2014
DOI: 10.1111/1540-6229.12039
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Mortgage Brokers, Origination Fees, Price Transparency and Competition

Abstract: This article examines the dynamics between mortgage broker competition, origination fees and price transparency. A reverse first-price sealed-bid auction model is used to motivate broker pricing behavior. Confirming the model predictions, our empirical analysis shows that increased mortgage brokerage competition at the Metropolitan Statistical Area level leads to lower fees. The findings are robust to different measures of fees as well as different measures of competition. We also provide evidence that broker … Show more

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Cited by 33 publications
(17 citation statements)
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“…This includes loan characteristics (fees charged on the loan, adjustable or fixed rate, loan amount, combined loan‐to‐value ratio, whether the loan has a prepayment penalty, purchase or refinance, cash‐out or rate/term refinance and whether the payments are interest‐only), property characteristics (two‐unit, condominium, owner‐occupied or investment property) and borrower characteristics (FICO score, borrower age, borrower income, whether the borrower is self‐employed, debt‐to‐income ratio). The area characteristics include the monthly MSA unemployment rate, the level of broker competition as computed in Ambrose and Conklin () and the Pahl index capturing the level of broker regulation at the state level. Higher values of the Pahl index indicate stricter regulation of brokers at the state level (Pahl ).…”
Section: Data and Empirical Strategymentioning
confidence: 99%
See 1 more Smart Citation
“…This includes loan characteristics (fees charged on the loan, adjustable or fixed rate, loan amount, combined loan‐to‐value ratio, whether the loan has a prepayment penalty, purchase or refinance, cash‐out or rate/term refinance and whether the payments are interest‐only), property characteristics (two‐unit, condominium, owner‐occupied or investment property) and borrower characteristics (FICO score, borrower age, borrower income, whether the borrower is self‐employed, debt‐to‐income ratio). The area characteristics include the monthly MSA unemployment rate, the level of broker competition as computed in Ambrose and Conklin () and the Pahl index capturing the level of broker regulation at the state level. Higher values of the Pahl index indicate stricter regulation of brokers at the state level (Pahl ).…”
Section: Data and Empirical Strategymentioning
confidence: 99%
“…A few recent papers highlight other ways that mortgage brokers positively impact mortgage markets. Ambrose and Conklin () show that mortgage broker competition reduces fees and increases pricing transparency. Ambrose and LaCour‐Little () provide evidence that mortgage brokers encourage optimal prepayment timing.…”
mentioning
confidence: 99%
“…We also take into account the impact of market competition on fees as the banking literature (Berger and Hannan, ; Liu and Skully, ) suggests that there is a positive relationship between the level of market concentration and the level of interest rates or yield spreads. Specifically, Ambrose and Conklin () document that market competition in the United States leads mortgage brokers to reduce fee charges significantly. As we examine mortgage corporations rather than mortgage brokers, this paper differs from that of Ambrose and Conklin ().…”
Section: Introductionmentioning
confidence: 99%
“…Ambrose and Conklin () use New Century data to study a different aspect of mortgage broker compensation. They focus on refinance loans and report the sum of percentage broker revenues and percentage lender fees as 3.8% for their sample.…”
mentioning
confidence: 99%
“…They prohibit mortgage broker compensation to vary based on loan terms other than principal. In particular, brokers can no longer receive yield spread premia from the lender.11 Ambrose and Conklin (2014) use New Century data to study a different aspect of mortgage broker compensation. They focus on refinance loans and report the sum of percentage broker revenues and percentage lender fees as 3.8% for their sample.12 About 24% of the YSP entries in our data are left blank.…”
mentioning
confidence: 99%