This document is provided for informational purposes only.
The Eﬀect of Mortgage Broker Licensing On Loan Origination
Standards: Evidence from Home Mortgage Disclosure Act
Department of Economics
University of Washington
November 18, 2011
We study the origination-to-distribution mortgage lending market from the mid 1990s to the
late 2000s. The current literature documents that securitization leads to weakened incentives
for lenders to screen applicants. Mortgage loan brokers originated close to two thirds of the
mortgage loans in this period. We examine whether stricter licensing requirements of loan bro-
kers 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 reduces brokers’ incentives to
take risks (e.g., by lowering current loan origination standards) that jeopardize their likelihood
of survival into the future. We exploit the cross-state variations in licensing requirements and
ﬁnd that states with more stringent requirements experienced higher standards in originating
loans, and consequently lower default rates. 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.
Lastly, we ﬁnd the eﬀect of licensing on loan origination standards is greater for neighborhoods
with greater minority percentages and lower incomes, and the eﬀect is stronger for lenders that
specialize in sub-prime lending.
Key words: Occupational Licensing; mortgage; professional labor market; expert; informa-
tion asymmetry; moral hazard; incentives
JEL codes: D82; G14; G21; J44; K2; L1; L5; L15
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: email@example.com.
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. 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. Another
argument against the use of licensing is that market mechanisms can work, i.e., players’ concern for
their reputation disciplines themselves. In what environment would the market mechanism work
and regulation hinders eﬃciency? And when would the reputation mechanism work poorly and
regulation 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 We
focus on the link between mortgage loan brokers and lenders. Given that lenders have little incen-
tives to screen applications,3 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 requirements, leads to greater
eﬃciency in the loan origination market. There are several channels that licensing helps raise the
See, e.g., Shapiro (1986).
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.
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.
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. 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. Looser regulation of the brokers
thus leads to lower quality in the loan-origination market.
A related yet distinct mechanism is that by raising the entry barrier, more proﬁt is gen-
erated, which raises the value of staying in the business for the long term. This may serve as a
deterrent for the brokers to take risks in the form of lowering lending standards. We therefore pre-
dict that loan-origination standards will be higher where licensing is more stringent in this period
of 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 start our analysis by comparing the state of Minnesota (MN) and Wisconsin (WI).
These two states are very similar in most aspects, but they diﬀer in how they regulate loan bro-
kers. WI has much stricter requirements than MN. We conduct a loan-level analysis and have
several ﬁndings. Noticing the great heterogeneity across lenders, which are likely aﬀected by the
licensing requirements and also aﬀecting the dependent variable (origination rate), we focus on
results with lender ﬁxed eﬀects. We ﬁnd that within-lenders and controlling for all observable
loan characteristics, including race, gender, loan/income, and tract demographic information, the
loan origination rate in WI is 3.5% lower than that in MN. Second, we ﬁnd that the eﬀect of WI
requirements is greater for loan applications in low income neighborhoods. Third, we ﬁnd that the
eﬀect is stronger for lenders that specialize in sub-prime lending. Results for reﬁnance loans are
very similar to the baseline results for home-purchase loans. Fourth, the eﬀect is absent in periods
after the credit crisis. Lastly, MN has a greater foreclosure rate than WI. All of this evidence
supports the conclusion that the origination standard is greater in WI, which has more stringent
licensing requirements for loan brokers.
We then test the eﬀect of loan broker licensing on loan origination market eﬃciency 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
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, requirements 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.
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.
To highlight the eﬀect that the absence of lender incentives had on our results, we also
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
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
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.
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, introduce
data in Section 4, compare MN and WI in Section 5, present and discuss results in Section 6, and
conclude in Section 7.
2 Background; Institutional Details
2.1 Purchase of Loans by Government Agencies and Securitization of Loans by
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
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 2006.4
The development of secondary markets has many beneﬁts. First, they increase compe-
Starting in mid-90s, private ﬁrms took up a greater market share in securitization.
tition by encouraging the development of a new industry of loan originators. Absent secondary
markets, the only institutions originating mortgage loans are those with the capacity to hold them
permanently, termed “portfolio lenders.” The entry of mortgage companies who can sell into the
secondary market increased the competition among lenders. Secondary markets also increase ef-
ﬁciency by encouraging a specialization of lending functions that reduces costs. Portfolio lenders
typically do everything connected to originating and servicing loans. Secondary markets, in con-
trast, create pressures to break functions apart and price them separately. Third, conversion of
mortgages into mortgage-backed securities permits a better distribution of the risk of holding ﬁxed-
rate mortgages. Historically, depository institutions were not well positioned to hold loans that
were long-term and had ﬁxed-rates because their deposits were short-term. Many pension funds, in
contrast, were well positioned to hold long-term investments. Finally, mortgage-backed securities
are also “liquid” while mortgages themselves are not. Because most investors value liquidity and
are willing to accept a lower yield to get it, converting illiquid mortgages to liquid securities puts
downward pressure on the rates charged to borrowers. Secondary markets have also vastly ex-
panded the size of the borrower pool. Portfolio lenders generally restrict their loans to “A-quality”
borrowers. Secondary markets, in contrast, can access investors who are prepared to hold risky
loans if the price is right. The result has been the emergence of the so-called “subprime market.”
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.5
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. These problems are traditionally mitigated by the market
by means of the issuer’s reputation with the investors and by the ratings from rating agencies, like Moody’s, which
served the role of rating the asset-backed securities. In addition, the incentive compensation of investment bankers
(arrangers) was focused on fees generated from assembling ﬁnancial products, rather than the performance of those
products and proﬁts generated over time.
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. A mortgage bank is a state-licensed
banking entity that makes mortgage loans directly to consumers.
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 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).6 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: How Loan Brokers Are
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
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.7
By selling them shortly after they are closed and funded, they are eligible for earning a service release premium.
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.
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).8
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. These types of loans are useful for borrowers who have certain needs or
face certain constraints, and if priced right, are acceptable to certain lenders (and investors). These
types of loans are certainly not for everyone.9 Because of the higher rates and greater proﬁts that
the lenders can receive from the secondary market, lenders often give higher rebates to brokers for
these 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 tradeoﬀ. 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 because of 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.10
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.
Yet, it is documented that a very high percentage of loans made to subprime borrowers are ARM, interest-only,
If the loan is originated by an employee, these rebates would be received by borrowers. Now with the use of
brokers, it is via brokers that the rebates go through.
2.4 Regulations in the Loan Origination Market
The ﬁve agencies — the Oﬃce of the Comptroller of the Currency (OCC), the Board of Governors of
the Federal Reserve System (FRS), the Federal Deposit Insurance Corporation (FDIC), the Oﬃce of
Thrift Supervision (OTS), and the National Credit Union Administration (NCUA) have regulatory
and supervisory responsibility over certain types of institutions. Compared with depository lenders,
non-depository mortgage lenders are lightly regulated (Keys et al., 2009).11
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 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.
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”.
On regulation of mortgage banks, Massachusetts laws have equity requirement ($200,000). Bond requirement is
often used. Some states require that a mortgage company have a license.
Licensing is implemented at various levels of the mortgage broker ﬁrms. 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
both the sum of all requirements and the speciﬁc requirements.
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. The proﬁt
rate depends on the type and the rate of the loans. Lenders then factor these variables into their
contracts with brokers. Since lenders do not hold the loans, they care less about the re-payment
risk.12 Indeed, Keys et al. (2010) and Purnanandam (forthcoming) showed that lenders have
reduced incentives to screen loans than in an origination-and-keep model.
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.
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’
Second, in a market with asymmetric information, competition may play a perverse role.
Looser requirements make it easier to enter the market; therefore there are more agents present in
response to a hot market. Agents need to complete a certain number of deals in order to survive or
support themselves. When there is more competition, the average number of deals is less, and the
incentives to lower standards (and thus quality) is greater. This is possible because borrowers are
not able to protect themselves since they are less informed than the brokers. A related mechanism
is oﬀered by Keeley (1990) and Hellmann et al. (2000), who argue that with entry barriers comes
Of course, the ﬁnal investors care. Yet we argue that the information asymmetry between issuers and investors,
and between lenders and issuers, is not fully addressed by the market mechanism, including the reputation mechanism.
less competition, more proﬁt, higher charter value, and less incentives for players to take risks that
might endanger their survival into the future. Lowering loan origination standards is a form of
risk-taking since there is the possibility that lenders can’t sell in the secondary market, and would
therefore have to hold the portfolio of originated loans.
Summing up, licensing enhances the broker selection process, as well as improves the incen-
tives for brokers. 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 licens-
ing requirements raise the standards of loans origination in the origination-to-distribute mortgage
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. Lastly, lenders diﬀer in
their market share in lending to less informed borrowers. We therefore predict that the eﬀect of
licensing is greater for lenders targeting less privileged borrowers — sub-prime borrowers.
4 Identiﬁcation and Econometric Speciﬁcation
The identiﬁcation strategy is the state-level variation in licensing regulations. Our baseline speci-
ﬁcation, 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
relevant information used by brokers in brokering the loan, greater licensing requirements should
lead to greater origination standards, i.e., we test whether β lic < 0.
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.
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 estimates, 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.
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 (nonmetropolitan) 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
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.13 For the overall requirement, FL and CA are among the top 5 most stringent states. For
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
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
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
States vary in various dimensions; therefore the issue of omitted variables is always a concern for
any state-level analyses. We therefore start our empirical analyses by comparing two states that
are very similar yet diﬀer greatly in how they regulate mortgage brokers: Minnesota (MN) and
6.1 Initial Results: Comparing Minnesota and Wisconsin
In 2007, Minnesota had 5.7 million people and 1, 354 census tracts, while WI had 5.9 million people
and 1, 351 census tracts. In 2005, the states’ race and ethnicity demographics were quite similar:
70.4 percent of the MN population and 75.4 percent of WI population were white. WI had stricter
broker laws.14 MN has no licensing requirement from 1996-98. From 1999 to 2007, it imposed
only one restriction: the registration requirement. WI had two requirements from 1996 to 1997:
registration for the licensees and the employees. From 1998 to 2004, the state imposed further
requirements: networth (coded value is 3) and surety bond (coded value is 1). From 2005 to 2007,
WI further required employees to pass exams. Over time, WI has a mean licensing requirements
of 6 while MN has 1.
The Division of Banking of the Wisconsin Department of Financial Institutions (DFI) regulates both mortgage
broker entities and their employees pursuant to Chapter 224 of the Wisconsin Statutes. Wisconsin registers ap-
proximately 793 brokerages and 14, 094 loan originators through the DFI. The Minnesota Department of Commerce
regulates mortgage brokers pursuant to its Residential Mortgage Originator and Servicer Licensing Act.
6.1.1 Summary Statistics and Regression at the Loan-level
In 2005, MN had 238,031 loan applications, and the origination rate was .53; WI had 192,052 loan
applications, and the origination rate was .58. The median of the loan/income ratio in MN is 2.30,
while that in WI is 2.04.
Since the origination decision depends on various factors, including borrower character-
istics, such as loan/income ratio, race, gender, and loan characteristics, we include all possible
information in the equation below
I originatedilhc = β i Xi + β l Xl + β h Xh + β c Xc + εilc (1)
where i refers to the borrower, l refers to the loan, h refers to the house, c refers to the census-tract
that the house is located in, Xi is the borrower-level variables, including their race and gender,
Xl is the loan-level variables, including the loan amount/income, Xh is the house-level variables,
including whether it is a single home, multi-family home, or manufactured home, and Xc are
census-tract-level variables. We use a linear probability model due to the ease of interpretation.
There were in total 1, 281 lenders operating in 2005 in these two states that have reported
to HMDA. Among them, 339 banks operated only in MN, 425 operated only in WI, and 517 lenders
operated in both states. For the 339 lenders solely in MN, the average number of loan application
was 28 and the average origination rate was .67. For the 425 lenders solely in WI, the average
number of loan application was 68 and the average origination rate was .77. For the 517 lenders,
the mean number of loan application was 442 in MN and 316 in WI, and the percentage originated
was .58 in MN and .56 in WI. It is apparent that lenders who operated solely in one state diﬀer in
their size across the two states.15
One concern is that states with diﬀerent licensing requirements might attract diﬀerent
kinds of lenders, which aﬀect the loan application outcome. This would cause a spurious eﬀect
between licensing and loan application outcome. We therefore add lender ﬁxed eﬀects in our
baseline speciﬁcation. Our identiﬁcation strategy is then within-lender across-state variation.
Table 2B reports the results from the loan-level analysis. Column 2 shows results after
including lending ﬁxed eﬀects to the speciﬁcation in Equation (1), and we ﬁnd that for lenders
which operate in both states, the loan origination rate is lower in WI.16
Smaller lenders appear to have greater origination rates. This may relate to the fact that smaller-sized lenders
may be better at processing soft information, whereas larger ones have to rely on hard information in underwriting
a loan (Stein, 2002).
The simple statistics show that WI has higher origination rates, yet after controlling for borrower, loan, and
This ﬁnding is consistent with our hypothesis that states that have more stringent broker
licensing requirements have greater standards for originating loans. The estimated coeﬃcient on
WI, −3%, is statistically signiﬁcant. That is, loans in WI are 3% less likely to be originated than
loans in MN for the same observables for the same lender.17
6.1.2 Tract-level Variation
We obtain tract-level data on the percent of population that is minority (minority percentage) and
the income percent compared with median MSA income (called MSA median income percentage).
We merge this data with the main HMDA data. The coeﬃcient on the minority variable is barely
signiﬁcant. It is likely due to the fact that there is not much variation in the minority percentage
across tracts in these two states. The coeﬃcient on the median MSA income percentage is signiﬁ-
cantly positive, i.e., a loan from a higher income tract is more likely to be originated, controlling
for observables. The signiﬁcant of the coeﬃcient of interest is unaﬀected by the inclusion of these
We hypothesize that loans by borrowers in low-income neighborhoods are what short-
term brokers target, since they are likely less informed. We deﬁne a dummy variable for tracts
with moderate to low income (whose MSA median income percent is lower than 80) and another
dummy variable for tracts with low income (whose MSA median income percent is lower than
50) and interact these two variables with the indicator variable for WI. Around 17 percent of all
applicants are from moderate to low income tracts, with 3.5 percent from low income tracts. The
results with the interaction term are in columns 6-8 of Table 2B. In column 8, for example, we
ﬁnd that the coeﬃcient on the WI variable is −.036, while the coeﬃcient on WI*low income tract
is −.03; i.e., a loan application in WI is 3.6% less likely to be originated, and this eﬀect is an
additional 3% for a loan from a low income tract.18
census-tract characteristics, MN has the higher origination rate. This contrast highlights the importance of controlling
for information that goes into the loan origination decision.
Other coeﬃcients are reasonably estimated. For example, for the speciﬁcation in the last column of Table 2B,
we ﬁnd that male applicants are 1.3 percent more likely to be originated; compared with loans that are sold to other
parties, loans that are orignated and sold to government agencies are 45 percent more likely to be originated; loans
that are sold to private entities are 71 percent more likely to be originated; loans for non-manufactured homes are
15 percent more likely to be originated; loans for Hispanics are 4 percent less likely to be originated; loans for whites
are 8 percent more likely; ﬁrst lien loans is 11 percent more likely to be originated than otherwise; and an increase
of 1 in loan/income ratio leads to 1.7 percent less likelihood to be originated. We ﬁnd that the coeﬃcient on WI
borders on statistial insigniﬁcance in a speciﬁcation without the lender ﬁxed eﬀects.
We try another way to measure information asymmetry by exploiting that lower income people are generally
6.1.3 Lender-level Variation — Being a Sub-prime Lender or Not
According to the U.S. Department of Housing and Urban Development (HUD), there were 210
sub-prime lenders in 2005. We merge this ﬁle with our main MN-WI data and ﬁnd that 113
sub-prime lenders were not present in MN and WI in 2005. The remaining 97 sub-prime lenders
received 65,700 loan applications in these two states, which is 15 percent of all loan applications.19
Subprime lenders on average operate in 1.7 states and non-subprime lenders operate in 1.4 states.
Sub-prime lender status is a state-invariant variable; we therefore use the speciﬁcation with-
out lender ﬁxed eﬀects. The coeﬃcient on WI is insigniﬁcant, the coeﬃcient on subprime lender it-
self is −.12 and is statistically signiﬁcant at conventional levels, and the coeﬃcient on WI*subprime lender
is −9.4% and statistically signiﬁcant. That is, across all lenders in the two states, subprime lenders
have lower origination rates (possibly due to the higher risk loan applications that they receive),
with sub-prime lenders in WI having even lower originating rates than in MN, which has lighter
Lastly, we examine the performance of mortgage loans. We focus on loans made in 2006.
From HUD data that is based on the Delinquency Survey of the Mortgage Banker Association, in
less able to evaluate the brokers’ recommendations. We create an interaction term, WI*(1-MSA median income
percentage), and include it in the regression. Seen in column 6, the coeﬃcient on this variable is statisitically
signiﬁcant and negative. We also examine whether the competitiveness in the loan origination market aﬀects the
lenders’ loan origination decision. We compute the Herfendial index per state-county by adding up the squared
market share of each lender in each state-county. We include it in the regression and ﬁnd that counties with less
competitiveness use higher standards. The coeﬃcient on our variable of interest is not aﬀected.
Among the 65,700 loan applications, the origination rate was .41. Loan applications with subprime lenders
totalled 38,765 in MN, and the origination rate was .43. Loan applications with subprime lenders in WI totalled
26,935, and the origination rate was .39.
We also examine MN vs. WI in other years. The summary statistics are in Table 2A and estimation results
are not shown. We use the same speciﬁcations as those for 2005. In year 2004, the eﬀect is equally strong. The
eﬀect was smaller in 2003. The eﬀect of WI in 1997 was quite similar to the eﬀect in 2005. We also examine data
for 2006-2009. We ﬁnd that the eﬀect of WI regulation goes down in years 2008-2009, particularly in reﬁnance.
This is re-assuring since during the post credit crisis period, securitization virtually ceased to exist, and thus the
lenders’ incentive to screen was restored. Hence the role of broker regulations should play a smaller role. We also
examine loan applications for reﬁnance purposes. Overall the results for reﬁnancing are quite similar to those for
home purchase. We also consider whether lender-level variation in their degree of selling to secondary markets aﬀects
their decision in originating loans. We compute the lender-level degree of keeping loans on their balance sheet. The
variation was large. When we include this variable in the regressions, the coeﬃcient on it is negative, i.e., lenders
that do not sell to secondary market use higher standards. This is consistent with Keys et al. (2010)’s ﬁnding that
the ease of securitization reduces lenders’ incentives in screening loans.
2008 the delinquency rate for MN was .048, while the rate for WI was .042. While comparing MN
and WI provides some insights, below we proceed to examine the whole country.
6.2 Basic Results: 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 3. 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.21 22 These pieces of evidence suggest that licensing
aﬀects entry.23 24
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).
We compare MN and WI and ﬁnd that WI showed a more restrained entry and exit than MN.
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.
More stringent licensing requirements on brokers may lead loan professionals to become loan oﬃcers instead.
6.3 Basic Results: 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 +loan amt/incomest,yr +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.25 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 4A. 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. 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 percent lower origination rates.
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.
Thus, we think our results are a lower bound of the true eﬀect of licensing on broker entry.
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.
The results are in column 8; the coeﬃcient on the summary licensing variable is of slightly lower
6.4 Which Speciﬁc Requirements Matter?
Table 4B 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.26 We ﬁnd that the
coeﬃcients on the requirement of passing exams and work experience are negative but statistically
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
6.4.1 Licensing on Branching
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
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.
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.
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.28
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 4B. 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 4B. 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.
6.5 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?29 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
4C. 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.30 In columns 2-4, we report the coeﬃcient on the education
The information is at http://raincityguide.com/2011/02/01/loan-originator-mortgage-broker-and-consumer-
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.
We report the summary statistics for speciﬁc requirements at diﬀerent levels of the ﬁrm. From 1997 to 2006,
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
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
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.31
6.6 Findings on Loan Riskiness
Lastly, we examine whether the stricter licensing requirements decrease the riskiness of the orig-
inated 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 4A.
We 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. The coeﬃcient implies that, compared with states at the 25th
percentile of the summary requirements (3), states at the 75th percentile (8) are .013∗(8−5) = .065
lower in loan amount/income. The mean of loan amount/income is 2.19, so .065 is 3 percent lower.
Together these pieces of evidence suggest that states that have looser licensing requirements
originated a higher proportion of their loan applications.32
6.7 Findings on the Default Rate
We obtain HUD county-level data on foreclosure rates that is based on the delinquency survey data
from the Mortgage Banking Association. We estimate the following equation:
foreclosure ratec,s = pct high cos tc,s +hs pr ch heightc,s +pct unemployc,s +Licensing reqs +εc,s, .
where c refers to county, s refers to state, pct high cost is percent of total loans made between
2004 and 2006 that are high cost, hs pr ch height is percent change in MSA OFHEO price index
required; in 9.2% state-years, managing principal’s passing exams is required.
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.
We also examine the state-level mean loan volume/income for the loans that are issued (originated and purchased
by lenders). We conﬁrm our ﬁndings from originated loans.
(current price relative to the maximum in the past 8 years), pct unemploy is percent unemployed
in the county in June 2008. OFHEO represents Oﬃce of Federal Housing Enterprise Oversight.
The dependent variable is the foreclosure rate in June 2008. The licensing requirement is from
2006, for it usually takes two years for loans to show up as delinquent and become foreclosed.33
We adjust the standard error for clustering at the state level. We try diﬀerent ways of
putting the licensing variables in the regression: using edu req + exp req + exam req only, or
bond networth req, or all included. Table 4D reports the regression results. We ﬁnd that in the
speciﬁcation of using bond networth req only, the coeﬃcient on the variable is signiﬁcantly negative
(at the 10-percent level). This is consistent with the prediction that more stringent requirements
raise loan origination standards and hence loan performance.
When we use edu req + exp req + exam req, the coeﬃcients on these variables are in-
signiﬁcant overall. Coeﬃcients on other variables are reasonably estimated. For example, a greater
percentage of high cost loans, a greater reduction in house prices from the height during the past
8 years, and a greater unemployment rate are associated with greater foreclosure rates.34
In sum, the previous 7 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 sections below, we explore the
variation ﬁrst at the tract-level, and then at the lender-level.
6.8 Tract-level Analyses
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 are in the process of securing data on delinquency of various types of loans, including ﬁxed-rate loans and
adjustable-rate loans. We expect that the eﬀect is greater for adjustable-rate loans.
Approximately 82 percent of U.S. mortgages issued to subprime borrowers were adjustable-rate mortgages. After
U.S. home sales prices peaked in mid-2006 and began their steep decline thereafter, reﬁnancing became more diﬃcult.
As adjustable-rate mortgages began to reset at higher interest rates, mortgage delinquencies soared.
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. In the section comparing MN and WI, we used this variable
in the loan-level analysis.
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 5A 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
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 5B. All standard errors are clustered at state county level. Column
1 introduces only the three speciﬁc licensing requirements: those on education, exam, and work
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.35 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.
We also ﬁnd that the requirement for exams is now negatively associated with the issuance rate.
6.8.1 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 5A shows that across tracts, the median of the
growth rate is .96. Table 5C 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.36 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.37 Table 5D 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.38
6.9 Lender-level Analyses
We conduct lender-level analyses. Table 6A provides some summary statistics.39 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
This ﬁnding conﬁrms what Mian and Suﬁ (2009) found: The number of loan originations increased more in
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.
These ﬁndings are also present in 2003-04.
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
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
We thus include lender ﬁxed eﬀects in our basic econometric speciﬁcation for lender-level
analysis of loan origination rates; 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 6B. 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
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
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.
6.9.1 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 6C.40 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.41
6.10 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.42 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
We are working on including results from other years.
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.
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.
6.11 Further Analyses
First, we will examine the loan delinquency rate in more detail, e.g., according to whether loans are
prime or subprime. Second, diﬀerent government agencies are designated to be responsible for the
licensing regulation. The two agencies that appear most frequently in the data are the Department
of Financial Institutions and the Department of Commerce. We would like to investigate whether
the identity of the regulating government agencies matters. Third, 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 examine
whether introducing this aspect would aﬀect our main ﬁndings.
7 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,
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. 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.
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(2006), “License to deal: Regulation in the mortgage broker industry,” Community Dividend, The
Federal Reserve Bank of Minneapolis.
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ings?” Journal of Financial Economics, 101 (1).
Dell’Ariccia, Giovanni, Deniz Igan, Luc Laeven (2009), “Credit Booms and Lending Standards:
Evidence from the Subprime Mortgage Market,” European Banking Center Discussion Paper No.
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Garmaise, Mark (2010) “After the Honeymoon: Relationship Dynamics Between Mortgage
Brokers and Banks,” mimeo, UCLA Anderson.
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licensed as loan originators,” Miami Herald.
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Hazard in Banking, and Prudential Regulation: Are Capital Requirements Enough?,” American
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Lead to Lax Screening? Evidence from Subprime Loans,” Quarterly Journal of Economics, 125
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Analyzing Earnings, Employment, and Outcomes for Consumers,” NBER Chapters, in: Studies of
Labor Market Intermediation, pages 183-231 National Bureau of Economic Research, Inc.
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from the U.S. Mortgage Default Crisis” Quarterly Journal of Economics, 124 (4): 1449-1496.
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1. (Mar.): 25-53.
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2006.” Federal Reserve Bank of Minneapolis Community Aﬀairs Report, No. 2007-2.
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Mortgage Crisis,” Review of Financial Studies.
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dence,” The Quarterly Journal of Economics, 109 (2): 399-441.
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A. 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.
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)
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 puchased 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. State-level Analysis of Delinquency Rate
We collect state-level data from LoanPerformance and the MBA Delinquency Survey. Our state-
level data from LoanPerformance covers only loans securitized by private ﬁrms. Our dependent
variable is percent of loans that are delinquent for 90 days or more as of 2011. For example, for
loans originated in 2006, the mean delinquency rate is around 30 percent. The rate for ARM loans
originated in 2006 and later securitized is around 35 percent. The state-level data from MBA are
for loans originated in Q1 of 2007, and the dependent variable is percent of loans that are seriously
delinquent, i.e., 90+ days delinquent or in foreclosure. The mean rate is around 6 percent. For
the state-level analyses, we include price change from the origination year to the current year. We
ﬁnd that for either data set, the coeﬃcient on the price change is negative, conﬁrming the intuition
that house price decreases raise delinquency rates. Yet when we include the licensing variables,
we consistently ﬁnd the result that the coeﬃcient on the summary licensing variable is positive.
We do note that Mian and Suﬁ (2009) pointed out that state-level analyses often yield misleading
results, likely because of the omitted variables problem. We are conducting further analyses on the
eﬀect of mortgage broker licensing on loan performance.
Table 1: 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
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.
Table 1B: Licensing Requirements for Mortgage Brokers, 1996-2006
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
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.
Table 2A: Summary Statistics, WI vs. MN, 1997-2009
MN WI MN WI Orig+ Orig+ MN WI Sold to
loan loan orig orig purchase purchase white white private
appl no appl no rate rate rt rt rate rate firms
1997 112k 102k .62 .67 .82 .79 .74 .83 .9%
2003 180k 152k .60 .62 .80 .81 .73 .77 1.7%
2004 199k 165k .57 .61 .79 .80 .72 .76 <1%
2004 refi 339K 342K .43 .48 .59 .63 .70 .73 .39%
2005 refi 335k 353k .40 .43 .55 .58 .71 .73 2%
2006 213k 180k .50 .55 .75 .77 .66 .72 2%
2007 140K 140k .51 .54 .76 .70 .70 .77 .5%
2008 97k 88k .57 .59 .77 .79 .75 .81 .5%
2008 refi 152k 241k .42 .49 .55 .63 .77 .82 .2%
2009 102k 86k .55 .57 .83 .81 .75 .81 1.3%
2009 refi 261k 370k .51 .57 .68 .74 .78 .83
Notes. Data are for home-purchase loans unless otherwise noted (refi represents refinance). The variable “loan appl no”
is the number of loan applications. The variable orig rate is the percentage of loans that are originated. The variable
“orig+purchase rt” is the percentage of loans that are originated or purchased by lenders. The variable “white rate” is
the percentage of population that is white. The variable “sold to private firms” is the percentage of loans that are sold to
private labels, e.g., investment banks.
Table 2B: Loan-level Analyses for MN and WI, 2005
(1) (2) (3) (4) (5) (6) (7) (8)
Dep. Var. Orig. Orig. Orig. Orig. Orig. Orig. Orig. Orig.
WI -.0072 -.0072* -.068*** .0064* -.03*** -.09** -.035 -.036
(.0037) (.0038) (.024) (.0034) (.01) (.04) (.012) (.012)
Loan/income -.029*** -.029*** -.076 Y Y Y Y Y
(.0008) (.004) (.0016)
kept -- -- -- Y .07 Y Y Y
Sell to private -- -- -- .45 .45 Y Y Y
Sell to gvnt -- -- -- .36 .71 Y Y Y
MSA_med_inc_ -- -- -- -- -- .0004 -- --
WI*(1-inc pct) -- -- -- -- -- -.0002** -- --
WI*mod_low -- -- -- -- -- -- -.0075 --
WI*low income -- -- -- -- -- -- -- -.030***
Specification Robust Robust Robust Robust Robust Lender Lender Lender
FE FE FE
Cluster State State- State- Sate- Agency Agency_ Agency_ Agency_
tract tract tract lender lender lender lender
# obs 371773 371773 371773 368083 368083 368083 368083
R-sqrd .18 .18 .03 .24 .20 .23 .21 .21
Notes. The dependent variable orig is an indicator variable that takes the value of 1 if the loans is originated. Column 3 is for loan
applications at mortgage companies. WI is an indicator variable for the state being Wisconsin. Loan/income is the ratio of loan
amount divided by applicants’ income. The variable “kept” is an indicator variable that the loan is kept by the lender instead of
being sold. Sell to private is an indicator variable for the loan being sold to private labels, e.g., investment banks. Sell to gvnt is
an indicator variable for the loan being sold to government-sponsored entities, like Fannie Mae or Freddie Mac.
Msa_med_inc_pct is the tract-level median income as a percentage of the MSA median income level. WI*(1-inc pct) is the
interaction term between WI and (1-Msa_med_inc_pct). Mod_low_income is an indicator variable for being in a tract whose
median income is moderate or low. The control variables include a dummy for being mortgage companies, a dummy for owner
occupy, a dummy for being male, a dummy for originated and kept, a dummy for being non-manufactured home, a dummy for
being Hispanic, a dummy for being white, a dummy for being first lien loan, the percent of minority in the tract, and a dummy for
being WI. “Y” means that the variable is included but the coefficient is not reported. Column 6 further includes variables “sell”
and “WI*sell” where sell is an indicator variable that takes the value of 1 if the loan is sold in secondary market. All standard
errors, in parentheses, are robust. *: significant at 10% level. **: 5% level, ***: 1% level.
Table 3: 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
Bond+net worth -.0084 -.03
lagged (.0051) (.02)
(p-value: .104) (p-value=.15)
Bond_all+net worth -.0082 -.014
lagged (.0050) (.010)
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 4A: Panel and Yearly Analysis at State-level, 1997 and 2003-06
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Pct_ orig Pct_ orig Pct_ orig Pct_ orig Pct_ orig Pct_ orig Pct_ orig Pct_issue, Loan
Ratio kept -.08 -.007 -.09 -.18 -.10 -.06 -.00083 -.21** .007
(.10) (.005) (.19) (.12) (.10) (.11) (.0090) (.10) (.023)
Summary -.0053*** .002 -.0045*** .0053*** -.0058*** -.0056*** .0041*** -.013*
lic. req. (.0013) (.004) (.0013) (.0015) (.0016) (.0016) (.0010) (.007)
Summary lic -.0054***
req, lag (.0014)
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)
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.
Table 4B: Panel Analysis, 1997 and 2003-06: Impact of 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*
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. 204 204 204 204 204 204 204 51
Years covered 2003-06 97 03 04 05 06 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 4C: Regulating Licensees vs. Employees
(1) (2) (3) (4) (5) (6) (7)
Dep var: pct Pct Edu req. is Edu req. is Edu req. Exam Exam Exam
originated originated for for is for req. is for req. is for req. is for
licensee managing employee licensee managing employee
Ratio kept -.006 Y Y Y Y Y Y
Loan income .005 Y Y Y Y Y Y
Home Price growth .11 Y Y Y Y Y Y
Licensee req -.013** -.022* -.017
(.0065) (.013) (.019)
Man principal req -.0068* -.015 -.002
(.0035) (.013) (.01)
Employee req -.0046 Y Y -.033** Y Y .001
(.0041) (.013) (.02)
Office in state -.0092 Y Y Y Y Y Y
Bond+Networth -.007** Y Y Y Y Y Y
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.
Table 4D. Foreclosure Rate and Licensing Requirements
No of obs 3137
Notes. Data are at the county level. The dependent variable is the percent of loans that were foreclosed as of June 2008. The
variable bond+networth is the sum of the surety bond requirement and net-worth requirement (see Table 1B) in year 2006. The
regression includes as control variables percentage of loans that are high cost, BLS unemployment rate, and house price change
from the height during the past 8 years. Standard errors, in parentheses, are clustered at state level.
Table 5A: Summary Statistics on Tract-level Data, 2007
Population number Minority pct HUD median Tract HUD inc
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 5B: 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
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
Tract mino_pct -.002 -.0018 Y
Tract HUD income pct .0005 .0005 Y
Exam*mino pct .00005
Edu*mino pct -.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
Table 5C: 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 5D: 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
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.
Table 6A: Summary Statistics at Lender-level
N Mean Median P1 P25 P75 P99
No. of lenders 7625 1052 71
Lender_loan_number 8244 1544 72 1 21 248 26636
Lender_number_state 8244 5 2 1 1 4 50
Lender_loan_number 8272 1843 69 1 21 261 29820
Lender_number_state 8272 5.3 2 1 1 4 50
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 6B: Lender-level Analysis, 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
Pct owner occupy -.04*** Y Y
Pct male .02*** Y Y
Pct Hispanic -.08*** Y Y
Pct single family .15*** Y Y
Pct white .08*** Y Y
Pct with first lien .024* Y Y
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.
Table 6C: 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)
Dum_subprime* -.015 -.0090
education requirement (.005) (.0059)
Dum_subprime* -.0016 -.0063
exam requirement (.0044) (.0048)
# 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.
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
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.