CONSUMER-LENDING DISCRIMINATION 

IN THE FINTECH ERA 

ROBERT BARTLETT, ADAIR MORSE, RICHARD STANTON, & 

NANCY WALLACE 

UNIVERSITY OF CALIFORNIA, BERKELEY 

APRIL 2019 
 

 

 

 

 

 

 

 

 

Setting: Household debt 

• Latinx & African-American ethnic groups hold $2.25 trillion of the $13 trillion of U.S. 

household debt. (Most is housing debt – our focus.) 

• Any interest rate discrimination is material: 

• A 1 basis point (0.01%) higher mortgage rate => minorities pay $160 million 

extra in interest per year 

• Any rejection rate discrimination is material: In 2017, homeownership rates: 

• Majority ethnicities (72.4%), Latinx (48.4%), African-American (43.0%) 

• If any of this disparity is due to discrimination => welfare inequalities caused 

by financial services 
 

 

 

 

 

 

 

 

 

 

Our goal 

When we started this project... 

• Wanted to estimate discrimination 

• Also wanted to understand what fin-technology means for discrimination going forward 

• Platforms: Quicken, largest GSE counterparty by volume 

• Non-platforms: Of 2,098 largest issuers, 945 were fully algorithmic by 2018 

• Ex ante: Unclear whether technology lessens discrimination: 

• Platforms remove loan officer biases from face-to-face interaction 

• But platforms increasingly use more data in statistical discrimination 

• Fuster, Goldsmith-Pinkham, Ramadorai, and Walther (2018): Machine-learning 

results in differential loan provision to minority borrowers. 
 

 

 

 

 

 

What we do 

◦ Establish an economic basis for the legal “standard of proof”. 

◦ In an era of Big Data, how can a court distinguish between legitimate and illegitimate 

discrimination? 

- Disparate treatment vs. disparate impact 

◦ Estimate level of ethnic discrimination (if any) and by whom 

◦ Study mortgages to address the two empirical challenges of data and identification 

◦ Identification aided by pricing rules of Fannie Mae and Freddie Mac 

◦ Allows us to avoid “omitted-variable” problem. 
 

 

 

 

Legal Standard 

Two U.S. Federal statutes prohibit discrimination in mortgage processes 

•Fair Housing Act of 1968 (FHA) 

•Equal Credit Opportunity Act of 1974 (ECOA) 

Issue is not the statutes, but how to implement them in the courts. 
 

 

 

 

 

 

 

 

Legal Standard 

• Imagine a lender cannot see wealth 

• Court: Lenders can use proxy variables (read: Big Data)... 

1. Proxies are allowable: 

◦ If lender can show that these variables have a legitimate business 

necessity. 

• where legitimate business necessity is the act of scoring credit risk. 

2. Proxies are not allowable: 

◦ If variables are used for other purposes, including a lender’s efforts to earn higher 

markup above costs on a minority group 
 

Outcomes of discrimination 

I. Pricing Decisions 

II. Accept/Reject Decisions 
 

 

 

 

 

GSEs help on Identification: Pricing of Loan 

Interest rate pricing 

= Market rate 

+ Expected cost of default 

(credit risk= G-Fee) 

+ Discretionary part for

lender profits & strategic

incentives 
 

G-Fees 

G-Fees are dictated by Loan Level Performance Adjustments (LLPA) grid 
 

0 2 4 6 8 10 12 14 16 

Percent 

Everyone Else 

Latinx/African-American 

0.02 0.04 0.06 0.08 0.10 0.12 

Mortgage Interest Rate 

Raw 

Interest 

Rates 
 

0 5 10 15 20 

Percent 

Everyone Else 

Latinx/African-American 

-0.04 -0.02 0.00 0.02 0.04 0.06 0.08 

Mortgage Interest Rate 

Interest 

Rates 

Adjusted

for GSE 

Grid 
 

 

 

 

 

 

Data 

• Focus on mortgages – because HMDA has ethnicity 

• Focus on 30-year fixed for comparability & conforming for GSE process 

• Datasets (2008–2015): 

• HMDA- ethnicity and income local geography but not address 

• ATTOM – origination, performance and exact location 

• McDash – detailed contract terms and performance 

• Equifax – consumer debt 

• Merge using performance strings 
Pricing Results 
 

Interest Rate Discrimination 

Dependent Variable: Mortgage Interest Rate 

Purchase Mortgages Refinance Mortgages 

(1) (2) (3) (4) 

Latinx/African- 0.000903*** 0.000792*** 0.000299*** 0.000356*** 

Americans [0.000101] [3.09e-05] [7.96e-05] [2.96e-05] 

Observations 1,480,186 1,480,186 2,068,453 2,068,271 

R-squared 0.003 0.729 0.000 0.693 

Year-Month FE N Y N Y 

GSE Grid FE N Y N Y 

• Minorities pay 9 basis points (bps) more in purchase interest rates. (3bps in refis) 

• Incorporating the credit risk model (time differences and GSE grid effects = R-square >0.70) 

discrimination remains. 

• Discrimination: 7.92 bps in purchases, 3.56 in refis 

• Is 7.92 bps worth talking about? 

• Mortgage Bankers Association: Average mortgage makes 50 bps in profit. 

• Together, $748 million more interest paid by minorities annually on existing stock of mortgages 
Interest Rate Discrimination –Issuances by FinTechs 

Latinx/AfricanAmericans 

Observations 

R-squared 

Year-Month FE 

GSE Grid FE 

Dependent Variable: Mortgage Interest Rate 

Purchase Mortgages Refinance Mortgages 

FinTech + FinTech + FinTech FinTech Algorithm Algorithm 

0.000534*** 0.000680*** 0.000200** 0.000249*** 

[4.36e-05] [5.50e-05] [6.34e-05] [3.08e-05] 

41,832 354,680 110,870 348,833 

0.731 0.607 0.707 0.65 

Y Y Y Y 

Y Y Y Y 

• FinTechs & Algorithm-Based Lenders Discriminate almost as much as traditional lenders 

• FinTechs cannot see borrowers. 

• Thus, it must be that the algorithms build in discrimination into their pricing strategies. Even if 

unintentional, this is still illegal. 

• Pricing for low shopping behavior, financial deserts, etc... correlated with ethnicity 
 

 

 

 

Omitted Variables - Costs vs Profit Margins 

Which costs? 

1. Geography: It could be that locations in areas with higher minority 

populations involve higher costs 

2. Lender: It could be that lenders servicing more minorities have higher 

fixed costs 
Interest Rate Discrimination: Results in Purchase Mortgages 

Robustness: Costs may vary by geography or by fixed cost of lender 

Dependent Variable: Mortgage Interest Rate 

Latinx/African- 0.000792*** 0.000701*** 0.000552*** 0.000525*** 

Americans [3.09e-05] [2.37e-05] [2.76e-05] [3.33e-05] 

Observations 1,480,186 1,480,170 1,478,947 1,453,606 

R-squared 0.729 0.733 0.747 0.758 

Year-Month FE Y Y Y Y 

GSE Grid FE Y Y Y Y 

County FE N Y Y N 

Lender FE N N Y N 

County-Lender FE N N N Y 

• Of the nearly 7.9 bps of potential discrimination 

• 0.9 bps might be due to varying location costs, 

• 1.5 bps might be due to varying lender fixed costs 

• 5.25 bps of discrimination is robust (probably more) 

• Follow-up work: “The geography of discrimination”. 

• Our view: what we are identifying is monopoly rent-seeking in financial deserts and other 

communities known for low shopping. Not allowed under court ruling. 
 

 

 

 

Omitted Variables - Costs vs Profit Margins 

Robustness 

1. Geography: It could be that locations in areas with higher minority 

populations involve higher costs 

2. Lender: It could be that lenders servicing more minorities have higher 

fixed costs 

3. Servicing costs 

4. Points 
 

 

 

 

 

Robustness Concerns 

Robustness 

Concern: Points 

Residual Risk via 

Servicing or MBS 

Holding 

Ethnicity 

Designation 

Sub-Sample: 0.795 < LTV 

<0.801 Small Lenders 

Only use HMDAClassified Ethnicity 

Observations 

Reasoning: At budget 

constraint 

Unlikely to service 

the loans or hold as 

MBS on balance 

sheet 

Eliminate software 

errors in 

classification 

Points: It could be that we are picking up non-minority borrowers lowering interest rates by 

paying points 

• Solution: Down-payment behavior 

─ Many borrowers do whatever they can to get the house they want 

─ Right at LTV of 0.80, borrowers have scraped to meet down-payment 

(Avoid paying insurance cost above 0.80) 

─ Limit to sample LTV = 0.80 (see histogram) 
LTV Histogram for Purchase Mortgages 
 

 

 

 

Robustness Concerns 

Residual Risk via 

Robustness Concern: Points 

Sub-Sample: 0.795 < LTV <0.801 

Servicing or MBS 

Holding 

Small Lenders 

Ethnicity Designation 

Only use HMDAClassified Ethnicity 

Observations 

Unlikely to service the Eliminate software errors Reasoning: At budget constraint loans or hold as MBS on in classification balance sheet 

Residual Risk: It could be that discrimination we show reflects residual risk for lenders 

exposed to default risk, not in the loan default, but in additional costs of servicing loans 

that are delinquent 

• Solution: Eliminate large lenders 
 

Robustness Concerns 

Residual Risk via Robustness Ethnicity Points Servicing or MBS Concern: Designation Holding 

Sub-Sample: 0.795 < LTV 

<0.801 Small Lenders 

Only use HMDAClassified Ethnicity 

Observations 

Panel A: Purchases 

Latinx-/African- 0.000944*** 0.000868*** 0.000814*** 

American [4.21e-05] [3.49e-05] [3.23e-05] 

Observations 327,355 833,579 1,356,670 

R-squared 0.739 0.730 0.729 

Month-Year FE Y Y Y 

GSE Grid FE Y Y Y 
 

 

2 Silver Linings for Fin-Technology 

1> Time pattern 

2> Rejection rates 
 

 

 

 

Purchase Refinance 

0 1 2 3 4 5 6 7 8 

Rate discrimination is declining 

Pattern consistent with: 

+ New entry of FinTech platforms 

+ Simpler and quicker online 

applications of Incumbents 

Resulting in 

=> Greater competition 

=> Less taking of monopoly rents 

2008 to 2009 2010 to 2011 2012 to 2013 2014 to 2015 2008 to 2009 2010 to 2011 2012 to 2013 2014 to 2015 

Graphs by loan 
 

 

Dependent Variable: Application Rejection 

FinTech Lenders: Buchak, et al FinTech Lenders with Full Online Small Lenders (2017) Application 2018 

(1) (2) (3) (4) (5) (6) (7) (8) (9) 

0.0934*** 0.0858*** 0.0639*** 0.023 0.0112 0.0259** 0.0531*** 0.0496*** 0.0344*** 

Latinx/African- [0.00646] [0.00563] 

American 

[0.00266] [0.0200] [0] [0.00881] [0.0160] [0.0136] [0.0118] 

Observations 2,007,520 2,007,520 2,007,520 116,893 116,893 116,893 926,542 926,542 926,542 

R-squared 0.058 0.064 0.329 0.082 0.088 0.301 0.039 0.046 0.345 

Application 

Variable Y Y Y Y Y Y Y Y Y 

Splines 

Year FE Y Y Y Y Y Y Y Y Y 

Census Tract 

%iles: LTV, 

Credit Score 

N Y Y N Y Y N Y Y 

County FE N N Y N N Y N N Y 

Lender F.E N N Y N N Y N N Y 

• 6.4% extra rejections overall: Consistent with material discrimination. 

• Yet (silver lining #2) FinTechs discriminate much less, if at all. 
 

 

 

 

 

 

 

 

Conclusions 

1. This work could provide a tool for regulators and the courts 

◦ Big data is just starting... lenders may be testing the waters on how courts will handle 

statistically discriminating variables 

2. Back of the envelope: $750 million in extra interest per year. Our estimates are a lower 

bound, but show that discrimination is happening (sometimes without purpose) 

3. Surprise contributions: 

◦ GSEs may be serving a role in preventing discrimination. 

◦ Algorithms may be improving competitive landscape 

4. Work in progress: 

o The targeting of monopoly rents & the geography of disparate impact