Fair value accounting, commonly used to value fund assets, tends to underestimate the true volatility of asset values, according to a research note published by PitchBook. While the findings are not new, NAV lenders may find the summary stimulating given their reliance on asset values.

Fair value accounting tends to smooth out changes in asset values ​​due to the scarcity of such valuations (usually quarterly) and because general practitioners tend to act cautiously in reflecting changes in the value of assets. assets up or down. This trend is understandable given the absence of frequent observable transactions in a particular asset and, from a fund perspective, it aligns with the long-term investment horizon in which funds typically operate.

The net asset value lenders, however, do not exactly share the fund’s point of view. Loan maturities are often shorter than the remaining life of the fund, and the lender is keen to be adequately secured at all times. It is a matter of first principles: Lenders do not participate in the rise of an investment and, therefore, have no interest in weathering declines in value along the way. Instead, the priority of the net asset value lender is to accurately assess the economic value of assets in its borrowing base throughout the life of the loan.

The smoothing returns approach highlighted in the PitchBook writing may be more relevant to historical analyzes of an LP than to NAV lenders, but here is what we think lenders can take away. First, where possible, it may be useful to supplement the fair value accounting estimates with additional data on the value of the assets. Second, incorporating a lender valuation challenge mechanism into the facility can be helpful in balancing potential gaps between fair value and true economic value. Our Samantha Hutchinson took a more in-depth look at lender reassessment negotiating points in a precedent. FFF item. Finally, at the portfolio level, lenders should ensure that correlation and diversification assumptions are based on fair value data.



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