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Loan fair value and exit price trends: Quarterly disclosure insights – Q1 2025

A consistent, data-driven approach to exit price analysis for loan portfolios

Financial institutions continue to operate in an environment of evolving interest rate trends, regulatory expectations, and heightened scrutiny over balance sheet transparency. As part of these regulatory requirements, fair value disclosure—particularly for loan portfolios—has become a critical component of quarterly and annual reporting.

Under ASU 2016-01, institutions are required to disclose the fair value of financial instruments using the exit price framework defined in ASC 820. This means estimating the price a market participant would pay to acquire a financial asset under current market conditions. For banks and credit unions, this disclosure most often centers around loan fair value, given its size and complexity within the balance sheet. Abrigo’s purchase accounting and valuation services team performs a quarterly analysis of estimated fair value for loan portfolios for a number of clients for disclosure purposes.

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A consistent methodology for fair value disclosures

Abrigo’s fair value disclosure review is a quarterly summary of the fair value analytics across clients for which our valuation services advisors provide exit price disclosure work. While individual results vary by institution, the report reflects how common methodologies and market-based assumptions are impacting loan fair value estimates in aggregate.

This recurring analysis is built on a consistent valuation framework that incorporates both observable market data and portfolio-specific factors.

The analysis focuses on:

Application of the income approach, appropriate for non-traded financial assets

Development of loan-level cash flow models

Use of market-based discount rates aligned with ASC 820

Integration of both Level 2 and Level 3 valuation inputs

Credit risk modeling using PD (probability of default) and LGD (loss given default) assumptions based on benchmarked data

The purpose of the review is to offer a reference point for institutions evaluating their own disclosures and to provide insight into how fair value estimates are evolving over time in response to interest rate changes and credit conditions.

Why exit price disclosure matters

While many institutions meet the minimum disclosure requirement using broad aggregations, loan-level fair value analysis can offer greater insight into the underlying performance and sensitivity of the balance sheet.

Exit price disclosure under ASC 820 differs meaningfully from traditional book valuation. It requires consideration of current market conditions and how market participants would assess asset-level risk. This approach becomes especially important during periods of market stress or rapidly changing interest rate environments.

Quarterly disclosure of loan fair value can also support:

Better understanding of how rate movements impact portfolio value

Internal discussions around credit quality and repricing behavior

Alignment with external auditor expectations and SEC reporting standards

What the quarterly review covers

The Fair Value Disclosure Review provides recurring insight into fair value trends across common loan portfolio segments, including:

Residential mortgage

Commercial real estate (CRE)

Commercial & industrial (C&I)

Consumer loans

While portfolio compositions vary by institution, the review captures how market discount rates, credit risk assumptions, and loan yields affect fair value outcomes quarter over quarter.

How institutions use the analysis

Institutions use this recurring review as a reference point for:

Supporting quarterly and year-end fair value disclosure under ASU 2016-01

Identifying and explaining changes in yield-related or credit-related valuation marks

Informing discussions with auditors, regulators, and executive stakeholders

Understanding the valuation impact of rate environments and market assumptions

The analysis does not prescribe a valuation, but it provides context that institutions can use to validate or benchmark their own internal models and assumptions.