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A TIME-SERIES ANALYSIS OF US SAVINGS AND
LOAN PERFORMANCE:
MAJOR TRENDS AND POLICY ISSUES AFTER THE
HOUSING CRISIS
Mine Aysen Doyran, Lehman College—CUNY1
ABSTRACT
The savings and loan (S&L) debacle of the 1980s produced one of the largest financial
crises since the Great Depression. Since then scholarly research has devoted substantial
attention to profitability and loan performance in US commercial banking. Yet, the
implications of these studies for research on non-bank institutions are limited. This paper
investigates the determinants of S&L asset quality and loan performance over the period
1978-2009. The aim of this paper is to establish which of these potential determinants of
loan performance prevail in the US S&L industry. An equation of loan performance ratio
is submitted using first-order differences of variables and least squares trend fitting. Our
analysis highlights that higher return on assets (ROA) and ratio of net worth to total
assets lead to a lower ratio of poor loan performance. Higher the ratio of consumers to
default on their loans, the lower the return on assets and hence less bank profits. In
addition, the leverage ratio has a significant negative coefficient on loan performance.
By and large, our analysis is consistent with earlier studies that diminishing net worth
and under-performing loans have the potential to render S&Ls vulnerable to financial
shocks, thus contributing to financial instability.
KEY WORDS: S&L Industry, Banking Regulation, Asset Quality, Subprime Mortgage Crisis,
Loan Performance
INTRODUCTION
Like the S&L crisis of the 1980s and early 1990s, the current financial crisis exposed
weaknesses in the U.S. financial system not seen since the Great Depression. During this
downturn, financial losses have been largely concentrated in the housing market;
mortgage market participants have been hard hit by declines in profitability, increases in
bad loans, and customer defaults. Since then the S&L industry has also realized sizable
1
I am are grateful to the Center of Excellence in Teaching and the Liberal Arts Department of the
Fashion Institute of Technology of the State University of New York’s for their outstanding and
generous support, both financial and moral. I am alone responsible for errors.
1
losses on loans and investments, thus resulting in a further rise in non-performing loans
and credit-related write-offs in mortgage lending related sectors.
The S&L industry in general responded to the crisis by using loan loss reserves—an
expense set aside as a cushion against customer defaults and bad loans. Thrifts were
reported as having set aside a record of $39.3 billion in loss reserves and an additional
$5.8 billion in the first quarter of 2009. Yet, reflecting the present housing market
downturn and increase in unemployment rates, asset quality and problem loans
(delinquent loans plus ―loans in nonaccrual status‖ and ―repossessed assets‖) have
increased to a degree not seen since the early 1990s (US Treasury Department, 2010).
Institutions of all types and business models have experienced an increase in problem
loans especially institutions offering subprime and other types of high-risk mortgages.
For example, the FDIC took control of the California based savings and loan association
IndyMac in what regulators called the second-largest bank failure in U.S. history.
Asset quality is one of the key indicators of financial viability and overall performance of
banking institutions. While S&L associations continue to expand their repertory of
savings deposits, mortgage loans and other financial products, the loan performance is
typically the principal indicator of their asset quality. Accordingly, loan quality remains a
key indicator of their overall condition and earnings. Since the banking crises of the early
1980s, scholarly research has devoted substantial attention to profitability and loan
performance in US commercial banking. Yet, the implications of these studies for
research on non-bank institutions are quite limited.
2
Accordingly, this paper examines the determinants of asset quality and loan performance
in the S&L industry over the period 1978-2009. An equation of asset quality ratio using
first-order differences of time-series values is estimated. Using ADF as a statistical test
by estimation of least squares trend fitting, the study takes into consideration industry
specific as well as macroeconomic indicator as control variables. The ADF (Augmented
Dickey Fuller) test is used for detecting the existence of a unit root in autoregressive
model and stationary trend fitting. Overall, our analysis highlights that higher return on
assets (ROA) and ratio of net worth to total assets leads to a lower ratio of poor loan
performance. Higher the ratio of consumers to default on their loans, the lower the return
on assets and hence less bank profits. In addition, the leverage ratio has a significant
negative coefficient on loan performance. By and large, our analysis is consistent with
the view that decreasing net worth and under-performing loans have the potential to
render S&Ls vulnerable to financial shocks, thus contributing to financial instability.
The rest of the article is organized as follows. The second section examines historical
developments in the S&L industry asset structure and underwriting standards since the
beginning of the current downturn. It discuses the significance of recent market and
financial innovations and assesses the role of regulation in light of historical trends. The
third section reviews the literature on asset quality and mortgage loan performance. The
fourth section introduces the data and illustrates the methodology and hypothesis; the
fifth section presents the empirical results. The sixth section briefly discusses the
implications of the federal loan modification programs for the prudential regulation of
S&Ls, which have sought to help households in foreclosure and prevent delinquency. The
3
final section draws strategic lessons from this experience for future researchers and
practitioners in the field of risk management.
BACKGROUND: THE S&L INDUSTRY PERFORMANCE DURING THE
FINANCIAL DOWNTURN
Like commercial banks, the S&L industry went through radical changes during the late
1980s and early 1990s. Because of rapid market innovations and deregulatory measures
instituted by the US government, S&Ls began to offer a more diversified range of
services than ever before. With their new freedoms, they were able to enter a wider range
of businesses that they might have otherwise sought, such as commercial lending, trust
services and nonresidential consumer lending. In 1980, Depository Institutions
Deregulation and Monetary Control Act was passed, increasing the deposit insurance
coverage for S&Ls from $40,000 to $100,000. During this period, many S&Ls failed due
to engagement in large-scale speculation, especially in real estate (Balderston, 1985).
However, a more unstable period ensued with the closing of the Resolution Trust
Corporation (RTC) in mid-1995—a government agency responsible for resolving 747
thrifts with total assets of $394 billion. From January 1986, to December 1995, the US
S&L industry lost nearly 50% of its total institutions—a period during which number of
federally insured S&Ls declined from 3,234 to 1,645 (Curry and Shibut, 2000)
During the 1990s, the S&L industry was shaped by the rapid growth of subprime
mortgage lending. Various federal government actions as well as market forces fostered
this development. The later revisions to the Community Reinvestment Act (CRA) of
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1977 chiefly offered banking institutions deregulatory incentives to provide loans to lowand moderate-income borrowers, mainly those classified as subprime borrowers.
Furthermore, the ―Federal Housing Administration, which guarantees mortgage loans of
many first-time borrowers, liberalized its rules for guaranteeing mortgages, increasing
competition in the market and lowering interest rates faced by some subprime mortgage
borrowers‖ (Gramlich, 2004). This group of borrowers included customers who were
previously denied credit for real estate and other consumer loans. While subprime
lending has provided new opportunities for increased homeownership among high risk
borrowers, it was also associated with poor loan performance related to higher levels of
delinquency, foreclosure, and in some cases predatory and discriminatory lending
practices.
While high interest rates and inflation during the early 1980s hampered financial growth,
mortgage loan volume has shown a dramatic increase since 2000. The recent financial
expansion illustrates the ―confluence of rising borrower demand, historically low interest
rates, intense lender competition, innovations in the structure and marketing of
mortgages, and an abundance of capital from lenders and mortgage securities investors‖
(Angell and Rowley, 2007). However, a booming housing market came at a cost of
deteriorating credit quality in light of lending practices. While a lack of oversight over
mortgage lending and deregulation were in part to blame for risky loans to homebuyers,
the securitization model generally involved larger risks. This was particularly true for
S&Ls which traditionally specialized in processing conventional home mortgages rather
than securitizing subprime loans with adjustable rates. The elimination of Regulation Q
5
in 1980 shifted the mortgage market dramatically from savings institutions to commercial
banks and to government-sponsored enterprises (GSEs). GSEs led the expansion of
secondary mortgage market by securitizing mortgages in the form of mortgage-backed
securities (MBS). This was the market for the sale of securities/bonds backed or
collateralized by the value of mortgage loans. By 2005, nearly 68% of home mortgages
were securitized. In 2005, total private-label MBS accounted for 29% of gross
outstanding MBS in 2005, doubling its share from 2003. During the same period, twothirds of private-label securitizations included nonprime loans, increasing from 46% in
2003 (Angel and Rowley, 2007). With the increased exposure to competition from other
lenders, S&Ls appeared willing to assume greater risk in their search for high profits.
Chart 1: S&L Share of Mortgage Originations, $ in Millions
900000
800000
700000
600000
500000
400000
MORIG
300000
Source:
OTS, 2010
200000
100000
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
0
Mortgage securitization enables lenders to shift credit risk and interest rate risk to
investors who have greater risk forbearance. As a recent by product of secondary
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mortgage markets, ―private-label‖ MBS’s are securitized by institutions other than
government-sponsored enterprises and therefore lack any explicit or implicit backing.
Since subprime loans lack an explicit guarantee, they elevate credit risks. Lenders
naturally take into account a borrower’s credit history when determining loan eligibility.
When ―compared with prime loans, subprime loans typically have higher loan-to-value
ratio, reflecting that subprime borrowers face in making down payments and the tendency
of these borrowers to seek equity during refinancing‖. Because of their tendency to go
into delinquency or default more often, subprime loans also carry higher interest rates
than prime loans. While subprime lenders are subject to certain lending standards, fraud,
abuse, and predatory lending problems are also problematic features of the subprime
market (Gramlich, 2004).
Yet none of these problems prompted subprime regulation. Over the period 1994-2003,
subprime mortgage loan originations increased by a rate of 25% per year, nearly a tenfold increase in nine years. Similarly, subprime originations as a share of total mortgage
originations grew from 4.5% in 1994 to 13.2% in 2000 and 8.8% in 2003. In 2002, only 5
commercial banks and 11 S&Ls offered subprime loans though these five banks were
large enough to represent 27% of the mortgage loans of the subprime lenders. Likewise,
35 affiliates of financial holding companies (for example, City Financial) constituted only
19% of the subprime lenders but accounted for 43% of the subprime loans. 11 S&Ls
constituted only 6% of subprime lenders but accounted for 13.8% of total subprime
mortgage loans. On the other hand, there were 135 independent mortgage companies, but
they only accounted for 12%, the relatively small share of subprime mortgage loans
7
(Gramlich, 2004). Chart 1 plots the S&L share of mortgage originations during the period
1978-2009. As can be seen, the mortgage originations illustrate some cyclicality but no
apparent trend in the 1980s and 1990s. It is important to note, however, that mortgage
originations increased sharply during the housing boom and then decreased during the
cyclical downturn of the early 2007 to levels statically below that of the S&L crisis
period.
Because of their larger share of the mortgage securities market, U.S. banks and securities
trading firms suffered the greatest losses when the market collapsed in 2008. Federally
insured S&Ls specialized in nonprime mortgages were also impacted. American Home
Mortgage, which operated as a real estate investment trust, collapsed and filed for
bankruptcy on August 6, 2007. In January 2008, Bank of America acquired Countrywide
Financial, the largest U.S. mortgage lender, for $4 billion after its stock prices had
dropped 80% in value since 2007 (Morgenson, 2007; Mildenberg, 2008). In April 2007,
New Century Financial Corporation, another real estate investment trust and second
biggest U.S. mortgage lender, filed for bankruptcy after effectively reducing its labor
force by 54% (CNN, 2007). IndyMac—a Pasedena based savings banks specialized in
Alt-A loans— also failed in July, 20008 and was seized by FDIC (Hudson, 2008).
IndyMac’s collapse was estimated to ―cost the FDIC between $4 billion and $8 billion,
potentially wiping out more than 10% of the FDCI's $53 billion deposit insurance fund‖
(Palette and Enrich, 2008). IndyMac’s failure was the third largest bank collapse in
FDIC’s history after Continental Illinois in 1986 and First Republic Bank in 1988.
8
FINANCIAL REGULATION AND LOAN QUALITY PERFORMANCE IN THE
S&L INDUSTRY
S&Ls have remained key facilitators of home mortgages and savings deposits throughout
American history. Yet there are few studies of the financial condition and performance of
savings and loans associations (Brigham, 1964; Benston, 1972; Verbrugge, Shick and
Thygerson, 1976; Geehan and Allen, 1978; Bradley, Gabriel and Wohar, 1995; Kaushik
and Lopez, 1996; Jahere, Page and Hudson, 2006). While the bulk of the research has
concentrated on the causes and consequences of the S&L crisis, savings and loan studies
have drawn largely from US banking experts. Since the S&L crisis, research has shifted
away from this area. However, the collapse of Indy Mac in July 2008, the largest S&L in
California specializing in Alt-A loans, have once again raised concerns about the viability
of S&Ls in their present form.
There are numerous investigations of mortgage lending institution performance,
particularly of commercial banks (Benston, 1972; Berger, 1995; Bourke, 1986). Most of
these studies, however, have examined the determinants of profitability rather than loan
or asset performance. Therefore, they used a number of indicators (return on assets,
return on equity) as proxies for profitability and regressed it with bank-specific, industryspecific and macroeconomic (external) indicators (Rasiah, 2010). Additionally, the
impact of market and financial structures on bank performance has received considerable
attention among American and European researchers (Short, 1979; Gilbert, 1984;
Bourke, 1986; Goldberg and Rai, 1996; Casu, Girardone and Molynuex, 2004;
Demirguc-Kunt and Huizinga, 2000). Further applications of profitability have been
9
critical in orienting methodology towards ―single country studies‖ and ―panel country
studies‖ (Naceur, 2003). Because of the emphasis on the role of market structures, most
of the studies fall under the Structure-Conduct-Performance (SCP) paradigm. Within
panel country studies, for example, explanatory variables unique to each country and
explanatory variables unique to each bank were highlighted in order to address the
relationship between market structures and bank performance.
Research on S&L performance has drawn primarily from US banking studies. Benston
(1972) investigated scale economies in a panel study of 83 commercial banks and 3159
S&Ls. Deposit and loan structure were used as proxies for bank size. He found that
greater operating costs were accompanied by larger size, but marginal cost increased at a
decreasing rate for branch banking. This illustrated the benefits of scale economies,
especially in demand deposit and real estate loans. A similar analysis of that period by
Verbrugge, Shick and Thygerson (1976) showed the effects of both bank-specific and
regulation variables on profitability measured as return on net worth. They claimed that
usury laws lessened fee income in S&Ls purchasing rather than servicing loans. This led
to less profits yet marginally decreasing operating costs. The authors also found loan
composition (―multi-family and other higher-risk non-single family‖) to be positively
correlated with operating costs.
In research into the effect of S&L crisis on ―mortgage-credit intermediation‖, Bradley,
Gabriel and Wohar (1995) sought to examine the root causes of housing sales and
purchase cycles. Their results indicated a highly correlated relationship between deposit
10
flows and S&L intermediation of mortgage credit. Their analysis also shed light on the
increasing disintermediation as a function of maladjustment to credit flows due to deposit
rate ceilings and other structural obstacles. This has led to sharp reductions in the
provision of mortgage credit, serving to increase the mortgage-Treasury interest rate
spread and facilitating the crash. Policy lessons are drawn for the impact of the crisis on
the housing sector. Results cast doubts on fixing the S&L crisis through increased
bailouts of remaining S&Ls.
While not directly related to the S-C-P paradigm, financial regulation is a structural
variable in most banking studies. This type of analysis extends beyond market structures
by taking into account the larger regulatory and policy environment of banks. The
premise of structural analysis is that the organizational make-up of the US financial
system impacts the viability of savings and loan associations. The claim is that
inefficiencies leading to inadequate oversight and poor performance stem from a
fragmented regulatory environment (Blair and Kushmeider, 2006; Kushmeider, 2005;
Matasar and Pavelka, 1998). Unlike other countries, the US regulatory structure is
marked by deference to competition, a dual banking system, federalist politics and the
choice of charter. In this system, a bank can designate which agency serves as its primary
regulator through its selection of a chartering source (federal or state) and choosing to
join the Federal Reserve System. It has long been claimed that the choice of regulatory
authority (ie, the ability of banks to choose their primary regulator) has produced a
clientele relationship between the regulators and banks. Clientalism implies giving bank
regulators the exceptional power and ―authority‖ from the reporting banks. Federal
11
regulators compete with each other to woo banks into choosing their chartering authority
so as to increase revenues from the number of reporting banks—a phenomenon of
regulatory capture known as ―competition in laxity‖ (Matasar and Pavelka 1998:57).
S&L industry performance is largely shaped by this contradictory relationship between
the state and federal level of regulation. Similar to the commercial banking industry, the
S&L industry organizes under a dual charter system. States provide a savings and loan
association (S&L) charter while some states also provide a savings bank charter. This has
allowed financial interests to switch charters and ―shop for‖ the regulator that sets the
lowest standards. Policing banks runs the risk that regulatory agencies will lose the fees
they rely on from the very institutions they are charged with regulating. At the federal
level, the OTS offers both a federal S&L charter and federal savings bank (FSB) charter.
All state-chartered thrifts (S&Ls and savings banks) are subject to regulation by their
state chartering authority (such as state banking department) and also by a federal
regulator—the OTS in the case of state-chartered S&Ls and FDIC in the case of statechartered savings banks. State-chartered banks enjoy the advantage of being more
leniently regulated than national banks under OCC. This is mainly because state
authorities alternate with federal regulators (FDIC and Federal Reserve) who do not
charge fees for examining the books of state banks. The OCC, on the other hand, must
cover the full cost of supervision. State-chartered saving banks, not charged by FDIC for
examination, are more cost efficient than state-chartered S&Ls which pay a double
supervisory assessment fee–both to their state-chartering authority and the OTS (Blair
and Kushmeider, 2006). Cost effectiveness highlights why S&Ls became savings banks
12
with the hope of more leniently regulated. It also highlights the decreasing number of
mortgage providers or increasing proliferation of lending categories in the S&L industry.
DATA AND ECONOMETRIC ANALYSIS
Most of the structure-performance studies have looked at profitability as a measure of a
bank’s financial performance or overall condition. While this relationship is recognized
in research that associates profitability with a variety of internal and external factors, the
discussion of loan performance received relatively little attention. However, loan
performance is difficult to measure due to the diversity of bank assets. Since S&L
associations are technically speaking providers of loan and deposit accounts, it is
important to understand their function in the financial system. Primarily, S&Ls are
―specialized mortgage lenders with considerable expertise in evaluating potential
borrowers, establishing long-term relationships with customers and designing loan
agreements that minimize adverse selection‖ (Bradley, Gabriel and Wohar, 1995:478).
Although governmental policies supported the diversification of S&L assets through
deregulation in order to address the ongoing crisis, S&Ls historically remained as
originators of home mortgages. As Chart 2 illustrates, the average amount of single and
multifamily loans between 1978 and 2009 was $521.7 billion comprising nearly 76% of
all loans in those categories (OTS, 2010).
Other measures of S&L credit provision (the share of non-home mortgage loans in
construction, non-residential, consumer, commercial and land) are shown in Chart 2.
These series diverged significantly for the second time during the cyclical downturn of
13
the early 2007, largely affecting the loan performance and resulting in a sharp decline in
S&L provision of mortgage credit (Chart 1). This certainly reflected the overall decline in
the supply of credit relative to credit demand and contraction in the housing market. The
reduction in the supply of credit resulted in higher mortgage interest rates but also in
higher ratio of non-performing to total loans or total assets. Therefore, the loan portfolio
performance appears as one of the most import proxies for financial well being of this
industry. To highlight the significance of this issue, the following section puts forward an
equation using a set of financial ratios that are expected to affect the loan performance of
the S&L industry during the period 1978-2009.
Chart 2: Average Assets by Loan Type, 1978-2009 ($ In Millions)
600000
500000
400000
300000
200000
100000
0
The data for this study is obtained from the Office of Thrift Supervision (OTS) database
as well as the World Bank and Pen World Table at the University of Pennsylvania. The
14
data set consists of aggregate information for which a variety of financial ratios were
calculated. Most savings associations, also known as thrifts, are small, private and
customer oriented institutions committed to providing home mortgages and other retail
lending to communities. The OTS, a bureau of the Department of the Treasury, is the
federal regulator of S&Ls (state or federal charter) and the holding companies that own
them (such as AIG). In 2009, the OTS supervised 765 thrifts with assets of $941.7
billion. Although there are sizable indicators of loan performance going back to 1964, we
were only able to collect information between 1978 and 2009 for which data was found:
Ratio of non-performing loans to total loans (DML_TLOANS) Return on assets (ROA),
leverage ratio (LEV), Real GDP capita income (RGDPL), growth rate of Real GDP
(GROWTH_RATE_GDP), ratio of net worth to total assets (NW_TA) and ratio of total
liabilities to new worth (TLI_NW). The source for the financial ratios is Office of Thrift
Supervision (OTS), 2009 Fact Book: A Statistical Profile of the Thrift Industry. Timeseries for RGDPL and GROWTH_RATE_GDP were obtained from the Pen World Table
of the University of Pennsylvania (1978-207) and World Development Indicators &
Global Development Finance (2008-2009) of the World Bank database.
What are the determinants of loan performance as measured by the ratio of total nonperforming (delinquent) loans to total industry loans? Our analysis seeks to evaluate
whether the specified financial ratios since 1979 have led to a loss of asset quality or poor
loan performance specified by DML_TLOANS. The aim of this analysis is to establish
which of these potential determinants of loan performance is dominant in S&L industry.
The analysis is based on a two-steps least squares regression. In the first equation, we test
15
the existence of unit roots in levels for control variables that are used to predict the value
of loan performance. In our second equation, we filter the effects of variables of unit
roots on the dependent variable by conduct of an ADF test. The financial ratios are in
accord with previously published studies in the field. We use the ratio of non-performing
loans to total loans (DML_TLOANS) as dependent variable; explanatory or control
variables are return on assets (ROA), leverage ratio (LEV), Real GDP capita income
(RGDPL), growth rate of Real GDP (GROWTH_RATE_GDP), ratio of net worth to total
assets (NW_TA) and ratio of total liabilities to new worth (TLI_NW). Meanwhile,
macroeconomic indicators of Real GDP capita income (RGDPL), growth rate of Real
GDP (GROWTH_RATE_GDP) are used as external variables. The calculation of the
external ratios (macroeconomic variables) was obtained from Demirguc-Kunt and
Huizinga (2000). We calculated industry endogenous ratios from the works of banking
industry experts (Verbrugge, Shick, and Thygerson, 1976; Gallick, 1976; Berger, 1995;
Chaudhry, Chatrath, and Kamath, 1995; Pervan, Pervan and Guadagnino, 2009;
Papanikolaou and Wolff, 2010).
Inter-temporal factors might misrepresent variables on financial sector performance in a
time-series analysis. Therefore, least squares trend fitting is necessary to capture the
significance of variables under consideration. When data is observed over a defined time
frame, autocorrelation may occur where the preceding and successive values of timeseries are highly correlated. This is due to the unit root in levels that cause non-stationary
trend in the mean. Variables with unit roots exhibit non-stationary or trending behavior
that also cause ―serial correlations‖ over time (Cromwell, Hannan, Labys, Terraza,
16
1994:23). Under panel data estimation, on the other hand, cross-sectional differences
across units of observation may not be easily estimated due to variations in industry
reporting standards. Since S&Ls service relatively homogeneous products (mainly real
estate and mortgage loans), the shortcoming in both types of estimations can be filtered
with a proper statistical technique. An ADF test (Augmented Dickey–Fuller Test) is a
well-known co-integration procedure that tests the existence of a unit root in a time-series
data. Since much of time-series theory is concerned with stationary time-series, an ADF
test is conducted to filter non-stationary behavior by means of first or second differencing
equations.
Our data temporal reference, t, in this case for a year, and i for parameter estimates,
variables, with autoregressive model of order.
t
captures the random error in time
denoted by white noise (residual) and Y and x are the observed value of time-series at
time t. Many macroeconomic variables such as Real GDP have unit roots in levels. As a
result, they exhibit trending behavior that results in high R Square values and t-ratios
with insignificant statistical meaning. To achieve our goal of transforming a nonstationary series into a stationary one, an ADF (Augmented Dickey Fuller) test is applied
to the regression residuals of auto-correlated time-series. This is done by inclusion of
lagged values of Y (DML_TLOANS) where
Y is the first difference of the variable Y,
indicating Y minus its one period prior value. The model parameters of the study are
indicated below where regression is performed in terms of
YDML _ TLOANS
Yt
0
x
0
i t i
x
1 t,1
x
2 t,2
t
i 6
17
x
3 t,3
4
Y rather than Y:
x t,4
x
5 t,5
x
6 t,6
t
Our regression equation estimates loan performance in terms of YDML _ TLOANS . As the proxy
for asset quality, the loan performance ratio is computed by dividing the amount of
delinquent loans by total loans. Delinquent mortgage loans are a form of non-performing
loans for which the borrower has failed to make payments as specified in the loan
agreement. If the borrower can’t pay the mortgage within a certain time period, the
lender can start foreclosure proceedings. Foreclosure starts only after the borrower has
completely defaulted on his or her payments. As a result of this lag factor, which may
misrepresent the true extent of delinquency, mortgage foreclosures were omitted from the
analysis. ROA is computed by dividing the net income over total assets. The leverage
ratio (LEV) is calculated as the ratio of debt to equity capital. Due to lack of debt figures,
debt was calculated as the ratio of (total liabilities minus equity) to equity. The ratio of
net worth to total assets (NW_TA) signifies the amount of debt a company has, as net
worth is calculated by the difference between total assets and total liabilities. The higher
the ratio the lower the amount of debt a bank has. The ratio of total liabilities to new
worth (TLI_NW) is an indicator of long-term debt since it implies the extent to which the
net worth of the enterprise can offset its liabilities. The higher the ratio, the lower the
ability of the enterprise to retain net worth or balance debt. On the other hand,
macroeconomic variables such as real GDP per capital income (RGDPL) and the annual
growth rate of Real GDP (GROWTH_RATE_GDP) are external factors that might affect
the long-run performance or profitability.
The unit root test gives the researcher an opportunity to re-estimate the slope coefficients
of variables in order to de-trendise a time-series or make it stationary. After identifying
18
variables with unit roots in levels, one can apply the first or second difference operator to
the auto-correlated time-series data. If the first operator shows the differenced time-series
to be stationary, then one can apply ordinary least squares to these variables to estimate
the slope coefficients. The ADF test specified that first level was required for all
variables in order to induce stationary. In a series of unit root tests, the coefficients did
not show the expected sign of significance in the level. Accordingly, our data was nonstationary and required transformation. A regression equation was then re-estimated
(below) taking first difference of variables that had unit roots in levels. The testing
procedure for the ADF test is the same as other autoregressive models, but it was mainly
applied to the first difference operator.
Re-estimated least squares (with ARMA) using DML_TLOANS as dependent variable:
Estimation Equation:
=====================
D(DML) = C(1) + C(2)*D(LEV) + C(3)*D(ROA) + C(4)*D(RGDPL) +
C(5)*D(GROWTH_RATE_GDP) + C(6)*D(NW_TA) + C(7)*D(TLI_NW)
Substituted Coefficients:
=====================
D(DML) = 466.3003279 - 8116.863112*D(LEV) - 599187.3712*D(ROA) +
1.218213703*D(RGDPL) - 85.37358754*D(GROWTH_RATE_GDP) 473451.7964*D(NW_TA) + 7096.695556*D(TLI_NW)
Re-estimated least squares (with ARMA) using DML as dependent variable:
Estimation Equation:
=====================
D(DML_TLOANS) = C(1) + C(2)*D(LEV) + C(3)*D(ROA) + C(4)*D(RGDPL) +
C(5)*D(GROWTH_RATE_GDP) + C(6)*D(NW_TA) + C(7)*D(TLI_NW)
Substituted Coefficients:
=====================
D(DML_TLOANS) = 0.001572363527 - 0.01118915311*D(LEV) 0.6273853931*D(ROA) - 1.075641852e-006*D(RGDPL) - 6.21887169e006*D(GROWTH_RATE_GDP) - 0.3667931234*D(NW_TA) +
0.01006688646*D(TLI_NW)
19
DISCUSSION OF FINDINGS
This section discusses loan performance in the US Savings and Loan Industry over the
period 1978-2009. A review of variables shows some variations in our data. Trends in
asset quality, earnings and profitability reflect the continuing US business cycle and
housing market weakness. From 1978 to 2009, non-performing loans increased by
506.065% against an increase of 89.369% in total industry assets. On the other hand,
total industry assets decreased by 21% over the period 2007-2008 to $1.20 trillion from
$1.51 trillion, reflecting the loss of one big S&L that failed during the period (OTS,
2009). When loans are past due by 30 or 89 date, they indicate the borrower’s failure to
pay monthly mortgage on due dates and therefore are classified as delinquent or nonperforming in this study. During the same period, the average number of delinquent loans
stood at $17,189.12 (Table 1) while the amount of non-performing loans increased from
$3.8 billion in 1978 to $23.1 billion in 2009. Max amount was $41.5 billion and
minimum amount was $3.8 billion. Chart 3 discusses trends in non-performing loans as
measured by the amount of delinquent loans in the same period and captures the impact
of business cycle on asset quality and loan performance. The cyclical component of
time-series indicates that non-performing loans reached the highest levels at the peak of
the S&L and sub-prime mortgage crises, and then started to decrease. According to linear
trend line, it seems that this trend will continue over time.
Reflecting the degree to which the credit supply was contracting, mortgage originations
began to decrease at an increasing rate since the beginning of the subprime financial
crisis (Chart 1). In 2008, total industry mortgage originations (multifamily and
20
nonresidential mortgages) were $404.9 billion, decreasing by 43% from $716.2 billion in
2007. In the fourth quarter of 2008, total mortgage originations decreased to $63.2 billion
from $166.6 billion in the fourth quarter one year ago. Since they represent the largest
category of loans in the S&L industry, single family (1-4) loans were impacted the worst.
In the fourth quarter of 2008, 1-4 family mortgage originations by S&Ls were $52.4
billion, down 64% from $143.9 billion in the fourth quarter of one year ago (OTS,
2009:4).
Chart 3: Delinquent Mortgage Loans ($ In Millions)
45000
40000
35000
30000
25000
y = 217.1x + 13607
20000
DML
Linear (DML)
15000
10000
Source:
OTS, 2009
5000
0
Chart 4 shows average net charge-offs by loan type. In 2007-2009, while single and
multi-family charge-offs constituted 55% of total charge-offs, net-charge offs in
consumer and commercial loans were also significant (45%) highlighting the significance
of non-collectible loans in non-mortgage category. Charge-offs arise when a bank is
unable to collect some of its loans and therefore are subsequently written-off or reported
as a ―bad debt expense‖ on a company’s financial statement. Net-charge off appears as a
21
form of debt or impairment of assets and negatively affect earnings, also resulting in a
―write-down‖ of some of the bank’s assets. Since total aggregated data goes back to 1998
only, we could not include net charge-offs into our regression analysis for estimating loan
performance. In addition, net charge-offs are form of non-collectible loans so they are not
essentially different from delinquent or non-performing loans. Therefore, including this
variable might have caused multi-colinearity (very high R Square) essentially measuring
the same variable as DML_TLOANS.
Chart 4: Avrg. Net Charge-Offs 2007-2009
Multi Fam
Charge-Offs; 2%
Consumer
Charge-Offs; 30%
Single Fam
Charge-Offs; 53%
Commercial
Charge-Offs; 15%
Source: OTS, 2009
Table 1: Basic Statistics of Variables
Var.
Mean
Max.
DML_TLOA
NS
0.0246223
0.0510772
Min.
Std.
Dev.
LEV
ROA
15.533
95
34.294
12
0.003364
9
0.0088065
7.3286
2
0.0118789
7.9055
26
0.012577
0.013206
8
0.006934
1
RGDP
L
33057.
61
42897.
42
GROWTH_G
DP
2.875
7
NW_TA
0.06706
18
0.10737
33
TLI_N
W
16.558
2
35.294
12
17189.
72
DML
41494
24160.
93
-3
0.02755
27
8.3133
3825
6013.2
77
2.12132
0.02427
43
7.9765
05
10565.
89
Table 2 shows the parameters for an autoregressive model where DML_TLOANS is a
22
measure of loan performance/asset quality. The same variables appear in Table 3 where
DML is a dependent variable of loan performance. We used DML without ratios in order
to detect the degree of autocorrelation. In the first specification, where DML_TLOANS
is the dependent variable, there is a low level of positive autocorrelation with DurbinWatson statistic of 2.272113. In the second model, autocorrelation is almost non-existent
at the significance of 1.998551 Durbin Watson value. Using DML instead of
DML_TLOANS as dependent variable allowed lower autocorrelation and relatively
significant ratio of net worth to total assets (NW_TA). Both specifications, however, are
significant at 1% level based on Probability (F Statistic). Overall, these measure the
mutual relationship between the predictor variables and dependent variable in each
model. Based on R Square values, the right hand side variables explain the dependent
variable by almost 44% and 50% and the F statistic supports the regression. Probability
(F-Statistic) suggests that both regression models are significant at a 1% level, so we can
be reasonably confident that the good fit of the equation is not due to chance.
Table 2: Parameters for an autoregressive model
Dependent Variable: D (DML_TLOANS)
Method: Least Squares; first difference operator
Sample (adjusted): 1979 2009
Included observations: 31 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D (LEV)
D (ROA)
D (RGDPL)
D (GROWTH_RATE_GDP)
D (NW_TA)
D (TLI_NW)
0.001572
-0.011189*
-0.627385**
-1.08E-06
-6.22E-06
-0.366793
0.010067**
0.000977
0.003416
0.269874
6.68E-07
0.000385
0.251581
0.003177
1.609487
-3.275475
-2.324735
-1.611253
-0.016149
-1.457953
3.168480
0.1206
0.0032
0.0289
0.1202
0.9872
0.1578
0.0041
R-squared
0.440618
23
Mean dependent var
0.001000
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.300773
0.006353
0.000969
116.8040
2.272113
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.007597
-7.084131
-6.760327
3.150752
0.020116
(*Significant at 1% or 0.01 level; ** Significant at 5% or 0.05 level; ***Significant at 10% or 0.1 level)
The regression analysis in Table 2 indicates that variables except RGDPL,
GROWTH_RATE_GDP and NW_TA are significant in explaining loan performance at
1% and 5% respectively. This seems to indicate that macroeconomic variables like GDP
per capita income have no impact on the likely of borrowers to default on mortgage
loans. While this seems to be inconsistent with Table 3, where DML is dependent
variable instead of ratio value, the coefficient of RGDPL is barely significant at 10%
level or P value of 0.0927. The unimportance of GDP per capita and Growth rate of GDP
is unexpected given that they are regarded as major financial crisis indicators at the
country level (Economics of Crisis, 2011). The impact of GDP on bank performance has
received attention in Demirguc-Kunt and Huizinga (2000) who found a link between
economic development and bank profitability. Banks in well-developed markets face
tougher competition but lower profitability. Yet, greater financial market development is
correlated with higher bank profits and net interest margins in less developed financial
systems. Applying this interpretation to our analysis, it is plausible to say that higher
profits lead to higher mortgage originations because it allows lenders to borrow more
capital in order to originate more mortgages; greater economic growth generates
profitable banking. Yet it is not conclusive from this analysis whether the determinants of
loan performance are bank-specific (profits) or macroeconomic (financial market
24
development). It was difficult to establish this factor due unavailability of appropriate
data.
Table 3: Parameters for an autoregressive model
Dependent Variable: D (DML)
Method: Least Squares, first difference operator
Sample (adjusted): 1979 2009
Included observations: 31 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
466.3003
969.2582
0.481090
0.6348
D(LEV)
D(ROA)
D(RGDPL)
D(GROWTH_RATE_GDP)
D(NW_TA)
D(TLI_NW)
-8116.863*
-599187.4**
1.218214**
-85.37359
-473451.8**
7096.696*
2355.956
284622.0
0.695624
297.2539
260465.2
2174.002
-3.445253
-2.105204
1.751253
-0.287208
-1.817716
3.264346
0.0021
0.0459
0.0927
0.7764
0.0816
0.0033
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.497549
0.371936
5470.263
7.18E+08
-306.8397
1.998551
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
624.4194
6902.496
20.24772
20.57152
3.960970
0.006804
(*Significant at 1% or 0.01 level; ** Significant at 5% or 0.05 level; ***Significant at 10% or 0.1 level)
There is evidence that non-performing loan as a share of total loans is an important
indicator of loan performance and subsequently liquidity crisis in the banking sector.
Since lenders generate income by writing loans, the price of these loans cover operating
costs and generates a profit. If a borrower fails to make payments for a long period of
time, the bank loses income and categorizes the debt as non-performing loan. High ratio
of non-performing loans can further cause contraction in lending, even causing the share
prices to fall. Yet the larger determinants of poor loan performance seem to be industry
and bank related as well as macroeconomic. Loss of income, for example, is a
25
macroeconomic factor that can certainly lead to foreclosure because its direct effect on a
borrower’s ability to make mortgage payments. It is plausible to say that job loss or
unemployment is inversely correlated with bank earnings through increased mortgage
defaults and foreclosures during a financial crisis. Due to unavailability of data, however,
borrower’s income and socio-economic variables had to be omitted from the analysis.
As clearly seen in Table 2, leverage ratio (LEV), return on assets (ROA) as a proxy for
profitability and ratio of total liability to net worth (TLI_NW) are significant in
explaining S&L loan performance. While LEV and ROA have a negative coefficient
sign, TLI_NW is positively correlated with DML_TLOANS. The most statistically
significant variables in Table 3 are LEV and TLI_NW at 1% with p values of 0.0021 and
0.0033 respectively. Leverage coefficients vary in significance depending on the loan
performance used but in both regressions carry negative signs, indicating inverse
relationship with respect to loan performance. This seems to indicate that higher the nonperforming loans as a share of total loans (DML_TLOANS), the lower the leverage and
vice versa. Similarly, higher the ratio of DML_TLOANS, lower the return on assets
(ROA), in other words less bank profits.
Given that profits are integral part of bank earnings, we expect non-performing loans to
decrease as profits increase. Everything else remaining equal, there is evidence that the
ratio of total liabilities to net worth (TLI_NW) affect loan performance positively.
Measuring the net worth of the enterprise to offset its liabilities (debt), TLI_NW reflects
on the relationship between assets and liabilities. Higher ratio is an indicator of higher
26
debt and thus less net worth. A bank with higher debt is also likely to display a higher
ratio of non-performing loans to total loans. As DML_TLOANS increases, the ratio of
net worth to total assets of the company decreases, consistent with negative coefficient
sign of NW_TA in Table 3. Overall, our analysis is coherent with the view that
diminishing net worth and under-performing loans have the potential to render S&Ls
vulnerable to financial shocks, thus contributing to financial instability.
POLICY
IMPLICATIONS
FOR
LOAN
PERFORMANCE
AND
PROFITABILITY
We can make some recommendations at the industry and country level that will help
improve the safety and soundness of loan portfolios at remaining S&Ls. A numbers of
forces affect the performance of financial intermediaries. While some of these forces are
external to institutions such as trends of interest rates and the strength of the economy
and regulations, some of them are internal. Internal forces reflect managerial capabilities
and the usefulness of operating policies and procedures. Return on Assets (ROA) and
ratio of total liabilities to net worth are important internal factors since they were found to
be complementary with loan performance. Another factor is loss provisions that represent
the reserves set aside for potential loan defaults. One possible way of protecting the
safety and soundness of S&Ls is to increase provisions for losses in interest bearing
assets. While S&L loss provisions have been consistently increasing since 1991, from
$4.9 billion to $19.5 billion in 2009, they were the highest level on record in 2007 ($11.6
billion), 2008 ($39.3 billion) and 2009 (OTS, 2010) Higher than average levels of loss
provisions reflected the credit cycle and persistent declines in home prices rather than a
27
standard policy. It would make sense to keep the average loss provisions substantial in
order to cushion the effect of loan defaults on bank soundness and safety. They key
question is whether sizable additions to loan loss reserves dampen earnings/profitability
or filter the impact of poor loan performance is difficult to predict. The impact of loss
provisions could have been estimated if data existed back to 1978.
The quality of loan portfolio reflects the degree of credit risk associated with an asset
(such as MBS) and maps to the overall riskiness of an institution. The improvement of
the loan quality of S&Ls needs to be based on reinforcement of the supervisory standards
through national regulation programs. Adequately regulating the proportion of nonperforming loans to total loans and monitoring the size of leverage are essential in
controlling risk. It is necessary to frequently monitor the adequacy of Loan Loss
Provisions concomitantly with risk management processes and internal regulations at
financial institutions.
This study only looked at one type of mortgage as a proxy for loan performance—
delinquent—when ―the borrower has missed one or more scheduled monthly payments‖
There are other types of classifications that were not included due to lag factor that might
exaggerate the size of poor performance. For example, default ―happens when is 90 or
more days delinquent. At this point, foreclosure proceedings against the borrower
become a strong possibility‖. Foreclosure is when ―the borrower has been delinquent for
more than 90 days, and the lender has elected to foreclose in what is an often lengthy
28
process with several possible outcomes. For instance, the borrower may sell the property
or the lender may repossess the home‖ (GAO, 2009:6-70).
In addition to adequately regulating loan performance and soundness at S&Ls, policy
interventions geared towards borrowers might be appropriate. Since the start of the
housing crisis, the government undertook a number of loan modification plans that
included making changes to the terms of loan agreement, reducing the interest rate,
extending the loan term or using ―forbearance plans‖. Under the Home Affordable
Modification Program (HAMP), for example, Department of the Treasury, Fannie Mae,
and Freddie Mac were called on to use up to $75 billion to promote loan modifications.
These plans were aimed at delaying foreclosure and making mortgage payments more
affordable, especially in the non-prime category (GAO, 2009:13).
While lenders and borrowers must work together to improve loan performance and help
reduce future home foreclosures, it is a challenge to determine the eligibility for loan
modification programs. The policy implications of our analysis (the need for loan
portfolio improvement) can be subject to a number of obstacles. As GAO noted, US
Treasury has estimated that up to 3 to 4 million borrowers with high risk profile (at risk
of default or foreclosure) could be part of the loan modification plan under HAMP.
However, as GAO noted again in July 2009, ―Treasury’s estimate reflects uncertainty
created by data gaps and the need to make numerous assumptions, and therefore may be
overstated‖ (GAO, 2009:13-14).
29
CONCLUSION
This paper investigated the determinants of asset quality and loan performance in the
S&L industry over the period 1978-2009. In particular, it sought to establish which
potential determinants of loan performance prevailed in the US S&L industry.
Furthermore, the paper discussed the significance of recent market and financial
innovations and assessed the role of regulation in light of the present crisis. The
development of the secondary mortgage market, especially private label MBS’s, has
helped expand the business of securitization but also adversely affected loan performance
and quality at S&Ls with fixed asset structures. The final section briefly discussed the
implications of loan modification programs for asset quality management and drew
strategic lessons for future researchers and practitioners in the field of risk management.
We obtained the data from the Office of Thrift Supervision (OTS) database between 1978
and 2009, thus covering both the years before the start of the present crisis as well as
those that followed. Despite the sizable indicators of loan performance going far back to
1964, this paper only included the period for which data on loan performance were found.
Applying ADF as a unit root test for filtering non-stationary effects by estimation of least
squares, we were able to establish meaningful trends in the loan performance of the S&L
industry from 1978 to 2009. To apply the test, we accepted the existence of a unit root
assuming that time-series variables were non-stationary. The model was then reestimated applying the first difference operator to the series and stationary de-trending.
30
Overall, the results of our analysis indicated that industry characteristics explain a
considerable part of the variation in loan performance measured by the ratio of nonperforming (delinquent) loans to total loans. The most statistically significant variables
are return on assets, leverage ratios and ratio of total liabilities to net worth. Poor loan
performance tends to be associated with banks holding less profits and net worth. Put
differently, this indicates higher return on assets (ROA) and ratio of net worth to total
assets leads to a lower ratio of poor loan performance. Higher the ratio of consumers to
default on their loans, the lower the return on assets. In addition, the leverage ratio has a
significant negative coefficient on loan performance. By and large, our analysis is
confirmed by earlier studies that decreasing net worth and under-performing loans have
the potential to render S&Ls vulnerable to financial shocks, thus contributing to financial
instability.
One of the limitations of the study is the use of time-series data for loan performance
indicators rather than cross-sectional data by sector. The other limitation is the exclusion
of other categories of data for measuring loan performance such as provisions for losses
on interest bearing assets. While this type of data is largely available for US commercial
banks, it only goes back to 1991 for S&Ls, making it difficult to generalize from a
limited period of time. Although this has made it difficult to examine the variations in
loan performance across institutions, the co-integration statistical procedure (ADF test)
was able to filter some of the trending behavior in our data.
31
Future research can benefit from the inclusion of exogenous variables in regression
analysis, such as policy interventions and regulatory capital requirements that can affect
the long-run performance of the S&L industry at the country level. Structure-conductperformance (S-C-P) theory highlights the contribution of market structures and financial
system variables to financial institution performance. This theory is used to analyze the
relation among firm performance, market conduct, and market structures. With an
industrial organization approach, it has led to useful empirical modeling of financial
changes, technological innovations, merger analysis, profitability and identification of
market power. If properly integrated into the S-C-P paradigm, the policy reforms
discussed above can be the starting point for regulators to design long-term policies that
can enhance financial institutional stability and sound lending practices, improved
schemes for asset quality management, and strengthened oversight of leverage, liquidity
and risk management.
BIBLIOGRAPHY
Angell, C. and Rowley, C.D. 2007. Breaking New Ground in U.S. Mortgage Lending,
FDIC
Outlook
[On
line,
March
21,
2007]
Available
at
http://www.fdic.gov/bank/analytical/regional/ro20062q/na/2006_summer04.html
[accessed: April 6, 2011].
Balderston, F. E. 1985. Thrifts in Crisis: Structural Transformation of the Savings and
Loan Industry. Real Estate Economics, Volume 14, Issue 1, pp.165-168, March.
Benston, G.J. 1972. Economies of Scale of Financial Institutions. Journal of Money,
Credit and Banking, Vol.4, No.2, pp.312-341.
Berger, A. N. 1995. The relationship between capital and earnings in banking. Journal of
Money, Credit and Banking, Volume 27, Issue 2, pp.432-456.
Blair, C. and Kushmeider, R.M. 2006. Challenges to the Dual Banking System: The
Funding of Bank Supervision. FDIC Banking Review, Volume 18, No.1.
32
Bourke, P. Concentration and Other Determinants of Bank Profitability in Europe, North
American and Australia. Journal of Banking and Finance 13, 65-79.
Bradley, M.G., Gabriel, S.A. and Wohar, M.E.1995. The Thrift Crisis, Mortgage-Credit
Intermediation, and Housing Activity. Journal of Money, Credit and Banking, Vol.27,
No.2, pp.476-497.
Brigham, E. 1964. Economies of Scale in the Savings and Loan Industry. Economic
Inquiry, Volume 3, Issue 1, September, pp.7-20.
Casu, B., Girardone, C. and Molyneux, P. 2004. Productivity Change in European
banking: A comparison of parametric and non-parametric approaches. Journal of
Banking and Finance, Volume 28, Issue 10, pp. 2521-2540.
Chaudhry, M., Chatrath, A. and Kamath, R. 1995. Determinants of bank profitability.
American Journal of Business. 1995, vol. 10, issue 1, pages 41-46
Cromwell, Jeff B., Labys, W.C. and Hannan, M.J. 1994. Multivariate Tests for TimeSeries Models. Sage Publications.
CNN. 2007. New Century files for Chapter 11 Bankruptcy, April 3.
Curry, T. and Shibut, L.2000. The Cost of the Savings and Loan Crisis: Truth and
Consequences. FDIC Banking Review, (12), 26–35.
Demirguc-Kunt, A. and Huizinga, H. 2000. Financial Structure and Bank Profitability.
The World Bank in its series Policy Research Working Paper Series with number 2430
[On line August 31, 2000] Available at http://ideas.repec.org/p/wbk/wbrwps/2430.html
[accessed: March 5, 2011].
Economics of Crisis. 2011. Financial Crisis Indicators in the Literature. Available on line
http://www.economicsofcrisis.com/economics_of_crisis/lit.html [accessed: April 17,
2011].
Geehan, R. and Allen, L. 1978. Measuring the real output and productivity of savings and
credit institutions. The Canadian Journal of Economics, Vol.11, No.4, November,
pp.669-679.
Gilbert, R.A. 1984. Bank Market Structure and Competition: A Survey. Journal of
Money, Credit and Banking, Volume 16, Issue 4, pp.650-656.
33
Golderberg, L.G. and Rai, A. 1996. The structure-performance relationship for European
banking. Journal of Banking & Finance, Volume 20, October, Issue 4, pp. 745-771.
Gramlich, E. M. 2004. Sub-prime Mortgage Lending: Benefits, Costs, and Challenges,
Governor of the Board of thee Federal Reserve System, at the Financial Services
Roundtable Annual Housing Policy Meeting, Chicago, Illinois, May 21.
GAO. 2009. Loan Performance and Negative Home Equity in the Non-Prime Mortgage
Market. US Government Accountability Office [On line, December 16] Available at
www.gao.gov/new.items/d10146r.pdf [accessed: April 20, 2011).
Jahera, J.S., Page, D.E. and Hudson, C.D. 2006. Financial Condition and Performance of
Savings and Loans: A Retrospective Look at Mutual to Stock Conversions. Investment
Management and Financial Innovations, Volume 3, Issue 2, 2006, pp.77-87.
Kaushik, S.K. and Lopez, R.H. 1996. Profitability of Credit Unions, Commercial Banks
and Savings Banks: A Comparative Analysis. The American Economist, Vol.40, No.1,
Spring, pp.66-78.
Kushmeider, R. M. 2005. The US Federal Financial Regulatory System: Restructuring
Federal Bank Regulation. FDIC Banking Review, Volume 17, No.4, pp. 1-29.
Matasar, A.B. and Pavelka, D.D. 1998. Federal Banking Regulators’ Competition in
Laxity: Evidence from CRA Audits. International Advances in Economic Research,
February, Vol.4, No.1, 56-69.
Mike Hudson IndyMac: What Went Wrong? How An Alt-A Leader Fueled its Growth
With Unsound and Abusive Mortgage Lending, Center for Responsible Lending, CRL
Report, June 30, 2008.
Mildenberg, D. 2008. Bank of America to Acquire Countrywide for $4 billion,
Bloomberg, January 11.
Morgenson, G. 2007. Inside the Countrywide Lending Sphere, New York Times, August
26.
Naceur, S.B. 2003. The Determinants of the Tunisian Banking Industry Profitability:
Panel Evidence. [On line] Available at www.mafhoum.com/press6/174E11.pdf
[accessed: February 27, 2011].
OTS. 2009. THRIFT INDUSTRY HIGHLIGHTS FOURTH QUARTER 2008 [On line.
February 26, 2009] Available at http://www.ots.treas.gov/?p=IndustryPerformance
[accessed: October 16, 2010].
OTS. 2010. 2009 Fact Book: A Statistical Profile of the Thrift Industry. [On line. March,
34
2010] Available at www.ots.treas.gov/_files/481165.pdf [accessed: October 16 2010].
Paletta, D. and Enrich, D. 2008. Crisis Deepens as Big Bank Fails, Wall Street Journal,
July 12.
Papanikolaou, N.I and Wolff, C.C.P. 2010. Leverage and risk in US Commercial
Banking in the light of the current financial crisis. Paper provided by Luxembourg School
of Finance, University of Luxembourg in its series LSF Research Working Paper Series
with number 10-12 [Online]. Available at http://ideas.repec.org/p/crf/wpaper/10-12.html
[accessed: March 15, 2011].
Pervan, M., Pervan, I. and Guadagnino, A. 2009. Market Structure and Performance of
Croatian Commercial Banks. Paper presented at July 2009 Business and Economics
Society International Conference, July 15-18, Kona-Hawaii, USA.
Rasiah, D.2010. Review of Literature and Theories on Determinants of Commercial Bank
Profitability. Journal of Performance Management 23.1, 23-49.
Short, B.K. 1979. The Relation Between Commercial Bank Profit Rates and Banking
Concentration in Canada, Western Europe and Japan. Journal of Banking and Finance 3,
209-219.
US Treasury Department. 2010. Overview of OTS-Regulated Mutual Thrifts. Available at
files.ots.treas.gov/upload/files/49005.pdf [accessed: April 6, 2011].
Verbrugge, J.A., Shick, R.A. and Thygerson, K.J. 1976. An Analysis of Savings and
Loan Profit Performance. The Journal of Finance, Vol.31, No.5, December, pp.14271442.
World Data Bank. 2010. World Development Indicators & Global Development Finance
[On
line]
Available
at
http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2 [accessed: March
13, 2011].
35