Mortgage securitization and information frictions in general equilibrium

Layer 1: Overview

This paper develops a quantitative general equilibrium model of the U.S. housing finance system that jointly determines mortgage credit and mortgage-backed security (MBS) issuance, with the aim of measuring how information frictions in the securitization market amplify aggregate credit cycles. The central motivation is the tight co-movement of mortgage credit and MBS issuance documented in HMDA data from 1990 to 2016: from 2000 to 2019, originators sold or securitized roughly 70 percent of all residential mortgages within the first year of origination, making securitization the dominant source of funding for new lending. When this source of liquidity collapsed during the Great Financial Crisis (GFC), aggregate residential mortgage credit contracted by roughly 41 percent and RMBS issuance contracted by roughly 37 percent on average from 2008 to 2013.

The model is a discrete-time, infinite-horizon DSGE framework with three types of agents: an impatient representative borrower household, a unit-mass continuum of heterogeneous lenders, and a government. Borrower households consume non-durables and housing services, take on long-term fixed-rate mortgages modeled as perpetuities with geometrically declining payments, and can endogenously default when idiosyncratic housing valuation shocks erode their equity. Lenders face stochastic loan origination costs drawn i.i.d. from a continuous distribution, can privately identify the quality of loans in their portfolios, and access a securitization market modeled after the to-be-announced (TBA) forward market for agency MBS — the largest liquid MBS market in the U.S. The TBA market features anonymous, non-exclusive trades at a single pooling price, and the “cheapest-to-deliver” convention gives sellers the incentive to offload their lowest-value loans, giving rise to a classic Akerlof-style adverse selection problem. The government captures GSE credit guarantees through a state-contingent subsidy to MBS buyers, financed by a distortionary fee on originators and lump-sum taxes on households. The model is calibrated to match key cross-sectional moments of the HMDA dataset for 1990 to 2006, including the distribution of lending: the top 1 percent of originators accounted for 62 percent of lending and the top 10 percent for 89 percent. These moments of market concentration are central to quantifying the amplification channel.

Two novel theoretical features distinguish this framework. First, the mortgage interest rate and the security price are jointly determined in equilibrium — a “joint price determination” property. Second, the severity of information frictions is itself an endogenous function of equilibrium prices, the household default rate, and lenders’ trading decisions. When household credit risk rises, more loans become low-quality, deteriorating the average quality of the pool offered by sellers. MBS buyers, aware of sellers’ incentives, demand a larger adverse selection discount; security prices fall; fewer lenders find it profitable to securitize; an endogenous liquidity shortage follows in the credit market; and tighter lending conditions further weaken household balance sheets. This feedback constitutes the adverse selection multiplier.

Quantitatively, when the calibrated model is fed the sequence of income and housing-valuation shocks observed from 2006 to 2016, it replicates two-thirds of the observed 41 percent contraction in mortgage lending and the full 37 percent contraction in MBS issuance from 2008 to 2013. A shock decomposition (Table 7) shows that, on average over 2008–2013, information frictions account for 40 percent of the model’s predicted decline in mortgage lending (52 percentage points from housing valuation shocks and 5 percentage points from income shocks make up the remainder; comparable shares hold in the securitization market). There is a 1.5 adverse selection multiplier: absent information frictions, credit would have contracted by 27 percent rather than 41 percent. Housing valuation shocks account for roughly half the total dynamics; income shocks account for about 5 percent.

Regarding the post-GFC structural changes, the paper evaluates the effect of GSEs expanding their market share to 100 percent (up from 69 percent in 1990–2006) and the threefold increase in the guarantee fee (from 20 to 60 basis points after 2012). These changes reduce the volatility of the mortgage spread from 6.3 to 4.7 percentage points and lower the unconditional probability of a securitization market collapse from 6.5 to near zero. However, the policy generates inefficiently high levels of liquidity, produces only small welfare gains for borrowers (0.06 percent in consumption-equivalent units), and distributes gains unequally — lenders gain approximately 1.3 percent. Households face higher interest rates (lenders pass through the guarantee fee) and higher taxes. The model corroborates other GE studies in finding that credit guarantees were underpriced before the GFC; the actuarially fair price is closer to the post-2012 fee.

Layer 2: Deep Dive

What is the paper’s identification strategy and what is the nature of the quantitative exercise?

The paper does not use a reduced-form empirical identification strategy; it is a structural DSGE model. The quantitative exercise feeds the calibrated model the observed sequences of aggregate household income shocks and housing valuation shocks from 2006 to 2016, with the model calibrated to match pre-GFC (1990–2006) moments of the U.S. mortgage market. The decomposition of information frictions is accomplished by simulating a complete-information counterfactual for the same shock sequence: the difference between the benchmark model and the complete-information economy quantifies the contribution of private information.

What is the securitization liquidity channel, and how does it operate mechanically in the model?

The securitization liquidity channel is the transmission mechanism from the securitization market to mortgage credit supply. In normal times, lenders with low origination costs (sellers) securitize their loan portfolios, freeing up funds to originate new loans, while high-cost lenders purchase securities rather than originate, effectively specializing their roles through the market. A shock that increases household default risk worsens pool quality. Buyers face a larger adverse selection discount, security prices fall, and the wedge between the market price and a seller’s valuation of high-quality loans widens. Many lenders switch from selling to holding, reducing the supply of liquidity in the securitization market. Constrained by limited access to debt markets, lenders cut new mortgage origination. The resulting tightening in credit further deteriorates household balance sheets, creating an amplification loop.

What are the three types of lenders in the model, and what determines their trading decisions?

Lenders endogenously sort into three groups based on their idiosyncratic origination cost draw z relative to two equilibrium cutoffs. Sellers (low-cost lenders, z below the first cutoff) find origination sufficiently profitable to sell their inventory of loans into the securitization market and originate new ones. Buyers (high-cost lenders, z above the second cutoff) find origination too costly and instead buy securities from sellers. Holders (lenders with z between the two cutoffs) neither sell at the prevailing adverse-selection-discounted price nor buy at the effective cost grossed up by the information wedge; they retain their illiquid loan portfolios and originate fewer new loans. The information wedge — the distance between the two cutoffs — is a decreasing function of the subsidy coverage and an increasing function of the adverse selection discount.

How is the adverse selection discount endogenously determined, and why does it amplify shocks?

The per-unit adverse selection discount mu_t is defined as the aggregate fraction of low-quality loans traded in the securitization market: mu_t = S_B_t / S_t, where S_B_t is the aggregate supply of low-quality loans and S_t is total loans traded. This fraction is endogenous: it depends on which lenders sort into the seller category and what quality distribution their portfolios have, which in turn depends on the household default rate and the equilibrium price. When household credit risk rises, the default rate increases, more loans become low-quality, and sellers selectively offload bad loans while retaining good ones. The endogenous deterioration in mu_t raises buyers’ required discount, further reducing the security price, which causes additional holders to switch away from selling, compounding the adverse selection problem. This self-reinforcing dynamic is the multiplier.

Under what conditions can the securitization market shut down entirely, and what happens to credit in that case?

Proposition 2 establishes that a sufficient condition for market shutdown in the steady state is that the market effective cost of buying securities exceeds the origination cost of the highest-cost lender in the economy. When this condition holds: (1) the securitization market does not operate; (2) every lender originates using only her own technology; and (3) the mortgage rate is higher than when the market operates. Critically, even when the securitization market collapses, the credit market continues to function, but with higher interest rates and lower intermediation volumes. The economy can transition between states with and without an active securitization market.

What role does market concentration of mortgage originators play in the quantitative results?

Market concentration is crucial for the magnitude of amplification. From 1990 to 2016, the top 1 percent of originators accounted for 62 percent of lending and the top 10 percent for 89 percent (from HMDA data). The model is calibrated to match these moments. Because large originators specialize as securitization sellers, their decision to switch from selling to holding — triggered by rising adverse selection discounts — produces very large contractions in aggregate credit supply. The calibrated lending-cost distribution shows a large discontinuity: the last marginal securitization seller originates a volume four times larger than the next marginal holder. When the most efficient, high-volume lenders exit the securitization market, the aggregate effect is disproportionately large.

How does the government subsidy policy interact with adverse selection, and what are its theoretical properties?

The GSE credit guarantee is modeled as a state-contingent subsidy tau_t = alpha_G * mu_t, where alpha_G in [0,1] represents the degree of insurance provided. Any positive subsidy reduces the adverse selection wedge by moving the second cutoff leftward, expanding the mass of security buyers. A full subsidy (alpha_G = 1) completely offsets buyers’ losses from default risk, stabilizing security demand regardless of household credit risk and minimizing the probability of market collapse. However, Proposition 3 establishes that a full subsidy generates inefficiently high levels of liquidity compared to the complete information benchmark: it expands the volume of MBS at lower average quality relative to an economy where low-quality loans are screened out. A full subsidy also fails to replicate complete-information allocations because the guarantee fee distorts lenders’ origination decisions and raises borrowers’ mortgage rates.

What are the welfare implications of the post-GFC policy changes?

The welfare analysis (Table 9) finds small positive but unequal welfare gains. The overall post-GFC policy changes (full subsidy plus higher guarantee fee) yield borrower welfare gains of 0.06 percent and lender welfare gains of 1.3 percent in consumption-equivalent units. Decomposing the changes: the increase in the subsidy (alpha_G from 69 to 100 percent) generates borrower welfare losses of -0.16 percent (due to higher taxes and interest rates, offset partially by lower volatility) and lender gains of 3.01 percent (from improved lending efficiency). The increase in the guarantee fee reverses some of this by generating borrower gains of 0.18 percent and lender losses of -1.53 percent. The paper characterizes these as upper bounds because the full subsidy may generate moral hazard by weakening originators’ incentives to screen loan quality.

How does this paper relate to and extend Justiniano et al. (2015, 2019) and Landvoigt (2016)?

Justiniano et al. (2015, 2019) argue that credit supply constraints — limits on the funds available to lenders — are quantitatively more important than credit demand forces in explaining mortgage credit fluctuations. This paper provides a microfoundation for those constraints by modeling securitization as the dominant source of liquidity for lenders and deriving endogenously how adverse selection limits that liquidity. Landvoigt (2016) introduces securitization in a DSGE housing model in reduced form. This paper goes further by modeling an endogenous securitization market where lenders optimally trade off liquidity benefits against information friction costs, so security prices and mortgage rates are jointly determined rather than imposed exogenously.

How does this paper relate to the Kurlat (2013) and Bigio (2015) models of adverse selection in asset markets?

The securitization design combines Kurlat (2013)’s framework of asset creation and reallocation with two additional features specific to the TBA market: (1) the cheapest-to-deliver convention, which means sellers can select the lowest-value loans in their inventory satisfying trade terms; and (2) the non-exclusive, anonymous nature of TBA trades, which ensures a pooling price. Bigio (2015) models endogenous liquidity and the business cycle through information frictions in interbank markets. This paper extends the adverse selection approach to the mortgage market specifically and provides an equilibrium linkage between the securitization market and the credit market rather than modeling them as a single market.

What are the non-targeted moments and how well does the model fit the data?

Three non-targeted moments are reported (Table 5). The model generates a fraction of loan sales of 73.9 percent (data: 61.8 percent from HMDA), a correlation between loan sales and new lending of 0.86 (data: 0.90), and a mortgage spread of 178 basis points (data: 330 basis points). The loan sales fraction is somewhat above data and the spread is substantially below. For targeted cross-sectional moments (Table 6), the model closely matches the distribution of lending by quartile, with Q4 market shares of 0.957 in the model versus 0.959 in the data. For the dynamic GFC episode, the model replicates two-thirds of the 41 percent contraction in mortgage lending and the full 37 percent contraction in MBS issuance.

What are the sources of aggregate shocks and how are they calibrated?

The two exogenous aggregate state variables are household income Y_t and the variance of idiosyncratic housing valuation shocks sigma_omega_t (the proxy for mortgage credit risk). They follow a first-order joint Markov process. Income is identified using the cyclical component of disposable personal income from the flow-of-funds accounts. The variance of housing shocks is calibrated to match the national delinquency rate for loans 90+ days delinquent or in foreclosure from the National Mortgage Database (FHFA). The calibrated states produce default rates of 1.8 percent in the low-risk state and 7.9 percent in the high-risk state, with an unconditional default rate of 2.6 percent.

What are the key limitations and caveats of the analysis?

Several limitations are noted. First, the welfare analysis of the full subsidy is characterized as an upper bound because moral hazard — the impact of guaranteed insurance on originators’ incentives to screen loan quality — is not modeled. Second, the model abstracts from other consequences of default for borrowers, such as reputation concerns and long-term credit market exclusion. Third, the paper focuses on information frictions between lenders and investors (the securitization chain), not between borrowers and lenders. Fourth, the non-targeted mortgage spread (178 bps in model versus 330 bps in data) suggests some quantitative limitations in matching all features of the credit market simultaneously. Fifth, the exercise is a structural model exercise and not empirically identified through exogenous variation.

Key Concepts

Securitization liquidity channel: The mechanism by which mortgage originator funding capacity depends on their ability to sell loan portfolios in the securitization market; when securitization demand falls, originators face an endogenous liquidity shortage and reduce new mortgage lending, transmitting shocks from the MBS market to the credit market.

Adverse selection multiplier: The amplification factor arising from private information in the securitization market: as household credit risk rises, sellers’ incentives to offload low-quality loans worsen pool quality, causing buyers to demand a larger discount, which causes more lenders to withdraw from selling, creating a feedback loop that magnifies the initial shock to credit supply. Quantified at 1.5 for the GFC episode.

TBA (to-be-announced) forward market: The dominant trading venue for agency MBS in the U.S., accounting for over 90 percent of MBS trading volume, where the specific securities to be delivered are not identified at the trade date and sellers can deliver the cheapest eligible pool (‘cheapest-to-deliver’), institutionalizing adverse selection incentives.

Cheapest-to-deliver convention: A TBA market practice by which a seller selects and delivers the lowest-value mortgage pools in its inventory that satisfy the terms of trade, giving sellers a systematic informational advantage and incentivizing selective retention of high-quality loans.

Adverse selection discount (mu_t): In this paper, the per-unit discount arising from adverse selection, defined as the endogenous equilibrium fraction of low-quality loans in the aggregate supply of traded loans (S_B_t / S_t); this fraction is determined jointly with prices and lenders’ trading decisions, and rises when household default risk increases.

Mortgage credit risk (sigma_omega_t): The standard deviation of idiosyncratic housing valuation shocks to household members, which is the exogenous aggregate state variable that drives default rates; when sigma_omega_t rises, more households fall below the default threshold, increasing the aggregate default rate and degrading the quality composition of lenders’ portfolios.

Joint price determination: A novel equilibrium property of the model in which the mortgage interest rate (in the credit market) and the price of securities (in the securitization market) are simultaneously determined; this interdependence means that adverse selection dynamics in the securitization market directly affect the cost of credit and vice versa.

GSE credit guarantee (subsidy policy): A state-contingent subsidy tau_t = alpha_G * mu_t paid to MBS buyers, representing the credit guarantees of Fannie Mae and Freddie Mac; financed by a guarantee fee (distortionary tax on originators) and lump-sum taxes on households; alleviates adverse selection by stabilizing security demand but generates inefficiently high liquidity and fails to deliver meaningful household welfare gains.