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Richard H. Stanton

Richard H. Stanton

· ProfessorVerified

University of California, Berkeley · Real Estate

Active 1990–2026

h-index34
Citations6.1k
Papers1236 last 5y
Funding
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About

Richard H. Stanton is a Professor of Finance and Real Estate at UC Berkeley Haas School of Business, holding the Kenneth Rosen Chancellor's Chair in Real Estate. His main research interests include mortgage and lease markets, term structure modeling, mutual funds and risk management, and employee stock options (ESOs). Since joining Haas in 1991, he has held positions from Assistant Professor to Professor, and has been recognized with numerous awards for teaching and research excellence. Stanton is a leading expert in mortgage markets and has contributed to understanding financial norms, credit default swaps, and the subprime mortgage crisis through his scholarly work.

Research topics

  • Financial system
  • Computer Science
  • Finance
  • Political Science
  • Sociology
  • Business
  • Economics
  • Actuarial science
  • Public administration
  • Law and economics
  • Law

Selected publications

  • Fairness by Design: Machine Learning and Interpretable Mortgage Lending

    SSRN Electronic Journal · 2026-01-01

    preprintOpen access
  • Improve the stewardship of federal research funds

    Science · 2025-09-04

    editorialSenior author

    Federal spending for scientific research in the United States is at a pivotal point of change. The funding system is now under close scrutiny by the Trump administration and Congress for the return it provides to taxpayers. Support from different federal agencies has already been diminished, and more change is coming. It is up to the research community to provide constructive solutions that improve accountability for federal money while preserving a partnership with the government that can continue to catalyze economic growth, place the US at the forefront of scientific knowledge, and improve lives.

  • The Deposit Business at Large vs. Small Banks

    National Bureau of Economic Research · 2023-11-01 · 32 citations

    reportOpen access

    The deposit business differs at large versus small banks.We provide a parsimonious model and extensive empirical evidence supporting the idea that much of the variation in deposit-pricing behavior between large and small banks reflects differences in "preferences and technologies."Large banks offer superior liquidity services but lower deposit rates, and locate where customers value their services.In addition to receiving a lower level of deposit rates on average, customers of large banks exhibit lower demand elasticities with respect to deposit rate spreads.As a result, despite the fact that the locations of large-bank branches have demographics typically associated with greater financial sophistication, large-bank customers earn lower average deposit rates.Our explanation for deposit pricing behavior challenges the idea that deposit pricing is mainly driven by pricing power derived from the large observed degree of concentration in the banking industry.

  • The Deposit Business at Large vs. Small Banks

    SSRN Electronic Journal · 2023-01-01 · 7 citations

    articleOpen access
  • Algorithmic Fairness

    Annual Review of Financial Economics · 2023-08-16 · 16 citations

    articleOpen access

    This article reviews the recent literature on algorithmic fairness, with a particular emphasis on credit scoring. We discuss human versus machine bias, bias measurement, group versus individual fairness, and a collection of fairness metrics. We then apply these metrics to the US mortgage market, analyzing Home Mortgage Disclosure Act data on mortgage applications between 2009 and 2015. We find evidence of group imbalance in the dataset for both gender and (especially) minority status, which can lead to poorer estimation/prediction for female/minority applicants. Loan applicants are handled mostly fairly across both groups and individuals, though we find that some local male (nonminority) neighbors of otherwise similar rejected female (minority) applicants were granted loans, something that warrants further study. Finally, modern machine learning techniques substantially outperform logistic regression (the industry standard), though at the cost of being substantially harder to explain to denied applicants, regulators, or the courts.

  • The Deposit Business at Large vs. Small Banks

    SSRN Electronic Journal · 2023-01-01 · 10 citations

    articleOpen access
  • Nonbanks and Mortgage Securitization

    Annual Review of Financial Economics · 2022 · 25 citations

    • Business
    • Financial system
    • Finance

    This article reviews the dramatic growth of nonbank mortgage lending after the Global Financial Crisis, especially to borrowers with lower credit scores, and the related importance of mortgage-backed securitization. Our literature review suggests that the existing theoretical and empirical work on securitization is more relevant to bank than to nonbank lenders, thus leaving outstanding questions as to why nonbank market shares have increased to their current levels and how best to structure nonbank oversight. To highlight key differences in the mortgage-lending incentives of banks and nonbanks, we build a simple theoretical model of bank versus nonbank mortgage lending and use it to generate and test empirical hypotheses. We find, in particular, that loans issued by nonbanks are more likely to prepay early than loans issued by banks, the difference not explainable by nonbank borrowers prepaying more rationally. Using regulatory filings from nonbanks that are typically unavailable to academic researchers, we examine the balance sheets and liquidity and capital positions of large Ginnie Mae nonbank servicers, which face and pose more risk in the current mortgage system. We find that on average these servicers have reasonable liquidity and capital positions relative to standard regulatory thresholds, particularly in 2022:Q1 after a few quarters of elevated profits. However, some large Ginnie Mae servicers appear to have inadequate capital, as gauged by risk-based capital measures. If defaults rise on a large scale, the liquidity and capital positions of these servicers may amplify the disruption in the mortgage and housing markets.

  • Consumer-lending discrimination in the FinTech Era

    Journal of Financial Economics · 2021 · 528 citations

    • Business
    • Actuarial science
    • Financial system

    Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.

  • LibGuides: HE Construction and Civil Engineering: Home

    2020-05-07

    libguides1st authorCorresponding
  • Algorithmic Discrimination and Input Accountability under the Civil Rights Acts

    SSRN Electronic Journal · 2020 · 16 citations

    Senior authorCorresponding
    • Political Science
    • Computer Science
    • Law and economics

Frequent coauthors

Education

  • PhD, Graduate School of Business

    Stanford University

    1992

Awards & honors

  • Financial Management best paper prize (2018)
  • Nomination for Journal of Finance Brattle best corporate-fin…
  • Earl F. Cheit Award for Excellence in Teaching, Undergraduat…
  • Nomination for Journal of Finance Smith-Breeden best-paper p…
  • Best Paper award, Utah Winter Finance conference (2006)
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