Paying Too Much: Bhutta, Fuster & Hizmo (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 42 pages. To replicate or extend it, access the full source at the original (paywalled).

TL;DR

Using a proprietary platform dataset of 3.6 million rate-lock agreements (Optimal Blue, Jan 2015 to Dec 2019) linked to real-time lender offer distributions, the paper constructs an Expected Gain from Additional Search (EGain) metric: the rate reduction a borrower could expect by obtaining one more quote from a randomly drawn lender. FHA borrowers average 28 bp of EGain; jumbo borrowers average 4 bp. Overpayment rises when market interest rates are low, consistent with behavioral factors reducing shopping effort. Substantial rate dispersion persists even within the same lender, branch, and loan officer on the same day. Expensive lenders earn higher profits: a 1 pp rate premium translates to $4.05 more gross income per $100 originated, with no evidence of better service quality. NSMO survey data show that a composite borrower sophistication index (shopping and knowledge) predicts rates 23 bp lower for the most vs. least sophisticated, and the benefit of lower market concentration accrues primarily to sophisticated borrowers.

Core results

Magnitudes and significance are as reported; */**/*** = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	FHA borrowers have the largest expected gain from additional search	Table II, p. 66; Figure 2, p. 65	Mean EGain = 28 bp for FHA vs. 17 bp conforming, 10 bp super-conforming, 4 bp jumbo; 25% of FHA borrowers have EGain above 40 bp
R2	Low-FICO and high-LTV borrowers overpay more, conditional on loan amount	Table III, p. 67	FICO>=740 vs. [640,660]: coef -0.097*** (col 1); adding lender-branch FEs reduces but does not eliminate the FICO gap
R3	EGain falls when market interest rates are higher	Table IV, p. 70; Figure 3, p. 69	A 1 pp rise in the 10-yr Treasury yield reduces average EGain by about 5 bp (coef -0.051***, col 1); effect persists after lender-branch FEs (coef -0.026**, col 3); stronger for unconstrained borrowers (DTI <= 36: interaction +0.012***)
R4	Substantial residual rate dispersion remains after controlling for all observable characteristics, lender, branch, and LO	Table V, p. 73; Table VI, p. 75	90-10 percentile gap: 55 bp raw (col 3); 26 bp after full lender, branch, and LO FEs (col 10); FHA 90-10 gap = 31 bp, jumbo = 24 bp in most restrictive spec; gap for same-branch same-day identical borrowers = 31 bp (col 8)
R5	Larger lenders and those with high FHA share are more expensive and more profitable	Table VII, p. 79; Table VIII, p. 81	Size quartile 4 lender FE: +0.127*** pp (col 1); FHA share coef: +0.385*** (col 2); 1 pp higher rate associated with $4.05*** more gross income per $100 originated (Table VIII)
R6	Expensive lenders have higher costs but higher net income; higher costs mostly reflect personnel, not service quality	Table VIII, p. 81	Gross expenses rise $3.50*** per $100 per 1 pp rate; net income rises $0.45** (residential) and $0.24** (all lines); technology and occupancy effects are small
R7	More expensive mortgages are not associated with better service; borrowers paying more are less satisfied	Table IX, p. 84	A 100 bp more expensive mortgage: satisfied with interest rate -13*** pp, satisfied with lender -2.3*** pp, application process -1.9** pp; overall satisfaction coefficient -0.035***
R8	Borrower sophistication (shopping and knowledge) strongly predicts lower mortgage rates; competition benefits sophisticated borrowers most	Table X, p. 86	Sophistication Index coef: -0.226*** (col 2); applied to 2+ lenders for better terms: -0.075***; knows their interest rate: -0.060***; interaction Sophistication x County HHI: +0.050** (col 3), meaning lower concentration helps sophisticated borrowers more

Overall (paper’s conclusion). A large fraction of U.S. borrowers, especially FHA and low-FICO borrowers targeted by government homeownership programs, overpay for mortgages. Limited borrower sophistication provides lenders with market power even in low-concentration markets. Overpayment also impedes pass-through of monetary policy easing to mortgage rates.

Datasets used

Dataset	Role in paper	Wiki page
Optimal Blue rate-lock and pricing-insight data (Jan 2015 to Dec 2019; ~3.6M locks; 20 MSAs for offer data)	Primary dataset: locked mortgage rates, lender offer distributions, loan/borrower characteristics; basis for EGain construction	No page yet
Mortgage Call Reports (MCR/NMLS), 2015:Q1 to 2019:Q4; 162 unique lenders	Nonbank lender income, expenses, and profitability; merged with Optimal Blue to study how rate premiums translate into lender margins and how competition moderates borrower overpayment	MCR (NMLS)
National Survey of Mortgage Originations (NSMO), waves 1-26, 2013-2019; 22,567 mortgages	Borrower sophistication, shopping behavior, knowledge, satisfaction; merged with administrative credit/servicing data	NSMO
HMDA (Home Mortgage Disclosure Act)	Lender classification (bank vs. nonbank); market concentration (HHI)	No page yet

Sample sizes: lock data 3.6M (full) / 67,537 (matched to offer data for EGain); dispersion analysis 2,996,149; MCR-Optimal Blue merge 162 lenders, 1,897 lender-quarters; NSMO 22,567 mortgages.

Theory / model

The paper has no original structural model. It is an empirical paper that tests predictions from the consumer-search and price-dispersion literature (Carlson and McAfee (1983), Baye et al. (2006)) and from the literature on limited financial sophistication in consumer finance (Woodward and Hall (2012), Agarwal et al. (2015, 2017)).

The central identification object is the Expected Gain from Additional Search (EGain). Given $n$ lenders posting rates $r_1 \leq r_2 \leq \cdots \leq r_n$ for an identical borrower type on a given day, and a borrower who has already found rate $r_k$, the expected gain from one more search is (eq. 1, p. 63):

$$\text{EGain}_k = \sum_{i=1}^{k-1} (r_k - r_i) \times \frac{1}{n-1} = \left[ r_k - \frac{\sum_{i=1}^{k-1} r_i}{k-1} \right] \times \frac{k-1}{n-1}$$

Intuitively, the bracketed term is the locked rate minus the expected rate among the $k-1$ cheaper lenders; this is scaled by $\frac{k-1}{n-1}$, the share of remaining lenders who offer cheaper rates. The measure accounts for both the borrower’s position in the distribution and the width of the distribution. Lenders that do not offer a given loan type are treated as $r_i = \infty$.

The main hypotheses tested:

• (H1) Borrowers with lower financial sophistication (less shopping, less knowledge) pay higher rates relative to available offers.
• (H2) Overpayment varies systematically with borrower type (FICO, LTV, loan program) even after controlling for risk-based pricing in offers.
• (H3) Market concentration interacts with borrower sophistication: lower concentration (HHI) benefits sophisticated borrowers more.
• (H4) Expensive lenders are more profitable but do not provide better service, implying pure market power rather than quality differentiation.

Identification for the EGain analysis rests on comparing each locked rate to the real-time distribution of lender offers for an identical loan (same MSA, same day, same FICO/LTV/program/amount/points), which is observed in the Optimal Blue Pricing Insight data. For the dispersion and sophistication analyses, identification uses within-market OLS with exhaustive fixed effects (lender, branch, loan officer, MSA x month) and the NSMO survey linked to administrative loan records.

Method

The estimating strategy has three parts: (1) constructing EGain from matched lock and offer data; (2) OLS regressions of EGain and locked rates on borrower/lender characteristics with layered fixed effects; (3) NSMO-based OLS regressions of contracted rates on borrower sophistication with administrative controls.

EGain construction. For each rate-lock, the authors match to the offer distribution on the same day in the same MSA for a loan with nearly identical FICO, LTV, program, purpose, and amount. They adjust locked rates for points paid using the empirical points-rate relationship. The EGain is then computed from equation (1) above. The matched sub-sample is 67,537 loans from the 20 MSAs with offer data.

Rate-dispersion regressions (Table V, p. 73). Locked rates are regressed on increasingly rich sets of fixed effects. The outcome of interest is the residual variance (measured as standard deviation and 90th-to-10th percentile gap) after absorbing each specification:

$$\text{Rate}_{ilt} = \beta \, X_{ilt} + \text{FE\_set} + \varepsilon_{ilt}$$

• $\text{FE\_set}$ escalates across columns:
• col (1): Lock Date x MSA F.E.
• col (2): + FICO x LTV x Program x Lock Month F.E., ZIP Code F.E., Discount Points x Program x Lock Month F.E.
• col (3): + Discount Points x Program x Lock Month F.E. (finer)
• col (4): + Lender F.E.
• col (5): + Lender x Lock-Day x Program x LTV x Loan Month F.E.
• col (6): + Lender x FICO x LTV x Program x Lock Month F.E.
• col (7): + Branch F.E.
• col (8): + Branch x Lock Month F.E.
• col (9): + Loan Officer F.E.
• col (10): + Loan Officer x Lock Year F.E. x Program
• SE: two-way clustered by month and lender.

Standard errors are two-way clustered by month and lender. Sample: 2,996,149 loans locked 2015 to 2019, 30-year fixed-rate, fully documented, owner- occupied, single-unit purchase mortgages in 277 MSAs.

EGain-on-Treasury-yield regression (Table IV, p. 70). The time-series variation in overpayment is estimated as:

$$\text{EGain}_{it} = \beta \cdot \text{TreasuryYield}_t + \gamma \, Z_{it} + \text{MSA F.E.} + [\text{MSA} \times \text{Month F.E.}] + [\text{Lender-Branch F.E.}] + \varepsilon_{it}$$

• $\text{TreasuryYield}_t$: daily 10-year Treasury yield at the lock date.
• $Z_{it}$: FICO, LTV, loan amount controls and a DTI-below-36 dummy.
• $\text{DTI} \leq 36$ is interacted with Treasury yield to test whether affordability-constrained borrowers drive the relationship.
• SE: two-way clustered by month and lender.
• Sample: 67,241 matched loans.

Empirical specifications

EGain cross-section (R1, R2; Table II and III). Cross-sectional differences in EGain by loan program, FICO, and LTV are first shown in summary statistics (Table II, p. 66), then confirmed in regressions:

$$\text{EGain}_{it} = \beta_1 \, I_{\text{FICO\_bin}} + \beta_2 \, I_{\text{LTV\_bin}} + \gamma \cdot \text{LoanOfficerComp}_{it} + \text{Loan Amount F.E. (\$10k bins)} + \text{MSA} \times \text{Month F.E.} + [\text{Lender-Branch F.E.}] + \varepsilon_{it}$$

• $I_{\text{FICO\_bin}}$: indicator dummies for FICO bins; omitted category [640, 660).
• $I_{\text{LTV\_bin}}$: indicator dummies for LTV bins; omitted category [60, 80].
• $\text{LoanOfficerComp}_{it}$: loan officer compensation controls.
• SE: two-way clustered by month and lender.
• Sample: 67,637 matched loans, 30-year fixed-rate purchase, 20 MSAs, 2016-2019.
• Key coefficients: FICO >= 740 vs [640,660) is -0.097*** (col 1); adding lender-branch FEs reduces but does not eliminate the FICO gradient (Table III).

EGain and market rates (R3; Table IV, p. 70). The core specification is:

$$\text{EGain}_{it} = \beta \cdot \text{TreasuryYield}_t + \gamma \, Z_{it} + \text{MSA F.E.} + \varepsilon_{it}$$

with columns progressively adding MSA x Month F.E. and Lender-Branch F.E. Key result: a 1 pp rise in Treasury yield reduces EGain by about 5 bp (col 1: -0.051***); with lender-branch FEs: -0.026** (col 3). This builds on Fuster, Lo, and Willen (2024) and their time-varying price-of-intermediation result: lenders also make worse offers when rates are low, and borrowers are less likely to shop (p. 71).

Rate dispersion (R4; Table V, p. 73). The 10-spec rate dispersion regression described in Method above. Key results: 90-10 gap = 55 bp in spec (3) (pure observable controls); = 26 bp in spec (10) after full lender, branch, and LO FEs. Same-branch same-day gap (spec 8) = 31 bp. This contrasts with Alexandrov and Koulayev (2017), who find that negotiation plays little role; the substantial within-lender within-branch-day dispersion here is consistent with negotiation (p. 52).

Lender expensiveness and profits (R5, R6; Tables VII-VIII). Lender FEs from spec (4) of Table V are the dependent variable in lender-level regressions on size quartiles, nonbank indicator, and FHA share (Table VII, p. 79). Lender expensiveness is then regressed on income/expense line items from MCR filings in median regressions with year-quarter FEs (Table VIII, p. 81):

$$\text{FinancialOutcome}_l = \beta \cdot \text{LenderExpensiveness}_l + \text{year-quarter F.E.} + \varepsilon_l$$

Key: 1 pp higher rate corresponds to $4.05*** extra gross income and $3.50*** extra gross expenses per $100 originated; net income rises $0.45** (residential originations, precorporate).

Service quality (R7; eq. 2, Table IX, p. 84). NSMO borrower survey outcomes are regressed on contracted rate, with rich controls:

$$Y_{ijtw} = \beta \cdot \text{Rate}_i + \Gamma \, Z_{ij} + \alpha_t + \delta_w + \varepsilon_{ijtw} \tag{2}$$

• $Y_{ijtw}$: binary satisfaction/delay indicator.
• $\text{Rate}_i$: contracted mortgage rate.
• $Z_{ij}$: credit-score and LTV flexible controls, county FEs, program indicators, income/employment/wealth/race/ethnicity controls, and likelihood-of-moving controls.
• $\alpha_t$: origination-month FEs.
• $\delta_w$: survey-wave FEs.
• SE: robust.
• Sample: 22,567 NSMO mortgages, 2013-2019.

Borrower sophistication and rates (R8; eq. 3, Table X, p. 86). NSMO contracted rates are regressed on a sophistication index and market concentration:

$$\text{Rate}_{ijtw} = \beta \, X_i + \Gamma \, Z_{ij} + \alpha_t + \delta_w + \varepsilon_{ijtw} \tag{3}$$

• $X_i$: either (col 1) individual shopping/knowledge binary indicators or (col 2) a composite Sophistication Index (sum of six shopping/knowledge dummies divided by 6, range 0-1), or (col 3) Sophistication Index plus County HHI (last year) and their interaction.
• $Z_{ij}$, $\alpha_t$, $\delta_w$: the same rich controls as eq. (2).
• SE: robust.
• Sample: 22,567 (cols 1-2), 22,563 (col 3).
• Key results: Sophistication Index coef = -0.226*** (col 2); HHI x Sophistication interaction = +0.050** (col 3), meaning lower concentration primarily benefits sophisticated borrowers.

When to read the full paper

Access the original if you are: replicating (code available in the journal’s Supporting Information); studying the EGain measure construction in detail (Section III and Internet Appendix Sections IV-V); reviewing robustness across loan programs and lender types; or tracing the monetary policy transmission implications (Section VI). The locators above point to the exact tables and figures. For “what did this paper find,” the table above is sufficient.

Attribution & rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026, pp. 49–90. © 2025 the American Finance Association. This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. The paper is paywalled; redistribution is extract-only. Cite as:

Bhutta, Neil, Andreas Fuster, and Aurel Hizmo. “Paying Too Much? Borrower Sophistication and Overpayment in the U.S. Mortgage Market.” The Journal of Finance 81, no. 1 (February 2026): 49–90. DOI: 10.1111/jofi.70001.