Prepayment-risk-modellling-on-MBS

Pavithra Saran

Mortgage Prepayment and Risk: A Data-Driven Overview
This analysis delves into how various loan characteristics and geographic factors affect prepayment rates within the mortgage securities dataset. By leveraging Power BI, we explore key insights from loan attributes like Original UPB, LTV ratio, DTI ratio, and credit scores. Our findings reveal that prepayment behaviour is consistent across most regions, with Vermont showing slightly higher rates. The data shows a peak in prepayment activity in the early 2000s, a decline post-2004, and recent fluctuations. The geographic analysis identifies states with varying prepayment activity levels, and loan purpose influences prepayment behaviour. This report provides actionable insights for strategic decision-making and risk management.
Like this project
0

Posted Aug 26, 2024

Mortgage Prepayment and Risk: A Data-Driven Overview This analysis delves into how various loan characteristics and geographic factors affect

Prepayment-risk-modellling-on-MBS
Prepayment-risk-modellling-on-MBS
Work-Life Balance and Engagement Analysis Report
Work-Life Balance and Engagement Analysis Report