220 Week 2 A /For WIZARD KIM

J Financ Serv Res (2016) 49:265–280 DOI 10.1007/s10693-014-0211-9

Transparency in the Mortgage Market

Andrey Pavlov · Susan Wachter · Albert Alex Zevelev

Received: 22 April 2014 / Revised: 17 July 2014 / Accepted: 27 November 2014 / Published online: 16 January 2015 © Springer Science+Business Media New York 2015

Abstract This paper studies the impact of transparency in the mortgage market on the underlying real estate market. We show that geographic transparency in the secondary mort- gage market, which implies geographic risk based pricing in the primary market, can limit risk-sharing and make house prices more volatile. Ex ante, regions prefer opaque markets to enable insurance opportunities. We discuss the implications for risk based pricing and house price volatility more generally. In addition, we investigate the specific conditions under which competitive lenders would optimally choose to provide opaque lending, thus reducing volatility in the real estate market. We show that in general the opaque competitive equilibrium is not stable, and lenders have an incentive to switch to transparent lending if one of the geographic regions has experienced a negative income shock. We propose market and regulatory mechanisms that make the opaque competitive equilibrium stable and insu- rance opportunities possible.

Keywords Housing finance · Mortgage · Transparency · Opacity · Real estate · Insurance · House price volatility

1 Introduction

One of the most often-cited causes for the severity of the 2008 financial crisis is that most housing-related financial instruments were highly opaque (see for example Gorton (2008)).

A. Pavlov (�) Beedie School of Business Simon Fraser University, Vancouver, British Columbia, Canada e-mail: [email protected]

S. Wachter · A. A. Zevelev The Wharton School University of Pennsylvania, Philadelphia, PA, USA

S. Wachter e-mail: [email protected]

A. A. Zevelev e-mail: [email protected]

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Since investors were unable to ascertain the exposure of separate financial institutions to these instruments and because the exposures were crosscutting, the entire financial system was at risk. As a result, numerous regulatory, policy, and institutional recommendations have called for greater transparency in mortgage portfolios and their derivatives French et al. (2010).1

Nonetheless, the design of transparency features matters. Transparency in some forms may in fact have negative side effects. In this paper, we build upon the literature on debt and insurance markets to investigate the impact of increased transparency in the mortgage mar- ket. The existing literature, discussed below, highlights a negative impact of transparency on liquidity in financial markets. In this paper, we introduce a model which shows that certain forms of transparency can lead to increased volatility in housing and mortgage markets. Specifically, we develop a model of a mortgage lending system that can be trans- parent or opaque and compare outcomes under both scenarios, as they relate to diversifiable region-specific risk.

We show that a transparent market may be undesirable because it increases real estate price volatility and magnifies the impact of income shocks. Under a transparent system, lenders (and investors), know the geographic location of each mortgage. When a local neg- ative income shock occurs, lenders (investors) rationally withdraw credit from that region in anticipation of future (auto-correlated) income and house price shocks. This withdrawal magnifies the price impact of the original income shock.

In our model, the withdrawal of loans from the city which experienced a bad income shock leads to an increase of loans to the city with stable income. However, this need not be the case. Our results hold if MBS investors have alternative methods to deploy their funds in fully diversified or risk-free investments. While we present our model in terms of income shocks to different cities/regions, our main points can easily be framed in terms of demand shocks to an entire sector of the economy (housing) as long as other sectors are not affected. This of course requires securitized instruments to be opaque with respect to the sectors in which they are invested. This excludes sectors of the economy with a substantial presence of publicly available investments, such as stocks, bonds, and derivatives.

In our setting, both borrowers and lenders may be worse off in a transparent system. This negative impact of transparency is due to two factors. First, transparent systems increase the volatility of the underlying real estate markets. Such volatility negatively impacts poten- tially risk-averse lenders and borrowers. While lenders can somewhat diversify the increased house price volatility, borrowers cannot. The impact on borrowers from switching to a transparent system is substantial and persistent.

The second factor that makes transparent systems undesirable for lenders and borrowers is that the price declines following an income shock are magnified. This effect remains in force even if all agents can fully diversify the increased volatility. As we show in our model, the magnified price declines occur when future income is also likely to be lower. For lenders, this means potential defaults on other loans, which in combination with the mortgage losses already discussed, can put the solvency of the lender in jeopardy. For borrowers, the transparent system magnifies the simultaneous decline of their two main assets: real estate

1In 2008, Fannie Mae briefly implemented a “Declining Markets Policy” by restricting the maximum CTLV for properties located within a declining market to five percentage points less than the maximum permitted for the selected mortgage product. Fannie Mae ended this policy in a few months.

J Financ Serv Res (2016) 49:265–280 267

and human capital. Beyond standard consumption implications, this can push borrowers into solvency or liquidity constraints.

We study mechanisms that preserve a stable opaque equilibrium that allow for insur- ance. One mechanism keeps a multitude of competitive lenders in the opaque equilibrium as long as they consider the long-term returns from that system. We show that in the case of multiple lenders, the presence of a short-term player in the market forces everyone to switch to a transparent system. The transparent equilibrium we derive is stable. Lenders require an external intervention or coordination to switch back to the preferred opaque equilibrium.

We proceed as follows. Section 2 reviews the relevant literature. Section 3.1 presents a theoretical model with a single lender. Section 3.2 extends the work to two lenders and dis- cusses the game-theoretic outcomes. Section 4 provides a numerical calibration. Section 5 discusses policy implications. Section 6 concludes.

2 Literature review

There are two major strands of literature related to transparency in financial markets. The first strand focuses on liquidity for debt markets.2 A major question in security design is whether securities should be made transparent (and therefore tranched) or made opaque (bundled). Papers in this literature include Dang et al. (2013), Pagano and Volpin (2010), and Farhi and Tirole (2012).

In a theoretical model, Pagano and Volpin (2010) show that issuers of asset-backed secu- rities, facing a tradeoff between transparency and liquidity, deliberately choose to release coarse information to enhance the liquidity of the primary market. Farhi and Tirole (2012) look at the implication of tranching versus bundling on liquidity. They show that tranching has adverse welfare effects on information acquisition as tranching provides an incen- tive against commonality of information that contribute to the liquidity of an asset. They also show that liquidity is self-fulfilling: a perception of future illiquidity creates current illiquidity.

Dang et al. (2013) argue that opacity is essential for liquidity. Investors in their models are not equally capable of processing the transparent information. When the composition of a security is opaque then all investors are symmetrically ignorant. If it is made transparent, investors will pay a cost to process the additional information. Since not all investors are capable of processing this information, transparency will create asymmetric information, which has an adverse effect on liquidity.3 To illustrate their logic, Holmstrom (2012) explains that DeBeers sells wholesale diamonds in opaque bags. If the bags were trans- parent, buyers would examine each bag individually leading to increased transaction costs due to time allocated to inspections and adverse selection among buyers. This would make the diamond market much less liquid.

2For a discussion of the liquidity of the MBS market and its benefits as measured in the TBA market see Vickery and Wright (2010). 3DGH argue that while symmetry of information about payoffs is essential for liquidity, transparency is not and opacity actually contributes to liquidity as symmetric information can be achieved through shared ignorance. Highly nontransparent markets can be very liquid (19th century clearinghouses, currency). When it is possible to obtain information about an asset, people invest in finding information differentially, resulting in lower overall liquidity.

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Nonetheless Downing et al. (2005), in the context of MBS, show that making available to investors information that informs on risk and reduces uncertainty enables tranching to be efficient by dividing informed investors willing to invest in riskier tranches from non- informed investors who are sheltered from the risk in higher tranches. This has been done in agency MBS and does not interfere with liquidity. But tranching for risk that is not trans- parent creates adverse selection and is not stable similarly to the situation demonstrated by Akerlof (1970). This happened in the private MBS and CDO markets over the crisis as shown in French et al. (2010) and Beltran et al. (2013).

This first set of studies focuses on the trade-off between the liquidity benefits of opaque- ness and the adverse selection implications. The lack of transparency can ensure symmetric information among actors, unless the issuers and institutions lead to differentially disclosed information.

Our model extends a second strand of literature that studies the relationship between transparency and risk pooling. Hirshleifer (1971),4 the seminal paper in this literature, shows how transparency can be harmful through its destruction of insurance opportunities. If as the insurance contract is being entered into, knowledge of the risk is made known to the actors, they will price it separately, even if the risk is diversifiable. If market participants have updated information about each other’s risk they will not want to insure each other. This mechanism has been applied to study the role of transparency among financial inter- mediaries (Bouvard et al. 2012). They find that transparency enhances the stability of the financial system during crises but has destabilizing effects in normal times.

While consistent with the literature on transparency and liquidity, our work predomi- nantly draws on the second strand discussed above to show that transparency limits risk pooling and reduces insurance opportunities. This is particularly relevant for transparency regarding exposure to macroeconomic shocks, modeled here as income shocks. Our model has no implications about transparency with respect to loan-specific risk characteristics and underwriting criteria.

Similarly, recent work by Hurst et al. (2014) studies regional risk sharing through the U.S. mortgage market. While our research studies the impact of geographic transparency on equilibrium house prices, Hurst et al. (2014) consider the impact on equilibrium interest rates. In addition they consider a fully dynamic model of housing with discrete adjustment.

3 Model

We develop a simple model that captures key features of residential real estate markets. The first assumption is that homes are purchased with mortgages from the financial system only, and homeowners cannot raise equity or issue debt directly to the market. We further assume that lenders are competitive, so they generate zero profits. This assumption is consistent with our discussion that local shocks are fully diversifiable to originators and MBS investors. The only choice lenders have is whether to be transparent or opaque in their lending deci- sions. Most importantly, lenders are not able to derive monopolistic/duopolistic profits in any scenario by altering their pricing and quantity mix.

A limitation of the model is the assumption that homeowners base their purchase deci- sions on their current income and current loan availability, with no foresight of potentially

4This is in contrast to Akerlof (1970) who shows that transparency is good in markets that suffer a “lemons” problem. Informing all parties who the lemons are will make the market function more smoothly.

J Financ Serv Res (2016) 49:265–280 269

changing availability of credit, and no ability to increase their investment if they perceive good opportunities.

We begin by describing the housing and credit markets under transparency and opacity. Our baseline model for both of these regimes utilizes a single loan originator (or lender) funded by the secondary market and two cities. We then expand this to two (or more) origi- nators, both funded by a secondary market, to analyze the coordination problem faced by individual originators under these circumstances.

3.1 One lender

We assume that the loan originators in our model are competitive (or face the threat of competition in the case of a single originator). Thus, the lending rate offered is determined entirely by the secondary market. We assume that the lenders charge a spread between their funding cost and lending rate to cover their costs. Also, originators can fully diversify their exposure to local income shocks. In other words, the interest rate, R = (1 + r), lenders charge their borrowers is exogenous. Lenders are funded by selling an unlimited volume of mortgage-backed securities (MBS) in the secondary market as long as those securities provide the prevalent expected rate of return.

Consider two cities denoted by A and B. Each city j (j ∈ {A, B}) has a representative household who receives income in period t , denoted y

j t . Income in the two cities follows a

correlated stochastic process (yAt , y B t ) ∼ F (defined below). In addition to income, homes

are also financed by loans L j t .

The demand for housing is given by:

Q j t = α + yjt + Ljt − γ pjt (3.1)

Where α is the intercept, γ is the slope and p j t is the price of housing in city j at

time t . The supply of housing is fixed: H j t = H . While we acknowledge that different sup-

ply elasticities can potentially affect the price adjustment process derived below, we justify this assumption by appealing to the fact that supply is fixed at least in the short-run, over which income shocks occur. Increased supply elasticity would not affect our results for the city with the negative income shock, as there would be no new supply there. It may very well affect the supply in the city with a positive income shock, thus reducing the quantitative magnitude of the effects we find for that city.

The market clearing condition is that supply equals demand, Q j t = H jt . This provides

the following price for real estate at each point in time in each city:

p j t =

1

γ

( α + yjt + Ljt − H

) (3.2)

The loan to the representative household in city j , L j t , is given by a risk-neutral loan

originator who operates in a competitive market. L j t satisfies a zero expected profit

condition. While we frame the model in terms of two competing cities, this need not be the case.

Our model can easily be framed in terms of one investment (residential MBS) and another investment with low or negative correlation to housing. This translates the implications of our model from regional to economy-wide shocks.

We consider two regimes. A loan in a transparent regime where each loan is city specific, L

j t , and a loan in an opaque regime where mortgage-backed securities investors cannot

geographically discriminate, Lt .

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We model transparent markets as those in which originators give loans to regions condi- tional on region-specific risks (i.e. geographic risk based pricing). If the secondary mortgage market sells securities that are geographically transparent then investors are able to tranche these securities according to their geographic risk. Demand for MBS based on geographic risk will make lenders in the primary mortgage market price and lend according to their geographic risk.

Consider two cities, A and B. If the secondary mortgage market is geographically opaque, then lenders will neglect city-specific risk. In this regime, loans would incorporate the average risk of both city A and city B. However, if the secondary market is geograph- ically transparent, investors will tranche the MBS into MBS A and MBS B. Demand for MBS will now reflect region-specific risk. Thus lenders will price their loans to each region based on that region’s local risk. This is how transparency would remove the ability to pool risk between city A and city B.

Transparent mortgage markets regime The lender’s expected profit for loans to city j at time t, denoted by π

j t , is given by the expected collection (loan amount plus interest if no

default, or house value if default) less the initial loan amount:

E

[ π

j t

] = −Ljt + ηEt min

[ L

j t R, p

j

t+1H ]

(3.3)

Where η is the lender’s discount factor. Credit markets are competitive so L j t is given by

a zero expected profit condition:

E

[ π

j t

] = 0 (3.4)

⇔ L

j t = ηLjt R · P

{ L

j t R ≤ pjt+1H

} (3.5)

+ηH Et [ p

j

t+1|L j t R > p

j

t+1H ]

· P { L

j t R > p

j

t+1H }

Opaque mortgage markets regime When markets are geographically opaque, the lender is not able to discriminate geographically and gives the same loan to both cities. The expected profits are:

expected profit at time t = −(amount lent to both cities at t) (3.6) + discounted expected payoff from the loan to A at t + 1 + discounted expected payoff from the loan to B at t + 1

We add the expected payoffs across cities, because each city decides individually whether to repay or default.

E[πt ] = −(Lt + Lt ) + ηEt min [ Lt R, p

A t+1H

] + ηEt min

[ Lt R, p

B t+1H

] (3.7)

= −2Lt +ηLt R · P

{ Lt R ≤ pAt+1H

}

+ηH Et [ p

A t+1|Lt R > pAt+1H

] · P

{ Lt R > p

A t+1H

}

+ηLt R · P { Lt R ≤ pBt+1H

}

+ηH Et [ p

B t+1|Lt R > pBt+1H

] · P

{ Lt R > p

B t+1H

}

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The corresponding zero expected profit condition is:

E[πt ] = 0 Lt = ηLt R · P

{ Lt R ≤ pAt+1H

}

+ηH Et [ p

A t+1|Lt R > pAt+1H

] · P

{ Lt R > p

A t+1H

}

+ηLt R · P { Lt R ≤ pBt+1H

}

+ηH Et [ p

B t+1|Lt R > pBt+1H

] · P

{ Lt R > p

B t+1H

}

⇔ Lt =

1

2 ηLt R ·

( P

{ Lt R ≤ pAt+1H

} + P

{ Lt R ≤ pBt+1H

})

+ 1 2 ηH

( Et

[ p

A t+1|Lt R > pAt+1H

] · P

{ Lt R > p

A t+1H

} (3.8)

+Et [ p

B t+1|Lt R > pBt+1H

] · P

{ Lt R > p

B t+1H

})

+ηH Et [ p

B t+1|Lt R > pBt+1H

] · P

{ Lt R > p

B t+1H

}

Under opacity the loan is made to average risk across cities.

Income shock We now consider a situation with two time periods t ∈ {0, 1}, and two income levels, y

j t ∈ {yL, yH } with yL < yH . Assume city A starts with the low income shock and

city B starts with the high income shock: yA0 = yL, yB0 = yH . The probability city A will have a low shock next period is given by:

P { y

A 1 = yL|yA0 = yL

} = 1 + ρ

2 (3.9)

Where ρ ∈ [−1, 1] is the auto-correlation for income.5 We assume income follows a two-state Markov chain:

y j t ∼

( 1+ρ

2 1−ρ

2 1−ρ

2 1+ρ

2

) (3.10)

For simplicity we assume that the spatial correlation in income shocks is perfectly neg- ative ρA,B ≡ −1, so whenever city A has a negative shock yAt = yL, city B will have a positive shock yBt = yH and vice-versa.

In a transparent market, the zero profit level of lending to each city is:

L A 0 =

η (

1+ρ 2

) ( 1 γ

( α + yL + LA1 − H

)) H

( 1 − η

( 1−ρ

2

) R

) ,

(3.11)

L B 0 =

η (

1−ρ 2

) ( 1 γ

( α + yL + LB1 − H

)) H

( 1 − η

( 1+ρ

2

) R

) (3.12)

5The exogenous auto-correlation in income we assume in the model generates an auto-correlation in house prices. For evidence on auto-correlation in house prices see Duca et al. (2010), Case and Shiller (1989), and Poterba et al. (1991).

272 J Financ Serv Res (2016) 49:265–280

In an opaque market, the lender’s zero profit level of lending (same in both cities) is:

L0 = 1 2 η

( 1 γ

(α + yL + L1 − H ) )

H (

1 − 12 ηR ) (3.13)

(See derivations in the Appendix).

Proposition 1 If income shocks are positively auto-correlated ρ > 0 and if the lender’s discount rate is less than the mortgage rate (ηR > 1), the transparent level of lending to the city with the bad shock is less than the opaque level, which is less than the transparent level of lending in the city with the good shock:

(3.14) This proposition is intuitive. Since income shocks are auto-correlated, the badly shocked

city is more likely to have more bad shocks. Hence lenders are more reluctant to lend.

Plugging this into the equilibrium price function: p j

0 = 1γ ( α + yj0 + L

j

0 − H )

provides

the important result that prices in the city which received a bad income shock are lower under the transparent regime relative to the opaque regime.

Proposition 2 House prices in the city with a bad income shock are lower under trans- parency than opacity:

p A,trans 0 =

1

γ

( α + yA0 + LA0 − H

) <

1

γ

( α + yA0 + L0 − H

) = pA,opaque0 (3.15)

House prices in the city with a good income shock are higher under transparency than opacity:

(3.16) We have assumed that city A starts with a bad income shock at time 0 and city B starts

with a good income shock. Ex ante with probability 12 we have y A 0 = yL and yB0 = yH , and

with probability 12 we have y A 0 = yH and yB0 = yL. However, ex ante neither city knows

which state of the world they will start in. Hence, ex ante they will prefer opacity to have less volatile house prices.

Proposition 3 The ex ante house price volatility is greater under transparency than under opacity:

σ 2 p,opaque < σ

2 p,trans (3.17)

3.2 Two lenders

Now consider two originators, each choosing independently whether to operate in a trans- parent or opaque way. As discussed above, the originators can place their mortgage-backed

J Financ Serv Res (2016) 49:265–280 273

securities in the secondary market as long as those securities provide zero expected profit to the investors. The price in each city is given by:

p j

0 = 1

γ

( α + yj0 + L

j,1 0 + L

j,2 0 − H

) (3.18)

where L j,k t denotes the lending of lender k in city j at time t . If both lenders operate the

same way (transparent or opaque), the equilibrium level of total lending is exactly the same as in the case with a single lender above, and satisfies the following inequality:

L A,1 0 + LA,20 < L0 < LB,10 + LB,20 (3.19)

However, if one lender deviates, then the above order extends to the following:

L A,1 0 + LA,20 < LA,10 + δL0 < L0 < LB,10 + δL0 < LB,10 + LB,20 (3.20)

where δ denotes the market share of lender 2 if both lenders choose to lend opaquely, e.g., δ = 1/2. Prices follow the same relationship, which is easily verified because a mixed scenario always results in a switch to transparent lending in period 1 (i.e., p

j

1 is given by the transparent lending expression given above (3.2)). While the profits of the two lenders in each of the above scenarios sum to zero, the lender who choses the transparent method has positive profits in the mixed scenario, at the expense of the lender who continues to lend in an opaque way. The second lender has no choice but to also switch to transparent lending.

The above conclusion indicates that if both originators lend opaquely, the MBS of both satisfy the zero-profit condition indefinitely. However, this equilibrium is unstable because each of the originators (and their investors) has an incentive to switch to transparent lending in case one of the cities experiences a negative income shock. The originator who switches can offer securities that generate positive profit for one period, after which the second originator also switches to transparent lending, and the transparent equilibrium continues indefinitely.

Note that the only choice originators (and their investors) have is between transparent and opaque lending. We are excluding any additional lending quantity choice because the market for MBS is assumed to be fully competitive. In other words, investors can choose between opaque or transparent portfolios, but have no ability to restrict lending to monopolistic levels.

Short-term and long-term lenders The model above implies the following payoff matrix for the MBS of the two originators at time zero, denoting the one-period profit of the lender who switches from opaque to transparent as π (Table 1).

Payoffs beyond time 0 are all zero as both originators switch to transparent lending for- ever. With these payoffs, both originators have incentives to switch to transparent lending the moment one of the cities experiences a negative income shock. To preclude this trivial solution, we assume that an originator (or its MBS investors) receives a (small) benefit, , (0 < < π), above it’s zero profit if that lender lends in an opaque way (Table 2). The one-period payoff matrix is given in Table 2.

Table 1 MBS 1, t = 0 Payoff Function MBS 1\ MBS 2 Transparent Opaque

Transparent 0 π

Opaque −π 0

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Table 2 MBS 1, t = 0 Payoff Function MBS 1\ MBS 2 Transparent Opaque

Transparent 0 π

Opaque −π

An originator who optimizes over a long (infinite) horizon has an incentive to remain in the opaque equilibrium, as receiving over a long time horizon dominates the one-time profit, π . However, if one of the originators switches to a short horizon view of the world, that originator would switch to transparent lending in case of a negative income shock to collect the one period positive profit, π .

There are two potential mechanisms that can make the opaque lending more stable. First, if each of the lenders can switch to transparent lending in the same period their competitor switches, then both lenders move to the fully transparent equilibrium and satisfy the zero profit conditions in this equilibrium. In this case, there is no incentive for a lender to switch away from the opaque equilibrium, so it can continue indefinitely.

The second mechanism is to increase the incentive, , for the lenders to stay in the opaque equilibrium. While a very short-term lender would still switch to transparent lending, this scenario is less likely. Also, if the short-term lender gets out of business or changes back to long-term optimization, then the probability that the remaining lender(s) return to opaque lending is higher.

4 Numerical calibration

In this section, we will provide a numerical exploration of the results in our model. Consider a world where the parameters are:

parameter description value

ρ autocorrelation 0.5 η discount factor .99 R gross interest rate 1.04 H exogenous housing supply 10 α demand intercept 15 yL low income level 5 yH high income level 8 L1 exogenous loan 10 γ demand slope on price 1

We assume city A has a bad income shock at time 0 and income shocks are negatively correlated across space: yA0 = 5, yB0 = 8.

The corresponding loans are:

(4.1)

J Financ Serv Res (2016) 49:265–280 275

Since city A is more likely to default than city B it will receive a smaller loan in a transparent world (with risk based pricing). However in an opaque world the lender averages risks across cities and both cities receive the same intermediate loan.

The corresponding house prices are:

p A,trans 0 = 209.97 < p

A,Opaque

0 = 214.04 (4.2) p

B,trans 0 = 230.296 > p

B,Opaque

0 = 217.04 (4.3)

Since city A is more risky, it receives a smaller loan in a transparent world and there- fore has lower house prices. Note that under opacity city A has lower house prices than city B even though they receive the same loan because city A has lower income than city B.

Figure 1 plots the loans LA0 , L B 0 , L0 as a function of the persistence of income ρ ∈ [0, .5).

This figure illustrates that the spread in loans LB0 − LA0 is an increasing function of persistence ρ. The intuition is that the more auto-correlated the shock is, the more likely the badly shocked region (city A) is to experience another bad shock. Hence it will receive a smaller loan with higher ρ (Fig. 1).

Figure 2 plots the prices pA,trans0 , p A,opaque

0 , p B,trans 0 , p

B,opaque

0 as a function of the persistence of income ρ ∈ [0, .5).

We have the same lesson. When ρ > 0, higher auto-correlation ρ corresponds to a bigger spread in house prices (Fig. 2).

The lesson is that in a world with greater persistence (ρ) the benefits from risk-pooling through opacity are even greater.

Fig. 1 Loan size as a function of auto-correlation

276 J Financ Serv Res (2016) 49:265–280

Fig. 2 Prices as a function of auto-correlation

5 Policy implications

The main point of this paper is to challenge the view that greater transparency in mortgage- backed securities is necessarily better. While transparency has advantages and is intuitively appealing, we show that it may actually leave both borrowers and lenders worse off. Specif- ically, we show that transparent lending results in larger changes in loan availability to areas which recently experienced a bad income shock. This leads to increased home price volatil- ity. This is clearly undesirable for homeowners, but can also hurt the MBS investors as well and overall system stability.

At the very least, this work suggests that calls for regulatory requirements for increased transparency in the MBS market may not necessarily achieve their original intent. In fact, such calls hurt the very borrowers they are trying to help and protect because transparency prevents lending markets from providing indirect insurance against idiosyncratic shocks and shocks over the cycle.

Preventing regulatory requirements for geographic transparency in mortgage origination is of course not sufficient. Policymakers also need to ensure that mortgage originators are protected in their decision to issue geographically opaque instruments. Regulatory steps are needed to specifically prevent changing the level of transparency for a particular MBS issue through time. In other words, if an MBS is issued and labeled as opaque, originators should keep it that way. They may not be able to do so alone, and would likely require legislative protection. At the minimum, such issues should be clearly labeled as opaque from the start.

While our work suggests that geographic transparency is not welfare improving, other types of transparency can still be desirable. For instance, transparency with respect to origination standards and other mortgage characteristics can improve pricing in secondary markets and help investors detect changes in these standards.

We further develop mechanisms to keep opaque MBS viable even if one region expe- riences a negative income shock. The natural inclination of originators (and their MBS customers) is to switch to transparent lending. First and foremost, as already discussed, it is imperative that the opaque instruments remain that way. Second, it is important for opaque instruments to continue to exist, and preferably dominate the market. Since MBS investors

J Financ Serv Res (2016) 49:265–280 277

have a short-term view of the underlying market to begin with (they are only exposed to the specific issue they hold), the only player who can ensure geographic opaqueness in the system is the originator.

Our work suggests two mechanisms to maintain opaqueness. First, all originators need to be long-term players in the market, so that they weigh the potential immediate ben- efit of switching to transparent issuance against the long-term benefit of keeping the system geographically opaque. Second, the originators do need to realize some (small) benefit, above their zero-profit condition, in case they issue opaque instruments. Such benefits can come from many sources, including simple customer loyalty built through being in the market for a long time or lighter regulatory burden in exchange for issu- ing opaque MBS. When both of these conditions are present, originators can remain in the opaque equilibrium, thus benefiting their customers and themselves in the long run. MBS investors are not worse off, as all lending we consider satisfies an ex ante zero-profit condition.

Finally, let us note that geographically opaque MBS is nothing new or exotic. In fact, it is the predominant form of securitization to date. Both agency MBS and private-label MBS have historically been opaque, and have been well received by investors. We have only recently seen attempts to offer geographic transparency in the private market. While one might argue that our historical experience is not supportive of opaqueness, as we show here, geographic transparency is unlikely to be an improvement. Transparency in other dimensions would likely be highly beneficial.

6 Conclusion

In this paper we develop a model to analyze the implications of a geographically transparent or opaque lending system on the underlying the real estate market. We show that a geo- graphically opaque lending system benefits homeowners as it allows them to insure against local income shocks. Under opaqueness, real estate price volatility is lower. Loan origina- tors and MBS investors are no worse-off under the opaque system, and in fact they can be better off in certain circumstances.

We further analyze the interaction of two (or more) originators and develop the con- ditions under which they can sustain an opaque system. These conditions involve a long-term benefit that offsets the potential immediate gain from switching to a trans- parent system. A sustained opaque system requires all originators to be long-term players.

While we frame our model in terms of geographic transparency and diversification, it can easily be re-framed in terms of shocks to an entire sector of the economy (housing). In that case, investors would withdraw funds from the troubled sector, thus magnifying the impact of the original, potentially modest, income shock.

Based on our model, we develop a number of policy implications focused on devel- oping and sustaining a geographically opaque lending system. Such a system can be achieved with no additional regulation, in fact we argue against introducing new regula- tion that potentially requires originators to be geographically transparent in structuring their MBS.

The results of the model we develop here points to the increased house price volatility that results from the withdrawal of credit in response to a diversifiable shock. While the transparency literature clearly points to the need to monitor origination of loans for secu- ritization to prevent lemons and adverse selection, the pricing of diversifiable risks has its

278 J Financ Serv Res (2016) 49:265–280

own negative consequences in terms of increased house price volatility. What is needed is transparency, monitoring and accountability for risk introduced in origination, without the pricing of risk that would make the pooling of risk infeasible.

Acknowledgments Dr. Pavlov acknowledges financial support from the Social Sciences and Humanities Research Council of Canada. Dr. Wachter acknowledges the assistance from the Research Sponsors Program of the Zell/Lurie Real Estate Center at Wharton.

Appendix: 2 periods, 2 states

Transparent Mortgage Markets The lender lends gets L

j

0 R if the borrower repays, and p j

1 H if the borrower defaults.

Assume city A starts with the low income shock and city B starts with the high income shock: yA0 = yL, yB0 = yH .

If we denote the ratio pAt pBt

with θt , then the income shock effects imply that θ1 < θ0.

Without loss of generality let assume that θ0 = 1, then the income shock effects are depicted in the fact that θ1 < 1.

The probability city A will have a low shock next period is given by: P

{ yA1 = yL|yA0 = yL

} = 1+ρ2 Where ρ ∈ [−1, 1] is the auto-correlation for income. We assume income follows a

two-state Markov Chain:

y j t ∼

( 1+ρ

2 1−ρ

2 1−ρ

2 1+ρ

2

) .

For simplicity we assume that the spatial correlation in income shocks is perfectly nega- tive ρA,B = −1, so whenever city A has a bad shock, city B will have a good shock vice-versa.

In a transparent market, the lender’s expected profit to city j is:

E

[ π

j t

] = −Ljt + ηEt min

[ L

j t R, p

j

t+1H ]

= −Ljt + ηLjt R · P { L

j t R ≤ pjt+1H

} + ηH Et

[ p

j

t+1|L j t R > p

j

t+1H ]

The zero expected profit condition implies:

LA0 = η (

1−ρ 2

) LA0 R + η

( 1+ρ

2

) pA1 H

⇔ LA0

( 1 − η

( 1−ρ

2

) R

) = η

( 1+ρ

2

) H pA1

⇔ LA0 =

η (

1+ρ 2

) H

( 1−η

( 1−ρ

2

) R

) pA1

J Financ Serv Res (2016) 49:265–280 279

Likewise,

L B 0 = η

( 1 + ρ

2

) L

B 0 R + η

( 1 − ρ

2

) p

B 1 H

L B 0 =

η (

1−ρ 2

) H

( 1 − η

( 1+ρ

2

) R

) pB1

In an opaque market, the lender’s zero profit condition is:

L0 = 12 ηL0R · (

1−ρ 2

) + 12 η

( 1+ρ

2

) H pA1

+ 12 η (

1+ρ 2

) L0R + 12 η

( 1−ρ

2

) H pB1

⇔ L0 = 12 ηL0R + 12 ηH

[( 1+ρ

2

) pA1 +

( 1−ρ

2

) pB1

]

⇔ L0

( 1 − 12 ηR

) = 12 ηH

[( 1+ρ

2

) pA1 +

( 1−ρ

2

) pB1

]

⇔ L0 =

1 2 ηH(

1− 12 ηR ) p1

Where p1 = [(

1+ρ 2

) pA1 +

( 1−ρ

2

) pB1

] , and we have pA1 < p1 < p

B 1 , also notice that

in the opaque market case, each city receives the same loan L0.

Proposition 1 If ρ > 0, then if ηR > 1: if ηR < 1: LA0 > L0 > L

B 0

If income is negatively correlated ρ < 0, then signs are reversed. But this is not the case we are interested in.

The case we study has ρ > 0 and ηR > 1. Plugging this into the equilibrium price function:

p j

0 = 1γ ( α + yj0 + L

j

0 − H )

Since the loan to city A under transparency is smaller than the loan to city A under opacity LA0 < L0, the transparent price is lower than the opaque price:

p A,trans 0 = 1γ

( α + yj0 + LA0 − H

) <

1 γ

( α + yj0 + L0 − H

) = pA,opaque0

Likewise: p

B,trans 0 = 1γ

( α + yj0 + LB0 − H

) >

1 γ

( α + yj0 + L0 − H

) = pB,opaque0

NOTE: we have assumed that city A starts with a bad income shock at time 0 and city B starts with a good income shock. Ex ante with probability 12 we have y

A 0 = yL and yB0 = yH ,

and with probability 12 we have y A 0 = yH and yB0 = yL. However, ex ante neither city knows

which state of the world they will start in they will prefer opacity to have smoother house prices.

The lesson from this model is that a geographically transparent mortgage market has more volatile house prices which are more strongly correlated to local risks.

280 J Financ Serv Res (2016) 49:265–280

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