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Community bank CECL: 3 external drivers that may strengthen your model

As community banks continue to evaluate their data and develop and test methodologies for CECL modeling, some might not have enough data within their existing loan portfolios. To more effectively estimate forward-looking losses, community institutions should apply external key data elements (KDE) to help obtain the results required to satisfy their need for “reasonable and supportable forecasts.”

Community institutions can access various data elements to enhance their CECL model. Since the potential list of external factors is numerous, we have honed in on three key drivers that are easily accessible and useful:

(1) Comparable Loan Pools

Some community banks may not have aggregated the right data points and/or stored data over a long enough period of time to gain a statistically relevant estimate. Community banks may benefit from working with a third-party CECL modeling firm that can legally access and utilize comparable pool data from peer institutions that have preserved that data. A bank may only need to augment their pool data with a few specific data points such as prepayment rates, and loan characteristics such as LTV, credit scores, average age, etc.

When working with a third party, institutions should use various sampling strategies to ensure the data is in some form representative of their current portfolio. This will give the institution and its auditors confidence the modeled result will reflect future performance of the portfolio until the bank can collect a sufficient internal history.

(2) Macroeconomic Factors 

While some community banks are operating on the premise that a simpler model is better, we believe that the application of external factors will enable institutions with a more robust and supportable data points that will satisfy auditors ― especially since many community banks have not captured updated obligor performance data. However, in the absence of obligor performance data, macroeconomic data at the national and local level will provide auditors with additional inputs and assumptions that impact loan performance. Some examples may include the unemployment rate, interest rates and even oil prices in some parts of the country. Many of these data points are available from government sources such as the US Census Bureau and the Federal Reserve Economic Data (FRED).

(3) Property Values and Behaviors

Depending on the asset type, understanding changes to the asset value historically and in the future may help predict loan performance and provide additional insight into the community bank’s portfolios. In one such example, community banks can use MSA-specific house price indices (Case Shiller) to gain general information, but they may also want to look at historical trends and patterns present in a range of economic environments. For example, a loan pool with a relatively short history may not capture the value risk present during a recession. Sources that may provide such data include appraisals, occupancy rates, default and foreclosure rates, etc.

As community banks work to develop their models, incorporating key factors will ensure you are factoring in risk and providing auditors with a supportable and more sophisticated analysis. To learn more about how Situs can help you determine what data and modeling solutions are available to you, please reach out to Andrew Phillips.