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.
FHFA: GSEs Have Sold Over 90,000 Nonperforming Loans Since 2014
Fannie Mae and Freddie Mac have sold 90,921 nonperforming loans (NPLs) as of December 31, 2017, with a total unpaid balance of $17.4 billion, according to the fifth Non-Performing Loan Sales Report released last week by the Federal Housing Finance Agency (FHFA). On average, the NPLs had a delinquency of 3.2 years and an average current loan-to-value ratio of 95 percent. Nearly half (46 percent) of the NPLs sold came from New Jersey, New York and Florida.
The report also surveyed borrower outcomes based on data from the 79,683 NPLs sold since June 30, 2017. As of December 31, 2017, 55 percent of NPLs had been resolved; 34 percent with foreclosure and 21 percent without. Foreclosure avoidances were highest when homes were occupied by borrowers – only 11 percent of vacant properties avoided foreclosure, compared to 25.7 percent when homes were borrower-occupied. The report also compared the foreclosure rate among sold NPLs to a benchmark of similarly delinquent loans that were not sold, noting that sold NPLs resulted in fewer foreclosures.
read more: ABA Banking Journal
Mortgage Rates Ricochet Higher After Brief Lull
Rates for home loans rebounded, with the benchmark touching the second-highest level of 2018, after a brief respite for borrowers came to an end ahead of a key Federal Reserve decision.
The 30-year fixed-rate mortgage averaged 4.62% during the June 14 week, up from 4.54%, mortgage provider Freddie Mac said late last week. The 15-year fixed-rate mortgage averaged 4.07%, up six basis points during the week. The 5-year Treasury-indexed hybrid adjustable-rate mortgage averaged 3.83%, up from 3.74%.
Those rates don’t include fees associated with obtaining mortgage loans.
Mortgage rates follow the path of the 10-year U.S. Treasury note, which has been under pressure in recent months. As bond prices decline, yields rise, and so do mortgage rates typically. Investors are facing the risk of higher inflation and a rush of supply of government paper, both of which will erode the value of bonds.
Read more: MarketWatch
Federal Reserve Limits Credit Exposure Big Banks Can Have with Each Other
A banking regulator capped the amount of exposure the largest banks operating in the U.S. can have with each other, putting in place a long-awaited rule to reduce the chance of snowballing credit risk in the financial system during times of crisis.
The rule, completed last week by the Federal Reserve, would limit the credit exposure that eight “systemically important” U.S. banks have with each other to 15% of high-quality capital, including JPMorgan Chase, Wells Fargo, and Citigroup. The same limit would apply to foreign banks with a U.S. holding company with $500 billion or more in assets.
Under the rule, all banks with $250 billion or more in total assets will have to cap their credit exposure to any other company to 25% of high-quality capital.
Read more: Wall Street Journal
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