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Bank Credit Standards*
BY MITCHELL BERLIN
B
anks’ lending standards at times seem too
stringent and at other times too lax. The
pattern seems to indicate that banks lend
more easily in good times but tighten credit
standards in lean times. But such a lending pattern may
also be attributable to changes in borrowers’ default risk
over the business cycle or changes in the demand for
loans, which rises and falls with GDP. Is there a systematic
reason why banks might be too lax or too stringent in their
lending? Economists have proposed a number of models to
explain a bank lending cycle, including changes in bank
capital, competition, or herding behavior. In this article,
Mitchell Berlin discusses these models and the empirical
evidence for each.
Bankers and the business press
often speak of cycles in bank credit
standards, periods in which banks’
lending standards are too lax, followed
by periods in which standards are too
stringent. In this view, bank lending
policies tend to amplify fluctuations in
GDP; easy money during the upturn
Mitchell Berlin
is a vice president
and economist
and head of the
banking section in
the Philadelphia
Fed’s Research
Department. This
article is available
free of charge at
www.philadelphiafed.org/research-and-data/
publications/.
www.philadelphiafed.org
sows the seeds of tight money episodes
in the downturn.1
But this pattern is also consistent
with variations in bank lending driven
by changes in borrowers’ default risk
1
Most observers view the expansion of high-risk
mortgage loans between 2004 and 2006 as a
particularly dramatic example of a widespread
decline in lending standards. While the
research on this episode is expanding rapidly,
the evidence is too recent to interpret with
confidence or to incorporate into this article.
Nonetheless, the theories I discuss in this article
will certainly be part of a full explanation for
the recent financial crisis.
*The views expressed here are those of the
author and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
over the business cycle or changes
in the demand for loans, which rises
and falls with GDP. To make sense
of the idea of a lending cycle, we
must uncover a systematic reason for
banks to make unprofitable loans
in an upturn and to forgo profitable
loans in a downturn. I emphasize that
the tendency must be systematic to
distinguish the idea of a credit cycle
from the truism that loans made
near the peak of an expansion are
more likely to go bad simply because
bankers (just like economists and
other businessmen) have difficulties
predicting downturns.
What is the evidence for an
independent effect for changing
bank lending standards — that is, a
systematic reason why banks might be
too lax or too stringent? And what
factors might explain this type of
behavior? Economists have proposed a
number of plausible models of a bank
lending cycle, emphasizing changes in
bank capital, competition, or herding
behavior. To date, only the channel
relating changes in bank capital to
lending standards has firm empirical
support. The available evidence is too
weak to give us much confidence in
assigning an important role for other
theories of bank lending standards.
WHAT ARE CREDIT
STANDARDS?
It is helpful to be a little clearer
about what we mean by a change in
bank credit standards. Let’s begin
with a straightforward prescription
from investment theory: A profitmaximizing bank should make any
loan with a positive net present value
(NPV). The NPV of a loan is just the
Business Review Q2 2009 1
sum of discounted future repayments
(principal plus interest) on the loan
minus the loan amount. Future
repayments must be discounted for
two different reasons: First, $10 in
the bank now is worth more than $10
paid a year from now. After all, the
bank could receive a year’s interest
by purchasing Treasury bills on the
$10 paid back tomorrow. Second, the
bank recognizes that the borrower may
default in the future, so the bank may
never receive some future payments.
The firm may have a healthy balance
sheet at the time the loan is made; a
year from now, the borrowing firm may
suffer financial setbacks and may be
unable to pay back its loan.2
Using this framework, we can
define a change in bank credit
standards as a change in a bank’s loangranting decisions for some reason
other than a change in the NPV of the
loan. We can define a credit cycle as a
systematic tendency to fund negative
NPV loans during an expansion and a
systematic tendency to reject positive
NPV loans during a contraction.
Since banks’ lending decisions also
involve the pricing and design of loan
contracts, a credit cycle might also
take the form of a systematic tendency
to relax or tighten loan terms by more
than would be justified by changes in
borrower risk.
Conceptually, it is not too
difficult to define a credit cycle.
Empirically, it may be much harder
to tell whether one has occurred. For
example, think about some of the
things that happen in an economic
downturn. As economic conditions
2
To keep the discussion simple, I focus here on
the loan-granting decision. Of course, the bank
will set the loan rate in light of the probability
of default. The bank will also design the loan
contract to reduce the likelihood of default
and to increase its payments in the event of
default by including covenants or requiring the
borrower to post collateral.
2 Q2 2009 Business Review
become more difficult, more firms
experience economic difficulties and
the probability that a firm will default
increases. This reduces the NPV of a
given stream of repayments and would
probably induce the bank to raise the
loan rate, impose new contractual
restrictions, or refuse to make the
loan at all. While these actions might
be interpreted as a tightening of
standards by an outside observer or by
an aggrieved borrower, credit standards
haven’t changed according to our
definition.
Figures 1a and 1b illustrate the
distinction between the effects of a
time as other borrowers in a bank’s
portfolio default. To see this, consider
a Detroit bank that has a portfolio
with a high concentration of loans
to auto parts suppliers. This bank is
evaluating two prospective loans with
identical probabilities of default. One
of the loans is to an auto parts supplier,
and the other is to a department store.
Even though the probability of default
is identical for both projects, the bank
will not charge the same default risk
premium to both. Instead, the bank
will charge a higher risk premium for
the loan to the auto parts supplier
because its performance is more highly
tightening of credit standards and
the effects of an increase in credit
risk. Figure 1a shows a probability
distribution of loan applicants’ NPVs.
The profit-maximizing rule for a bank
is to make a loan as long as its NPV
is positive (the sum of the shaded
regions). If the bank tightens its credit
standards, for example, making only
loans with an NPV greater than $A,
the bank will make a smaller number
of loans (just the darker region).
Figure 1b illustrates the effects of a
downturn: Loans become riskier and
the distribution of NPVs shifts to the
left. But this figure shows a bank that
retains the profit-maximizing rule.
Note that the number of loans made
falls in this case also (from the sum of
the shaded regions to just the darker
region).
Slightly more subtly, in a
downturn many loans often go bad
at once. Typically, a bank will charge
a borrower a higher loan rate if the
borrower is likely to default at the same
correlated with the rest of the bank’s
portfolio.
Taking this idea a step further,
economists have found that firms’
defaults tend to be correlated.3
Thus, we should not be surprised
that a bank would demand a higher
premium for default risk in a downturn
as compensation for the higher
probability that many loans will go bad
at the same time. Although the bank
has charged borrowers a higher price
for bearing risk, this should not be
viewed as a change in credit standards.
In an economic downturn,
nonfinancial firms also cut back on
investments in plant and equipment
and inventories, and, in turn, they cut
back on borrowing. A decline in the
demand for loans should certainly not
be viewed as a change in bank credit
standards.
3
See the article by Sanjiv Das, Darrell Duffie,
Nikunj Kapadia, and Leandro Saita.
www.philadelphiafed.org
FIGURES 1a and 1b
A Tightening of Credit Standards
An Increase in Credit Risk
We can see the empirical
challenge in identifying an
independent effect for lending
standards on the quantity of loans.
Consider an economic downturn. In
a downturn, default risk increases,
risks become more correlated, and
the demand for loans declines. None
of these factors reflects a change in
lending standards, but all lead to
a decline in the quantity of loans
made. To uncover a lending cycle,
the researcher must find some way to
disentangle the effects of changing
lending standards from these other
effects.
THE BROAD FACTS
Economists have documented
a number of empirical observations
that are broadly consistent with the
existence of a lending cycle.4 The
first empirical observation is that
declines in bank capital are associated
with declines in bank lending. Ben
Bernanke and Cara Lown (among
many others) have found evidence that
large negative shocks to bank capital
— such as those experienced by banks
in New England at the end of the
1980s — are associated with declines
in bank lending. The relationship
between capital and lending is a robust
empirical finding, but since the weak
economic conditions associated with
a decline in bank capital are also
associated with higher default risk,
more correlated risks, and a decline in
loan demand, economists have had to
be ingenious in providing compelling
evidence for the capital channel (as I
discuss in the next section).
A second observation is the
well-documented flight to quality
4
Note that not all the researchers who made
these observations were concerned with lending
cycles or with identifying an independent role
for bank credit standards.
www.philadelphiafed.org
Business Review Q2 2009 3
during economic downturns. For
example, William Lang and Leonard
Nakamura show that bank portfolios
shift from high- to low-risk loans
during a downturn; specifically, they
show that bank portfolios shift away
from loans made above the prime
rate.5 Their finding is consistent with
evidence that during a downturn,
banks systematically shift their
portfolios toward larger borrowers and
toward borrowers with pre-existing
loan commitments.6 While these
studies shed light on the ways that
bank lending may amplify negative
economic shocks, the observed
portfolio shifts may simply reflect a
rise in default risk during an economic
downturn, rather than an independent
role for lending standards, according
to our definition. With a rise in default
risk, some borrowers are shut out of
public debt markets and shift toward
bank borrowing, while bank portfolios
shift toward lower risk borrowers.
A third observation is that loan
terms vary systematically over the
business cycle in a way that may
amplify economic fluctuations. Patrick
Asea and Asa Blomberg find that
commercial loan markups (the spread
between the loan rate and the rate
on a riskless Treasury security) fall
continuously right up to the beginning
of a recession. Their interpretation of
this finding is that credit standards
are excessively easy at the end of an
expansion, sowing the seeds of future
portfolio problems.
Jianping Mei and Anthony
Saunders provide evidence of trend-
5
Traditionally, the prime rate is defined as the
rate offered to a bank’s best customers. Loans
made above the prime rate are typically made
to smaller borrowers and borrowers who do not
have access to money market financing.
6
See the article by Ben Bernanke, Mark
Gertler, and Simon Gilchrist for a review of the
empirical literature on the flight to quality.
4 Q2 2009 Business Review
chasing behavior by banks. They find
that banks increase real estate lending
when past real estate returns are high,
but that bank real estate investments
are unprofitable, on average. These
results are consistent with a systematic
tendency for excessively lax credit
standards during an expansion,
and they may also be evidence of
a tendency for banks to invest in
a herd-like manner. However, the
evidence from commercial lending
and real estate lending markets
on lending standards, suggesting an
independent role for credit standards.
While this is perhaps the most
convincing evidence that changes
in bank credit standards have an
independent effect, Lown and Morgan
do not provide evidence that banks
systematically choose excessively lax or
risky lending standards.
To sum up, there is survey
evidence of an independent role for
bank credit standards, and a number
of empirical observations are broadly
!
" #
may simply mean that banks have
difficulty predicting a downturn (just
like everyone else). Thus, banks may
continue lending strongly even as the
downturn begins.
The most direct evidence for a
direct role for bank credit standards
comes from survey results. Cara Lown
and Donald Morgan analyze the
Federal Reserve Board’s Senior Loan
Officer Opinion Survey, in which
bankers are asked periodically whether
they changed their credit standards
in the previous three months. They
are also asked to explain how their
standards changed, e.g., changes in
collateral requirements, covenants,
and loan markups, as well as the
underlying reasons for any change.
Using a statistical analysis called a
vector autoregression (VAR), Lown
and Morgan find that changes in
credit standards (as measured by
survey responses) have a significant
effect on both the quantity of bank
loans and GDP.7 Interestingly, changes
in GDP do not have a significant effect
consistent with the existence of a
lending cycle. Making further progress
requires a theoretical framework
that would permit us to disentangle
the various effects on banks’ lending
behavior.
CAPITAL CONSTRAINTS
LEAD BANKS TO TIGHTEN
STANDARDS
Bank Lending Is Limited by
Bank Capital. A wide range of models
show that a firm’s investments in plant,
equipment, and inventories are limited
by the firm’s capital, i.e., the funds
committed by the firm’s owners. A
bank is just a particular type of firm,
but instead of investment in goods
and machines, its main investments
are loans. While the precise link
between capital and investment differs
7
In a VAR model, each variable (e.g., change
in credit standards, change in GDP, change
in loans) is regressed on past values of itself
and the other variables. Thus, each variable is
permitted to affect the others.
www.philadelphiafed.org
from model to model, the element
common to all of them is that agency
problems limit firms’ access to outside
funding. In our context, the term
“agency problem” refers to a conflict
of interest between a firm’s insiders
— owners and top managers, who are
influential in a firm’s decision-making
— and outside investors — depositors,
bondholders, and perhaps small
stockholders, who control only their
willingness to provide funds.
For example, in Bengt Holmstrom
and Jean Tirole’s model, the bank’s
insiders have a choice between
carefully monitoring borrowers and
avoiding the costs of monitoring.8 A
carefully monitored loan has low risk
and positive NPV; a loan that is not
monitored has a high risk of default
and a negative NPV. The underlying
agency problem is that a firm’s insiders
will forgo monitoring and make
high-risk loans unless they receive
a sufficiently large share of the total
profits.9 But providing insiders with
incentives to monitor limits the share
of the returns left over for outside
investors, who will refuse to provide
funds unless their own expected rate of
return is adequate.
The role of bank capital in all
this is that a firm’s insiders have a
stronger incentive to engage in costly
monitoring of loans when more of their
own funds are at risk, i.e., when bank
capital is higher. Outside investors
will refuse to provide funds to banks
8
I am interpreting Holmstrom and Tirole’s
model in a banking context. Their model is
actually cast in more general terms. Bernanke,
Gertler, and Gilchrist’s article describes some
other agency-based models that yield results
similar to Holmstrom and Tirole’s.
9
In the Holmstrom and Tirole model, insiders
can’t promise to monitor carefully or to fund
only positive NPV loans because outsiders
have too little information about the details of
lending decisions to ensure that the promise is
kept.
that are not well-capitalized.10 In
Holmstrom and Tirole’s model, a bank
with insufficient capital may be unable
to convince outside investors to fund
loans that would have positive NPV
if the bank could make a credible
guarantee to monitor.
When Bank Capital Falls,
Banks Tighten Lending Standards.
Loan losses are countercyclical; in
particular, in an economic downturn,
more borrowers default and loan
losses increase (Figure 2). Higher
loan losses reduce bank capital, and
the availability of outside financing
also decreases. In turn, banks may
be forced to forgo loans with positive
NPV (if properly monitored); that
10
The concept of capital used in Holmstrom
and Tirole’s study is often called net worth or
economic capital. This is not exactly the same
thing as regulatory capital, although net worth
corresponds fairly closely to tier 1 capital, which
mainly includes equity.
is, banks will have excessively tight
lending standards. Most models that
focus on the link between capital
and the availability of outside funds
focus on economic capital, but similar
limits on lending arise if regulators
limit bank lending when loan losses
press banks against regulatory capital
requirements.
Note that this model predicts
that capital shortages will restrict
lending but it doesn’t predict that
banks would ever have excessively lax
credit standards. That is, according
to Holmstrom and Tirole, banks will
forgo positive NPV loans when access
to outside funds is restricted because
their capital is low, but high bank
capital doesn’t increase the likelihood
that a bank will make a negative NPV
loan.
Empirical Evidence for the
Capital Channel. A large empirical
literature documents the effect of
negative shocks to banks’ capital
FIGURE 2
Loan Losses Rise in Downturns
Loan Loss Provisions / Assets
0.80
0.75
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
70
19
73 975 977 979 981 983 985 987 989 991 993 995 997 999 001 003 005 007
1
1
2
1
1
2
1
1
1
1
1
1
2
1
1
1
2
19
Source: Report of Condition
www.philadelphiafed.org
Business Review Q2 2009 5
on bank lending. In particular, a
number of studies of the 1990-92
credit crunch in the U.S. show
that declines in bank capital were
systematically associated with declines
in bank lending, consistent with the
statements of bankers, borrowers, and
bank regulators at the time.11 While
consistent with an independent effect
for bank capital on lending standards,
these studies are not fully convincing
because the same factors that led to
declines in bank capital also led to a
decline in the demand for loans and to
a decline in loans’ NPV. Specifically,
the credit crunch occurred following
an economic downturn triggered,
in part, by serious downturns in the
commercial real estate markets in
New England, California, and the
Southwest. At a minimum, these
studies don’t fully disentangle the
relative importance of demand effects,
changes in credit risk, and declines in
bank capital.
Joe Peek and Eric Rosengren’s
studies of Japanese banks’ lending in
the U.S., following the collapse in
Japanese equity prices in 1989-92 and
the precipitous decline in the Japanese
real estate market beginning in 1991,
provide the most convincing evidence
for a significant, independent channel
relating capital to lending standards.
In these studies, which cover the 198996 period, Peek and Rosengren find
that U.S. branches of Japanese banks
reduced commercial and industrial
loans and real estate loans when their
parent bank’s capital fell.12 So, for
example, the U.S. branch of a Japanese
bank operating in New York would
11
Joe Peek and Eric Rosengren’s articles provide
the main references.
12
They also find a strong negative effect for
nonperforming loans. Peek and Rosengren
argue that Japanese banks postponed
recognizing loan losses, so nonperforming loans
may be a proxy for unrecognized loan losses.
6 Q2 2009 Business Review
reduce its commercial real estate loans
in the state when its parent suffered
a decline in capital, even though
U.S. commercial banks operating in
the same state were increasing their
commercial real estate loans. Peek and
Rosengren’s studies provide convincing
evidence that the decline in capital
was a major cause of the decline in
Every episode
in which lending
#
#
lending, because the U.S. banks and
U.S. branches of Japanese banks
both faced essentially the same local
business conditions (default risk and
loan demand) in the U.S.
COMPETITION MAY AFFECT
LENDING STANDARDS
Every episode in which lending
expands rapidly and loan terms
become more lenient is accompanied
by statements from bankers and other
market players that competition drives
them to relax lending standards.
For example, a manager at Standard
and Poor’s, a credit rating agency,
explained the growth of “covenantlite” loans during a fiercely competitive
loan market in 2006 as follows: “When
you have a lot of money chasing deals,
lenders may lose their appetite for
enforcing covenants and are more
willing to waive them.”13
Competition and the Winner’s
Curse. Economic theorists have
explored the possibility that aggressive
competition might lead to a decline in
lending standards. In particular, they
have argued that economic booms
generate competitive pressures that
may induce banks to screen borrowers
less carefully. An element common to
a number of the theoretical models is
a phenomenon that will be familiar
to anyone who has purchased a home
in a bidding war or won an online
auction and worried, “I must have paid
too much. If I had offered less, I still
would have won.” When a bank knows
that a successful loan applicant has
approached multiple banks, it worries
that it has won the firm’s business only
because other banks have decided that
the borrower was not creditworthy.
Economists call this effect the winner’s
curse. In these models, banks compete
more aggressively when the winner’s
curse is less serious, as may be true
in an economic expansion. Notably,
aggressive competition may lead
banks to lend without screening some
borrowers.14
13
Quoted in Serena Ng’s article.
14
Not all models of competition and lending
standards build on the idea of the winner’s
curse. For example, Gary Gorton and Ping
He’s interesting model views a credit crunch
as a breakdown in oligopolistic cooperation
among banks. In their model, banks shift
between periods when they cooperate and
perform a normal level of monitoring, and
periods in which cooperation breaks down and
banks engage in excessive monitoring. Robert
Hauswald and Robert Marquez argue that
competition reduces market power over repeat
customers, thus reducing incentives to monitor.
I focus on theories of lending cycles, rather
than on theories of the effects of secular
changes in competitive conditions — for
example, due to regulatory reforms — on banks’
incentives to take risks. There is a large, and
largely inconclusive, literature on the effects of
competitive conditions on risk-taking. For an
account of this literature, see Elena Carletti’s
article.
www.philadelphiafed.org
Martin Ruckes proposes a model
of lending booms, in which underlying
economic conditions affect bank
screening decisions. In his model,
borrowers approach multiple banks
that can respond in one of three ways:
(i) screen the applicant (and make
loans only to applicants who appear
creditworthy); (ii) reject the applicant
out-of-hand; or (iii) make a loan offer
without screening.15
In a recession, when default risk is
high, banks believe that customers are
not likely to be creditworthy. Consider
a lender’s thought process when a
borrower applies for a loan and average
credit risk is high. Since average credit
risk is high, the bank worries that the
loan applicant has failed competitors’
credit screens. Thus, the bank would
never lend without carefully screening
loan applicants. Even if the customer
passes the lender’s screen, the bank
still charges a high loan rate because
it worries that it has missed something
other lenders have noticed. When
economic conditions are very poor, the
winner’s curse can become so severe
that banks will simply turn away some
borrowers without screening.
During an economic boom,
borrowers’ creditworthiness improves.
Of course, not all borrowers are
good risks, but the likelihood that
any particular borrower will prove
to be creditworthy increases in good
economic times. Thus, the winner’s
curse is less severe, and banks will
tend to compete more aggressively for
customers. This competition takes
an interesting form. In addition to
charging a low loan rate to those
customers they find to be creditworthy,
banks make some loans without
screening at all.
15
To be precise, lenders may also play mixed
strategies; for example, a loan applicant may be
screened with some probability and given a loan
without screening with some probability.
www.philadelphiafed.org
Ruckes’s model yields outcomes
that look like a credit cycle. In
particular, the fierce competition in
the upturn yields high loan default
rates (because of lax screening) and
low expected bank profits. Credit
standards are much more stringent
in a downturn, and borrowers may
be turned away altogether, a model
prediction that resembles a flight to
quality.
Empirical Evidence for the Competition Channel. The evidence for
an independent effect for competition
is mainly anecdotal. One piece of evidence comes from the Senior Loan Officer Opinion Survey, which asks those
bankers who tightened or loosened
standards to provide a reason. Respondents typically emphasize competitive
factors, even though they are also
given the chance to ascribe the change
in lending standards to a number of
factors reflecting credit risk.16
Respondents code their responses,
with 1 denoting “not important,” 2
denoting “somewhat important,” and 3
denoting “very important.” So, for example, in the November 2004 survey,
respondents ascribed their easing of
loan terms primarily to more aggressive competition, with an average score
of 2.54. (That is, most respondents
said that competitive conditions were
either somewhat important or very
important.) At the same time, they
noted that easier loan terms were also
partially due to a more favorable economic outlook, with an average score
of 1.87. These responses correspond
16
Respondents are given different
(nonexclusive) choices to explain why they
changed their lending standards, including (i)
more (less) aggressive competition from other
banks or nonbank lenders; (ii) more (less)
favorable or uncertain business environment;
(iii) improvement (worsening) of industryspecific problems; and (iv) increased (reduced)
tolerance for risk. Choices (ii)-(iv) are all
reasonably interpreted as factors related to
default risk.
to press reports that competition was
heating up in 2004.
While this type of survey evidence
provides a fairly accurate indicator
of bankers’ own views of the forces
underlying changes in credit standards,
most economists remain skeptical. In
particular, without convincing econometric evidence, economists worry
that respondents haven’t adequately
distinguished the relative roles of
default risk and competitive pressures
that drive their lending decisions.
Indeed, Ruckes’s model, which emphasizes the close connection between the
creditworthiness of borrowers and the
aggressiveness of competition, suggests
that these will be very difficult to disentangle, not only for econometricians
but also for a banker who has made a
loan.
HERDING MAY AFFECT
CREDIT STANDARDS
Reputational Concerns Can
Induce Banks to Herd. Many commentators suggest that lenders’ credit
standards are interdependent even
when they are not competitors; for example, banks often seem to postpone
recognizing loan losses until they all
jointly tighten standards in a herd-like
movement. A famous example is Citicorp’s May 20, 1987, announcement
that it was increasing loan-loss reserves
against its loans to less developed
countries (LDC), following a long
period in which banks had dealt with
their troubled LDC debt either by providing borrowers new funds to pay off
old loans or by rescheduling old loans.
By the end of June 1987, 32 banks had
increased their own loan-loss reserves
against LDC debt.17
In Raghuram Rajan’s model,
banks may act this way because bank
managers have reputational concerns
17
Theoharry Grammatikos and Anthony
Saunders discuss this episode in detail.
Business Review Q2 2009 7
that lead them to focus on short-term
results. For example, top bank managers are more likely to be promoted
or recruited by other banks if recent
financial results have been strong. In
his model, some lenders have superior
ability in identifying profitable loans.
Crucially for Rajan’s analysis, differences in ability matter primarily when
loan market conditions are favorable.
When economic conditions are good,
only the loans originated by highability lenders have a low probability of
default. However, in a downturn, loans
turn out poorly for both high- and
low-ability lenders. Also important for
Rajan’s conclusions, bank managers’
information — both about their own
portfolio and about general loan market conditions — is superior to that of
other market participants.18
Consider a lender’s decision when
he or she discovers that a number of
the bank’s loans are having serious
problems. The lender can recognize
losses immediately or relax credit
standards — provide new funds or
reschedule loan payments — in the
hope that the borrower’s situation
will turn around. By assumption,
the bank’s profits are maximized by
recognizing losses now, rather than by
throwing good money after bad.
But the lender is concerned about
his or her current reputation, as well
as the profitability of the bank’s loan
portfolio. Concerns about reputation
generate a systematic bias toward
excessively lax credit standards. Note
that unlike Holmstrom and Tirole’s
model, Rajan predicts that banks
have a systematic tendency to make
negative NPV loans.
18
The assumption that bankers have better
information about general loan market
conditions may seem unrealistic. However,
it is enough that bank managers learn about
loan market conditions before other market
participants for Rajan’s model to work.
8 Q2 2009 Business Review
To see why, think about how
market players update their view
of a lender’s ability when the bank
recognizes losses. Loan losses are
bad news about the lender’s ability
when market conditions are good.
Unless market participants are quite
sure that loan market conditions are
unfavorable, the lender’s reputation
will suffer; that is, market participants
will downgrade their view of the
lender’s ability. To avoid taking a hit
to his or her reputation, the lender
will knowingly throw good money
after bad, unless market conditions are
widely viewed to be poor.
But how does this lead to herding
behavior? The key is that the lender’s
reputation also depends on what
other banks do. If other banks have
written down loans, a lender can
recognize losses and the market will
not judge the lender harshly. Market
participants will simply infer that loan
market conditions are poor and that
all banks are facing a difficult lending
environment. But if one bank alone
writes down its bad loans, its lender’s
reputation will take a hit.
Thus, banks have a systematic bias
toward lax credit standards because
of reputational concerns. But when
the economy moves into a downturn,
banks ultimately shift toward a strict
lending policy as all banks recognize
losses in a herd. While a single
bank in isolation would choose lax
standards in a downturn to avoid
taking a negative hit to its reputation,
the existence of other banks permits
all banks to jointly tighten lending
standards. In effect, banks achieve a
form of coordination; as long as they
tighten jointly, market participants
assign a high probability of a harsh
lending environment.
Herding Without Reputation.
Other models predict herding behavior
in bank credit standards but without
reputational effects. In the herding
models described in the article by
Sushil Birkchandan, David Hirshleifer,
and Ivo Welch, banks place excessive
reliance on decisions made by other
banks, sometimes overriding the
decision they would make based on
their own information. How does this
work?
Each banker has some useful, but
idiosyncratic information about the
profitability of a loan. Note that it
makes complete sense for one banker
to take account of a previous banker’s
lending decision, since each banker
knows that others also have useful
information. If each lender could
actually observe the information used
by previous lenders, lending decisions
would become progressively more
informed. Each lender would be adding
its own information to that of previous
banks.
Things are different if bankers
observe only the decisions made
by previous lenders (as is realistic),
rather than the information on which
the decisions were based. In this
case, sequential decision-making
can lead to what economists call an
informational cascade. That is, the
decisions of previous banks ultimately
lead subsequent banks to override
their own information. So a bank will
rationally follow the crowd even if its
own credit analysis suggests that a
lending decision is too risky.
Consider an example. First
Bank might view an investment in a
shopping mall as marginally profitable.
The bank’s risk managers are actually
quite worried about a possible
downturn in the real estate market.
But a number of First Bank’s past
commercial real estate investments
are maturing and the bank does
not intend to replace them. So the
lending officers argue that the risk is
not so great after all, and First Bank
decides to make the loan. Imagine that
Second Bank views shopping malls as
www.philadelphiafed.org
a profitable investment and also makes
the loan.
Now consider Third Bank’s
decision. Third Bank has evaluated
the shopping mall and decided that
it is too risky based on its own cash
flow projections. Third Bank has also
observed that both First Bank and
Second Bank have decided to lend, but
the bank is not privy to First Bank’s
future plans to limit its real estate
exposure. On this basis, Third Bank
might (rationally) decide to override its
own cash flow projections and make
the investment anyway.
What about Fourth Bank? Fourth
Bank and all subsequent banks will
never know that Third Bank’s cash
flow analysis was negative, only that
the bank decided to invest. In this
example, had banks shared their
information collectively, they might
have decided that shopping malls were
a negative NPV investment.
Empirical Evidence for Herding.
While stories about informational
cascades abound in the business press,
there is, as of yet, no econometric
evidence that permits us to distinguish
informational cascades from
reputational explanations (such as
Rajan’s), which also predict herding
behavior. Also, it is very difficult to
distinguish herd-like behavior from
instances in which banks act in a
correlated way because they share
common information or even because
of regulatory pressures.19 That many
www.philadelphiafed.org
banks make similar investments
that ultimately turn out badly is not
necessarily evidence of herding.
CONCLUSION
Bankers, business analysts,
and economists often speak of a
credit cycle, in which bankers adopt
excessively lax credit standards in
an upturn and excessively stringent
credit standards in a downturn.
The expansion in mortgage loans,
cycle. In one explanation, banks’
lending standards are driven by shocks
to bank capital. This explanation
has both well-founded theoretical
foundations and convincing empirical
support. Second, there are also many
interesting and plausible models in
which competitive conditions can be
shown to affect lending standards,
but there is little hard econometric
evidence that competitive pressures
have an empirically significant
!
#
particularly the growth in low- and
no-doc loans in 2006-07, and the
widespread cutback in mortgage
loans during the financial crisis that
followed, is the most recent episode.
Broadly, three classes of explanations
might generate this type of credit
cycle or, at least, some aspects of a
19
Viral Acharya and Tanju Yorulmazer argue,
for example, that banks may choose correlated
investment strategies because they know that
regulators will bail out banks when a large
number of banks fail at the same time. To
explain their finding that banks’ real estate
investments reflect trend chasing, Mei and
Saunders suggest that bank regulation may lead
to correlated investment strategies. They argue
that once examiners have permitted one bank
to make an investment, others can follow.
effect. Finally, there are a number
of plausible models in which lending
standards are driven by herding
behavior. In particular, reputational
concerns or informational cascades
can lead lenders to follow correlated
lending strategies, even when loans
have negative NPV. To date, there
is insufficient empirical evidence
to support either competition or
herding as explanations for lending
cycles. Learning more about the
underlying sources of variation in
lending standards is an important
area for further economic research.
A careful examination of the recent
episode in credit markets should lead
to valuable insights for researchers and
policymakers. BR
Business Review Q2 2009 9
REFERENCES
Acharya, Viral, and Tanju Yorulmazer.
“Too Many to Fail: An Analysis of TimeInconsistency in Bank Closure Policies,”
Journal of Financial Intermediation, 16
(2007), pp. 1-31.
Das, Sanjiv, Darrell Duffie, Nikunj
Kapadia, and Leandro Saita. “Common
Failings: How Corporate Defaults Are
Correlated,” Journal of Finance, 62
(February 2007), pp. 93-118.
Mei, Jianping, and Anthony Saunders.
“Have U.S. Institutions’ Real Estate
Investments Exhibited ‘Trend-Chasing’
Behavior?,” Review of Economics and
Statistics, 79 (1997), pp. 248-58.
Asea, Patrick, and Asa Blomberg.
“Lending Cycles,” Journal of Econometrics,
83, 1998. pp. 89-128.
Gorton, Gary, and Ping He. “Bank Credit
Cycles,” Review of Economic Studies 75:4
(2008).
Ng, Serena. “Easy Money? Banks Get
Lenient on Loans,” Wall Street Journal,
April 6, 2006.
Berger, Allen, and Gregory Udell. “The
Institutional Memory Hypothesis and the
Procyclicality of Bank Lending Behavior,”
Journal of Financial Intermediation, 13
(2004), pp. 458-95.
Grammatikos, Theoharry, and Anthony
Saunders. “Additions to Bank Loan-Loss
Reserves: Good News or Bad?,” Journal
of Monetary Economics, 25 (1990), pp.
289-304.
Peek, Joe, and Eric Rosengren. “The
International Transmission of Financial
Shocks: The Case of Japan,” American
Economic Review, 87 (1997), pp. 495-505.
Bernanke, Ben, Mark Gertler, and Simon
Gilchrist. “The Financial Accelerator and
the Flight to Quality,” Review of Economics
and Statistics, 78 (February 1996), pp. 1-15.
Hauswald, Robert, and Robert Marquez.
“Competition and Strategic Information
Acquisition in Credit Markets,” Review of
Economic Studies, 19 (2006), pp. 967-1000.
Bernanke, Ben, and Cara Lown. “The
Credit Crunch,” Brookings Papers on
Economic Activity, 2 (1991), pp. 204-48.
Holmstrom, Bengt, and Jean Tirole.
“Financial Intermediation, Loanable
Funds, and the Real Sector,” Quarterly
Journal of Economics, 112 (1997), pp. 66392.
Birkchandan, Sushil, David Hirshleifer,
and Ivo Welch. “Learning From the
Behavior of Others: Conformity, Fads,
and Informational Cascades,” Journal of
Economic Perspectives, 12 (Summer 1998),
pp. 151-70.
Carletti, Elena. “Competition and
Regulation in Banking,” in Anjan Thakor
and Arnoud Boot, eds., Handbook of
Financial Intermediation and Banking.
North-Holland, 2008, pp. 448-82.
10 Q2 2009 Business Review
Lang, William, and Leonard Nakamura.
“’Flight to Quality’ in Banking and
Economic Activity,” Journal of Monetary
Economics, 36 (1995), pp. 145-64.
Peek, Joe, and Eric Rosengren. “Collateral
Damage: Effects of the Japanese Bank
Crisis on Real Activity in the United
States,” American Economic Review, 90
(March 2000), pp. 30-45.
Rajan, Raghuram. “Why Bank Credit
Policies Fluctuate: A Theory and Some
Evidence,” Quarterly Journal of Economics,
109 (May 1994), pp. 399-401.
Ruckes, Martin. “Bank Competition and
Credit Standards,” Review of Financial
Studies, 17 (Winter 2004), pp. 1073-1102.
Lown, Cara, and Donald Morgan. “The
Credit Cycle and the Business Cycle: New
Findings Using the Loan Officer Opinion
Survey,” Journal of Money, Credit, and
Banking, 38 (September 2006), pp. 1575-97.
www.philadelphiafed.org
Regulating Short-Sales*
by Ronel Elul
S
hort-selling, the practice of selling a security
the seller does not own, is done in an attempt
to profit from an expected decline in the price
of the security. During the recent financial
turmoil, many press accounts blamed short-selling for
declines in stock prices and even for the collapse of some
firms. In this article, Ronel Elul discusses the issue of
short-selling. He notes that research has shown that shortselling plays a valuable role in setting accurate prices for
securities but that it can also be used to facilitate market
manipulation. This latter consideration may provide
justification for restricting short-sales under certain
circumstances.
During the recent financial
turmoil, many press accounts blamed
short-selling for declines in stock prices
and even for the collapse of some
firms. Regulators in many countries
responded by restricting or banning
short-sales. This critical attitude to
short-selling has been a feature of
many financial crises, including the
stock market crash of 1929 and even
the collapse of the South Sea Bubble
in 1720.
Ronel Elul is a
senior economist
in the Philadelphia
Fed’s Research
Department.
This article is
available free of
charge at www.
philadelphiafed.org/research-and-data/
publications/.
www.philadelphiafed.org
Short-selling, or “shorting,” is the
practice of selling a security or other
financial instrument the seller does
not own, in the hope of repurchasing
it later at a lower price. This is done in
an attempt to profit from an expected
decline in the price of the security.1
Since the investor does not own
the security he is shorting, he must
typically borrow (or, rather, “rent”) it
This is not the only way to profit from declines
in the price of an asset. Depending on the
security in question, an investor may also be
able to enter into a short futures contract,
which locks in the price at some future date, or
to buy a put option, which allows the holder of
this option to sell an asset at a specified price in
the future. In either of these cases, the investor
will profit if the market price ends up below the
price he has locked in.
1
*The views expressed here are those of the
author and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
from someone who does own it. Thus,
short-selling is closely linked to the
securities lending market.
Economists who have studied
short-selling have shown that it plays
a valuable role in setting accurate
prices for securities and in aggregating
dispersed information. However, they
have also shown that it can be used
to facilitate market manipulation.
This may provide a justification for
restricting short-sales under certain
circumstances.
KEY FEATURES OF A TYPICAL
SHORT-SALE
Suppose that shares in Highflier,
Inc. currently sell for $10 a share.
An investor believes that the stock is
overvalued and would like to profit
from this by selling Highflier short.
He borrows 100 shares and then
immediately sells them for a total of
$1000. This transaction is typically
intermediated through the investor’s
brokerage house, which buys and sells
the securities on his behalf and also
often arranges the loan of the shares.
If the investor is correct and
the price later falls to $8 a share, the
investor would then buy 100 shares
back for $800, return the shares to
their original owner, and make a $200
profit (minus the transaction fees for
borrowing the shares). This practice
has the potential for losses as well. For
example, if the shares of Highflier in
fact went up to $25, the short-seller
would have to buy back all of the
shares at $2500, losing $1500.2
2
Since the lender often retains the right to
“recall” the security, as discussed below, the
short-seller may not be able to wait for the price
to go back down.
Business Review Q2 2009 11
Margin Requirements. The
short-seller cannot simply pocket
the $1000 he receives from selling
the stock. Rather, Federal Reserve
Regulation T requires the shortseller to deposit 150 percent of the
proceeds into his margin account. In
our example, this means the $1000
proceeds of the short-sale, together
with another $500 (in cash or
securities). This margin is designed to
protect the broker from losses due to
failure by the short-seller to return the
security.3 In addition to this purchase
margin, most exchanges also impose
a maintenance margin of 25 percent;
that is, at any point in time, the value
of the margin account must be at least
125 percent of the current value of the
securities that have been borrowed.
For example, if the stock price rises
to $13 per share, the short-seller
would need to add another $125 to
his margin account in order to meet
the maintenance margin requirement
and avoid having his position closed
out.4 These margin requirements are
costly, since the money cannot be used
for other purposes and the short-seller
often does not accrue any interest
on his margin account. (A valued
customer might receive some interest,
but typically it will be at below-market
rates.)
The Securities Lending Market.
Where are the borrowed shares
obtained? In the simplest case, the
brokerage houses may be able to lend
other customers’ shares, when those
customers have bought their stock on
margin.5 If the broker does not have
the particular security in its inventory,
however, it must turn to outside
sources. Institutional investors such
as mutual funds, pension funds, and
insurance companies often lend shares
in their portfolios to short-sellers.6
This is particularly attractive for them,
since they generally do not anticipate
needing to sell those shares. However,
they typically retain the right to
“recall” the shares at any time.7
Institutional investors such as mutual
funds and pension funds often lend shares
in their portfolios to short-sellers.
The borrowed shares do not come
free. The broker will deposit part of
the margin that the short-seller posted
as collateral with the lender. The
interest rate received on this collateral
is typically below market interest rates,
and this represents the opportunity
cost of borrowing the security. This
cost is borne by the short-seller
because it reduces the interest he
receives on the cash in his margin
account (if any). Moreover, if the cost
of borrowing shares is sufficiently high,
not only will the short-seller receive no
interest, but he may actually have to
pay a fee to borrow the securities.
5
Buying on margin means borrowing money
(typically from one’s broker) in order to buy
securities. The securities thus purchased remain
in the buyer’s margin account, since they serve
as collateral for the loan and so are available to
the broker for lending.
This is typically done through “custodian
banks,” which hold the institutional investors’
shares.
6
This is most likely to occur if the price of the
stock goes up, since, in that case, the shortseller would need to come up with additional
cash in order to close out his position.
3
Since he initially deposited $1500 in his
margin account, and the securities he has
borrowed are now worth $1300 (so the margin
requirement is 125 percent of this, or $1625).
4
12 Q2 2009 Business Review
Christopher Geczy, David Musto,
and Adam Reed document costs in the
securities lending market. They find
that if the security is not in particular
demand by short-sellers, the difference
between the market interest rate and
that paid on the collateral is small
(typically less than 20 basis points).
However, if the security is in high
demand, the cost of borrowing it may
be rather high; that is, the interest rate
received by the short-seller will be very
Pension funds and mutual funds are in
fact required to retain the right to recall the
securities, according to the provisions of the
Employee Retirement Income Security Act
(ERISA) and the Investment Company Act,
respectively.
7
low. In this case the stock is said to be
“on special.” Geczy, Musto, and Reed
find that, on average, about 7 percent
of stocks are on special at any one
time. For example, companies involved
in mergers often tend to be expensive
to short.8 In addition, new issues
(IPOs) are also not infrequently on
special. Furthermore, sometimes it may
be virtually impossible to borrow the
shares of a particular company – which
makes short-selling infeasible.9 This
inability to short-sell may occasionally
lead to a striking mispricing of these
stocks, as we discuss below.
Naked Short-Selling. According
to the Securities and Exchange
Commission’s (SEC) regulation SHO,
In particular, the acquiring company is often
on special. The reason is that a standard
“merger arbitrage” strategy — often practiced
by hedge funds — involves buying shares of
the target and shorting shares of the acquirer
(since in a successful merger the target’s shares
commonly rise, and the acquirer’s fall).
8
9
This may occur particularly for certain new
issues. One reason is that the underwriters
(the investment banks that helped issue the
stock) are not permitted to lend out the stock
for 30 days following the IPO. Also, many IPOs
involve the issue of a relatively modest amount
of shares.
www.philadelphiafed.org
a broker-dealer10 cannot accept a shortsale order unless he has “reasonable
grounds” for believing that the security
can be borrowed; this is known as
“locating” the stock.11 But what if the
short-seller has not actually located
the shares? Or does not actually
borrow those shares (because they are
expensive)? This is known as naked
short-selling. Such a strategy may be
attractive if the shares are difficult
(i.e., expensive) to borrow. If the
short-seller obtains and delivers the
shares by the settlement date (within
three days of the sale, in the U.S.), the
naked short-sale is essentially invisible.
A naked short-sale may become
apparent, however, if the short-seller
fails to deliver the stock in time, either
by design or due to circumstances
beyond his control. Failing to deliver
imposes two costs on the short-seller.
First, the seller does not receive the
sale proceeds (and so forgoes interest).
Second, if the buyer demands the
physical shares, the seller may be
“bought in” immediately.12 That is,
the security will be purchased on the
open market by the broker on behalf of
the buyer (typically at an unattractive
price).
Since naked short-selling can, in
principle, lead to the level of shortselling exceeding the actual number of
10
A broker-dealer is a company or other
organization that trades securities for its own
account or on behalf of its customers. Although
many broker-dealers are independent firms
solely involved in providing broker-dealer
services, others are business units or subsidiaries
of commercial banks, investment banks, or
investment companies.
shares outstanding, some executives of
troubled companies have charged that
it can also facilitate manipulation.13 As
discussed below, the SEC has sought
to restrict naked short-selling in recent
years.
On the other hand, in some cases
naked short-selling can in fact facilitate
Company. This pattern — the collapse
of a share-price bubble followed by attempts to prohibit short-selling — has
repeated itself many times. In another
example, England banned short-sales
in 1733, following the collapse of the
South Sea Bubble.16
In the United States, the
In the United States, the stock market
crash of 1929 led to public attacks on
short-sellers, a strident defense by the
New York Stock Exchange, many years of
congressional hearings, and new regulation.
market liquidity. Market makers14
in particular will often engage in a
modest amount of naked short-selling,
since they must stand ready to sell
shares even if there is a limited supply
of those shares. In recognition of their
role, market makers are exempt from
some of the requirements to locate a
lender before shorting a stock.
REGULATING SHORT-SALES
We have referred to short-sale
restrictions, but what form do these
regulations take in practice?
History of Short-Sale Regulation. Among the first countries to
restrict short-sales was Holland,15
which banned them in 1610, following
the collapse of shares in the East India
This criticism of short-selling was also made
following the crash of 1929 (see the book by
J. Edward Meeker). See also the discussion of
Owen Lamont’s paper, below.
13
stock market crash of 1929 led to
public attacks on short-sellers, a
strident defense by the New York
Stock Exchange,17 many years of
congressional hearings, and new
regulation. One example of this new
regulation was the Federal Reserve’s
power to set margin requirements.
Another important regulation
first adopted during that period
was the uptick rule, which restricted
short-selling to taking place only at an
“uptick,” that is, at a price above the
previous trade’s price.18 That is, shortselling was not permitted in a falling
market. The uptick rule was adopted
by the SEC in 1938 and remained in
force until 2007. It was a response to
allegations that bear raids contributed
to the 1929 crash. A bear raid is a
strategy in which a trader (or group
of traders) attempts to force down the
The “locate” rules were originally instituted
by the various exchanges. In 2004 the SEC
adopted Regulation SHO, which instituted a
uniform locate requirement, and as discussed
below, the SEC has recently tightened these
rules further.
14
A market maker is an individual or firm that
quotes prices for a security and stands ready
to buy and sell (modest amounts) for its own
account on a regular basis at those prices.
Market makers in equity options also sometimes
short-sell the underlying stock, to either hedge
or close out a position.
16
The law remained in force until 1820 but had
little effect on actual market practice.
See the paper by Richard Evans, Christopher
Geczy, David Musto, and Adam Reed for more
details on fails and buy-ins.
15
See the book by Meeker for further discussion
of the history of short-sale regulations up until
the 1930s.
18
11
12
www.philadelphiafed.org
17
In particular, Meeker (who was economist
to the New York Stock Exchange) explicitly
dedicated his 1932 book to the defense of shortselling.
More precisely, a short-sale was permitted
at the same price as the previous trade if that
previous trade itself represented an uptick.
Business Review Q2 2009 13
price of a stock, for example, to cover
a short position. This can be done by
spreading negative rumors about the
target, or alternatively, the traders
take on very large short positions,
with the large volume of selling itself
causing the price to fall. Allegations of
bear raids have also been made in the
current financial crisis.19 Even the SEC
cited the “market impact of rumors”
preceding the collapse of Bear Stearns
in enacting its short-sale restrictions in
2008.
Recent Restrictions on Naked
Short-Selling and Failures to Deliver.
In recent years, the SEC has enacted
rules to restrict naked short-selling
and failures to deliver. Regulation
SHO (enacted in 2004) instituted
a requirement for short-sellers —
other than market makers — to be
reasonably certain that they have
“located” a lender of the stock. In
2008, in response to the financial
crisis, these regulations were tightened
further. Currently, they (i) require
short-sellers in 19 financial stocks to
actually enter into an agreement to
borrow shares before short-selling,20 (ii)
explicitly prohibit market participants
from deceiving others regarding their
ability to borrow or deliver stock by
the settlement date,21 and (iii) require
all “fails” to be closed out on the first
trading day following the settlement
date.22
Other Recent Restrictions.
During the recent financial turmoil,
many countries have instituted
outright bans on short-selling stock.
In the U.S., on September 19, 2008,
the SEC temporarily prohibited
short-selling for nearly 1000 stocks
whose business related in some way
to the financial sector.23 The ban
was unpopular and was allowed to
expire after less than a month. Many
other countries also banned shortsales of at least some stocks around
the same time.24 The SEC also
Another outcome
of the current crisis
has been a decline
in the amount of
securities available
for borrowing.
recently instituted a requirement that
investment managers (including hedge
funds) must report their short-sales.25
Another outcome of the current
crisis has been a decline in the amount
of securities available for borrowing.
Some institutional investors have
announced that they have curtailed
securities lending programs, either
because of bad publicity (from
accusations that short-sellers were
manipulating financial stocks) or
because of losses realized from their
lending activities.26
23
Release number 34-58592.
For example, the UK, Australia, Korea, and
Taiwan. Most of the countries that imposed
bans eliminated or relaxed them within several
months, although Australia’s ban was extended
at least through March 2009.
24
See, for example, the article “Bringing Down
Bear Stearns” in the August 2008 issue of
Vanity Fair.
19
An “emergency order” promulgated in release
number 34-58166 (July 15, 2008).
THE POSITIVE ROLE OF
SHORT-SALES
Despite the public appetite for
short-sale regulations, economists
have shown that short-sales play an
important role in financial markets
and that restricting them may have
negative effects.
Short-Sale Constraints and
Overvaluation. One of the first to
argue that restricting short-sales can
lead to overvaluation of securities
was Edward M. Miller. In particular,
Miller showed that if short-selling is
restricted and investors have different
opinions about the underlying value of
the security, its price does not reflect
the beliefs of all potential investors but
only the opinion of the most optimistic
ones. This, he argued, will tend to
bias the price of the stock upward.
The reason is that those investors who
value the stock less are limited in their
ability to act on their beliefs when
short-selling is not possible.
Aside from restrictions on shortselling, another key assumption that
Miller makes is that investors have
different beliefs: Some are innately
optimistic about the firm, while
others are pessimistic. Note that this
is not just a matter of the optimists
having different information about
the firm than the pessimists. There
is some empirical support for this
connection between differences in
opinions and overvaluation. A study
by Karl Diether, Christopher Malloy,
and Anna Scherbina finds that stocks
for which there is wide dispersion in
analysts’ forecasts subsequently tend
to perform badly, perhaps reflecting
overpricing at the time of the forecasts.
20
21
SEC Rule 10b-21.
SEC Temporary Rule 204T, effective from
September 18, 2008 – July 31, 2009. Prior to
this, broker-dealers had 13 days in which to
close out fails.
22
14 Q2 2009 Business Review
On September 18, 2008, the SEC required
institutional investment managers with assets
under management of at least $100 million
(including hedge funds) to report their shortsales weekly; this requirement is set to expire
on August 1, 2009. Meeker notes that a similar
reporting requirement was instituted by the
NYSE during the First World War.
25
As reported in the Wall Street Journal on
October 20, 2008, the losses were incurred
because the banks that were managing the
programs invested the cash collateral in
securities backed by subprime mortgages.
26
www.philadelphiafed.org
In another study, Michael
Harrison and David Kreps argue that
the overvaluation may be even more
dramatic than that suggested by Miller.
They show that restricting short-sales
will lead the price of the security to
exceed the valuation that even the
most optimistic investor attaches to
it today. The reason is that investors
anticipate that, at some point in the
future, someone else may be even more
optimistic about the stock than they
are. This is even true for the investor
who is most optimistic about the
stock’s fundamental value today.27 He
knows that he may be able to sell the
stock for more than its fundamental
value at some point in the future, and
thus he will be willing to pay a little
bit more than this value today. As for
Miller, restrictions on short-sales are
necessary for this to occur because
otherwise those investors who believe
that the asset is currently priced above
its fundamental value would sell it
short. Like Miller’s model, Harrison
and Kreps’s model also assumes that
investors disagree about the value of
the asset.
But why would investors disagree
about the value of the security?
Neither Miller nor Harrison and
Kreps specify the reasons for this.
However, José Scheinkman and Wei
Xiong suggest that one reason may be
investor overconfidence. In particular,
if investors put more weight on their
own information than on others’, they
may form different opinions about
the value of the asset, even when
evaluating the same information.
Scheinkman and Xiong then show that
this can lead to overpricing.
Owen Lamont and Richard
Thaler present several cases of
The fundamental value of a security may be
defined as the present value of the security’s
future cash flows.
27
www.philadelphiafed.org
overvaluation facilitated by difficulty
in short-selling. One very prominent
example is that of Palm and 3Com.
On March 1, 2000, 3Com sold a small
(5 percent) stake in its subsidiary
Palm through an initial public offering
(IPO) while retaining the rest (this
is an example of an equity carve-out).
The company also announced that it
would give the remaining Palm shares
to 3Com shareholders by the end of
the year in a spin-off; in particular,
each 3Com shareholder would receive
of [95×1.5]-82 = $60 today, with a
further possible profit from the residual
3Com value after the remaining Palm
shares were spun off.
Arbitragers were not able to
exploit this mispricing because, as a
practical matter, it was very difficult to
borrow Palm shares. Thus, the frenzy
for tech stocks allowed this overpricing
of Palm shares to persist for months.29
However, Geczy, Musto, and Reed
argue that Palm is an unusual case.
They show that most tech stocks
If investors put more weight on their own
information than on others’, they may form
different opinions about the value of the asset,
even when evaluating the same information.
approximately 1.5 shares of Palm. This
transaction is illustrated in the figure
on page 16.
How did the market price this
transaction? On the day of the IPO,
Palm closed at $95 a share, while
3Com closed below $82. That is, even
though each 3Com shareholder had
the right to receive 1.5 shares of Palm,
3Com shares traded well below Palm’s.
This meant that the implied value
of 3Com, less the Palm shares that
were to be distributed, was actually
negative!28 Clearly, Palm’s shares were
vastly overpriced relative to 3Com’s.
How could one exploit this
overvaluation? If short-selling Palm
were possible, there would be a clear
profit opportunity: to buy one share
of 3Com and short 1.5 shares of Palm,
and use the Palm shares received (by
the end of the year) to close out the
short position. This would give a profit
28
As Lamont and Thaler point out, this is
particularly surprising given that 3Com had
ample holdings of cash and profitable ongoing
operations.
were not that difficult to short in
practice, and so this cannot provide an
explanation for the broad-based techstock bubble of the late 1990s.
In another paper, Owen Lamont
examines a sample of 300 firms that
tried to fight short-selling, for example,
by publicly attacking short-sellers or by
taking legal action. He shows that their
stock prices tended to subsequently
perform worse than the market, which
also suggests overvaluation may be
facilitated by impediments to shortselling.
Short-Sale Constraints and the
Revelation of Information. A key
role of prices in financial markets is to
aggregate dispersed information.30 For
example, if an investor has negative
information about a company’s
prospects, he may short-sell that stock
29
Lamont and Thaler show that this overpricing
did diminish over time and in most cases was
eliminated by the time the actual date of the
distribution was announced.
30
An early exposition of this idea is featured in
Friedrich Hayek’s critique of socialism.
Business Review Q2 2009 15
FIGURE
The Palm Equity Carve-Out
Company before carve-out (February 28, 2000)
3Com without Palm
Palm
Shareholders implicitly own 100% of Palm through their 3Com shares.
Company following carve-out (March 1, 2000)
3Com without Palm
5% of Palm sold
to market for cash
in IPO
Shareholders now own 100% of
3Com (without Palm) and 95% of
Palm implicitly through their 3Com
shares
5%
of
Palm
95% of
Palm
Stock Market
Company after spin-off (Before year-end)
3Com after spin-off
Each 3Com
shareholder receives
1.5 Palm shares
Palm
3Com Shareholders
16 Q2 2009 Business Review
if there are no restrictions on shortselling. In order to clear the market,
the stock price must fall, and this
will alert other investors to the fact
that the company may be troubled.
As Douglas Diamond and Robert
Verrecchia demonstrate, this role
may be compromised by short-sale
restrictions.
Diamond and Verrecchia show
that even if short-sales are restricted,
prices will not be biased upward;
that is, shares will not be overvalued
(unlike in the studies by Miller and
Harrison and Kreps). The reason is
that in Diamond and Verrecchia’s
model, investors differ only in the
information they possess. They are
all equally innately optimistic (or
pessimistic) about the company’s
prospects and — had they all had
access to the same information
— would all come to the same
conclusion about the firm’s value.
While constraints on short-selling do
affect the ability of those investors
who possess negative information to
trade on that information, market
participants understand this. So when
the market observes thin trading, it
will infer that there is a reasonable
chance that negative information
exists concerning this stock; this will
lead to a reduction in its price.
Nevertheless, Diamond and
Verrecchia point out that since lack
of trade is a less informative signal of
low firm quality than actual selling
pressure, short-sale constraints will
have a negative effect on the speed of
information transmission: They slow
the rate at which information becomes
public. Although Diamond and
Verrecchia do not model this, this slow
transmission of information could lead
to inefficient investments by allowing
bad firms to survive for longer than
they should.
In a recent paper, Arturo Bris,
William Goetzmann, and Ning Zhu
www.philadelphiafed.org
compare stock market regulation
around the world and find that prices
do indeed seem to incorporate negative
information more slowly in those
countries where short-sales are either
not allowed or not practiced, providing
empirical support for Diamond and
Verrecchia’s model.
WHEN DOES RESTRICTING
SHORT-SALES MAKE SENSE?
The models presented above
highlight the important role played by
short-sales. Nevertheless, we do see
cases in which governments restrict
them. What might be the rationale for
doing so?
A paper by Itay Goldstein and
Alexander Guembel provides one
possible justification for short-sale
restrictions.31 Their work can be
viewed as a model of bear raids, and
it also provides an explanation of why
restricting short-sales will prevent
such raids. They argue that restricting
short-sales can prevent manipulation of
stock prices by investors. The reason is
that, by selling large amounts of stock,
a short-seller can force the price of the
firm down, because other investors
(who are not fully informed about the
firm) may interpret this selling pressure
as reflecting negative information
about the firm’s prospects. Once the
price has fallen, the short-seller can
See the article by Yaron Leitner for further
discussion of Goldstein and Guembel’s model.
31
www.philadelphiafed.org
close out his position at a profit; thus
to the extent that this strategy is selffulfilling, it will be profitable for the
short-seller.
The particular case they study
is one in which the low stock price
may convince the firm’s management
that its prospects are poorer than
they previously believed, so that the
firm forgoes profitable investment
opportunities, thereby lowering its
value. However, they also discuss
another interpretation of their model,
one in which the low stock price
affects the firm’s access to other
sources of financing (for example,
investors may be reluctant to extend
the firm credit or may demand more
collateral on outstanding derivative
contracts) and may thus force the firm
into bankruptcy. This interpretation
formalizes the view — expressed in the
popular press — that bear raids may
have contributed to the recent collapse
of some financial institutions (such as
Bear Stearns).
Intuitively, this provides a
rationale for restricting short-sales.
In addition, Goldstein and Guembel
point out that, rather than banning
short-sales altogether, it may be better
to make them more costly in some
manner. The reason is that in their
model short-selling is more profitable
for those who truly have negative
information about a firm than for
those attempting to manipulate its
stock price. Thus, the latter group
may be discouraged when short-selling
becomes more expensive, without
undermining the market’s role in
aggregating information about the
firm. This is not discussed in their
article, but many current regulations
have this effect, such as the less
favorable tax treatment of short-sale
profits (they are considered income
rather than capital gains), and
restrictions on naked short-selling
(since, as we have seen, borrowing
stock can be costly).
CONCLUSION
Short-selling plays a valuable
economic role in preventing
overvaluation of securities and
facilitating the incorporation of
negative information about a company
into its stock price. This role is
supported by empirical studies.
But under certain conditions,
short-selling can also be used to
manipulate the market. By selling large
amounts of stock, a short-seller may be
able to convince other investors and
lenders that the company’s prospects
are poor, thereby shutting off its access
to outside financing and forcing it
into bankruptcy. This also provides an
argument for regulations that make
short-sales more costly or difficult,
since such costs make manipulation
more difficult, while still allowing
those with truly negative information
about the company to profit. Further
work is also needed on evaluating
the tradeoff between the positive and
negative effects of these regulations,
as well as on better understanding the
securities lending market. BR
Business Review Q2 2009 17
REFERENCES
Bris, Arturo, William N. Goetzmann, and
Ning Zhu.“Efficiency and the Bear: Short
Sales and Markets Around the World,”
Journal of Finance, 62:3 (June 2007), pp.
1029-79.
Geczy, Christopher C., David K. Musto,
and Adam V. Reed. “Stocks Are Special
Too: An Analysis of the Equity Lending
Market,” Journal of Financial Economics, 66
(2002), pp. 241-69.
Diamond, Douglas W., and Robert E.
Verrecchia. “Constraints on ShortSelling and Asset Price Adjustment to
Private Information,” Journal of Financial
Economics, 18 (1987), pp. 277-311.
Goldstein, Itay, and Alexander Guembel.
“Manipulation and the Allocational Role
of Prices,” Review of Economic Studies, 75
(2008), pp, 133-64.
Diether, Karl B., Christopher J. Malloy,
and Anna Scherbina. “Differences of
Opinion and the Cross Section of Stock
Returns,” Journal of Finance, 57:5, (October
2002), pp. 2113-41.
Evans, Richard B., Christopher C. Geczy,
David K. Musto, and Adam V. Reed.
“Failure Is An Option: Impediments to
Short Selling and Options Prices,” Review
of Financial Studies (forthcoming).
18 Q2 2009 Business Review
Hayek, Friedrich A. “The Use of
Knowledge in Society,” American Economic
Review, 35 (1945), pp. 519-30.
Harrison, J. Michael, and David M. Kreps.
“Speculative Investor Behavior in a Stock
Market with Heterogeneous Expectations,”
Quarterly Journal of Economics, 92:2 (May
1978), pp. 323-36.
Lamont, Owen A. “Go Down Fighting:
Short Sellers vs. Firms,” NBER Working
Paper 10659 (August 2004)
Lamont, Owen A., and Richard H. Thaler.
“Can the Market Add and Subtract?
Mispricing in Tech Stock Carve-Outs,”
Journal of Political Economy 111:2 (2003),
pp. 227-68.
Leitner, Yaron. “Stock Prices and Business
Investment,” Federal Reserve Bank of
Philadelphia Business Review (Fourth
Quarter 2007).
Miller, Edward M. “Risk, Uncertainty, and
Divergence of Opinion,” Journal of Finance,
32:4 (September 1977), pp. 1151-68.
Scheinkman, José A., and Wei Xiong.
“Overconfidence and Speculative
Bubbles,” Journal of Political Economy, 111:6
(2003), pp. 1183-1219.
www.philadelphiafed.org
Residential Housing
And Personal Bankruptcy*
B
by Wenli Li
ankruptcy filings are on the rise, and millions
of households have either lost their homes to
foreclosure or are on the verge of losing them.
One subject of debate amid this rising number
of bankruptcies is how personal bankruptcy laws deal with
residential housing. This subject centers on two main
issues: First, how do personal bankruptcy laws affect the
availability of mortgages and the terms on which borrowers
obtain mortgages? Second, how do personal bankruptcy
filings affect the outcome of mortgage foreclosures? In this
article, Wenli Li discusses these questions and examines
the economic literature to shed some light on the
legislative and policy debates that are likely to recur after
the current crisis is over.
The subprime mortgage crisis
that started in late 2006 has caused a
sharp correction in the U.S. housing
market. By the second quarter of 2008,
real housing prices had dropped for
four consecutive quarters, year over
year, according to the Federal Housing
Wenli Li is an
economic advisor
and economist in
the Philadelphia
Fed’s Research
Department.
This article is
available free of
charge at www.
philadelphiafed.
org/research-and-data/publications/.
www.philadelphiafed.org
Finance Agency house price index.1
Meanwhile, lenders have tightened
credit conditions by either charging
higher rates or denying credit to
those who would have gotten credit
before the crisis. As a result, many
1
The Economic Recovery Act of 2008, which
was enacted on July 30, 2008, established the
Federal Housing Finance Agency (FHFA)
by combining the Office of Federal Housing
Enterprise Oversight (OFHEO) and the Federal
Housing Finance Board (FHFB). The legislation
calls for OFHEO and the FHFB to be abolished
one year from the date of enactment.
*The views expressed here are those of the
author and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
households, especially those whose
adjustable mortgage rates are scheduled
to increase, are struggling to pay their
bills. Bankruptcy filing rates have gone
up – following the sharp rise and even
sharper decline that accompanied the
2005 changes in the bankruptcy law –
and millions of households have either
lost their homes to foreclosure or are
on the verge of losing them (Figure 1).
One subject that has received
some attention, particularly from
policymakers, as a result of the current
crisis is how personal bankruptcy
laws deal with residential housing.
Although the Housing and Economic
Recovery Act of 2008 does not contain
direct changes to the current personal
bankruptcy laws, proposals to reform
bankruptcy laws were a central part
of the debate. For instance, the
Helping Families Save Their Homes
in Bankruptcy Act, introduced in
October 2007 but not included in
the final law, amends the federal
bankruptcy law to permit a bankruptcy
plan to modify the mortgages of
certain debtors and to provide for
payment of such a loan at a fixed
annual interest rate over a 30-year
period.
There are two main issues
concerning residential housing and
personal bankruptcy law. One is how
personal bankruptcy laws affect the
availability of mortgages and the
terms at which borrowers obtain their
mortgages. The other is how personal
bankruptcy filing affects the outcome
of mortgage foreclosure. Economists
have studied both issues, though the
first issue has received somewhat more
attention in the economic literature.
Although the literature hasn’t yet
Business Review Q2 2009 19
FIGURE 1
Bankruptcy and Foreclosure Starts
Units
Rate
1.20
700000
bankruptcy filings (left axis)
600000
foreclosure starts as % of total
outstanding loans (right axis)
1.00
500000
0.80
400000
0.60
300000
0.40
200000
0.20
100000
0.00
0
20001
20012
20023
20034
achieved complete agreement on either
question, it does shed some light on
the legislative and policy debates that
are likely to come up again after the
dust settles somewhat on the current
crisis.
EFFECT OF PERSONAL
BANKRUPTCY LAWS ON
AVAILABILITY AND PRICE OF
MORTGAGES
There are two broad categories
of household debt. Secured
(collateralized) debt allows creditors
to reclaim the collateral if the debtor
defaults on the loan. The main
examples of secured debt are mortgages
and automobile loans. Unsecured
debt – mainly credit card debt and
installment credit – has no collateral
that creditors can seize. Foreclosure
laws govern the default on secured
mortgage loans and are unique to each
state. (See The Foreclosure Process for a
20 Q2 2009 Business Review
20051
20062
20073
short description of the main features
of state foreclosure laws.) However,
consumers can forestall foreclosure by
electing bankruptcy, which is governed
by the federal bankruptcy code.
Personal Bankruptcy Laws.
There are two separate bankruptcy
procedures: Chapters 7 and 13. The
two chapters differ in that debtors
who file under Chapter 7 are obliged
to repay debt out of their assets, to
the extent that their assets exceed
predetermined exemption levels.
Debtors who file under Chapter 13
are obliged to repay debt out of their
income over a period of time after
deducting reasonable living expenses.
Personal bankruptcy is governed
by federal law, and there are separate
federal exemption levels for the
household’s homestead (home
equity in residential housing) and
nonhomestead or other personal
property (jewelry, furniture, savings,
and so forth). States also set their own
exemptions. While some states allow
filers to opt out of the state exemptions
for federal ones, other states disallow
the use of federal exemptions. As
mentioned above, Chapter 7 filers
surrender all of their assets above the
exemption levels in exchange for the
discharge of their remaining unsecured
debt not covered by the asset seized.
Exemptions also have significance in
Chapter 13 through the “best interests
of the creditors” test, which states that
creditors are entitled to receive at least
as much in Chapter 13 as they would
have received in Chapter 7. Thus, in a
state with high exemptions, creditors
should also expect lower repayments in
Chapter 13.
Bankruptcy laws reduce (“strip
down”) debts secured by cars to the
fair market value of the car at the time
of the bankruptcy filing, and debts that
exceed the fair market value become
unsecured. But they do not allow for
modification of mortgage loans secured
solely by the borrower’s principal
residence. Nevertheless, Chapter 7
bankruptcy voids deficiency payments2
in the same way that it voids
unsecured debt whose value exceeds
total nonexempt assets. Homeowners
who file for bankruptcy under Chapter
13 are allowed to repay arrears on their
mortgages over a three- or five-year
period. Furthermore, bankruptcy
filing puts an automatic stop to
lenders’ collection actions, including
foreclosure on the debtor’s house. The
stay can be lifted only by the court or
after the bankruptcy case is dismissed
or terminated.
The Determination of Mortgage
Borrowing and Interest Rates. Like
other goods and services, mortgage
2
A deficiency judgment is a judgment lien
against a borrower whose foreclosure sale
did not produce sufficient funds to pay the
mortgage in full.
www.philadelphiafed.org
The Foreclosure Process
W
hen a borrower defaults on a home
mortgage, the lender may attempt to
recover its losses by repossessing and
selling the property. This process is
governed by three types of state property
laws: the judicial foreclosure process,
statutory rights of redemption, and deficiency judgments.
These laws vary widely across states (see the Table on
pages 22-23 for a summary of the differences).
Under state property laws, two types of foreclosure
are widely used. The more important type, foreclosure
by judicial sale, is available in every state and required
in many. It involves the sale of the mortgaged property
under the supervision of a court, with the proceeds going
first to satisfy the mortgage holder, then to satisfy other
lien holders, and finally to the borrower. The second
type is foreclosure by power of sale. Here, the mortgage
holder is permitted to sell the property without court
supervision. Again, proceeds from the sale go first to the
mortgage holder, then to other lien holders, and finally to
the borrower.a If the proceeds do not pay off the existing
mortgage on the property plus costs, most states allow
the lender to collect a deficiency judgment against the
borrower’s other assets equal to the lender’s foreclosure
losses. Deficiency judgments are thus unsecured debt
that remains after repossession or sale and has the same
priority as other unsecured debt.
After the foreclosure sale is complete, the
homeowner can still regain the property if his or her
state grants a statutory right of redemption. Up to a year
after the sale, depending on the state, homeowners
can redeem their property for the foreclosure sale price
plus foreclosure expenses. The existence of redemption
rights has resulted in investors’ reluctance to purchase
a foreclosed property during the redemption period and
a large percentage of properties become lender-owned
instead of being sold to a third party immediately after
the foreclosure.b
Foreclosure is a costly process. A typical foreclosure
process can last anywhere from a few months to a year,
depending on the state. The total costs of the foreclosure
process consist of accrued interest, advances, cost of
the lawsuit, attorney’s fees, publication fees, and the
fee of the sheriff or selling officer from the filing of the
complaint through the foreclosure sale.c Everybody loses
in foreclosure. Lenders are estimated to lose almost 30
percent of their investment in a foreclosure,d and debtors,
at the least, lose their homes, an outcome that disrupts
families and communities.
Where it is available, foreclosure by power of sale is generally faster than foreclosure by judicial sale. From the borrowers’ perspective, the
requirements of a judicial sale provide several months of free rent and protection against lenders’ imposing excessive fees on borrowers.
a
One practical solution is to buy the redemption rights from the owner, either shortly before or shortly after purchasing the property at auction at a
negotiated price. Typically, redemption rights are sold for amounts ranging from a few hundred to a few thousand dollars. In most cases, an owner
facing foreclosure who sees no realistic way to either avoid the foreclosure or recover the property afterwards is willing to sell rights he never expects
to use.
b
c
Researchers have found that the costs amounted to 19.1 percent of the final judgment amount – the amount mortgage borrowers owed to lenders —
in the case of foreclosure sales in 1993 and 18.43 percent of the final judgment in the case of foreclosure sales in 1994. (See Debra Stark’s article.)
GMAC-RFC (Residential Funding Corporation), America’s largest private issuer of mortgage-backed securities and a leading warehouse lender,
estimates that it loses over $50,000 per foreclosed home. This number, together with the average loan size of $201,000 at origination in 2004, yields
a loss rate of over 25 percent. A warehouse loan is a line of credit that a financial institution extends to a loan originator to fund a mortgage used to
purchase property. (See page 2 of the article by Desiree Hatcher, which cites a GMAC-RFC estimate.)
d
loans and interest rates are determined
by mortgage supply and demand
(Figures 2 and 3 on page 24). Lines
labeled L represent the supply of
mortgages. A particular supply curve
shows the amount of mortgage loans
www.philadelphiafed.org
(in dollars) that lenders want to
provide at each interest rate. Holding
everything else the same, including
estimated default risk, the higher
the interest rate lenders can charge,
the more willing they are to provide
mortgage loans. So, the supply curve is
upward sloping. Anything that affects
lenders’ ability to make a profit, such
as the probability that borrowers will
default on their mortgages and the
lenders’ losses when they do, will affect
Business Review Q2 2009 21
TABLE
State Foreclosure Laws — Comparison
State
Judicial Requirement
Deficiency Judgment
Effective
Judicial/Nonjudicial
Actual
Law
Alabama
NJ
B
61.5
365
Allowed
Alaska
NJ
B
105
365
Judicial foreclosure only
Arizona
NJ
B
90
105
Varies
Arkansas
E
B
70
365
Nonjudicial foreclosure only
California
NJ
B
117
365
Yes, judicial foreclosure only
Colorado
NJ
B
91
75
Yes
Connecticut
J
J
62
Court Decides
Yes
Delaware
J
J
190
0
No
NJ
NJ
47
0
Yes
Florida
J
J
135
0
Yes
Georgia
NJ
B
37
0
Yes
Hawaii
E
B
220
0
Yes
Idaho
NJ
B
150
365
Yes
Illinois
J
J
300
90
Indiana
J
J
261
0
Yes
Iowa
J
B
160
20
No
Kansas
J
J
130
365
Yes
Kentucky
J
J
147
365
Yes, with restrictions
Louisiana
J
J
180
0
Yes
Maine
J
J
240
90
Yes
Maryland
J
J
46
Court Decides
Yes
Massachusetts
J
J
75
0
No
Michigan
NJ
NJ
60
197.5
Varies, case by case
Minnesota
NJ
B
95
1825
Yes
Mississippi
NJ
B
90
0
No
Missouri
NJ
B
60
365
No
Montana
NJ
B
150
0
Judicial foreclosure only
Nebraska
J
J
142
0
No
Nevada
NJ
B
116
0
Yes
New Hampshire
NJ
NJ
59
0
Yes
J
J
270
10
Dist of Columbia
New Jersey
22 Q2 2009 Business Review
Process Period
(Days)
Statutory
Redemption
Redemption Period
(Days)
Varies
Yes, restricted
www.philadelphiafed.org
TABLE ... continued
State Foreclosure Laws — Comparison
State
Judicial Requirement
Deficiency Judgment
Effective
Judicial/Nonjudicial
Actual
Law
New Mexico
J
J
180
270
Yes
New York
J
J
445
0
Yes
North Carolina
NJ
B
110
0
Varies case by case
North Dakota
J
J
150
180-365
Yes
Ohio
J
J
217*
0
Yes
Oklahoma
J
B
186
0
Yes, with time limitation
NJ
B
150
180
Pennsylvania
J
J
270
0
Yes
Rhode Island
NJ
B
62
0
Yes
South Carolina
J
J
150
0
Yes
South Dakota
J
B
150
197.5
Tennessee
NJ
NJ
42.5
730
Yes
Texas
NJ
B
27
0
Yes
Utah
NJ
NJ
142
Court Decides
Yes
Vermont
J
J
95
272.5
Yes
Virginia
NJ
B
45
0
Yes
Washington
NJ
B
135
0
Yes, only in judicial foreclosure
West Virginia
NJ
NJ
75
0
No
Wisconsin
J
B
290
365
Wyoming
NJ
B
60
227.5
Oregon
Process Period
(Days)
Statutory
Redemption
Redemption Period
(Days)
Yes, only with judicial foreclosure
Varies case by case
Yes, unless waived
Yes
* Before confirmation of foreclosure sale.
Note:
J: judicial foreclosure; NJ: nonjudicial foreclosure; B: both judicial and nonjudicial foreclosure are allowed; Actual: what is required by law;
Effective: what is carried out in practice. In general, a nonjudicial foreclosure will proceed in states where a power-of-sale clause can be
written into the contract. There are a few states (MI, IA, SD, and OK), however, where a judicial foreclosure is pursued, i.e., effective, even
though it is not required by law.
Source: http://www.foreclosures.com/www/pages/state_laws.asp and http://www.realtytrac.com/foreclosure-laws/foreclosure-laws.asp.
Compiled by Kelly D. Edmiston and Dan Reichgott.
the supply of mortgages. Graphically,
this is represented as a shift in the
supply curve, say, from L1 to L2 (if the
factor makes mortgage lending more
profitable).
By contrast, mortgage demand,
www.philadelphiafed.org
as depicted by lines labeled D, moves
in the opposite direction to interest
rates. A particular demand curve
shows the amount of mortgage loans
(in dollars) that households wish to
borrow at each interest rate holding
everything else constant, including the
default rate. The higher the interest
rate, the smaller will be households’
demand for mortgages. Thus, the
demand curve is downward sloping.
Anything (other than the interest rate)
Business Review Q2 2009 23
FIGURE 2
Mortgage Demand and Supply
(Mortgage Exemptions)
D2
L3
D1
C
L1
Loan supply
L2
Mortgage
interest
rate
B
A
Loan demand
Loan amount
FIGURE 3
Mortgage Demand and Supply
(Automatic Stay)
D2
L2
D1
B
L1
Loan supply
Mortgage
interest
rate
A
Loan demand
Loan amount
24 Q2 2009 Business Review
that affects households’ incentives to
borrow will affect the position of the
demand curve. Graphically, a factor
that makes mortgage borrowing more
or less attractive is represented as a
shift in the demand curve, say, from D1
to D2 (if the factor makes taking out a
mortgage more attractive).
The final market interest rate and
mortgage loan amount, or the market
equilibrium rate and loan amount,
are determined by the intersection
of the demand and supply curves.
Economists have identified several
channels through which the provisions
of personal bankruptcy laws affect
mortgage demand and supply.
Debt Discharge and Bankruptcy
Exemptions. The first channel comes
from partial or full discharge of
unsecured debt and car loans under
personal bankruptcy. When debtors
are in financial distress, they can file
for bankruptcy, obtain discharge of
their nonmortgage debts, and use
the funds that would otherwise go to
nonmortgage lenders to repay their
mortgages and thereby keep their
homes, at least for a time. Figure
2 depicts how debt discharge and
bankruptcy exemptions affect mortgage
loan amounts and mortgage interest
rates.
The more generous the homestead
and nonhomestead exemptions, the
more funds borrowers are likely to have
after filing for bankruptcy. In addition, higher homestead exemptions
directly protect debtors’ home equity
and, consequently, reduce borrowers’
incentive to default on mortgage loans.
These positive effects of bankruptcy
debt discharge on mortgage payment
are termed “wealth effects,” since they
leave borrowers with more wealth or
funds that can be used to make their
mortgage payments, which subsequently increase lenders’ profits for a given
mortgage demand. The supply curve
will shift out because of this effect.
www.philadelphiafed.org
But exemptions also have a
counteracting effect on supply. To
the extent that higher exemptions
increase households’ incentives to file
for bankruptcy (and perhaps default
on their mortgage) or increase lenders’
losses in the event of a mortgage
default, the supply curve will shift
inward. The total effect of exemptions
on the supply of loans depends on the
relative strength of these two effects:
The supply curve will shift out from
L1 to L2 if the first effect dominates,
and it will shift in from L1 to L3 if the
second force dominates.
Generous bankruptcy exemptions
affect mortgage borrowers’ loan
demand as well. In particular, if
borrowers are better sheltered by
bankruptcy laws in the event of
financial distress, they will be more
likely to demand larger mortgages. As
a result, mortgage demand will shift
out (for example, from D1 to D2 in
Figure 2).
To see the net effects of
exemptions, consider a state that
increases its exemption level. The new
equilibrium loan amount and interest
rate are determined by the new loan
supply and demand curves. If supply
shifts out, say, from L1 to L2, the
equilibrium loan amount will definitely
be higher (see point B). Whether the
interest rate will be higher depends on
whether demand increases more than
supply. (As drawn, the interest rate is
higher.) If the loan supply curve shifts
inward, for example, from L1 to L3, the
interest rate will certainly be higher,
but it is unclear whether equilibrium
loan supply will be higher or lower
(see point C). (As drawn, the dollar
amounts of loan supply are smaller
than at point B.)
Automatic Stay. The second
channel concerns the automatic
stay provision in bankruptcy law.
A bankruptcy filing imposes an
automatic stay on all collection efforts,
www.philadelphiafed.org
including foreclosure sales. The
stay can be lifted only by the court.
In other words, foreclosure cannot
occur without the court’s approval.
This generates substantial costs for
lenders in dealing with borrowers
who are incapable of maintaining
their mortgage payments despite their
A bankruptcy filing
imposes an automatic
stay on all collection
efforts, including
foreclosure sales.
The stay can be lifted
only by the court.
bankruptcy filing. The longer these
households get to stay in the house,
the more likely it is that the house may
be damaged, since these households
no longer have the incentive to do
regular maintenance, since it’s likely
they will lose the house. In addition,
if foreclosure turns out to be the final
outcome, the lender loses the profits
from having sold the house earlier.
Both of these effects reduce lenders’
profits and thus reduce loan supply. In
Figure 3, this corresponds to an inward
shift of the supply curve from L1 to L2.
By contrast, the automatic stay
on collection efforts will increase
borrowers’ demand for mortgages,
because they will be able to stay in
their homes for some period in the
event of financial distress. As the
new demand curve shifts out, say,
from D1 to D2, the new equilibrium
rate and loan amount will be at point
B. The new interest rate will be
higher; whether the loan amount will
be higher depends on whether the
increase in loan demand more than
offsets the decline in supply.
What Economists Have Found.
Taken together, whether bankruptcy
requirements make the provision of
equilibrium credit more extensive
or more difficult depends on the net
effect of the forces mentioned above.
In their 1999 paper, Jeremy
Berkowitz and Richard Hynes examine
Home Mortgage Disclosure Act
((HMDA) data3 and find significant
wealth effects associated with higher
homestead exemptions. In particular,
they find that higher homestead
exemptions have tended to reduce
mortgage rates and the probability
of being denied a mortgage. In other
words, higher exemptions shift the
supply curve out. Personal exemptions,
on the other hand, do not have a
statistically significant impact.
By contrast, Emily Lin and
Michelle White argue that Berkowitz
and Hynes’s empirical results are
biased because they estimate a
model that takes into account only
the household’s decision to default
on its mortgage. Instead, Lin and
White argue that an empirical model
should include both the household’s
decision to file for bankruptcy, and its
decision as to whether to default on its
mortgage. Examining the same HMDA
data as Berkowitz and Hynes, they
find a positive relationship between the
homestead exemption levels and the
probability of borrowers being denied
both mortgage and home improvement
loans after taking into consideration
borrowers’ incentive to file for
bankruptcy. The relationship between
personal property exemptions and the
probability of being denied either loan,
however, is insignificant, as found
by Berkowitz and Hynes. Explaining
these results, Lin and White argue
that borrowers’ increased incentives
to default on mortgages because of
3
http://www.ffiec.gov/hmda/
Business Review Q2 2009 25
more generous bankruptcy provisions
and the provision of an automatic stay
are much more important than the
“wealth effects.” Although lenders are
entitled to collect additional interest to
compensate for the delay, the available
assets may not be sufficient to pay
this interest, nor will these additional
assets necessarily compensate lenders
for all the associated costs.
Several other studies find
supporting evidence for Emily Lin
and Michelle White’s argument. For
example, using the Panel Study of
Income Dynamics (PSID), a survey
that provides detailed financial and
income information about households,4
Scott Fay, Erik Hurst, and Michelle
White find that higher homestead
and personal bankruptcy exemptions
increase the likelihood that borrowers
will file for bankruptcy. Numerous
studies confirm that bankruptcy
lengthens the foreclosure process and
thus incurs substantially more cost to
lenders.5
Furthermore, in a separate but
related paper, Reint Gropp, John
Scholz, and Michelle White show
that more generous bankruptcy laws
disproportionately affect low-asset
households. In particular, using data
from the Survey of Consumer Finances
(SCF),6 they find that generous state
bankruptcy exemptions increase the
amount of credit held by high-asset
households and reduce the availability
and amount of credit to low-asset
households, taking account of other
observable characteristics that might
differ across households. They also
4
find that interest rates on car loans
are higher for low-asset households in
high-exemption states. In other words,
bankruptcy redistributes credit toward
high-asset borrowers.7
In summary, although the jury is
still out, the weight of the evidence
is that more generous bankruptcy
laws tend to restrict the availability of
credit.
EFFECT OF PERSONAL
BANKRUPTCY LAWS ON
HOMEOWNERSHIP OUTCOME
Another aspect of the issue
concerning personal bankruptcy
laws and residential housing is
whether personal bankruptcy laws
A bankruptcy filing helps debtors save their
homes (at least temporarily) by stopping
lenders from closing and by giving debtors
extra time to repay their overdue
mortgage payments.
help financially distressed borrowers
save their homes. This question is of
particular importance in light of the
current financial crisis.
The same forces that affect
mortgage demand and supply discussed
earlier also affect homeowners’ ability
to keep their homes. Again, the first
force is the wealth effect. Under either
Chapter 7 or Chapter 13, bankruptcy
exemptions allow borrowers to shift
their resources toward mortgage
payments and thereby help them keep
http://psidonline.isr.umich.edu/
5
These studies include articles by Thomas
Springer and Neil Waller; Brent Ambrose,
Richard Buttimer, and Charles Capone; and
Dennis Capozza and Thomas Thomson.
6
http://www.federalreserve.gov/pubs/oss/oss2/
scfindex.html
26 Q2 2009 Business Review
their homes. The second force comes
from the automatic stay on lenders’
collection activity imposed by the
bankruptcy court. A bankruptcy filing
helps debtors save their homes (at
least temporarily) by stopping lenders
from closing and by giving debtors
extra time to repay their overdue
mortgage payments. This second force
is particularly strong under Chapter
13, which allows debtors to have
a repayment plan that spans three
to five years. Bankruptcy trustees
may also help debtors challenge
excessive fees and penalties imposed
by lenders. Katherine Porter, in her
study, finds that mortgage lenders add
questionable or excessive fees in half
7
In their paper, Souphala Chomsisengphet and
Ronel Elul argue that bankruptcy exemptions
affect lenders’ credit supply and mortgage
loan terms only to the extent that they affect
borrowers’ payment behavior and, thus, their
credit bureau score.
of all foreclosures. Lower fees in turn
increase borrowers’ ability to keep their
homes.8
Finally, Melissa Jacoby argues that
even in cases where debtors do end up
losing their houses to foreclosure sale,
bankruptcy filing gives them time to
avoid a fire sale, in which the house is
sold at a large discount.
Of course, other forces
counterbalance the aforementioned
positive effects. A bankruptcy filing
delays the foreclosure process and
imposes costs on both borrowers
and lenders. Borrowers have to pay
bankruptcy filing fees, lawyer fees,
8
These arguments are nicely laid out in
Michelle White and Ning Zhu’s article.
www.philadelphiafed.org
trustee fees, and so forth. In a Chapter
13 filing, trustee fees alone amount to
between 6 to 10 percent of the total
payments borrowers have to make
through the repayment plan. The
cost to lenders is even higher, and it
includes lost mortgage interest, the
time cost of money, and depreciated
property value.
Do Homeowners Keep Their
Homes? The empirical evidence
on whether bankruptcy filing helps
homeowners retain their homes is
mixed.
First, the treatment of
homeownership is an important
matter for many bankrupt households.
Economists have found that the
majority of Chapter 13 filers are
homeowners who (presumably) wish to
save their homes. For example, Hülya
Eraslan, Pierre-Daniel Sarte, and I
studied Chapter 13 bankruptcy filings
in Delaware between 2001 and 2002
and found that over 80 percent of the
filers owned homes at the time of filing
and that their mortgage loan-to-value
ratio exceeded 90 percent. In another
study, Michelle White and Ning Zhu
also found that the vast majority (96
percent) of their bankrupt Delaware
households were homeowners. This
is despite the fact that a major
bankruptcy reform adopted in 2005
was intended to force some bankruptcy
filers to repay their unsecured debts
in Chapter 13. Even in Chapter 7, the
homeownership rate approached 50
percent, according to Ning Zhu’s 2007
article.9
On the other hand, it is not clear
whether the bankruptcy filing helped
borrowers remain homeowners in
the long run. First and foremost, the
failure rate of Chapter 13 repayment
9
In their 2005 article, Raisa Bahchieva, Susan
Wachter, and Elizabeth Warren document
similar findings for an earlier period.
www.philadelphiafed.org
plans is surprisingly high. In separate
studies, Scott Norberg and Andrew
Velkey and Hülya Eraslan, PierreDaniel Sarte, and I document that
the final discharge rates of Chapter 13
cases are as low as 33 percent. That
is, only about 33 percent of Chapter
13 filers successfully completed their
repayment plans. Borrowers who fail to
complete their repayment plan will not
have their unsecured debt discharged,
and lenders will immediately resume
Researchers
find that filing for
bankruptcy prolongs
borrowers’ stay in
their home before
they eventually lose it
to foreclosure sales.
their collection efforts as soon as
borrowers exit bankruptcy. These low
discharge rates are also corroborated
by anecdotal evidence in the legal
literature.
Second, despite their bankruptcy
filing, a significant number of
homeowners still end up losing their
houses to foreclosure sales within
five to six years of their bankruptcy
filing. Sarah Carroll and I studied
homeowners who filed for bankruptcy
between 2001 and 2002 in New
Castle County, Delaware, until
2007 and found that close to 30
percent of these filers still lost their
houses to foreclosure sales. The rate
increases substantially, to 40 percent,
if we consider homeowners who
were already one year late on their
mortgage payments at the time of
filing, compared to 43 percent of those
homeowners who went to foreclosure
without filing for bankruptcy. This
finding is consistent with Raisa
Bahchieva, Susan Wachter, and
Elizabeth Warren’s survey result that
many homeowners in financial distress
are simply hanging on to their houses
without any realistic hope of repaying
their mortgages.
The Costs of Borrowers Staying
in their Homes. Researchers find
that filing for bankruptcy prolongs
borrowers’ stay in their home before
they eventually lose it to foreclosure
sales. For example, Thomas Springer
and Neil Waller find that bankruptcy
filing lengthens the foreclosure process
by half a year to one year. Sarah
Carroll and I find that a Chapter 13
bankruptcy filing adds, on average,
one year to the borrower’s foreclosure
process. A study by Brent Ambrose,
Richard Buttimer, and Charles
Capone, and another by Dennis
Capozza and Thomas Thomson
also find supporting evidence that
bankruptcy filing delays foreclosure
sales but has little effect in helping
mortgage loans to become current.
But this result is a double-edged
sword. While borrowers may have
enjoyed additional benefits from
staying in their own homes, the cost
to lenders is high. In addition to the
added cost mentioned earlier in the
event that the bankruptcy plan fails
and the foreclosure process begins
again, lenders collect very little in
cases under Chapter 13. For example,
Norberg and Velkey find that the
average repayment rate for secured
lenders under Chapter 13 is 31 percent,
and Hülya Eraslan, Pierre-Daniel
Sarte, and I find the rate to be a mere
22 percent.10
Finally, there is also evidence that
final sale price is negatively correlated
with the length of a borrower’s stay in
bankruptcy and foreclosure together.
For instance, Sarah Carroll and I find
See my 2007 Business Review article for more
details.
10
Business Review Q2 2009 27
that longer time-to-sale is associated
with lower sale price; the correlation
coefficient of the gap between
bankruptcy filing and foreclosure sale
and the final foreclosure sale price
adjusted for inflation and house price
growth is -0.16.
Although the existing literature
finds that bankruptcy filing offers
extra breathing room to homeowners
who try to keep their homes, the
eventual success rate is low and the
added cost to lenders is high.
A Caveat. Before concluding,
it is worth noting that many of the
empirical studies cited in this section
are based on a sample of bankruptcy
filers, instead of a random sample
of households in the U.S. consisting
of both bankruptcy filers and
nonbankruptcy filers. This can lead to
what economists call a selection bias.
The outcomes for the bankruptcy filers
may not be the result of the features
of the bankruptcy process but may be
the result of some factor common to
households that file for bankruptcy.
For example, the fact that a large
number of homeowners lose their
houses despite filing for bankruptcy
may be simply because only households
in desperate financial straits file for
bankruptcy. In a properly designed test
we would be comparing outcomes for
essentially identical households: some
who file for bankruptcy and some who
don’t. Therefore, while the stylized
facts remain true, it is hard to conclude
definitely whether bankruptcy
helps homeowners preserve their
homeownership. Since any changes
in bankruptcy law would not only
alter the bankruptcy outcome but also
affect households’ decision to file for
bankruptcy, a fully convincing analysis
should take account of both effects.
WHAT’S NEXT?
The existing literature on
bankruptcy and homeownership has
28 Q2 2009 Business Review
focused on two questions. First, how
do personal bankruptcy provisions
affect credit supply? Second, how do
the personal bankruptcy provisions
affect households’ homeownership
outcome? While the literature
generally supports the conclusion that
more generous bankruptcy provisions
lead to more restrictive credit supply,
answers to the second question are
mortgages, and automobile loans to
borrow. In the second period, upon
learning their income and asset value,
they must decide whether to repay
their loans, default on their mortgages,
and/or enter bankruptcy. While
instructive, this framework doesn’t
allow researchers to explore certain
types of long-term decisions. For
example, Chapter 7 bankruptcy filers
Any analysis that examines only those
that have entered bankruptcy may lead to
relationships that appear much stronger than
they actually are or, in some cases, relationships
that are completely illusory artifacts.
much more mixed. Economists agree
that homeowners take advantage of
personal bankruptcy to try to retain
their homes, particularly under
Chapter 13. Nonetheless, only a small
proportion of households succeed in
keeping their homes in the long run.
Furthermore, while bankruptcy filing
adds to the length of the foreclosure
process, the cost to lenders is high.
Proponents of recent legislation
are likely to argue again that existing
mechanisms to avoid foreclosure in
bankruptcy need to be strengthened.
To better evaluate such proposals,
research needs to advance on two
fronts.
First, we need to build a
consistent framework that takes into
consideration the effect of bankruptcy
provisions and filings on credit supply
and demand as well as mortgage
payments and homeownership
retention. Michelle White and Ning
Zhu have taken the first step and
provided a highly simplified framework
in which households live only two
periods. In the first period, households
decide how much unsecured debt,
cannot file for bankruptcy for the next
six years, a factor that households will
take into account when they decide
whether to enter bankruptcy. The next
step will be to extend this framework
to a dynamic setting in which
households will enjoy or suffer the
effects of their decisions beyond the
current period in which the decision is
made and its immediate future.
Second, we need to collect
additional national data, particularly
in panel form, that will allow
researchers to follow households
over time. Such data will help us
overcome the selection bias that the
existing literature suffers from. Any
analysis that examines only those
that have entered bankruptcy may
lead to relationships that appear
much stronger than they actually
are or, in some cases, relationships
that are completely illusory artifacts.
A national database will also help
us overcome regional bias, since
bankruptcy exemptions and foreclosure
laws differ substantially from state to
state. BR
www.philadelphiafed.org
REFERENCES
Ambrose, Brent W., Richard J. Buttimer,
Jr., and Charles A. Capone, Jr. “Pricing
Mortgage Default and Foreclosure Delay,”
Journal of Money, Credit, and Banking, 29:3
(1997), pp. 314-25.
Bahchieva, Raisa, Susan Wachter, and
Elizabeth Warren. “Mortgage Debt,
Bankruptcy, and the Sustainability of
Homeownership,” in Patrick Bolton and
Howard Rosenthal, eds., Credit Markets
for the Poor. New York: Russell Sage
Foundation, 2005.
Fay, Scott, Erik Hurst, and Michelle J.
White. 2002. “The Household Bankruptcy
Decision,” American Economic Review, 92:3
(2002), pp. 706-18.
Pence, Karen. “Foreclosing on
Opportunity: State Laws and Mortgage
Credit,” Review of Economics and Statistics,
88 (2006), pp. 177-82.
Gropp, Reint, John Scholz, and Michelle
White. “Personal Bankruptcy and Credit
Supply and Demand,” Quarterly Journal of
Economics, 112:1 (1996), pp. 217-51.
Porter, Katherine. “Mistake and
Misbehavior in Bankruptcy Mortgage
Claims,” University of Iowa Legal Studies
Research Paper 07-29 (2007).
Hatcher, Desiree. “Foreclosure Alternative:
A Case for Preserving Homeownership,”
Profitwise News and Views (2006), pp. 1-5.
Springer, Thomas, and Neil Waller.
“Lender Forbearance: Evidence from
Mortgage Delinquency Patterns,” Journal of
American Real Estate and Urban Economic
Associations, 21:1 (1993), pp. 27-46.
Berkowitz, Jeremy, and Richard Hynes.
“Bankruptcy Exemptions and the Market
for Mortgage Loans,” Journal of Law and
Economics 42:2 (1999), pp. 809-30.
Jacoby, Melissa. “Bankruptcy Reform
and Homeownership Risk,” University
of Illinois Law Review 323:1 (2007), pp.
323-46.
Capozza, Dennis, and Thomas Thomson.
“Subprime Transitions: Lingering or
Malingering in Default,” Journal of Real
Estate Finance and Economics, 33:3 (2006),
pp. 241-58.
Li, Wenli. “What Do We Know About
Chapter 13 Personal Bankruptcy Filings?,”
Federal Reserve Bank of Philadelphia
Business Review (Fourth Quarter 2007), pp.
19-26.
Carroll, Sarah, and Wenli Li. “The
Homeownership Experience of Households
in Bankruptcy,” Federal Reserve Bank of
Philadelphia Working Paper 08-14 (2008).
Lin, Emily Y., and Michelle J. White.
“Bankruptcy and the Market for Mortgage
and Home Improvement Loans,” Journal of
Urban Economics, 50 (2001), pp. 138-62.
Chomsisengphet, Souphala, and Ronel
Elul. “Bankruptcy Exemptions, Credit
History, and the Mortgage Market,” Journal
of Urban Economics, 5 (2006), pp. 171-88.
Norberg, Scott, and Andrew Velkey.
“Debtor Discharge and Creditor
Repayment in Chapter 13,” Creighton Law
Review, 39:3 (2007), pp. 473-560.
Stark, Debra P. “Facing the Facts: An
Empirical Study of the Fairness and
Efficiency of Foreclosures and a Proposal
for Reform,” University of Michigan Journal
of Law Reform, 30 (1997), pp. 639-88.
White, Michelle, and Ning Zhu. “Saving
Your Home in Chapter 13 Bankruptcy,”
NBER Working Paper 14179 (2008).
Zhu, Ning. “Household Consumption and
Personal Bankruptcy,” Journal of Legal
Studies (forthcoming).
Eraslan, Hülya, Wenli Li, and PierreDaniel Sarte. “The Anatomy of U.S.
Personal Bankruptcy under Chapter 13,”
Federal Reserve Bank of Philadelphia
Working Paper 07-31 (2007).
www.philadelphiafed.org
Business Review Q2 2009 29
RESEARCH RAP
Abstracts of
research papers
produced by the
economists at
the Philadelphia
Fed
You can find more Research Rap abstracts on our website at: www.philadelphiafed.org/research-and-data/
publications/research-rap/. Or view our working papers at: www.philadelphiafed.org/research-and-data/
publications/.
ACCOUNTING FOR HOUSING IN
CONSUMER PRICE INDEXES
In this paper, the authors take stock of
how statistical agencies in different nations
are currently accounting for housing in their
consumer price indexes (CPIs). The rental
equivalence and user cost approaches have
been favorites of economists. Both can be
derived from the fundamental equation
of capital theory. Concerns about these
approaches are taken up. They go on to argue
that an opportunity cost approach is the correct
theoretical framework for accounting for
owner-occupied housing (OOH) in a CPI. This
approach, first mentioned in a 2006 OECD
paper by Diewert, is developed more fully here.
The authors explore the relationship of this new
approach to the usual rental equivalency and
user cost approaches. The new approach leads
to an owner-occupied housing opportunity cost
(OOHOC) index that is a weighted average of
the rental and the financial opportunity costs.
The authors call attention to the need
for more direct measures of inflation for
owner-occupied housing services. In a 2007
paper, Mishkin argues that central banks with
supervisory authority can reduce the likelihood
of bubbles forming through prudential
supervision of the financial system. However,
the official mandates of central banks typically
focus on managing measured inflation. Barack
Obama has pledged to give the Federal Reserve
greater oversight of a broader array of financial
institutions. They believe that an important
addition to this pledge should be to give the
BLS, BEA, and Census Bureau the funds and
the mandate to aggressively develop improved
measures of inflation for owner-occupied
housing services. Central banks and national
governments have many policy instruments at
their disposal that they could use, in the future,
to control inflation in housing markets. What
30 Q2 2009 Business Review
they lack are appropriate measures of inflation in
the market for owner-occupied housing services.
The proposed new opportunity cost measure for
accounting for OOH in a CPI will not be simple
or cheap to implement. However, the current
financial crisis makes it clear that the costs of not
having an adequate measure for inflation in the
cost of owner-occupied housing services can be
far greater.
Working Paper 09-4, “Accounting for Housing
in a CPI,” W. Erwin Diewert, University of British
Columbia, and Alice O. Nakamura, University of
Alberta School of Business, and Visiting Scholar,
Federal Reserve Bank of Philadelphia
OPPORTUNITY COST TREATMENT
OF OWNER-OCCUPIED HOUSING IN
MEASURES OF INFLATION
This paper provides a brief introduction to
a proposed new opportunity cost treatment of
owner-occupied housing in measures of inflation
for the United States. In addition, the paper
introduces, and provides links to, a collection
of nine other papers that discuss various aspects
of the treatment of owner-occupied housing in
measures of inflation for a number of nations,
including Canada, Germany, Iceland, and the
United States.
Working Paper 09-5, “Introduction to Price
and Productivity Measurement for Housing,” Bert
M. Balk, Erasmus University Rotterdam; W. Erwin
Diewert, University of British Columbia; and Alice
O. Nakamura, University of Alberta School of
Business, and Visiting Scholar, Federal Reserve Bank
of Philadelphia
MODELING APPROACHES TO LABOR
MARKETS AND IMPLICATIONS FOR
INFLATION DYNAMICS
This paper reviews recent approaches to
modeling the labor market and assesses their
implications for inflation dynamics through both
www.philadelphiafed.org
their effect on marginal cost and on price-setting behavior.
In a search and matching environment, the authors consider
the following modeling setups: right-to-manage bargaining
vs. efficient bargaining, wage stickiness in new and existing
matches, interactions at the firm level between price and
wage-setting, alternative forms of hiring frictions, search onthe-job and endogenous job separation. They find that most
specifications imply too little real rigidity and, so, too volatile
inflation. Models with wage stickiness and right-to-manage
bargaining or with firm-specific labor emerge as the most
promising candidates.
Working Paper 09-6, “Inflation Dynamics with Labor
Market Matching: Assessing Alternative Specifications,” Kai
Christoffel, European Central Bank; James Costain, Banco
de España; Gregory de Walque, National Bank of Belgium;
Keith Kuester, Federal Reserve of Philadelphia; Tobias Linzert,
European Central Bank; Stephen Millard, Bank of England; and
Olivier Pierrard, Banque Centrale de Luxembourg
A MODEL OF HOUSING AND CONSUMPTION
WITH REALISTIC LABOR INCOME AND
HOUSE-PRICE UNCERTAINTIES
The authors estimate a structural model of optimal
life-cycle housing and consumption in the presence of
realistic labor income and house-price uncertainties. The
model postulates constant elasticity of substitution between
housing service and nonhousing consumption and explicitly
incorporates a house adjustment cost. Their estimation fits
the cross-sectional and time-series household wealth and
housing profiles from the Panel Study of Income Dynamics
quite well and suggests an intra-temporal elasticity of
substitution between housing and nonhousing consumption
of 0.33 and a housing adjustment cost that amounts to
about 15 percent of house value. Policy experiments with
estimated preference parameters imply that households
respond nonlinearly to house price changes with large
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house price declines leading to sizable decreases in both the
aggregate homeownership rate and aggregate nonhousing
consumption. The average marginal propensity to consume
out of housing wealth changes ranges from 0.4 percent to
6 percent. When lending conditions are tightened in the
form of a higher down payment requirement, interestingly,
large house-price declines result in more severe drops in
the aggregate homeownership rate but milder decreases in
nonhousing consumption.
Working Paper 09-7, “Housing Over Time and Over the
Life Cycle: A Structural Estimation,” Wenli Li, Federal Reserve
Bank of Philadelphia; Haiyong Liu, East Carolina University;
and Rui Yao, Zicklin School of Business, Baruch College
OPTIMAL INFLATION RATE AND POLICY
TRADE-OFFS IN A TWO-SECTOR MODEL
The authors develop a two-sector monetary model
with a centralized and a decentralized market. Activities in
the centralized market resemble those in a standard New
Keynesian economy with price rigidities. In the decentralized
market agents engage in bilateral exchanges for which money
is essential. The model is estimated and evaluated based
on postwar U.S. data. They document its money demand
properties and determine the optimal long-run inflation rate
that trades off the New Keynesian distortion against the
distortion caused by taxing money and hence transactions in
the decentralized market. The authors find that target rates
of -1 percent or less are desirable, which contrasts with policy
recommendations derived from a cashless New Keynesian
model.
Working Paper 09-8, “Sticky Prices Versus Monetary
Frictions: An Estimation of Policy Trade-offs,” S. Boragan
Aruoba, University of Maryland, and Visiting Scholar, Federal
Reserve Bank of Philadelphia, and Frank Schorfheide, University
of Pennsylvania, and Visiting Scholar, Federal Reserve Bank of
Philadelphia
Business Review Q2 2009 31
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