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

Hospital Billing Regulations and Financial Well-Being: Evidence from California’s Fair Pricing Law

WP 25-39 – We examine the financial consequences of the 2007 California Fair Pricing Law, which places a price ceiling on hospital bills for financially vulnerable individuals.

Education Finance

Working Paper

Financial Consequences of Student Loan Delinquency, Default, and Servicer Quality

WP 25-38 – Using anonymized consumer credit bureau data, the author examines the credit market consequences of student loan delinquency and default and the role that student loan servicers play in contributing to borrower outcomes.

A woman reviews paperwork at the kitchen table.

LIFE Survey Report – October 2025

This report is part of a quarterly series on key observations from the Labor, Income, Finances, and Expectations (LIFE) Survey. Data from the survey provide insight into consumers’ recent financial lives and their future expectations.

Mortgage Markets

Working Paper

Single-Family REITs and Local Housing Markets

WP 25-37 – This paper documents the growth of single-family REITs (SF-REITs) and their (non)effects on housing markets by constructing a novel dataset of SF-REITs’ underlying properties.

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 Finance

Discussion Paper

Combining AI and Established Methods for Historical Document Analysis

DP 25-02 – This paper describes methodological approaches for extracting structured data from historical documents. We show the benefits of an "adaptive modular" approach leveraging optical character recognition, full-text search, and frontier LLMs.