Shi_mortgage_licensing_mar_1_2012 by liwenting

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									      The Effect of Mortgage Broker Licensing On Loan Origination
         Standards: Evidence from Home Mortgage Disclosure Act
                                                  Lan Shi∗
                                         Department of Economics
                                         University of Washington

                                                 March 1, 2012



                                                    Abstract
            We study the origination-to-distribution mortgage lending market from the mid 1990s to
        the late 2000s. Mortgage loan brokers originated close to two thirds of the mortgage loans in
        this period. We examine whether stricter licensing requirements of loan brokers raise lending
        standards by i) admitting only higher quality brokers who benefit more from a long-term career
        and thus have greater incentives to protect borrowers’ long-term interests, ii) raising entry
        costs and thus generating higher future rents that reduce brokers’ incentives to chase short-
        term profits, e.g., by lowering loan origination standards, that jeopardize their likelihood of
        winning future business from borrowers and lenders. We exploit the cross-state and over time
        variations in licensing requirements and find that originated loans in states with more stringent
        requirements had higher standards: FICO score were higher and LTV were lower and there were
        fewer negative amortization, interest only, balloon, ARM, and No Doc loans. The requirements
        on surety bonds and net worth, education, and office in state have the greatest impact on
        loan origination standards. The education (and exam) requirements for employees are more
        effective than those for licensees. The effect of licensing on loan origination standards is greater
        for neighborhoods with greater minority percentages and lower incomes, and for lenders that
        specialize in sub-prime lending. Corrobarating findings on loan origination standards, states
        with more stringent licensing requirements had lower default rates: Moving from 25th to 75th
        percentile in licensing is associated with close to 20 percent reduction in 90 days plus delinquency
        rate. These findings point to the value of licensing when incentives are compromised with
        securitization.
            Key words: Occupational Licensing; mortgage; professional labor market; expert; informa-
        tion asymmetry; moral hazard; incentives
  ∗
      I would like to thank Yoram Barzel, Bo Becker, Fahad Khalil, Jacques Lawarree, Erica Clower, Troy J. Scott,
Hendrik Wolff, Rong Zhao, and participants at the University of Washington Economics Department brownbags for
their comments. Contact information: Lan Shi, UW Econ, Box 353330, Seattle, WA 98195. Email: lanshi@uw.edu.


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              JEL codes: D82; G14; G21; J44; K2; L1; L5; L15


1        Introduction
In markets for expert services, e.g. lawyers, doctors, and loan brokers, there are usually various
types of regulations, e.g., licensing.1 Advocates for the use of licensing argue that by requiring
credentials, licensing admits only those who can meet the requirements, and thus raises the quality
of the service providers. Opponents claim that the licensing requirements serve as a barrier to entry,
thus limiting the supply of providers, reducing competition, raising the price, and even possibly
lowering the quality supplied due to the reduced competition. However, countering this argument,
Keeley (1990) and Hellmann et al. (2000) argue that raising the barrier generates greater profits
and charter value for players, which serve as a deterrent to players’ risk-taking behavior. A central
question is when market mechanisms can work, i.e., when players’ concern for their reputation
disciplines themselves? In what environment would the market mechanism work and government
interventions hinder efficiency? And under what conditions would the reputation mechanism work
poorly and regulations would help improve efficiency?
             This paper aims to shed light on these questions by examining the licensing of mortgage
loan brokers in the U.S. from 1996-2009. The market during this period has some features that
make it a good candidate to examine this question. Starting in early 2000s, the U.S. mortgage
financing industry gradually moved from an origination-and-keep to an origination-to-distribute
model. Under the new system, lenders originate loans and then sell them in a secondary market.
While the potential advantage is more efficient risk-sharing, many researchers (Keys et al., 2010;
Purnanandam, forthcoming) documented that lenders have less incentives to screen loans.2 Ac-
cording to a 2004 study by Wholesale Access Mortgage Research & Consulting, Inc., close to two
thirds of all residential loans in the U.S. were originated by brokers. We focus on the link between
mortgage loan brokers and lenders.
             One form of monitoring can potentially come from the borrowers. However, borrowers are
on average less informed than the brokers about what are the right products for their needs. Still,
another potential mechanism is the broker’s concern for his/her reputation with the borrowers. We
argue that it is poorly functioning: It takes some time for borrowers to gauge the true performance
of the brokers, i.e., whether he or she recommended the right product to the borrower; in fact,
brokers’ self-serving behavior, which causes long-term harm to borrowers, can, in the short-term,
be thought of as good behavior, and their short-term reputation is enhanced.
    1
        See, e.g., Shapiro (1986).
    2
        It appears that the reputation mechanism is not fully functioning between lenders and issuers (the parties that
securitize the debt), and between issuers and investors.



                                                            2
            Given that lenders have little incentives to screen applications and borrowers are less in-
formed than mortgage brokers, would regulation of loan brokers make a difference in the efficiency
of the loan-origination market? We examine whether regulation, in the form of licensing require-
ments, leads to greater efficiency in the loan origination market. There are several channels that
licensing helps raise the standard of loan origination. First is the selection effect. More stringent
requirements raise the bar of becoming a loan broker, and thus the average quality of loan brokers.
For higher quality brokers, a longer-term career is more valuable, and this reduces their incentives
to lower current standards to earn business. Putting it another way, looser regulations make it
easier for brokers aiming to profit from short-term gains to enter the market. These brokers care
little about their reputation, and thus the loan origination standard is lowered to generate fees by
issuing as many loans as possible.3
            A related yet distinct mechanism is that by raising the entry barrier, more profit is gener-
ated, which raises the value of staying in the business for the long term and thus reduce brokers’
incentives to pursue short-term profit that might jeopardize their prospect of long-term profits.
Those risky actions include the brokers’ actions to take advantage of borrowers and lenders in the
current period; over time, borrowers and lenders alike could find out that the brokers were not
acting in their best interests and are less likely to give the brokers future business or referrals. We
therefore predict that loan-origination standards will be higher where licensing is more stringent
under the originate-to-distribute mortgage financing model.
            We exploit the cross-state variations in licensing requirements to measure the ease of en-
tering the mortgage loan market. Commonly used licensing requirements include surety bonds,
requirements of net worth, work experience, education, and passing exams. We rely on the mea-
surement of licensing requirements by Pahl (2007) who carefully coded the requirements. Licensing
requirements evolve over time. It usually takes several years before an initiative becomes a piece
of law (Kleiner and Todd, 2009).
            For the loan origination data, we use Home Mortgage Disclosure Act data, which contains
the details of each loan application, including whether it is originated, turned own, whether it is
sold by the lender, the name of the lender, characteristics of the loan, and characteristics of the
borrower. We also use tract-level Census data which includes various information on census tracts.
            We test the effect of mortgage broker licensings on the efficiency of mortgage loan origina-
tion market at the national level. We first document that loan brokers enter the market in response
to how strict the regulation is. Second, we find states that sell a greater percentage of their loans
pass a greater percentage of loan applications, suggesting that lenders in states where investors are
   3
       Facing the pressure from brokers who take up a greater market share by lowering loan standards, other loan
brokers also lower standards to win business, especially in an environment where consumers reward agents with good
short-term performance.



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further away from the loan-issuers use looser standards in issuing loans. Third, states that have
more stringent requirements pass a lower percentage of loan applications, and those loans have a
lower loan amount/applicant income ratio. Fourth, among the requirements, those on surety bonds
and net worth, education level of employees, and office in state have the greatest impact on the
standards of loan origination. Finally, states that have more stringent requirements have lower
foreclosure rates.
        If the licensing is effective in raising loan origination standards, it should lead to two things.
First, loan origination standards are higher. We thus test whether FICO score, LTV (loan to value
ratio) vary with the licensing stringency. Second, brokers are less likely to use predatory terms,
including interest only, negative amortization, balloon payment, ARM, and nod-doc, etc. We thus
test whether the percentage of loans having these terms are lower in states with greater licensing
requirements.
        We then conduct tract-level analyses. First, we confirm our findings from the state-level
analysis. Second, we test whether tracts that have more severe information asymmetry prob-
lems experience lower loan standards, and whether the effect of licensing is greater. We measure
the degree of information asymmetry between the broker and the borrower using the tract-level
percentage of minority population. Our findings confirm the predictions.
        We also conduct lender-level analyses. First, lender-level analysis confirms our findings
from the state-level analysis. The Department of Housing and Urban Development maintains a list
of lenders that specialize in subprime lending. We find that the effect of licensing requirements on
loan origination standard is greater for lenders who specialize in subprime lending.
        Since information on the loan application is not complete, one worries about the omitted
variable problem. We thus follow up the loan origination analyses with loan performance analyses.
If loan origination standards were higher, it should lead to better performance for loans in states
with more stringent licensing requirements. First, we show that loan performance varies with the
loan terms. Second, we show that loan performance is better; 90+ days delinquency rate is lower
in states with more stringent requirements. The economic magnitude is large; moving from 25th
to 75th percentile in the licensing requirement is associated with close to 20 percent decrease in
delinquency rate.
        We investigate whether the finding of lower origination rates in states with more stringent
licensing requirements is due to omitted state-level laws on mortgage lending. In particular, many
states adopted anti-predatory lending (APL) laws in 2000s to combat predatory mortgage lending.
We found that introducing APL laws does not affect our results.
        The closest paper is Kleiner and Todd (2009), who examine the effect of licensing require-
ments on the labor market outcome for brokers and loan quality for consumers. Their focus is on
the usefulness of different licensing requirements, in particular, surety bonds. Our paper differs


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from theirs in two ways. First, we examine the impact of all types of requirements, and the impact
of licensing on different levels of loan brokerage firms (the licensees or the employees). Second, we
want to shed light on the mechanism of how licensing helps improve efficiency. Our findings that
surety bonds/net worth and education requirements matter (especially for employees) suggest that
both the charter value mechanism and the channel of raising the quality of players are at work.4
             Other related works include Dell’Ariccia et al. (2009) who study the relationship between
credit expansion and loan-issuance standards at the MSA level, Becker and Milbourn (2011) who
find that the entry of a third credit-rating agency reduces the quality of credit-rating agencies,
Garmaise (2010) who examine the relationship dynamics between mortgage brokers and lenders,
and Jiang et al. (2010) who find that brokers issue lower quality loans than lender employees.
             We provide background information in Section 2, develop hypotheses in Section 3, present
identification strategy and econometric specification in Section 4, introduce data in Section 5,
examine loan origination in Section 6, conduct analysis of loan performance in Section 7, explored
alternative hypotheses in Section 8, discuss further analyses in Section 9, and conclude in Section
10.


2        Background; Institutional Details
2.1        Purchase and Securitization of Loans by GSEs and Investment Banks

In the U.S., the Federal government created several programs, or government sponsored entities, to
foster home ownership. These programs include the Government National Mortgage Association
(known as Ginnie Mae), the Federal National Mortgage Association (known as Fannie Mae) and the
Federal Home Loan Mortgage Corporation (known as Freddie Mac). These government sponsored
enterprises (GSEs) purchase conforming loans from lenders, allowing lenders to originate more
loans.
             The GSEs then securitize these loans, and these are known as mortgage-backed securi-
ties (MBS). Whether or not a loan is conforming depends on the size and a set of guidelines.
Non-conforming mortgage loans which cannot be sold to Fannie or Freddie are either “jumbo” or
“subprime”, and can be packaged into mortgage-backed securities and sold to investors by invest-
ment banks. Since 1970s, markets developed for mortgage-backed securities. For example, the
securitized share of subprime mortgages increased from 54 percent in 2001 to 75 percent in 2006.5
    4
        To highlight the effect that the absence of lender incentives had on our results, we also plan to examine periods
when lenders had incentives to check their loans’ quality — period prior to the origination-and-distributions model
and the period when the securitization market froze up — 2008 and onward.
   5
     Starting in mid-90s, private firms took up a greater market share in securitization. The non-agency securitization
market has grown dramatically since 2000 (Key et al., 2010).



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            The development of secondary markets has many benefits. First, they increase competition
by encouraging the development of a new industry of loan originators. Second, conversion of
mortgages into mortgage-backed securities permits a better distribution of the risk of holding
mortgages. Third, mortgage-backed securities are also “liquid” while mortgages themselves are
not. Several important checks are designed to prevent mortgage fraud, the first being the due
diligence of the issuer. In addition, the originator typically makes a number of representations
and warranties about the borrower and the underwriting process. When these are violated, the
originator generally must repurchase the problem loans. However, in order for these promises to
have a meaningful impact on the friction, the originator must have adequate capital to buy back
the loans, which they do not always have.6


2.2       Players in the Market

The lenders are commercial banks, thrifts (which include savings and loan associations and savings
banks), credit unions, and mortgage banks in the United States. The first three are depository
institutions. States and the federal government each issue bank charters. State-chartered banks
operate under state supervision. National banks are chartered and regulated by the Office of the
Comptroller of the Currency (OCC), a division of the Treasury Department. A Mortgage bank
specializes in originating and/or servicing mortgage loans, and is state-licensed.
            A mortgage bank is not regulated as a federal or state bank and does not take deposits
from consumers or businesses. Generally, a mortgage bank originates a loan and places it on a
pre-established warehouse line of credit with large lenders until the loan can be sold to an investor
such as Fannie Mae or Freddie Mac. Their two primary sources of revenue are loan origination
fees and loan servicing fees (provided they are a loan servicer).7 Mortgage banks now dominate
the US market. Of the 10 largest lenders last year, 9 were mortgage banks and only one was a
portfolio lender. However, many of the large mortgage banks, such as Chase Manhattan Mortgage
and Wells Fargo Mortgage, are affiliated with large commercial banks.


2.3       The Use of Employees and Brokers by Lenders

In the U.S., the process by which a mortgage is secured by a borrower is called origination. This
involves the borrower submitting a loan application and documentation related to his/her financial
history and/or credit history to the lender. Loans are originated in two ways. One route is that
lenders use their employees (loan officers) to originate a loan, called retail lending. The other
   6
       The issuer underwrites the sale of securities secured by the pool of subprime mortgage loans to an asset manager,
who is an agent for the ultimate investor. An important information asymmetry also exists between the issuer and
the investors concerning the quality of mortgage loans.
   7
     By selling them shortly after they are closed and funded, they are eligible for earning a service release premium.


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is that lenders set aside a wholesale branch, to which independent brokers have access. A broker
establishes wholesale relations with several lenders. Brokers serve the role of bringing the applicants
and collecting information, and the lenders provide the funds. Most lenders have both employees
and wholesale lending programs. To expand business, it is often more expedient to use brokers.8

2.3.1       The Arrangement Between Lenders and Brokers

Brokers are compensated for their services in two ways. On the one hand they receive fees paid
directly by the borrower. These include the loan origination fee, credit fee, etc. In addition, the
broker is paid a yield spread premium by the lender. A yield spread premium is the fee paid by a
lender to a broker for higher-rate loans (Federal Reserve Press Reserve. Dec. 18, 2007).9
            Lenders also provide brokers different compensations for different types of loans. The types
of loans include, e.g., adjustable interest rate loans (“ARM”, which often have a low initial rate
but potentially sharply increased rate after the first couple of years), interest-only loans, or no-doc
(no-documentation) loans. Because of the higher rates and greater profits that the lenders can
receive from certain types of loans in the secondary market, lenders often give higher rebates to
brokers for those types of loans. The fee schedule by the lenders give brokers two incentives. The
first is the incentive to charge above-market rates. The second is the incentive to steer borrowers
to the types of loans that give brokers the highest commission.
            Brokers do need to compete with other brokers and lenders to find borrowers. Therefore
there is a trade-off. If the broker charges a higher rate, it is more profitable for himself and the
lender, but he is less likely to obtain the business from the borrower. However, when the borrowers
do not shop around for rates, which is made difficult by the various kinds of charges across loans, or
when they do not have the ability to truly understand the risks and costs of various loan products,
borrowers can be led by the broker into loans that gives the broker the highest commission instead
of what best meets her needs.
   8
       A third way to lend is via correspondent lending programs (other mortgage banks, commercial banks, Savings
and Loans, and credit unions.) They are usually small and receive funding from wholesale lenders. They differ from
brokers in that they fund the loans.
   9
     Lenders pay brokers rebates on high-rate loans, and charge points on low-rate loans. Points are upfront payments
from the brokers to the lender, and rebates are upfront payments from the lender to the brokers. For example, on
November 7, 2007, wholesale lenders quoted the following prices to brokers for a 30-year fixed-rate mortgage: 6% at
zero points, 5.75% at 1.25 points, 6.25% at 1 point rebate and 6.50% at 2 points rebate. This means that the lender
wants to be paid 1.25% of the loan amount upfront for 5.75% loans, and will pay 1% or 2% rebates upfront for 6.25%
and 6.5% loans, respectively. Brokers can choose to use part of the rebates to cover borrowers’ closing costs.




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2.4       Regulations in the Loan Origination Market

The five agencies — OCC, FRS, FDIC, OTS, and NCUA have regulatory and supervisory respon-
sibility over national banks and their subsidiaries, member state-chartered banks and their sub-
sidiaries and subsidiaries of bank holding corporations, non-member state-chartered banks, savings
and loans institutions and their subsidiaries, and credit unions, respectively. Compared with the
depository-taking lenders, non-depository mortgage lenders are lightly regulated, at the state level
(Keys et al., 2009).

2.4.1       Regulation of Loan Brokers

At the federal level, loan origination is regulated by laws including the Truth in Lending Act and
the Real Estate Settlement Procedures Act (1974). Credit scores are often used, and these must
comply with the Fair Credit Reporting Act. The majority of mortgage brokers are regulated to
ensure compliance with banking and finance laws in the jurisdiction of the consumer; however, the
extent of the regulation, often in the form of licensing, depends on the jurisdiction.
            Commonly used methods are i) education requirements, e.g., number of hours of in-class
training, ii) work experience requirements, iii) passing exams, and iv) posting surety bonds, or
meeting net-worth requirements, or both. Surety bonds are a common practice. These bonds func-
tion as a guarantee from a Surety to a government and its constituents (obligee) that a company
(principal) will comply with an underlying statute, state law, municipal ordinance, or regulation.
The principal will pay a premium (usually annually) in exchange for the bonding company’s finan-
cial strength to extend surety credit. In the event of a claim, the surety will investigate it. If it
turns out to be a valid claim, the surety will pay it and then turn to the principal for reimbursement
of the amount paid on the claim and any legal fees incurred. Because of this, the most important
thing a surety looks for in the principal is the net worth. Some states require surety bonds, while
others require net worth. We thus create a variable that is the sum of both.10
            Licensing is implemented at various levels of the mortgage broker firms. Financial re-
quirements are certainly at the firm level. For the qualification requirements, some states impose
requirements only at the applicant (licensee) level, some include requirements for the managing
principal (who actively runs the firm), and some further include requirements at the employee level.
Pahl (2007) did a very careful job in coding the various requirements for each party. We will use
  10
       A typical clause regarding education is “The applicant (for Mortgage Broker Individual license) must have
completed 24 hours of classroom education on primary and subordinate financing transactions and the laws and
rules.” A typical clause regarding examination is “The applicant must pass a written test adopted by the department
that is designed to determine competency in primary and subordinate mortgage financing transactions as well as to
test knowledge of the laws and the rules”.




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both the sum of all requirements and the specific requirements.11


3         Hypothesis Development
Under an origination-to-sell business model, lenders’ profit depends on the quantity and profit rate
of loans that will be sold to issuers who then package and sell them to investors. Since lenders
do not hold the loans, they care less about the re-payment risk. Indeed, Keys et al. (2010) and
Purnanandam (forthcoming) showed that lenders have reduced incentives to screen loans than in
an origination-and-keep model.
              There are two ways brokers may affect the loan origination outcome. First, they can
falsify the documents, by inaccurately recording data, omitting data, or intentionally falsifying
information. Second, they can win the trust of the lenders and then reduce their loan screening
efforts, and the effect of broker screening on loan origination outcome is particularly true for low-
doc or no-doc loans. In these loans, hard information is not required, yet after all, brokers have
soft information on the borrowers and the loans.12 The brokers may be less diligent in verifying
information.13
              In the preceding Section 2.3.1, we examined the contracts between brokers and lenders.
Given the contracts, we predict that brokers have incentives to i) increase earnings by closing more
deals, which is facilitated by lowering loan origination standards, and ii) steer borrowers to the
types of loan products that give them the greatest compensation instead of what maximizes the
borrowers’ interests. In such a environment, we argue that licensing can play a role in improving
efficiency in the loan origination market. There are two mechanisms that licensing may improve
efficiency.
              First, stricter licensing prevents the less qualified individuals from entering the market.
Less qualified brokers are less likely to survive, while high ability ones are more likely to survive.
Thus the latter benefit more from a long-term reputation, which encourages them to pursue clients’
long-term interests, or prevents them from exploiting the clients and pursuing short-term profits
in the form of charging excessive fees or rates and granting loans to applicants who can not repay.
              Second, there is the effect of concern for future profits (charter-value argument by Keeley
    11
         While loan brokers are regulated, employees (loan originators) of regulated financial institutions or insurance
companies, lawyers, and real estate agents were often exempt. License can be suspended or revoked.
  12
     Garmaise (2011) showed that brokers first established certain reputation with the lenders and then decreased
their loan-screening efforts. Over time, brokers present more loans to the bank, and these loans have higher default
probabilities.
  13
     Jiang et al. (2011) showed the broker channel is worse among low-doc loans. Correspondent brokers have stronger
reputation concerns due to their exclusive or long-term relationships with lenders. They show that lenders did not
factor risk in pricing.



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(1990) and Hellmann et al (2000). With greater entry costs comes greater profit and higher
future profit staying in the industry. By lowering lending standards or engaging improper lending
practices, the broker may gain more business currently, but their lenders and clients would find
out his practices (over time) and are less likely to fund loan applications or seek loans brokered
by him. This loss of future profit is greater when competition in the loan brokering market is
smaller. That is, more stringent licensing requirements improve the brokers’ incentive to uphold
borrowers’ long-term interest. Summing up, licensing enhances the broker selection process, as well
as improves the incentives for brokers.
        We note that lenders, not brokers, make the final funding (loan origination) decision.
However, brokers literally do every step of the loan application except the funding decision. Since
lenders have little incentives to screen loans because of the availability of secondary markets,
loans brought by brokers determine the pool of loan applications that lenders face. In Section 3,
we argued that brokers have different incentives under different licensing environments, we thus
predict that the loan origination quality (standard) is higher in states with more stringent licensing
requirements. Our data does not include information on whether the loan is ARM, interest-only,
or no-doc. We thus focus on loan origination standards, and predict that more stringent licensing
requirements raise the standards of loans origination in the origination-to-distribute mortgage
financing model.
        Among the various means of licensing, education and exam requirements are likely more
difficult to satisfy, especially for employees. Therefore, to examine whether the selection mechanism
is at work, we shall test whether the effect of education and exam requirements for employees
have a particularly greater effect. Practitioners point out that the greatest entry barrier is surety
bonds/net worth. Therefore, to examine whether the charter value mechanism is at work, we will
test whether the effect of licensing is particularly greater for those requirements.
        Since borrowers differ in their susceptibility to fall for brokers’ self-serving behavior, we
predict that the effect of licensing is greater for less informed borrowers. Along the same line
of reasoning, lenders differ in their emphasis on sub-prime lending market. We therefore predict
that the effect of licensing is greater for lenders targeting less informed borrowers — sub-prime
borrowers. Finally, mortgage companies, unlike depository-taking institutions which have offices
and loan officers, likely rely on mortgage brokers to a larger degree, and therefore the effect of
mortgage broker licensing should be greater for these types of lenders.




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4    Identification and Econometric Specification
The identification strategy is the state-level variation in licensing regulations. Our baseline econo-
metric specification, which is at the loan-application level, is

           I originatedilhc = β lic Licst + β i Xi + β l Xl + β h Xh + β c Xc + β h m Xh m + εilhc

where i refers to the borrower, l is the loan, h is the house, c is the community level, st represents
state, h m refers to housing market, Licst is the licensing requirements at state-level, Xi is the
borrower-level variables, including their race, gender, and income, Xl is the loan-level variables,
including the outcome of the loan application (originated by the lender, purchased by the lender,
denied), loan amount, the loan purpose (home-purchase or re-finance), whether it is a first lien
loan, and the type of the loan (guaranteed by government agencies or not), Xh is the house-level
variables, including the census tract it is in, whether it is a single home, multi-family home, or
manufactured home, and Xh m are variables capturing the local housing market, e.g., the average
house price change. Summing these over a cross-section of people and then dividing by the size of
the population, we have

P ct originatedarea = β lic Licst + β i Xi area + β l Xl area + β h Xh area + β c Xc area + β h m Xh m + εarea ,

where the area can be state, census tracts, or lenders. If the control variables fully capture the
hard relevant information used by brokers in brokering the loan, brokers in states with greater
licensing requirements have incentives better aligned with borrowers and lenders and will use soft
information to a large degree, which should lead to greater origination standards. For example,
brokers in states with more stringent requirements are less likely to falsify documents for no-doc
loans, and thus loan origination likelihood is lower. That is, we test whether β lic < 0.
         An ideal test of the prediction is to look at the loan origination standards across state.
We do not have such data, and therefore look at the action of loan origination to “back out”
the loan origination standards. A further test will be to look at the loan performance. If loan
origination standards were lowered, it should lead to worse performance of the originated loans.
We will examine loan defaults in Section 7.
         One concern with this identification strategy is that states might use regulations to adjust
to changing economic environments. However, we take comfort in the fact that it usually takes
years for a legislative initiative to become law. Therefore, the licensing requirements are likely ex-
ogenous. In addition, if there is any remaining endogeneity, it is likely that states that experienced
predatory lendings (higher origination rate), for example, adopt more stringent licensing require-
ments, creating a possible positive correlation between the dependent variable and the licensing
variables. Yet our hypothesis is that the coefficient on the licensing variable is negative. Therefore,
this mechanism, if present, biases us against finding β lic < 0.

                                                      11
        Another concern with this econometric specification is that the available control variables
are not exhaustive, and the decision to originate or not relies on variables not included, like FICO
scores, etc. For this to bias our estimate of the effect of licensing requirements, it has to be that
the omitted loan-level variables vary with the state-level licensing variables in a systematic way,
which appears unlikely. Still, we will try include as many relevant variables as possible, including
using variables from other data sources.
        It is possible that licensing requirements impacts the quality of loan applications; antici-
pating that brokers and lenders are more diligent in screening, applicants of lower quality are less
likely to apply. If there is such an effect, this should lead to a positive relation between licensing
requirements and the likelihood of origination, i.e., this mechanism would bias us against finding
the hypothesized negative effect of licensing on loan origination likelihood.
        Still, different lenders attract loan applications of different quality. This consideration
suggests that we should include lender-type dummies to control for unobservable loan application
quality. There appears to have evidence that different lenders attract different applications; the
coefficient on the lender-type dummy variables are significant in a loan-level analysis. In addition,
the rich data allow us to further test the prediction by exploiting variation in borrowers’ degree of
being informed proxied by the tract-level minority percentage, and variation in lender-type.
        We also test the prediction by directly examining the loan characteristics, including FICO
scores and LTV, and loan terms, including negative amortization, interest only, balloon payment,
and no-doc. More stringent requirements should lead to better loan characteristics and terms.
Finally, assuming state characteristics do not change over time, including state fixed effects would
take care of the unobserved heterogeneity across states.


5    Data and Summary Statistics
Our main data set comes from HMDA (Home Mortgage Disclosure Act) 1996-2009, which provides
loan-application-level data. The Home Mortgage Disclosure Act (HMDA) was enacted by Congress
in 1975 and is implemented by the Federal Reserve Board’s Regulation C. For example, in 2010
almost 19.5 million loan records for calendar year 2009 were reported by 8,124 institutions. HMDA
applies to most depository institutions (commercial banks, savings associations, and credit unions)
with home or branch offices in MSAs. Depositories that are exempt are small (those with assets of
less than $35 million for the 2006 HMDA reporting year), or are not in the home-lending business,
or have offices exclusively in rural (non-metropolitan) areas. The 2004 revision required more
information to be disclosed, including pricing if the treasury rate is exceeded by a certain amount.
        HMDA also extends to mortgage and consumer finance companies, whether such companies
are independent, subsidiaries of banking institutions, or affiliates of bank or thrift institution


                                                 12
holding companies. Covered mortgage and consumer finance companies (referred to henceforth as
“mortgage companies”) include those that extend 100 or more home purchase or home refinancing
loans per year; such institutions are deemed to have an office in a metropolitan area if they receive
five or more applications for properties in such areas.
            It is estimated that the more than 8,800 lenders currently covered by the law account for
approximately 80% of all home lending nationwide. Because of its expansive coverage, the HMDA
data likely provide a representative picture of most home lending in the United States. Depository
institutions account for the bulk of the reporting institutions, but mortgage companies report the
majority of the applications and loans. In 2005, for example, nearly 80% of the 8,850 reporting
institutions were depository institutions, but together they reported only 37% of all the lending-
related activity. Mortgage companies accounted for 63% of all the reported lending; 70% of these
institutions were independent and not related in any way to a depository institution. Appendix A
offers more details on constructing the sample. The summary statistics show that about one in five
loan applications is denied, while about one-fourth of all loans are extended by subprime lenders.
            We control for changes in the economic environment in the MSA by including variables
that have been shown to be good predictors of loan denial decisions at the individual level (see
Munnell et al., 1996). In particular, we include a measure of house price appreciation to take into
account the role of collateral.
            For across-state variation in regulation, we use Pahl (2007). Data on the number of brokers
comes from the Occupation Employment Survey, which can be categorized by state. Data on fore-
closure comes from summary reports from LoanPerformance and Mortgage Bankers Association’s
(MBA) National Delinquency Survey. Census-level data provide tract-level data on race, including
the percent of minority population.
            Appendix B shows how the variables are defined. For example, for surety bond require-
ments, a requirement of bond amount over $50,000 is codes 3, $20,000-$30,000 is coded 2, under
$25,000 is coded 1, and none is coded 0. States differ greatly in how strict their licensing re-
quirements are. The top panel of Table 1B lists the 5 states that score the highest in the various
requirements, while the bottom panel lists the states that score the lowest in the various require-
ments.14 For the overall requirement, FL and CA are among the top 5 most stringent states. For
surety bond/net worth, New Jersey and WI are among the top 5. For the exam requirement and
the education requirement, FL and CA are consistently the top 2 states. The mean value of the
indicator variable for surety bonds is 1.2, and the mean for net-worth is .46. In 166 out of the
561 state-years, the surety bonds requirement is absent. In 450 out of the 561 state-years, the
  14
       Loan officers of lenders work under the umbrella license of their employers. Depository institutions are usually
more heavily regulated than mortgage broker firms. Often, mortgage firms are similarly regulated as mortgage broker
firms.



                                                          13
net-worth requirement is absent. Figure 1 shows the average licensing requirement over time. It is
apparent that states became more stringent in their requirements.
              Coded licensing information is only available for 50 states and D.C. area and for years
1996-2006. We obtained HMDA data for 1997 and 2003-2009. Therefore the data that merged
HMDA and licensing data covers years 1997 and 2003-2006.


6         Econometric Analyses of Loan Origination
6.1         State-level Analyses of Entry of Loan Brokers and Officers

First, we examine whether looser licensing regulations lead to more elastic entry of loan brokers with
respect to loan market conditions. In California, for example, the number of newly licensed brokers
increased from 1, 000 in 1996 to 7, 000 in 2006, and then started to decrease (shown in Figure 2). We
obtain employment and earning data from the Occupation Employment Survey, 1999-2010. The
data does not distinguish the specific occupations of loan brokers. Instead, it covers occupations
listed as loan counselling, loan interviewers and clerks, and loan officers. Loan counsellors provide
guidance to prospective loan applicants who have problems qualifying for traditional loans. Loan
interviewers and clerks interview loan applicants to elicit information. Specializing in commercial,
consumer, or mortgage loans, loan officers include mortgage loan officers and agents, collection
analysts, loan servicing officers, and loan underwriters. These occupations likely include both loan
brokers and loans officers working as employees in lending firms.
              It is well understood that entry and exit also respond to the house price change. We thus
estimate an equation of the below form:

                             Emp grst,t = β 1 licst + h s price appreciationst,t−1 + εst ,

where emp gr is the growth in employment for the three occupations listed above, lic is the various
licensing requirements, hs refers to house, and st represents state. The regression results are in
Table 2A. We find that the employment growth is negatively associated with the various require-
ments, especially those for education, exams, and surety bonds and net worth requirements. But
the estimates border on statistical insignificance. We also tried to use the lagged regulation vari-
ables, which appear to produce a better fit. The most prominent variable in affecting employment
growth is the bond and networth requirement.15                  16 These   pieces of evidence suggest that licensing
    15
         This is consistent with an article on surety bonds being the true entry barrier for loan brokers: (“Largest Barrier
to Mortgage Licensing” by Integrity Mortgage Licensing).
  16
     We compare MN and WI and find that WI showed a more restrained entry and exit than MN.




                                                              14
affects entry.17      18



6.2       State-level Origination Rate

We examine the loan origination outcome at the state-level. We examine the percentage of loans
that are originated among applications. Specifically, we estimate an equation of the below form:

          P ct originatedst, yr = β 1 licst, yr + β 2 securitization rtst + β 3 loan amt/incomest,yr
                                        +β 4 h s price changest,yr + εst,yr ,

where lic is the various licensing variables, st refers to state, yr refers to year, securitization rt
is the percent of loans that are not kept by the lenders, and hs price change is the change of
house price.19 The origination rate depends on many factors. For example, states that have more
stringent requirements likely have lower quality loans, and consequently should be turned down.
Therefore, in all regressions, we include the state-level average of loan amount/income.
            We conduct the analyses in two ways. First, we run the regression every year. The results
for individual years are reported in columns 3-7 of Table 2B. Across the years, we find that the
coefficient on the percent sold is positive, which is consistent with the claim that greater rates of
securitization leads to lower standard for loans. For our key variables, i.e., the summary variable for
licensing requirements, the coefficient is consistently negative in years 2003-2006. The coefficient
on the house price change is positive, although it is not always significant.
            We then pool all years of data together, and the results are in columns 1-2.20 For this
specification, we add year fixed effects. Since standard errors are likely correlated over time, we
cluster them at the state level. The coefficient on the summary licensing requirement variable is
significantly negative. We find that the coefficient on the summary licensing requirement variable
from 2003 to 2006 to be −.0053 and statistically significant. The 25th percentile value of code is 4
and the 75 percentile is 9. The estimated coefficient suggests that, compared with states that are
at the 25th percentile value of the summary licensing variable, states that are at the 75th percentile
have .0053 ∗ (9 − 4) = 2.7 percentage point lower origination rates.
  17
       We find that earning increase is higher in less regulated states. Since we also find that entry is more elastic in
less regulated states, the finding that earning increases are higher in less regulated states suggests that the entry did
not totally drive away the profits.
  18
     More stringent licensing requirements on brokers may lead loan professionals to become loan officers instead.
Thus, we think our results are a lower bound of the true effect of licensing on broker entry.
 19
    We also included other state-level variables, like GDP per capita. We find that the most significant state-level
economic variable is house price change.
  20
     We are currently conducting further analysis of the effect of the over-time variation in licensing across all states
in the nation.




                                                           15
            We conduct a couple of robustness checks. For instance, CA and FL have stringent laws,
and their origination rates are low. We check whether CA and FL dominate our results, and find
that they do not. Second, the effect of licensing requirements might take time to show. We thus
use the lagged value of the summary licensing variable. The results are in column 2. We find that
the coefficient changes little. Third, we use the sample where all loan applications (including those
purchased by lenders) and use the percent originated or purchased as the dependent variables.
The results are in column 8; the coefficient on the summary licensing variable is of slightly lower
magnitude.


6.3       Which Specific Requirements Matter?

Table 2C reports results examining the individual requirements. Instead of the summary require-
ments, we include the requirements for passing exams, education, work experience, continued
education, the sum of surety bonds and net-worth requirements, and finally the requirement that
a lender that operates a branch in another state must have an office in that state. All are indicator
variables, as defined in Appendix B.
            Column 1 reports results using all years from 2003 to 2006. We find that the coefficient
on education is −.021 and statistically significant. The 25th percentile value of the education
requirement is 0 and the 75th percentile is 1. The estimated coefficient suggests that, compared
with states that are at the 25th percentile value of the education requirement, states that are at
the 75th percentile have .021 ∗ (1 − 0) = 2.1 percent lower origination rates.21 We find that the
coefficients on the requirement of passing exams and work experience are negative but statistically
insignificant.
            We also find that the coefficients on net-worth plus surety bonds requirements is −.0088
and statistically significant. The 25th percentile value of the sum of the net-worth and surety
bonds requirements is 1 and the 75th percentile is 2. The estimated coefficient suggests that,
compared with states that are at the 25th percentile value of the sum of net-worth and surety
bonds requirements, states that are at the 75th percentile have .0088 ∗ (2 − 1) = .88 percent lower
origination rates.22
  21
       Education requirements include two kinds, one being a certain number, usually 24-40 hours of classroom educa-
tion, and the other being a degree in related fields, e.g., finance, real estate, business, etc. No states specifically require
earning of a formal degree; several states specify that a formal degree can be used as a substitute for requirements
of exams or work experience.
  22
     The coefficient on house price change is .16. The 25th percentile of house price growth is .05 and the 75th
percentile is .11. The coefficient suggests that moving from 25th to 75th percentile of the house price growth value,
the origination rate is .16 ∗ (.11 − .05) = 1 percent higher.




                                                            16
6.3.1      Licensing on Office in State

A recent phenomena is that there arose many internet firms that offered loan brokerage services,
firms like Quiken Loan. Licensing entities have regulated such brokerage service branches. For
example, the state of IL requires brokers to have in-state offices. One state regulates that the
requirement of in-state offices can be waived if brokers put up extra bonds. In the state of WA,
for example, in 2010 close to half of all loan originators were from out of state.23
            A requirement of in-state offices increases the cost of doing business in a state for out of
state broker firms. It is likely this reduces entry of brokerage firms and raises the rent for current
brokers, and thus they have less incentives to pursue current profits by reducing lending standards.
We include a dummy variable that takes the value of 1 if state laws require in-state offices for
mortgage loan brokerage firms. The regression results are in column 7 of Table 2C. We find that
the coefficient on the variable is negative, yet its statistical significance is borderline. The scale
of internet lending picks up over time, and we thus focus on latest year in the data — 2006. The
results are in column 8 of Table 2C. We find that the coefficient on the variable to be −.022 and
significant at the 7% level. The estimate is economically significant. The magnitude suggests that
compared with a state that is at the 25th percentile value of the requirement (0), a state that is
at the 75th percentile (1) has 2.2% lower origination rates.
            We plan to examine the effect of the amount of surety bond; instead of category variables,
we use the raw amount. In particular, we plan to use the ratio of surety bond over the local
personal income.


6.4       Regulation on Licensee- or Employee-level

What is the most effective way to regulate mortgage loan brokerage firms, licensing the applicants
(licensees), managing principals (the one who is actively running the firm), or employees?24 To
examine this, we sum all requirements on each party, creating variables called licensee requirement,
managing principal requirement, and employee requirement. The results are in column 1 of Table
2D. We find that the requirement for licensees has the greatest impact. The −.013 coefficient
suggests that compared with states at the 25th percentile (0) of the licensee requirement, the state
that is at the 75th percentile (1) has a 1.3% lower origination rate.
            We focus on the requirement for education, i.e., the education requirements for licensee,
managing principals, and employees.25 In columns 2-4, we report the coefficient on the education
  23
       The information is at http://raincityguide.com/2011/02/01/loan-originator-mortgage-broker-and-consumer-
loan-license-numbers-for-jan-2011/.
  24
     Miami Herald published an article showing how criminals jump in to become loan orignators to feed the lending
frenzy. FL regulates the mortgage brokers only at the licensee level, not at the loan originator level.
   25
      We report the summary statistics for specific requirements at different levels of the firm. From 1997 to 2006,



                                                       17
requirement at each level. Among the three specifications, the coefficient on employee education
requirement has the greatest magnitude. The coefficient on employee education (−.033) suggests
that one standard deviation in employee education (.34) is associated with a 1.1 percent lower
origination rate. We notice that the requirement for employee education and employee exams is
highly correlated (.67). For the requirement on exams, the effect is greatest for licensee, although
all of them are insignificant.26


6.5     Working on Identification

We explore the sensitivity of results to the alternative identification strategy of using within-MSA
cross-state variation. We note that many MSAs have asymmetric presence across states; in many
cases, the majority of the MSA economy is in one state, often surrounding a major city in that state.
Therefore comparing the economies across the two states (within one MSA) could capture the city
vs town effect. We therefore control for all possible variables that capture economic environment,
like tract-level income, minority percentage, etc. We are currently working on it.

6.5.1    Over-time Variation in Licensing

States vary in various dimensions; therefore the issue of omitted variables is always a concern for
any state-level analyses. Note that the finding that origination ratio is lower in high licensing
states can mean two things. One is that loans in those states are worse than in other states, thus
origination ratio is low. This bias us for finding our results. Including state fixed effects helps
address this. If for some reason, states have worse loans, state fixed effects can capture that. The
over-time change in licensing is then used to identify the effect.
         Regression results are in Table 2E. We find that the coefficient on the summary licensing
requirement variable is mostly insignificant. We also find that the coefficient on the exam require-
ment is negative in OLS regression. The insignificant coefficient on the licensing variable could be
due to the possibility that loans in states with higher licensing requirements began to have better
characteristics, which raises their likelihood of being originated.
         The effect of licensing shows in two ways. First, brokers would turn down loans that
they deem too risky, which shows up in origination rate. Second, those risky loans could only be
the average value of the licensee education requirement is 14%, the employee education requirement is 9.7%, and the
managing principal education requirement is 6.3%. The average value of the licensee work experience requirement
is 20%, the employee work experience requirement is 2%, and the manager work experience requirement is 12.7%.
In 4.3% of state years, licensees’ passing exams is required; in 10 percent of state years, employees’ passing exams is
required; in 9.2% state-years, managing principal’s passing exams is required.
  26
     The effect of licensees’ work experience requirement on loan origination standards is mostly negative, while the
effect of employee work experience requirement is significantly positive.



                                                          18
originated by using risky terms, like negative amortization (negam), ARM, no doc, interest only
(io) etc. In turning down those risky loans, the use of risky terms, in originating loans, were down.
In the following subsection, we thus test this prediction using data on characteristics and terms of
loans.


6.6      The Effect of Licensing Requirements on Characteristics and Terms of Orig-
         inated Loans

We ask whether characteristics of originated loans were worse in states that had looser license
regulation. Critical loan characteristics (for later loan performance) include many: Loan to Value
ratio (LTV), FICO (credit) scores, whether the loan is ARM or fixed rate, whether the loan has
negative amortization (negam), whether the loan is IO (interest only), whether the loan has a
balloon payment towards the end of duration, and whether the loan is no-doc or full-doc.
           The loan performance data provide information on various characteristics of loans and
their origination year. We focus on loans originated 2000-2007. For each characteristics, we use as
dependent variable the proportion of loans, in terms of originated loan value, that have the focal
characteristics, at state level for each year, i.e., we estimate an equation of the below form:

                             P ct loan value with f eatureit = βlicit + αt + αi + εit,

where i is state, t is origination year, αt are year fixed effects, and αi are state fixed effects where
applicable.
           The LTV ratio increased in 2004-06. Column 1 of Table 2F.2 shows regression results. In
results with state fixed effects, states with higher summary licensing requirements had lower LTV
ratio; work experience requirement has the most significant impact among all requirements.
           FICO score dipped in 2004-06. Column 2 of Table 2F.2 shows regression results with state
fixed effects. States with higher summary licensing requirements had higher FICO (the estimated
coefficient on FICO is .61). In particular, requirements on employees and the requirement of office
in state raise FICO scores.
           The mean of the dummy variable for non negam is .96 across 2000-07. Column 4 shows
regression results using the specification with state fixed effects. States with higher summary
licensing requirements had higher proportion of loans that are non negam; the estimated coefficient
is .0023**. Individual dimension of licensing has insignificant impact.
           The proportion of loans that were non-IO drastically decreased 2002-2007: it was .98 in
2002, .75 in 2005, and .67 in 2007. Regression results for IO, in column 5 of Table 2F.2, are unlike
what we found for other loan characteristics.27
 27
      More investigation is needed.



                                                     19
            Prior to 2004, 5 percent loans were balloon. In 2005-06, 10+ percent were balloon. Re-
gression results, in column 6, show that states with higher summary license requirement variable
have higher percentage of loans that are non balloon. Office-in-state requirement has significant
impact, so does the employee requirement. Licensee requirement in fact raises the proportion of
balloon loans, and so does work experience requirement.
            Loan can be originated by brokers or loan officers of lenders. The effect of licensing should
be greater for brokers. Unfortunately, eighty seven percent of loans have no data on who originated
the loans.

6.6.1       Effect of Licensing on Loans Being ARM

Column 3 shows results from examining the percent of loans that are fixed rate. We found that
in the specification with state fixed effects, states with higher licensing requirements had greater
proportion of loans being fixed-rate. Among the various requirements, the exam requirement raises
the ratio being fixed-rate. Education requirement reduces it. Requirements on licensees raise the
percent of fixed-rate loan more than the employee requirement. The results are robust on inclusion
of house price appreciation. In addition, the effect is also true for the percent of all loan counts
(instead of amount) that are fixed-rate. The effect is particularly strong for the period 2002-07.
            The results using OLS specification are different. Upon examination, we found that CA,
FL, and NV had both high license requirements and high ratio of ARM. Excluding CA, FL, and
NV in the OLS specification makes the coefficient on license variable to be consistent with the
results from state FF specification.

6.6.2       Effect of Licensing on Loans Being No Doc

We consider no/low-doc loans. No doc loans refers to no income verification. No doc loans are
attractive to self-employed people whose income can be erratic. Lenders do check FICO, and charge
higher rate. The percent of no doc loans shot up during 2002-2007. Table 2F.1 shows that the
percentage of home purchase loans that are full doc decreased from .68 in 2001 to .35 in 2007.
There was no apparent increase in the percent of self-employed people. Among them, CA, FL
are among the states that experienced the largest reduction.28 One apparent explanation is the
lowering of loan origination standards.
            Regression results with state FE are in column 8 of Table 2F.2. They are consistent with
the overall big picture: States with higher summary license requirements have greater percentage
  28
       We find that large states have lower than average (often lower than .50) full doc ratio, in particular, CA, FL,
NV, the three states that later have higher delinq ratio. CA and FL ranked the 1st and 3rd in state GDP. TX and
NY is the 2nd. Notes that these state experience high increase in house price and so low housing affordability.




                                                          20
of loans that are full doc. Office in state requirement has impact, and so does exam requirement.
Work experience requirement reduces the proportion that is full doc, though.29
            In sum, the previous sub-sections provide state-level analyses on the relation between
loan origination standards and state licensing strictness. However, we are concerned that possible
omitted variables can explain the correlation. We thus explore further variations that can help tell
apart our hypothesis from alternative explanations. In the two sub-sections below, we explore the
variation first at the tract-level, and then at the lender-level.

6.7       Tract-level Analyses of Loan Origination

Our hypothesis predicts that the effect of mortgage broker licensing on loan origination standard
is greater when the client (the borrower) is less able to evaluate the broker’s suggestions. The pay
arrangement between loan brokers and lenders is such that brokers’ pay increases with the number
of loans closed and the interest rate charged. Borrowers who are less informed are at a greater
danger of being misled by brokers to loan products that maximize the brokers’ pay instead of the
borrowers’ best interests. We hypothesize that licensing is more helpful for borrowers who do not
have the ability to see through loan brokers’ recommendations.
            We argue that borrowers who are less educated are less able to protect their own interests.
However, the borrower-level education variable is not present in HMDA data. Education level is
closely correlated to minority status.
            HMDA provides information on the tract that the borrower resides in, and tracts differ in
the minority percentage. We thus measure the degree of un-informedness by using several variables
at the tract-level: the minority percentage in the tract, and the percentage of low income families
in the tract. The tract-level data are available from the 2000 Census.
            Table 3A provides summary statistics of tract-level data from 2007. There were 65,515
census tracts; the median number of population is 4, 043, the median minority percent is 19 percent,
and the median HUD income is $59, 100. Our current analysis uses the sample that includes loans
that were purchased, so we examine the percent of loans that were originated or purchased, termed
percent issued.
            We first estimate an equation of the following specification:

               P ct issuedtract, year = β 1 licensing rqmtstate, year + β 2 percent soldtract,year +

   β 3 pct minoritytract, year + β 4 licensing rqmtstate, year ∗ pct minoritytract, year + εtract,state,yr .
            Results are in Table 3B. All standard errors are clustered at state county level. Column
1 introduces only the three specific licensing requirements: those on education, exam, and work
  29
       We conducted preliminary county-level analyis. We found that counties in states with more stringent experience
requirement have lower delinquency rate.


                                                          21
experience. Column 2 further introduces the tract-level information, column 3 introduces the
interaction term, and column 4 uses the summary licensing requirement variable and its interaction
with the minority percentage variable.
           Across the columns, we confirm the finding from state-level analysis that more stringent
requirements, particularly those for education, are associated with lower issuance rates.30 Tracts
that have higher percentages of minorities have lower percentages of loan applications being is-
sued. For the variable of interest — licensing rqmtstate, year ∗pct minoritytract, year , the coefficient
is insignificant. It is likely that tracts differ in unobservable ways. In the following analysis, we
therefore examine the over time change in issuance rates.

6.7.1      Tract-level Change in Issuance Rate

We examine the change in issuance rate at the tract-level. The percent issued in 2004 was .51 and
for 2005 was .49 (Table 1). Confirming this, Table 3A shows that across tracts, the median of the
growth rate is .96. Table 3C reports results of the estimation of the same equation as the one above
with the exception that the dependent variable is the change of issuance rate from 2004 to 2005.
           Column 1 uses the tract minority percent variable and the summary state-level licensing
requirements only. We find that the coefficient on tract minority percent is positive, implying that
tracts that have a greater minority percentage experienced a disproportionate increase in issuance
rates.31 The coefficient on the licensing variable is negative, implying that tracts in states with
more stringent requirements experienced smaller increases in issuance rates.
           We add an interaction term of state-level summary licensing variable and tract-level minor-
ity percentage in columns 2-4. We find that it is within tracts that have a high minority percentage
that the state licensing requirements suppress the disproportionate increases in issuance rates. The
result is robust to using various ways of computing standard errors and adding state fixed effects.
           A different way to examine the credit expansion is to look at the growth of the originated
amount.32 Table 3D reports the results. The results are for home purchase loans in 2004-05. We
find that i) high minority percentage tracts experienced greater increase in loan originations, and
ii) this effect is smaller in states that have more stringent licensing requirements.33
  30
       We also find that the requirement for exams is now negatively associated with the issuance rate.
  31
       This finding confirms what Mian and Sufi (2009) found: The number of loan originations increased more in
lower-income neighborhoods.
  32
     It is worth pointing out that it could be efficient to have disproportional credit expansion in low income neigh-
borhoods since the risk diversification was infeasible for them before the financial innovation, but it should not be
at the cost of lowering loan standards.
  33
     These findings are also present in 2003-04.




                                                        22
6.8       Lender-level Analyses of Loan Origination

We conduct lender-level analyses. Table 4A provides some summary statistics.34 We observe that
the number of lenders increased from 1997 to 2005, and then decreased. Second, there are huge
variations in the size of the lenders. For example, row 1 shows the number of lender-level loan
applications. In year 2005, for example, the mean is 1, 843, and the median is 69. Row 2 shows
the mean and median value of lender-state. On average, a lender has presence in 5 states, and the
median value is 2 states.
            It appears that lenders of differing sizes have differing origination rates. For example, in
2005, lenders that had a larger than median number of loan application had .74 origination rate,
and lenders that had a smaller number of loan applications than the median had a .83 origination
rate.
            It is also possible that different licensing requirements attract certain lenders. We therefore
use an econometric specification that includes lender fixed effects; therefore the coefficient on
the policy variable captures the effect of within-lender across-state licensing requirements on the
loan application outcome. We cluster the standard error at the lender level. That is, our basic
econometric specification is

            Ylender,state,year = β L Licensestate + β X Xlender,state,year + αlender + εlender,state,year .

            The results are in Table 4B. Column 1 includes the summary licensing variable only, and
the coefficient on the variable is significantly negative. Column 2 introduces the four categories of
licensing requirements: passing exams, education, work experience, and the sum of bond and net
worth. We find that the coefficients on exams and education are significantly negative, confirming
the evidence found in state-level analysis. The coefficient on the sum of bond and networth is
negative, but its statistical significance is borderline. Column 3 introduces the other two licensing
variables, continuing education (contedu) and branch in-state requirements, and also includes other
across-lender variables, like the proportion of applicants who are male, the proportion of first lien
loans, and the proportion of houses that are owner occupied. We find that the coefficient on
contedu is significantly negative and the coefficient on the branch-in-state requirement is negative
too.
            Column 4 uses the indicator variable for the employee exam requirement, and column 5 uses
the indicator variable for the licensee exam requirement. It appears that the effect of exams mainly
comes from the employee requirement, confirming our findings in tract-level analysis. Column 6
  34
       Lenders are regulated by different agencies. In 2005, for example, state banks, regulated by FDIC, had the
greatest number of lenders, but mortgage companies, regulated by HUD, issued the largest number of loans. Percent
originated is greast for FDIC regulated state banks. Mortgage companies have the lowest keeping ratio for loans that
they originated.


                                                          23
reports results from year 2005 that uses the summary licensing variable. The results are utterly
the same as in 2006.
            Other variables are reasonably estimated. For example, the states that have a higher
holding rate see higher standards within a lender. States that have greater house price increases
see a lower standard within the same lender. Overall, findings at the lender-level are consistent
with those from the state-level.

6.8.1       Subprime Lenders vs Non-subprime Lenders

HUD (Housing and Urban Development) maintains a list of subprime lenders from the mid-1990s
to mid-2000s. We merge the yearly list of subprime lenders with the yearly HMDA data. There
were 211 lenders on the list. When we merge the list with the HMDA data set, we find that 189
of the listed subprime lenders appear in the HMDA data set. These lenders on average originate
in 20 states. The median lenders originate in 14 states.
            We examine whether the effect of licensing on loan origination standards is stronger for
the list of subprime lenders. Our current analysis uses the sample that includes loans that are
purchased, so we examine the percent of loans that are originated or purchased (issued). Results
for years 2004 and 2005 are in Table 4C.35 The dependent variable is the percent of loans that
are originated or purchased (loans issued) among all loan applications at the lender level. We
first introduce the dummy variable for subprime-lender status, and then the interaction variable of
subprime-lender and the licensing variables. Since being a subprime lender or not is a time-invariant
characteristics of a lender, we drop lender fixed effects. We find that i) the education requirement
is negatively associated with the issuance rate, ii) being a sub-prime lender is associated with a
lower issuance rate (possibly because of the lower quality loans that the lenders tend to attract),
and iii) the effect of the education requirement on loan origination standards is stronger for lenders
that are on the HUD subprime lender list.36
            While the preceding results are consistent with higher origination standards in states with
more stringent licensing requirements, omitted variable bias remains a concern — could the lower
origination rate in a state a reflection of the worse loan applications, which prompts the state to
strengthen its licensing? If our hypothesis that licensing raises loan origination standards is right,
loans originated in heavily licensed states would perform better. Yet if the alternative hypothesis
  35
       We are working on including results from other years.
  36
       We conduct robustness checks. First, we are concerned that the list is incomplete; therefore using a dummy
variable and implementing an interaction term might not be the best way. We try focusing on data that belong
to subprime lenders. We find that the coefficients on the summary licensing variable, or education, passing exams,
and work experience are similar to the baseline results, and are slightly higher. Second, we exclude the lenders that
issued loans in one state and the results changed little.




                                                            24
is right, heavily licensed states would have worse loan applications and worse originated loans, and
thus worse performance. The following section examines loan performance.


7         Econometric Analyses of Loan Performance
7.1         Data on Loan Performance

We use a dataset that follow the performance of mortgage loans that are securitized.37 Shown
in Table 5A, a very high percentage of loans were securitized. For example, as of Dec. 2008, 53
percent of all home purchase loans originated in 2005 were securitized.


7.2         Statel-level Panel Analysis of Loan Performance and Licensing

We first examine whether loan performance varies with loan characteristics. Table 5B shows the
90+ delinquency rate various with the presence of various loan characteristics. ARM loans have
10 percentage greater delinquency rate than fixed rate loans. Loans with negative amortization
have close to 6 percentage higher delinquency rate. Interest-only loans have close to 3 percentage
higher delinquency rate than non-IO loans. Loans with balloon payment have close to 6 percentage
higher delinquency rate. And low-doc loans have 2 percentage higher delinquency rate than full-
doc loans. In addition, regression results show that loans with higher FICO scores have lower
delinquency rate.
              We collect data on loan performance as of December 2008 for loans originated 2000-2007.
The dependent variable is percent of loans that are 90+ delinquency as of Dec 2008. The econo-
metric specification is as follows:

         loan perofrmancest = β p hs pr ch heightst + β u pct unemployst + β l licen sin g reqst + εst ,

where s refers to state, t refers to year, hs pr ch height is percent change in state-level OFHEO
price index of the current price relative to the maximum in the past 4 years, pct unemploy is per-
cent unemployed in the state as of December 2008. OFHEO represents Office of Federal Housing
Enterprise Oversight. The dependent variable is the delinquency rate (and the seriously delin-
quency) rate as of December 2008. The licensing requirement is 2-year lagged, for it usually takes
two years for loans to show up as delinquent.
    37
         The sample for securitized loans differs from that for non-securitized loans in potentially two ways. First, having
to meet the requirements for securitization, they likely have higher FICO scores. Second, given that they can be
securitized, lenders (and brokers) collected less information on their repayment risk (since they won’t keep them.)
These two forces work in opposite directions, therefore bias from focusing on securitzed loans only should be mitigated
and limited.




                                                              25
            Results are in Table 5C. OLS regression (in column 1) yields positive sign on the summary
license requirement variable. States might differ in unobservable ways, which might affect the
license requirement and the dependent variable. One way to capture it is to include state fixed
effects. The over-time variation is then exploited, and the estimate is a diff-in-diff, i.e., comparing
states with over—time change in license with states with no over-time change in license.38
            Shown in Table 5C, results of panel regressions with state fixed effects show that increases
in license requirements are associated with reduced delinquency rate, either 90+ or seriously delin-
quent. (We note in regression without state fixed effects, the negative effect mainly comes from
the work experience requirement.) The estimated coefficient on the summary licensing requirement
variable is -.24. The 25th percentile value of summary licensing variable is 4 and the 75 percentile is
9, and the mean delinquency rate 90+ is 6.3 percent. That is, moving from 25th percentile to 75th
percentile in licensing is associated with -.24*5=-1.2, which is 19 percent reduction in delinquency
rate from the mean.
            We examine house purchase loans originated in 2003-2006. The coefficient is -.46. The 25th
to 75th percentile of code is 4 to 9, i.e., 5. The mean dependent variable value is 12.6 percent. That
is, moving from 25th to 75th percent can lead to .46*5=2.3 percentage decrease in delinquency
rate, a 18 percent decrease from the mean.39
            We also used as the dependent variable the year end performance for loans originated in the
same year. The mean of the dependent variable is 1.45. The results are in Table 5D. The coefficient
is .04. So the 25-75th percentile translates into .2 percentage point, a 14 percent decrease from the
mean. The effect mainly comes from loans that are ARM. The coefficient is -.08. The effect on
fixed rate loans were negative at -.026.
            In addition, from columns examining individual licensing requirements, we see that educa-
tion and experience requirements matter, and so does requirements on employees. The coefficient
on education requirement is -.39, i.e., moving from 0 (25th percentile) to .5 (75th percentile) in
exam requirement is associated with .195 percent reduction in delinquency 90+ rate, which is a 13.5
percent reduction from the mean (1.45%). Results are better for delinquency 90+ and seriously
delinquent than for default or foreclosure (not shown).
  38
       More than half of the states changed their licensing regulation. For example, the state of North Carolina (NC), in
2004, passed a law that enabled licensing regulation of mortgage bankers, brokers, and officers. Most of the changes
took place around 2002, before the housing market and the mortgage credit boom took off.
  39
     For all loans, including hp and refi and home equity 2003-2006?, the coefficient for code is -.30. the mean of dep
var is 12.3. That is, 25 to 75 change lead to .30*5=1.5 percentage decrease in delq rate. a 12 percent decrease from
the mean.




                                                            26
7.3     Effect of Licensing Is Greater for Loan Terms Where Brokers Have Discre-
        tion

7.3.1    Doc vs low/no doc

If licensing matters, brokers in states with more stringent requirements, due to both selection and
incentive effects, are less likely to abuse no-doc loans. No-doc loans in those states would experience
better performance than those in states whose brokers abused the terms to profit themselves without
regards to borrower or lender interests. Meanwhile, for full-doc loans, there is less room for brokers
to profit themselves at the cost of borrower or lender, the effect of licensing should be smaller.
         We thus test whether the effect of licensing is greater for loan terms where brokers have
discretion, in particular, low or no doc loans. We note that the sum of loan origination amount
is 833 billion for full-doc (no obs=430), 1000b for low-doc (no obs==430), and 50.7b for none
doc (no obs=414). Therefore the true difference is between full doc and low doc. We use house
purchase loan only 02-07 and include HPI and unemployment rate rate. We include the house
price appreciation since borrowers probably have anticipate a refinance in the near future and thus
used lower loan origination standards. We find that for low-doc, the coefficient on licensing is
negative at -.57 (.28)**, and for full-doc, the coefficient is -.17 (.15). That is, the effect of licensing
is significant — high licensing states have lower delinquency rate for low doc loans. The effect is
absent for full doc loans.

7.3.2    ARM vs Fixed

ARM is a term where brokers have freedom in steering borrowers into loans that can get passed,
often by using other potentially predatory terms. Licensing is hypothesized to reduce the abusing.
That is, states with high license will use ARM less, or less loans are originated with ARM. ARM
loans in high licensing states would perform better than ARM loans in low state. Results are in
Table 5D. Using performance data of current year performance for loans originated in the same
year, we find that the negative coefficient on code is mainly driven by the effect for ARM loans.
The effect of individual requirement is also greater for ARM loans.
         Licensing on brokers are hypothesized to change the selection and incentives of brokers.
We thus use the data to exam whether the effect of licensing is stronger for broker-originated loans
than for retail loans. Results are shown in the last column of Table 5C. We find that for 2003-2006,
the effect is negative for broker originated loans. It was absent for retail loans (not shown).
         In summary, this section provides evidence on the effect of licensing on the loan perfor-
mance. We found that more stringent licensing requirements are associated with better perfor-
mance, and the channel is that less loans with features that are positively correlated with default
were originated in those states.

                                                  27
8     Alternative Explanations
8.1   Federal and State Regulations of Anti-Predatory Lending

Federal agencies have applied provisions of laws to seek redress for consumers who have been
victims of predatory lending. Among the most frequently used laws are TILA, HOEPA, the Real
Estate Settlement Procedures Act (RESPA), and the FTC Act. Congress has also given certain
federal agencies responsibility for writing regulations that implement these laws. For example, the
Board writes Regulation Z, which implements TILA and HOEPA, and HUD writes Regulation X,
which implements RESPA.
        Truth in Lending Act (TILA), which became law in 1968, was designed to provide con-
sumers with accurate information about the cost of credit. In 1994, Congress enacted the HOEPA
amendments to TILA in response to concerns about predatory lending. First, it places restrictions
on loans that exceed certain rate or fee thresholds, which the Board can adjust within certain
limits prescribed in the law. For these loans, the law restricts prepayment penalties, prohibits
balloon payments for loans with terms of less than 5 years, prohibits negative amortization, and
contains certain other restrictions on loan terms or payments. Second, HOEPA prohibits lenders
from routinely making loans without regard to the borrower’s ability to repay. RESPA, passed in
1974, seeks to protect consumers from unnecessarily high charges in the settlement of residential
mortgages by requiring lenders to disclose details of the costs of settling a loan and by prohibiting
certain other costs. The FTC Act, enacted in 1914 and amended on numerous occasions, provides
the FTC with the authority to prohibit and take action against unfair or deceptive acts or practices
in or affecting commerce.
        However, with the exception of loans covered under HOEPA, there are no federal statutes
that expressly prohibit making a loan that a borrower will likely be unable to repay. In response
to the lack of protection of consumers in mortgage lending, many states adopted anti-predatory
lending laws, which are often more stricter than that at the federal level.
        In 1999, North Carolina passed the first comprehensive state law that was modeled after
the federal HOEPA (mini-HOEPA law). Prompted by growing concerns over the explosion in
subprime lending, many other states also enacted anti-predatory lending laws. As of 2007, Bostic,
Engel, McCoy, Pennington-Cross, and Wachter (2008a) found that 29 states and the District of
Columbia had mini-HOEPA laws in effect and another 14 states had some types of older anti-
predatory lending laws that were still in effect, which were adopted prior to 2000 and restricted
prepayment penalties, balloon payments, or negative amortization for all mortgages only.
        These laws, which could potential correlate with state licensing, may affect the loan origi-
nation, causing a spurious relation between licensing and loan origination standards. We use coded
APL law data from Ding et al. (2010). In the loan performance regression, we include house price

                                                 28
change from the loan origination year to 2008:

                Loan perfit = β a AP Lit + β p h p 2008 over orig yrit + β l licit + εit

    Results are in Table 6. State-level panel analyses found that APL has insignificant effect on
loan performance, and states with stronger licensing requirements have lower delinquency rate.

8.2   Regulation on Mortgage Banks

Another concern is that there exists difference in laws for consumer loan companies. Currently,
under state laws, mortgage banks need to be licensed before they can do business in a state. In WI,
for instance, the net worth requirement is the same as that for mortgage brokers, and the surety
bond requirement for mortgage bankers is $50k and that for brokers is $10k. In MN, mortgage
banks and brokers are together licensed as mortgage loan originators. Across the states, it appears
that the variations in licensing on mortgage banks are very similar to those on mortgage brokers.
        More lenient requirement on mortgage banks invites more entry of mortgage companies,
which caused greater competition in the loan origination market. This may increase their incentives
to win market share by lowering loan origination standards. It also results in less charter value,
therefore mortgage companies are more likely to take risks in issuing low-standard loans either by
lowering loan-origination standards or by selling riskier loan products. These mechanisms might
confound the effect of broker licensing on loan origination standards.
        To assess the existence of these mechanism, we first check whether the percentage of
mortgage banks in all loan applications (loan originations) is smaller in WI, the state with more
stringent requirement on mortgage companies than MN. We find that it is true that, in 2008, 2006
and in 2004, the percent loan applications or loan originations by mortgage companies is higher in
MN than in WI. More investigations are being conducted to disentangle the effect of licensing of
mortgage broker from that of mortgage companies. One way is to exploit the cross-state variation
in mortgage banker licensing as opposed to mortgage broker licensing.


9     Further Analyses
We investigate whether licensing requirements have negative consequence, in particular, do they
cause higher prices, i.e., higher interest rate charged? To answer this question, we focus on data
after the credit crisis where the secondary market shrank quite a bit and the incentives for lenders
to screen were improved. The majority of loan applications in 2009 was for refinance. HMDA
provides data on rate charged for loans that have high rates. For these loans, the rate in WI is
insignificantly different from that in MN. This evidence does not support the prediction that more
stringent requirements raise price.

                                                  29
9.1       Cost-benefit Analysis

According to the Board of Federal Reserve, the residential mortgage loan outstanding in 2007 was
$11.9 trillion. Moving from 25th to 75th percentile of licensing requirements leads to .30*5=1.5
percentage decrease in 90+ delinquency rate, i.e., if the whole nation moved from 25th to 75th per-
centile in licensing requirements, the nation would have 11.9*1.5/100= .18t = $180b less foreclosed
loans.
            Using 2004 data, we have 13m loan application, 132k being the average loan amount and
.68 being the origination rate. This means in year 2004, 13m ∗ 132k ∗ .69 ∗ 1.5/100 = $18 billion can
be saved from foreclosure if the nation moves from 25th to 75th percentile in licensing requirements.
Considering the loan outstanding is an accumulation of loans made over time, these two numbers
are roughly consistent.
            According to a 2004 study by Wholesale Access Mortgage Research & Consulting, Inc.,
there are approximately 53,000 mortgage brokerage companies that employ an estimated 418,700
employees and originate 68% of all residential loans in the U.S. In 2005, the per capita income
was $35, 452, i.e., $17.7/hr. Assume the in-class room education is 40 hours, the maximum lost
productivity (and earning) is 418, 700 ∗ 40 ∗ 17.7 = 296m = $.3b per year. This back-of-the-
envelope calculation suggests that there is huge welfare gain from using more stringent licensing
requirements.
       Effect of Licensing in a New Era — Years After 2008. The mortgage loan securitization market
basically shut down starting in 2008. We plan to examine the effect of licensing requirements in
this new era. Since lenders will have to hold the loans on their balance-sheet, examining this period
will help us understand what happens to loan origination quality when the lenders have incentives
to screen.40 Also,we plan to focus on the effect of licensing regulation on pricing, and therefore
this analysis will help shed light on any possible downside of licensing regulations.


10        Concluding Remarks
This paper examines whether licensing regulation of mortgage loan brokers improves loan orig-
ination outcomes when the lenders have few incentives to screen loans in an origination-to-sell
mortgage financing model. The mechanisms are i) more stringent licensing requirements screen
out low quality brokers, who place a lower value on future profits, and are more inclined to pursue
current profits by using lax standards in issuing loans, and ii) more stringent licensing require-
ments block entry, raising the long-term profits, which reduces the brokers’ incentives to take risks,
  40
       Note that in normal business cycles, there is variation in lending standards, as higher house prices in booming
periods make refinancing easier in the future, which reduces current lending standards (Rajan, 1994). Yet in my
study, the elephant in the room is the securitization of mortgage loans.


                                                           30
by lowering lending standards in current periods, that jeopardize their rate of survival into the
future. We find that states that use the more stringent licensing requirements have higher lending
standards and better loan characteristics and terms that facilitate better loan performance. Two
requirements, one for bonding and net worth, and the other for education (and exam) requirements,
have the greatest effect on loan origination standards. The education (and exam) requirements are
most effective at the employee level. The effect of licensing is greater in neighborhoods with higher
minority percentages, and for lenders that focus on sub-prime lending. Finally, states that have
more stringent requirements have lower default rates for their loans.
        This paper examines one particular form of regulation — via licensing — in addressing the lax
lending standards in the mortgage loan origination market due to securitization diminishing lenders’
incentives to screen. Backley et al. (2006) recommends that registration and certification be used as
alternative ways to regulate. More works are needed to further evaluate what are the best solutions
to address the general inefficiencies arising from information asymmetry between customers and
their better informed service providers. The relation between brokers and borrowers depends on
the state law. Some advocate a fiduciary relation, some states list the relation as principal-agent,
while others list the relation as fair-dealing. We plan to take up these issues in future research.


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                                                 Appendix

   A. Data Issues
   A1. Sample Construction
   HMDA requires lenders to report the outcome of loan applications, including denied, approved (by
lenders) but not accepted (by borrowers), originated, or purchased. For loans processed by loan officers
of a lender, the reporting is straightforward. Loans arranged by independent loan brokers are recorded
by the party which provides the funding, and therefore are reported by the lenders, since brokers do not
provide funding. Loans originated by correspondent lenders, which fund the loans and often immediately
sell to the lenders which usually provides a line of credit for the correspondent lenders, are reported by the
correspondent lenders as originated and as purchased by the warehouse lenders in the HMDA dataset. The
last type of loan is therefore double-counted in the HMDA dataset. In our main analysis, we use observations
excluding records that show the loans to be purchased.
        It is possible that some correspondent lenders do not meet the HMDA’s requirements to report, and
thus the last type of loans go unreported. In those cases, those loans are likely reported by the lenders that
purchase them since the buyers are usually large institutions. For example, in 2006 3.3 million records show
that the lender purchased the loans. Yet only 1.8 mil records show that they are originated and then sold to
the categories of purchasers which consist of commercial banks/savings, credit unions, and affiliates. These
numbers suggest that the loans made by correspondent lenders are under-reported. Therefore, to examine
the sensitivity of our results, in robustness checks we include all records showing loans as purchased, and
use as the dependent variable the percentage of loans originated or purchased.
   A2. Authenticity of Data
   Since lenders input and submit the data to FFIEC (Federal Financial Institution Examination Council),
one concern is the authenticity of the data. One particular suspect is the reported income of the applicant.
This authenticity of this information can be altered by actions of the borrowers, brokers, and lenders.
HMDA data in 2007, compared with previous years, started to include tract-level data from the Census. In
particular, the census-level income and minority percentage data are provided. We thus compare the median
of the reported applicant income from HMDA with the median census income from Census



                                                     33
        In 2007, there were around 66k census tracts. The median tract-level (median) family income was
53.75k. The median tract-level (median) applicant income was 69k. There appears to be some exaggerations
since family income is usually less than applicant (individual) income. However, it is possible that the buying
family, in order to be able to afford the houses in a neighborhood, are slightly richer than the current residents
who may have purchased the house earlier at a lower price.
    A3. Mortgage Applications that Are Submitted More Than Once (to Different Lenders)
    It is likely that applicants will seek alternative lenders after their mortgage loan applications are denied.
We think this data issue will not affect our analysis as long as it does not systematically correlate with the
state licensing of mortgage brokers.
    B. Variable definitions (from Pahl, 2007)
    REG = Licensing/registration of entities, sole proprietors, and individuals acting as mortgage brokers
(Licensed/registered = 1; None=0)
    LIC-EDU=Specific education requirement for licensing/registration (Required of many principals=2;
Required of one principal=1; None=0)
    LIC-EXP=Specific experience requirement for licensing/registration (Required of many principals=2;
Required of one principal=1; None=0)
    LIC-EXAM=Examination required to obtain license/registration (Required of many principals=2; Re-
quired of one principal=1; None=0)
    LIC-CONT-EDU=Examination required to obtain license/registration (Required of many principals=2;
Required of one principal=1; None=0)
    NET WORTH=Net worth requirement for licensing/registration (Net worth over $50,000=3; Net worth
$25,000—$50,000=2; Net worth under $25,000=1; None=0)
    BOND=Bond requirement for licensing/registration (Bond over $50,000=3; Bond $25,000—$50,000=2;
Bond under $25,000=1; None=0)
    MAN-LIC=Regulation of managing principals (Licensed/registered as individual mortgage broker=2;
Licensed/registered as employee=1; None=0)
    MAN-EDU=Specific education required for managing principal status (Required=1; None=0)
    MAN-EXP=Specific experience required for managing principal status (Required=1; None=0)
    MAN-EXAM=Examination required for managing principal status (Required=1; None=0)
    MAN-CONT-EDU=Continuing education for managing principal (Required=1; None=0)
    BRANCH-BOND=Bond requirement for licensing/registration (Bond over $50,000=3; Bond $25,000—
$50,000=2; Bond under $25,000=1; None=0)
    BRANCH-IN-STATE=In-state office required for licensing/registration (Yes =1; No=0)
    BRANCH-MAN-LIC=Regulation of branch managers (Licensed/registered as individual mortgage bro-
ker=2; Licensed/registered as employee=1; None=0)
    BRANCH-MAN-EDU=Specific education requirement for branch manager status (Required=1; None=0)


                                                       34
    BRANCH-MAN-EXP=Specific experience requirement for branch manager status (Required=1; None=0)
    BRANCH-MAN-EXAM=Examination required for branch manager status (Required =1; None=0)
    BRANCH-MAN-CONT-EDU=Continuing education for branch manager (Required =1; None=0)
    EMP-REG=Employees regulated (Employees regulated=1; Employees not regulated=0)
    EMP-EXP=Specific experience requirement for licensing/registering employee (Required=1; None=0)
    EMP-EDU=Specific education requirement for licensing/registering employee (Required=1; None=0)
    EMP-EXAM=Examination required for licensing/registering employee (Required=1; None=0)
    EMP-CONT-EDU=Continuing education for employee (Required=1; None=0)
    SUMMARY CODE=Summation of all variables
    C. Tract-level Change in Keeping Rate and Securitization Rate
    HMDA records whether loans originated or purchased are kept or sold, and the entity of the purchaser.
To inquire whether the disproportionate increase in origination rates is due to the ease of securitization (and
the decreased incentives to screen), we examine whether high minority tracts experience disproportionate
increases in securitization rates. We find that high minority-percentage tracts experienced lower keeping
rates, greater rate increase of loans sold to private securitizers, and greater rate decrease of loans sold to
government agencies. Note that government agencies follow relatively stricter issuance guidelines.
    D. Further Analyses
    We did a brief analysis of the effect of federal preemption of state APL laws on lenders’ loan origination
standards. We find that national banks increased their originations more dramatically starting in 2004 when
OCC succeeded in exempting national lenders from state APL laws. Meanwhile, the market share of state-
chartered banks shrunk, and so did the independent mortgage companies. We are investigating whether the
expanded market share of national banks was mainly attributed to activities of their subsidiaries.
    E. Robustness Checks
    Our main dependent variable is an indicator variable for whether the loan is originated. We plan to use
an alternative dependent variable — whether a loan application is denied. Second, it is possible that it takes
time for broker licensing to affect the behavior and selection of mortgage broker; we thus plan to explore
using lagged instead of current licensing variables.
    F. Findings on Loan Riskiness
    We examine whether the stricter licensing requirements decrease the riskiness of the originated loans.
We measure the riskiness of the quality using the ratio of loan volume divided by borrowers’ income. If our
variables capture the information used by lenders, the higher the ratio is, the less likely that the borrower
can afford the payment for the loan, and the more risky the loan is. The results from state-level analysis are
in column 9 of Table 2B. yWe regress the state-level mean loan amount/income for all observations on the
same set of variables. We find that the estimated coefficient on the summary requirement variable is −.013
and significant at the 10 percent level.




                                                       35
                                  Table 1A: Summary Statistics of HMDA Data
             # loan appli-          Percent             Percent           Percent kept           Applicant            Loan
                cations          originated or         originated                                 income,            Amount,
                                  purchased                                                       median             median
  1997           8.02 m               .65                   .49                 .21                 43k                77k
  1998            7.9m                .57                    --                  --                 51k                97k
  1999            8.4m               .575                    --                  --                 53k               101k
  2000            8.2m                .58                    --                  --                 57k               106k
  2001            7.6m                .64                    --                  --                 59k               115k
  2002            7.3m                .69                    --                  --                 62k               125k
  2003           10.5 m               .75                   .53                 .21                  ?                  ?
  2004           12.7m                .71                   .51                 .18                 66k               132k
  2005           15.2m                .69                   .49                 .19                 72k               133k
  2006           14.7 m               .69                  .458                 .18                 77k               135k
 Notes. The sample includes loans for home purchase only. The sample includes observations where loans are purchased by
lenders. Percent originated or purchased is the percent of loan applications that are either originated or purchased by the lenders.
The percent kept is the percent of loan applications that are originated and kept (not sold) by the lenders. In 2007, 2008, and
2009, the numbers of loan applications (the percent originated or purchased) are 10.8m (.725), 6.79m (.69), and 6.2m (.738),
respectively. In 2009, the origination rate was .47 for the 12.4m applications for re-finance. For sample where purchased loans
are dropped, there were 12.1m loan applications, and the origination rate was .612 in 2005. In 2006, there were 11.3 million loan
applications, and the origination rate was .595. From 1998 to 2002 where data comes from Urban Institute HMDA, data is for h-p
for 1-4 family units.

                     Table 1B: Licensing Requirements for Mortgage Brokers, 1996-2006
                    Summary                            Surety
                    licensing                          bond+                              Exam                           Education
   States         requirements         States         Net-worth         States         requirement        States        requirement
  Florida            14.5833         Tennessee        5.583333         Florida              3            Florida             3
 New Jersey             13           New Jersey           5           California            2           California           2
 California             10            Arkansas            4           New Jersey            2            Indiana         1.166667
   Ohio              8.91667           Idaho             3.5            Ohio                1           Kentucky         1.166667
  Nevada               8.75          Wisconsin        3.333333         Arizona              1           Alabama              1

 S. Dakota             1.75          California            0          Minnesota              0           Alaska               0
 Wyoming                 1           Colorado              0          Colorado               0           Virginia             0
                                      Massa-
  Minnesota            0.75           chusetts             0            Hawaii               0            Hawaii              0
  Colorado               0             South
                                      Dakota               0             Idaho               0          Minnesota             0
    Alaska               0           Minnesota             0          S. Carolina            0          Tennessee             0
    Notes. The top panel of this table lists the five states that score the highest for the relevant licensing requirements. The
    variable “summary licensing requirements” is the sum of all licensing requirements. Bond+networth is the sum of the bond
    requirement and the networth requirement. Bond requirement is coded as follows: Bond over $50,000=3; Bond $25,000-
    $50,000=2; Bond under $25,000=1; None=0, where bond is the surety bond requirement for licensing/registration. Networth
    requirement is coded as follows: Net worth over $50,000=3; Net worth $25,000-$50,000=2; Net worth under $25,000=1;
    None=0, where net worth is the requirement for licensing/registration. Education requirement is the sum of specific education
    requirement for licensing/registration (Required of many principals=2; Required of one principal=1; None=0), specific
    education required for managing principal status (Required=1; None=0), specific education requirement for branch manager
    status (Required=1; None=0), and specific education requirement for licensing/registering employee (Required=1; None=0),
    where specific education requirement is, for example, hours of classroom education. The definition for the exam variable is
    the same as that for education except that the requirement is passing of an exam.


                                                                36
                           Table 2A: Entry of Brokers as a Function of Licensing Regulation
                                     Gr_employment for        Gr_emp for all loan         Gr_employment               Gr_emp for all
                                      loan officers only          workers                 for loan officers            loan workers
                                                                                                only
        Bond+net worth                        -.0084                    -.03
          lagged                             (.0051)                   (.02)
                                         (p-value: .104)           (p-value=.15)
        Bond_all+net worth                                                                       -.0082                   -.014
          lagged                                                                                (.0050)                   (.010)
                                                                                             (p-value:.11)
        Education requirement                  Y                       -.06                         Y                      -.05
          lagged                                                       (.06)                                               (.05)
        Experience                             Y                        .14                       Y                         .14
          requirement lagged                                           (.12)                                               (.13)
        Exam-passing                           Y                       -.03                       Y                        -.03
          requirement lagged                                           (.04)                                              (.037)
        Year dummy                            Yes                       Yes                      Yes                        yes
        No obs                                447                       449                      447                        449
        R-squared                             .11                       .03                      .11                        .03
    Notes. Employment of loan officers are from Occupation Employment Survey, 2002-2006. Gr_employment_loan_officers is the
    change in the number of loan officers. All loan workers include loan counseling, loan interviewers and clerks, and loan officers.
    Bond, net worth, education, and passing exam are defined in notes of Table 1B. Bond_all is the sum of bond requirement for the
    licensee and the branches. “Y” means that the variable is included but the coefficient is not reported. All columns include year
    fixed effects. Standard errors, in parentheses, are clustered at the state level.


        Table 2B: Panel and Yearly Analysis of Loan Origination at State-level, 1997 and 2003-06
                    (1)          (2)           (3)          (4)           (5)         (6)          (7)          (8)           (9)
                   Pct_         Pct_        Pct_ orig      Pct_          Pct_        Pct_         Pct_        Pct_issue      Loan
                                                                                                                ,with      amt/incom
                   orig         orig                       orig          orig        orig         orig                          e
                                                                                                              purchase
                                                                                                                  d
Summary             -                         .002          -            -             -            -             -         -.013*
  lic. req.     .0053***                     (.004)     .0045***     .0053***      .0058***     .0056***      .0041***      (.007)
                 (.0013)                                 (.0013)      (.0015)       (.0016)      (.0016)       (.0010)
Summary lic                       -
  req, lag                    .0054***
                               (.0014)
Ratio kept         -.08        -.007           -.09        -.18          -.10         -.06       -.00083       -.21**        .007
                   (.10)       (.005)         (.19)        (.12)         (.10)       (.11)       (.0090)        (.10)       (.023)
House price         .11         .13*           .21         .24*           .09        .094          -.04          .09       2.48***
  Gr               (.08)        (.08)         (.51)        (.14)         (.14)       (.10)         (.03)        (.07)        (.43)
Loan_              .003         .012         .17***         .01          .008        -.008          .13         .006
  income          (.024)       (025)          (.05)        (.03)         (.03)       (.03)         (.18)       (.019)
State fe
No. of obs.        204           204            51           51            51          51           51          204          204
Yrs covered       03-06        03-06          1997         2003          2004        2005         2006        2003-06      2003-06
R-squared          .33          .324           .27          .32           .26         .23          .21          .35          .33
    Notes. The data are for home purchase only unless otherwise noted. Data excluded loans that were purchased by lenders, unless
    otherwise noted. The dependent variable is percent of loans that were originated at the state-level. Pct_issue is the percentage of
    loans that were originated or purchased by lenders. Ratio kept is the percent of loans that were not sold by the lenders (in the
    secondary market) among all originated loans at the state-level. The summary licensing requirement variable is defined in notes
    of Table 1B. House_price gr is the percentage change of home price over the last year. The house price index is from FHA.
    Loan_income is the state average of loan amount/applicants_ income. All columns include year dummies for 2003-2006. Note
    that in column 2, the state-level loan/income variable is the mean of all observation on loan/income, not the mean of loan/income
    for originated loans. Estimation method is OLS. In all columns, standard errors, in parentheses, are clustered at state level. *:
    significant at 10% level. **: 5% level, ***: 1% level.


                                                                    37
     Table 2C: Panel Analysis of Loan Origination, 1997 & 2003-06: Specific Licensing Requirements
                               (1)            (2)               (3)              (4)                (5)           (6)                 (7)              (8)
                           Pct_orig        Pct_orig         Pct_orig         Pct_orig          Pct_orig       Pct_orig          Pct_orig          Pct_orig
Ratio kept                   -.0062          -.07              -.19            -.098               -.05         -.0005               -.04            -.003
                            (.0055)         (.20)             (.13)            (.11)              (.13)         (.011)              (.11)           (.011)
Exam-passing                 -.0032            Y              -.002           -.0009              -.002         -.011*            -.0036            -.0099
   requirement              (.0053)                          (.008)           (.007)               .007          .006              .0052             .006*
Education                  -.021***                Y          -.013           -.017*           -.024***       -.025***           -.018**          -.025***
   requirement               (.008)                          (.010)           (.009)             (.008)         (.009)            (.007)            (.009)
Experience                   -.0032              -.02         -.005           -.0013              -.006                           -.0034              .003
   requirement              (.0051)             (.02)        (.006)           (.006)             (.007)                          (.0056)            (.008)
Continuing                  -.0097*             -.05*        -.010*          -.017**               -.01        -.0082             -.0077             -.005
   education                (.0042)             (.03)       (.0055)           (.007)             (.007)       (.0074)            (.0046)            (.007)
Bond+networth             -.0088***            .0040          -.006          -.009**           -.0092**      -.0089**           -.0079**         -.0096***
                            (.0034)            (.012)        (.004)           (.004)            (.0038)       (.0038)            (.0033)           (.0036)
Office in state                                                                                                                    -.013            -.022*
                                                                                                                                  (.011)            (.012)
House price gr               .16**               .44             .22            .20             .14              .20                .073               .22
                              (.08)             (.59)           (.15)          (.13)           (.11)            (.19)             (.071)              (.18)
Loan_income                  -.004            .21***             .01            -.02           -.024           -.064*               .017            -.07**
                              (.03)             (.06)           (.03)          (.04)           (.03)           (.035)             (.023)              (.03)
No. of obs.                    204                 51             51              51             51               51                 204                51
Years covered              2003-06              1997            2003           2004            2005             2006            2003-06               2006
R-squared                      .39                .29            .36             .36            .31              .34                  .29              .37
Note. Data are for home purchase loans only. Data excludes loans that were purchased by lenders. The dependent variable is the percent of loans
that were originated at state-level. Column (8) covers year 2006. All columns include year fixed effects for year 2003-06. The variables
ratio_kept, requirements on exam, education, surety bonds and net-worth, house price gr, and loan_income are defined in the notes of Tables 1B
and 4A. Experience is the sum of requirement of work experience at the licensee-, managing principal-, branch managers-, and employee-level.
Contedu is the sum of requirement of continuing education at the licensee-, managing principal-, branch managers-, and employee-level. The
variable “Office in state” is an indicator variable for the requirement that a branch needs to have an office in the state for it to legally operate in
the state. “Y” means that the variable is included but the coefficient is not reported. Estimation method is OLS. Standard errors, in parentheses,
are clustered at state level. *: significant at 10% level. **: 5% level, ***: 1% level.



                             Table 2D: Loan Origination: Regulating Licensees vs. Employees
                                     (1)               (2)              (3)                (4)            (5)              (6)             (7)
Dep var: pct originated              Pct          Edu req. is       Edu req. is         Edu req. is   Exam req.        Exam req. is     Exam req.
                                 originated       for licensee     for managing            for           is for       for managing        is for
                                                                     principal          employee       licensee         principal       employee
Ratio kept                             -.006              Y              Y                  Y              Y                Y               Y
                                      (.006)
Loan income                            .005               Y                Y               Y               Y              Y               Y
                                       (.02)
Home Price growth                       .11               Y                Y               Y               Y              Y               Y
                                       (.08)
Licensee req                         -.013**           -.022*                                            -.017
                                     (.0065)           (.013)                                           (.019)
Man principal req                    -.0068*                             -.015                                          -.002
                                     (.0035)                            (.013)                                          (.01)
Employee req                          -.0046                                           -.033**                                          .001
                                     (.0041)                                            (.013)                                          (.02)
Office in state                       -.0092              Y                Y               Y               Y              Y               Y
                                      (.013)
Bond+Networth                        -.007**              Y                Y               Y               Y              Y               Y
                                      (.003)
R-squared                               .39              .29              .27             .31             .32            .32             .32
Notes. Data are for home purchase loans only. Data excludes loans that were purchased by lenders. The dependent variable is the percent of loans
that were originated at state-level. Includes year fixed effects for year 2003-06. The number of observations is 204. The control variables are
defined in the notes of Tables 4B, 4A, and 1B. Licensee requirement is the sum of all requirements, such as education, work experience, exam,
etc. that are at the licensee level. Similarly, managing principal requirement is the sum of all requirements at the managing principal level. And
employee req is the sum of all at the employee level. In column 2, the licensing requirement is focused only on the education requirement at the
licensee level. In column 3, the licensing requirement is focused only on the education requirement at the managing principal level, and so on.
The “Y” means that the variable is included but the coefficients are not reported. Estimation method is OLS. Standard errors, in parentheses, are
clustered at state level. *: significant at 10% level. **: 5% level, ***: 1% level.



                                                                          38
                             Table 2E: Over-time Variation in Licensing Requirement
                                             (1)                (2)            (3)             (4)
                                        Pct_ orig       Pct_ orig         Pct_ issue      Pct_ issue
           Summary                         .00053                             .0008             --
             lic. req.                    (.00094)                           (.0008)
           Exp                                               .02***                          .008**
                                                               (.01)                          (.003)
           Edu                                                .007*                         .014***
                                                              (.004)                          (.005)
           Exam                                             -.013***                        -.015***
                                                              (.005)                          (.004)
           Contedu                                             .002                            .005
                                                              (.006)                          (.004)
           Networth                                           .0004                              Y
                                                              (.005)
           Office in state                                     -.02                             Y
                                                               (.02)
           House price                     .17***           .16***              .06*             .05
             Gr                             (.04)             (.04)             (.03)           (.03)
           Ratio kept                        -.05             -.05              -.08             -.09
                                            (.09)             (.08)             (.06)           (.06)
          Loan_                            .06***           .06***            .05***           .06***
             income                         (.02)             (.02)             (.02)           (.02)
          State fe                             Y                Y                 Y               Y
          No. of obs.                        204               204               204             204
          Yrs covered                       03-06            03-06             03-06            03-06
          States covered                     All               All               All             All
          R-squared                          .11                                 .10             .06
          Within                              .71                                .58             .62
          Between                            .01                                 .02             .01
        Notes. Consistent standard errors are in parenthesis. *: significant at 10% level. **: 5% level, ***: 1% level.



                    Table 2F.1: Summary Statistics of Loan Characteristics Over 2000-07
            Sum of full doc loans       Sum of all loans           Percent of full doc   Mean across states in
                securitized               securitized             among all secu loans     the pct of full doc
                                                                                                  loans
   2000               74b                       108b                 0.685185                      .73
    01                159b                      233b                 0.682403                      .69
    02                215b                      343b                 0.626822                      .69
    03                293b                      509b                 0.575639                      .64
    04                408b                      786b                 0.519084                      .62
    05                483b                     1090b                 0.443119                      .57
    06                365b                      994b                 0.367203                      .51
    07                124b                      353b                 0.351275                      .47
The difference between percent of full doc among all using all observation vs using across state observation suggests
that large states have lower percentage of full doc loans.




                                                           39
Table 2F.2: The Impact of Licensing Regulation on Loan Characteristics of Originated Loans:
State-Year Panel Analysis
                          (1)          (2)             (3)             (4)              (5)               (6)           (7)         (8)
                         LTV          FICO            FICO            FICO          Not negative         Not           Not          Full
                                                                                    amortization       interest     balloon         doc
                                                                                                         only       payment
Summary lic req          -.044        .62***           .35           .60***           .0023**           .0006        .0014*       .0026*
                        (.046)         (.23)          (.22)           (.22)            (.0010)         (.0030)       (.0007)      (.0014)
HPI over past yr           --            --             --               4                --               --            --          --
                                                                       (11)
HPI over min past          --           --              4.5             --                --               --           --           --
four yr                                                (10)
Yr fixed effects          Y              Y               Y               Y                Y               Y           Yes           Yes
No obs                                 408             204              408              408            408           408           408
Range                                 00-07           03-06           00-07         00-07             00-07        00-07          00-07
R-sqrd                    .14           .28                             .29              .48             .61           .78          .53
Within                    .61           .79                             .79              .71             .82           .85          .91
Between                 .0007         .0003                            .003              .08             .02         .0009          .06
Mean of dep var          77.5          671                                               .96             .84           .95          .62
The summary lic req in CA, FL, and NV in 2005 were 10, 16, and 12, respectively, which were among the highest among
all 51 states in 2005. These three states were known to have high rates of loan default. . Consistent standard errors are in
parenthesis. *: significant at 10% level. **: 5% level, ***: 1% level.




 Table 2F.3. The Impact of Licensing Regulation on Being ARM for Originated Loans: State-Year Panel Analysis
                           (1)               (2)          (3)            (4)             (5)            (6)            (7)            (8)
                       Pct of fixed    Pct of fixed   Pct of fixed   Pct of fixed    Pct of fixed   Pct of fixed   Pct of fixed   Pct of fixed
                       in loan amt     in loan amt    in loan amt    in loan amt     in loan amt     in hp loan    in loan cnt    in loan cnt
                                                                                                        amt
Summary lic req          .00092          .0044*        .0039***           --          .0047***         To do          .0014        .0043***
                          (.002)         (.0024)        (.0015)                        (.0018)                       (.0017)        (.0015)
Exp                                                                      .008
                                                                        (.01)
Edu                                                                     -.004
                                                                       (.008)
Exam                                                                      .02
                                                                       (.006)
Contedu                                                                  .003
                                                                       (.004)
Networth_bond                                                             .01
                                                                       (.007)
Office in state                                                          -.02
                                                                        (.02)
Hpi over min of last     -.12***                        -.06***          -.17                                        -.13***        -.19***
4 yrs                      (.04)                          (.01)         (.03)                                          (.03)          (.04)
State FE                     N             N                Y              Y              Y                              N              Y
No obs                      408            48              408           407             306                            408            408
Range                     00-07          2005            00-07         00-07            02-07          00-07          00-07          00-07
States                      All         Exclude            All             all           all            All             All            All
                                         CA,
                                        FL,NV
R-sqrd                     .75            .07             .74            .74             .57                           .64            .63
Within                      --             --             .92            .92             .85                            --            .85
Between                     --             --             .04            .04            .0000                           --            .04
Notes. Mean across 432 state-yr obs of pct fixed in counts is .53. Median is .52. Year fixed effects included. Consistent
standard errors are in parenthesis. *: significant at 10% level. **: 5% level, ***: 1% level.




                                                                      40
              Table 3A: Loan Origination. Summary Statistics on Tract-level Data, 2007
                          Population number              Minority pct             HUD median                Tract HUD inc
                                                                                    income                    percentage
         N                        65515                     65488                    65550                      65351
       Median                     4043                       19                      59100                        97
Notes. Minority pct is the percent of population that is not-white. HUD median income is the median income at the tract-level.
Tract_hud_inc_percentage is the tract-level median income as a percentage of the MSA median income.




                           Table 3B: Loan Origination. Tract-Level Analysis, 2007
                                   Pct_issued               Pct_issued               Pct_issued               Pct_issued
Ratio kept                             -.19                     -.20                     -.20                     -.21
                                      (.01)                    (.01)                    (.01)                    (.01)
Summary licensing                                                                                               -.0033
  requirements                                                                                                  (.0006)
Exam requirement                        -.01                    -.005                    -.006                     Y
                                      (.004)                   (.003)                  (.0025)
Education requirement                  -.026                     -.02                    -.018                     Y
                                      (.004)                   (.003)                  (.0024)
Experience                             .007                      .01                      .011                     Y
  requirement                         (.004)                   .(003)                  .(0035)
Population                                                    3.8e-06                 3.80e-06                     Y
                                                             (6.2e07)                 (5.7e-07)
Tract mino_pct                                                  -.002                   -.0018                     Y
                                                              (.0001)                  (.0002)
Tract HUD income pct                                           .0005                     .0005                     Y
                                                             (.00007)                 (.00007)
Code*mino_pct                                                                                                   .00003
                                                                                                               (.00003)
Exam*mino pct                                                                          .00005
                                                                                      (.0001)
Edu*mino pct                                                                           -.0001
                                                                                      (.0001)
# obs                                66317                      --                     64245                   64245
R-squared                             .09                      .36                       .36                    .35
Standard error                    3183 clusters            3132 clusters            3132 clusters           3132 clusters
Notes. Data are for home purchase loans in year 2007 only. Data includes loans that were purchased by lenders. The dependent
variable is the percent of loans that were originated or purchased by lenders at census-tract-level. The control variables are
defined in the notes of Tables 4B, 4A, and 1B. The variable “tract mino_pct” is the percent of minorities in a tract. Tract HUD
income pct is the tract-level median income as a percentage of MSA median income. The “Y” means that the variable is included
but the coefficients are not reported. Estimation method is OLS. Standard errors, in parentheses, are clustered at state-county
level.




                                                               41
                         Table 3C: Changes in Issue Rate at Tract Level, 2004-2005
                                Growth of pct           Growth of pct           Growth of pct            Growth of pct
                                    issued                   issued                  issued                  issued
    Tract Minority pct              .00006                .0004***                   .0004                 .0003***
                                   (.00005)                 (.0001)                 (.0001)                 (.0001)
    Summary licensing            -.00074***                  .0004                   .0004                      --
       requirement                 (.00027)                 (.0003)                 (.0005)
    Summary lic. req*                  --                -.0004***                -.0004***                -.00003***
       mino_ pct                                           (.00001)                (.00015)                  (.00001)
    # obs                           64108                    64108                   64108                    64108
    Adj R-sqrd                      .0001                    .0003                   .0003                     .0084
    Standard error                  Robust             Cluster at MSA           Cluster at state-             Robust
                                                              level              county level
Notes. Data are for home purchase loans in year 2004-05. Data includes loans that were purchased by lenders. The dependent
variable is the change in percent issued from 2004 to 2005 at census-tract-level. The control variables are defined in the notes of
previous tables. The minority percent is from 2007 HMDA. Estimation method is OLS, except that column 4 uses include state
FE. Standard errors are in parentheses. *: significant at 10% level. **: 5% level, ***: 1% level.


.




                  Table 3D: Changes in Issued Loan Amount at Tract Level, 2004-2005
                                 Growth of issued        Growth of issued        Growth of issued       Growth of issued
                                     loan amt                loan amt                loan amt               loan amt
    Tract Minority pct              .0043***                .0049***                .0055***               .0004***
                                      (.0004)                 (.0004)                 (.0012)                (.0001)
    Tract MSA income pct                                                              -.00016
                                                                                     (.00011)
    Summary licensing                                                                  .01**                    .0004
       requirement                                                                     (.005)                  (.0005)
    Summary lic. req.*                                                                                      -.00004***
       minority pct                                                                                          (.000015)
    # obs                               64924                  64294                   64294                    64294
Notes. Data are for home purchase loans in year 2004-05. Data includes loans that were purchased by lenders. The dependent
variable is the change in total amount for loans that were originated or purchased by lenders from 2004 to 2005 at census-tract-
level. The control variables are defined in the notes of previous tables. The minority percent is from 2007 HMDA. Estimation
method is OLS and standard errors are robust in column 1. The specification in column 2 includes MSA FE. The adjusted R-
squared is .0031 and .0022 in columns 1 and 2. Robust standard errors are in parentheses. *: significant at 10% level. **: 5%
level, ***: 1% level.




                                                                42
                      Table 4A: Loan Origination. Summary Statistics at Lender-level
                                     N            Mean          Median            P1            P25           P75            P99
                                                                 1997
      No. of lenders               7625           1052            71

                                                                  2004
 Lender_loan_number                8244           1544             72              1             21           248           26636
 Lender_number_state               8244            5                2              1              1            4             50

                                                                  2005
 Lender_loan_number                8272           1843             69              1             21           261           29820
 Lender_number_state               8272            5.3              2              1              1            4             50

                                                                  2007
 Lender_loan_number                6561           1644             80              1             20           288           19751
 Lender_number_state               6561           3.84              1              1              1            3             34
Notes. Lender_loan_number is the number of loan applications that a lender receives. Lender_number_state is the
number of states in which a lender originates or purchases loans. P1 represents percentile 1, and so on.



     Table 4B: Loan Origination. The Effect of Mortgage Broker Licensing Requirements for
                                      Subprime Lenders
                                                  2004                         2005                         2005
                                               Pct_issued                  Pct_issued                    Pct_issued
     Ratio kept                                    .049                        .007                         .067
                                                 (.011)                       (.009)                        (.10)
     Exam requirement                             -.004                       -.008                        -.005
                                                (.0015)                       (.001)                      (.0015)
     Education requirement                       .0018                       -.0018                        .0015
                                                (.0016)                      (.0015)                      (.0015)
     Work Experience                             .0025                        .0015                       .00036
       requirement                              (.0015)                      (.0015)                      (.0014)
     Dum_subprime                                  -.30                        -.31                         -.30
                                                  (.02)                        (.18)                        (.02)
                                                                         (p-value=.082)
     Dum_subprime*                                -.015                                                    -.0090
       education requirement                      (.005)                                                  (.0059)
                                                                                                       (p-value=.13)
     Dum_subprime*                               -.0016                                                    -.0063
       exam requirement                          (.0044)                                                  (.0048)
                                                                                                       (p-value=.19)
     # obs                                        34697                       36817                        36817
     R-squared                                     .16                         .50                           .15
     OLS method                                    OLS                      Lender FE                       OLS
Notes. Data are for home purchase loans in year 2006. Data includes loans that were purchased by lenders. The dependent
variable is the percent of loans that were originated or purchased at the lender-level. The control variables are defined in the notes
of previous tables. Dum_subprime is an indicator variable for the lender being in the list of subprime lenders of the HUD list.
Standard errors, in parentheses, are clustered at lender level. The number of lenders is a bit above 8,000. *: significant at 10%
level. **: 5% level, ***: 1% level.




                                                                 43
                          Table 4C: Lender-level Analysis of Loan Origination, 2006
                                 (1)               (2)              (3)            (4)             (5)              (6)
                                 Pct               Pct              Pct            Pct             Pct              Pct
                            originated         originated      originated      originated      originated       originated
 Rate kept                   -.027***          -.025***         -.017**         -.017**         -.017**              Y
                               (.008)            (.008)           (.008)         (.008)          (.008)
 Loan_amt/ income              -.0013            -.0012         -.00097          -.0010          -.0010              Y
                              (.0019)           (.0010)         (.00076)        (.0008)         (.0008)
 Summary licensing          -.0025***                                                                          -.0025***
   requirement                (.0003)                                                                            (.0003)
 Price_gr                     .21***             .25***              .24         .21***          .21***             Y
                                (.03)              (.03)            (.03)          (.03)           (.03)
 Exam requirement                               -.011***          -.0092        -.012***          -.0008
                                                 (.0014)         (.0014)         (.0026)          (.004)
 Education                                     -.0091***          -.0075        -.009***        -.012***
   requirement                                   (.0016)         (.0017)         (.0016)          (.002)
 Experience                                       .0010         .0042**         .005***          .0031*
   requirement                                   (.0015)         (.0017)          (.002)         (.0017)
 Continuing                                                      -.0029*        -.0025**
   education req.                                                (.0012)         (.0011)
 Bond+networth                                   -.0015*         -.00080          -.0006          -.0009
   requirement                                  (.00086)        (.00085)         (.0009)         (.0009)
 Office in state                                               -.0064***        -.0059**        -.0064**
   requirement                                                   (.0025)         (.0025)         (.0026)
 Pct no guarantee                                                 -.04**             Y               Y
                                                                    (.02)
 Pct owner occupy                                                -.04***            Y               Y
                                                                   (.005)
 Pct male                                                        .02***             Y               Y
                                                                   (.006)
 Pct Hispanic                                                    -.08***            Y               Y
                                                                    (.01)
 Pct single family                                                .15***            Y               Y
                                                                    (.02)
 Pct white                                                       .08***             Y               Y
                                                                   (.007)
 Pct with first lien                                               .024*            Y               Y
                                                                   (.013)
 No. of obs                    34011             34011             34011         34011           34011            33592
 R-squared                     .0074             .0050             .0906         .0925           .0956            .0009
Notes. Data are for home purchase loans in year 2006. Data excludes loans that were purchased by lenders. The dependent
variable is the percent of loans that were originated at the lender-level. The exam requirement variable in column (4) refers to
exam requirement for employee while the exam requirement variable in column (5) refers to exam requirement for licensees.
Column 6 uses data from 2005. The control variables are defined in the notes of previous tables. “Pct no guarantee” is the percent
of loans that were not guaranteed by government agencies, like FHA, etc. “Pct owner occupy” is the percent of loan applications
for homes that were occupied by owners instead of for investment purposes. “Pct male” is the percent of males among loan
applicants. “Pct Hispanic” is the percent of Hispanics among applicants. “Pct single family” is the percent of homes that are
single family instead of manufactured homes among loan applicants at the lender-level. “Pct white” is the percent of whites
among applicants for the lender. “Pct with first lien” is the percent of first lien loans. In columns 4 and 5, the exam requirement is
superseded by an indicator variable for employee exam requirement and licensee requirement, respectively. “Y” means that the
variables are included, but coefficients are not reported. Estimation method is OLS that includes lender FE. The number of lender
is close to 8200. Robust standard errors are in parentheses. *: significant at 10% level. **: 5% level, ***: 1% level.




                                                                 44
                         Table 5A. Summary Statistics of Loans That Are Securitized
               # loan     Percent         Loan      Total hp             Amt            Ratio                Amt          Mean            Delq 90+       Mean
               appli-    originated      Amount,    loan amt          securitize       secure-             secure-        Delq 90+        as of the    Delq 90_
              cations       (hp)         Median    (billion $)         d for hp         tized                tized        as of Dec        end of      as of Dec
             for home                     (hp)                        (billion $)                         (billion $)     2008            orig year      2008,
             purchase                                                                                                                                  weighted
                (hp)                                                                                                                                   orig_inv_
                                                                                                                                                          bal
 1997         8.02 m        .49            77k
 2000                                                                     64                                 114               12.4          1.9           12.0
 2001                                                                     85                                 238               11.6          1.2           8.9
 2002                                                                    112                                 350                7.7          .70           4.9
 2003         10.5 m        .53                                          173                                 542                5.0          .55           3.2
 2004         12.7m         .51           132k      854.964              360                .42              802               10.6          .63           8.7
 2005         15.2m         .49           133k      990.584              526                .53             1100               14.8          1.0           18.0
 2006         14.7 m       .458           135k      908.901              454                .50              996               18.9          1.4           24.8
 2007                                                                    141                                 354               12.6          4.2           15.0
Notes. Data securitized for hp and for all are based on the original investor balance as of Dec. 2008 for loans that
were originated in each year.

       Table 5B: Loan Performance as a Function of Loan Characteristics: Summary Statistics
  Features      ARM      Fixed    Nega     Not      IO        Not        ballo       No           Full      Low         No       retail   broke    whol     No-
                                   m       nega               IO          on        ballo         doc       doc         doc                 r      esale    data
                                            m                                        on                                                                      on
                                                                                                                                                            orig.
                                                                                                                                                            chan
                                                                                                                                                             nel
Mean of 90+      17.3     7.5     15.6     10.0    9.5        12.3       16.8       11.2          11.2      13.2        11.4     13.9     16.7     20.2     11.5
delinquency
Total amt        2.9tr   1.6tr    .4tr     3.2tr   1.3tr      3.2tr         .3tr    4.2tr         2.1tr     2.2tr       .1tr     .12tr    .04tr    .23tr    3.9tr




               Table 5B.1: Loan Performance as a Function of Loan Characteristics, Cont’d
                                                           90+ delinquency rate                                                90+ delinquency rate
LTV                                                                -.06                                                                 -.13
                                                                   (.14)                                                               (.27)
FICO                                                             -.14***                                                              -.09**
                                                                   (.03)                                                               (.04)
Yr fe                                                                 Yes                                                               Yes
spec                                                                  ols                                                            State fe
obs                                                                   406                                                               406
r-sqrd                                                                .53                                                                .51
Within                                                                                                                                   .62
between                                                                                                                                  .35
Notes. The dep. var. is the percent of loan balance that is 90+ days delinquent as of Dec. 2008 for loans originated in
years 2000-07. . Consistent standard errors are in parenthesis. *: significant at 10% level. **: 5% level, ***: 1%
level.




                                                                  45
     Table 5C: Loan Performance and Licensing, Dec. 2008 Performance for Loans Originated in 2000-07
Dep. Var.: 90+              All      All          All      All            All             HP      HP           HP            Refi          Refi      HP        HP,      Broker
                           loans     loans        loans    loans          loans                                                                      low      Full
Delinquency rate                                                                                                                                     doc       doc
Summary licensing           .23*       -.17       -.30**                             -.46***                  -.32**        -.24*       -.21       -.57**      -.17      -.48
                            (.13)     (.13)       (.145)                               (.15)                   (.14)        (.13)      (.16)        (.28)     (.14)     (.33)
req.
Lag lic. req.                                               -.34***                              -.47***
                                                              (.11)                                (.14)
Average of curr and                                                       -.44***
                                                                            (.16)
lag
Hpi change over the          -18       -14          Y           -21         -22           Y       -21           Y             Y             Y        -19       -13        N
focal yr                    (1.8)      (3)                      (5)         (5)                   (6)                                                (6)       (4)
State unemployment           .39       -.12         Y            Y          Y             Y        Y            Y             Y             Y        -.64      Y          N
rate                        (.32)     (.40)                                                                                                         (.85)
Yrs covered                00-07      00-07       03-06     03-06         03-06          03-06   03-06        02-06         00-07      03-06        03-07     03-07     03-06
State FE                     N          Y           Y         Y             Y              Y       Y            Y             Y          Y            Y         Y         Y
No of obs                   406        406         204       204           204            204     204          255           405        204          296       304       185
R-sqrd                      .65        .57         .68       .66           .63            .58     .58          .60           .53        .71          .41       .49       .33
Within r-sq                  --        .71         .85       .85           .85            .83     .83          .78           .69         Y           .67       .73       .53
Between r-sq                 --        .28         .27       .22           .16            .13     .12          .20           .18         Y           .03       .18       .02
     Notes. The dep. var. is the percent of loan balance that is 90+ days delinquent as of Dec. 2008 for loans originated in
     years 2000-07. State unemployment rate is included as a control variable. Mean delinquency rate for loans
     originated in 2004 was 10.6, 14.7 (2005), 18.6 (2006), and 12.6 (2007). Across 00-07, mean delinquency rate was
     11.7. Mean HPI change from origination year to 2008 is 1.32 for 2003, 1.2 for 2004, 1.1 for 2005 and 1.00 for 2006.
     Mean unemployment rate was 4.91 across states and 00-07. Mean of dep. Var. 2003-06 hp was 12.6. p25 of the code
     variable is 4. P75 code is 9.The standard deviation is 3.8. “Y” means that the variable is included but the estimated
     coefficient is not reported. Consistent standard errors are in parenthesis. *: significant at 10% level. **: 5% level,
     ***: 1% level.

                   Table 5D: Loan Performance at the End of the Year of Origination 2000-07, by Fixed vs ARM
     Dep var: 90+ Delinquency         All loans      All loans        All loans      ARM           Fixed         All loans          ARM           All loans    ARM
     rate                                                                             loans      rate loans                         loans                      loans
     Summary licensing req.              .01          -.052**          -.04*         -.08**         -.026              --             --             --          --
                                        (.02)          (.023)          (.02)          (.03)        (.019)
     Dummy for ARM loans               1.2***            1.2            .84             --            --            1.2***            --             1.2           --
                                        (.06)           (.07)          (.07)                                         (.07)                          (.07)
     Edu                                                                                                            -.39**          -.495*            --           --
                                                                                                                     (.18)          (.255)
     Exp                                                                                                             -.20*          -.39**           --            --
                                                                                                                     (.12)           (.19)
     Exam                                                                                                             .14             .15            --            --
                                                                                                                     (.20)           (.26)
     Total employee req.                                                                                               --               .-.13*
                                                                                                                                       --            -.22**
                                                                                                                                       (.076)         (.10)
     Licensee req.                                                                                              --             --        .15*           .19
                                                                                                                                         (.08)        (.13)
     Bond+networth                                                                                            -.02           -.02         -.05         -.06
                                                                                                             (.11)          (.16)        (.11)        (.15)
     Office in state                                                                                           .12            .23         -.25         -.27
                                                                                                             (.35)          (.56)        (.24)        (.37)
     Hpi change over past 4 yrs             N             N            N            N             N             N              N            N            N
     State unemployment rate                N             N            N            N             N             N              N            N            N
     Yrs covered                          00-07         00-07        03-06        00-07        00-07         00-07         00-07        00-07         00-07
     State fixed effects                    N             Y            Y            Y             Y             Y              Y            Y            Y
     No of obs                             800           800          402          405          395           798            404          798          404
     R-sqrd                                 Y            .44          .20          .48           .20           .43            .47         .42          .45
     Within r-sq                            --           .53          .31          .62           .28           .53            .62          .53          .62
     Between r-sq                           --           .02          .01          .02          .004          .005         .0006           .09          .11
     Notes. The dep. var. is the percent of loan balance that is 90+ days delinquent as of the end of the year of the origination. Mean of dep var is 1.45
     (# obs is 810). The median is .80. The “Y” means that the variable is included but the estimated coefficient is not reported. Consistent standard
     errors are in parenthesis. *: significant at 10% level. **: 5% level, ***: 1% level.


                                                                                    46
       Table 6. Effect of Other Government Interventions on Loan Performance: APL Laws
Dep var: 90+                    (1)             (2)             (3)            (4)             (5)             (6)
delinquency rate
APL in effect                   .81             -.21           .76              --               --              .67
                               (.96)           (.89)          (.76)                                             (.97)
HPI change from yr               --             -18            -14              --              -15               --
orig. to Dec. 2008                             (1.9)          (2.6)                            (2.6)
Summary licensing var             --            .25            -.15          -.26**           -.164           -.252*
                                               (.12)          (.14)           (.13)           (.134)           (.131)
State fe                          N              N               Y              Y                Y                Y
No obs                          406             406            406             406              406              406
r-sqrd                           .41            .64            .58             .35              .58              .36
Within                            --             --             .71            .62              .71              .62
Between                           --             --            .31             .10              .30              .10
Notes. Year fixed effects are included in all columns. The unit of observation is state-year 2000-2007. The dep var is
the percent of loans that are 90 days + delinquency as of Dec. 2008 for loans originated in years 2000 to 2007. APL
in effect is from Ding et al. (2010). HPI change is the HPI in Dec 2008 over the HPI in year of origin. Summary lic
var. is the licensing requirements in the year of origin. Consistent standard errors are in parenthesis. *: significant at
10% level. **: 5% level, ***: 1% level.




                                                           47
              Figure 1: Summary of Licensing Requirements Over Time




The graph plots the sum of all licensing requirements (code), the requirements on surety bond amount
(bond_ttl), and the requirements on surety bond and net-worth (bond_ttl_networht). For more details on the
definition, refer to notes of Table 1B.




                  Figure 2: Grant of Licenses in California, 1999-2009
                                           sls_issued

8000

6000

4000                                                                                    sls_issued

2000

  0
  1994     1996    1998    2000    2002    2004     2006   2008    2010    2012

The graph plots the number of mortgage loan broker licenses issued each year in California.




                                               48

								
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