Recession/Recovery Impact Model
Assessing Recession Risk & Recovery Impact
Purpose of the model
AKUVO is helping credit unions understand how a potential COVID-19 related recession can have an effect on its members and recovery timeframe.
Although the current economic environment is not like any that we’ve previously seen, we believe we can provide meaningful data, scores and analysis to help provide a foundation for a better understanding of a recession impact and its recovery.
How the Model Works
The model produces Recession, Recovery, and Long-Term Recovery Scores for every ZIP+4 (nine-digit zip code). There are 40 million ZIP+4s averaging 4 households, which makes the data extremely granular. These scores are based on:
- Analyzing IRS tax return line item detail for all U.S. households and individuals, as well as all for-profit businesses before, during, and after the 2008 recession, leveraging an extensive consumer database containing detailed tax return data of every ZIP+4 in the U.S. which covers 150+ million households and 200+ million adults.
- Utilizing over 1,000 data transformations derived from the tax return variables
- Ranking each ZIP+4 in the U.S.
- Using an Ordinary Least Squares (OLS) regression analysis
Recession Impact Score
The Recession Impact Score is developed by looking at the pre-recession financial strength of each ZIP+4 and then analyzing the change that occurred during the recession and measures the impact of that change.
The chart on the right shows the distribution of members in risk segments and how those ZIP+4s that are ranked in the highest segments had the most charged-off loans.
Short Term Recovery Score
The Short-Term Recession Recovery Score measures the degree of improvement in the first full year after the Recession.
The chart on the right shows the distribution of members in risk segments and how those ZIP+4s that are ranked in the highest segments correlated to the percentage of charged-off loans.
Long Term Recovery Score
The Long-Term Score measures the recovery through 2019.
The chart on the right shows the distribution of members in risk segments and the correlation of those ZIP+4s that are ranked in the highest segments represent the highest percentage of charged-off loans in the portfolio.
Value of the Model
The scores from this unique model can provide additional insights into which geographies (at a granular level) may be better situated for a quicker versus prolonged recovery period.
When applying these scores to a credit union’s portfolio data, a credit union can potentially identify which members may be impacted the hardest by another recession and may require additional assistance or support.
Some additional examples:
- Apply these scores to assist in evaluating which members are in the high-risk segments
- Use these scores when creating collection strategies
- Factor in these scores when considering a loan modification
- Combine these scores with additional data in the credit union’s data warehouse for more robust analytics
AKUVO has additional score models and an extensive library of data that can be combined with these scores for more robust analytics. Check out our DataMart that has data that’s been collected and developed just for credit unions.