail system currently consists of 62 stations and several more are in the planning and/or design stage.

In addition to operating its own services Metro funds 16 municipal bus operators and funds a wide array of transportation projects including bikeways and pedestrian facilities, local road and highway improvements, goods movement, and the popular Freeway Patrol and Call Boxes.

Recognizing that no one form of transit can solve urban congestion problems, Metro’s multimodal approach uses a variety of transportation alternatives to meet the needs of the highly diverse population in the region.

Metro’s Mission is to insure the continuous improvement of an efficient and effective transportation system for Los Angeles County. In support of this mission, our team members provide expertise and leadership based on their distinct roles: operating transit system elements for which the agency has delivery responsibility, planning the countywide transportation system in cooperation with other agencies, managing the construction and engineering of transportation system components and delivering timely support services to the Metro organization.

Metro was created in the state legislature by Assembly Bill 152 in May 1992. This bill merged the Los Angeles County Transportation Commission (LACTC) and the Southern California Rapid Transit District (RTD) to become the Los Angeles County Metropolitan Transportation Authority. The merger became effective on April 1, 1993.

Metro is governed by a 13-member Board of Directors comprised of: the five Los Angeles County Supervisors, the Mayor of Los Angeles, three Los Angeles mayor-appointed members, four city council members representing the other 87 cities in Los Angeles County and one non-voting member is appointed by the Governor of California.

SEMINAR MEMBERS

UCLA Anderson Forecast, September 2015 Seminar Members–99

The Legislature and Governor created the California Research Bureau (CRB) within the California State Library in the 1991 Budget Act. The bureau provides objective, nonpartisan, timely, and confidential research to the Governor’s Office, members of both houses of the Legislature, and other state constitutional officers. The Bureau provides these clients with research, policy assistance through written reports and other documents, consultations, seminars, and other training and assistance in preparing legislative proposals. The Bureau has five branches: Environmental and Natural Resources; Education and Human Services; Economics; General Law and Government; and Information Services. It maintains a small office at the State Capitol in Room 5210 to make reference services conveniently available.

The nonpartisan Legislative Analyst's Office (LAO) has been providing fiscal and policy advice to the California Legislature for more than 65 years. It is particularly well known for its fiscal and programmatic expertise and nonpartisan analyses relating to the state budget, including making recommendations for operating programs in the most effective and cost-efficient manner possible. Its responsibilities also include making economic and demographic forecasts for California, and fiscal forecasts for state government revenues and expenditures. It also prepares fiscal analyses for all propositions that appear on the California statewide ballot, including bond measures.

For more information about the LAO, please visit our website at www.lao.ca.gov or call us at 916-445-4656.

SEMINAR MEMBERS

100–Seminar Members UCLA Anderson Forecast, September 2015

The Los Angeles Magazine has named Hermosa an "outstanding coastal town" praising many of our businesses and shops. From traditional Surf and Turf to more exotic cuisines, from Comedy to Jazz, Hermosa Beach has many fine dining and entertainment places from which to choose. Our hotel and lodging facilities offer breath taking ocean views and all the comforts of home which are surrounded by a Mecca of restaurants, upscale shops and tourist delights. Come to Hermosa Beach, relax and enjoy the warmth of our hospitality.

City of Hermosa Beach

The State of California’s Department of Finance is responsible for submitting to the State’s fiscal year budget to the Governor in January of each year. The Department is part of the State’s Executive Branch and part of the Governor’s Administration. The Director of Finance is appointed by the Governor and is his chief fiscal advisor. The Director sits as a member of the Governor’s cabinet and senior staff. Principal functions include:

Establish appropriate fiscal policies to carry out the Administration’s Programs.

Prepare, enact and administer the State’s Annual Financial Plan.

Analyze legislation which has a fiscal impact.

Develop and maintain the California State Accounting and Reporting System (CALSTARS).

Monitor/audit expenditures by State departments to ensure compliance with approved standards and policies.

Develop economic forecasts and revenue estimates.

Develop population and enrollment estimates and projections.

Review expenditures on data processing activities of departments.

In addition, the Department of Finance interacts with the Legislature through various reporting requirements, by presenting and defending the Governor’s Budget and in the legislature.

The Department interacts with other State departments on a daily basis on terms of administering the budget, reviewing fiscal proposals, establishing accounting systems, auditing department expenditures and communicating the Governor’s fiscal policy to departments.

SEMINAR MEMBERS

UCLA Anderson Forecast, September 2015 Seminar Members–101

Health Net, Inc. is among the nation’s largest publicly traded managed health care companies. Its mission is to help people be healthy, secure and comfortable. The company’s health plans and government contracts subsidiaries provide health benefits to approximately 6.7 million individuals across the country through group, individual, Medicare, Medicaid and TRICARE and Veterans Affairs programs. Health Net’s behavioral health subsidiary, MHN, provides mental health benefits to approximately 7.0 million individuals in all 50 states. The company’s subsidiaries also offer managed health care products related to prescription drugs, and offer managed health care product coordination for multi-region employers and administrative services for medical groups and self-funded benefits programs.

The Employment Development Department’s Labor Market Information Division (LMID) regularly collects, analyzes, and publishes information about California’s labor market, which has approximately 1,068,000 employers covered by Unemployment Insurance and a civilian labor force of approximately 16.6 million. In addition to employment and unemployment data, LMID provides economic development and planning information; industry and occupational characteristics, trends, and wage information; and social and demographic information. Most of these data are available for the state and counties. Some data are available for other geographic regions a well.

In addition to basic labor market information, the LMID provides technical assistance, training seminars for data users, and standard and customized reports for state and sub-state geographic areas. Labor market information is available electronically and in print.

For more information, visit our website at www.calmis.ca.gov or call 916-262-2162.

SEMINAR MEMBERS

102–Seminar Members UCLA Anderson Forecast, September 2015

The energy industry is changing rapidly and dramatically. As global competition transforms the way companies do business, energy issues are no longer simply local, or even national. At the same time, its clear that the importance of providing reliable local service has never been more important.

Our heritage at Southern California Edison is based on reliability. For more than 100 years we have provided high-quality, reliable electric service to more than 4.2 million business and residential customers over a 50,000 square mile service area in coastal, central, and southern California.

Of course, recent changes in the California’s electric industry have affected us as well. In 1997, as part of the restructuring of the electric industry in our state, SCE sold its 12 fossil fuel generating stations and overhauled nearly every aspect of its business to prepare for the changing environment. While we still own and operate hydro and nuclear power facilities that serve our area, our main role is that of power transmission and distribution. The power needed for our customers is largely purchased from the California Power Exchange and provided by SCE to our customers without a price markup.

At SCE we want you to know that even in times of change, we retain our proven commitment to service, reliability, innovation, and the community.

Celebrating its 150th anniversary in 2014, the Irvine Company is one of America’s most respected and diversified real estate companies. The Company is renowned for its investment properties across coastal California and its stewardship and master planning of The Irvine Ranch in Orange County, California.

The Irvine Company’s property portfolio exceeds 105 million square feet and includes 500 office buildings, 41 retail centers, 130 apartment communities, five marinas, three hotels, and three golf courses, primarily in Orange County, with one-third of the Company’s investment properties in Los Angeles, San Diego, Silicon Valley and Chicago.

As master planner of the historic Irvine Ranch, the Irvine Company plans and brings to life balanced, sustainable communities with a full range of housing, job and retail centers, schools, recreation and permanently preserved open spaces. Nearly 60% of the 93,000-acre Irvine Ranch — or 55,000 acres — has been preserved in perpetuity as parklands and open space.

Donald Bren is Chairman of the Irvine Company. He has been deeply involved in California real estate as a master planner, master builder and long-term investor for more than 50 years. He oversees a Board of Directors that includes some of the nation’s most accomplished and respected business leaders and former public officials.

UCLA Anderson Forecast, September 2015 Sponsors-103

104-Sponsors UCLA Anderson Forecast, September 2015

MEMBERS

UCLA Anderson Forecast, September 2015 Members - 105

Corporate 6

California Energy CommissionThe California Endowment

Corporate 4

ADPCFA Society Los AngelesCity National Bank - CosciaCity of Los AngelesHomeStreet BankIBIS World, Inc.IS AssociatesMedPOINT Management, Inc.Southern California Association of Governments

Corporate 3

Ameron InternationalCitizens Business BankCity of El SegundoCity of Manhattan BeachCity of Santa MonicaHanmi BankKia Motors America, Inc.Los Angeles Police Federal Credit UnionMcMaster-Carr Supply CompanyMetropolitan Water DistrictMitsubishi Cement Corp.Pacific Western BankPepperdine UniversityRPAState Farm Insurance Co.The Newhall Land and Farming CompanyUnified Grocers, Inc.WCIRBWinreal Operating Co.

Individual Member

ALGAlliance BernsteinAustrian Trade CommissionBay Area EconomicsBBCN BankBrand Management IncBRE Properties, INCCal RecycleCalifornia Air Resources BoardCalifornia Association Of RealtorsCalifornia Department of TransportationCalifornia Public Utilities CommissionCalifornia State Board of EqualizationCalifornia State Polytechnic University, PomonaCalifornia State University, SacramentoCalifornia Steel Industries, IncCathay BankChartwell Capital SolutionsChicago TitleChu & Waters, LLPCity of CarlsbadCity of Garden GroveCity Of SacramentoCity of San DiegoCity of San JoseCity of Santa ClaraCity of TorranceCity of Torrance - Kenneth FlewellynCommunity BankConsulate General of JapanCounty of San DiegoCTBC Bank USADesmond, Marcello & AmsterEast West BankFDIC

MEMBERS

106 - Members UCLA Anderson Forecast, September 2015

Goodwin Procter LLPGranite Rock CompanyHarold Davidson & Associates Inc.Heritage Bank of CommerceHR and A Advisors, Inc.JETRO, Los AngelesKinecta Federal Credit UnionKPMGLehigh Southwest Cement CompanyLloyd Management CorporationLogix Federal Credit UnionLondre Marketing Consultants, LLCLos Angeles Public Library - Business Economics DeptMaynard Consulting ServicesNewland Real Estate GroupNorthern California Power AgencyOrange County Executive Office - BudgetOrange County Transportation Authority 2PG&EPreferred Employers Insurance CompanyRBC Capital Markets

Redwood Credit UnionSan Diego Gas & Electric Co.School Services of California Inc.SMUDStanford UniversityState Compensation Insurance FundState of Hawaii - Department of TaxationSully-Miller Contracting CoThe Aerospace CorporationThe Olson CompanyUnited Methodist F.C.U.University of California Library, BerkeleyUniversity of California San DiegoUniversity of CincinnatiUniversity of RichmondUSS-POSCO IndustriesVulcan MaterialsVulcan Materials CompanyWarland InvestmentsWells Fargo SecuritiesYork University Libraries

SPEAKERS

UCLA Anderson Forecast, September 2015 Speakers–107

Edward E. Leamer is the Chauncey J. Medberry Professor of Management, Professor of Economics and Professor of Statistics at UCLA. He received a B.A. degree in mathematics from Princeton University and a Ph.D. degree in economics and an M.A. degree in mathematics from the University of Michigan. After serving as Assistant and Associate Professor at Harvard University he joined the University of California at Los Angeles in 1975 as Professor of Economics and served as Chair from 1983 to 1987.

In 1990 he moved to the Anderson Graduate School of Management and was appointed to the Chauncey J. Medberry Chair. Professor Leamer is a Fellow of the American Academy of Arts and Sciences, and a Fellow of the Econometric Society. He is a Research Associate of the National Bureau of Economic Research and a visiting scholar at the International Monetary Fund and the Board of Governors of the Federal Reserve System. Dr. Leamer has published over 100 articles and 4 books . This research has been supported by continuous grants for over 25 years from the National Science Foundation, the Sloan Foundation and the Russell Sage Foundation. His research papers in econometrics have been collected in Sturdy Econometrics, published in the Edward Elgar Series of Economists of the 20th Century. His research in international economics and econometric methodology has been discussed in a chapter written by Herman Leonard and Keith Maskus in New Horizons in Economic Thought: Appraisals of Leading Economists. Recent research interests of Professor Leamer include the North American Free Trade Agreement, the dismantling of the Swedish welfare state, the economic integration of Eastern Europe, Taiwan and the Mainland, and the impact of globalization on the U.S. economy.

Edward E. LeamerDirector

David ShulmanSenior Economist

David Shulman is currently managing member of his own LLC and engages in educational and charitable ac-tivities, including being a Distinguished Visiting Professor at Baruch College and a Visiting Professor at the Univer-sity of Wisconsin. Dr. Shulman is currently a member of NAREIT’s Real Estate Investment Advisory Council. He blogs at Shulmaven.blogspot.com. Shulman received a bachelor’s degree from Baruch College in 1965, an MBA in 1966 from the Graduate School of Management at UCLA; and his Ph.D. in 1975 with a specialization in Finance.

From 1986 to 1997, Dr. Shulman was employed by Sa-lomon Brothers Inc. in various capacities. He was their director of real estate research from 1987 to 1991 and be-came Chief Equity Strategist from 1992 to 1997. As Chief Equity Strategist, he was responsible for developing the firms overall equity market view and maintaining their list of recommended stocks. Dr. Shulman was widely quoted in print and electronic media and he coined the terms “Gold-ilocks Economy” and “New Paradigm Economy.” In 1991, he was named a Managing Director; and in 1990, he won the First Annual Graaskamp Award for Excellence in real estate research from the Pension Real Estate Association.

In March 2005, Dr. Shulman retired from Lehman Broth-ers, where he was Managing Director and head Real Estate Investment Trust Analyst. Before joining Lehman Brothers in 2000, he was a member and Senior Vice President at Ulysses Management LLC from 1998-1999, an Investment Manager of a private investment partnership and an offshore corporation, whose invest-ment capital approximated $1 billion at the end of 1999.

SPEAKERS

108–Speakers UCLA Anderson Forecast, September 2015

Jerry NickelsburgSenior Economist

Jerry Nickelsburg joined the UCLA Anderson Forecast in 2006 as an economist. At the Anderson Forecast he plays a key role in the economic modeling and forecasting of the Los Angeles, Southern California and California economies. He has conducted special studies into the future of manufacturing in Los Angeles, the distribution of income, the economic impact of the writer’s strike, the aerospace industry, the undocumented construction and manufacturing labor force, the ports of Los Angeles and Long Beach and the garment industry, focusing on the development of new data and the application of economic theory and statistical methods to sectoral issues. He is a regular presenter at the Los Angeles Mayor’s Economic Conference and has been cited in the national and local media including the Financial Times, New York Times, Los Angeles Times, Reuters, Variety, CNBC, NBC, PBS, and L.A. Business Journal.

He received his Ph.D. in economics from the University of Minnesota in 1980 specializing in monetary economics and econometrics. He was formerly a professor of Economics at the University of Southern California and has held executive positions with McDonnell Douglas, Flight Safety International, and Flight Safety Boeing during a fifteen year span in the aviation business.

From 2000 to 2006, he was the Managing Principal of Deep Blue Economics, a consulting firm he founded. He held a position with the Federal Reserve Board of Governors developing forecasting tools, and has advised banks, investors and financial institutions. He has been the recipient of the Korda Fellowship, USC Outstanding Teacher, India Chamber of Commerce Jubilee Lecturer and is a Fulbright Scholar. He has published over 40 articles on monetary economics, econometrics, aviation economics, and industrial organization.

William YuEconomist

William Yu joined the UCLA Anderson Forecast in 2011 as an economist. At Forecast he focuses on the economic modeling and forecasting of Los Angeles and other regional economies in California. He also conducts research and forecast on Asian emerging economies, especially China, and their impacts on the US economy. His research interests include a wide range of economic and financial issues, such as time series econometrics, stock, bond and commodity price dynamics, public health, human capital, higher education, and economic sustainability. He has published over a dozen research articles in Journal of Forecasting, International Journal of Forecasting, Journal of International Money and Finance, Journal of Health Care Finance, Journal of Education Finance, Economic Affairs, and Global Economic Review, etc. He has also served as a reviewer for various journals, such as Journal of Money, Credit, and Banking, Journal of Banking and Finance, Japan and the World Economy, and Energy Journal, etc.

He received his bachelor’s degree in finance from National Taiwan University in 1995 and was an analyst in Fubon Financial Holding in Taipei from 1997 to 2000. In 2006, he received his Ph.D. degree in economics from the University of Washington where he was also an economics instructor and won two distinguished teaching awards. In 2006, he worked for the Frank Russell Investment Group for Treasury and corporate yields modeling and forecasting. From 2006 to 2011, he served as an assistant and an associate professor of economics at Winona State University where he taught courses including international economics, forecasting methods, intermediate macroeconomics, introductory macroeconomics, money and banking, and Asian economies.

September 2015 Report - UCLA Anderson Forecast · 2016-10-14 · September 2015 Report THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA. FORECASTS: 2015 3. rd. ... Patricia

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