A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes

Author:

Chamboko RichardORCID,Bravo Jorge MiguelORCID

Abstract

This paper proposes a novel system-wide multi-state framework to model state occupations and the transitions among current, delinquency, default, prepayment, repurchase, short sale and foreclosure on mortgage loans. The approach allows for the modelling of the progression of borrowers from one state to another to fully understand the risks of a cohort of borrowers over time. We use a multi-state Markov model to model the transitions to and from various states. The key factors affecting the transition into various loan outcomes are the ability to pay as measured by debt-to-income ratio, equity as marked by loan-to-value ratio, interest rates and the property type. Our findings have broader policy implications for better decision-making on granting loans and the design of debt relief and mortgage modification policies.

Publisher

MDPI AG

Subject

Strategy and Management,Economics, Econometrics and Finance (miscellaneous),Accounting

Reference82 articles.

1. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY

2. An empirical transition matrix for nonhomogeneous Markov chains based on censored observations;Aalen;Scandinavian Journal of Statistics,1978

3. A comparative study on base classifiers in ensemble methods for credit scoring

4. Policy Intervention in Debt Renegotiation: Evidence from the Home Affordable Modification Program

5. Mortgage Refinancing, Consumer Spending, and Competition: Evidence from the Home Affordable Refinancing Program;Agarwal,2015

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Digital financial services adoption: a retrospective time-to-event analysis approach;Financial Innovation;2024-01-18

2. Credit Risk Scoring: A Stacking Generalization Approach;Lecture Notes in Networks and Systems;2024

3. Backtesting Recurrent Neural Networks with Gated Recurrent Unit: Probing with Chilean Mortality Data;Lecture Notes in Networks and Systems;2022

4. The Demographics of Defense and Security in Japan;Smart Innovation, Systems and Technologies;2021-10-29

5. A conservative approach for online credit scoring;Expert Systems with Applications;2021-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3