Inspiration
Financial stress varies across multiple structural factors. As students interested in economics and consulting, we aimed to understand what drives financial vulnerability and to develop data-driven solutions.
What it does
We identify high-risk households and measure financial resilience using the Debt-to-Income Ratio (DTI): $$ DTI = \frac{Total\ Debt}{Annual\ Income} $$ We apply regression modeling to determine key financial and demographic drivers, and conduct a 10% rent shock simulation to evaluate how vulnerability changes under stress. We then translate findings into actionable policy recommendations.
How we built it
- Cleaned and preprocessed the dataset
- Defined and constructed the Debt-to-Income Ratio (DTI)
- Built a linear regression model to analyze the relationship between key factors and DTI
- Conducted a deeper analysis on the most influential factor groups
- Performed shock simulation to assess financial resilience under stress
Challenges we ran into
- Handling skewed, real-world data with outliers
- Translating model results into practical solutions
- Using Git with Jupyter Notebook for version control for the first time
Accomplishments that we're proud of
- Isolating the strongest predictors of financial vulnerability
- Combining predictive modeling with scenario simulation
- Translating analytical insights into actionable strategies
What we learned
- Financial vulnerability is driven by overlapping structural factors rather than a single variable.
- Our analysis shows that housing tenure, age group, and other factors interact and jointly increase financial stress.
Log in or sign up for Devpost to join the conversation.