Case Study: How To Win More Loan Business

Improving Loan Production

We work on thousands of lending transactions every year with hundreds of community banks across the country.  We participate and help structure financing on commercial real estate, C&I and Ag properties ranging in size from a few hundred thousand to over $100mm, and we collaborate with community bank lenders and underwriters that span the whole gamut of experience.  We witness the good, the bad, the ugly, and occasionally the very bizarre in bank marketing, underwriting, and documentation.  Occasionally we see a brilliant strategy that works out well for a bank, and we feel compelled to share it with our readers - we came across an example recently.

At the beginning of 2016, a banker, Danny, proposed a $2.7mm loan to Mr. Borrower to purchase a medical office building in a large MSA. The strategy was to offer Mr. Borrower some options that other banks did not consider.  Danny showed Mr. Borrower nine different financing options that spanned five to ten-year commitments, and 70 to 80% LTV, and various prepayment provisions.  Each financing option had a separate credit spread – this is not common in community banking.  Most banks do not have differential pricing for the same contemplated loan.  Many banks, in an effort to win business, gravitate to the borrower’s most extreme underwriting demand – highest LTV, weakest credit support, longest commitment term, and lowest pricing.  

Danny’s approach was to entice the borrower to consider the following loan parameters:

  1. Lowest LTV (highest debt yield),
  2. Highest credit support,
  3. Commitment term that made the most credit sense and resulted in the highest ROE in the bank’s RAROC model,
  4. Robust prepayment provision, and
  5. Price the loan with different credit spread based on the parameters above.

Danny optimized the borrower’s options to come up with the best deal for the bank and the borrower by offering a lower credit spread for lower LTV, full guaranty, ten-year term, and a robust prepayment provision.  The bank and borrower agreed to a ten-year loan, 70% LTV (just over 10% debt yield).  The borrower was willing to take a much lower advance rate and accept the robust prepayment provision to obtain a very competitive credit spread of 2.00%.

There were several bankers at the time of loan origination who expressed concern about the credit spread.  However, with good cash flow, low LTV, high debt yield, full guaranty, and good asset class (medical office), the RAROC model showed an 18.2% ROE.  What the model did not show, but Danny understood, was the critical leverage and the financial benefit the bank obtained with the robust prepayment provision.

The loan performed as expected, and the borrower did not miss any payments.  However, in November 2019, the borrower chose to accept an offer to sell the underlying collateral for a significant gain – this is good for the borrower, but not necessarily the bank.  Mr. Borrower is a long-term investor, and proceeds from the sale of this property were already earmarked for the purchase of another office building with a higher cap rate.  Mr. Borrower proposed to Danny to make a loan to be secured on the new purchased office building.  But the reality in 2019 is that competing banks are showing loan terms up to 80% LTV, and with the lower NOI on the new building, the debt yield would only be 6.5%.  Danny’s bank had no interest in making that type of loan. Had Danny’s initial strategy been an average lending strategy, the loan would pay off, and Mr. Borrower would get his 80% LTV loan with no recourse from “stupid” competition and the bank would need to find a substitute earning asset which would be subject to continued “stupid” market pricing and structure. 

But Danny’s initial strategy of thin pricing but a robust prepayment provision had a profound impact on the bank.  The prepayment cost on the loan was almost $250k.  The prepayment provision was tied to the actual cost to prepay the contract on the fixed term of the loan and could not be easily negotiated away by the borrower.  Mr. Borrower simply could not stomach writing the prepayment check to the bank.  Instead, Danny proposed to port the rate on the existing loan and substitute the collateral.  Mr. Borrower obtained a new 10-year loan, secured by the purchased office building, the prepayment fee was not charged, but the bank did approve 75% LTV on the appraised value of the office building (better for the bank then the competition that was offering 80% LTV and two-years IO).      

It wasn’t apparent to everyone in 2016 what parameters of the loan structure would drive profitability for the bank.  The credit quality was acceptable, but pricing seemed thin compared to comparable market deals.  What Danny recognized was that commercial loans to Mr. Borrower are continually morphing.  Mr. Borrower, who is a serial investor looking to profit from property improvements and real estate valuations, will always find borrower-friendly (and lender-unfriendly) terms 11 years into an economic expansion.  If you can establish the initial credit relationship in more lender-friendly times offer attractive pricing, the correct structure, and the proper prepayment provision, you create a much more profitable loan for the bank when Mr. Borrower needs to change his credit arrangements in the future.

How to Implement at Your Bank

In this article, we credit Danny with the initial strategy to price and structure the loan to maximize lifetime value for the bank.  In reality, Danny would have chosen a different approach if he worked for another bank.  The majority of bank competitors price for immediate yield or NIM and do not have the tools to quantify the tradeoff between LTV, DSCR, debt yield, commitment term, prepayment provision, and loan yield.  Danny’s executive team developed a lending strategy that allows lenders to structure loans and propose pricing that can result in competitive differentiation, and optimize the profit outcome for the bank.  Every bank needs to acquire the tools necessary to quantify relationship ROE based on changes in LTV, DSCR, debt yield, commitment term, prepayment provision, and loan yield.