Private credit valuation
Improving the private debt valuation process
Valuation in private credit markets involves balancing quantitative methods with subjective judgment. Yet the increasing use of private data sets for level 3 debt valuations raises concerns about transparency and replicability. Discover how this data may contrast with publicly available market data and present challenges for auditors, regulators, and investors seeking to validate valuations.
Key takeaways
- Valuation combines quantitative methods that are transparent and replicable with subjective elements, often leading to debates over the extent of subjectivity allowed in models.
- Using private data sets compiled from client loan originations and trades in valuations can lead to transparency challenges, investor concerns, and regulatory and board oversight issues.
- Private market databases can support valuations if used to corroborate a final selected discount spread or yield developed using transparent, replicable data that meets stakeholder standards.
Private data sets
Valuation is often viewed as part science and part art, where science is quantitative, replicable, and transparent, and art is subjective. Over the past few years, the use of private data sets—typically information collected on recent loan organizations and trades across many clients—as the primary input to the valuation of level 3 debt has crept into use. Proponents argue that private data more accurately reflects the current market dynamics of private credit investments.
Private data set concerns
Private credit investors may worry that using this data set for external valuations complicates replication, potentially increasing audit fees to address concerns about subjectivity. Regulators and LP investors may also be concerned with anonymized private data sets. Valuation committees and boards must have adequate support to fulfill oversight responsibilities such as SEC Rule 2a-5.
Improving the valuation process
Valuations improve by emphasizing recent transaction data from investors’ own portfolios, using credit rating models to monitor borrower conditions, and avoiding subjective adjustments by quantitatively accounting for illiquidity.
Role of private data sets
A private market database can play a useful role in valuations if used to corroborate a final selected discount spread or yield developed using transparent, replicable, and auditable data that meets the standards of auditors, regulators, and LP investors.
Looking ahead
Private market databases can support valuations when derived from transparent, auditable data, rather than serving as the sole basis for valuations.
Deloitte’s Portfolio Valuation Operate offers portfolio valuation services combining technologyand expert knowledge to enhance valuation efficiency, reduce risk, and support investment management across asset classes.
