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Federal Reserve Bank of Chicago

Credit Supply and the Housing Boom
Alejandro Justiniano, Giorgio E. Primiceri, and
Andrea Tambalotti

June 2014
WP 2014-21

CREDIT SUPPLY AND THE HOUSING BOOM
ALEJANDRO JUSTINIANO, GIORGIO E. PRIMICERI, AND ANDREA TAMBALOTTI
Abstract. The housing boom that preceded the Great Recession was due to an increase
in credit supply driven by looser lending constraints in the mortgage market. This view
on the fundamental drivers of the boom is consistent with four empirical observations:
the unprecedented rise in home prices and household debt, the stability of debt relative to
house values, and the fall in mortgage rates. These facts are difficult to reconcile with the
popular view that attributes the housing boom to looser borrowing constraints associated
with lower collateral requirements. In fact, a slackening of collateral constraints at the
peak of the lending cycle triggers a fall in home prices in our framework, providing a
novel perspective on the possible origins of the bust.

1. introduction
The U.S. economy has recently experienced a very severe financial crisis, which precipitated the worst recession since the Great Depression. The behavior of the housing and
mortgage markets in the first half of the 2000s was the crucial factor behind these events.
Four key empirical facts characterize this behavior in the period leading up to the collapse
in house prices and the ensuing financial turmoil.
Fact 1: House prices rose dramatically. Between 2000 and 2006 real home prices increased anywhere between 40 and 70 percent, as shown in Figure 1.1. This boom is unprecedented in U.S. history, and was followed by an equally spectacular bust after 2006.
Fact 2: Households borrowed against the rising value of their real estate, expanding
mortgage debt relative to income by a substantial amount. This fact is illustrated in
figure 1.2, for both the entire household sector and financially constrained households—the
group that is most informative for the parameterization of our model. Both measures of
Date: First version: March 2014. This version: June 2014.
We thank Simon Gilchrist and seminar participants at the European Central Bank, the Padova Macroeconomics Meetings, CEMFI, the Graduate Institute of International and Development Studies, Universitat
Autonoma de Barcelona, Bilkent University, Bocconi University, and EIEF for comments and suggestions.
Giorgio Primiceri thanks Bocconi University and EIEF for their hospitality while conducting part of this
research. The views expressed in this paper are those of the authors and are not necessarily reflective of
views at the Federal Reserve Banks of Chicago, New York or the Federal Reserve System.
1

CREDIT SUPPLY AND THE HOUSING BOOM

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Figure 1.1. Real house prices. The two measures of house prices are the FHFA
(formerly OFHEO) all-transactions house price index and the CoreLogic repeatedsales index. Both indexes are deflated by the consumer price index, and normalized
to 100 in 2000:Q1.

indebtedness were stable in the 1990s, but increased by about 30 and 60 percentage points
between 2000 and 2007, before beginning to fall during the financial crisis.
Fact 3: The increase in mortgage debt kept pace with the rapid appreciation of house
prices, leaving the ratio of mortgages to the value of real estate roughly unchanged. This
often under-appreciated fact is shown in figure 1.3, where we can also see that this measure
of household leverage actually spiked when home values collapsed immediately before the
recession.
Fact 4: Mortgage rates declined substantially. Figure 1.4 plots the 30-year conventional
mortgage rate minus various measures of inflation expectations from the Survey of Professional Forecasters. Real mortgage rates were stable around 5% during the 1990s, but
declined substantially afterward, with a fall of about 2.5 percentage points between 2000
and 2005.
We argue that the key factor behind these four stylized facts was the relaxation of
lending constraints since the late 1990s, which led to a significant expansion in the supply
of mortgage loans. This simple mechanism provides a much more successful explanation of
the boom phase of the cycle than the relaxation of borrowing constraints alone, which have

CREDIT SUPPLY AND THE HOUSING BOOM

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Figure 1.2. (a): Mortgages-to-GDP ratio (Flow of Funds). Mortgages are defined as home mortgages from the balance sheet of U.S. households and nonprofit
organizations in the Flow of Funds. (b): Mortgages-to-income ratio (SCF). Ratio
between mortgage debt and income for the households with little financial assets
in the Survey of Consumer Finances (see section 4.1 for details).

been the main focus of the literature so far. In fact, a slackening of borrowing constraints
that takes place when the credit boom is still in full swing triggers a fall in home prices in
our framework, providing a novel perspective on the origins of the bust.
We develop this argument in a very simple model, to make the distinction between credit
supply and loan demand as stark as possible. As in the literature spawned by Kiyotaki
and Moore (1997), a collateral constraint limits households’ ability to borrow against the
value of real estate, thus affecting their demand for credit. In addition, a lending constraint
limits the supply of credit to the mortgage market. For simplicity, we impose this constraint

CREDIT SUPPLY AND THE HOUSING BOOM

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Figure 1.3. (a): Mortgages-to-real estate ratio (Flow of Funds). Real estate is
defined as the market value of real estate from the balance sheet of U.S. households
and nonprofit organizations in the Flow of Funds. Mortgages are defined as in figure
1.2. (b): Mortgages-to-real estate ratio (SCF). Ratio between mortgage debt and
the value of real estate for the households with little financial assets in the Survey
of Consumer Finances (see section 4.1 for details).

directly on the savers, but we show that a leverage restriction on financial intermediaries
would produce the same results.
The lending constraint, which is the key new feature of our model, captures a combination of technological, institutional, and behavioral factors that restrain the flow of funds
from savers to mortgage borrowers. Starting in the late 1990s, the diffusion of securitization and market-based financial intermediation lowered many of these barriers, relaxing the constraint. For example, the pooling and tranching of mortgages into mortgage

CREDIT SUPPLY AND THE HOUSING BOOM

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Figure 1.4. Real mortgage interest rates. The three real rates correspond to
the 30-year conventional mortgage rate minus the three measures of expected inflation available from the Survey of Professional Forecasters, i.e. the expectation of
10-year-ahead CPI inflation, 1-year-ahead CPI inflation, and 1-year-ahead GDPdeflator inflation.

backed securities (MBS), with the resulting creation of highly-rated assets, contributed
to channel into mortgage products a large pool of savings traditionally directed towards
government debt. Thanks to this innovation, institutional investors that face restrictions
on the riskiness of their assets—such as money market mutual funds, pension funds and
insurance companies—could finance mortgages through their investments in MBS (Brunnermeier, 2009). Furthermore, by allowing banks to convert illiquid loans into liquid funds,
securitization reduced banks’ holdings of liquid securities and increased their ability to
lend (Loutskina, 2011). On the regulatory side, banks’ capital requirements imply lower
charges for agency MBS (and the senior tranches of private-label ones) than for mortgages
themselves. The resulting regulatory arbitrage, combined with the rise of off-balance-sheet
vehicles, which allowed their sponsors to increase leverage without raising new capital, significantly increased the ability of banks to supply credit (Brunnermeier, 2009, Acharya and
Richardson, 2009, Acharya, Schnabl, and Suarez, 2013). Some of the changes in financial
intermediation we just described date back to the 1970s, when the Government Sponsored
Enterprises created the first mortgage backed securities (e.g. Gerardi, Rosen, and Willen,
2010, Fostel and Geanakoplos, 2012). However, securitization did not take off until the

CREDIT SUPPLY AND THE HOUSING BOOM

6

late 1990s and early 2000s, with the expansion of private-label MBS beyond conforming
mortgages and ultimately into subprime products. This is the phase of the credit boom we
focus on.
International factors also played an important role in increasing the supply of funds
to U.S. mortgage borrowers (Justiniano, Primiceri, and Tambalotti, 2014b). The rapid
deterioration of the current account deficit in the first half of the 2000s channeled large pools
of international savings, mostly from East Asia and the oil-exporting countries, towards
domestic borrowers. Many of these funds found their way into the mortgage market through
the purchase of MBS, as documented by Bernanke, Bertaut, DeMarco, and Kamin (2011).
In our simple model, this inflow of foreign funds into mortgage products is isomorphic to a
slackening of the domestic lending constraint, resulting in a shift in the overall amount of
funds available to the borrowers.

We interpret these developments in financial intermediation as sources of more relaxed
lending constraints, and use our model to analyze their impact on the rest of the economy,
both qualitatively and quantitatively. For the quantitative part of the analysis, we calibrate
the model to match some key properties of the balance sheet of U.S. households in the 1990s,
using the Survey of Consumer Finances.
A key assumption underlying this quantitative exercise is that the US economy in the
1990s was constrained by a limited supply of funds to the mortgage market, rather than
by a scarcity of housing collateral. Starting from this situation, we show that a progressive
loosening of the lending constraint in the residential mortgage market increases household
debt (fact 2). If the resulting shift in the supply of funds is sufficiently large, the availability
of collateral also becomes a binding constraint. As a consequence, a further expansion of
the lending limit boosts the collateral value of houses, increasing their price (fact 1), while
the interest rate falls (fact 4). The “endogenous” relaxation of the borrowing constraint
associated with higher real estate values leads to a parallel increase in household debt,
leaving leverage unchanged (fact 3). This is in contrast with the effects of an “exogenous”
relaxation of the borrowing constraint through lower down-payments, which would not
lead to lower interest rates and would produce an increase in household leverage. When
combined with a binding lending constraint, however, a marginal relaxation of collateral
requirements produces a decline in house prices, pointing to a potential trigger for the crisis.

CREDIT SUPPLY AND THE HOUSING BOOM

7

This reconstruction of the stylized facts that characterize the recent credit and housing
cycle is consistent with the micro-econometric evidence of Mian and Sufi (2009, 2011). They
show that an expansion in credit supply was the fundamental driver of the unprecedented
boom in household debt, and that borrowing against the increased value of real estate by
existing homeowners accounts for a significant fraction of this build-up in debt. Our model,
with its emphasis on the role of lending as opposed to borrowing constraints, provides a clean
theoretical framework to interpret this evidence. Such a framework is particularly relevant
because subsequent work has demonstrated the far-reaching repercussions of the boom
and bust in household debt and in real estate values on other macroeconomic outcomes,
such as defaults, durable consumption and employment, both before and after the crisis
(Mian and Sufi, 2010, 2014a,b, Mian, Rao, and Sufi, 2013, Baker, 2014, Charles, Hurst, and
Notowidigdo, 2014a,b, Palmer, 2014, Di Maggio and Kermani, 2014).

This paper is related to the recent literature on the causes and consequences of the
financial crisis. As Eggertsson and Krugman (2012), Guerrieri and Lorenzoni (2012), Hall
(2012), Midrigan and Philippon (2011), Favilukis, Ludvigson, and Nieuwerburgh (2013),
Boz and Mendoza (2014), Justiniano, Primiceri, and Tambalotti (2014a,b), and Huo and
Rios-Rull (2014), we use a model of household borrowing to analyze the drivers of the boom
and bust in credit and house prices that precipitated the Great Recession. As in many of
these studies, borrowing is limited by a collateral constraint à la Kiyotaki and Moore
(1997), backed by houses as in Iacoviello (2005) and Campbell and Hercowitz (2009b). Our
main innovation relative to this literature, which focuses exclusively on borrowing limits,
is the introduction of a lending constraint. The interaction between lending and borrowing
constraints in our model generates rich patterns of debt and home values that resemble
those in the data, both during the boom, as well as around the cycle’s turning point, when
the boom turns into bust. This interaction between the constraints also sets our work
apart from Kiyotaki, Michaelides, and Nikolov, 2011, Adam, Kuang, and Marcet (2012),
Garriga, Manuelli, and Peralta-Alva (2012) and Kermani (2012), who study the effects of a
reduction in the world interest rate on a small open economy with borrowing constraints.
These effects are qualitatively similar to those of a relaxation of the lending constraint in our
framework. However, they miss the interaction between collateral and lending constraints
that is at the center of our analysis of the boom and of the spark that triggered the bust.

CREDIT SUPPLY AND THE HOUSING BOOM

8

Another important difference is that we interpret the financial liberalization of the early
2000s as a slackening of lending constraints. On the contrary, most of this literature captures
variation in the availability of credit only through changes in the tightness of the borrowing
constraint. This approach to modeling changes in credit conditions is virtually a default
option for these papers, since they do not consider any impediment to lending.1 We deviate
from this practice and model financial liberalization as a relaxation of lending constraints
for two reasons. First, the micro-econometric evidence of Ambrose and Thibodeau (2004),
Mian and Sufi (2009), Dell’Ariccia, Igan, and Laeven (2012), Favara and Imbs (2012) and
Di Maggio and Kermani (2014) attributes the debt expansion of the first half of the 2000s,
and the resulting boom in house prices, to a shift in credit supply. This evidence translates
directly into a relaxation of lending constraints in our model. Second, in models with a
borrowing constraint à la Kiyotaki and Moore (1997), looser collateral requirements lead
to an outward shift in the demand for credit. Such a shift is difficult to reconcile with the
falling interest rates and stable debt-to-collateral values seen in the data, as we show in our
analysis.
The reference to looser collateral requirements as a credit demand shock might sound
surprising, since it is common to think of loan-to-value ratios as set by financial intermediaries. If this is the case, it would seem plausible for intermediaries to accept lower
down-payments following an increase in their ability to lend. This link between collateral
requirements and lending limits is absent in the workhorse model of collateralized borrowing
of Kiyotaki and Moore (1997), but it might play a role in practice, resulting in a connection
between movements in the supply and demand of credit. Even in this case, however, our
results suggest that a satisfactory account of the credit boom requires credit supply to shift
more than loan demand in response to their common determinants.
Even though our approach to modeling financial liberalization focuses on lending constraints, it does not rule out a parallel loosening of collateral requirements, which arguably
also took place as house prices were rising. Duca, Muellbauer, and Murphy (2011) and
Favilukis, Ludvigson, and Nieuwerburgh (2013), for instance, document a reduction in

1

This modeling device is so common in the literature on household debt that it has also become the foundation of many recent normative studies on macroprudential regulation (e.g. Bianchi, Boz, and Mendoza,
2012, Mendicino, 2012, Bianchi and Mendoza, 2012, 2013, Lambertini, Mendicino, and Punzi, 2013 Farhi
and Werning, 2013, Korinek and Simsek, 2014).

CREDIT SUPPLY AND THE HOUSING BOOM

9

down-payments on new mortgages to first-time home buyers and subprime borrowers during the boom. Our results, however, do imply that the aggregate impact of looser collateral
requirements during the boom was smaller than that of the expansion in credit supply
brought about by a progressive erosion of the existing barriers to lending.
Our study also builds on a vast literature that studies the microeconomic foundations
of leverage restrictions on financial intermediaries, in environments with agency or informational frictions (e.g. Holmstrom and Tirole, 1997, Adrian and Shin, 2008, Geanakoplos,
2010, Gertler and Kiyotaki, 2010, Gertler and Karadi, 2011, Christiano and Ikeda, 2013,
Bigio, 2013, Simsek, 2013). As Adrian and Shin (2010a), Gertler, Kiyotaki, and Queralto
(2012), Adrian and Boyarchenko (2012, 2013), Dewachter and Wouters (2012), He and Krishnamurthy (2013), and Brunnermeier and Sannikov (2014), we take these restrictions as
given. These papers focus on risk as the fundamental determinant of credit supply through
its effects on asset prices and intermediaries’ leverage, on their fragility when leverage rises
in tranquil times, and on the consequences of this fragility when tranquillity gives way to
turbulence. In contrast, we abstract from risk entirely, to concentrate on the link between
the availability of credit, household debt and home prices. The result is a very simple
model of the causes of the credit and housing boom and of a possible trigger of its demise.
Central to these results is the interaction between lending and borrowing constraints, which
is instead absent in these papers.2
The paper closest to ours is Landvoigt (2014), who also stresses the interaction between
supply and demand of mortgage debt. He proposes a very rich model of borrowing and
lending with intermediation, mostly focused on the effects of securitization on mortgage
finance over the past several decades. In this model, mortgages can default, and securitization allows to transfer this risk from leverage-constrained intermediaries to savers with
low risk aversion. The final section of the paper studies the boom and bust of the 2000s,
as we do here. In this experiment, the credit cycle is driven by a slackening of collateral
requirements, along with a perceived decline in the riskiness of mortgages, which turns out
to be incorrect. Qualitatively, this combination of shocks generates a plausible boom and
bust in debt and real estate values. However, the response of house prices is small, partly
2

Our paper is also broadly related to the work of Gerali, Neri, Sessa, and Signoretti (2010) and Iacoviello
(2014), who estimate large-scale dynamic stochastic general equilibrium models with several nominal and
real frictions, including collateral constraints for households and entrapreneurs, and leverage restrictions
for financial intermediaries. These papers, however, investigate the properties of business cycles, and do
not have a particular focus on the recent boom-bust cycle.

CREDIT SUPPLY AND THE HOUSING BOOM

10

because the MBS yield rises during the boom. This counterfactual effect of mortgage rates
is presumably due to the slackening of the collateral constraint, which increases the demand
for credit.
The rest of the paper is organized as follows. Section 2 presents the simple model of
lending and borrowing with houses as collateral. Section 3 analyzes the properties of this
model and characterizes its equilibrium. In section 4, we conduct a number of quantitative
experiments to study the impact of looser lending and collateral constraints. Section 5
concludes.

2. The model
This section presents a simple model with heterogeneous households that borrow from
each other, using houses as collateral. We use the model to establish that the crucial factor
behind the boom in house prices and mortgage debt of the early 2000s was an outward shift
in the supply of funds to borrowers, rather than an increase in the demand for funds driven
by lower collateral requirements, as mostly assumed by the literature so far. We illustrate
this point in the simplest possible endowment economy, abstracting from the complications
arising from production and capital accumulation.
2.1. Objectives and constraints. The economy is populated by two types of households,
with different discount rates, as in Kiyotaki and Moore (1997), Iacoviello (2005), Campbell
and Hercowitz (2009b) and our own previous work (Justiniano, Primiceri, and Tambalotti,
2014b,a). Patient households are denoted by l, since in equilibrium they save and lend.
Their discount factor is βl > βb , where βb is the discount factor of the impatient households,
who borrow in equilibrium.
At time 0, representative household j = {b, l} maximizes utility
E0

∞
�

βjt [u (cj,t ) + vj (hj,t )] ,

t=0

where cj,t denotes consumption of non-durable goods, and vj (hj,t ) is the utility of the
service flow derived from a stock of houses hj,t owned at the beginning of the period. The
function v (·) is indexed by j for reasons that will become clear in section 2.3. Utility

CREDIT SUPPLY AND THE HOUSING BOOM

11

maximization is subject to the flow budget constraint
cj,t + pt [hj,t+1 − (1 − δ) hj,t ] + Rt−1 Dj,t−1 ≤ yj,t + Dj,t ,
where pt is the price of houses in terms of the consumption good, δ is their depreciation
rate, and yj,t is an exogenous endowment of consumption goods and new houses. Dj,t
is the amount of one period debt accumulated by the end of period t, and carried into
period t + 1, with gross interest rate Rt . In equilibrium, debt is positive for the impatient
borrowers and it is negative for the patient lenders, representing loans that the latter extend
to the former. Therefore, borrowers can use their endowment, together with loans, to buy
non-durable consumption goods and new houses, and to repay old loans with interest.
Households’ decisions are subject to two more constraints. First, on the liability side of
their balance sheet, a collateral constraint limits debt to a fraction θ of the value of the
borrowers’ housing stock, along the lines of Kiyotaki and Moore (1997). This constraint
takes the form
(2.1)

Dj,t ≤ θpt hj,t+1,

where θ is the maximum admissible loan-to-value (LTV) ratio.3 Higher values of θ capture
looser collateral requirements, such as those brought about by mortgages with higher initial LTVs, multiple mortgages on the same property (so-called piggy back loans), and home
equity lines of credit. Together, they contribute to enhance households’ ability to borrow
against a given value of their property. An emerging consensus in the literature identifies
these looser credit conditions and their reversal as the fundamental force behind the credit
cycle of the 2000s. Recent papers based on this hypothesis include Eggertsson and Krugman
(2012), Guerrieri and Lorenzoni (2012), Hall (2012), Midrigan and Philippon (2011), Garriga, Manuelli, and Peralta-Alva (2012), Favilukis, Ludvigson, and Nieuwerburgh (2013),
and Boz and Mendoza (2014).
The second constraint on households’ decisions applies to the asset side of their balance
sheet. This constraint consists in an upper bound on the total amount of mortgage lending

3This type of constraint is often stated as a requirement that contracted debt repayments (i.e. principal plus

interest) do not exceed the future expected value of the collateral. The choice to focus on a contemporaneous
constraint is done for simplicity and it is inconsequential for the results, since most of the results pertain
to steady state equilibria.

CREDIT SUPPLY AND THE HOUSING BOOM

12

they can engage in, as in
(2.2)

−Dj,t ≤ L̄.

Expression (2.2) is meant to capture all implicit or explicit regulatory and technological
constraints on the economy’s ability to channel funds towards the mortgage market.4 In this
simple model without banks, we impose this constraint directly on the lenders. However, in
appendix B we show that it can be interpreted as stemming from a leverage (or regulatorycapital, or “skin in the game”) constraint on financial intermediaries. If raising capital is
costly—as typically assumed in the literature, e.g. Jermann and Quadrini (2012)—these
leverage restrictions translate into a restraint on overall mortgage lending. In the limit
in which equity adjustments become very onerous, these leverage restrictions on financial
intermediaries would produce the same results of (2.2).
We focus on this stark formulation of the constraint to create the cleanest possible contrast with the more familiar collateral constraint imposed on the borrowers. From a macroeconomic perspective, this lending limit produces an upward sloping supply of funds in the
mortgage market, which mirrors the downward sloping demand for credit generated by the
borrowing constraint. We illustrate this point in the next section, which characterizes the
equilibrium of the model. In section 4, we will use this equilibrium, and its observable
implications, to argue that the boom in credit and house prices of the early 2000s is best
understood as the consequence of the relaxation of constraints on lending, rather than on
borrowing, as most of the literature has been assuming (i.e. as an increase in L̄, rather
than in θ.)
2.2. Equilibrium conditions. Given their lower propensity to save, impatient households
borrow from the patient in equilibrium, and the lending constraint (2.2) does not influence
their decisions. As a consequence, their optimality conditions are
(2.3)
(2.4)

(1 − µt ) u� (cb,t ) = βb Rt Et u� (cb,t+1 )
�
�
(1 − µt θ) u� (cb,t ) pt = βb vb� (hb,t+1 ) + βb (1 − δ) Et u� (cb,t+1 ) pt+1

4In our extremely stylized economy, this constraint results into a limit on households’ overall ability to save.

But this is simply an artifact of the simplifying assumption that mortgages are the only financial assets in
the economy, and is not important for the results.

CREDIT SUPPLY AND THE HOUSING BOOM

(2.5)
(2.6)

13

cb,t + pt [hb,t+1 − (1 − δ) hb,t ] + Rt−1 Db,t−1 = yb,t + Db,t
µt (Db,t − θpt hb,t+1 ) = 0,

µt ≥ 0,

Db,t ≤ θpt hb,t+1 ,

where u� (cb,t ) · µt is the Lagrange multiplier on the collateral constraint.

Equation (2.3) is a standard Euler equation weighting the marginal benefit of higher

consumption today against the marginal cost of lower consumption tomorrow. Relative to
the case of an unconstrained consumer, the benefit of higher current consumption is reduced
by the cost of a tighter borrowing constraint. Equation (2.4) characterizes the borrowers’
housing demand: the cost of the forgone consumption used to purchase an additional unit
of housing today must be equal to the benefit of enjoying this house tomorrow, and then
selling it (after depreciation) in exchange for goods. The term (1 − µt θ) on the left-hand

side of (2.4) reduces the cost of foregone consumption, because the newly purchased unit of
housing slackens the borrowing constraint when posted as collateral. Equation (2.4) makes
it clear that the value of a house to a borrower is higher when the borrowing constraint
is tighter, and when the ability to borrow against it (i.e. the maximum loan-to-value
ratio) is higher. Finally, equation (2.5) is the flow budget constraint of the borrowers, and
expressions (2.6) summarize the collateral constraint and the associated complementary
slackness conditions.
Since the patient households lend in equilibrium, the lending constraint is the relevant
one in their decisions. Their equilibrium conditions are
(2.7)
(2.8)
(2.9)
(2.10)

(1 + ξt ) u� (cl,t ) = βl Rt Et u� (cl,t+1 )
�
�
u� (cl,t ) pt = βl vl� (hlt+1 ) + βl (1 − δ) Et u� (cl,t+1 ) pt+1
cl,t + pt [hl,t+1 − (1 − δ) hl,t ] + Rt−1 Dl,t−1 = yl,t + Dl,t
�
�
ξt −Dl,t − L̄ = 0,

ξt ≥ 0,

− Dl,t ≤ L̄,

where u� (cl,t ) · ξt is the Lagrange multiplier on the lending constraint. When this constraint

is binding, the lenders would like to save more at the prevailing interest rate, but they cannot. To make it optimal for them to consume instead, the multiplier ξt boosts the marginal

CREDIT SUPPLY AND THE HOUSING BOOM

14

benefit of current consumption in their Euler equation (2.7), or equivalently reduces their
perceived rate of return from postponing it. Contrary to what happens with the borrowers,
who must be enticed to consume less today not to violate their constraint, the lenders must
be driven to tilt their consumption profile towards the present when their constraint is
binding. Unlike the collateral constraint, though, the lending constraint does not affect the
demand for houses, since it does not depend on their value. Otherwise, equations (2.3)-(2.6)
have a similar interpretation to (2.7)-(2.10).
The model is closed by imposing that borrowing is equal to lending
(2.11)

Db,t + Dl,t = 0,

and that the housing market clears
hb,t + hl,t = h̄,
where h̄ represents a fixed supply of houses.
2.3. Functional forms. To characterize the equilibrium of the model, we make two convenient functional form assumptions. First, we assume that the lenders’ utility function
implies a rigid demand for houses at the level h̄l . As a consequence, we replace equation
(2.8) with
hl,t = h̄l .
This assumption implies that houses are priced by the borrowers, which amplifies the potential effects of collateral constraints on house prices, since these agents face a fixed supply
equal to h̄b ≡ h̄ − h̄l , and they are leveraged. This assumption is appealing for several
reasons. First, housing markets are highly segmented in practice (e.g. Landvoigt, Piazzesi,
and Schneider (2013)) and the reallocation of houses between rich and poor, lenders and
borrowers, is minimal. By assuming a rigid demand by the lenders, we shut down all reallocation of houses between the two groups of agents, thus approximating reality. In addition,
this simple modeling device captures the idea that houses are priced by the most leveraged
individuals, as in Geanakoplos (2010).
The second simplifying assumption is that utility is linear in consumption. As a consequence, the marginal rate of substitution between houses and non-durables does not depend
on the latter, and the level and distribution of income do not matter for the equilibrium in

CREDIT SUPPLY AND THE HOUSING BOOM

15

the housing and debt markets. Therefore, the determination of house prices becomes very
simple and transparent, as we can see by re-writing equation (2.4) as
(2.12)

pt =

βb
[mrs + (1 − δ) Et pt+1 ] ,
(1 − µt θ)

�
�
where mrs = v � h̄ − h̄l , with the constant marginal utility of consumption normalized to
one.

According to this expression, house prices are the discounted sum of two components:
the marginal rate of substitution between houses and consumption, which represents the
“dividend” from living in the house, and the expected selling price of the undepreciated
portion of the house. The discount factor depends on the maximum LTV ratio θ—prices
are higher if a larger fraction of the house can be used as collateral—and on the tightness
of the borrowing constraint µt . The tighter the constraint, the more valuable is every unit
of collateral.
Although extreme, the assumption of linear utility has the virtue of simplifying the
mathematical structure of the model, as well as its economics. With a constant marginal
rate of substitution, the only source of variation in house prices is the tightness of the
borrowing constraint, as captured by the multiplier µt . To the extent that, in the data,
part of the movement in prices is driven by changes in the marginal utility of housing
services relative to other consumption, this simplification stacks the deck against our goal
of explaining the behavior of the housing market in the 2000s.
3. Characterization of the Equilibrium
The model we presented in the previous section features two balance sheet constraints,
both limiting the equilibrium level of debt in the economy. The collateral constraint on
the liability side of the borrowers’ balance sheet—a standard tool to introduce financial
frictions in the literature—limits directly the amount of borrowing by impatient households
to a fraction of the value of their houses (Db,t ≤ θpt h̄b ). The lending constraint, instead,
puts an upper bound on the ability of patient households to extend mortgage credit. But in

our closed economy, where borrowing must equal lending, this lending limit also turns into
a constraint on borrowing (Db,t ≤ L̄).5 Which of the two constraints binds at any given
5In an open economy model with borrowing from abroad, such as Justiniano, Primiceri, and Tambalotti

(2014b), this constraint would become Db,t ≤ L̄ + Lf,t , where Lf,t denotes the amount of foreign borrowing.
Therefore, in such a model, Lf,t plays a similar role to L̄ in relaxing or tightening the constraint.

CREDIT SUPPLY AND THE HOUSING BOOM

16

R

1/!b

1/!l

Demand of funds

Supply of funds

θ ! p ! hb

Db

Figure 3.1. Demand and supply of funds in a model with collateral constraints.

point in time depends on the parameters θ and L̄, but also on house prices, and is therefore
endogenous. Moreover, both constraints bind simultaneously when θpt h̄b = L̄, a restriction
that is not as knife-edge as one might think, as illustrated below.
To illustrate the interaction between the two balance sheet constraints, start from the
standard case with only a borrowing limit, which is depicted in figure 3.1. The supply of
funds is perfectly elastic at the interest rate represented by the (inverse of the) lenders’
discount factor. The demand of funds is also flat, at a higher interest rate determined
by the borrowers’ discount factor, but only up to the borrowing limit, where it becomes
vertical. The equilibrium is at the (gross) interest rate 1/βl , where demand meets supply
and the borrowing constraint is binding. This implies a positive value of the multiplier of
the collateral constraint (µt ) and a house price determined by equation (2.12), which in
turn pins down the location of the kink in the demand of funds in figure (3.1).
Consider now the case of a model with a lending constraint as well, depicted in figure 3.2.
With a lending constraint, the supply of funds also has a kink, at the value L̄. Whether
this constraint binds in equilibrium depends on the relative magnitude of L̄ and θpt h̄b . If
L̄ > θpt h̄b , as in the figure, the equilibrium is the same as in figure 3.1.6 In particular, small
variations of the lending limit L̄ do not affect equilibrium interest rates and house prices.
6For this to be an equilibrium, the resulting house price must be such that L̄ > θp h̄ .
t b

CREDIT SUPPLY AND THE HOUSING BOOM

17

R

1/!b

1/!l

Demand of funds

Supply of funds

θ ! p ! hb

L

Db

Figure 3.2. Demand and supply of funds in a model with collateral and lending
constraints, with the latter not binding.

!

If instead L̄ < θpt h̄b , the model behaves differently, as shown in figure 3.3. In this case,
it is the lending limit to be binding and the interest rate settles at the level 1/βb , higher
than before. Borrowers are limited in their ability to anticipate consumption not by the
value of their collateral, but by the scarcity of funds that the savers are allowed to lend
in the mortgage market. At the going rate of return, savers would be happy to expand
their mortgage lending, but they cannot. As a result, the economy experiences a dearth of
lending and a high interest rate. House prices are again determined by equation (2.12), but
with µt = 0, which puts them below those in the scenario of figures 3.1 and 3.2.
Qualitatively, the transition from a steady state with a low L̄ (figure 3.3) to one with a
higher L̄ (figure 3.2), in which the lending constraint no longer binds, matches well what
happened in the U.S. in the first half of the 2000s, with interest rates falling and household
debt and house prices rising. Section 4 shows that this parallelism also works quantitatively,
and that a slackening of the constraint on mortgage lending is also consistent with other
patterns in the data.
In contrast, a slackening of the borrowing constraint caused by an increase in the LTV
parameter θ would have opposite effects, making it an unlikely source of the boom observed
in the U.S. in the 2000s. Assuming that the borrowing constraint binds initially, as in figure
3.2, an increase in θ that is sufficiently large leads to an increase in interest rates from 1/βl
to 1/βb , as the vertical “arm” of the demand for funds crosses over the lending limit L̄,

CREDIT SUPPLY AND THE HOUSING BOOM

18

R

1/!b

1/!l

Demand of funds

Supply of funds

L

θ ! p ! hb

Db

Figure 3.3. Demand and supply of funds in a model with collateral and lending
constraints, with the latter binding.

!

causing the latter to become binding. Moreover, as the borrowing constraint ceases to
bind, the multiplier µt falls to zero, putting downward pressure on house prices.7
Intuitively, changes in θ affect the demand for credit by its ultimate users, so that an
increase in this parameter increases credit demand, driving its price (the interest rate)
higher. And with higher mortgage rates, house prices fall. On the contrary, the lending
limit L̄ controls the supply of funds from lenders, so that an increase in the limit drives the
prices of mortgage credit down and house prices up, also leading to more household debt,
at roughly unchanged levels of leverage.
Before moving on, it is useful to consider the case in which L̄ = θpt h̄b , when the vertical
arms of the supply and demand for funds exactly overlap. This situation is not an unimportant knife-edge case, as the equality might suggest, due to the endogeneity of home prices.
In fact, there is a large region of the parameter space in which both constraints bind, so
that pt =

L̄
.
θ h̄b

Given pt , equation (2.12) pins down the value of the multiplier µt , which, in

turn, determines a unique interest rate
Rt =

1 − µt
βb

7Starting instead from a situation in which the lending constraint is binding, as in figure 3.3, an increase

in θ would leave the equilibrium unchanged.

CREDIT SUPPLY AND THE HOUSING BOOM

19

via equation (2.3). This is an equilibrium as long as the implied value of µt is positive, and
the interest rate lies in the interval [1/βl , 1/βb ].
We formalize these intuitive arguments through the following proposition.
Proposition 1. In the model of section 2 there exist two threshold house prices, p ≡
βb mrs
1−βb (1−δ)

(i) if

and p̄ (θ) ≡

β̃(θ) mrs
,
1−β̃(θ)(1−δ)

such that:

L̄ < θph̄b , the lending constraint is binding and
pt = p,

(ii) if

and

Rt =

1
;
βb

L̄ > θp̄ (θ) h̄b , the borrowing constraint is binding and
pt = p̄ (θ) ,

(iii) if

Db,t = L̄

Db,t = θp̄ (θ) h̄b

and

Rt =

1
;
βl

θph̄b ≤ L̄ ≤ θp̄ (θ) h̄b , both constraints are binding and

L̄
pt =
,
θh̄b

Db,t = L̄

and

�
�
where mrs ≡ v � h̄ − h̄l , β̃ (θ) ≡

�
�
1
1 − βb (1 − δ) − mrs · βb θh̄b /L̄
Rt =
1−
;
βb
θ

βb β l
θβb +(1−θ)βl

and p̄ (θ) ≥ p for every θ ≥ 0.

Proof. See appendix A.

�

To further illustrate Proposition 1, figure 3.4 plots the equilibrium value of house prices,
debt and interest rates, as a function of the lending limit L̄, for a given value of the loan-tovalue ratio θ. On the left of the three panels, tight lending constraints are associated with
high interest rates, low house prices and low levels of indebtedness. As L̄ rises and lending
constraints become looser, interest rates fall, boosting house prices and allowing people
to borrow more against the increased value of their home. However, the relation between
lending limits and house prices is not strictly monotonic. When lending constraints become
very relaxed, the model is akin a standard model with collateral constraints, and lending
limits become irrelevant for the equilibrium.
The qualitative consequences of a transition towards looser lending constraints—a reduction in interest rates, an increase in house prices and debt, with a stable debt-to-collateral
ratio—square very well with the stylized fact of the period preceding the Great Recession
that we have outlined in the introduction. In the next section we will use a calibrated
version of our model to analyze its performance from a quantitative standpoint.

CREDIT SUPPLY AND THE HOUSING BOOM

20

p
p(" )

!!
p

" p(" )h b

" ph b

!

Db

!

!

L

!

" p(" )h b

!
!

" p(" )h b

" ph b

R

!

!

L

!

1
"b

!
!

1
"l

" ph b

" p(" )h b

L

!

Figure 3.4. Equilibrium house price, debt and interest rates as a function of L̄,
for a given θ.

!

!

!

4. Quantitative analysis
This section provides a quantitative perspective on the simple model illustrated above.
The model is parametrized so that its steady state matches key statistics for the period of
relative stability of the 1990s. We interpret this steady state as associated with a binding
lending constraint, as in figure 3.3 above. This assumption seems appropriate for a period
in which mortgage finance was still relatively unsophisticated, securitization was a nascent
phenomenon, and as a result savers faced relatively high barriers to accessing mortgage
loans.
We then analyze the extent to which a lowering of these barriers, in the form of a
progressive increase in the lending limit L̄, generates a surge in debt and house prices and a
fall in interest rates comparable to those observed in the early 2000s. The main conclusion

CREDIT SUPPLY AND THE HOUSING BOOM

δ

βb

βl

θ

21

ρ

0.003 0.9879 0.9938 0.80 0.0056
Table 1. Calibration of the model.

we draw from this experiment is that looser lending constraints are key to understanding the
dynamics of debt, house prices and interest rates in the period leading up to the financial
crisis. In contrast, a slackening of borrowing constraints, which we also consider as an
alternative source of those dynamics, has entirely counterfactual implications for the boom
phase of the credit and housing cycle. In fact, in our framework, a relaxation of collateral
requirements at the peak of the boom triggers a fall in house prices.

4.1. Parameter values. Table 1 summarizes the model’s calibration, which is based on
U.S. macro and micro data.
Time is in quarters. We set the depreciation rate of houses (δ) equal to 0.003, based on
the Fixed Asset Tables. Real mortgage rates are computed by subtracting 10-year-ahead
inflation expectations from the Survey of Professional Forecasters from the 30-year nominal
conventional mortgage rate published by the Federal Reserve Board. The resulting series is
plotted in figure 1.4. The average real rate in the 1990s is slightly less than 5% (4.63%) and
it falls by about 2.5% between 2000 and 2005. Therefore, we set the discount factor of the
borrowers to match a 5% real rate in the initial steady state, and we calibrate the lenders’
discount factor to generate a fall in interest rates of 2.5 percentage points following the
relaxation of the lending constraint. The heterogeneity in discount factors consistent with
this decline, βb = 0.9879 and βl = 0.9938, is in line with that chosen by Krusell and Smith
(1998) or Carroll, Slacalek, and Tokuoka (2013) to match the distribution of household
wealth in the US.
For the calibration of the remaining parameter—the maximum allowed LTV ratio (θ)—
we face two main challenges, due to some aspects of the theoretical model that are stark
simplifications of reality. First, the model assumes a collateral constraint with a constant
loan-to-value ratio over the entire life of a loan. This simple specification, which is by far
the most popular in the literature, works well to provide intuition about the working of
the model. However, calibrating θ to the initial loan-to-value ratio of the typical mortgage,

CREDIT SUPPLY AND THE HOUSING BOOM

22

say around 0.8, would overstate the aggregate debt-to-real estate ratio in the economy.8
This is because, in reality, mortgage contracts require a gradual repayment of the principal,
leading to the accumulation of equity in the house and therefore to average loan-to-value
ratios that are lower than the initial one.
To capture this feature of reality in our quantitative exercises, we follow Campbell and
Hercowitz (2009b) and generalize the model by replacing the collateral constraint (2.1) with
(4.1)

(4.2)

Db,t ≤ θpt Hb,t+1
Hb,t+1 =

∞
�
j=0

(1 − ρ)j [ht+1−j − (1 − δ) ht−j ] ,

where the last expression can be written recursively as
(4.3)

Hb,t+1 = (1 − ρ) Hb,t + [hb,t+1 − (1 − δ) hb,t ] .

The variable Hb,t+1 denotes the share of the housing stock that can be used as collateral,
which does not necessarily coincide with the physical stock of houses Hb,t+1 . Equation

(4.2) describes the evolution and composition of Hb,t+1 . The housing stock put in place
in the latest period (ht+1 − (1 − δ) ht ) can all be used as collateral, and hence it can “sustain” an amount of borrowing equal to a fraction θ of its market value. However, only a

fraction (1 − ρ)j of the houses purchased in t − j is collateralizable, with the remaining

share representing the amortization of the loan and the associated accumulation of equity
by the borrowers. If ρ = δ, amortization and depreciation of the housing stock proceed
in parallel, so that the entire housing stock can always be used as collateral. As a result,
the new collateral constraint is identical to (2.1). If ρ > δ, however, amortization is faster
than depreciation, reducing the borrowing potential of the housing stock and the average
debt-to-real estate ratio in the economy, for any given value of the initial LTV θ. Appendix
C characterizes the solution of the model with this generalized version of the collateral
constraint.
To calibrate θ, we should look at the mortgages of households that resemble the borrowers
in the model. One option would be identify as borrowers all households with mortgage debt
in the micro data. However, in the data many borrowers also own a substantial amount of
8In this case, the effects of looser lending constraints would be even larger than in the baseline calibration.

CREDIT SUPPLY AND THE HOUSING BOOM

23

financial assets (Campbell and Hercowitz, 2009a, Iacoviello and Pavan, 2013, Kaplan and
Violante, 2014). Our model is not equipped to capture this feature of the data, since in it
agents only borrow because they are impatient, and as a result they do not own any asset,
aside from their house. In the data, instead, people might choose to borrow against their
houses (instead of running down their financial assets) for a variety of other reasons that
are not explicitly considered in our model. To avoid including in the group of borrowers
also many individuals possibly with the characteristics of a lender, an alternative, more
prudent choice is to identify as borrowers all households with little financial assets.
More specifically, we separate the borrowers from the lenders using the Survey of Consumer Finances (SCF), which is a triennial cross-sectional survey of the assets and liabilities
of U.S. households. We identify the borrowers as the households that appear to be liquidity
constrained, namely those with liquid assets whose value is less than two months of their
total income. Following Kaplan and Violante, we compute the value of liquid assets as
the sum of money market, checking, savings and call accounts, directly held mutual funds,
stocks, bonds, and T-Bills, net of credit card debt. We apply this procedure to the 1992,
1995 and 1998 SCF.
To calibrate the initial loan-to-value ratio θ, we take an average of the loan-to-value ratios
of the mortgages of all borrowers who purchased their home (or refinanced their mortgage)
in the year immediately before the surveys.9 An average of these ratios computed over
the three surveys of 1992, 1995 and 1998 yields a value for θ of 0.8, a very common initial
LTV for typical mortgages, which is also broadly in line with the cumulative loan-to-value
ratio of first-time home buyers estimated by Duca, Muellbauer, and Murphy (2011) for the
1990s. As forρ, the parameter that governs the amortization speed on the loans, we pick a
value of 0.0056, so as to match the average ratio of debt to real estate for the borrowers,
which is 0.43 in the 1990s SCFs. Finally, the lending limit L̄ is chosen in the context of the
experiments described in the next subsection.
4.2. An expansion in credit supply. In this subsection we study the quantitative effects
of a relaxation of lending constraints in the mortgage market. The premise for this exercise
is that, at the end of the 1990s, the U.S. economy was constrained by a limited supply of
9Following Campbell and Hercowitz (2009b), among these households, we restrict attention to the ones

who borrow at least half of their home value, since those with lower initial LTVs are probably not very
informative on the credit conditions experienced by the marginal buyers.

CREDIT SUPPLY AND THE HOUSING BOOM

24

1.3

1.2

1.1

1

0.9

0.8

0.7

0.6
1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Figure 4.1. Evolution of the lending limit relative to GDP
credit, as in figure 3.3 above. Starting in 2000, the lending constraint is gradually lifted,
following the linear path depicted in figure 4.1. Each movement of L̄ is unanticipated by
the agents. The experiment is timed so that the lending constraint is no longer binding in
2006. The dotted part of the line in figure 4.1 corresponds to the time periods in which the
lending constraint becomes irrelevant for the equilibrium.
In the barebones model presented so far, an increase in L̄ affects the equilibrium house
prices and interest rates only in the region in which both the lending and borrowing constraints are binding, as shown in the proposition 1. This is the region in which our numerical
experiments put the economy between 2000 and 2006, because the dynamics associated with
a relaxation of L̄ in this region are very consistent with the empirical facts we highlighted
in the introduction. However, we think that it is plausible that the relaxation of lending constraints was in fact an ongoing phenomenon, that probably started in the 1990s,
and possibly earlier. However, the model suggests that this process would have had relatively modest effects as long as the maximum volume of lending was far enough below the
borrowing limit, which is why we ignore this earlier period in the simulations.
What real world phenomena are behind the relaxation of the lending limit in the model?
Broadly speaking, we think of an increase in L̄ as capturing technological and regulatory

CREDIT SUPPLY AND THE HOUSING BOOM

25

developments that made it easier for savings to flow towards the mortgage market. Securitization, for instance, turned mortgages into MBS. These standardized products could
easily be traded on financial markets, and their higher-rated tranches could be acquired
by institutional and other investors whose portfolios are restricted by regulation and their
own statutes to include only safe assets. This innovation, in turn, opened up the possibility
for a large pool of savings previously directed towards Government debt to get exposed to
mortgage products, tapping a source of funds that was previously unavailable. Similarly,
the capital requirements imposed by regulation on commercial banks imply lower charges
for agency MBS (and the senior tranches of private label ones) than for the mortgages
themselves. In addition, the rise of highly levered off-balance-sheet special purpose vehicles
allowed banks to bypass capital regulations altogether, further increasing the amount of
funds directly intermediated towards mortgage lending. Finally, the rapid deterioration of
the current account deficit in the first half of the 2000s channeled large pools of international savings, mostly from East Asia and the oil-exporting countries, towards domestic
borrowers. Many of these funds found their way into the mortgage market through the
purchase of MBS, as documented by Bernanke, Bertaut, DeMarco, and Kamin (2011).
While some of these developments date back at least to the 1970s, they really took off
in the late 1990s and early 2000s. From the perspective of the model, this expansion in
credit starts affecting the economy only when the lending limit becomes loose enough to
approach the borrowing limit. Circumstantially, it seems plausible to assume that this interaction between the two constraints started in the early 2000s. This is around the time
when enough funds had become available to mortgage finance that borrowers’ collateral
(and creditworthiness) begun to represent a material impediment to further credit expansion. The fact that these borrower-side factors, more than the ability of lenders to direct
their savings towards the mortgage market, started to represent the relevant constraint is
confirmed by the pressure to looser credit standards that lead to the boom in subprime,
high-LTV, piggy back and other risky mortgages that sowed the seeds of the subsequent
financial crisis.
Figure 4.2 plots the response of the key variables in the model to the loosening of the
lending constraints described above. The expansion in credit supply lowers mortgage rates
by 2.5 percentage points. This decline reflects the gradual transition from a credit-supplyconstrained economy, where the interest rate equals

1
βb ,

to a demand-constrained economy,

CREDIT SUPPLY AND THE HOUSING BOOM

Annualized mortgage rate

26

House prices

6
150
5
140
4
130
3
120
2
110
1
100
0
1990

1995

2000

2005

1990

Debt−to−GDP ratio

1995

2000

2005

Debt−to−real estate ratio
0.55

1.2
0.5
1

0.45
0.4

0.8
0.35
0.6
1990

1995

2000

2005

0.3
1990

1995

2000

2005

Figure 4.2. The response to a loosening of lending constraints

with interest rate

1
βl .

This permanent fall in mortgage rates is a distinctive feature of our

environment with lending constraints, and it would be difficult to obtain in the context of
more standard models with only collateral constraints, in which the steady state interest
rate is always pinned down by the discount factor of the lenders. The magnitude of the
decline is in line with the evidence presented in the introduction, but this is just a function
of our calibration of the discount factors of the two sets of households.
When the supply of credit is tight, the borrowers are unconstrained, and the interest
rate is equal to the inverse of their discount factor. However, when lending constraints
become looser and mortgage rates fall below

1
βb ,

the impatient households increase their

demand for credit up to the limit allowed by their collateral constraint, which becomes
binding. The lower the mortgage rate, the more desirable is borrowing, and the higher is
the shadow value µt of the collateral constraint. According to equation (2.12), a rise in µt
increases the value of houses to the borrowers, who are the agents pricing them, because
their collateral services become more valuable. In our calibration, house prices increase

CREDIT SUPPLY AND THE HOUSING BOOM

27

by almost 40 percent in real terms following a lending liberalization, a magnitude that is
comparable to the U.S. experience depicted in figure 1.1.
The substantial increase in house prices then relaxes the collateral constraint of the
impatient households, allowing them to borrow more against the higher value of their
homes. In the model, mortgage debt raises by approximately 30 percentage points of GDP.
However, the debt-to-real estate ratio remains unchanged, with debt and home values rising
in parallel. This is precisely what happened in the data through 2006, as shown in figure
1.3.

4.3. Looser collateral requirements. In this section, we contrast the implications of the
loosening of lending constraints described above to those of a slackening of the borrowing
constraint, either through a higher initial LTV, or through slower amortization. This comparison is important, because most of the literature focuses on an increase in borrowing
limits as the trigger of the boom in household debt and, to a certain extent, in house prices.
Our results suggest that this view is difficult to reconcile with some of the key stylized facts
discussed in the introduction. On the contrary, we show that a relaxation of collateral requirements can even generate a decline in home values, rather than a rapid increase. This is
an appealing feature of our model because it suggests a possible trigger for the turnaround
in house prices that started in the fall of 2006, at a time when financial liberalization was
still in full swing.
We start this analysis by slackening the collateral constraint in an economy without
lending limits, which is the case typically considered in the literature. The model is parameterized to match the same targets used in section 4.1, which produces the same values
for most parameters, except for βl and βb . In particular, βl is set equal to 0.9879 to match
the 5 percent average real mortgage rate in the 1990s, since the equilibrium interest rate in
this model corresponds to

1
βl .

As for βb , we choose the value 0.9820 to maintain the same

gap from the discount factor of the lenders as in the previous calibration.
Given this parametrization, we study the effects of a gradual increase in the maximum
LTV from 0.8, the baseline value of θ, to 1.02, representing a situation in which borrowers
can borrow up to the entire amount of their house (panel a in figure 4.3). This change in
θ generates the same increase in household debt as in the previous experiment, making the
two simulations easily comparable.

CREDIT SUPPLY AND THE HOUSING BOOM

(a): Maximum LTV

−3

7

x 10

28

(b): Speed of repayment

6.5

1.1

6
1

5.5

0.9

5
4.5

0.8

4
0.7

0.6
1990

3.5

1995

2000

2005

3
1990

1995

2000

2005

Figure 4.3. Increase in LTV and reduction in the speed of repayment

Figure 4.4 plots the behavior of debt, interest rates and house prices in response to
this change in θ, contrasting them to the responses of these variables to the relaxation in
the lending constraint described above. Even though the model is extremely stylized, the
contrast between the responses highlights the remarkable success of the first experiment in
generating the stylized facts we are focusing on. In comparison, not much happens when θ
increases.
First, interest rates remain unchanged as a result of the looser collateral requirements,
since lenders are unconstrained and their discount factor pins down the interest rate. In a
model with short-run dynamics, interest rates would even increase, so as to convince the
patient households to lend additional funds to the now less constrained borrowers (Justiniano, Primiceri, and Tambalotti, 2014a). Second, house prices move very little in response
to an increase in the maximum LTV, which is consistent with the results of Iacoviello
and Neri (2010) and Kiyotaki, Michaelides, and Nikolov (2011). As a result, the increase
in household debt stems from a combination of slightly higher house prices and a higher
debt-to-collateral ratio, as shown in the lower-right panel. This increase in leverage is counterfactual, as we already stressed, because the debt-to-real estate ratio was essentially flat
over this period.
Results are very similar if we drive an increase in household debt via a reduction in the
speed of amortization ρ, rather than via a rise in θ. This experiment is depicted by the green
dashed-dotted line in figure 4.4. In this case, the change in ρ is calibrated to generate the
same dynamics of household debt as in the other two experiments, which requires gradually
decreasing ρ from the initial value of 0.0056 to a value of 0.0041, as shown in panel b of

CREDIT SUPPLY AND THE HOUSING BOOM

Annualized mortgage rate

29

House prices

6
150
5
140
4
130
3
120
2
1

Lending constraints
Collateral constraints:θ
Collateral constraints:ρ

0
1990

1995

2000

110
100
2005

1990

Debt−to−GDP ratio

1995

2000

2005

Debt−to−real estate ratio
0.55

1.2
0.5
0.45

1

0.4
0.8
0.35
0.6
1990

1995

2000

2005

0.3
1990

1995

2000

2005

Figure 4.4. Comparison of the responses to a loosening of lending and collateral constraints.

figure 4.3. The resulting dynamics of debt and house prices are similar to those generated
by the previous experiment, and they are equally counterfactual.

4.4. Why house prices started to fall: a potential trigger for the bust. The previous two experiments analyze the consequences of looser collateral constraints when lending
constraints are absent, or so slack that they are irrelevant for the equilibrium. In this subsection, we show that the same relaxation of collateral constraints has substantially different
effects if lending constraints are in fact present, as in our model, and eventually become
binding. In this scenario, an increase in θ lifts interest rates and the debt-to-collateral
ratio, and depresses house prices. These outcomes are consistent with the data between
2006 and 2008, when the mature phase of the credit boom gave way to the bust. This novel
account of the turnaround in the cycle is appealing because it does not rely on a reversal of
the forces behind the boom, unlike most of the literature (see Burnside, Eichenbaum, and
Rebelo, 2013 for an exception to this statement, in a model with houses, but no credit).

CREDIT SUPPLY AND THE HOUSING BOOM

Annualized mortgage rate

30

House prices

6
150
5
140
4
130
3
120
2
110
1
100
0
1990

1995

2000

2005

2010

1990

Debt−to−GDP ratio

1995

2000

2005

2010

Debt−to−real estate ratio
0.55

1.2
0.5
0.45

1

0.4
0.8
0.35
0.6
1990

1995

2000

2005

2010

0.3
1990

1995

2000

2005

2010

Figure 4.5. The response to a loosening of lending constraints (an increase in
the lending limit L̄) followed by a relaxation of collateral constraints (an increase
in the maximum loan-to-value ratio θ).

To illustrate this point, we modify the baseline experiment of section 4.2 and assume
that the increase in L̄ between 2000 and the end of 2005 is followed by a rise in θ from 0.8
in 2006 to 1.02 at the end of 2008. The results of this combined experiment are depicted in
figure 4.4. These simulations are identical to those of figure 4.2 until the end of 2005. At
that point, following the expansion in L̄, the lending constraint is no longer binding and
the equilibrium is determined by the collateral constraint, as in figure 3.2. Starting in 2006,
however, the relaxation of the collateral constraint shifts the kink of the demand for funds
to the right, to the point where the collateral and the lending constraint are both binding
again.
At this point, a further increase in θ reduces house prices, as in case (iii) of Proposition
1, since L̄ = θpt h̄b . Intuitively, a slackening of the borrowing constraint (i.e. a higher θ)
reduces its shadow value (µt ) by more when the amount of borrowing is constrained by the
supply of funds rather than by their demand. Moreover, mortgage rates increase, debt is
stable and the debt-to-collateral ratio rises, as shown in figure 4.4.
In the experiment of this subsection, we increased L̄ and θ sequentially, to isolate their
relative role in the boom-bust episode of the 2000s. In reality, the relaxation of lending

CREDIT SUPPLY AND THE HOUSING BOOM

31

and borrowing constraints probably proceeded in parallel, rather than sequentially, since
they are both manifestations of a broader process of financial liberalization. However,
our model’s key insight is that an increase in θ will trigger a fall in house prices even in
these more complex and realistic environments, as long as the resulting expansion in credit
demand eventually exceeds that in supply.
Finally, we have no ambition to capture with such a simple model the intricate dynamics
of the acute phase of the financial and economic crisis that followed the collapse of Lehman
Brothers at the end of 2008. The model does suggest, however, that a successful account of
this phase of the bust must feature a sharp contraction in the demand for credit, relative to
its supply, to simultaneously match the decline in mortgage rates and house prices observed
during this period.

5. Concluding Remarks
An unprecedented boom and bust in house prices and household debt have been among
the defining features of the U.S. macroeconomic landscape since the turn of the millennium.
Most accounts of this cycle in credit and collateral values—and of the Great Recession and
slow recovery that accompanied it—have pointed to changes in the tightness of borrowing
constraints, and the consequent change in the demand for credit, as its key drivers. In
this paper, we argued that the focus of this discussion should shift from constraints on
borrowing to impediments to lending, in particular when it comes to understanding the
boom phase of the cycle.
We make this point in a stylized model of borrowing and lending among households,
which features both a collateral constraint on the borrowing side, and a constraint on
households’ ability to lend in the mortgage market. A progressive loosening of this lending
constraint is consistent with four key empirical facts characterizing the boom—the large
increase in house prices and in mortgage debt, the stability of the ratio between mortgages
and the value of the real estate collateralizing it, and the fall in mortgage interest rates.
The empirical success of the model depends on the interaction between the borrowing and
and lending constraints, but it cannot be reproduced with either of the two constraints in
isolation. In fact, the interaction of the two constraints produces rich dynamics of interest
rates, debt and house prices, which might account for both the boom and bust phases of the

CREDIT SUPPLY AND THE HOUSING BOOM

32

cycle. A fuller analysis of these dynamics, in a model in which the tightening and loosening
of the two constraints is intertwined, is on our research agenda.
Appendix A. Proof of proposition 1
To prove part (i) of the proposition, consider first the case in which the lending constraint
is binding, but the collateral constraint is not, so that Db,t = L̄ < θpt h̄b , ξt > 0 and µt = 0.
With linear utility in consumption, Rt = 1/βb follows from equation (2.3), and equation
(2.4) implies pt =

βb mrs
1−βb (1−δ)

≡ p. For this to be an equilibrium, it must be verified that the

collateral constraint is not binding, as assumed initially. This requires L̄ < θph̄b .

To prove part (ii) of the proposition, consider the opposite case in which the collateral
constraint is binding, but the lending constraint is not. It follows that Db,t = θpt h̄b < L̄,
ξt = 0 and µt > 0. We can now derive Rt = 1/βl from equation (2.7), while equation
(2.3) implies µt = βb /βl − 1. Substituting the expression for µt into equation (2.4) yields
pt =

β̃(θ) mrs
1−β̃(θ)(1−δ)

L̄ > θp̄ (θ) h̄b .

≡ p̄ (θ), where β̃ (θ) ≡

βb βl
θβb +(1−θ)βl .

This is an equilibrium, provided that

To prove part (iii) of the proposition, we must find the equilibrium in the region of the
parameter space in which θph̄b ≤ L̄ ≤ θp̄ (θ) h̄b . Equations (2.3) and (2.7) together imply

that at least one of the two constraints must be binding in this region, but parts (i) and (ii)
of the proposition imply that we cannot have only one of them binding in this region of the
parameter space. It follows that both constraints must be binding simultaneously, implying
Db,t = L̄ = θpt h̄b and pt =
compute µt =

L̄
. Substituting the
θ h̄b
1−βb (1−δ)−mrs·βb θ h̄b /L̄
and, using
θ

expression for pt into equation (2.4), we can
�
�
b θ h̄b /L̄
(2.3), Rt = β1b 1 − 1−βb (1−δ)−mrs·β
.
θ

Finally, µt satisfies µt ≥ 0 as long as θph̄b ≤ L̄ ≤ θp̄ (θ) h̄b , which concludes the proof.

Appendix B. A Simple Model with Financial Intermediaries and Capital
Requirements
This appendix shows that our simple baseline model with a parametric lending limit L̄ is
equivalent to the limiting case of a more realistic model with financial intermediation. In this
model, intermediaries face a capital requirement that their equity be above a certain fraction
of their assets, i.e. a “skin in the game” constraint, as in He and Krishnamurthy (2013),
and Brunnermeier and Sannikov (2014). Intermediaries finance mortgages by collecting
savings from the patient households in the form of either debt (i.e. deposits) or equity,

CREDIT SUPPLY AND THE HOUSING BOOM

33

where the latter can only be adjusted by paying a convex cost, similar to Jermann and
Quadrini (2012). In the limit in which the marginal cost of adjustment tends to infinity, so
that equity is fixed in equilibrium, the capital requirement becomes a hard constraint on
the funds supplied to the borrowers, exactly as in the baseline model.
Although this case with infinite adjustment costs is extreme, it is qualitatively consistent
with the evidence on the stickiness of intermediaries’ equity first uncovered by Adrian
and Shin (2010b). If the marginal cost of adjusting the intermediaries’ capital were not
prohibitively large, as assumed here, the resulting supply of funds would be differentiable,
rather than having a kink, but it would still be upward sloping. This property of the supply
of mortgage finance is the key driver of our results.
In the model with intermediaries, competitive “banks” finance mortgages with a mix
of equity and deposits collected from the savers. Although the borrowers receive funds
from the intermediaries, rather than directly from the savers, their optimization problem
is identical to the one in section 2. The lenders, in contrast, maximize the same utility
function of section 2, but subject to the flow budget constraint
D
E
cl,t + pt [hl,t+1 − (1 − δ) hl,t ] − Dl,t + Et ≤ yl,t − Rt−1
Dl,t−1 + Rt−1
Et−1 ,

where −Dl,t represents “deposits”, which pay a gross interest rate RtD , and Et represents

equity capital, with rate of return RtE . These interest rates can differ from the borrowing
rate Rt .
With linear utility in consumption, the first order conditions of the problem of the lenders
become
RtD = RtE =

(B.1)

1
,
βl

together with the condition hl,t = h̄l following from our maintained assumption that the
lenders’ demand for houses is rigid.
The competitive financial intermediaries maximize profits
(B.2)

Rt Db,t + RtD Dl,t − RtE [1 + f (Et )] Et

subject to the constraints that assets must equal liabilities,
(B.3)

Db,t + Dl,t = Et ,

CREDIT SUPPLY AND THE HOUSING BOOM

34

and to a “capital requirement” that limits lending to a multiple of equity,
(B.4)

Db,t ≤ χEt .

The function f (Et ) represents a convex cost of issuing equity. As in Jermann and
Quadrini (2012), this cost is positive, creating a pecking order of liabilities whereby debt is
preferred to equity. We parametrize it as
� �
� �γ
Et
Et
f
=τ
Ē
Ē
so that the bank’s first order conditions become
(B.5)

Rt − RtD = φt

(B.6)

�
� �γ �
Et
RtE 1 + τ (1 + γ)
− RtD = χφt ,
Ē

where φt is the Lagrange multiplier on the capital requirement.
Combining these two conditions with the fact that RtD = Rt,E we find that the interest
rate on loans is a weighted average of the cost of funding these loans with a combination
of equity and deposits
(B.7)

�
� �γ �
1 D
Et
χ−1 D
Rt = Rt 1 + τ (1 + γ)
+
Rt .
χ
χ
Ē

In this expression, 1/χ is the share of bank liabilities held as equity when the capital
requirement is binding. Its cost is a markup over the interest rate on deposits RtD , which
reflects the marginal cost of issuing equity.
Since this marginal cost is everywhere positive, debt is always preferable to equity, making
the capital requirement constraint always binding for the financial intermediary. Therefore,
we can turn equation (B.7) into the supply of funds by substituting Et = Db,t /χ to obtain
�
�
� �
1
τ (1 + γ) Db,t γ
Rt =
1+
.
βl
χ
χĒ
This supply function is increasing and convex for γ > 1. When γ → ∞, the function
exhibits a kink at Db,t = χĒ, thus establishing the equivalence between this model with

intermediation and the simple model with a lending constraint, as long as L̄ = χĒ. This
equivalence furthermore provides an interpretation for changes in the lending limit L̄, as

CREDIT SUPPLY AND THE HOUSING BOOM

35

stemming from changes in the leverage ratio of intermediaries χ, or in their cost of issuing
equity.

Appendix C. Solution of the model with home equity accumulation
The model used in section 4 to generate the quantitative results differs from the baseline specification because the collateral constraint allows for the gradual repayment of the
mortgage principal. This generalization involves replacing expression (2.1) with (4.1) and
(4.3). The optimality conditions of the problem of the borrowers become
(C.1)
(C.2)
(C.3)

(1 − µt ) u� (cb,t ) = βb Rt Et u� (cb,t+1 )
�
�
(1 − ζt ) u� (cb,t ) pt = βb vb� (hb,t+1 ) + βb (1 − δ) Et (1 − ζt+1 ) u� (cb,t+1 ) pt+1
�
�
(ζt − θµt ) u� (cb,t ) pt = βb Et (1 − ρ) ζt+1 u� (cb,t+1 ) pt+1

(C.4)

cb,t + pt [hb,t+1 − (1 − δ) hb,t ] + Rt−1 Db,t−1 = yb,t + Db,t

(C.5)

µt (Db,t − θpt Hb,t+1 ) = 0,

(C.6)

µt ≥ 0,

Db,t ≤ θpt Hb,t+1 ,

Hb,t+1 = (1 − ρ) Hb,t + [hb,t+1 − (1 − δ) hb,t ]

where u� (cb,t ) · µt and u� (cb,t ) · pt · ζt are the Lagrange multipliers on the constraint Db,t ≤

θpt Hb,t+1 and the evolution of Hb,t+1 respectively. The optimality conditions of the problem
of the lenders and the market clearing conditions are the same as in the baseline.
To solve this model, first note that
Hb,t+1 =

δ
h̄b .
ρ

Suppose now that the lending constraint is binding and the collateral constraint is not, so
that Db,t = L̄ < θpt ρδ h̄b , ξt > 0 and µt = 0. With linear utility in consumption, Rt = 1/βb
follows from equation (C.1), and equations (C.2) and (C.3) imply pt =

βb mrs
1−βb (1−δ)

≡ p. For

this to be an equilibrium, the collateral constraint must actually not be binding, as assumed
above. This requires L̄ < θp ρδ h̄b .

CREDIT SUPPLY AND THE HOUSING BOOM

36

Suppose now to be in the opposite situation in which the collateral constraint is binding,
while the lending constraint is not. It follows that Db,t = θpt ρδ h̄b < L̄, ξt = 0 and µt > 0. We
can now derive Rt = 1/βl from equation (2.7), while equation (C.1) implies µt = βb /βl − 1.
Substituting the expression for µt into equation (C.3) and combining it with (C.2) yields
pt =

βb mrs
1 − βb (1 − ρ)
·
≡ p̄ (θ, ρ) > p.
1 − βb (1 − δ) 1 − βb (1 − ρ) − θ (1 − βb /βl )

This is an equilibrium, provided that L̄ > θp̄ (θ) ρδ h̄b .
Finally, we must find the equilibrium of the model in the region of the parameter
space in which θp ρδ h̄b ≤ L̄ ≤ θp̄ (θ) ρδ h̄b . Combining equations (C.1) and (2.7) implies

that at least one of the two constraints must be binding, and the results above show
that the value of the parameters in this region is inconsistent with only one of them
being binding. It follows that both constraints must bind at the same time, implying
Db,t = L̄ = θpt ρδ h̄b and pt =

ρ L̄
δ θ h̄b .

Substituting the expression for pt into equations (C.2)
1−βb (1−δ)−mrs·βb δθh̄b /(ρL̄) 1−βb (1−ρ)
and (C.3), we can compute the equilibrium value of µt =
· 1−βb (1−δ) ,
θ
and verify that it is positive if θp ρδ h̄b ≤ L̄ ≤ θp̄ (θ) ρδ h̄b . We can then obtain Rt =
�
�
1−βb (1−δ)−mrs·βb δθh̄b /(ρL̄) 1−βb (1−ρ)
1
· 1−βb (1−δ) using (C.1).
βb 1 −
θ
These results can be summarized in the following proposition.

Proposition 2. In the model of section 4 there exist two threshold house prices, p ≡
βb ·mrs
1−βb (1−δ)

(i) if

and p̄ (θ, ρ) ≡

L̄ <

θp ρδ h̄b ,

βb ·mrs
1−βb (1−δ)

such that:

Db,t = L̄

and

Rt =

1
;
βb

L̄ > θp̄ (θ, ρ) ρδ h̄b , the borrowing constraint is binding and
pt = p̄ (θ, ρ) ,

(iii) if

1−βb (1−ρ)
1−βb (1−ρ)−θ(1−βb /βl ) ,

the lending constraint is binding and
pt = p,

(ii) if

·

δ
Db,t = θp̄ (θ, ρ) h̄b
ρ

and

Rt =

1
;
βl

θp ρδ h̄b ≤ L̄ ≤ θp̄ (θ, ρ) ρδ h̄b , both constraints are binding and
pt =

ρ L̄
,
δ θh̄b

Db,t = L̄

and

�
�
� �
1 − βb (1 − δ) − mrs · βb δθh̄b / ρL̄ 1 − βb (1 − ρ)
1
Rt =
1−
·
;
βb
θ
1 − βb (1 − δ)
�
�
where mrs ≡ v � h̄ − h̄l and p̄ (θ) ≥ p for every 0 ≤ θ ≤ 1.

CREDIT SUPPLY AND THE HOUSING BOOM

37

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Federal Reserve Bank of Chicago
E-mail address: ajustiniano@frbchi.org
Northwestern University, CEPR, and NBER
E-mail address: g-primiceri@northwestern.edu
Federal Reserve Bank of New York
E-mail address: a.tambalotti@gmail.com

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Corporate Average Fuel Economy Standards and the Market for New Vehicles
Thomas Klier and Joshua Linn

WP-11-01

The Role of Securitization in Mortgage Renegotiation
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-11-02

Market-Based Loss Mitigation Practices for Troubled Mortgages
Following the Financial Crisis
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-11-03

Federal Reserve Policies and Financial Market Conditions During the Crisis
Scott A. Brave and Hesna Genay

WP-11-04

The Financial Labor Supply Accelerator
Jeffrey R. Campbell and Zvi Hercowitz

WP-11-05

Survival and long-run dynamics with heterogeneous beliefs under recursive preferences
Jaroslav Borovička

WP-11-06

A Leverage-based Model of Speculative Bubbles (Revised)
Gadi Barlevy

WP-11-07

Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation
Sule Alan, Bo E. Honoré, Luojia Hu, and Søren Leth–Petersen

WP-11-08

Fertility Transitions Along the Extensive and Intensive Margins
Daniel Aaronson, Fabian Lange, and Bhashkar Mazumder

WP-11-09

Black-White Differences in Intergenerational Economic Mobility in the US
Bhashkar Mazumder

WP-11-10

Can Standard Preferences Explain the Prices of Out-of-the-Money S&P 500 Put Options?
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-11-11

Business Networks, Production Chains, and Productivity:
A Theory of Input-Output Architecture
Ezra Oberfield

WP-11-12

Equilibrium Bank Runs Revisited
Ed Nosal

WP-11-13

Are Covered Bonds a Substitute for Mortgage-Backed Securities?
Santiago Carbó-Valverde, Richard J. Rosen, and Francisco Rodríguez-Fernández

WP-11-14

The Cost of Banking Panics in an Age before “Too Big to Fail”
Benjamin Chabot

WP-11-15

1

Working Paper Series (continued)
Import Protection, Business Cycles, and Exchange Rates:
Evidence from the Great Recession
Chad P. Bown and Meredith A. Crowley

WP-11-16

Examining Macroeconomic Models through the Lens of Asset Pricing
Jaroslav Borovička and Lars Peter Hansen

WP-12-01

The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell, Jonas D.M. Fisher, and Alejandro Justiniano

WP-12-02

Macroeconomic Effects of Federal Reserve Forward Guidance
Jeffrey R. Campbell, Charles L. Evans, Jonas D.M. Fisher, and Alejandro Justiniano

WP-12-03

Modeling Credit Contagion via the Updating of Fragile Beliefs
Luca Benzoni, Pierre Collin-Dufresne, Robert S. Goldstein, and Jean Helwege

WP-12-04

Signaling Effects of Monetary Policy
Leonardo Melosi

WP-12-05

Empirical Research on Sovereign Debt and Default
Michael Tomz and Mark L. J. Wright

WP-12-06

Credit Risk and Disaster Risk
François Gourio

WP-12-07

From the Horse’s Mouth: How do Investor Expectations of Risk and Return
Vary with Economic Conditions?
Gene Amromin and Steven A. Sharpe

WP-12-08

Using Vehicle Taxes To Reduce Carbon Dioxide Emissions Rates of
New Passenger Vehicles: Evidence from France, Germany, and Sweden
Thomas Klier and Joshua Linn

WP-12-09

Spending Responses to State Sales Tax Holidays
Sumit Agarwal and Leslie McGranahan

WP-12-10

Micro Data and Macro Technology
Ezra Oberfield and Devesh Raval

WP-12-11

The Effect of Disability Insurance Receipt on Labor Supply: A Dynamic Analysis
Eric French and Jae Song

WP-12-12

Medicaid Insurance in Old Age
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-12-13

Fetal Origins and Parental Responses
Douglas Almond and Bhashkar Mazumder

WP-12-14

2

Working Paper Series (continued)
Repos, Fire Sales, and Bankruptcy Policy
Gaetano Antinolfi, Francesca Carapella, Charles Kahn, Antoine Martin,
David Mills, and Ed Nosal

WP-12-15

Speculative Runs on Interest Rate Pegs
The Frictionless Case
Marco Bassetto and Christopher Phelan

WP-12-16

Institutions, the Cost of Capital, and Long-Run Economic Growth:
Evidence from the 19th Century Capital Market
Ron Alquist and Ben Chabot

WP-12-17

Emerging Economies, Trade Policy, and Macroeconomic Shocks
Chad P. Bown and Meredith A. Crowley

WP-12-18

The Urban Density Premium across Establishments
R. Jason Faberman and Matthew Freedman

WP-13-01

Why Do Borrowers Make Mortgage Refinancing Mistakes?
Sumit Agarwal, Richard J. Rosen, and Vincent Yao

WP-13-02

Bank Panics, Government Guarantees, and the Long-Run Size of the Financial Sector:
Evidence from Free-Banking America
Benjamin Chabot and Charles C. Moul

WP-13-03

Fiscal Consequences of Paying Interest on Reserves
Marco Bassetto and Todd Messer

WP-13-04

Properties of the Vacancy Statistic in the Discrete Circle Covering Problem
Gadi Barlevy and H. N. Nagaraja

WP-13-05

Credit Crunches and Credit Allocation in a Model of Entrepreneurship
Marco Bassetto, Marco Cagetti, and Mariacristina De Nardi

WP-13-06

Financial Incentives and Educational Investment:
The Impact of Performance-Based Scholarships on Student Time Use
Lisa Barrow and Cecilia Elena Rouse

WP-13-07

The Global Welfare Impact of China: Trade Integration and Technological Change
Julian di Giovanni, Andrei A. Levchenko, and Jing Zhang

WP-13-08

Structural Change in an Open Economy
Timothy Uy, Kei-Mu Yi, and Jing Zhang

WP-13-09

The Global Labor Market Impact of Emerging Giants: a Quantitative Assessment
Andrei A. Levchenko and Jing Zhang

WP-13-10

3

Working Paper Series (continued)
Size-Dependent Regulations, Firm Size Distribution, and Reallocation
François Gourio and Nicolas Roys

WP-13-11

Modeling the Evolution of Expectations and Uncertainty in General Equilibrium
Francesco Bianchi and Leonardo Melosi

WP-13-12

Rushing into American Dream? House Prices, Timing of Homeownership,
and Adjustment of Consumer Credit
Sumit Agarwal, Luojia Hu, and Xing Huang

WP-13-13

The Earned Income Tax Credit and Food Consumption Patterns
Leslie McGranahan and Diane W. Schanzenbach

WP-13-14

Agglomeration in the European automobile supplier industry
Thomas Klier and Dan McMillen

WP-13-15

Human Capital and Long-Run Labor Income Risk
Luca Benzoni and Olena Chyruk

WP-13-16

The Effects of the Saving and Banking Glut on the U.S. Economy
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-13-17

A Portfolio-Balance Approach to the Nominal Term Structure
Thomas B. King

WP-13-18

Gross Migration, Housing and Urban Population Dynamics
Morris A. Davis, Jonas D.M. Fisher, and Marcelo Veracierto

WP-13-19

Very Simple Markov-Perfect Industry Dynamics
Jaap H. Abbring, Jeffrey R. Campbell, Jan Tilly, and Nan Yang

WP-13-20

Bubbles and Leverage: A Simple and Unified Approach
Robert Barsky and Theodore Bogusz

WP-13-21

The scarcity value of Treasury collateral:
Repo market effects of security-specific supply and demand factors
Stefania D'Amico, Roger Fan, and Yuriy Kitsul
Gambling for Dollars: Strategic Hedge Fund Manager Investment
Dan Bernhardt and Ed Nosal
Cash-in-the-Market Pricing in a Model with Money and
Over-the-Counter Financial Markets
Fabrizio Mattesini and Ed Nosal
An Interview with Neil Wallace
David Altig and Ed Nosal

WP-13-22

WP-13-23

WP-13-24

WP-13-25

4

Working Paper Series (continued)
Firm Dynamics and the Minimum Wage: A Putty-Clay Approach
Daniel Aaronson, Eric French, and Isaac Sorkin
Policy Intervention in Debt Renegotiation:
Evidence from the Home Affordable Modification Program
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
Tomasz Piskorski, and Amit Seru

WP-13-26

WP-13-27

The Effects of the Massachusetts Health Reform on Financial Distress
Bhashkar Mazumder and Sarah Miller

WP-14-01

Can Intangible Capital Explain Cyclical Movements in the Labor Wedge?
François Gourio and Leena Rudanko

WP-14-02

Early Public Banks
William Roberds and François R. Velde

WP-14-03

Mandatory Disclosure and Financial Contagion
Fernando Alvarez and Gadi Barlevy

WP-14-04

The Stock of External Sovereign Debt: Can We Take the Data at ‘Face Value’?
Daniel A. Dias, Christine Richmond, and Mark L. J. Wright

WP-14-05

Interpreting the Pari Passu Clause in Sovereign Bond Contracts:
It’s All Hebrew (and Aramaic) to Me
Mark L. J. Wright

WP-14-06

AIG in Hindsight
Robert McDonald and Anna Paulson

WP-14-07

On the Structural Interpretation of the Smets-Wouters “Risk Premium” Shock
Jonas D.M. Fisher

WP-14-08

Human Capital Risk, Contract Enforcement, and the Macroeconomy
Tom Krebs, Moritz Kuhn, and Mark L. J. Wright

WP-14-09

Adverse Selection, Risk Sharing and Business Cycles
Marcelo Veracierto

WP-14-10

Core and ‘Crust’: Consumer Prices and the Term Structure of Interest Rates
Andrea Ajello, Luca Benzoni, and Olena Chyruk

WP-14-11

The Evolution of Comparative Advantage: Measurement and Implications
Andrei A. Levchenko and Jing Zhang

WP-14-12

5

Working Paper Series (continued)
Saving Europe?: The Unpleasant Arithmetic of Fiscal Austerity in Integrated Economies
Enrique G. Mendoza, Linda L. Tesar, and Jing Zhang

WP-14-13

Liquidity Traps and Monetary Policy: Managing a Credit Crunch
Francisco Buera and Juan Pablo Nicolini

WP-14-14

Quantitative Easing in Joseph’s Egypt with Keynesian Producers
Jeffrey R. Campbell

WP-14-15

Constrained Discretion and Central Bank Transparency
Francesco Bianchi and Leonardo Melosi

WP-14-16

Escaping the Great Recession
Francesco Bianchi and Leonardo Melosi

WP-14-17

More on Middlemen: Equilibrium Entry and Efficiency in Intermediated Markets
Ed Nosal, Yuet-Yee Wong, and Randall Wright

WP-14-18

Preventing Bank Runs
David Andolfatto, Ed Nosal, and Bruno Sultanum

WP-14-19

The Impact of Chicago’s Small High School Initiative
Lisa Barrow, Diane Whitmore Schanzenbach, and Amy Claessens

WP-14-20

Credit Supply and the Housing Boom
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-14-21

6