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Owner-Occupancy Fraud and Mortgage Performance

Author

Listed:
  • Ronel Elul
  • Aaron Payne
  • Sebatian Tilson

Abstract

We use a matched credit bureau and mortgage dataset to identify occupancy fraud in residential mortgage originations, that is, borrowers who misrepresented their occupancy status as owner-occupants rather than residential real estate investors. In contrast to previous studies, our dataset allows us to show that – during the housing bubble – such fraud was broad based, appearing in the government-sponsored enterprise market and in loans held on bank portfolios as well, and increases the effective share of investors by 50 percent. We show that a key benefit of investor fraud was obtaining a lower interest rate, particularly for riskier borrowers. Mortgage borrowers who misrepresented their occupancy status performed substantially worse than otherwise similar owner-occupants and declared investors, and constituted one-sixth of the share of loans in default by the end of 2008. We show that these defaults were also significantly more likely to be “strategic,” further highlighting the contribution of fraud in the housing bust.

Suggested Citation

  • Ronel Elul & Aaron Payne & Sebatian Tilson, 2019. "Owner-Occupancy Fraud and Mortgage Performance," Working Papers 19-53, Federal Reserve Bank of Philadelphia.
  • Handle: RePEc:fip:fedpwp:86686
    DOI: 10.21799/frbp.wp.2019.53
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    Cited by:

    1. Albanesi, Stefania & DeGiorgi, Giacomo & Nosal, Jaromir, 2022. "Credit growth and the financial crisis: A new narrative," Journal of Monetary Economics, Elsevier, vol. 132(C), pages 118-139.
    2. Griffin, John M. & Kruger, Samuel & Maturana, Gonzalo, 2021. "What drove the 2003–2006 house price boom and subsequent collapse? Disentangling competing explanations," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1007-1035.
    3. Peter Rosenblatt & Steven J. Sacco, 2018. "Investors and the Geography of the Subprime Housing Crisis," Housing Policy Debate, Taylor & Francis Journals, vol. 28(1), pages 94-116, January.
    4. Chen, Denghui & Kiefer, Hua & Liu, Xiaodong, 2022. "Estimation of discrete choice network models with missing outcome data," Regional Science and Urban Economics, Elsevier, vol. 97(C).
    5. Mikhed, Vyacheslav & Raina, Sahil & Scholnick, Barry & Zhang, Man, 2024. "Debtor income manipulation in consumer credit contracts," Journal of Financial Economics, Elsevier, vol. 157(C).
    6. Christa Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott Nelson & Wilbert van der Klaauw & Jialan Wang, 2025. "Consumer Credit Reporting Data," Journal of Economic Literature, American Economic Association, vol. 63(2), pages 598-636, June.
    7. Peter Ganong & Pascal J. Noel, 2020. "Why Do Borrowers Default on Mortgages?," NBER Working Papers 27585, National Bureau of Economic Research, Inc.
    8. Eid, Nourhan & Maltby, Josephine & Talavera, Oleksandr, 2016. "Income Rounding and Loan Performance in the Peer-to-Peer Market," MPRA Paper 72852, University Library of Munich, Germany.
    9. Daniel I. García, 2022. "Second‐home buying and the housing boom and bust," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 33-58, March.
    10. Yiwei Dou & Stephen G. Ryan & Biqin Xie, 2018. "The Real Effects of FAS 166/167 on Banks’ Mortgage Approval and Sale Decisions," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 56(3), pages 843-882, June.
    11. Lauren Lambie‐Hanson & Wenli Li & Michael Slonkosky, 2022. "Real estate investors and the U.S. housing recovery," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(6), pages 1425-1461, November.

    More about this item

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    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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