Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Streaming Media
Document Type
Event
Start Date
15-4-2025 4:00 PM
Description
This study explores the performance of multifamily loans using a logistic regression model to predict loan outcomes as either “closed” or “current”. Utilizing a dataset of over one million observations and 54,771 unique loan observations, we classify loan status based on Freddie Mac’s mortgage performance codes, with closed loans including modification with a loss, foreclosures, real estate owned, and fully closed loans. Through explanatory analysis, it reveals a nearly balanced distribution between the binary variables. This dataset supports the use of a logistic regression to model the probability of loan default or completion. The findings have implications for risk mitigation in underwriting practices, helping lenders avoid loans with characteristics like those that historically defaulted.
Included in
UC-016 Multifamily Loan Performance
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
This study explores the performance of multifamily loans using a logistic regression model to predict loan outcomes as either “closed” or “current”. Utilizing a dataset of over one million observations and 54,771 unique loan observations, we classify loan status based on Freddie Mac’s mortgage performance codes, with closed loans including modification with a loss, foreclosures, real estate owned, and fully closed loans. Through explanatory analysis, it reveals a nearly balanced distribution between the binary variables. This dataset supports the use of a logistic regression to model the probability of loan default or completion. The findings have implications for risk mitigation in underwriting practices, helping lenders avoid loans with characteristics like those that historically defaulted.
