While community banks have typically enjoyed a steady, but smaller, share of the overall mortgage origination business as compared with their independent lender counterparts, record-low interest rates have skyrocketed both purchase and refinance volume across the board. Increased demand translates into higher profits — a welcome sight on any bank’s balance sheet. However, given the level of volumes at play in the current market, banks need to focus on managing mortgage pipeline risk to ensure the long-term health and stability of their mortgage operations.

Many banks choose a bulk mandatory commitment model to take advantage of the better pricing available over best effort, and it is easy to see why. On average, mandatory delivery provides a quarter-point improvement or more over best effort pricing, and given bank executives’ desire to see steady returns, bank mortgage divisions would naturally opt for the delivery mechanism that yields a greater return on investment.

As is often the case, greater returns also yield greater risk, and many banks typically manage the risk of mandatory execution using a combination of institutional/loan-level knowledge, spreadsheets and, in the case of experienced secondary marketing managers, gut instinct. In an average market, this strategy works, as the size of the pipeline is such that the secondary/capital markets department can use the “eyeball test” or a spreadsheet to manage the bank’s position on its commitments.

However, the current market is anything but average, and when volumes reach the point where the number of loans exceeds the department’s ability to track each one individually, the “eyeball test” is no longer sufficient to ensure the bank meets its commitments and avoids the financial consequences that result from failure to deliver loans as promised. To truly manage the risks associated with mandatory execution, banks must invest in more sophisticated modeling, reporting and analytics to track market movements, accurately calculate expected as well as actual pull-through rates and ultimately maximize profitability.

For example, when a lender fails to make good on a mandatory commitment, Fannie Mae and Freddie Mac typically charge a pair-off fee. Once upon a time, this only occurred when there was a loss. However, the GSEs will now pay lenders’ pair-off gains when applicable. In either case, the GSEs take out a one-eighth of a point administration fee for mandatory pair-offs, and for bank lenders using the “eyeball test” method, this results in significant inefficiencies even when receiving a pair-off gain, since the GSEs are taking out a 12.5 basis point administrative fee.

Utilizing predictive analytics and modeling, bank lenders can not only avoid mandatory pair-offs (both positive and negative) but also take advantage of pay-ups for specific loan groups/types that are in high demand, such as low-balance loans, by moving from bulk commitments to committing loans on an more individualized basis. The pay-ups available for individual commitments can be tremendous, ranging from 30 to 300 basis points or more, and the total cost of implementing a system to help achieve these pay-ups is typically less than six basis points, which is less than half of the admin fee the GSEs charge for pair-offs. Furthermore, a bank can easily cover the cost of such a system with only two small-balance (under $225k) loan pay-ups per month.

While $10 million in monthly production is the oft-quoted benchmark, banks can begin to realize the benefits of a more sophisticated mandatory execution strategy with pipelines as small as $5 million, as it not only helps maximize profitability but also provides the bank with flexibility when consumers need to change loan programs or lock pricing structures after the loan has been locked or require a rate lock extension. Furthermore, a more sophisticated mandatory execution strategy enables banks to be more strategic in retaining versus releasing servicing if desired by allowing the bank to make these decisions after loans have closed.

With the massive amount of mortgage business currently available, banks would be doing themselves a serious disservice by not fully capitalizing on this opportunity. Simply put, eschewing the “eyeball test” in favor of advanced modeling, reporting and analytics is an overall better way to manage mandatory execution risk, as it allows the bank to more reliably predict monthly revenue from loan commitments, avoid costly mandatory pair-offs and maximize the overall profitability of its loan sales.





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