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December 2008
(Covering November 14, 2008 to December 11, 2008)

In This Issue
Inﬂation and Prices
October Price Statistics
Financial Markets, Money, and Monetary Policy
The Yield Curve, November 2008
International Markets
Japan’s Quantitative Easing Policy
Economic Activity and Labor Markets
Industrial Production, Commodity Prices, and the Baltic Dry Index
GDP: Third-Quarter Preliminary Estimate
The Employment Situation, October 2008
Metro-Area Diﬀerences in Home Price Indexes
Regional Activity
Fourth District Employment Conditions, October 2008
Banking and Financial Markets
Fourth District Community Banks

Inﬂation and Prices

October Price Statistics
11.25.08
by Brent Meyer

October Price Statistics
Percent change, last
2007
avg.

1mo.a

3mo.a 6mo.a 12mo. 5yr.a

−10.9
−0.9

−4.4
1.1

2.3

2.2

2.4

Medianb

1.8

2.7

3.3

3.2

2.8

3.1

16% trimmed meanb

−0.6

0.7

3.1

3.0

2.6

2.8

0.5

5.1

4.0

7.1

4.6

4.4

2.3

2.1

Consumer Price Index
All items
Less food and energy

2.8

3.7

3.2

4.2

Producer Price Index

−28.5 −15.2

Finished goods
Less food and energy

5.1

4.4

a. Annualized.
b. Calculated by the Federal Reserve Bank of Cleveland.
Sources: U.S. Department of Labor, Bureau of Labor Statistics; and Federal
Reserve Bank of Cleveland.

Trimmed-Mean CPI Measures
Annualized monthly percent change
8
7
6
5

Median CPIa

4
3
2
1
16% trimmed-mean CPIa
0
-1
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
a. Calculated by the Federal Reserve Bank of Cleveland.
Sources: U.S. Department of Labor, Bureau of Labor Statistics, and the Federal
Reserve Bank of Cleveland.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

The Consumer Price Index (CPI) decreased at an
annualized rate of 10.9 percent in October. It was
the largest monthly decrease on record in the series
(which goes back to 1947), conjuring the specter
of deﬂation just four short months after inﬂation
looked to be a major concern. Moreover, the core
CPI fell 0.9 percent in October, its ﬁrst price dip
since December 1982. Energy prices decreased
dramatically during the month (65.9 percent at an
annualized rate) and were responsible for much of
the headline price decrease. However, falling gas
prices are not the whole story, as evidenced by the
declining core CPI.4.9 percent. The longer-term
trends in the core and trimmed-mean measures
remained somewhaelevated in September, ranging
between 2.5 percent and 3.4 percent.
Short-term (one-year ahead) average inﬂation
expectations, measured by the University of Michigan’s Survey of Consumers, remained at 4.6 percent
in October, as energy and commodity prices continued to fall from recent highs. Long-term (5-10
year) average inﬂation expectations decreased from
3.3 percent in September to 2.9 percent in October, their lowest value since March 2003.
Turning to the measures of underlying inﬂation
calculated by the Federal Reserve Bank of Cleveland, the median CPI rose 1.8 percent, while the
16 percent trimmed–mean CPI fell 0.6 percent
(its second price decrease since the series began in
1982). The CPI trimmed–mean estimators exclude
the components in the CPI that show the most
extreme monthly price changes and are much less
volatile than either the CPI or the more traditional
core CPI, making them more useful guides in
evaluating inﬂation trends. Lately, the 16–percent
trimmed-mean CPI and median CPI have been
diverging somewhat. For example, over the past
three months, the median CPI is up 2.7 percent,
while the 16–percent trimmed-mean has increased
only 0.7 percent. Over the past four months, the
16 percent trimmed-mean measure has been pick2

CPI Component Price Change Distributions
Share of CPI, weighted by expenditure
50
September 2008
45
July 2008
40
2008 year-to-date average
35
30
25
20
15
10
5
0

0 to 1
1 to 2
2 to 3
3 to 4
4 to 5
Annualized monthly percentage change

Source: Bureau of Labor Statistics.

CPI, Core CPI, and Trimmed-Mean CPI
Measures
12-month percent change
6

An investigation into the price-change distribution of CPI components may reveal why the overall
index exhibited such a large decline. In October, 33
percent of the CPI’s components (by expenditure
weight) decreased, while only 9 percent fell in July.
Also, only 34 percent rose at rates exceeding 3.0
percent in October, compared to 60 percent in July
and a year-to-date average of roughly 50 percent.
Deﬂation requires sustained, broad–based price
declines. We can see that the rate of price increases
slowed in October, and the prices of some components did actually decline during the month. However, based on this report alone, it would be more
than a stretch to declare that deﬂation has set in.
Over the past 12 months, the CPI is up 3.7 percent, down considerably from July’s recent year–
over–year high of 5.6 percent. The 12–month
trends in the underlying inﬂation measures (core,
trim, and median) have fallen as well, and are ranging between 2.2 percent and 3.2 percent.

ing up on some of the extreme prices swings that
are excluded from the median. At least 50 percent
of the CPI’s components (by expenditure weight)
exhibited price decreases or increases at rates exceeding 5 percent.

CPI
Median CPIa

1
1998

Core CPI

16% trimmedmean CPIa
2000

2002

2004

2006

2008

a. Calculated by the Federal Reserve Bank of Cleveland.
Sources: U.S. Department of Labor, Bureau of Labor Statistics, and the Federal
Reserve Bank of Cleveland.

Household Inflation Expectations
12-month percent change
7.5
7.0
6.5
6.0
5.5
5.0
4.5
One-year ahead
4.0
3.5
3.0
Five to ten-years ahead
2.5
2.0
1.5
1.0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

According to the November preliminary release of
the University of Michigan’s Survey of Consumers,
21 percent of respondents expect zero inﬂation in
the year ahead, while 16 percent actually expect deﬂation. Consequently, short–term (one–year ahead)
average inﬂation expectations fell to 2.9 percent in
early November. Long–term (5–10 year) average inﬂation expectations have remained more stable over
the past few months, and actually increased from
3.1 percent in October to 3.3 percent in November.

Note: Mean expected change as measured by the University of Michigan’s
Survey of Consumers.
Source: University of Michigan.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Financial Markets, Money, and Monetary Policy

The Yield Curve, November 2008
11.26.08
by Joseph G. Haubrich and Kent Cherny

Yield Spread and Real GDP Growth
Percent
12
10

R eal G DP growth
(year-to-year
percent change)

8
6
4
2
0

Ten-year minus three-month
yield s pread

-2
-4
1953

1963

1973

1983

1993

2003

Note: Shaded bars represent recessions
Sources: Bureau of Labor Statistics and the Federal Reserve Board.

Yield Spread and One-Year Lagged Real
GDP Growth
Percent
12
One year lagged real GDP growth
(year-to-year percent change)

6
4
2
0

-4
1953

Ten-year minus three-month
yield spread
1963

1973

1983

1993

2003

Sources: Bureau of Economic Analysis and the Federal Reserve Board.

Those relationships underlie the use of the slope of
the yield curve as a simple forecaster of economic
growth. The rule of thumb is that an inverted yield
curve (short rates above long rates) indicates a
recession in about a year, and yield curve inversions
have preceded each of the last six recessions (as deﬁned by the NBER). Very ﬂat yield curves preceded
the previous two, and there have been two notable
false positives: an inversion in late 1966 and a very
ﬂat curve in late 1998. More generally, though, a
ﬂat curve indicates weak growth, and conversely, a
steep curve indicates strong growth. One measure
of slope, the spread between 10–year bonds and
3–month T–bills, bears out this relation, particularly when real GDP growth is lagged a year to line
up growth with the spread that predicts it.
The ﬁnancial crisis showed up in the yield curve,
with short rates falling since last month, as investors ﬂed to quality. The 3–month rate dropped
from an already low 0.46 percent down to a miniscule 0.07 percent (for the week ending November
21), the lowest level it has been since the Treasury
constant maturity series started in 1982.

-2

In the midst of the horrendous economic news of
the last month, the yield curve might provide a slice
of optimism. Since last month, it has ﬂattened, as
short rates fell more than long rates. On the other
hand, the historic turmoil in the ﬁnancial markets
also suggests that the historical relationships on
which our interpretation of the yield curve depends
may not be holding up in times of stress.

The 10-year rate fell from 4.06 percent to 3.38 percent. Consequently, the slope decreased by 29 basis
points to 331, down from 360 in October, but still
above the 290 basis points for September and the
205 for August.
The ﬂight to quality and the turmoil in the ﬁnancial markets may aﬀect the reliability of the yield
curve as an indicator, but projecting forward using
past values of the spread and GDP growth suggests

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

that real GDP will grow at about a 3.4 percent rate
over the next year. This remains on the high side
of other forecasts, many of which are predicting
reductions in real GDP.
While such an approach can predict when growth
is above or below average, it does not do so well in
predicting the actual number, especially in the case
of recessions. Thus, it is sometimes preferable to
focus on using the yield curve to predict a discrete
event: whether or not the economy is in recession.
Looking at that relationship, the expected chance
of the economy being in a recession next November
stands a miniscule 0.05 percent, equal to October
and down from September’s already low 0.2 percent.

Yield Spread and Predicted GDP Growth
Percent
6
Real GDP growth
(year-to-year percent change)

5
4

Predicted
GDP growth

3
2
1
0
Ten-year minus three-year
yield spread

-1
-2
2002

2003

2004

2005

2006

2007

2008

2009

Sources: Bureau of Economic Analysis and the Federal Reserve Board.

Probability of Recession Based on the
Yield Spread
Percent
100
90

ty of recession
n
Probability

80
70

F
st
Forecast

60
50
40
30
20
10
0
1960

1966

1972

1978

1984

1990

1996

2002

2008

Note: Estimated using probit model.
Sources: Bureau of Economic Analysis, the Federal Reserve Board, and authors’
calculations.

The probability of recession predicted by the yield
curve is very low, and may seem strange the in the
midst of the recent ﬁnancial news, but one aspect
of those concerns has been a ﬂight to quality, which
lowers Treasury yields. Furthermore, both the
federal funds target rate and the discount rate have
remained low, which tends to result in a steep yield
curve. Remember also that the forecast is for where
the economy will be next November, not earlier in
the year. On the other hand, in the spring of 2007,
the yield curve was predicting a 40 percent chance
of a recession in 2008, something that looked out
of step with other forecasters at the time.
To compare the 0.05 percent to some other probabilities and learn more about diﬀerent techniques
of predicting recessions, head on over to the Econbrowser blog. It might not be advisable to take this
number quite so literally, for two reasons. First,
this probability is itself subject to error, as is the
case with all statistical estimates. Second, other
researchers have postulated that the underlying
determinants of the yield spread today are materially diﬀerent from the determinants that generated
yield spreads during prior decades. Diﬀerences
could arise from changes in international capital
ﬂows and inﬂation expectations, for example. The
bottom line is that yield curves contain important
information for business cycle analysis, but, like
other indicators, should be interpreted with caution.
For more detail on these and other issues related to

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

using the yield curve to predict recessions, see the
Commentary “Does the Yield Curve Signal Recession?”
To see other forecasts of GDP growth:
http://www.cbo.gov/ftpdocs/89xx/doc8979/02-15-EconForecast_
ConradLetter.pdf
To see other probabilities of recession:
http://www.bloomberg.com/apps/news?pid=20601087&sid=aEX73
qWiBrb4
Econbrowser blog is available at:
http://www.econbrowser.com/archives/2008/02/predicting_rece.html
Does the Yield Curve Signal Recession?,” by Joseph G. Haubrich.
2006. Federal Reserve Bank of Cleveland, Economic Commentary,
is available at:
http://www.clevelandfed.org/Research/Commentary/2006/0415.pdf

International Markets

Japan’s Quantitative Easing Policy
12.10.08
by Owen F. Humpage and Michael Shenk

Japanese Real GDP
Trillions of yen
600
1.9%
550
0.5%
500
450

4.7%

400
350
300
1985

1988

1991

1994

1997

2000

2003 2006

Quantitative Easing Policy
Source: International Monetary Fund, International Finance Statistics Database,
October 2008.

The Federal Open Market Committee has lowered
its federal funds rate target 4.5 percentage points
since August 2007. It is now at 1 percent, and
ﬁnancial markets expect a further substantial cut.
The United States has entered a recession, and the
outlook for next year seems so somber that some
economists are asking if deﬂation—a drop in overall prices—is not a distinct possibility.
Very low interest rates and deﬂation are a precarious combination. At zero interest rates, open market operations are less eﬀective than under normal
circumstances because reserves and short–term
Treasury bills are near perfect substitutes on banks’
balance sheets. As a result, open market operations,
which substitute reserves for Treasury bills, may not
spur bank lending. In addition, declining prices
discourage consumption and investment spending,
especially when interest rates approach zero.
Japan underwent a decade–long odyssey with deﬂation and the zero–bound problem. The Bank of
Japan’s experience during this period oﬀers a guide
for getting back to more familiar economic turf.
Economic activity in Japan slowed precipitously
following the collapse of the so–called bubble economy in December 1989, and Japan began to expe-

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

rience deﬂation by early 1995. During this initial
period, while the economy was slowing, forecasters
and policymakers consistently underestimated the
extent of Japan’s economic malaise. Consequently,
while monetary policy seemed appropriate in terms
of the prevailing outlook, the loosening proved
woefully inadequate in hindsight.

Japanese Inflation
12-month percent change
5
Data distorted by taxes
4
3
2
1
0
-1
-2
1986

1989

1992

1995

1998

2001

2004

2007

Quantitative Easing Policy
Source: International Monetary Fund, International Finance Statistics Database,
October 2008.

Quantitative Easing
Trillions of yen
40
35
Target range
30
25

Excess reserves

20
15
10

Current account balance

5
0
3/2001

3/2002

3/2003

3/2004

3/2005

3/2006

3/2007

Source: Bank of Japan.

After a series of fairly ineﬀectual policy actions, the
Bank of Japan undertook its famous quantitative
easing policy from March 19, 2001, to March 9,
2006. Under this policy, the Bank shifted its day–
to–day operating target from the overnight, call–
money rate to the level of current–account balances
(reserves) at banks. Over the ﬁve years that the
program was in place, the Bank of Japan raised its
current–account target nine times. In implementing the quantitative easing policy, the Bank of Japan also increased its outright purchases of longerdated Japanese government securities. The objective
was to ﬂood banks with excess reserves, which, of
course, would keep the call-money rate at zero.
The Bank’s previous policy, maintained between
February 1999 and August 2000, had been a zero
interest rate policy, but the Bank had supplied only
enough reserves to keep the call–money rate at zero.
Hence, the quantitative easing was a more profound and visible policy shift.
When it introduced the quantitative easing policy,
the Bank of Japan also promised to maintain the
policy until the core CPI either reached zero or rose
on a year–over–year basis for several months. This
inﬂation target was stronger than the Bank’s zero
interest rate policy, which only promised to maintain zero interest rates until the economy showed
signs of recovery. Since inﬂation lagged economic
activity and since the Bank had a history of being
hawkish on inﬂation, the zero interest rate policy
was not a strong commitment to a positive inﬂation
rate. In contrast, the new commitment required
clear evidence that deﬂation had ended.
Analytically, the quantitative easing policy consisted
of three distinct parts: a commitment to maintain
zero interest rates until deﬂation clearly ended;
a signiﬁcant increase in the Bank of Japan’s balance sheet; and a change in the composition of the
Bank’s balance sheet. Generally, empirical analysis

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Japanese Policy Rates
Percent
3.0
2.5
2.0
1.5
1.0

Nominal
discount rate

Nominal call
rate

0.5
0.0
-0.5

Real call rate

-1.0
-1.5
-2.0
1995

1998

2001

Zero Interest Rate Policy

2004

2007

Quantitative Easing Policy

Source: International Monetary Fund, International Finance Statistics Database,
October 2008.

seems to suggest that the quantitative easing policy
lowered the term structure of government securities
by increasing expectations that future short–term
interest rates would remain near zero, but not by
aﬀecting term premia on government securities or
risk premia on other assets. In addition, the quantitative easing policy eventually helped banks—and
ﬁrms that depended on them for ﬁnancing—by
indicating that the Bank of Japan would continue
to provide liquidity. This reduced uncertainty about
future funding.
The connection between the quantitative easing
policy and the macroeconomic recovery remains
somewhat more ﬂimsy. Most observers believe that
because the quantitative easing policy aided the
banking sector, economic activity at least did not
deteriorate further. The pace of economic activity
did pick up, with contributions from consumer
spending and investment, but exports, which beneﬁted from growth among Japan’s trading partners,
spurred much of the improvement. Although deﬂation ended in 2006, along with the quantitative
easing policy, it returned after a very short hiatus in
2007, and continued until the recent commodity
price boom.
The Japanese experience suggests that when inﬂation and short–term interest rates approach zero,
central banks should act aggressively, giving greater
than normal weight to downside risks. Moreover,
they should commit to an inﬂation target and clearly explain their actions in terms of that target.
To read more on the empirical analyses of Japan’s quantitative
easing policy:
http://www.boj.or.jp/en/type/ronbun/ron/wps/data/wp06e10.pdf

Economic Activity

Industrial Production, Commodity Prices, and the Baltic Dry Index
11.28.08
by Beth Mowry and Andrea Pescatori
Industrial production rebounded in October, rising
1.26 percent after declining a downwardly revised
3.7 percent in September. The revision to September output was caused, in part, by a larger–than–
anticipated estimate of the impact of hurricanes
Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Gustav and Ike on the chemical industry. The September drop resulted in the largest month–over–
month percent decline in the series since February
1946.

Industrial Production and Its Trend
Component
Index, 2002=100
120

100

80
Actual value
Trend component
60

40
1972

1978

1984

1990

1996

2002

2008

Notes: Data are seasonally adjusted. The trend component was constructed with
the Hodrick-Prescott filter.
Source: Federal Reserve Board/Haver Analytics.

Industrial Production, Cyclical Component
Percent change
4
3
2
1
0
-1
-2
-3
-4
-5
1972

1978

1984

1990

1996

2002

2008

Note: Data are seasonally adjusted.
Source: Federal Reserve Board/ Haver Analytics.

Industrial Production Trends
Index, 2002=100
120

100

Industrial production
Construction component
Manufacturing component

40
1972

1978

1984

1990

1996

2002

2008

Notes: Data are seasonally adjusted. The trend components were constructed with
the Hodrick-Prescott filter.
Source: Federal Reserve Board/Haver Analytics.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Splitting the series into its cyclical and trend components, we notice two things about the behavior
of industrial production (IP). First, the trough
currently observed in the cyclical component is of
a similar magnitude to that of the 1973–74 recession. Second, since the 1970s, the trend in IP has
been lagging the dates of recessions: The trough in
the trend is reached at the very end of recessions. (A
caveat: The ﬁlter used to construct the trend might
have introduced an artiﬁcial phase shift on the
order of 2–4 months for the most recent data.)
Industrial production reached its peak in January
2008, with an index reading of 112.6. It has since
fallen about 4.7 percent to 107.3. It is worth noting that the 1974 and 2001 recessions were each
preceded by an exceptional increase in IP and then
followed by a trough. The current situation seems
more similar to the 1980-81 scenario, when IP did
not surge before turning downward.
Industrial production can also be divided by major
market and industry groups. Market groups include, for example, ﬁnal products, nonindustrial
supplies, and materials. Construction lies within
the nonindustrial supplies group. It is interesting to
note that, while more volatile, the IP–construction
series was well-synchronized with the total IP series
up until recently. This seems to have changed now,
as the trend in construction production is leading
the decline in total IP, conﬁrming the fact that the
current decline in economic conditions started in
the housing sector.
The manufacturing sector is the most important
component of industrial production in most
countries, emerging countries included. Its deﬁnition excludes construction and comprises “establishments engaged in the mechanical, physical, or
chemical transformation of materials, substances, or
components into new products.” The inputs used
and transformed by manufacturing establishments
are raw materials that are products of agriculture,
forestry, ﬁshing, mining, or quarrying as well as
products of other manufacturing establishments.
9

Those materials are usually purchased directly from
producers or obtained through customary trade
channels. Consequently, most of the commodities
traded throughout the world are (directly or indirectly) the main input materials of the manufacturing sector.

Spot Commodity Price Indexes
1967=100
1,000

Metals
Raw industrials
All commodities
Foodstuffs
Textiles and fibers

800

600

400

200
2006

2007

2008

Notes: The Commodity Research Bureau’s spot index is an unweighted geometric
mean of the individual commodity price relatives, i.e., of the ratios of the current prices
to the base period prices. The advantage of using the geometric mean is that the index
is not dominated by extreme price movements of individual commodities.
Source: Commodity Research Bureau/Haver Analytics.

Spot Commodity Price Indexes
Reference year=100a
900

1600

Precious metals
Wheat
Industrial metals
Corn

700

1200

500

800

300

400

100
2006

0
2007

2008

a. The reference years for each commodity are different. For industrial metals,
1970=100; for precious metals, January 2, 1973=100; for wheat and corn,
Decemeber 31, 1969=100. Indexes represent monthly average prices.

Source: Standard & Poor’s.

Baltic Dry Index
January 1985=1000
14000
12000
10000
8000
6000
4000
2000
1/7/2007

1/7/2005

1/7/2003

1/7/2001

1/7/1999

1/7/1997

1/7/1995

1/7/1993

1/7/1991

1/7/1989

1/7/1987

1/7/1985

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

The fact that those commodities have a worldwide
market means that their prices are a good barometer for economic activity around the world, and,
when those prices are available at a high frequency,
they can be used as an indicator of future economic
conditions. Recent data on commodity prices conﬁrm that the slowdown in industrial production is
a worldwide phenomenon.
The overall spot commodity price index (published
by the Commodity Research Bureau) peaked between May and July 2008 and has greatly retreated
since then. That index is clearly strongly aﬀected
by oil prices, which peaked in the ﬁrst half of July.
West Texas Intermediate (WTI) oil sold for more
than $145 per barrel until July 14. However, other
commodities peaked earlier than that. For example,
metals peaked around April and May 2008, textiles
and ﬁbers peaked in March (although the steep
decline in this index did not begin until after July),
raw industrials peaked between March and May,
and foodstuﬀs peaked in July. The industrial and
precious metals spot indexes (published by Standard and Poor’s) peaked in March, while wheat and
corn peaked in March and June, respectively.
Another indicator that is supposed to be a relatively
accurate barometer of global trade volume and, in
turn, global production, is the Baltic dry index. It is
issued daily by the London–based Baltic exchange
and is considered useful in part because it contains
no speculative content. World economic activity is
the most important determinant of the demand for
transport service, and the Baltic dry index, loosely
speaking, provides an assessment of the price of
moving major raw materials by sea.
This “price” reﬂects the demand for shipping capacity with respect to the supply of dry bulk carriers,
which is inelastic in the short run. Hence, the index
indirectly measures global demand for the commodities shipped aboard dry bulk carriers, such as
building materials, coal, crude oil, metallic ores,
10

and grains. These materials function as raw material
inputs to the production of intermediate or ﬁnished
goods, such as concrete, electricity, steel, and food,
which makes the index an economic indicator of
future manufacturing activity and, more generally,
worldwide industrial output.
The Baltic index, after skyrocketing to almost
12,000 in mid–November 2008, now sits around
840 (about a 93 percent drop!). Part of the run-up
reﬂected oil–price patterns, given that bunker fuel
is a signiﬁcant part of shipping costs. However,
bunker fuel prices can explain only a small fraction
of the Baltic index’s volatility. Moreover, WTI oil
prices were still above $146 on July 14, while the
index was already receding from its peak. In fact,
it peaked back in May (on a daily basis it peaked
June 5 at 11,689) and by the beginning of July was
already below 9000 (a 25 percent decline).
All in all, data on commodity prices and freight
rates suggest that world industrial production (or
economic activity) was still exceptionally buoyant
during the winter of 2007. Furthermore, world
production was not perfectly synchronized with
U.S. industrial production, which saw its turning
point in January 2008. This created a situation
resembling the 1970s, where producer prices were
skyrocketing due to increasing commodity prices,
while industrial production was stagnating. However, unlike the 1970s, the initial shock behind the
current slowdown (stemming from the ﬁnancial
industry and the housing market) has restrained
the ability to lend, an eﬀect more similar to the one
caused by the disinﬂation shock engineered by Fed
Chairman Volcker in the early 1980s. This eﬀect,
coupled with a better management of inﬂation expectations by central banks, has avoided a persistent
world rise in inﬂation together with the downturn.
Hence, the lack of perfect business cycle synchronization, especially between developed and emerging economies (which, like China, now represent
a large share of the world manufacturing sector),
contributed to the spectacular rise in commodity
prices up until the summer of 2008. However, this
clearly could not last for long. The spillover from
credit markets and the drop in U.S. imports had already hit world industrial production by the spring
Federal Reserve Bank of Cleveland, Economic Trends | December 2008

of 2008 (as reﬂected in some commodity prices and
especially in freight rates). This also suggests that
the high oil prices were not the outcome of pure
speculation but were reﬂecting demand pressure
originating in the manufacturing sector; at most,
we can say that it took it a little bit too long before
oil prices started to decrease.

Economic Activity

GDP: Third–Quarter Preliminary Estimate
12.02.08
by Brent Meyer

Real GDP and Components, 2008:Q3
Preliminary Estimate
Annualized percent change, last:
Quarterly change
(billions of 2000$)

Quarter

Four quarters

Real GDP

−15.1

−0.5

0.7

Personal consumption

−79.2

−3.7

0.2

Durables

−49.5

−15.2

−5.7

Nondurables

−42.9

−6.9

−0.9

0.1

0.0

1.1

Services

−5.3

−1.5

1.7

Equipment

−15.5

−5.6

−2.6

Structures

5.5

6.6

10.5

Business fixed investment

Residential investment

−17.5

−17.6

−20.9

Government spending

27.0

5.3

3.0

National defense

22.4

18.1

7.7

Net exports

29.0

—

Exports

13.1

3.4

6.2

Imports

−15.8

−3.2

-3.4

Private inventories

−29.1

—

Source: Bureau of Labor Statistics.

Third–quarter real GDP was revised down 0.2
percentage point, to −0.5 percent, according to
the preliminary estimate released by the Bureau
of Economic Analysis. The downward revision,
which was largely anticipated, reﬂected downward
adjustments to personal consumption and exports,
which were somewhat oﬀset by an upward adjustment to inventories, and a downward revision to
imports (which subtracts from real GDP). Personal
consumption was revised down from −3.1 percent
(annualized rate) to −3.7 percent—its largest decrease since the second quarter of 1980. Real export
growth was adjusted down to 3.4 percent from the
5.9 percent of the advance release. Also, imports are
now reported as decreasing at an annualized rate
of 3.2 percent, as opposed to −1.9 percent before.
Residential investment in the third quarter was
revised up from −19.1 percent to −17.6 percent, a
slight improvement over its four–quarter growth
rate and a somewhat welcome development given
the current status of the housing sector.
According to the preliminary estimate for the
third quarter, personal consumption expenditures
subtracted 2.7 percentage points from real GDP
growth, whereas in the four quarters prior, they had
added an average 0.9 percentage point. The real
change in private inventories added 0.9 percentage
point to growth, up 0.2 percentage point from the
advance release. Also, the contribution to growth
from exports decreased, but was oﬀset by a gain
from imports.
The most recent economic indicators indicate

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Contribution to Percent Change in Real GDP
Percentage points
2.5
2.0

2008:Q3 advance
2008:Q3 preliminary

Government
spending

1.5
Exports

1.0
0.5

Residential
investment

Personal
consumption

0.0

Change in
inventories

Business
fixed
investment

-0.5
-1.0

Imports

-1.5
-2.0
-2.5
-3.0
Source: Bureau of Economic Analysis

Recent Economic Indicators
Annualized percent change
Personal
consumption

The forecast from the Blue Chip panel continues
to deteriorate. The consensus estimate is now for
year–over–year growth of −0.4 percent in 2009,
compared to 0.5 percent in the October forecast.
Perhaps more indicative of how gloomy the outlook
has become is that the Blue Chip optimists (the
average of the top–ten forecasts) are now expecting
the economy to eke out a growth rate of only 0.3
percent in 2009.

2008:Q3
October 2008

Nonfarm
payrolls
Durable goods

Retail sales
Industrial
production
-60

-50

-40

further weakness moving forward. Most notably,
durable goods decreased at an annualized rate
of 53.3 percent in October, compared to an 8.3
percent decline in the third quarter. Also, retail
sales fell 28.6 percent (annualized rate) in October,
much further than the 4.8 percent decrease seen in
the third quarter. Industrial production increased
16.3 percent (annualized rate) in October, following a 36.8 percent decrease in September. However,
much of the headline volatility in this series during
September and October was due to “hurricane–related disruptions, which are now estimated to have
been larger than previously reported,” according to
the Federal Reserve. In fact, “excluding [the effects from the hurricanes], total industrial production is estimated to have fallen around 0.7 percent
(nonannualized) in both September and October.”

-30

-20

-10

Source: Bureau of Economic Analysis

Real GDP Growth
Annualized quarterly percent change
6

Final estimate
Advance estimate
Preliminary estimate
Blue Chip consensus
forecast

5
4
3

2
1
0
-1
-2
-3
Q1

Q2 Q3
2007

Q2 Q3
2008

Q2 Q3
2009

Source: Blue Chip Economic Indicators, September 2008; Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Economic Activity

The Employment Situation, November 2008
Average Nonfarm Employment Change
Change, thousands of jobs
300
200
100
0
-100
-200
-300
-400
-500
-600

Revised
Previous estimate
2005 2006 2007 2008
YTD

IV
2007

III

Sep Oct

Nov

2008

Source: Bureau of Labor Statistics.

12.05.08
by Murat Tasci and Beth Mowry
November employment fell by 533,000 in the largest one-month drop since December 1974, coming
in far worse than expectations. Additionally, payrolls in September and October were revised down
to losses of 403,000 and 320,000, respectively.
Since the start of the recession in December 2007,
job losses in the United States have totaled about
1.9 million, roughly 1.3 million of which have
come in just the past three months. The unemployment rate also continued its upward path, increasing 20 basis points to 6.7 percent, the highest rate
seen since September 1993.
The diﬀusion index of employment change also
sank from 37.8 to an unprecedented low of 27.6,
meaning that only 27.6 percent of employers are
hiring, while the remaining 72.4 percent are cutting jobs.
Job losses were across the board, with the only
major areas posting any sort of gain being education and health services (+52,000) and government (+7,000). Goods-producing industries lost a
total of 163,000 jobs, and this was spread evenly
between construction (−82,000) and manufacturing (−85,000). Within manufacturing, the durable
goods category shed almost triple the number of
jobs that nondurables shed.
Service-providing industries dropped a massive
370,000 jobs in November, after experiencing
downwardly revised losses of 286,000 and 183,000
in September and October. The only other time
since the series began in 1939 that service industries
lost more jobs was in August 1983. The retail trade
sector lost 91,300 jobs, a large part stemming from
auto dealers (−24,000). Declines in leisure and
hospitality totaled 76,000, and information services
lost 19,000. Professional and business services and
ﬁnancial activities each saw record losses (−136,000
and −32,000, respectively). Within professional and
business services, the employment services sector

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

alone lost 100,000 jobs.

Labor Market Conditions

Average monthly change (thousands of employees, NAICS)
2005

2006

2007

2008 YTD

November 2008

Payroll employment

211

175

−174

−533

Goods-producing

−38

−96

−163

Construction

−19

−47

−82

Heavy and civil engineering

−1

−7

−12

Residentiala

−2

−20

−27

−35.7

Nonresidentialb

−13

−33.8

−7

−14

−22

−55

−85

−4

−16

−41

−62

Manufacturing
Durable goods

−8

−10

−6

−14

−23

Service-providing

Nondurable goods

179

172

130

−78

−370

Retail trade

−40

−91.3

−9

−13

−32

Financial

activitiesc

PBSd

−49

−136

Temporary help svcs.

−7

−36

−78.2

Education and health svcs.

Leisure and hospitality

−14

−76

Government

Local educational svcs.

−4.2

5.6

6.7

Average for period (percent)
Civilian unemployment rate

5.1

4.6

a. Includes construction of residential buildings and residential specialty trade contractors.
b. Includes construction of nonresidential buildings and nonresidential specialty trade contractors.
c. Includes the finance, insurance, and real estate sector and the rental and leasing sector.
d. PBS is professional business services (professional, scientific, and technical services, management of companies and enterprises, administrative and support, and waste management and remediation services.
Source: Bureau of Labor Statistics.

The three-month moving average of private sector employment growth dropped all the way from
−295,000 to −429,000 last month. Private payrolls
have seen losses in every month since December
2007, while government payrolls have declined in
only one month during that same period.

Private Sector Employment Growth
Change, thousands of jobs: three-month moving average
300
250
200
150
100
50
0
-50
-100
-150
-200
-250
-300
-350
-400
-450
-500
2003

2004

2005

2006

2007

2008

Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Economic Activity

Metro-Area Diﬀerences in House Price Indexes
Case-Shiller Composite Home Price
Indexes
Index, January 2000 = 100
235
215
10-city index

195
175

20-city index

155
135
115
95
75
1995

1997

1999

2001

2003

2005

2007

12.11.08
Michael Shenk
Home price indexes have been providing homeowners with nothing but bad news for the better part of two years now. On the last Tuesday of
every month, when the monthly S&P/Case-Shiller
housing price indexes are released, newspapers ﬁll
up with dour headlines about another new record
drop in home prices. While these headlines may
be factually correct, it’s important to realize that
the numbers being quoted are almost always the
composite ﬁgures. Since real estate markets are local, national or composite ﬁgures should have only
limited meaning to homeowners concerned about
their home’s value.

Source: S&P, Fiserv, and MacroMarkets LLC.

Case-Shiller Individual City Home Price
Indexes
Index, January 2000 = 100
300
275

High appreciation
cities

250
225
200

Mid-level
appreciation
cities

175
150
125
100

Low appreciation
cities

75
50
1995

1997

1999

2001

2003

2005

2007

Source: S&P, Fiserv, and MacroMarkets LLC.

As the picture below shows, home price appreciation patterns vary tremendously by metro area. Cities like Miami, Los Angeles, San Diego, and Washington, D.C. all saw tremendous growth in home
prices during the boom and have all subsequently
seen massive declines in values. On the other hand,
cities like Denver and Charlotte saw little to no
unusual home price appreciation during the boom
and have seen home prices decline only modestly
during the bust. For simplicity’s sake, the 20 metro
areas that the S&P/Case–Shiller indexes measure
can be arranged into three groups of similar appreciation rates: high–appreciation cities, mid–appreciation cities and low–appreciation cities.
These aggregates are not weighted in any way, so
what they are actually showing is the average index
value in the cities included. This information, while
still an aggregate measure, is potentially more useful
to homeowners living in a city that is not directly
measured by the indexes. These measures show
pretty clearly that the size of the decline in home
prices in a given metro area is directly related to the
size of the run up in prices during the boom.
Another factor that the aggregate home price indexes tend to hide is that not all homes in a metro
area experience the same price patterns. Homes in

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Case-Shiller Home Price Indexes Grouped
by Appreciation Level
Index, January 2000 = 100
255
High appreciation cities

235
215
195
175

Mid-level
appreciation
cities

155
135
115

Low appreciation cities

95
75
1995

1997

1999

2001

2003

2005

2007

Source: S&P, Fiserv, and MacroMarkets LLC.

Case-Shiller Tiered Home Price Indexes
Index, January 2000 = 100
250
Average low tier
225
200
Average middle tier
175
150

Average high tier

125
100
75
50
1995

1997

1999

2001

2003

2005

2007

Source: S&P, Fiserv, and MacroMarkets LLC.

Change in Home Prices
12-month percent change as of September 2008
City

Low Tier

Middle Tier

High Tier

Aggregate

Atlanta

- 13.78

- 10.12

- 8.62

- 9.47

Boston

- 12.10

- 8.36

- 3.22

- 5.71

Chicago

- 13.80

- 10.73

- 8.82

- 10.08

Cleveland

- 13.63

- 7.73

- 5.29

- 6.37

- 6.80

- 4.79

- 5.41

- 5.40

- 37.42

- 30.10

- 29.81

- 31.33

Denver
Las Vegas
Los Angeles

- 39.07

- 29.45

- 19.99

- 27.57

Miami

- 39.73

- 30.57

- 25.44

- 28.40

Minneapolis

- 19.15

- 14.33

- 13.13

- 14.43

New York

- 10.36

- 8.11

- 5.73

- 7.29

Phoenix

- 38.97

- 30.99

- 29.91

- 31.90

Portland

- 6.27

- 8.29

- 9.59

- 8.62

San Diego

- 33.35

- 25.43

- 17.98

- 26.34

San Francisco

- 43.16

- 27.26

- 14.39

- 29.51

Seattle

- 10.52

- 10.22

- 9.20

- 9.82

Tampa

- 25.85

- 20.04

- 16.66

- 18.51

Washington DC

- 29.35

- 21.22

- 11.69

- 17.16

Largest Decline

Middle Decline

Smallest Decline

Source: S&P, Fiserv, and MacroMarkets LLC.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

diﬀerent price ranges have diﬀerent demand and
supply curves and, as a result, appreciate and depreciate in diﬀerent ways. A signiﬁcant portion of the
housing boom was driven by a loosening in lending
standards, which one might expect to disproportionately aﬀect lower–priced homes. When credit
standards loosen, a whole new group of people who
previously were unable to aﬀord homes are suddenly capable of buying a home. The majority of the
people in this group are naturally going to demand
lower-priced homes. All else equal, the increase in
demand is going to push prices for these types of
homes upward. Mid– and high–priced homes are
aﬀected by these developments, too. More readily
available credit may mean that a person previously
able to aﬀord only a low-priced home can now afford a mid–priced home. In addition, the increase
in home prices creates positive feedback such that
people who already owned homes are now able to
sell their homes at higher prices and buy a more
expensive home.
Again creating some unweighted aggregates gives
a better view of how homes in speciﬁc price tiers
have changed in value. S&P breaks the Case–Shiller
metro area indexes down into price tiers. Each price
tier is unique to a speciﬁc area, meaning that the
maximum value of low-tier homes in Cleveland is
diﬀerent than that of low–tier homes in Miami.
Each tier in each metro area represents one–third of
the sales in a given period that are used to formulate an area’s overall index. The averages shown
below ignore the diﬀerences in price level between
metro areas and instead show the average index
value of the respective price tiers across metros. The
least expensive third of homes clearly has the largest
appreciation and subsequent depreciation in value,
while the most expensive third of homes has seen
the smallest run up and decline in home prices.
This pattern holds true in all but two of the 17
metro areas that the index breaks down into tiers.
What does all this mean to homeowners wondering
what their home is worth? Not as much they might
like. Ultimately, the value of a home is what a
buyer is willing to pay for it, and that is determined
by the individual characteristics of a home as well
as many economic factors. But in the absence of
a pending oﬀer, these diﬀerent breakdowns of the
17

data provide some insight into how home prices in
diﬀerent areas and diﬀerent price tiers have behaved
on average. Given the data, it seems safe to assume
that those homes that experienced the largest price
increases during the boom have likely given a great
deal, if not all of that gain back. Homes whose prices held pretty steady during the good times likely
have experienced only modest declines to date.

Regional Activity

Fourth District Employment Conditions, October 2008
12.11.08
Kyle Fee

Unemployment Rates
Percent
8
7

Fourth Districta

6
5
United States
4
3
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
a. Seasonally adjusted using the Census Bureau’s X-11 procedure.
Notes: Shaded bars represent recessions; Some data reflect revised inputs,
reestimation, and new statewide controls. For more information, see
http://www.bls.gov/lau/launews1.htm.
Sources: U.S. Department of Labor, Bureau of Labor Statistics.

County Unemployment Rates
U.S. unemployment rate = 6.5%

4.6% - 5.9%
6.0% - 6.9%
7.0% - 7.9%
8.0% - 8.9%
9.0% - 9.9%
10.0% - 11.9%
Note: Data are seasonally adjusted using the Census Bureau’s X-11 procedure.
Sources: U.S. Department of Labor, Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

The District’s unemployment rate rose 0.1 percent,
reaching 7.0 percent in October. The increase in
the unemployment rate is attributed to increases in
the number of people unemployed (2.2 percent)
and a decrease in the number of people employed
(−0.2 percent). The District’s rate was again higher
than the nation’s (by 0.5 percentage point), as it
has been since early 2004. Since this time last year,
the District’s unemployment rate has increased 1.5
percentage points, and the nation’s has increased
1.7 percentage points.
There are considerable diﬀerences in unemployment rates across counties in the Fourth District.
Of the 169 counties that make up the District,
42 had an unemployment rate below the national
average in October, and 127 had a higher one.
District counties reporting double–digit unemployment rates numbered 19, while only 1 county had
an unemployment rate below 5.0 percent. Rural
Appalachian counties continue to experience higher
levels of unemployment, and those counties along
the Ohio–Michigan border have begun to see more
elevated rates of unemployment.
The distribution of unemployment rates across
Fourth District counties ranges from 4.6 percent to
11.9 percent, with a median county unemployment
rate of 7.5 percent. Counties in Fourth District
West Virginia and Pennsylvania populate the lower
half of the distribution, while 55 percent of Fourth
District Kentucky counties and 60 percent of
Ohio’s counties are in the upper half of the distri18

County Unemployment Rates
Percent
13.0

Ohio
Kentucky
Pennsylvania
West Virginia

12.0
11.0
10.0

bution. These county–level patterns are reﬂected in
statewide unemployment rates. The states of Ohio
and Kentucky have unemployment rates of 7.3 and
6.8 percent, respectively, compared to Pennsylvania’s 5.8 percent and West Virginia’s 4.7 percent.

Median Unemployment Rate = 7.5%

9.0
8.0
7.0
6.0
5.0
4.0
3.0
County
Note: Data are seasonally adjusted using the Census Bureau’s X-11 procedure.
Source: U.S. Department of Labor, Bureau of Labor Statistics.

Banking and Financial Institutions

Fourth District Community Banks
Annual Asset Growth
Percent
5.0

12.11.08
by Joseph G. Haubrich, Kent Cherny, and Saeed
Zaman
Most of the 262 banks headquartered in the Fourth
Federal Reserve District as of September 30, 2008,
are community banks—commercial banks with
less than $1 billion in total assets. There are 238
such banks headquartered in the District, a number
that, as a result of bank mergers, has declined since
1998, when there were 337.

4.0
3.0
2.0
1.0
0.0
-1.0
-2.0
-3.0
-4.0
-5.0
-6.0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3
Note: 2008:Q1, 2008:Q2 and 2008:Q3 growth rates are annualized year-to-date asset
growth. For other years, Q4/Q4 growth rates are used.
Source: Author’s calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Total asset growth for Fourth District community
banks decreased 4.84 percent (annualized rate) in
the third quarter, but this rate has ﬂuctuated quite
a bit in the last few years. These ﬂuctuations do
not necessarily reﬂect falling asset values, though
this may partially be the case given the recessionary environment of the last four quarters. Another
possibility for the decrease in asset growth is that
some banks are merging with other Fourth District
banks in a way that pushes their assets above $1
billion, and therefore out of our “community bank”
sample. A bank’s assets may also be bought and
transferred to a bank holding company in another
state, which would again remove them from our
sample.
19

The structure of the market with respect to bank
size has changed since 2000. Back then the majority of the community banks in the district had less
than $100 million in total assets. Since then, banks
in the mid–size category ($100 million to $500
million) have constituted the majority.

Fourth District Community Banks by
Asset Size
Number of community banks
200

Assets < $100 million
Assets $100 million-$500 million
Assets $500 million-$1 billion

175
150
125
100
75
50
25
0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3
Source: Author’s calculation from Federal Financial Institutions Examination
Council, Quarterly Banking Reports of Condition and Income.

Income Stream
Percent of assets

Percent
5.0

1
Income earned
but not received

4.5
4.0
3.5
3.0
2.5

0.9

Net interest margin
0.8
ROA before tax and
extraordinary items

0.7

2.0
0.6

1.5
1.0

0.5

0.5
0.0

The income stream of Fourth District community
banks has deteriorated slightly in recent years. The
return on assets (ROA) fell from 1.7 percent in
1998 to 0.76 percent in the third quarter of this
year. (ROA is measured by income before tax and
extraordinary items, because one bank’s extraordinary items can distort the averages in some years.)
The decline is due in part to weakening net interest
margins (interest income minus interest expense divided by earning assets). Currently at 2.65 percent,
the net interest margin for Fourth District community banks is at its lowest level in over a decade, as
the deposit interest rate market remains competitive
and the prime rate stays low.

0.4
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3

Source: Author’s calculation from Federal Financial Institutions Examination
Council, Quarterly Banking Reports of Condition and Income.

Balance Sheet Composition
Percent of assets
52
Real Estate Loans
42

One possible cause of concern for Fourth District
community banks is their level of income earned
but not received, which currently stands at 0.59
percent of assets. If a loan agreement allows a
borrower to pay an amount that does not cover
the interest accrued on the loan, the uncollected
interest is booked as income even though there is
no cash inﬂow. The assumption is that the unpaid
interest will eventually be paid before the loan
matures. However, if an economic slowdown or
other some other factor forces an unusually large
number of borrowers to default on their loans, the
bank’s capital may be impaired. Income earned but
not received has been elevated since 2006, but it
has not reached the level seen following the 2001
recession, though it could again approach that level
depending on the severity of the current economic
downturn.

Consumer Loans

Mortgage-Backed Securities

Commercial Loans
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3

Source: Author’s calculation from Federal Financial Institutions Examination
Council, Quarterly Banking Reports of Condition and Income.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Real estate lending continues to be the primary
focus of community banks in the Fourth District.
When mortgage-backed securities are included,
59.7 percent of bank assets are tied to real estate.
Consumer and commercial loans (as a percentage
of assets) have been declining and ﬂat, respectively,
over the last few years and account for 9.3 percent
of assets. Our last report on Fourth District bank
20

holding companies showed that BHC asset portfolios contain slightly diﬀerent allocations. Although
both types of bank predominantly hold real estate loans, community banks focus more heavily
on them, while consumer and commercial loans
account for a large share—25 to 30 percent—of
regional bank holding companies’ balance sheets.

Liabilities
Percent of liabilities
90
80
70

Total time deposits

60
50
40
30
20

FHLB advancesa,b

Total demand deposits

10
0

Total brokered deposits
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3

a. Federal Home Loan Bank Advances.
b. Data starts in 2001
Source: Author’s calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income.

Problem Loans
Percent of loans
5.0
4.5
4.0 Commercial loans
3.5
3.0
2.5
2.0
1.5

Real estate loans

1.0
0.5

Consumer loans

0.0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3
Source: Author’s calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income.

Net Charge-Offs
Percent of loans
4.0
3.5
3.0
2.5
2.0

Commercial loans

1.5
1.0
0.5

Consumer loans
Real estate loans

0.0
1998 1999 2000 2001 2002 2003 2004 2005 2006 20072008:Q3
Source: Author’s calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income.

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Fourth District community banks consistently
ﬁnance their assets primarily through time deposits
(about 75 percent of total liabilities). Brokered deposits, which are a riskier type of deposit for banks
because they chase higher yields and are not a dependable source of funding, are used less frequently.
Federal Home Loan Bank (FHLB) advances are
loans from the FHLBs that are collateralized by the
bank’s small business loans and home mortgages.
Although they have gained some popularity in recent years, FHLB advances are still a small fraction
of community banksâ€™ liabilities (7.5 percent of
total liabilities) and remain an important source of
backup liquidity for most Fourth District community ﬁnancial institutions.
Problem loans are those that are more than 90
days past due, as well as those no longer accruing
interest. Problem commercial loans rose sharply
in 2001, returned to their 1998–2000 levels in
2005–2006, and have again begun increasing in
the last two years. Currently, 2.91 percent of all
commercial loans are problem loans. About 1.58
percent of all outstanding real estate-related loans
are 90 days or more past due, which is the highest
level in more than a decade. The trend in problem
real estate loans lately has mirrored that of housing
prices nationwide. Problem consumer loans (credit
cards, installment loans, etc.) have increased 0.10
percent from 2007 levels, and currently account for
0.56 percent of consumer loans.
Net charge–oﬀs are loans that are removed from the
balance sheet because they are deemed unrecoverable, less any loans that were deemed unrecoverable
in the past but are recovered in the current year. As
with problem loans, there was a sharp increase in
the net charge-oﬀs of commercial loans during and
following the 2001 recession. Consumer loans saw
a similar increase during that recession, and their
charge–oﬀ rate has remained near those levels since
21

Capitalization
Percent
12.0
11.5
11.0
Risk-based capital ratio

10.5
10.0

Leverage ratio
9.5
9.0
8.5
8.0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3
Source: Author’s calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income.

Coverage Ratio
Dollars
25

then. Net charge–oﬀs in the third quarter of 2008
reached 0.57 percent of outstanding commercial
loans, 0.55 percent of outstanding consumer loans,
and 0.15 percent of outstanding real estate loans.
These numbers could rise going forward if the
increasing number of problem loans these banks
are documenting ultimately translates into more
“unrecoverable” loans.
Capital is a bank’s cushion against unexpected
losses. The recent trend in the capital ratio indicates
that Fourth District community banks are protected by a large cushion. While the leverage ratio
(capital over total assets) remained above 10 percent, the risk-based capital ratio (a ratio determined
by assigning a larger capital charge on riskier assets)
was about 11 percent in the third quarter of 2008.
The growing capital ratio is a sign of strength for
community banks.
An alternative measure of balance sheet strength is
the coverage ratio. The coverage ratio measures the
size of the bank’s capital and loan loss reserves relative to its problem assets. As of the third quarter,
Fourth District community banks had about $11 in
capital and reserves for each $1 of problem assets.
The coverage ratio has declined over the last few
years, as problem loans have increased, but balance
sheets remain strong.

0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008:Q3
Note: Ratio of capital and loan loss reserves to problem assets.
Source: Author’s calculation from Federal Financial Institutions Examination
Council, Quarterly Banking Reports of Condition and Income.

To read our last report on Fourth District bank holding companies:
http://www.clevelandfed.org/research/trends/2008/0808/01banfin.
cfm

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ISSN 0748-2922

Federal Reserve Bank of Cleveland, Economic Trends | December 2008

Full text of Economic Trends (Federal Reserve Bank of Cleveland) : Economic Trends - December 2008

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