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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

In the wake of historic levels of mortgage defaults, regulators have debated how to regulate certain high-risk loans because of the risks of foreclosure involved. This study examines state laws that required loan applicants to receive information about the risks of foreclosure before they could sign certain mortgage contracts. Skeptics suggest that disclosures are largely ignored by consumers, yet controlling for other factors this study shows that loan applicants in states with enhanced warnings about foreclosures were more likely to reject high-cost refinance mortgage loan offers from a lender. Enhanced disclosures with features such as risk warnings, signatures, and referrals to counseling are being implemented as part of Dodd–Frank consumer finance reforms. This study suggests these strategies may be useful to balance consumer protection and access to high-risk credit.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

In the early 2000s, lenders developed more refined techniques for assessing and pricing consumer credit risk, which in turn expanded the ability of borrowers with poor credit to gain access to the mortgage market (Gramlich 2007). This expansion of access to credit was largely unprecedented, opening homeownership to some classes of borrowers for the first time (Schwartz 2011). However, even during the boom, consumer advocates worried that lenders were taking advantage of consumers and exposing borrowers—and ultimately society as a whole—to too much risk (Barr 2012; Engel and McCoy 2011). The surge in mortgage foreclosures in 2008 and subsequent years have heightened the debate over whether high-risk consumers should be offered mortgage contracts at all. Banning certain kinds of loans or borrowers is not always practical. Some consumers who could successfully comply with high-risk loan terms may be left worse off by being excluded from the market.

One partial solution is to implement policies that help potential borrowers to more realistically assess the risks of default before signing a loan contract with a willing lender. Providing mortgage loan applicants with information about the risks of losing their home in foreclosure may help those consumers who would otherwise undervalue the risks involved in a mortgage contract to re-evaluate their options and decide not to borrow. Consumer protection advocates who worry that myopic borrowers do not understand or appreciate the risk of default may find that carefully designed risk warnings can provide at least a partial remedy for uninformed borrowers agreeing to risky loan terms. More typically, however, mortgage disclosures are viewed as offering little or no real information, instead drowning loan applicants in a sea of legalese (for a discussion, see Durkin 2002; Kroszner 2007). The impact of mortgage information disclosures is an issue that needs to be understood as regulators implement credit market reforms and consumer protection rules in the aftermath of the mortgage crisis.

The debate over whether disclosure policies are a mechanism to facilitate informed consumer choices or a wasteful bureaucratic requirement is one that has not been carefully addressed in the empirical literature. This article examines whether loan applicants who were required to receive enhanced risk disclosures during the housing boom were more likely to reject a loan offer from a lender compared with similar loan applicants not subject to these disclosures. The study uses individual-level refinance mortgage loan applications from data provided under the Home Mortgage Disclosure Act (HMDA), and identifies effects of enhanced disclosures using the variation in state disclosure laws. The findings suggest that risk disclosures, particularly those requiring a signature, are associated with loan applicants rejecting approved loan offers at higher rates compared with similar borrowers not subject to augmented disclosure policies.

BACKGROUND

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

All loan applicants receive a Truth in Lending Act of 1968 (TILA) disclosure as part of the credit application process (Durkin 2002; Durkin and Elliehausen 1999). In theory, TILA disclosures help loan applicants by lowering the costs of acquiring and processing information about the interest rates and fees on prospective loans, resulting in loan applicants examining more alternatives in the marketplace (for the classic paper on the economics of information search, see Stigler 1961). In this way TILA disclosures standardize price comparisons and have the potential to encourage consumers to shop around for a loan that best meets their needs.

Another role for disclosures in credit markets is to deliver information not related to the price of a loan, but rather the risks of a loan. Here the information is intended to aid the consumer in deciding whether or not to enter the credit market at all. These disclosures warn about potential risk of default in order to influence the consumer's subjective perception of default risk. This, in turn, may reduce the consumer's expected value for the loan being evaluated. While TILA generally does not require risk disclosures, an amendment to TILA called the Home Ownership and Equity Protection Act (HOEPA) requires loan applicants seeking “high-cost” mortgage refinance loans to receive a disclosure form with the following text: “You are not required to complete this agreement merely because you have received these disclosures or have signed a loan application. If you obtain this loan, the lender will have a mortgage on your home. YOU COULD LOSE YOUR HOME, AND ANY MONEY YOU HAVE PUT INTO IT, IF YOU DO NOT MEET YOUR OBLIGATIONS UNDER THE LOAN.”

HOEPA was enacted under the Riegle Community Development and Regulatory Improvement Act of 1994 in response to abusive practices in mortgage refinance and home-equity lending. The Board of Governors of the Federal Reserve System issued regulations implementing HOEPA in 1995, and then broadened the scope of the regulation to more refinance loans in 2001. HOEPA did not apply to mortgages used to initially purchase a home until 2010 with the Dodd–Frank Wall Street Reform and Consumer Protection Act (Dodd–Frank). The new Consumer Financial Protection Bureau implemented new HOEPA regulations effective January 10, 2014 to expand regulations to cover more loans, including purchase mortgages and loans with other terms found to be potentially problematic for consumers (78 Federal Register 21, 2013). This study focuses on the pre-Dodd–Frank reform period, but is illustrative of how new forms of disclosure may be associated with borrowing decisions.

Before Dodd–Frank, and during the housing and mortgage boom of the 2000s, HOEPA was only required for refinance mortgages—that is, for loans being used to pay off an existing mortgage or to borrow against a home that has no mortgage. Loan applicants in this market already have a home and are borrowing to change the terms of their loan, including extracting home equity and converting it into debt in exchange for immediate cash. The HOEPA disclosure must be provided at least three business days before the closing of the mortgage contract, and borrowers have up to three days after signing the mortgage contract to rescind their agreement. (1) The Dodd–Frank Act has added further enhancements to HOEPA, including restricting certain terms and adding in counseling referrals for loan applicants. (2) Even under recent amendments, however, HOEPA retains the requirement to explain the consequences of default in a disclosure form.

Importantly for this study, a subgroup of states enacted enhanced HOEPA regulations during the 2000s before Dodd–Frank reforms were implemented. State statutes typically prescribed the wording of the disclosure, in some cases also including a signature by the borrower, a provision never included in HOEPA even more recently under Dodd–Frank. Other states also recommend or require borrowers to be notified that they can obtain counseling before they sign a high-risk loan contract, a provision HOEPA only adopted in 2014. Some states even require written risk disclosures for non-HOEPA loans, creating a subset of high-cost mortgage applications subject to differing regulations within and across states. This creates a useful natural experiment to compare the effects of augmented default risk disclosures. These state-by-state experiments are potentially informative for how Dodd–Frank amendments will affect loan applicants, especially as HOEPA is being expanded to cover more mortgages and counseling referrals are being required nationally. It also may provide evidence for further enhancements for financial disclosures more generally beyond what is currently being implemented.

The Refinance Mortgage Application Process

Figure 1 illustrates the application process for a subprime home mortgage refinance loan. Three stages are described, beginning with an applicant's entrance to the market, then the lender's evaluation of the application and finally mortgage origination if the applicant accepts the lender's offer.

image

Figure 1. Home Mortgage Refinance Decision Nodes

Download figure to PowerPoint

The first phase, labeled ENTRANCE, occurs when the consumer makes an application for a home refinance mortgage. The decision to apply with a particular lender may be the result of an information search process, including the lender's advertised loan products and product terms, or the borrower may be approached directly by a lender with an invitation to submit an application. Mortgages are offered by lenders along a continuum of interest rates and borrowers cannot be sure of the price for the loan for which they ultimately will qualify. How lenders present interest rates and fees related to a loan are regulated under TILA at this stage.

The second phase, defined in the illustration as LENDER EVALUATION, is the “underwriting” process used to determine whether a loan application meets the minimum threshold for approval for a given type of loan. This phase is centered on the decision of the lender based on information provided by the loan applicant at the prior stage. The lender considers the past credit history, income, and the value of the property being financed, among other factors. There are no relevant consumer disclosure provisions at this stage as the decision resides with the lender. A portion of loans are denied, ending the process for these loans. Approved loans move onto the next stage.

The final phase is CONSUMER EVALUATION and applies only to applicants who were approved by the lender at the prior stage. Here the decision is left to the loan applicant who can reject an approved loan offer from a lender by simply refusing to sign the loan documents or by exercising a right of rescission up to three business days after the contract is signed.1 In the refinance market a consumer might reject the offer and simply remain out of the market, or he/she might repeat the process in search of a better offer.

Disclosures about default risks, such as those found in HOEPA, are binding at this stage. An economic approach to understanding the influence of risk disclosures on behavior relies largely on the subjective utility model (Edworthy and Adams 1996). The loan applicant has a perceived expected value of the loan if they sign the mortgage contract, based on the benefits and costs of the loan. One of the costs is in a situation where the borrower fails to make timely payments, they could go into default and lose the home to a foreclosure. Loan applicants may assign a low or null value for the probability of default, however. This may be because they are unaware that the home is being used as collateral and subject to being taken by the lender. It may also be because of myopia where the applicant underestimates the risks of default occurring. Warning about foreclosure risks may cause the loan applicant to reconsider the transaction. The applicant may reassess the immediate benefits of a loan in light of the potential costs, including the risk of losing their home. Risk disclosures potentially offer new information that will: (1) increase the expected default rate, and (2) increase the perceived costs associated with default. These two channels will lower the expected net present value of the loan, increasing the probability that loan applicants will not accept a mortgage loan offer in the final stage of the application.

Before Dodd–Frank, HOEPA only applied to loans with an interest rate of at least 11−13%. Mortgages covered by HOEPA have always been a share of the market since the interest rate and fee thresholds were set relatively high. In 2005, HOEPA lending was at the highest level recorded, amounting to just one-half of one percent of covered mortgage loans made that year.2 By 2011, after the market retreated from high-risk lending, the market share of HOEPA mortgages relative to all refinance and home improvement loans dropped to 0.05%. Nevertheless, this segment of the market continues to exist, and as housing and mortgage markets recover, high-cost lending is likely to resume at some level.

Prior Literature

The literature on information mandates in consumer markets, including labels, disclaimers, and disclosures, extends back to a classic article in economics by Beales, Craswell, and Salop (1981). Disclosures are distinct from labels, the latter of which mainly provide information consumers use to decide how to use a product or how to evaluate product claims (for an example, see Wansink 2003). Kozup et al. (2012) provide a very useful discussion of the specific aspects of disclosures and what makes for sound disclosure policies. Ideally, disclosures provide relevant product information useful during the search process (Lynch and Wood 2006). Disclosures can play several roles, including addressing cognitive biases or myopia (for an example, see Bertrand and Morse 2011). One specific role of disclosures can be to influence how a consumer perceives risks (see Wogalter and Laughery 1996; Wogalter et al. 1991 for a discussion). Several studies in psychology and economics have addressed how people perceive risks (Arrow 1982; Weber and Milliman 1997; Slovic 1987; Viscusi 1983; Viscusi, Magat, and Huber 1987a, 1987b). Disclosures are common in the context of communicating health risks, but are not common in most credit markets.

Ko and Lee (2011) review the literature in economics and propose a framework where consumer protection policies in credit markets are targeted at differing problems depending on whether the fundamental economic problem is that consumers are being myopic or time inconsistent. Being myopic means the consumer is too shortsighted to anticipate accurately negative events or costs. Being time inconsistent means the consumer prefers not to make choices that harm them in the future, but makes biased choices in the present that are the opposite of their true long-run preferences. Bond, Musto, and Yilmaz (2009) describe problems of time-inconsistency concluding that regulations may be welfare improving in this context. But consumer myopia can be addressed to some extent by presenting information in a form and format that garners cognitive attention and engages the consumer in formulating a decision.

The literature on conveying risk information suggests that certain kinds of risk information will be more likely to gain attention from consumers. The more a risk evokes visceral feelings, the more a consumer will “dread” that risk and seek to avoid that risk (Slovic 1987). The HOEPA disclosure focuses on “LOSE YOUR HOME” (as opposed to “property” or “equity”), which may be more likely to cause consumers to connect the risk of default to their way of life, instead of the less personal “collateral” for a loan. Cox, Cox, and Zimet (2006) find in experiments that risks of more permanent detrimental harm provoke stronger reactions from consumers in terms of avoiding potential losses. Hobson et al. (1998) show that losing a home to foreclosure is perceived in surveys as one of the top most stressful life events, even ahead of a divorce. These studies support wording of the HOEPA disclosure and its focus on the risk of “losing your home and all the money you have put in it.”

There may also be some aspects of time inconsistency among HOEPA borrowers. In the high-cost segment of the market, borrowers are generally not seeking to refinance in order to obtain a more advantageous interest rate or term. Borrowers seek high-cost refinance loans primarily because they want to convert home equity into liquid cash. “Cash-out” means borrowers can use home equity for consumption or to pay off other obligations (Gist, Figueiredo, and Verma 2012). Behavioral economics suggests this may be a situation where consumers neglect long-term costs. The transfer of illiquid home equity to expendable funds may trigger consumers to underestimate the consumption burden a loan will impose on their financial situation in the future and to overestimate the benefit of the conversion of home equity into cash in the present (Laibson 1997).

There are few studies that are specific to risk disclosures in credit markets. One notable exception is by Camerer et al. (2003), who suggest that HOEPA disclosures help uninformed or naive consumers, who may be less capable of making an informed choice as other consumers. Prior studies are suggestive that some classes of consumers may systematically be uninformed about mortgages (Bucks and Pence 2008; Campbell 2006). Information disclosures might be more potent for these loan applicants.

HOEPA (pre-Dodd–Frank) focused on refinance mortgages. Unlike home purchase mortgage applicants who are seeking a loan while also trying to bid to purchase a home, refinance borrowers have more time and more discretion to decide to accept a new loan or not (Gibler and Nelson 2003). Refinance borrowers also have at least one prior experience with obtaining a mortgage (Hilgert, Hogarth, Beverly 2003). There are a few studies on the consumer decision to refinance, but none that focus on high-cost HOEPA mortgages specifically. The decision to refinance has been modeled in previous studies as being influenced by changes in home prices, income, household type, borrower demographics, as well as the overall interest rate environment (Hurst and Stafford 2004).

While the loan approval or denial decision of lenders has been studied previously (LaCour-Little 1999; Ladd 1998), there are no published studies on the consumer decision to accept an approved mortgage loan offer from a lender. There are a number of studies on how borrowers obtained high-cost loans during the housing boom (Anacker and Crossney 2013; Engel and McCoy 2011, for examples). There are also several studies emerging on how state regulations impacted supply and demand for high-cost mortgages (for examples, see Bostic et al. 2012; Ding et al. 2012). No prior studies have examined state mortgage disclosure laws, however, or the loan applicant's rejection of high-cost loan offers from lenders.

DATA

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

This analysis is conducted with data on refinance loan applications as reported to bank regulatory agencies in data provided under the HMDA. These data are publically available (http://www.ffiec.gov) and document each mortgage loan application submitted to regulated mortgage lenders in a given year. The unit of observation is a loan application, including borrower, loan, and lender characteristics, as well as the lender's approve–deny decision and the applicant's accept–reject decision (for approved loans).

This study is based on 2005 HMDA data. This year represents a year near the peak of the subprime lending boom and the peak market share for HOEPA loans. It is also before state and federal regulations restricting lending began in 2006–2007, and before capital markets retreated from funding mortgages in the high-cost market in 2007–2008. While a few states have adopted new disclosure rules since 2005, these legislative changes were mainly implemented after the high-cost mortgage market began to collapse, ruling out a longitudinal approach.

The 2005 HMDA data include 13.1 million refinance mortgage applications for owner-occupied single family homes in metropolitan areas submitted to 7,749 lending institutions. Each record includes the census tract of the property being financed, the amount of the loan requested, the applicant's race and gender, the applicant's annual income as listed in the loan application, the financial institution receiving the application, and other features of the loan. The lender is required to record whether the mortgage loan application was:

  1. denied
  2. withdrawn by the applicant
  3. too incomplete to evaluate
  4. approved but rejected by the applicant
  5. approved and originated as a loan.

The key metric for this study is the likelihood approved loan applicants rejected loan offers from lenders (option d., above). Of 6.6 million lender-approved applications in 2005, just over 954,300 or 14.5%, were rejected by the applicant. These consumers submitted complete loan applications to a lender, the lender approved the loan, but the consumer decided not to proceed with the loan.

Among loans that were approved and originated, the relative interest rate is recorded as the annual percentage rate (APR) compared with the rate on a Treasury security of a similar duration, but only if the difference is at least 3 percentage points.3 In addition, approved and originated loans include an indicator of whether the loan was subject to HOEPA. APR spread and HOEPA status is not reported in HMDA data for lender-approved loan applications rejected by the applicant, however. The APR spread and HOEPA status of loan offers must therefore be estimated based on approved and originated loans in the same market, defined as census tract, loan type, and lender. This estimation approach rests on the assumption that applicant characteristics are strongly correlated and that loan applications in the same census tract with the same lender seeking the same type of loan are likely to have similar loan terms and interest rates.

HOEPA status was estimated with a lender fixed effects regression using the 6.6 million originated applications, controlling for applicant and census tract characteristics. The values from this estimate were then imputed for all loan applications as the predicted HOEPA status. This produces an estimate of 17,715 HOEPA loan applications, including 14,903 approved and originated loans and 2,812 loans approved by lenders but rejected by applicants. This suggests a 15.9% rejection rate, similar to the 14.5% rate overall in the data discussed above. This estimation procedure correctly identified 84% of actual HOEPA-originated loans when comparing imputed HOEPA status to actual HOEPA status.

The APR spread for approved loan applications rejected by the loan applicant was also estimated using approved and originated mortgage loans. Rather than a regression, however, APR was estimated using means calculated for 66,000 census tracts across two loan types (government insured vs. conventional) and over 7,749 lending institutions. This resulted in approximately 3.5 million cell means, which were then merged back into each tract-lender-loan type combination. This approach correctly identified the APR spread category for loans with recorded APRs in 81% of cases. This approach tended to underestimate the actual APR for originated loans with an APR recorded, suggesting a lower bound estimate when identifying loans subject to interest rate triggered disclosures. If there is any APR-based search behavior on the part of consumers, they will reject higher APR loan offers but will be more likely to accept lower APR loan offers. This means that approved loans that were rejected by loan applicants most likely have a higher APR on average than is estimated in this analysis. This again would suggest these estimates will be conservative.

The final data set includes 166,355 high-cost refinance loan applications: just under 18,000 are estimated to be subject to HOEPA and the remainder to have interest rates of 10.5% or more. The means, standard deviations, and number of observations for variables used in this analysis with these data are displayed in Table 1. The final sample consists of loan applications from all 50 states, in addition to Washington, DC.

Table 1. Descriptive Statistics for 2005 HMDA Data on High-Cost Loan Applications
 Mean  SD
  1. Note: Number of observations (high-cost/HOEPA loan applications) = 166,355

Dependent variable:  
Loan application back out dummy0.159(0.142)
Explanatory variables:  
Lending law index3.187(2.668)
Log income applicant3.83(3.840)
Ratio income to loan amount54.959(54.851)
Minority applicant indicator0.241(0.238)
Lender regulated by OCC indicator0.122(0.106)
Lender regulated by OTS indicator0.079(0.075)
Lender subprime market share0.0821(0.078)
Government insured loan indicator0.001(0.001)
Loan > $360 conforming limit indicator0.038(0.045)

Nondisclosure Mortgage Regulations

States with augmented HOEPA laws are not distributed at random and generally states with added mortgage risk disclosures also implemented other regulations on mortgages. These other state laws may affect loan applicants or lenders of high-cost loans, including the prohibition of certain loan terms or enforcement provisions. Ho and Pennington-Cross (2006) conducted a detailed analysis of state laws and how various regulations impact the supply of and demand for mortgages. An index score is constructed from the work of Ho and Pennington-Cross including: (1) state prepayment penalty restrictions, (2) balloon payment restrictions, and (3) restrictions on mandatory arbitration. Each of these might discourage lenders and borrowers from being party to a HOEPA loan. The index is used to proxy for the strength of regulations affecting loan applications and intended to isolate the effects of state risk disclosure provisions over and above other state restrictions.

EMPIRICAL STRATEGY

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

The loan applicant's decision to reject an approved high-cost mortgage refinance loan offer from a lender is modeled as a dichotomous choice where reject = 1 and accept = 0. The predicted finding is that state risk disclosure regulations will result in borrowers being more likely to perceive the net expected benefits of a high-cost refinance loan offer as being negative, all else equal, which is observed in the data as a loan applicant rejecting an approved loan offer from a lender.

All HOEPA loans have an unsigned written disclosure of the risk of losing a home, regardless of state laws. Additional disclosures vary by state and by loan features.4 State laws take several forms: (1) adding a written warning in addition to the federal HOEPA disclosure, (2) adding a signed warning disclosure, (3) adding a warning disclosure for non-HOEPA loan applications, and (4) referring borrowers to counseling as part of the disclosure. It is expected that these each will provide loan applicants information about default risks in an attention-getting format that other disclosures do not achieve. Including loans covered by augmented disclosures laws and similar high-cost loans not covered by disclosure laws allows for estimates of the relative effect of each type of state law between states and within states. The empirical specification is as follows:

  • display math

where Bi,s is 1 if a loan applicant in state s backed out of a loan application, i. Di,s is a dichotomous variable for a loan application subject to state disclosure regulations. Di,s alternatively includes applications covered by any disclosure, a signed disclosure, or a disclosure that includes a referral to counseling. Hi is an indicator for a loan subject to HOEPA disclosure. This allows the behavior of these loan applicants to vary from those seeking non-HOEPA high-cost loans. Loan applications are identified as being “covered” by a state law based on the loan application's estimated APR or HOEPA status, taking into account any exemptions (see Table 2 for details). β3 is estimated from the interaction of loans covered by state laws and also by federal HOEPA disclosures to estimate the combined effect. Mi is an indicator for a loan applicant of non-white race. β5 is the interaction of a loan covered under a state law and a minority applicant. This interaction may provide evidence about the role of risk disclosures by borrower racial background—this is included in part because of prior studies showing minority race mortgage borrowers have less knowledge about their mortgage terms and costs, and disclosures may have differential effects for this population of loan applicants. Laws is the index of other state mortgage regulations described previously.

Table 2. Summary of State Risk Disclosure Laws for High-Cost Mortgages, 2005
State (FIPS code)APR TriggerFormSignedCounselWordsExempta
  1. GSE = Freddie Mac/Fannie Mae loan; FHA = government insured loan.

  2. a

    Maximum loan amount, unless range.

  3. b

    Actual APR not spread to Treasury.

  4. c

    Income limit.

Arkansas (5)8%  Yes 150k;GSE/FHA
California (6)8%YesYes 362360k
Colorado (8)8%YesYes 254 
Connecticut (9)8%Yes  58 
Florida (12)8%Yes  289GSE
Georgia (13)Prime + 4%  Yes 360k
Illinois (17)6%YesYes 220 
Indiana (18)8%Yes  234360k
Kentucky (21)8%Yes  22215k–200k;GSE
Maryland (24)7%   30187kc
Massachusetts (25)8%  Yes  
Michigan (26)AllYes  217None
Minnesota (27)All   33 
New Jersey (34)8%YesYesYes240360k
New Mexico (35)7%YesYes 239360k
New York (36)8%YesYes 339300k
North Carolina (37)8%  Yes 300k
Ohio (39)8%YesYes 59 
Oklahoma (40)8%YesYes 250 
Pennsylvania (42)8%Yes  217100k
South Carolina (45)8%  Yes 360k
Texas (48)12%bYes  33620k-192k
Utah (49)8%Yes  160 
Vermont (50)9%b Yes 20 
Washington, DC (11)6%YesYes 1407360k/107kc;FHA
Wisconsin (55)8%YesYes 68 
Federal HOEPA Law8%Yes  93 

Xi includes the natural log of loan applicant annual household income as reported in HMDA, a proxy of economic status. Xi also includes the ratio of the applicant's income to the amount of the loan, a measure of how much debt relative to income the applicant seeks. Lower incomes relative to larger mortgage sizes suggest higher risks of foreclosure. Also contained in Xi is an indicator of whether the amount of a loan exceeds $360,000, primarily because loans over this amount are exempted from some state laws. Borrowers seeking such large loans may also exhibit different behavior, which this could also capture. L is a matrix of lender characteristics, including an indicator for the lender's regulatory agency. The regulator of each lender is an important control to include because certain regulators could have preempted state disclosure laws.5 Applications to lenders regulated by the Office of the Comptroller of the Currency (OCC) and the Office of Thrift Supervision (OTS) are included. Also included in L is the lender's market share of subprime loans in the state as a potential measure of market power. Fixed effects for counties (ηt) are also included to account for unobserved housing and labor market factors that may also influence rejection rates.

All the loan applications in this analysis are predicted to have high interest rates and/or be predicted to be HOEPA loan application based on lender, loan, and census track characteristics. Each loan application is either in a state with no risk disclosure policy or in a state with such a policy but where the loan is not subject to the disclosure regulation. The comparison group for these estimates is another high-cost loan application.

This specification is estimated using a linear probability model with robust standard errors, in part to address the heteroskedastic distribution of the error term of a binary dependent variable (Greene 2011). A linear probability model is used instead of a maximum likelihood for ease of interpretation, especially when using an interaction between state-law dummies and loan-level dummies (Hoetker 2007; Norton, Wang, and Ai 2004). The linear probability model is also more computationally efficient than a maximum likelihood model, especially given the use of nearly 3,000 county fixed effects.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

The results of this analysis are presented as regression coefficients in Table 3. There are four columns, each displaying a variation in the specification equation described above. The dependent variable in all cases is a loan applicant rejecting an approved loan offer from a lender. All columns are on the same sample of 166,355 high-cost loan applications. The state law covering the loan application is used to identify the effects of policies, using similar loans not subject to state disclosure policies as a comparison group. A negative coefficient means that the variable reduces the likelihood of a loan applicant “backing out” and a positive coefficient means the loan applicant is more likely to reject an approved loan offer from a lender. A positive—and statistically significant—sign is consistent on the state policy variables and signifies that the state disclosure policies are associated with a loan applicant behaving in a manner that is consistent with risk disclosures being used to reweight the costs and benefits of agreeing to a particular loan.

Disclosures Are Associated with Borrowers Rejecting Loan Offers

In Table 3, column 1 includes indicators only for loan applications subject to any warning-type disclosure (18 states, all of which at a minimum use the language “you could lose your home”) in addition to an indicator for a subset of the laws that require the applicant to sign the disclosure form. The dependent variable is that a loan applicant rejected an approved loan offer from a lender. This model shows no effects for disclosures in general (shown in the first row of the Table 3). However, the next variable (shown in the second row) for signed disclosures shows that loan applicants are 6.4 percentage points more likely to reject a loan offer from a lender. Column 2 adds an interaction of loans covered by a state signed-disclosure provision and the loan being covered by the federal HOEPA law. Conditional on a loan application being subject to a state signature requirement and the loan covered by HOEPA, loan applicants were 10.7 percentage points more likely to reject a loan offer from a lender (or “back out” of the application). The signature dummy alone does not show a statistically significant effect on non-HOEPA loans in column 2, although the direction and magnitude are promising (HOEPA has no signature provision; state laws layer on a disclosure and signature requirement). Column 3 adds an additional interaction between a signed disclosure and a variable flagging if the loan applicant is of a minority race. The coefficient estimated on this interaction is not statistically significant. The effect of a signature among HOEPA loans is similar to column 2. The main effect of a signed disclosure provision is now statistically significant, however. This estimate produces a 3.2 percentage point increase in loan applicants backing out of an approved loan offer when state laws require a signature among non-HOEPA loans, without reducing the estimated coefficient on applicants with HOEPA loans.

Table 3. State Disclosure Provisions Increase the Rate Loan Applicants Reject Loan Offers from Lenders: Dependent Variable—Borrower Rejects Approved Loan Offer for Subprime Loan Application
 (1)(2)(3)(4)
  1. Notes: n = 166,355. Linear probability model; robust standard errors clustered at the state level; models include county fixed effects; T-stats in parentheses.

  2. *p<.10, **p<.05, ***p<.01.

Loan requires any disclosure dummy−0.039   
 (1.29)   
Loan disclosure requires signature dummy0.064**0.0530.032**0.043
 (2.45)(1.49)(2.05)(1.15)
Loan disclosure requires signature × HOEPA application 0.107**0.107**0.097*
  (2.23)(2.22)(1.91)
Loan disclosure requires signature × minority applicant  0.028 
   (0.83) 
Loan disclosure counseling dummy   0.047**
    (2.09)
HOEPA application dummy−0.024*−0.042***−0.037***−0.045***
 (1.72)(3.24)(2.74)(3.42)
Lending law index0.0030.0040.0030.001
 (0.93)(1.24)(0.94)(0.44)
Log income applicant−0.033***−0.033***−0.033***−0.033***
 (3.05)(3.07)(3.07)(3.06)
Ratio income: loan amount0.012***0.004***0.004***0.002***
 (3.74)(3.73)(3.73)(3.73)
Minority applicant dummy0.024***0.024***0.024***0.024***
 (4.58)(4.53)(4.28)(4.42)
Lender regulated by OCC dummy0.292***0.292***0.293***0.292***
 (10.41)(10.5)(10.5)(10.47)
Lender regulated by OTS dummy0.085***0.084***0.085***0.084***
 (9.45)(9.42)(9.44)(9.40)
Lender % subprime APR in state−0.002***−0.002***−0.002***−0.002***
 (7.92)(7.95)(7.97)(7.98)
Loan > $360 conforming limit0.052***0.053***0.053***0.055***
 (3.06)(3.20)(3.13)(3.22)
Constant0.275***0.271***0.272***0.275***
 (5.68)(5.71)(5.72)(5.82)
R-squared0.1160.1160.1150.116

While not a focus of this study, it should be noted that the controls included in the various models all provide estimates in plausible directions. HOEPA applications show lower applicant rejection rates, likely because of these applicants being highly credit constrained with few other options. The state lending law index does not produce large or statistically significant estimates in any of the models. Higher incomes are associated with fewer loan applicants rejecting loan offers, although the ratio of loan amount to income does produce a positive estimated coefficient. The income variable is likely picking up less price sensitivity among higher income borrowers, after controlling for debt burden in the ratio. Smaller loans relative to income are more likely to be rejected by borrowers, which again may be because of the intensity of the consumer's need for credit and other available options. Minority race applicants are generally more likely to reject a loan offer after controlling for other factors. This is conditional on a minority loan applicant being approved for a loan, which is different from prior studies on lender denials by race or even the decision to apply for a loan. Loan applications for mortgages with balances below the conforming loan limit are more likely to be rejected by applicants (only 3.8% of loans in this analysis sample of loans are greater than $360,000, however). Loan applications submitted to lenders regulated by the OCC or OTS also show strongly higher rates of borrowers rejecting a loan offer.

Counseling Is Associated with Borrowers Rejecting Loan Offers

Column 4 is similar to the model estimated in Column 2, only adding in one added variable for a loan application subject to a state disclosure, which requires or recommends that the loan applicant review the disclosure and the loan terms with a certified mortgage counselor. Attending such counseling might directly provide a loan applicant with information, or the recommendation to attend counseling might serve as a signal to the loan applicant that the loan deserves special attention or scrutiny. The coefficients of interest remain a loan application subject to a state signed-disclosure provision, as well as a HOEPA loan subject to a state signed-disclosure provision. Adding a counseling provision to the disclosure requirement weakens the effects of signed disclosures overall, but the effects remain for HOEPA loan applications, albeit at low levels of statistical significance. Applications with disclosures that contain counseling requirements have about 4.7 percentage point higher rates of loan applicants rejecting approved high-cost loan offers.

Results Are Robust to Alterative Specifications

One problem with the estimates in Table 3 is that states where augmented disclosure regulations were put into place may have had unobserved factors that might also be correlated with loan applicants rejecting loan offers. For example, consumers may be more focused on consumer protection and education in a state, and show a preference for electing officials who pursue consumer protection laws. The behavior of loan applicants may stem from the state's collective preferences rather than as a result of a disclosure law. Table 4 presents another set of estimates building from Table 3. Again the dependent variable is a loan applicant backing out of an approved loan offer and the samples are high-cost loan applications. The coefficients of interest are a loan application being covered by any state augmented disclosure provision, and then a state signed-disclosure provision. In Column 1, a dummy variable for all states with disclosure laws is included to pick up otherwise unobserved “disclosure state” effects. The results still show that applications requiring a signed disclosure have a significant coefficient. Loan applicants are about 5.8 percentage points more likely to reject an approved loan offer in this estimate. The dummy variable for the 18 states with augmented state laws is significant and negative; the state disclosure provision is still significant, however.

Table 4. Results Are Robust to Including State Law Fixed Effects: Dependent Variable—Borrower Rejects Approved Loan Offer for Subprime Loan Application
 (1)(2)(3)(4)
  1. Notes: n = 166,355. Linear probability model; robust standard errors clustered at the state level; include county fixed effects and all controls from Table 3; T-stats in parentheses; models contain interactions of law and HOEPA applications and law and minority loan applicant status; loans in New Jersey require a signed disclosure and counseling.

  2. *p<.10, **p<.05, ***p<.01.

Loan requires any disclosure dummy−0.021−0.024  
 (0.8)(0.93)  
Loan disclosure requires signature dummy0.058**0.062**0.047**−0.036
 (2.07)(2.33)(2.66)(0.94)
State has any disclosure law (18 states)−0.047***−0.023  
 (3.9)(1.4)  
State has signed disclosure law (10 states)  −0.044***−0.042***
   (3.63)(3.48)
State has counseling requirement (six states) 0.053***  
  (3.41)  
Loan disclosure counseling dummy 0.019  
  (1.15)  
Loan in New Jersey −0.112***  
  (5.53)  
Loan disclosure requires signature × HOEPA 0.101*  
  (1.99)  
R-squared0.1160.1170.1160.116

Adding an additional dummy variable for states with counseling requirements in column 2 of Table 4 shows similar results. This model also includes a dummy for the state of New Jersey, as it has signed disclosures and counseling requirements. While signed disclosures are associated with higher rates of applicants rejecting an approved loan offer from a lender, disclosures with counseling provisions are no longer statistically significant (although still positive). Column 3 includes a dummy for the 10 states with signed disclosure laws, showing that these states, all else equal, have lower rejection rates but that applications subject to a signed disclosure still are statistically significant at similar orders of magnitude as the prior estimates. Column 4 adds the interaction of signed disclosures and a HOEPA loan, showing about a 10.1 percentage point higher rate of loan applicants rejecting approved high-cost loan offers from a lender. (Although not displayed, estimated coefficients for controls were similar to the prior estimates.)

These models are reassuring that the estimates presented in Table 3 are not driven by unobserved factors correlated with state politics, culture, or other unobserved characteristics.6 Counseled and signed disclosures appear to be related to applicants being 3 to 6 percentage points more likely to reject a loan offer from a lender. The effects are concentrated among applications predicted to be under federal HOEPA regulations rather than high-cost loans generally. Given an overall rate of loan applicants rejecting a loan offer of 15.9%, a three-point estimated effect is about a 33% marginal effect at the mean. This is consistent with these state disclosure laws being associated with about a one-third increase in the rate of loan applicants backing out of high-cost loan offers. If the goal of policymakers is to have potential borrowers of high-risk loans think twice and reassess the costs and benefits of a loan offer, signed-disclosure provisions, as well as counseling referrals, may be a useful strategy.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

State laws that required loan applicants to sign risk disclosures are associated with greater probabilities that a loan applicant will reject a loan offer from a lender. Applications predicted to be covered by unsigned disclosures regulations do not appear to have as robust of effects as signed disclosures; counseling requirements or recommendations as part of the disclosure also have effects in some models.

At least some of the estimated effect sizes shown in this analysis are quite large, especially given the relatively low occurrence of applicants rejecting an approved loan offer overall. A 10 percentage point increase in applicants rejecting loans given a base rate of 15.9% would suggest a 63% marginal effect, for example. There is certainly noise introduced to the estimates because of the imputation techniques used, but overall the direction of the estimates is suggestive that risk disclosure requirements may influence consumer perceptions of loan offers.

These results are based on high-cost refinance loans reported in HMDA data during 2005 and may not be generalizable to purchase or home improvement loans, but are at least encouraging of further study.

It is important to note that state laws could influence an applicant's decision to enter the market and lender's approval of applications, as well as an applicant's decision to accept an approved loan offer. Loan applicants in jurisdictions lacking mortgage information disclosures may perceive greater risks of receiving bad loan offers and decide not to enter the market at all—an example of Akerlof's lemons problem (Akerlof 1970). Previous studies suggest that state mortgage regulations may encourage more or different types of borrowers to enter the mortgage market, although the effects are neither strong nor consistent (Bocian, Ernst, and Li 2008; Harvey and Nigro 2004; Ho and Pennington-Cross 2006). There also remains the possibility the laws examined in this study are correlated with other unobserved statewide factors. The estimates presented here may approximate a composite effect of state laws and other conditions rather than a casual model of disclosure itself. If warning-type information discourages borrowers from agreeing to high-cost loans, one concern may be that borrowers overreact to these disclosures and will avoid beneficial credit. (Lenders, loan applicants, and borrowers remained active in markets with risk warnings, however.)

These cautions aside, this study contributes to the literature by focusing on loan applicants rejecting approved loan offers, a consumer choice that would appear to have strong policy and practitioner interest in the credit market. High-cost mortgage refinance applications are an applied example of how variations in state information policies influence consumer decision making.

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES

One of the many issues raised by Dodd–Frank reforms is how regulations should treat home mortgages. The so-called “Qualified Mortgage” standards debate has been controversial with both industry and consumer advocates. Again, restrictive regulations may result in otherwise qualified loan applicants being pushed out of the mortgage market (see Quercia, Ding, and Reid 2012, for a discussion). The harm of restricting access to credit can be far reaching. For example, the primary mechanism for the accumulation of net wealth by minority race households is homeownership (Shapiro 2006). Without access to mortgages some groups of households may not be able to purchase a home. Even existing homeowners may have their wealth and/or consumption reduced if they cannot access home refinance or home improvement loans. Some loan terms may in fact simply be too hard for consumers to understand, or too easy for lenders to exploit, and outright bans may be appropriate. But enabling forms of nontraditional, higher-risk mortgage lending may be a policy priority.

Providing an otherwise myopic consumer with a warning about the risks of foreclosure before signing a mortgage contract is a relatively unobtrusive approach to consumer protection. Loan applicants potentially can make more informed choices about the costs and risks of borrowing against their home. At least based on the high-cost home refinance market as it existed in 2005, information disclosures related to risks may have more influence on decisions than is commonly presumed. Well-designed risk disclosures are one means to open access to credit in demand by borrowers while balancing default risks. These findings are instructive regarding the role and format of disclosures provided to consumers of a range of financial products under Dodd–Frank reforms.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. BACKGROUND
  5. DATA
  6. EMPIRICAL STRATEGY
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSIONS
  10. REFERENCES
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  1. 1

    There are several exceptions to the right of rescission. It is only available for home equity or refinance loans and only for loans with a new lender (refinancing with the holder of the existing note is exempt).

  2. 2

    Based on loan originations for refinance or home-improvement loans secured by a one-to-four family home reported in the Home Mortgage Disclosure Act.

  3. 3

    For example, if a loan is originated with an APR of 12.5% and the Treasury rate on the date of the loan closing is 4.5%, for example, the APR spread recorded in HMDA would be 8.0. Meanwhile, a loan with an APR of 6.5% would have no APR spread recorded.

  4. 4

    The exceptions are Michigan and Illinois, where all loans in this sample have a state disclosure requirement.

  5. 5

    In 2006, the Supreme Court ruled in Watters vs. Wachovia Bank NA that nationally chartered banks can preempt state regulations in favor of national laws; this ruling was not in place during the study period.

  6. 6

    Although not shown, variations in the sample, such as dropping Michigan and Illinois, states where disclosures apply to most loans, also produced similar results. Variations in the model, including a sequential logit to first predict if a loan applicant applied for a loan, then predict if a lender approved the loan, also provided similar results.