nach oben

The Journal of Real Estate Finance and Economics

Erschienen in:

01.01.2012

Asymmetric Information in the Subprime Mortgage Market

verfasst von: James B. Kau, Donald C. Keenan, Constantine Lyubimov, V. Carlos Slawson

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 1-2/2012

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Because of impersonal securitization in the secondary market, the ultimate investors in a mortgage have only a limited amount of information about the borrower’s characteristics. This creates an asymmetric information problem because of hidden knowledge on the part of the primary lenders, who naturally have much better access to this information. This is aggravated by the free rider problem when there are multiple investors. We discuss to what extent the secondary market then seeks to sort the loans to ameliorate this problem and what role reputations play. More importantly, however, the actions of the primary lender in terms of which kinds of loans they choose to approve are partly hidden, and this typical principal-agent situation importantly aggravates the incentive problem. To judge the nature and magnitude of this moral hazard dilemma, we use data to compare how well investors in the secondary mortgage market can predict default given the information they typically have access to as compared to the ability of primary lenders to similarly predict default given the larger set of information they typically will have access to. Finally, the implications of these results are indicated, particularly in light of the recent mortgage crisis.

Vorheriger Artikel The Subprime Crisis and House Price Appreciation

Nächster Artikel Information, Search, and House Prices: Revisited

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Hidden knowledge is also commonly referred to as hidden or private information.

The credit crisis is sometimes attributed, instead, more to the drying up of overall liquidity than to asymmetry of any given transaction, so that even if the potential purchaser had a realistic notion of the worth of an asset he could not count on being able to resell it at a price reflecting this knowledge. However, this is, of course, just evidence of adverse selection problems in this market further down the line impinging on a particular transaction, even though there would be no asymmetric information to the given transaction, and thus an example of how adverse selection in some potential transactions can indeed propagate and distort the entire market. One could take the alternative view that all that happened was that everyone discovered credit instruments were more risky than theretofore thought, but in such a case of symmetric information, the assets should simply have lost value in a mutually understood way, with no resulting seizing up of markets.

As with the literature on insurers, it is often regarded as a bit of a puzzle why the private secondary market does not make use of more information than it does, when as we will see there is good evidence that some of it is relevant, and where this information is potentially available to these final investors. (See, e.g., Finkelstein and Poterba (2006), who describe the U.K. annuities market, where insurers fail to use policyholders’ residential addresses.) Some of the reason, besides the obvious costs of such determinations, may involve wariness of running afoul of various anti-discrimination statutes or suffering regulatory disfavor. For our purposes, it is adequate that, for whatever reason, the secondary market does not in fact appear to exploit this information.

One could argue that the distinction from the classical moral hazard problem is unimportant, and that the situation is, in fact, rather like the classical one, where an employer sees the output of a worker, but not his effort. But in that situation, the moral hazard depends on there being noise in the relation of effort to output, since otherwise the employer could indeed infer the effort. While there may well be noise in loan approval, nonetheless, the moral hazard dilemma arising does not depend on it: even without noise, the buyer will not be able to infer the quality of the loan, since knowledge of this depends on factors known to the originator but hidden from the buyer. Thus, hidden information plays a critical factor in the secondary market’s moral hazard problem absent from the classical, pure moral hazard problem.

Of course, if the rule is applied by the primary lender to enough loans, the ultimate investors will eventually acquire enough information to statistically infer the rule, at least in retrospect, which is to say to that a longstanding primary lender will then acquire a reputation, so that credible commitment to a continued presence in the market creates incentives to avoid exploiting the immediate advantage to be had from hidden knowledge.

Note that, in both examples, while buyers in the secondary market see what is offered to them, they do not know what was not offered, in the former case presumably because the potential loan was not good enough and in the latter case presumably because because the loan was too good.

See DeMarzo and Duffie (1999) for a model in which the informed lender would prefer to sell loans with the lowest degree of asymmetry.

Downing et al. (2009), in their study of mortgage-backed securities backed by Freddie Mac participation certificates, find that pools underlying multi-class mortgage-backed securities (MBSs) allocated to Real Estate Mortgage Investment Conduits (REMICs) fare worse than non-REMIC pools, which they interpret as evidence of Government Sponsored Enterprise (GSE) exploitation of information asymmetries. An analysis of the pricing of commercial mortgage-backed security (CMBS) pools by An et al. (2009) suggests that “conduit lending”, which is analogous to the originate-to-distribute model, may have mitigated the potential adverse selection problem associated with choosing mortgages for securitization from a portfolio. They find that portfolio-originated loans are priced at a discount in the CMBS market, after controlling for credit quality, which they find to be lower than that of the conduit loans. Ambrose et al. (2009) find that moral hazard problem for the master servicers in CMBS deals is exacerbated by adverse selection, when the master servicer expects also to be the special servicer for the pool.

Ben-Shahar (2008) reviews much of this literature, as well as offering yet another adverse selection model.

The one field, other than insurance, where empirical work on asymmetric information is the most developed is that of auctions, where extensive applied techniques have been employed with considerable success (see Paarsch and Hong 2006).

The true type of the borrower, θ = (a,b), would then consist of his private as well as non-private characteristics.

The fact that c is regressed against a and y rather than h(y) against a and c is unimportant to the conditional independence test, though the fact that ex post claim occurrence y, instead of ex ante probability h(y), is used does pose a difficulty for the Puelz and Snow analysis, as pointed out by Dionne et al. (2001) and Chiappori and Salanie (2000).

The unused observables tests also opens the way for distinguishing moral hazard from adverse selection in those cases where it can be argued that the known characteristic can be convincingly argued not to be associated with the latter. This is never possible with the positive correlation test, since one never knows what is the missing factor causing the correlation.

We take as subprime, mortgages whose FICO scores fall below 720. While some Alt A mortgages may be included in the sample as a result of this metric, the mean FICO score remains near 613 (see Table 1).

Table 1 presents summary statistics, while Fig. 1 displays the observed counts of default and prepayment.

We use the MABLE geographic data base (http://mcdc2.missouri.edu/websas/geocorr2k.html) to match census tracts to zip codes. We rely on information concerning the location of the property, original loan balance, purpose of the loan, lien status, and occupancy to match our loans.

The variables \(\lambda_1^\ell,\lambda_2^\ell,\ldots, \lambda_{360}^\ell\) are mass points of the discrete baseline cumulative hazard function \(\Lambda_0^\ell,\) and I is the standard indicator function. See Kalbfleisch and Prentice (2002) for the statistical technique appropriate to this specification.

The latter includes the current yield, Yield1CMT, current unemployment, Unempl, current house price appreciation, HPNom, and the Spring Season indicator, SeasonSpring.

Properties of the contract include OrigLoanSize, MargNM, Margin, Teaser, Ceiling, and Floor, while properties of the economic environment at origination include TermStrOrig, TermStrSlope, HousePrice, and Y2007Vint, where all variables are briefly 2.

Characteristics of the borrower include OtherVsWhite, HispVsWhite, BlVsWhite, FemAppl, IncomePersNM, IncomePers, and IncPersLoDoc, while neighborhood characteristics include IncZcode, RaceZcode, and EducZcode. Obviously neighborhood characteristics could just as well be considered as properties of the economic environment at origination, but we tend to view them with regard to what they suggest about the borrower’s unseen characteristics. As this may be, the important property is that they can be taken to be unobserved by the secondary lenders.

It is notable that the addition of the six contractual variables does not change the signs of any of the previous covariates in the hazard of default equation, and that only one of these 24 variables, PenaltyFlag, changes in significance, becoming insignificant. For prepayment, only FicoScoreOrig apparently reverses sign (see Tables 4 and 5), but it is seen to be insignificantly different from zero in both cases.

One might argue that if one of margin or loan size is specified, then the other is redundant, since the tradeoff of available choices must determine the other, but besides it being harmless to include both, this argument ignores the role of other contractual properties that are part of the fuller menu.

There is the problem, present in all such analyses, that we may not have included all the information available to the primary market, and that the information provided by contractual variables is merely information available to the originator, but not to us. We can only say that we have gone to considerable lengths to get what is a broad set of additional variables, and that it is not immediately obvious to us what is missing and not adequately covered by what we do have. Further, as should have been made clear from the discussion of the secondary market into which these mortgages are necessarily being sold, the originator has limited incentives to engage in much costly acquisition of further information.

The present result is not very surprising given our earlier observation in regard to Tables 3 and 5 that the introduction of the contractual variables did not alter the pattern of the previously used variables in Tables 12 and 4 and so apparently provide independent information.

As an alternative robustness check, we also used residuals from the models of the borrower’s choice variables instead of the expected values of the choice variables in the estimation of default hazard in Table 10 (see, e.g. Richaudeau 1999). Our results do not qualitatively change compared to the reported specification.

Since the BLIS data well represents the information available to the secondary market, we can be confident of their information set. On the other hand, while the primary lender clearly has access to the additional information we then introduce, it is not clear that all this information does get used, and indeed, there are anti-discriminatory statutes which might seem to prevent the use of some of these variables. In contrast to the secondary market, however, it would be hard for regulators (who themselves then face a moral hazard problem with hidden knowledge) to demonstrate that originators were not using such information, except by a possibly inadmissible statistical analysis after the fact, and since originators have partial incentives to use this information, which, in the case of neighborhood characteristics, they often know at no cost and would have difficulty not using, we do not want to preclude the likelihood that they indeed do so. Further, regressing out the variables that the primary market might not use only serves to confirm the role of the ones that the primary market does use.

Akerlof, G. (1970). The market for lemons: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500.CrossRef

Ambrose B. W., LaCour-Little, M., & Sanders, A. B. (2005). Does regulatory capital arbitrage, reputation, or asymmetric information drive securitization? Journal of Financial Services Research, 28, 113–133.CrossRef

Ambrose, B. W., Sanders, A. B., & Yavas, A. (2009). Special servicers and adverse selection in informed intermediation: Theory and evidence. Working paper.

An, X., Deng, Y., & Gabriel, S. A. (2009). Is conduit lending to blame? Asymmetric information, adverse selection, and the pricing of CMBS. IRES working paper.

Ben-Shahar, D. (2008). Default, credit scoring and loan-to-value: A theoretical analysis of competitive and non-competitive mortgage markets. Journal of Real Estate Research, 30(2), 161–190.

Brueckner, J. K. (2000). Mortgage default with asymmetric information. Journal of Real Estate Finance and Economics, 20(3), 251–274.CrossRef

Cawley, J., & Philipson, T. (1999). An empirical examination of information barriers to trade in insurance. American Economic Review, 89, 827–846.CrossRef

Chiappori, P.-A., & Salanie, B. (2000). Testing for asymmetric information in insurance markets. Journal of Political Economy, 108, 56–78.CrossRef

Cutler, D. M., & Zeckhauser, R. J. (2000). The anatomy of health insurance. In A. J. Culyer, & J. P. Newhouse (Eds.), Handbook of health economics (Vol. 1). Elsevier.

Dell’Ariccia, G., Igan, D., & Laeven, L. (2008). Credit booms and lending standards: Evidence from the subprime mortgage market. IMF working paper WP/08/106.

DeMarzo, P. M., & Duffie, D. (1999). Liquidity-based model of security design. Econometrica, 67(1), 65–99.CrossRef

Dionne, G., Gourieroux, C., & Vanasse, C. (2001). Testing for evidence of adverse selection in the automobile insurance market: A comment. Journal of Political Economy, 109(2), 444–451.CrossRef

Downing, C., Jaffee, D., & Wallace, N. (2009). Is the market for mortgage–backed securities a market for lemons? Review of Financial Studies, 22(7), 2457–2494.CrossRef

Edelberg, W. (2004). Testing for adverse selection and moral hazard in consumer loan markets. Board of Governors of the Federal Reserve System, Finance and Economics Discussion Series 2004-09.

Elul, R. (2009). Securitization and mortgage default: Reputation versus adverse selection. FRB of Philadelphia, working paper.

Finkelstein, A., & Poterba, J. (2004). Adverse selection in insurance markets: Policyholder evidence from the U.K. annuity market. Journal of Political Economy, 112, 183–208.CrossRef

Finkelstein, A., & Poterba, J. (2006). Testing for adverse selection with ‘unused observables’. NBER working paper no. 12112.

Gjesdal, F. (2007). Moral hazard with hidden information. In R. Antle, F. Gjesdal, & P. J. Liang (Eds.), Essays in accounting in honor of Joel S. Demski. Springer.

Kalbfleisch, J., & Prentice, R. (2002). The statistical analysis of failure time data (2nd ed.). Wiley.

Keys, B., Mukherjee, T., Seru, A., & Vig, V. (2010). Did securitization lead to lax screening? Evidence from subprime loans. Quarterly Journal of Economics, 125(1), 307–362.CrossRef

Macho-Stadler, I., & Pérez-Castrillo, J. D. (2001). Introduction to the economics of information: Incentives and contracts (2nd ed.). Oxford.

McCarthy, D., & Mitchell, O. (2010). International adverse selection in life insurance and annuities. In S. Tuljapurkar, N. Ogawa, & A. Gautheir (Eds.), Riding the age wave: Responses to aging in industrial societies. Elsevier (forthcoming).

Mian, A., & Sufi, A. (2009). The consequences of mortgage credit expansion: Evidence from the U.S. mortgage default crisis. Quarterly Journal of Economics, 124(4), 1449–1496.CrossRef

Paarsch, H. J., & Hong, H. (2006). An introduction to the structural econometrics of auction data. MIT Press.

Puelz, R., & Snow, A. (1994). Evidence on adverse selection: Equilibrium signaling and cross-subsidization in the insurance market. Journal of Political Economy, 102, 236–257.CrossRef

Richaudeau, D. (1999). Automobile insurance contracts and risk of accident: An empirical test using french individual data. The Geneva Papers on Risk and Insurance Theory, 24, 97–114.CrossRef

Rothschild, M., & Stiglitz, J. (1976). An essay on the economics of imperfect information. Quarterly Journal of Economics, 90, 629–649.CrossRef

Titel: Asymmetric Information in the Subprime Mortgage Market
verfasst von: James B. Kau
Donald C. Keenan
Constantine Lyubimov
V. Carlos Slawson
Publikationsdatum: 01.01.2012
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 1-2/2012
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-010-9288-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1-2/2012

Presale Contract and its Embedded Default and Abandonment Options

Introduction to Special Issue on the Economics of Information in Real Estate Markets

Cash Flow Volatility, Prices and Price Volatility: An Experimental Study

Information Uncertainty and the Post-Earnings-Announcement Drift Anomaly: Insights from REITs

Distressed Properties: Valuation Bias and Accuracy

The Subprime Crisis and House Price Appreciation