Mortgage Default Rates and Borrower Race

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)

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Refinance and Mortgage Default: A Regression Discontinuity Analysis of HARP’s Impact on Default Rates

The Journal of Real Estate Finance and Economics ◽

10.1007/s11146-016-9566-z ◽

2016 ◽

Vol 55 (4) ◽

pp. 457-475 ◽

Cited By ~ 1

Author(s):

Kadiri Karamon ◽

Douglas McManus ◽

Jun Zhu

Keyword(s):

Regression Discontinuity ◽

Mortgage Default ◽

Default Rates

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

Managerial Finance ◽

10.1108/mf-08-2019-0391 ◽

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.

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Co-Movement of State’s Mortgage Default Rates: A Dynamic Factor Analysis Approach

SSRN Electronic Journal ◽

10.2139/ssrn.2079813 ◽

2011 ◽

Author(s):

Yaw Owusu-Ansah

Keyword(s):

Factor Analysis ◽

Analysis Approach ◽

Dynamic Factor Analysis ◽

Dynamic Factor ◽

Mortgage Default ◽

Default Rates

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Epidemiological spreading of mortgage default

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-05-2017-0047 ◽

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.

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