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DOI 10.1007/s11146-013-9449-5

First Mortgages, Second Mortgages, and Their Default
James B. Kau · Donald C. Keenan ·
Constantine Lyubimov
Published online: 24 November 2013
© Springer Science+Business Media New York 2013
Abstract Using 35,437 pairs of first and second mortgages
matched from within a
much larger set of subprime mortgages, this paper tracks and
describes the tendency
for either one of the mortgages to enter default, as well as the
tendency for either
the one or the other mortgage to ever return to being current, all
this in a possibly
repeated manner. Thus, the entire, interconnected default
history of pairs of first and
second mortgages is explored, as well as compared to
theoretical predictions.
Keywords Piggyback mortgages · Default · Loan modifications
Introduction
There have been a number of papers in the mortgage literature
looking at the effect
of individual second-lien loans on the default behavior of
primary residential mort-
gages (Gerardi et al. 2009; Sherlund 2008; Demyanyk and Van
Hemert 2011; Elul
et al. 2010; Jagtiani and Lang 2010; Eriksen et al. 2011), along
with a smaller number
The views expressed here are those of the authors and do not
indicate opinions of other members of

the research staff of FNMA.
J. B. Kau
Department of Insurance, Legal Studies and Real Estate, Terry
College of Business,
University of Georgia, Athens, GA, USA
D. C. Keenan
Department of Economics and Management, Université de
Cergy-Pontoise & THEMA,
Cergy-Pontoise Cedex, France
C. Lyubimov (�)
Federal National Mortgage Association, Washington DC, USA
e-mail: Konstantin [email protected]
mailto:[email protected]
562 J. B. Kau et al.
of papers looking at the default behavior of second loans in the
presence of firsts
(Agarwal et al. 2006a, b; Jagtiani and Lang 2010). To the best
of our knowledge,
however, this is the first paper to consider pairs of such loans
simultaneously, and so
the timing of default for either the first or second loan as
affected by the status of
the other loan. Furthermore, our pairs of loans continue to be
observed beyond the
initial default, and so, an accounting is made of whether the
other loan also eventu-
ally defaults and whether either loan ever returns to being
current, possibly only to
become delinquent again at a later date, and so on, in a

recurrent fashion.
This is achieved by employing a multistate competing hazard
framework, much
like a Markov chain, where the states are the various
combinations of being current
or in default for the two mortgages, yielding four such primary
states.1 The various
transitions between these states are modeled and estimated,
including both the for-
ward directions, where one or the other mortgage becomes
delinquent, as well as
the backward directions, where one or the other loan returns to
being current. See
Fig. 1 for the definition of the states and the transitions between
them. In addition,
we account for unobserved heterogeneity among the mortgages
and across the transi-
tions, thus creating a dependency among all the various
transitions, which must then
be estimated in a simultaneous fashion.2
Piggyback Loans
The pairs of mortgages are determined by matching the times of
origination, as well
as the combined loan-to-value (CLTV) ratios, borrower’s FICO
(Fair Isaac Corpo-
ration) score, and zip code of both first and second loans found
in a large pool
of securitized GMAC mortgages originated between 1999 and
2007 and observed
from January 2002 until June 2011.3,4 This matching yielded
35,437 loan pairs, of
which 30,314 primary loans are ARMs (adjustable rate
mortgages) and 5,123 primary

loans are FRMs (fixed rate mortgages.) Observation of the loan
pair’s status is made
1 Technically, what we have is a semi-Markov process, since
the transition probabilities are allowed to
depend on time spent in the state. The baseline hazard is
completely free, being estimated using a sequence
of dummy variables.
2 While these papers have not allowed for recurrence, a separate
literature employing multiple states has
followed the process from default through foreclosure (Ambrose
and Capone 1998; Capozza and Thomson
2006; Pennington-Cross 2010; Chan et al. 2011). Since we
suppress this foreclosure process, this literature
is in some sense complementary to ours. One reason we avoid
this further distinction as to the fate of
mortgages, beyond the need to keep our state model within
tractable dimensions, is that there have, as yet,
been relatively few actual foreclosures in our data.
3 GMAC is the acronym of the General Motors Acceptance
Corporation, now rebranded as Ally Financial
Inc.
4 The matching task is not a trivial one; in the words of
LaCour-Little (2007): “While an important area
for future research, the data requirements to jointly analyze the
performance of first and junior loans are
quite daunting.”
First Mortgages, Second Mortgages, and Their Default 563
C
7

8
6
2
10
1
9
5
4
3
B
DA
Fig. 1 The scheme of transitions and states determined by
possible statuses of the first and the second
loans, without prepayment. A both loans are not in arrears, B
the second lien in arrears, C the first lien in
arrears, D both loans are in arrears
monthly and default is also indicated on a 30-day delinquency
basis. Table 1 gives
a summary of typical characteristics of the entire set of
mortgages, whereas Table 2
breaks down the loans by year of origination and type, as well
as listing average val-
ues for some of these loans’ more important characteristics.
Note that our matching
procedure assures that these second loans are so-called
piggybacks, originated at the
same time as the primary loan. The usual explanation for such

loans is that they per-
mit the primary loan to be of 80 % or less LTV (loan-to-value)
ratio, even for a person
who wants to make less than a 20 % overall down payment, and
so avoids the need
for mortgage insurance on the primary loan. It might occur to
most economists that
the resulting benefit for the primary loan would need to be
offset by the higher rates
on the second loan, given an efficient market for insurance, but
it should be observed
that it is only the 80 % or less LTV ratio loans that are
traditionally securitizable, and
so for which a deep secondary market exists. The piggyback
arrangement is then a
convenient device for extracting, from a non-conventional loan,
that part which can
be expected, because of its greater market liquidity, to have
particularly favorable
terms not available through the equivalent larger loan.5,6
Previous Literature
As indicated, there is by now a substantial literature on the
default behavior of pri-
mary loans as they are affected by the presence of second-lien
loans. We mention
5 It has also been suggested (Calhoun 2005) that originating
piggybacks in place of higher LTV single
loans helped banks avoid certain capital requirements, another
explanation for better terms being offered
on the pair of loans than on an equivalent single loan.
6 While we cannot entirely preclude the possibility of additional
so-called “silent seconds”, which are
unobserved second loans occurring at a later date, typically

home-equity loans for a purpose other than
funding the house itself, this seems especially unlikely in our
sample, given that there is already an explicit
second loan at origination.
Table 1 Summary statistics for select variables
Variable No. obs. Mean St. dev. Min Max
Adjustable-rate first mortgages
Rt1 30314 7.90 1.41 3.4 13.88
Rt2 30314 11.61 1.69 6.49 16.99
LTV1 30314 80.91 2.64 54 90
LTV2 30314 18.61 3.14 4 43
Term1 30314 360 0.49 300 360
Bal1 30314 162.7 95.8 22 880
Bal2 30314 38.0 24.6 6 250
Marg1 30314 6.13 1.53 1.8 12.7
origCLTV 30314 1.00 0.02 0.73 1.01
No. modif. 3357
Fixed-rate first mortgages

Rt1 5123 8.44 1.61 4.85 13.88
Rt2 5123 11.48 2.01 6.70 16.99
LTV1 5123 80.90 3.11 22 90
LTV2 5123 18.33 3.41 4 33
Term1 5123 327.9 68.25 120 360
Bal1 5123 123.4 72.4 21 840
Bal2 5123 27.9 17.8 8.25 197.8
origCLTV 5123 0.99 0.02 0.40 1.01
No. modif. 495
Rt the rate at origination; Term the contract term, months; Bal
the balance of the loan at origination,
thousands of dollars; Marg the contract margin; origCLTV the
combined loan-to-value ratio at origination;
No. modif. number of modified first liens
Gerardi et al. (2009), Sherlund (2008), Demyanyk and Van
Hemert (2011) and Elul
et al. (2010) as outstanding examples. Except for Eriksen et al.
(2011) and Jagtiani
and Lang (2010), however, these papers lack further information
on the the second
loan beyond origination, except possibly as is reflected in the
combined loan-to-value
ratio. Eriksen et al. (2011) does have full information on second
loans, as well as the
firsts, for a smaller set of 3,078 FRM mortgages (taken from the

same data set as the
current one), but they do not fully exploit that data, in the sense
that they look only at
the effect of seconds on firsts, rather than treating them
simultaneously.7 The same
limitation exists in the nonetheless exceptional work of Jagtiani
and Lang (2010),
who match home equity loans with primary loans, but
concentrate on such issues as
7 The data set of Eriksen et al. (2011) is a bit small to engage in
the sort of analysis done here, and so in
most of their analysis the matched FRMs are combined with
other primary FRM loans who have no known
second match, thus making these latter loans subject to “silent
seconds.” The latter is a problem typically
encountered in most empirical mortgage analysis, though as
noted, the problem is minimal here.
Table 2 Sample by year of origination
Origination No. of Perc. of No. of Perc. of CLT V Rt1 Rt2
year ARM ARM FRM FRM
1999 234 0.8 44 0.9 0.78 10.98 14.36
2000 954 3.1 116 2.3 0.84 11.38 14.47
2001 1640 5.4 838 16.4 0.87 10.06 12.97
2002 3463 11.4 793 15.5 0.89 9.28 12.27

2003 4426 14.6 1239 24.2 0.89 8.28 10.86
2004 2867 9.5 747 14.6 0.89 7.39 9.40
2005 5540 18.3 363 7.1 0.98 7.17 8.29
2006 10467 34.5 856 16.7 1.07 7.75 8.72
2007 723 2.4 127 2.5 1.13 7.39 8.76
Total 30314 100 5123 100
The third and the fifth column represent the share of
originations in a given year to the total number
of, respectively, adjustable- and fixed-rate first mortgages in
our sample. The sixth column (“CLT V ”)
displays the mean current combined LTV over time for that
origination cohort (both ARM and FRM first
liens), the seventh and the eighth columns (“Rt1” and “Rt2”,
respectively) display the average current
rates on the first and the second lien over time for that
origination cohort
who continues to maintain their second loan while nonetheless
defaulting on the first,
rather than providing a comprehensive estimation of all default
activity among the
loan pairs over time.
Agarwal et al. 2006a, b face the opposite problem to most of
those articles men-
tioned above, in the sense that they have full information on the
second-lien loans but
little information on the first loan, other than as reflected in
combined loan-to-value

ratios. Their analysis is restricted to lines of credit (Agarwal et
al. 2006b) or to home
equity loans together with lines of credit (Agarwal et al. 2006a).
LaCour-Little et al. (2011) engage in matching of piggyback
loans, but keep the
analysis at the state or zip code level, rather than the individual
loan level. Finally,
while it did not engage in a similar empirical analysis, since it
was written before the
recent events which now provide us with so much information
on default behavior,
we would remiss if we did not mention Calhoun (2005), whose
prescient analysis of
piggyback loans portended many of the difficulties which have
more recently came
to pass.
We note, finally, that our approach, with its multiple states and
the risk of default,
is reminiscent of the vast literature on rating transitions of
corporate debt (see, for
instance, Lando (2004) for a partial review.) One important
difference, though, is
that this rating transition literature is necessarily concerned
with the market’s view of
the imminence of default, whereas we are concerned with
actually occurring default,
and not market perceptions. The occurrence of actual corporate
default is, of course,
a much rarer event, particularly in absolute numbers, than is
default on residential
mortgages.

The Empirical Framework
Default
An overall theme of this paper is that there is not just a first
loan that is influenced by
a second, nor just a second that is influenced by a first, there is
a pair of loans that the
borrower considers together at all times, whether one or the
other is in default, until
such time as there is final foreclosure on the house. Since this is
how the borrower is
presumed to think, this is how we must approach the problem:
we have tried to take
this view seriously in developing our estimation model.
As already indicated, Fig. 1 illustrates the overall setup of our
state transition
scheme. State A is the initial state of both loans being current,
state B is the second
being in default with the first current, state C is the opposite,
and state D is both loans
being in default.
Pride of place among the forward transitions is given to
transition 1, where begin-
ning from both loans being current, only the second loan goes
into default, whereas
transition 2 is where, instead, only the first loan goes into
default. In between is
transition 3, where both loans go into default simultaneously.
Unlike much analysis, we do not, however, stop with these
competing risks from

the initial state A, but instead follow the pairs of loans
throughout their lives. Tran-
sition 4 is the transition from only the second in default to both
being in default,
whereas transition 5 is the corresponding transition from only
the first being in
default to both being in default. Note that one could have
treated transition 3, both
simultaneously defaulting, as transition 1 immediately followed
by transition 4, or
alternatively, as transition 2 immediately followed by transition
5, but besides the
question of which way to treat it, this simultaneous decision to
default seemed a
distinct and significant enough choice to warrant its own
transition.8,9
Not only have we included all the possible forward transitions
toward default,
but we have also included the corresponding backward
transitions restoring loans
to currency. After some preliminary investigation, it was
decided in the backwards
direction to treat the pair of paths 6 and 10 as obeying the same
transition law, as well
as treating the pair of paths 7 and 9 in the same manner. Given
the large number of
possible transitions, further elaborated below, and the limited
amount of data, it was
necessary that some consolidation occur, and the backward
directions seemed the
most promising candidates, given that they are of less
importance to us and usually
come with less observations. Note that comparing the two paths
in each of above
pairs, the same mortgage is returning to currency, it is just a

matter of whether the
other mortgage is in default or not.
While some transitions are obviously more common than others,
none are vac-
uous: all possibilities occur with some frequency in our data.
Furthermore, it is
8 In part, the distinction is warranted because while the logic of
why one would default on, say a first
and not a second has been called into question, no one questions
that one might default on the two loans
together.
9 We treat movements from B to C or vice versa as a return to A
followed immediately by the other leg of
the trip.
7 5
4
1
86
9
11
2 10
3

E
A’’
A’
C’
C’’
B
D
Fig. 2 The scheme of transitions and states determined by
possible statuses of the first and the second
loans, prepayment of the first lien included. A′ both loans are
not in arrears and the second loan has not
been prepaid, A′′ the first loan is not in arrears and the second
loan has been prepaid, B the second lien in
arrears, C′ the first lien in arrears and the second loan has not
been prepaid, C′′ the first lien in arrears and
the second loan has been prepaid, D both loans are in arrears
possible, and sometimes happens, that one or the other of a loan
pair may enter into
default, then one or the other may return to being current, and
then, once again, a
default reoccurs for one or the other loan. Indeed, our scheme
permits any history of
recurrent default behavior to be accounted for among the loan
pairs.10
Prepayment
It must now be admitted that we have not been entirely

forthcoming as to the com-
plexity of the situation. In order to stress what we are primarily
interested in, default,
we have avoided mention, till now, of another possibility,
prepayment. We have not in
fact ignored prepayment, though we have treated it in a rather
more cursory fashion
than default. The first point to note is that, though we continue
to follow a loan pair if
only the second prepays, if the first prepays we cease observing
the pair. We thus have
an additional state E representing the first loan having prepaid,
which constitutes the
only absorbing state of the model. See Fig. 2 for an illustration.
What we have referred to as state A is then formally two states,
A′ and A′′, where
A′ is both loans fully current and A′′ is the first loan fully
current but the second
prepaid. The same distinction exists for state C (and, if you
wish, for state E, though
not for B, nor D), so C′ is the first loan in default with the
second current, whereas
C′′ is the first loan in default with the second prepaid. The
reason we feel entitled
to refer to either A′ or A′′ as state A is that we assume that
transition 2 is unaffected
by which state, A′ or A′′, the pair is in, though, of course, for
transitions 1 and 3 it
does make a difference, in the somewhat trivial sense that a pair
in state A′′ cannot
actually transition to state B or D, since a prepaid second loan
can obviously never
10 Note that the unobserved heterogeneity assigned to an
individual for a particular transition may vary

with the recurrence.
go into default.11 The same obvious logic applies to other states
and transitions, both
forward and backward. The consequences are further illustrated
in Fig. 2. Note, also,
that in the spirit of limiting the complications arising from the
opportunity to prepay,
we have treated all transitions to state E as obeying the same
law, that of transition
11, no matter the state of origin. There are then 9 different
transitions that need to be
estimated.
The Statistical Technique
The statistical framework is essentially the same as the other
mixed proportional haz-
ard models that have already been widely employed for
mortgages facing competing
risks, given unobserved heterogeneity.12,13 The main
difference is that here a loan
does not necessarily terminate or cease being observed after its
first transition, as
in the standard competing risk models of default and
prepayment, and, indeed, here
there is the possibility of repeated returns to the same state,
limited in principle only
by the finite life of the loan. Note that it is assumed that the
hazard from a particular
state depends only on the the most recent duration in that state,
though of course the

covariates affecting the baseline may evolve in either mortgage
or calendar time.
No distributional assumptions were made as to the frailty
distribution, which is
approximated by masspoints.14 The advantage of the discrete
masspoint method is
that it can arbitrarily well approximate any actual distribution
and need not result in
the biases inherent in the choice of a specific functional form
for the frailty distribu-
tion, as is inevitably required when adopting a continuous
frailty distribution. (See,
for instance, the discussion in Han and Hausman 1990).15 In
order to assure computa-
tional feasibility, though, we did limit ourselves to four
masspoints. As noted earlier,
though, the assigned frailty term of an individual may vary by
the source state, the tar-
get state, and the particular recurrence. Prior experience with
competing risk models
(see, for instance, Deng et al. 2000) showed that using only two
masspoints seemed
adequate to the task of treating unobserved heterogeneity among
mortgage holders.
Contractual Features Affecting the Transition Hazards
Note that the setup and estimation technique permits covariates
to vary at will among
the various transitions, but that we typically keep them the
same, except when inves-
tigating some particular feature of default. This is with the
notable exception of
11 That is, if one is in, say, state A′, then one can technically

only move to C′, but not to C′′ and if one is in
state A′′, one can move to C′′, but not C′. This is, however, of
little importance for these transitions, given
that we have assumed the rules of the transitions are the same,
though for further possible transitions, we
do need to keep track of which state the pair is actually in.
12 See Clapp et al. (2006) for a discussion of the use of such
models in the context of mortgages.
13 Identification of our model is achieved by results going back
to at least (Sueyoshi 1992). See Brinch
(2009) for a more recent discussion of such identification
results.
14 See discussions in Wienke (2011) or Bijwaard (2011) for the
importance of treating unobserved
heterogeneity in the context of duration models.
15 Thanks to Simen Gaure and Knut Røed for graciously
sharing their code. This software has also been
used, for example, in the estimation of models of employment
transitions; see, e.g. Gaure et al. (2008).
transition 11, prepayment, which is modeled with a rather
different set of covariates
than are the default transitions.
The key contractual variables of the mortgages are in general
dynamic, being
at their current values, and include the ones most widely
recognized in the mort-
gage literature: i.e. the contract rate, the loan size, and the loan-
to-value ratio. Being
dynamic and current,16 these features are as applicable to a
variable rate mortgage as

a fixed one. Note, though, partly to conserve on variables, we
have invoked elements
of the combined loan hypothesis (see further discussion below),
having aggregated
such things as the loan sizes, in balcomb, and the contract rates,
in ratecomb (see
below for the exact definitions). We have, however, in the most
basic model (Table 7)
kept distinct what is traditionally considered the most important
of these contractual
variables, the two loan-to-value ratios. One non-dynamic, non-
current contractual
variable we do include among the covariates, though, is the
original combined loan-
to-value ratio, origCLTV, whose effect is sometimes thought to
reflect self-selection
of different borrower types, not fully captured by, say, their
FICO scores. We note
that these FICO scores have, indeed, also been included as
another static covariate,
fico.17 Other static covariates include lowdoc, indicating
whether it is a low doc-
umentation loan, together with a dummy variable distinguishing
an ARM from an
FRM, arm.18
Preliminary Data Analysis
In the lower triangle of Table 3, we present, near the lower right
hand corner of each
cell, the number of mortgages ever making the transition from
the source state of
that column to the target state of that row, and then conversely,
near the upper left
hand corner, the number of mortgages ever making the
transition from the source

state of that row to the target state in that column.
Corresponding transitional prob-
abilities are displayed in Table 4. The diagonal elements of
Table 3 represent loans
where, from the state of both being current, the second prepays
(for states other than
A and C this is not possible, so no number is indicated.) In the
upper triangle of
the same Table, we list in parentheses only the number of loans
that are making the
transition for the first time. While some transitions are
obviously more frequent than
others, most are well populated, giving one confidence that the
various rules of tran-
sition can be estimated, despite the general need in hazard
analysis that there be a
16 Case-Shiller HPA index series were used to derive the
current loan-to-value ratio for properties located
in 20 largest MSA’s; for the rest of the sample, FHFA state-
level series were used.
17 Loans, particularly, adjustable rate mortgages have many
additional features, such as margins, teasers,
caps and floors, but these can be regarded as adequately
reflected in the current state of the dynamic
features of the loans which we do account for, e.g. the current
contract rate, though it must be admitted
that in a truly rational model they might exercise an additional
influence on the future terms of the loan
anticipated by the borrower, and, as with our motivation for
including the original combined loan-to-value
ratio, they constitute potential, though increasingly obscure,
margins on which borrowers might self-select.
18 The covariate modif is a dynamic indicator variable activated
when the loan is modified and will be
discussed further below.

Table 3 Transitions by source and destination
State A State B State C State D
State A 805 (4439) (6912) (10444)
(3450) (4290) (3255)
State B 4987 (930) (3443)
7779 (859) (2394)
State C 6288 987 99 (6118)
10966 1037 (3139)
State D 4425 3075 4457
13684 4395 8248
Prepay (E) 7780 0 0 277
The lower left triangle of the transition matrix displays total
numbers: the total number of transitions from
the source row state is displayed in the upper left corner of a
cell, whereas the total number of transitions
from the source column state is displayed in the lower right
corner of a cell. The upper right triangle of
the transition matrix contains the counts of first-time
transitions: transitions from the source column state
are displayed in parenthesis in the lower left corner, whereas

transitions from the source row state are
displayed in the upper right corner of respective cells. The top
left corner of the first cell on the main
diagonal contains the number of the second loans prepaid from
state A, the same spot in the third cell on
the main diagonal contains the number of the second loans
prepaid from state C; the bottom row displays
the number of the first loans prepaid from the respective column
state
substantial number of observations before accurate estimation
of the effect of
covariates can be achieved.
In Table 5 we list typical values of some of the characteristics
of the loans at the
time of a transition from state A to either state B, state C, or
state D, respectively.
We note that the average loan-to-value ratios are not as high as
one might imagine,
indicating that an overreliance on the principle that the
borrower must be acting to
Table 4 Transition probability matrix
Destination Source state
state A A′ B C C′ D E
A 0.952 0.214 0.157 0.018 0
A′ 0.001 0.951 0.036 0
B 0.009 0.553 0.013 0
C 0.013 0.610 0.018 0

C′ 0 0.029 0.003 0.962 0
D 0.016 0.187 0.206 0.950 0
E 0.009 0.026 0.046 0.024 0.002 0.001 1
The empirical transition probability from the source column
state to the destination row state averaged
over all durations is displayed in a cell
Table 5 Summary statistics for select variables at the time of
select transitions
Variable No. obs. Mean St. dev. Min Max
Transition 1
Ht/H0 7779 1.031 0.133 0.441 1.358
Rt1t /Rt10 7779 1.037 0.182 0.235 1.972
LTV1 7779 0.79 0.141 0.217 1.833
currCLTV 7779 0.962 0.181 0.334 2.287
DurSource 7779 9.66 12.1 1.00 104
Transition 2
Ht /H0 10966 1.003 0.142 0.426 1.357
Rt1t /Rt10 10966 1.064 0.187 0.343 2.16

LTV1 10966 0.816 0.153 0.369 1.88
currCLTV 10966 0.988 0.194 0.459 2.338
DurSource 10966 12.47 12.97 1.00 105
Transition 3
Ht /H0 13684 0.987 0.144 0.439 1.338
Rt1t /Rt10 13684 1.058 0.171 0.164 2.076
LTV1 13684 0.827 0.161 0.225 1.901
currCLTV 13684 1.018 0.206 0.341 2.328
DurSource 13684 13.32 13.03 1.00 112
Ht /H0 the ratio of derived house value at the time of transition
to the house price at origination, Rt1t /Rt10
the ratio of contract rate on the first loan at the time of
transition to that rate at origination, DurSource
number of months that the borrower spent in the state from
which a given transition occurred
minimize the market cost of the loan (discussed further below)
is liable to run into
difficulties.19
Rationality and Value Maximization
Value Maximization without Transaction Costs
We make the distinction between being rational, which in
economics means acting in
a goal-seeking manner, and so responding appropriately to

incentives, and the much
more narrow assumption, often employed in the finance-
oriented mortgage literature,
19 There is of course also the inevitable problem that, even
looking only at the averages, one would still
expect our constructed loan-to-value ratios to underestimate the
“actual” loan-to-value ratios of those
houses going into default, since default will presumably be
especially chosen among houses experiencing
exceptionally high falls in their value compared to those in the
region represented by the house price index.
It is not, however, even …
J Financ Serv Res (2016) 49:265–280
DOI 10.1007/s10693-014-0211-9
Transparency in the Mortgage Market
Andrey Pavlov · Susan Wachter · Albert Alex Zevelev
Received: 22 April 2014 / Revised: 17 July 2014 / Accepted: 27
November 2014 /
Published online: 16 January 2015
© Springer Science+Business Media New York 2015
Abstract This paper studies the impact of transparency in the
mortgage market on the
underlying real estate market. We show that geographic
transparency in the secondary mort-
gage market, which implies geographic risk based pricing in the
primary market, can limit
risk-sharing and make house prices more volatile. Ex ante,
regions prefer opaque markets

to enable insurance opportunities. We discuss the implications
for risk based pricing and
house price volatility more generally. In addition, we
investigate the specific conditions
under which competitive lenders would optimally choose to
provide opaque lending, thus
reducing volatility in the real estate market. We show that in
general the opaque competitive
equilibrium is not stable, and lenders have an incentive to
switch to transparent lending if
one of the geographic regions has experienced a negative
income shock. We propose market
and regulatory mechanisms that make the opaque competitive
equilibrium stable and insu-
rance opportunities possible.
Keywords Housing finance · Mortgage · Transparency · Opacity
· Real estate ·
Insurance · House price volatility
1 Introduction
One of the most often-cited causes for the severity of the 2008
financial crisis is that most
housing-related financial instruments were highly opaque (see
for example Gorton (2008)).
A. Pavlov (�)
Beedie School of Business Simon Fraser University, Vancouver,
British Columbia, Canada
e-mail: [email protected]
S. Wachter · A. A. Zevelev
The Wharton School University of Pennsylvania, Philadelphia,
PA, USA
S. Wachter

A. A. Zevelev
266 J Financ Serv Res (2016) 49:265–280
Since investors were unable to ascertain the exposure of
separate financial institutions to
these instruments and because the exposures were crosscutting,
the entire financial system
was at risk. As a result, numerous regulatory, policy, and
institutional recommendations
have called for greater transparency in mortgage portfolios and
their derivatives French et al.
(2010).1
Nonetheless, the design of transparency features matters.
Transparency in some forms
may in fact have negative side effects. In this paper, we build
upon the literature on debt and
insurance markets to investigate the impact of increased
transparency in the mortgage mar-
ket. The existing literature, discussed below, highlights a
negative impact of transparency
on liquidity in financial markets. In this paper, we introduce a
model which shows that
certain forms of transparency can lead to increased volatility in
housing and mortgage
markets. Specifically, we develop a model of a mortgage
lending system that can be trans-

parent or opaque and compare outcomes under both scenarios,
as they relate to diversifiable
region-specific risk.
We show that a transparent market may be undesirable because
it increases real estate
price volatility and magnifies the impact of income shocks.
Under a transparent system,
lenders (and investors), know the geographic location of each
mortgage. When a local neg-
ative income shock occurs, lenders (investors) rationally
withdraw credit from that region
in anticipation of future (auto-correlated) income and house
price shocks. This withdrawal
magnifies the price impact of the original income shock.
In our model, the withdrawal of loans from the city which
experienced a bad income
shock leads to an increase of loans to the city with stable
income. However, this need not be
the case. Our results hold if MBS investors have alternative
methods to deploy their funds
in fully diversified or risk-free investments. While we present
our model in terms of income
shocks to different cities/regions, our main points can easily be
framed in terms of demand
shocks to an entire sector of the economy (housing) as long as
other sectors are not affected.
This of course requires securitized instruments to be opaque
with respect to the sectors in
which they are invested. This excludes sectors of the economy
with a substantial presence
of publicly available investments, such as stocks, bonds, and
derivatives.
In our setting, both borrowers and lenders may be worse off in a

transparent system. This
negative impact of transparency is due to two factors. First,
transparent systems increase
the volatility of the underlying real estate markets. Such
volatility negatively impacts poten-
tially risk-averse lenders and borrowers. While lenders can
somewhat diversify the increased
house price volatility, borrowers cannot. The impact on
borrowers from switching to a
transparent system is substantial and persistent.
The second factor that makes transparent systems undesirable
for lenders and borrowers
is that the price declines following an income shock are
magnified. This effect remains in
force even if all agents can fully diversify the increased
volatility. As we show in our model,
the magnified price declines occur when future income is also
likely to be lower. For lenders,
this means potential defaults on other loans, which in
combination with the mortgage
losses already discussed, can put the solvency of the lender in
jeopardy. For borrowers, the
transparent system magnifies the simultaneous decline of their
two main assets: real estate
1In 2008, Fannie Mae briefly implemented a “Declining
Markets Policy” by restricting the maximum CTLV
for properties located within a declining market to five
percentage points less than the maximum permitted
for the selected mortgage product. Fannie Mae ended this policy
in a few months.
J Financ Serv Res (2016) 49:265–280 267

and human capital. Beyond standard consumption implications,
this can push borrowers
into solvency or liquidity constraints.
We study mechanisms that preserve a stable opaque equilibrium
that allow for insur-
ance. One mechanism keeps a multitude of competitive lenders
in the opaque equilibrium
as long as they consider the long-term returns from that system.
We show that in the case
of multiple lenders, the presence of a short-term player in the
market forces everyone to
switch to a transparent system. The transparent equilibrium we
derive is stable. Lenders
require an external intervention or coordination to switch back
to the preferred opaque
equilibrium.
We proceed as follows. Section 2 reviews the relevant
literature. Section 3.1 presents a
theoretical model with a single lender. Section 3.2 extends the
work to two lenders and dis-
cusses the game-theoretic outcomes. Section 4 provides a
numerical calibration. Section 5
discusses policy implications. Section 6 concludes.
2 Literature review
There are two major strands of literature related to transparency
in financial markets. The
first strand focuses on liquidity for debt markets.2 A major
question in security design is
whether securities should be made transparent (and therefore
tranched) or made opaque
(bundled). Papers in this literature include Dang et al. (2013),

Pagano and Volpin (2010),
and Farhi and Tirole (2012).
In a theoretical model, Pagano and Volpin (2010) show that
issuers of asset-backed secu-
rities, facing a tradeoff between transparency and liquidity,
deliberately choose to release
coarse information to enhance the liquidity of the primary
market. Farhi and Tirole (2012)
look at the implication of tranching versus bundling on
liquidity. They show that tranching
has adverse welfare effects on information acquisition as
tranching provides an incen-
tive against commonality of information that contribute to the
liquidity of an asset. They
also show that liquidity is self-fulfilling: a perception of future
illiquidity creates current
illiquidity.
Dang et al. (2013) argue that opacity is essential for liquidity.
Investors in their models
are not equally capable of processing the transparent
information. When the composition of
a security is opaque then all investors are symmetrical ly
ignorant. If it is made transparent,
investors will pay a cost to process the additional information.
Since not all investors are
capable of processing this information, transparency will create
asymmetric information,
which has an adverse effect on liquidity.3 To illustrate their
logic, Holmstrom (2012)
explains that DeBeers sells wholesale diamonds in opaque bags.
If the bags were trans-
parent, buyers would examine each bag individually leading to
increased transaction costs
due to time allocated to inspections and adverse selection

among buyers. This would make
the diamond market much less liquid.
2For a discussion of the liquidity of the MBS market and its
benefits as measured in the TBA market see
Vickery and Wright (2010).
3DGH argue that while symmetry of information about payoffs
is essential for liquidity, transparency is
not and opacity actually contributes to liquidity as symmetric
information can be achieved through shared
ignorance. Highly nontransparent markets can be very liquid
(19th century clearinghouses, currency). When
it is possible to obtain information about an asset, people invest
in finding information differentially, resulting
in lower overall liquidity.
268 J Financ Serv Res (2016) 49:265–280
Nonetheless Downing et al. (2005), in the context of MBS,
show that making available
to investors information that informs on risk and reduces
uncertainty enables tranching to
be efficient by dividing informed investors willing to invest in
riskier tranches from non-
informed investors who are sheltered from the risk in higher
tranches. This has been done
in agency MBS and does not interfere with liquidity. But
tranching for risk that is not trans-
parent creates adverse selection and is not stable similarly to
the situation demonstrated
by Akerlof (1970). This happened in the private MBS and CDO
markets over the crisis as
shown in French et al. (2010) and Beltran et al. (2013).

This first set of studies focuses on the trade-off between the
liquidity benefits of opaque-
ness and the adverse selection implications. The lack of
transparency can ensure symmetric
information among actors, unless the issuers and institutions
lead to differentially disclosed
information.
Our model extends a second strand of literature that studies the
relationship between
transparency and risk pooling. Hirshleifer (1971),4 the seminal
paper in this literature,
shows how transparency can be harmful through its destruction
of insurance opportunities.
If as the insurance contract is being entered into, knowledge of
the risk is made known to
the actors, they will price it separately, even if the risk is
diversifiable. If market participants
have updated information about each other’s risk they will not
want to insure each other.
This mechanism has been applied to study the role of
transparency among financial inter-
mediaries (Bouvard et al. 2012). They find that transparency
enhances the stability of the
financial system during crises but has destabilizing effects in
normal times.
While consistent with the literature on transparency and
liquidity, our work predomi-
nantly draws on the second strand discussed above to show that
transparency limits risk
pooling and reduces insurance opportunities. This is particularly
relevant for transparency
regarding exposure to macroeconomic shocks, modeled here as
income shocks. Our model
has no implications about transparency with respect to loan-

specific risk characteristics and
underwriting criteria.
Similarly, recent work by Hurst et al. (2014) studies regional
risk sharing through the
U.S. mortgage market. While our research studies the impact of
geographic transparency
on equilibrium house prices, Hurst et al. (2014) consider the
impact on equilibrium interest
rates. In addition they consider a fully dynamic model of
housing with discrete adjustment.
3 Model
We develop a simple model that captures key features of
residential real estate markets. The
first assumption is that homes are purchased with mortgages
from the financial system only,
and homeowners cannot raise equity or issue debt directly to the
market. We further assume
that lenders are competitive, so they generate zero profits. This
assumption is consistent with
our discussion that local shocks are fully diversifiable to
originators and MBS investors.
The only choice lenders have is whether to be transparent or
opaque in their lending deci-
sions. Most importantly, lenders are not able to derive
monopolistic/duopolistic profits in
any scenario by altering their pricing and quantity mix.
A limitation of the model is the assumption that homeowners
base their purchase deci-
sions on their current income and current loan availability, with
no foresight of potentially
4This is in contrast to Akerlof (1970) who shows that

transparency is good in markets that suffer a “lemons”
problem. Informing all parties who the lemons are will make the
market function more smoothly.
J Financ Serv Res (2016) 49:265–280 269
changing availability of credit, and no ability to increase their
investment if they perceive
good opportunities.
We begin by describing the housing and credit markets under
transparency and opacity.
Our baseline model for both of these regimes utilizes a single
loan originator (or lender)
funded by the secondary market and two cities. We then expand
this to two (or more) origi-
nators, both funded by a secondary market, to analyze the
coordination problem faced by
individual originators under these circumstances.
3.1 One lender
We assume that the loan originators in our model are
competitive (or face the threat of
competition in the case of a single originator). Thus, the lending
rate offered is determined
entirely by the secondary market. We assume that the lenders
charge a spread between their
funding cost and lending rate to cover their costs. Also,
originators can fully diversify their
exposure to local income shocks. In other words, the interest
rate, R = (1 + r), lenders
charge their borrowers is exogenous. Lenders are funded by
selling an unlimited volume

of mortgage-backed securities (MBS) in the secondary market
as long as those securities
provide the prevalent expected rate of return.
Consider two cities denoted by A and B. Each city j (j ∈ {A,
B}) has a representative
household who receives income in period t , denoted y
j
t . Income in the two cities follows a
correlated stochastic process (yAt , y
B
t ) ∼ F (defined below). In addition to income, homes
are also financed by loans L
j
t .
The demand for housing is given by:
Q
j
t = α + yjt + Ljt − γ pjt (3.1)
Where α is the intercept, γ is the slope and p
j
t is the price of housing in city j at
time t . The supply of housing is fixed: H
j
t = H . While we acknowledge that different sup-
ply elasticities can potentially affect the price adjustment
process derived below, we justify
this assumption by appealing to the fact that supply is fixed at

least in the short-run, over
which income shocks occur. Increased supply elasticity would
not affect our results for the
city with the negative income shock, as there would be no new
supply there. It may very
well affect the supply in the city with a positive income shock,
thus reducing the quantitative
magnitude of the effects we find for that city.
The market clearing condition is that supply equals demand, Q
j
t = H jt . This provides
the following price for real estate at each point in time in each
city:
p
j
t =
1
γ
(
α + yjt + Ljt − H
)
(3.2)
The loan to the representative household in city j , L
j
t , is given by a risk-neutral loan
originator who operates in a competitive market. L
j

t satisfies a zero expected profit
condition.
While we frame the model in terms of two competing cities, this
need not be the case.
Our model can easily be framed in terms of one investment
(residential MBS) and another
investment with low or negative correlation to housing. This
translates the implications of
our model from regional to economy-wide shocks.
We consider two regimes. A loan in a transparent regime where
each loan is city specific,
L
j
t , and a loan in an opaque regime where mortgage-backed
securities investors cannot
geographically discriminate, Lt .
270 J Financ Serv Res (2016) 49:265–280
We model transparent markets as those in which originators
give loans to regions condi-
tional on region-specific risks (i.e. geographic risk based
pricing). If the secondary mortgage
market sells securities that are geographically transparent then
investors are able to tranche
these securities according to their geographic risk. Demand for
MBS based on geographic
risk will make lenders in the primary mortgage market price and
lend according to their

geographic risk.
Consider two cities, A and B. If the secondary mortgage market
is geographically
opaque, then lenders will neglect city-specific risk. In this
regime, loans would incorporate
the average risk of both city A and city B. However, if the
secondary market is geograph-
ically transparent, investors will tranche the MBS into MBS A
and MBS B. Demand for
MBS will now reflect region-specific risk. Thus lenders will
price their loans to each region
based on that region’s local risk. This is how transparency
would remove the ability to pool
risk between city A and city B.
Transparent mortgage markets regime The lender’s expected
profit for loans to city j at
time t, denoted by π
j
t , is given by the expected collection (loan amount plus interest
if no
default, or house value if default) less the initial loan amount:
E
[
π
j
t
]
= −Ljt + ηEt min

[
L
j
t R, p
j
t+1H
]
(3.3)
Where η is the lender’s discount factor. Credit markets are
competitive so L
j
t is given by
a zero expected profit condition:
E
[
π
j
t
]
= 0 (3.4)
⇔
L
j

t = ηLjt R · P
{
L
j
t R ≤ pjt+1H
}
(3.5)
+ηH Et
[
p
j
t+1|L
j
t R > p
j
t+1H
]
· P
{
L
j
t R > p
j
t+1H

}
Opaque mortgage markets regime When markets are
geographically opaque, the lender is
not able to discriminate geographically and gives the same loan
to both cities. The expected
profits are:
expected profit at time t = −(amount lent to both cities at t)
(3.6)
+ discounted expected payoff from the loan to A at t + 1
+ discounted expected payoff from the loan to B at t + 1
We add the expected payoffs across cities, because each city
decides individually whether
to repay or default.
E[πt ] = −(Lt + Lt ) + ηEt min
[
Lt R, p
A
t+1H
]
+ ηEt min
[
Lt R, p
B
t+1H
]
(3.7)

= −2Lt
+ηLt R · P
{
Lt R ≤ pAt+1H
}
+ηH Et
[
p
A
t+1|Lt R > pAt+1H
]
· P
{
Lt R > p
A
t+1H
}
+ηLt R · P
{
Lt R ≤ pBt+1H
}
+ηH Et
[
p

B
t+1|Lt R > pBt+1H
]
· P
{
Lt R > p
B
t+1H
}
J Financ Serv Res (2016) 49:265–280 271
The corresponding zero expected profit condition is:
E[πt ] = 0
Lt = ηLt R · P
{
Lt R ≤ pAt+1H
}
+ηH Et
[
p
A
t+1|Lt R > pAt+1H
]

· P
{
Lt R > p
A
t+1H
}
+ηLt R · P
{
Lt R ≤ pBt+1H
}
+ηH Et
[
p
B
t+1|Lt R > pBt+1H
]
· P
{
Lt R > p
B
t+1H
}
⇔
Lt =

1
2
ηLt R ·
(
P
{
Lt R ≤ pAt+1H
}
+ P
{
Lt R ≤ pBt+1H
})
+ 1
2
ηH
(
Et
[
p
A
t+1|Lt R > pAt+1H
]
· P

{
Lt R > p
A
t+1H
}
(3.8)
+Et
[
p
B
t+1|Lt R > pBt+1H
]
· P
{
Lt R > p
B
t+1H
})
+ηH Et
[
p
B
t+1|Lt R > pBt+1H
]
· P

{
Lt R > p
B
t+1H
}
Under opacity the loan is made to average risk across cities.
Income shock We now consider a situation with two time
periods t ∈ {0, 1}, and two income
levels, y
j
t ∈ {yL, yH } with yL < yH . Assume city A starts with the low
income shock and
city B starts with the high income shock: yA0 = yL, yB0 = yH .
The probability city A will
have a low shock next period is given by:
P
{
y
A
1 = yL|yA0 = yL
}
= 1 + ρ
2
(3.9)

Where ρ ∈ [−1, 1] is the auto-correlation for income.5 We
assume income follows a
two-state Markov chain:
y
j
t ∼
(
1+ρ
2
1−ρ
2
1−ρ
2
1+ρ
2
)
(3.10)
For simplicity we assume that the spatial correlation in income
shocks is perfectly neg-
ative ρA,B ≡ −1, so whenever city A has a negative shock yAt =
yL, city B will have a
positive shock yBt = yH and vice-versa.
In a transparent market, the zero profit level of lending to each
city is:
L
A

0 =
η
(
1+ρ
2
) (
1
γ
(
α + yL + LA1 − H
))
H
(
1 − η
(
1−ρ
2
)
R
)
,
(3.11)
L
B

0 =
η
(
1−ρ
2
) (
1
γ
(
α + yL + LB1 − H
))
H
(
1 − η
(
1+ρ
2
)
R
) (3.12)
5The exogenous auto-correlation in income we assume in the
model generates an auto-correlation in house
prices. For evidence on auto-correlation in house prices see
Duca et al. (2010), Case and Shiller (1989), and
Poterba et al. (1991).

272 J Financ Serv Res (2016) 49:265–280
In an opaque market, the lender’s zero profit level of lending
(same in both cities) is:
L0 =
1
2 η
(
1
γ
(α + yL + L1 − H )
)
H
(
1 − 12 ηR
) (3.13)
(See derivations in the Appendix).
Proposition 1 If income shocks are positively auto-correlated ρ
> 0 and if the lender’s
discount rate is less than the mortgage rate (ηR > 1), the
transparent level of lending to
the city with the bad shock is less than the opaque level, w hich
is less than the transparent
level of lending in the city with the good shock:
(3.14)

This proposition is intuitive. Since income shocks are auto-
correlated, the badly shocked
city is more likely to have more bad shocks. Hence lenders are
more reluctant to lend.
Plugging this into the equilibrium price function: p
j
0 = 1γ
(
α + yj0 + L
j
0 − H
)
provides
the important result that prices in the city which received a bad
income shock are lower
under the transparent regime relative to the opaque regime.
Proposition 2 House prices in the city with a bad income shock
are lower under trans-
parency than opacity:
p
A,trans
0 =
1
γ

(
α + yA0 + LA0 − H
)
<
1
γ
(
α + yA0 + L0 − H
)
= pA,opaque0 (3.15)
House prices in the city with a good income shock are higher
under transparency than
opacity:
(3.16)
We have assumed that city A starts with a bad income shock at
time 0 and city B starts
with a good income shock. Ex ante with probability 12 we have
y
A
0 = yL and yB0 = yH , and
with probability 12 we have y
A
0 = yH and yB0 = yL. However, ex ante neither city knows
which state of the world they will start in. Hence, ex ante they
will prefer opacity to have
less volatile house prices.

Proposition 3 The ex ante house price volatility is greater under
transparency than under
opacity:
σ
2
p,opaque < σ
2
p,trans (3.17)
3.2 Two lenders
Now consider two originators, each choosing independently
whether to operate in a trans-
parent or opaque way. As discussed above, the originators can
place their mortgage-backed
J Financ Serv Res (2016) 49:265–280 273
securities in the secondary market as long as those securities
provide zero expected profit
to the investors. The price in each city is given by:
p
j
0 =
1
γ
(

α + yj0 + L
j,1
0 + L
j,2
0 − H
)
(3.18)
where L
j,k
t denotes the lending of lender k in city j at time t . If both
lenders operate the
same way (transparent or opaque), the equilibrium level of total
lending is exactly the same
as in the case with a single lender above, and satisfies the
following inequality:
L
A,1
0 + LA,20 < L0 < LB,10 + LB,20 (3.19)
However, if one lender deviates, then the above order extends to
the following:
L
A,1
0 + LA,20 < LA,10 + δL0 < L0 < LB,10 + δL0 < LB,10 + LB,20
(3.20)
where δ denotes the market share of lender 2 if both lenders
choose to lend opaquely, e.g.,
δ = 1/2. Prices follow the same relationship, which is easily

verified because a mixed
scenario always results in a switch to transparent lending in
period 1 (i.e., p
j
1 is given by the
transparent lending expression given above (3.2)). While the
profits of the two lenders in
each of the above scenarios sum to zero, the lender who choses
the transparent method has
positive profits in the mixed scenario, at the expense of the
lender who continues to lend in
an opaque way. The second lender has no choice but to also
switch to transparent lending.
The above conclusion indicates that if both originators lend
opaquely, the MBS of both
satisfy the zero-profit condition indefinitely. However, this
equilibrium is unstable because
each of the originators (and their investors) has an incentive to
switch to transparent lending
in case one of the cities experiences a negative income shock.
The originator who switches
can offer securities that generate positive profit for one period,
after which the second
originator also switches to transparent lending, and the
transparent equilibrium continues
indefinitely.
Note that the only choice originators (and their investors) have
is between transparent and
opaque lending. We are excluding any additional lending
quantity choice because the market
for MBS is assumed to be fully competitive. In other words,
investors can choose between

opaque or transparent portfolios, but have no ability to restrict
lending to monopolistic
levels.
Short-term and long-term lenders The model above implies the
following payoff matrix for
the MBS of the two originators at time zero, denoting the one-
period profit of the lender
who switches from opaque to transparent as π (Table 1).
Payoffs beyond time 0 are all zero as both originators switch to
transparent lending for-
ever. With these payoffs, both originators have incentives to
switch to transparent lending
the moment one of the cities experiences a negative income
shock. To preclude this trivial
solution, we assume that an originator (or its MBS investors)
receives a (small) benefit,
, (0 < < π), above it’s zero profit if that lender lends
in an opaque way (Table 2). The
one-period payoff matrix is given in Table 2.
Table 1 MBS 1, t = 0 Payoff
Function MBS 1 MBS 2 Transparent Opaque
Transparent 0 π
Opaque −π 0
274 J Financ Serv Res (2016) 49:265–280
Table 2 MBS 1, t = 0 Payoff
Function MBS 1 MBS 2 Transparent Opaque

Transparent 0 π
Opaque −π
An originator who optimizes over a long (infinite) horizon has
an incentive to remain
in the opaque equilibrium, as receiving over a long time
horizon dominates the one-time
profit, π . However, if one of the originators switches to a short
horizon view of the world,
that originator would switch to transparent lending in case of a
negative income shock to
collect the one period positive profit, π .
There are two potential mechanisms that can make the opaque
lending more stable. First,
if each of the lenders can switch to transparent lending in the
same period their competitor
switches, then both lenders move to the fully transparent
equilibrium and satisfy the zero
profit conditions in this equilibrium. In this case, there is no
incentive for a lender to switch
away from the opaque equilibrium, so it can continue
indefinitely.
The second mechanism is to increase the incentive, , for
the lenders to stay in the opaque
equilibrium. While a very short-term lender would still switch
to transparent lending, this
scenario is less likely. Also, if the short-term lender gets out of
business or changes back to
long-term optimization, then the probability that the remaining
lender(s) return to opaque
lending is higher.
4 Numerical calibration

In this section, we will provide a numerical exploration of the
results in our model. Consider
a world where the parameters are:
parameter description value
ρ autocorrelation 0.5
η discount factor .99
R gross interest rate 1.04
H exogenous housing supply 10
α demand intercept 15
yL low income level 5
yH high income level 8
L1 exogenous loan 10
γ demand slope on price 1
We assume city A has a bad income shock at time 0 and income
shocks are negatively
correlated across space: yA0 = 5, yB0 = 8.
The corresponding loans are:
(4.1)
J Financ Serv Res (2016) 49:265–280 275
Since city A is more likely to default than city B it will receive
a smaller loan in a
transparent world (with risk based pricing). However in an
opaque world the lender averages
risks across cities and both cities receive the same intermediate
loan.

The corresponding house prices are:
p
A,trans
0 = 209.97 < p
A,Opaque
0 = 214.04 (4.2)
p
B,trans
0 = 230.296 > p
B,Opaque
0 = 217.04 (4.3)
Since city A is more risky, it receives a smaller loan in a
transparent world and there-
fore has lower house prices. Note that under opacity city A has
lower house prices than
city B even though they receive the same loan because city A
has lower income than
city B.
Figure 1 plots the loans LA0 , L
B
0 , L0 as a function of the persistence of income ρ ∈ [0, .5).
This figure illustrates that …
2014 V42 2: pp. 472–496

DOI: 10.1111/1540-6229.12030
REAL ESTATE
ECONOMICS
The Influence of Fannie and Freddie
on Mortgage Loan Terms
Alex Kaufman*
This article uses a novel instrumental variables approach to
quantify the effect
that government-sponsored enterprise (GSE) purchase eligibility
had on equi-
librium mortgage loan terms in the period from 2003 to 2007.
The technique is
designed to eliminate sources of bias that may have affected
previous studies.
GSE eligibility appears to have lowered interest rates by about
ten basis points,
encouraged fixed-rate loans over ARMs and discouraged low
documentation
and brokered loans. There is no measurable effect on loan
performance or
on the prevalence of certain types of “exotic” mortgages. The
overall picture
suggests that GSE purchases had only a modest impact on loan
terms during
this period.
In 2011, over 75% of all mortgages that were originated in the
United States—
over $1 trillion worth—passed through the hands of the Federal
National Mort-
gage Association (Fannie Mae) and the Federal Home Loan
Mortgage Cor-
poration (Freddie Mac) (Inside Mortgage Finance 2012). These

institutions,
known as the Government-Sponsored Enterprises (GSEs), have
traditionally
been private corporations with a public charter, operating with
the implicit
backing of the U.S. government.1 Their mission, as defined in
their charters,
is to promote stability, liquidity and affordability in the U.S.
mortgage market.
The GSEs are meant to accomplish these goals by purchasing
mortgage loans
on the secondary market, which they then package into
securities or hold in
portfolio. In September 2008, the GSEs’ implicit government
backing became
explicit when in the throes of the financial crisis and facing
possible bankruptcy,
both Fannie and Freddie were placed in conservatorship by their
regulator, the
Federal Housing Finance Agency (FHFA). The cost to taxpayers
of their bailout
has been estimated at $317 billion so far (Congressional Budget
Office 2011).
*Board of Governors of the Federal Reserve System,
Washington, D.C. 20551
or [email protected]
1Technically the term Government-Sponsored Enterprise also
applies to the 12 Federal
Home Loan Banks, which are much smaller than Fannie Mae
and Freddie Mac. For
simplicity in this article, the term “GSE” is used to refer only to
Fannie and Freddie.
C© 2013 American Real Estate and Urban Economics
Association

The Influence of Fannie and Freddie 473
Given the GSEs’ vast scale, the liability they represent to
taxpayers and the
decisions that must soon be made about their future, it is crucial
to understand
how exactly they affect the mortgage markets in which they
operate. Unfortu-
nately, modeling GSE activity and estimating its effect is a
challenge. Fannie
and Freddie are for-profit enterprises bound by a government-
mandated mis-
sion that is likely at odds with their profit motive (Jaffee and
Quigley 2011).
As such, it is unclear what they maximize. Furthermore, they
are large relative
to the market. How they affect consumer outcomes, each other
and the rest of
the market depends upon details of market structure. For
instance, Passmore,
Sparks and Ingpen (2002) show that whether or not lower
capital costs (due to
the implicit government subsidy) are ultimately passed on to
borrowers in the
form of lower mortgage rates depends crucially on the degree of
competition
or collusion between Fannie and Freddie, which is theoretically
ambiguous.2
The GSEs’ huge market share may also affect their behavior in
other ways.
Bubb and Kaufman (2009), for instance, explore how the GSEs’
size may allow

them to incentivize mortgage originators using a toolbox of
strategies that is
unavailable to private-label securitizers.
In addition to these theoretical challenges, empirical estimation
of the GSEs’
impact on outcomes such as interest rates, default rates and
contract structures
faces at least three important obstacles: externalities, selection
bias and sorting
bias.
Externalities can arise because GSE purchase activity may
affect the equilib-
rium characteristics of all loans that are eligible for GSE
purchase, including
loans that are not purchased by the GSEs ex post. Just as the
presence of an
orthodox Jewish community in the United States has encouraged
most large
food manufacturers to produce foods according to kosher
dietary standards,
the presence of Fannie and Freddie may change prevailing loan
standards. If
one were to try to estimate the effect of orthodox Jews on food
standards by
comparing the food that they purchase with food purchased by
other people,
one would incorrectly conclude that they have little effect
because non-Jews
also tend to buy kosher food. To the contrary, it is likely that
without orthodox
Jews, no one would buy kosher food because manufacturers
would not bother
to follow kosher standards.

2In the Passmore, Sparks and Ingpen (2002) model, it is even
possible that the estab-
lishment of the GSEs can raise equilibrium interest rates. For
this to happen, it must
be the case that the GSEs behave collusively and that the
liquidity of mortgage-backed
securities issued by private-label institutions is lowered because
the market share of the
GSEs cuts into private securitizers’ economies of scale.
474 Kaufman
Analogously, it is not enough simply to compare the
characteristics of GSE-
bought loans and non-GSE-bought loans.3 GSE purchase
eligibility may affect
the characteristics of both groups of loans. Instead, the ideal
experiment is
to compare loans in two similar markets: one in which the GSEs
can make
purchases and one in which they cannot.4 The difference in
mean characteristics
between loans in one market and loans in the other will be an
estimate of the
effect of GSE purchase eligibility on these outcomes.
Second, estimates of the effect of GSE eligibility may suffer
from selection
bias. Due to the GSEs’ government mandate, the loans Fannie
and Freddie
can buy are not a random subset of all loans. GSE-eligible
mortgage loans,
on average, differ along several dimensions, including loan size
and borrower

creditworthiness, from loans purchased by private-label
securitizers or left in
the portfolio of originating lenders. Such selection must be
separated from the
true treatment effect of GSE eligibility.
Third, to the extent that GSE purchase eligibility may lead to
loan terms that
are more (or less) favorable to borrowers, potential borrowers
may adjust their
loan attributes in order to qualify for (or avoid) loan categories
that the GSEs
are likely to buy. Such customer sorting is another potential
source of bias. If
borrowers that sort into GSE-eligible loans are different from
other borrowers,
and if those differences influence the features of the loans they
receive—for
instance, due to preferences or risk-based pricing—then
customer sorting will
lead to biased estimates of GSE treatment effects.
To illustrate this point with a fanciful example, imagine that
GSE purchase eli-
gibility lowers interest rates by 20 basis points, and GSEs
follow a government-
mandated rule that they will only buy loans made to people who
live in red
houses. Suppose further that potential borrowers who know this
rule and are
savvy enough to paint their homes red are also, on average,
better credit risks
(in a way that is apparent to a loan underwriter but not to an
econometrician
with limited data) and so would naturally receive loans that are
cheaper by

15 basis points, regardless of house color. If we were to
estimate the effect
of GSE eligibility on interest rates using the idiosyncrasies of
the house color
rule, we would incorrectly find that it is 35 basis points because
we would have
conflated the true treatment effect with the sorting effect.
3Data sources such as FHFA
(www.fhfa.gov/Default.aspx?Page=313), Inside Mortgage
Finance (2012) and Lender Processing Services all suggest that
between a fifth and a
quarter of all securitized conforming loans during this period
were bought by private-
label securitizers.
4Estimates of the conforming/jumbo spread can be thought of as
approximations to this
ideal experiment. What matters is whether a loan is conforming
and thus eligible for
purchase, not whether it was, in fact, purchased.
This article estimates the equilibrium treatment effect of GSE
purchase eligi-
bility on interest rates, loan delinquency rates and mortgage
contract features
using an instrumental variables regression discontinuity design
meant to ad-
dress externalities, selection bias and sorting bias. The strategy
takes advantage
of the interaction of two features of the mortgage market: the
conforming size
limit and the ubiquity of 20% down payments.

By law, the GSEs are only allowed to buy loans smaller than the
conforming
loan limit, an upper bound that varies from year to year. In 2006
and 2007, for
instance, the limit was $417,000 in the continental United
States. Loans that
exceed the conforming size limit are referred to as jumbo. This
purchase rule
is fairly rigorously observed: in 2007, for instance, the GSEs
bought 88% of
all loans in the $5,000 window just below the conforming size
limit, but only
3% of loans in a similar window just above the limit.5
Researchers can potentially overcome two of the three
previously mentioned
sources of bias—externalities and selection—by exploiting the
discontinuity in
GSE intervention across the conforming size limit. By
comparing loans made
in a segment of the market where GSEs dominate (the
conforming market)
with otherwise similar loans made in a segment of the market
where GSEs do
not operate (the jumbo market), one can obtain estimates that
incorporate the
externalities of GSE purchases on the rest of the market. Also,
because the GSE
purchase eligibility is discontinuous while other relevant loan
features (absent
any sorting effects) vary smoothly with loan size, loans just
above the thresh-
old form a natural comparison group for loans just below (see,
for example,
DiNardo and Lee 2004). A regression discontinuity design can

therefore be
used to overcome bias due to loan selection.
However, a comparison of loans just above and below the
conforming loan
limit may still be biased due to customer sorting. Indeed,
histograms such as
Figure 1 suggest that customers bunch just below the
conforming loan limit,
choosing a larger down payment to avoid getting a jumbo loan.
If borrowers
that do this are unobservably different from borrowers that do
not, estimates
of the GSE treatment effect that use this discontinuity will be
contaminated by
sorting. Indeed, if sorting on unobservables is similar to sorting
on observables
(Altonji, Elder and Taber 2005), then the evidence is stark: the
average credit
score of borrowers in the sample who are just below the
conforming cutoff
is nearly 45 points higher than it is for those just above the
cutoff. It thus
appears that more-creditworthy borrowers are better able to take
advantage of
conforming loans.
5This and other statistics cited in text, unless otherwise noted,
estimated using data from
Lender Processing Services (LPSs).
476 Kaufman
Figure 1 � Histogram of loan origination amounts for 2006–

2007 continental U.S.
subsample.
0
.0
0
5
.0
1
.0
1
5
.0
2
D
e
n
si
ty
50 100 150 250 300 350 450 500 550 650 700 7500 200 400 600
800
Origination Amount (in $1,000s)
Continental US 2006−2007
Histogram of Origination Amount
Note: The vertical line is the $417,000 conforming size limit.

To address simultaneously all three sources of bias, this article
uses a slightly
different approach. Rather than directly compare loans above
and below the
conforming loan limit, I instrument for whether a loan is
conforming using a
discontinuous function of home appraisal value. As will be
explained in detail
in the Estimation Strategy section of this article, certain
features of the loan
origination process ensure that at particular home appraisal
values, the chance
that a borrower gets a conforming loan jumps significantly. In
particular, above
some appraisal values, it is impossible to get a conforming loan
without putting
more than 20% down, inducing a jump in the number of jumbo
loans at those
values. Evidence suggests that these key appraisal values are
not salient to either
lenders or borrowers, and there is little evidence of
manipulation of appraisals
around these values.
This article thus compares prices and attributes of loans made to
borrowers
whose homes happen to be appraised just below one of these
values with those
of borrowers whose homes happen to be appraised just above. I
argue that
the resulting differences are most plausibly attributed to the
different rates at
which these borrowers get conforming rather than jumbo loans.
Because GSE
purchase eligibility is the essential difference between the
conforming and

jumbo markets, this quasi-random assignment to the conforming
loan market
allows for a clean estimate of the equilibrium impact of GSE
purchase eligibility
on loan attributes.
Using this method, I find only modest impacts of GSE activity.
For a sample
of loans originated between 2003 and 2007, I estimate that GSE
purchase
eligibility lowered interest rates in the conforming market by 8–
12 basis points,
which is slightly smaller than previous estimates of the
conforming/jumbo
spread. I find no significant effect on loan default or
foreclosure rates. GSE
activity appears to have promoted fixed-rate mortgages over
adjustable-rate
mortgages: I estimate an increase of 5.3 percentage points on a
base of 61.9%
fixed-rate loans. It also appears to have discouraged low
documentation loans
and loans bought through a broker. I find no effect on debt-to-
income ratios, nor
on the prevalence of contract features such as prepayment
penalties, negative
amortization, interest-only loans and balloon loans.
This article joins a growing literature that attempts to measure
the impact of
GSE intervention on residential mortgage markets. Previous

work has largely
focused on determining the effect of GSE intervention on
contract interest
rates. McKenzie (2002) performs a meta-analysis of eight
studies that attempt
to quantify the size of the conforming/jumbo rate spread and
concludes that
the spread has averaged 19 basis points over the years 1996–
2000.6 Studies
in this literature generally run regressions in which a “jumbo”
dummy is the
coefficient of interest, and they control for observables that
covary with jumbo
status. Though extremely useful, such studies are potentially
vulnerable to
selection bias and sorting bias. Later studies, such as Passmore,
Sherlund and
Burgess (2005) and Sherlund (2008), yield similar estimates in
the 13–24 basis
point range while attempting to address sources of bias better.7
Another important strand of the literature has attempted to
determine the effect
of GSE intervention on the supply of mortgage credit. Ambrose
and Thibodeau
(2004) use a structural model to argue that subsequent to the
establishment in
1992 of a set of “Affordable Housing Goals” for the GSEs, the
total supply
of credit increased slightly more in metropolitan areas with
higher proportions
of underserved borrowers. Bostic and Gabriel (2006) investigate
the same set
6Studies include Hendershott and Shilling (1989), ICF
Incorporated (1990), Cotterman

and Pierce (1996), Ambrose, Buttimer and Thibodeau (2001),
Naranjo and Toevs (2002),
U.S. Congressional Budget Office (2001), Passmore, Sparks and
Ingpen (2002) and
Pearce (2002).
7Sherlund (2008), for instance, uses geographic location to
control for unobserved
borrower characteristics.
478 Kaufman
of housing goals but use the regulation’s definition of what
constitutes a “low-
income neighborhood” to compare areas that the GSEs were
supposed to target
with areas where they had no particular mandate, finding no
effect of GSE
targeting on outcomes such as homeownership rates and
vacancy rates.
This article contributes to this literature in two ways. First, its
estimation
strategy is designed to eliminate biases that may have affected
previous studies.
Second, it expands the set of outcomes examined to include
contractual forms
and features, as well as measures of loan performance.
Since the original version of this article appeared, Adelino,
Schoar and Sev-
erino (2011) and Fuster and Vickery (2012) have used similar
methodologies
instrumenting for conforming status using appraisal limits in
order to study re-

lated research questions. Adelino, Schoar and Severino (2011)
exploit changes
in the conforming limit over time in order to study the effect of
GSE loan
purchases on house prices, while Fuster and Vickery (2012) use
the post-2007
credit freeze in order to estimate the effect of GSE purchases on
the supply of
fixed-rate mortgages during times of financial distress.
The next section presents a brief history of the GSEs and
provides background
on conforming loan limits. The Estimation Strategy section
describes the es-
timation strategy in greater detail, while the Data and
Specifications section
discusses the dataset and the econometric specifications used.
The Results
section presents results, and the last section concludes.
Background
History of the GSEs
The Federal National Mortgage Association (Fannie Mae) was
established in
1938 as a federal agency fully controlled by the U.S.
government (Fannie
Mae 2010). Its mission was to provide liquidity in the mortgage
market by
purchasing loans insured by the Federal Housing Administratio n
(FHA). In
1948 that mandate was expanded to include loans insured by the
Veterans
Administration, and by the early 1950s Fannie Mae had grown
to such a

point that pressure mounted to take it private. In 1954, a
compromise was
reached whereby Fannie privatized but was still controlled by
the government
through Treasury ownership of preferred stock. Fannie was also
granted special
privileges, such as exemption from local taxes, which it
maintains to this day.
The Housing and Urban Development Act of 1968 took the
privatization of
Fannie Mae a step farther, splitting it by spinning off its
functions buying FHA-
and VA-insured loans into the wholly government-controlled
Ginnie Mae, while
preserving the rest of its business in the now supposedly fully
private Fannie
Mae.8 However, Fannie Mae continued to enjoy implicit
government backing
for its debt.
In 1970, the government chartered the Federal Home Loan
Mortgage Corpora-
tion (Freddie Mac) as a private company. Its mission—buying
and securitizing
mortgages to promote liquidity and stability—was similar to
Fannie Mae’s mis-
sion, though initially Freddie Mac was only meant to buy
mortgages originated
by savings and loan associations. With time this distinction
eroded. Like Fannie

Mae, Freddie Mac was perceived by most as having the implicit
backing of the
government.
In the wake of the savings and loan crisis, Congress in 1992
passed the Federal
Housing Enterprises Financial Safety and Soundness Act, which
established the
Office of Federal Housing Enterprise Oversight (OFHEO) as the
new regulator
for the GSEs. The act also expanded the GSEs’ mandate to
improve access and
affordability for low-income borrowers by creating the
affordable housing goals
studied in Ambrose and Thibodeau (2004) and Bostic and
Gabriel (2006). The
rules require the GSEs to buy a certain proportion of their loans
from households
defined as mid or low income and from neighborhoods defined
as low income.
The GSEs’ market share ballooned throughout the 1990s and
early 2000s.
During this time, both institutions expanded their loan
purchases and securi-
ties issuance, and they also began holding more MBS and
mortgage loans in
portfolio, which they financed by issuing debt.9 Spurred by
competition from
private-label securitizers, in the mid-2000s, the GSEs began
expanding their
operations into the subprime and Alt-A mortgage markets,
which they had tra-
ditionally avoided. With the collapse of the housing bubble in
mid-2007, the
GSEs’ subprime MBS holdings put them at risk of insolvency.

The Housing
and Economic Recovery Act (HERA) of 2008 replaced the
regulator OFHEO
with FHFA and granted it the power to place the GSEs in
conservatorship,
which FHFA did in late 2008, finally making explicit the
government’s long-
standing implicit backing of GSE debt. Since then the GSEs
have been held in
conservatorship, and their future remains uncertain.
8An often-cited reason for this division is that a 1968 change in
public accounting rules
made it so that additions to Fannie Mae’s balance sheet would
be treated as public
expenditures. Privatizing Fannie Mae made federal debt appear
smaller.
9Lehnert, Passmore and Sherlund (2008) investigate whether the
expansion of the GSEs’
portfolios were a major force affecting the mortgage rate and
conclude it was not.
480 Kaufman
Conforming Loan Limits
By law, the GSEs are only allowed to purchase loans smaller
than the conform-
ing loan limit (Federal Housing Finance Agency 2010). The
conforming loan
limit varies by both year and location. Prior to 2008, the size
limit increased at
most once a year and was constant across all locations within
the continental

United States and Puerto Rico.10
In 2008, the passage of HERA retroactively changed the
conforming size
limits of loans originated after July 1, 2007, allowing the GSEs
to guarantee
more loans. Because the act passed in 2008, it is unlikely that
the retroactive
changing of the conforming limit in some areas affected loans
terms at the
time of origination.11 Our only variables measured after
origination, default
and foreclosure are likely functions of house price appreciation,
loan terms and
borrower credit risk, and as such they would not be expected to
be affected
directly by retroactive eligibility for GSE purchase. After
HERA, it is no
longer the case that all continental U.S. locations are treated
equally—the Act
designated a set of “high-cost” counties with higher conforming
loan limits.
Estimation Strategy
The estimation strategy in this article employs a discontinuous
function of
home appraisal value as an instrument for conforming loan
status. Appraisal
value is related to conforming status for obvious reasons: more
expensive
houses are more likely to require mortgage loans larger than the
conforming
limit. However, the relationship between appraisal value and
conforming loan
status is not smooth. It is discontinuous because loan-to-value

(LTV) ratios of
exactly 80 (equivalent to a down payment of 20%) are
extremely modal in the
U.S. mortgage market. An LTV of 80 is common in part because
borrowers
are typically required to purchase private mortgage insurance
(PMI) for loans
above 80 LTV. In addition, 80 is considered “normal” and may
function as a
default choice for many people who would otherwise choose a
different down
payment. Figure 2 provides a histogram of the LTV ratios of
first-lien mortgage
loans, illustrating the importance of 80 LTV.
10Hawaii, Alaska, Guam and the U.S. Virgin Islands were
considered “high-cost areas”
and had a conforming limit 50% higher than the rest of the
country.
11If the law’s passage were anticipated, there could be an
influence. However, even if
passage were anticipated, the exact formulas determining which
counties were affected
may not have been anticipated. If such anticipation did occur, it
would tend to bias the
results of this article toward zero. The data over this period
show bunching of loans at
the limits that were in force at the time of origination but not at
the retroactively imposed
limits, suggesting that the law changes were not anticipated.
Figure 2 � Histogram of LTV ratios for the 2006–2007

continental U.S. subsample.
0
0
.0
5
0
.1
0
.1
5
0
.2
D
e
n
si
ty
5 10 15 25 30 35 45 50 55 65 70 75 85 90 950 20 40 60 80 100
Loan−To−Value Ratio
Continental U.S. 2006−2007
Histogram of Loan−To−Value Ratios
To see why the widespread use of 80 LTV induces a
discontinuity in the
relationship between appraisal value and conforming status,
note that the LTV

ratio equals the origination amount divided by the appraisal
value. In order to
have an LTV of 80 while staying under the conforming limit, a
home cannot be
appraised at more than the conforming limit divided by 0.8. For
a conforming
limit of $417,000, for instance, this appraisal limit, as I will
refer to it, would be
$417,000/0.8 = $521,250. Borrowers with homes appraised
above $521,250
must choose whether to put 20% or less down and get a jumbo
loan or put
greater than 20% down and get a conforming loan; conforming
loans with 20%
down payments are impossible for such borrowers. Because of
the stickiness
of 80 LTV, borrowers whose homes are appraised above this
appraisal limit are
discontinuously more likely to get a jumbo loan. Figure 3
illustrates the first-
stage relationship between appraisal value and jumbo status for
the 2006–2007
subsample.
Effectively, the empirical strategy compares the loan terms of
borrowers whose
homes were appraised just below the limit with those whose
homes are ap-
praised just above. The only difference between these two
groups is that those
in the former group have a discontinuously higher likelihood of
ending up with
482 Kaufman

Figure 3 � Proportion of loans smaller than the conforming
limit, by home appraisal
amount, for 2006–2007 continental U.S. subsample.
0
.4
0
.6
0
.8
1
P
e
rc
e
n
t
C
o
n
fo
rm
in
g

450 500 550 600
Appraisal Amount (in $1,000s)
Continental U.S. 2006−2007
Percent in Conforming Market by Appraisal Amount
Note: The vertical line is the $521,250 “appraisal size limit”
equal to the conforming limit divided
by 0.8.
a conforming loan.12 The resulting difference in loan terms is
then scaled by the
size of the difference in the likelihood of getting a conforming
loan in order to
yield the appropriate two-stage least squares IV estimate of the
causal impact
on loan terms of being in the conforming market.
So long as borrowers do not sort themselves by finely
manipulating values
around the appraisal limit, this method will be unbiased. How
easy is it to
manipulate appraisal values? Dennis and Pinkowish (2004)
provide an overview
of the home appraisal process. Independent appraisals are
needed because a
mortgage lender cannot rely on selling price as a measure of the
collateral
value of the home. Typically, the lender or mortgage broker
contracts a third
party to provide an appraisal (Hutto and Lederman 2003).
Borrowers are not
allowed to contract appraisers themselves for fear they will
shop around for

12The likelihood of getting a conforming loan does not change
from 0 to 1; instead, it
increases by about 8.8 percentage points. Such a situation is
typically referred to as a
“fuzzy” regression discontinuity.
an appraiser willing to inflate the appraisal and thus low er the
borrower’s
LTV. The appraiser estimates the probable market value of the
home by taking
into account the neighborhood, the condition of the home,
improvements to
the home and recent sale prices of comparable homes in the
area. Appraisals
usually cost $300–$500, and the fee is paid by the borrower
when the loan
application is filed.
When applying to refinance, the appraisal value is the sole
determinant of the
denominator of LTV. For home purchase loans, however, the
denominator of
LTV is the minimum of the appraisal value and the purchase
price.13 Borrowers
purchasing a home might therefore ignore the formal appraisal
and attempt to
manipulate the purchase price instead. If such manipulation
happened on a
large enough scale, it would create customer sorting and
potentially bias the
results. However, such manipulation can be observed: it would
create a lump

of borrowers with “appraisals” just below the appraisal limit.
As will be shown
in the Data and Specifications section, there appears to be no
bunching around
the appraisal limit, suggesting that such manipulation did not
occur on an
appreciable scale.
Borrowers aside, appraisal manipulation by the lender remains a
concern. Anec-
dotal evidence suggests lenders sometimes leaned on appraisers
to inflate values
to make loans more attractive for resale on the secondary
market.14 Appraisers
unwilling to inflate values may have seen a loss of business as a
result. Such
manipulation may indeed have occurred, but it is only relevant
for this article
if it occurred across the particular appraisal limit used in the
regression dis-
continuity. If the efforts of lenders to encourage appraisal
inflation were less
targeted, targeted at another goal or occurred in small enough
numbers, such
manipulation would not pose a threat to the empirical strategy.
The lack of
bunching around the appraisal limit (again shown in the Data
and Specifica-
tions section of this article) suggests that lenders’ manipulation
of appraisals
around this particular limit was not a widespread phenomenon.
Another potential cause of concern about the estimation strategy
is the avail-
ability of outside financing that is not observable in the dataset.
During the

2003–2007 period, it became tolerated practice to fund down
payments with
second-lien mortgages. These so-called “silent seconds” were
often 15-LTV
(or even 20-LTV) second-lien …
Do real estate loans
reflect regional banking and
economic conditions?
Amit Ghosh
Department of Economics, Illinois Wesleyan University,
Bloomington,
Illinois, USA
Abstract
Purpose – Using state-level data, the purpose of this paper is to
examine state banking-industry
specific as well as region economic determinants of real estate
lending of commercial banks across all 51
states spanning the period 1966-2014.
Design/methodology/approach – Using both fixed-effects and
dynamic-generalized method of
moments (GMM) estimation techniques the study compares the
sensitivity of different categories of real
estate loans to regional banking and economic conditions.
Finally, it provides a comparative perspective
by comparing the results for real estate loans with other
categories of loans given out by banks.
Findings – Greater capitalization, liquidity and overhead costs
reduce real estate lending, while banks
diversification and the size of the banking industry in each state
increase such lending. Moreover, real

estate loans are found to be procyclical to state economic cycles
with a rise in state real gross domestic
product (GDP) growth, increase in state housing price index
(HPI) and decline in both inflation and
unemployment rates, increasing real estate loans. Within
disaggregated loan types, construction and
land development and single-family residential loans are most
responsive to state banking and
economic conditions.
Originality/value – The recent financial turmoil is to a large
extent attributable to excessive
risk-taking by banks, particularly in terms of real estate
lending. Hence, it is of paramount importance
to empirically address the various determinants of real estate
lending. With most banks restricting their
operations in either one or a few states only, real estate lending
in any given state may be more sensitive
to regional banking and economic conditions than national
aggregates. The present study is the first of
its type to perform such an analysis.
Keywords Mortgages, Banks, Financial institutions and
services, Models with panel data,
Real estate services
Paper type Research paper
1. Introduction
The US banking industry was at the center of the 2007-2009
financial crises that had
deleterious consequences for banks’ financial health. Banks
across the USA were hit by
a sharp decline in their profitability along with an erosion of
their capital cushions,
which put severe pressure on their liquidity positions. These
developments along with

the overall poor health of the US economy imposed serious
strains on banks’ balance
sheet position and potentially impaired their ability to provide
new loans. At the same
JEL classification – R10, R11, E32, G21, G28, C23
Comments by two anonymous referees and the Editors of the
journal are gratefully
acknowledged.
The current issue and full text archive of this journal is
available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
Regional
banking and
economic
conditions
37
Received 11 September 2015
Revised 9 October 2015
Accepted 2 November 2015
Journal of Financial Economic
Policy
Vol. 8 No. 1, 2016
pp. 37-63
© Emerald Group Publishing Limited
1757-6385

DOI 10.1108/JFEP-09-2015-0050
http://dx.doi.org/10.1108/JFEP-09-2015-0050
time, the origins of the recent financial turmoil are to a large
extent attributable to
excessive risk taking by banks, particularly in terms of real
estate lending. In the build
up to the crisis, concerns loomed amongs t the federal banking
regulatory agencies that
concentration in commercial real estate loans has reached a
level that could lead to
undesirable outcomes in the event of a significant downturn.
Such concerns became true
from late 2008 onwards, with a precipitous decline in housing
prices followed by large
scale loans defaults, leading to a spat of bank failures, and the
ensuing credit crunch that
declined real estate lending (Lu and Whidbee, 2013; Rioja et
al., 2014). This has sparked
a burgeoning body of literature examining different aspects of
research on bank lending,
including real estate lending (Berrospide and Edge, 2010;
Contessi and Francis, 2013;
Ivashina and Scharfstein, 2010; Igan and Pinheirp, 2010; Peni et
al., 2013). However,
most studies use micro datasets and macro level empirical
research is somewhat
lacking. Pointedly, real estate loans are by far the largest loan
category in the loan
portfolios of most banks. Therefore, it is of paramount
importance to empirically
address the various determinants of real estate lending in the
USA. Formal empirical

research has also been very limited on the role of regional
banking and economic
conditions in affecting real estate loans. To the best of my
knowledge, the present study
is the first of its type to perform such an analysis.
Against this background, the focus of this paper is to examine
the sensitivity of real
estate loans to state-level macroeconomic conditions, while at
the same time controlling
for different state-level banking conditions. With this aim in
mind, a panel econometric
approach is used, encapsulating the largest time period of 1966-
2014, and spanning
across all 50 US states and District of Columbia. First, the real
estate loans-elasticities
with respect to both state-level economic as well as banking
conditions are estimated.
Thereafter, different categories of real estate loans data are used
to calculate the impact
of both state-level economic and banking variables, given
different types of real estate
loans are associated with different risk characteristics. Finally,
a comparative
perspective is provided by comparing the results for real estate
loans with other
categories of loans given out by banks.
The use of state-level data is motivated by the fact that the US
commercial banking
industry had restrictions on branching geographically due to its
unique historical
institutional origins. As a legacy of this, until today, most banks
restrict their operations
in either one or a few states only. Thus, bank lending in any
given state may be more

sensitive to regional conditions than national aggregates.
Significant heterogeneity
among banks across states also persists. Therefore, regional
trends in real estate loan
expansion and contraction may be increasingly sensitive to state
macroeconomic
conditions. The role of regional economic indicators in
influencing real estate lending is
further motivated by the fact that many states with large
declines in house prices also
experienced relatively large declines in personal income and
gross state product and
relatively large increases in unemployment rates (Depken et al.,
2011). Hence, it remains
interesting to examine the extent to which changes in real estate
loans are causally
associated with such changes in regional economic conditions
across states. In general,
the use of real estate as collateral lets businesses and consumers
borrow more during
regional economic booms (e.g. high state income growth and
low inflation), which
generally coincide with state real estate booms. As they borrow
more, demand for real
estate increases, pushing prices even higher and banks keep on
lending. However, when
the cycle starts turning (generally coinciding with decreasing or
negative state income
JFEP
8,1
38

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