Consumer Finance

Our Consumer Finance Institute researches how people earn, spend, save, and invest, as well as how credit markets and payment systems affect the economy. Our goal is to foster a healthy consumer sector, a stable financial system, and a resilient regional and national economy.

Consumer Credit

Working Paper

Financial Fraud Through the Lens of Extended Fraud Alerts

WP 25-29 – We use extended fraud alerts in anonymized credit reports to examine how identity theft, and subsequent cleanup, affects consumers’ credit outcomes.

Consumer Experiences with Auto, Home, and Rent Insurance Costs in 2024 – Survey Data

Housing and transportation compose a large portion of consumers’ annual spending. A portion of that annual spend comes from insurance on residences (home or renters insurance) and vehicles (auto insurance); insurance costs can make up 3 to 5 percent of expenditures (2 to 3 percent of gross income).

A close up of a financial document.

Consumer Credit

Discussion Paper

How Well Do Survey Self-Reports Align with Administrative Data? The Case of U.S. Consumer Credit Records

DP 25-01 – This paper assesses how closely consumers’ self-reported credit and debt attributes (such as credit account ownership and balances) align with administrative credit bureau records for mortgages, auto loans, and credit cards.

Aerial view of a suburb

Home Mortgage Disclosure Act (HMDA) Lender File

The HMDA Lender File includes characteristics of firms receiving mortgage applications and originating loans. The data set enables users to connect HMDA filers to their parent organizations and compare a filer’s lending over time.

Consumer Credit

Working Paper

Does CFPB Oversight Crimp Credit?

WP 21-08/R - We study how regulatory oversight by the Consumer Financial Protection Bureau (CFPB) affects mortgage credit supply and other aspects of bank behavior.

Mortgage Markets

Working Paper

Precision Without Labels: Detecting Cross-Applicants in Mortgage Data Using Unsupervised Learning

WP 25-25 – We develop an algorithm to detect loan applicants who submit multiple applications in a loan-level dataset without personal identifiers. Our method detects applicants that submit multiple mortgage applications with 92.3 percent precision.