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Mortgage Default and Foreclosure

Paper Session

Friday, Jan. 5, 2024 10:15 AM - 12:15 PM (CST)

Marriott Rivercenter, Conference Room 14
Hosted By: American Real Estate and Urban Economics Association
  • Chair: Erica Moszkowski, Federal Reserve Board

Strategic Default and Renegotiation: Evidence from Commercial Real Estate Loans

I. Serdar Dinc
,
Rutgers University
Erkan Yonder
,
Concordia University

Abstract

We study strategic default and its role in the renegotiation of debt contracts in a setting where the borrowers hold multiple loans and the borrower cash flow is disclosed: commercial real estate loans to real estate companies. We find that the majority of defaulted loans are to borrowers that have cash flow to meet their payments on the defaulted loan and that continue to meet their payments in other loans. The pervasiveness of strategic defaults is robust to alternative characterizations. Strategic defaults make loan renegotiations more likely. Our analysis controls for unobservable time-dependent borrower-, lender-, and market-level factors.

Borrowers Signal, Lenders Respond: The Strategic Value of Foreclosure Delay

Henry J. Munneke
,
University of Georgia
Nicholas B. Smith
,
Federal Reserve Bank of Philadelphia

Abstract

This study examines lender reactions to signals of a delinquent borrower’s intent to pay and self-cure the delinquency. Due to high costs of modification and foreclosure, lenders strategically delay acting on defaults with a high likelihood of self-cure. However, information on self-cure is asymmetric which leads to higher rates of foreclosure. High self-cure borrowers thus signal their intent to cure by making payments while delinquent. Results show that lenders foreclose less often relative to inaction on signaling loans. Lenders also suffer fewer costs due to foreclosures on signaler loans. Lenders also foreclose less often on borrowers making more payments than the average in their Zip 3. We also examine a California law which enforced foreclosure delays. We provide evidence that this regulatory loan forbearance led lenders to foreclose more often and suffer greater costs after the policies end which we attribute to a build-up in low self-cure delinquencies during the law. We estimate that lenders incurred over $372M in additional losses due to these laws. This suggests lenders pay the costs of laws benefiting borrowers.

Strategic Default, Foreclosure Delay and Post-Default Wealth Accumulation

Gianluca Marcato
,
University of Reading
Shotaro Watanabe
,
University of Reading
Bing Zhu
,
Technical University of Munich

Abstract

This paper conducts research on the growth of household wealth after the Great Recession, with a particular focus on mortgage defaults and their motives (strategic and non-strategic). Ambrose, Buttimer and Capone (1997) theorise that mortgage defaulters benefit from two channels: negative equity resolution and free rent. We examine these channels by quantifying post-default changes in household wealth as net worth and housing capital user cost and rent levels, and household’s post-default tenure choice. We find that strategic defaulters with severe negative equity (negative equity resolution channel) in states with significant delays in foreclosure processes during the recession (free rent channel) improved their overall balance sheets after default, with less decrease in housing user costs. These insights were not found for non-strategic defaulting households.

Mortgage Default: A Heterogeneous-Agent Model

Philip Lewis Kalikman
,
Cambridge University
Joelle Scally
,
Federal Reserve Bank of New York

Abstract

We introduce a loan-level model of mortgage default with heterogeneity in borrower characteristics and mortgage terms, including idiosyncratic penalties for default. Borrowers’ penalties determine how closely their behavior hews to the predictions of the double-trigger or strategic models. The state space varies loan-to-loan based on all of the loan’s, borrower's, property's, and neighborhood's idiosyncratic characteristics. We test the model on a high-performance computing cluster against real data drawn from linked databases with billions of observations of hundreds of simultaneous attributes. The model predicts defaults out-of-sample, fits cross-sectional characteristics of the distribution of mortgage performance, and classifies likelihood of default with high accuracy and better than all known benchmarks.

Discussant(s)
Taha Ahsin
,
Duke University
David Chester Low
,
Consumer Financial Protection Bureau
Darren James Aiello
,
Brigham Young University
Lara Pia Loewenstein
,
Federal Reserve Bank of Cleveland
JEL Classifications
  • G2 - Financial Institutions and Services