This discussion analyzes the potential impact of loan repricing on ServisFirst's net interest margin (NIM) following maturities in Q1 2026.
Generated by Dafinchi AI. Source-grounded AI analysis, not investment advice.
Can you quantify the incremental NIM pickup if $1.2B of maturing loans reprice to current going‑on yields?
ServisFirst disclosed that it has $1.2 billion of loan maturities at a fixed rate over the next 12 months, with a weighted average yield of 5.19% today. 1
It also disclosed that its “going on rate” for new loan activity is 6.5%. 1
So the gross asset-yield step-up on that $1.2B (ignoring competitive/structural mix effects and assuming full repricing at the going-on rate) is:
If those loans reprice into new loans at the going-on rate, the incremental annual net interest income from the $1.2B would be approximately:
To convert that into an incremental NIM pickup, you need an assumption about the denominator (average earning assets). The excerpts do not provide average earning assets for the quarter/year, so we cannot produce an exact NIM change from first principles using only the provided text.
However, we can express the relationship clearly:
Without the average earning asset base, any single-number “NIM pickup” would be guesswork, not an evidence-based estimate.
Management explicitly cautioned that it is “not” saying it will get 131 bps on every single loan that we reprice, but rather expects “some decent sized pickup” on repricing. 2
That warning means the real pickup is likely below the full 131 bps yield-step, due to partial prepayment/mix, not all principal repricing at the same terms, and/or other nuances. 2
If you want, you can supply the relevant average earning assets figure (from the same period’s deck/filing), and then the incremental NIM in bps can be computed directly from the $15.7M/year estimate.
Disclaimer: The output generated by dafinchi.ai, a Large Language Model (LLM), may contain inaccuracies or "hallucinations." Users should independently verify the accuracy of any mathematical calculations, numerical data, and associated units, as well as the credibility of any sources cited. The developers and providers of dafinchi.ai cannot be held liable for any inaccuracies or decisions made based on the LLM's output.
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