Did Bankruptcy Reform Cause Mortgage Defaults to Rise?

2011 ◽  
Vol 3 (4) ◽  
pp. 123-147 ◽  
Author(s):  
Wenli Li ◽  
Michelle J White ◽  
Ning Zhu
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Budget Constraints ◽  
Mortgage Default ◽  
Subprime Mortgage ◽  
Default Rates ◽  
Mortgage Defaults

Homeowners in financial distress can use bankruptcy to avoid defaulting on their mortgages, since filing loosens their budget constraints. But the 2005 bankruptcy reform made bankruptcy less favorable to homeowners and therefore caused mortgage defaults to rise. We test this relationship and find that the reform caused prime and subprime mortgage default rates to rise by 23% and 14%, respectively. Default rates rose even more for homeowners who were particularly negatively affected by the reform. We calculate that bankruptcy reform caused mortgage default rates to rise by one percentage point even before the start of the financial crisis. (JEL D14, G01, G21, K35)

Default Option Exercise over the Financial Crisis and beyond*

2020 ◽  
Author(s):  
Xudong An ◽  
Yongheng Deng ◽  
Stuart A Gabriel
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Financial Crisis ◽  
Panel Data Analysis ◽  
Mortgage Default ◽  
Time Varying ◽  
Crisis Period ◽  
Default Option ◽  
Run Up ◽  
Mortgage Defaults

Abstract We document changes in borrowers’ sensitivity to negative equity and show heightened borrower default propensity as a fundamental driver of crisis period mortgage defaults. Estimates of a time-varying coefficient competing risk hazard model reveal a marked run-up in the default option beta from 0.2 during 2003–06 to about 1.5 during 2012–13. Simulation of 2006 vintage loan performance shows that the marked upturn in the default option beta resulted in a doubling of mortgage default incidence. Panel data analysis indicates that much of the variation in default option exercise is associated with the local business cycle and consumer distress. Results also indicate elevated default propensities in sand states and among borrowers seeking a crisis-period Home Affordable Modification Program loan modification.

Markov-switching impacts of housing-market expectations on credit markets

2019 ◽  
Vol 46 (3) ◽  
pp. 381-400
Author(s):  
MeiChi Huang
Keyword(s):  
Housing Market ◽  
Design Methodology ◽  
Credit Markets ◽  
Markov Switching ◽  
Good Time ◽  
Mortgage Default ◽  
Housing Crisis ◽  
Content Type ◽  
Default Rates ◽  
Mortgage Defaults

Purpose The purpose of this paper is to investigate linkages between households’ expectations and credit markets in the housing crisis. Design/methodology/approach In the Markov-switching framework, the sample period is classified into high- and low-impact regimes based on impacts of expectations on default rates, and the good-time-to-buy (GTTB) index is chosen to proxy for expectations toward the housing-market dynamics. Findings The results suggest that in high-impact regimes, optimistic expectations are substantially associated with lower defaults for all default rates analyzed, and second mortgage defaults are more sensitive to households’ expectations than first mortgage defaults. In low-impact regimes, the GTTB index significantly influences composite and first-mortgage default rates, but its impact is insignificant for second mortgage and bankcard default rates. Originality/value The results provide compelling evidence that households’ expectations play more important roles in credit markets in turmoil periods.

scholarly journals Mortgage-Default Research and the Recent Foreclosure Crisis

2018 ◽  
Vol 10 (1) ◽  
pp. 59-100 ◽  
Author(s):  
Christopher L. Foote ◽  
Paul S. Willen
Keyword(s):  
Life Events ◽  
Financial Distress ◽  
House Prices ◽  
Mortgage Default ◽  
Reverse Causality ◽  
Adverse Life Events ◽  
Foreclosure Crisis ◽  
Housing Boom ◽  
New Research ◽  
Mortgage Defaults

This article reviews the surge in research on mortgage default inspired by the recent foreclosure crisis. Economists already understood a great deal about default, both theoretically and empirically, when the crisis began, but new research has moved the frontier further by improving data sources, building dynamic optimizing models of default, and explicitly addressing reverse causality between rising foreclosures and falling house prices. Mortgage defaults also featured prominently in early papers that pointed to subprime and other privately securitized mortgages as fundamental drivers of the housing boom, although this research has been criticized recently. Going forward, improvements to data and models will allow researchers to make progress on the two central questions in this literature. First, what are the relative contributions of adverse life events and negative equity to mortgage default? Second, why is default so rare, even among people with deep negative equity or acute financial distress?

scholarly journals Epidemiological spreading of mortgage default

2019 ◽  
Vol 12 (1) ◽  
pp. 74-93
Author(s):  
Jochen Schweikert ◽  
Markus Höchstötter
Keyword(s):  
Design Methodology ◽  
Forecast Accuracy ◽  
Mortgage Default ◽  
Logit Models ◽  
Content Type ◽  
Employment Conditions ◽  
Level Data ◽  
Default Rates ◽  
The Usa ◽  
Mortgage Defaults

Purpose This paper aims to introduce mathematical models to capture the spreading of epidemics to explain the expansion of mortgage default events in the USA. Design/methodology/approach The authors use the state of infectiousness and death to represent the subsequent steps of payment elinquency and default, respectively. As the local economic structure influences regional unemployment, which is a strong driver of mortgage default, the authors model interdependencies of regional mortgage default rates through employment conditions and vicinity. Findings Based on a large sample between 2000 and 2014 of loan-level data, the estimation of key parameters of the model is proposed. The model’s forecast accuracy shows an above-average performance compared to well-known approaches such as linear regression or logit models. Originality/value The key findings may be useful in understanding the dynamics of mortgage defaults and its spatial spreading.

Subprime Mortgage Default

2010 ◽  
Author(s):  
James B. Kau ◽  
Donald C. Keenan ◽  
Constantine Lyubimov ◽  
V. Carlos Slawson
Keyword(s):  
Mortgage Default ◽  
Subprime Mortgage

Asymmetric Information and Subprime Mortgage Default

2015 ◽  
Author(s):  
Xudong An ◽  
A. Quang Do ◽  
Timothy J. Riddiough ◽  
Vincent W. Yao
Keyword(s):  
Asymmetric Information ◽  
Mortgage Default ◽  
Subprime Mortgage

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

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