Accounting & Valuation
Pricing and relative value
Pricing and relative value
Pricing in ABF isn’t a single number. It’s a negotiation over multiple components, each compensating for different risks. If you understand what you’re paying for, you can negotiate more effectively. If you don’t, you’ll overpay for risks that don’t apply to your deal or underprice risks that will cost you later.
This topic covers the analytical framework for ABF pricing. You’ll learn how to decompose a spread, build a relative value view, and identify when a “cheap” deal is actually expensive. For the hands-on execution guide (building your own comps table, sourcing market data), see How to Benchmark Your Deal Against the Market.
How ABF pricing works
Every ABF deal prices as a spread over a benchmark rate. The all-in yield you pay (or receive) breaks down into distinct components:
All-in Yield = Benchmark Rate + Credit Spread + Liquidity Premium + Complexity Premium
Each component compensates for something specific:
| Component | What It Compensates For | Typical Range |
|---|---|---|
| Benchmark rate | Time value of money, macro rate environment | SOFR, Treasury (market-driven) |
| Credit spread | Expected and unexpected credit losses | 100-400+ bps depending on asset class |
| Liquidity premium | Difficulty of selling/exiting the position | 25-100+ bps vs. public ABS |
| Complexity premium | Analytical effort, structural opacity | 0-50+ bps for bespoke structures |
Benchmark rates
Most ABF facilities float over SOFR. Some insurance placements and longer-dated notes price as a spread to Treasuries. The benchmark choice affects your basis risk and investor base.
- SOFR: Standard for warehouse facilities and most floating-rate structures
- Treasuries: Common for fixed-rate term ABS and insurance placements
- Swap rates: Relevant when you’re evaluating fixed vs. floating trade-offs
What “spread” means in different contexts
You’ll encounter several spread measures. They’re not interchangeable.
Nominal spread: Simple difference between yield and benchmark. Ignores optionality and cash flow timing.
Z-spread (Zero-volatility spread): Constant spread added to the entire Treasury curve that makes the present value of cash flows equal to price. Accounts for cash flow timing but not optionality.
OAS (Option-Adjusted Spread): Z-spread minus the value of embedded options (prepayment, call). The “pure” credit spread after stripping out optionality.
DM (Discount Margin): Spread over the forward curve (SOFR) for floating-rate securities. The floating-rate equivalent of Z-spread.
For most ABF analysis, you’ll use DM (floating) or Z-spread (fixed). OAS matters when prepayment optionality is significant (RMBS, certain consumer loans).
Spread decomposition
To negotiate effectively, you need to know what you’re paying for. Here’s how to break down a spread.
Credit spread
The credit spread compensates for losses. It has two components:
-
Expected loss compensation: The actuarial cost of defaults. If you expect 2% cumulative losses on a 3-year WAL deal, that’s roughly 65-70 bps annually.
-
Unexpected loss compensation: The additional spread for variance around expected loss. The wider the potential loss distribution, the higher this component.
You can back out the credit component from comparable transactions. If public auto ABS (essentially zero liquidity premium) prices at SOFR+125 for BBB-rated tranches, and your BBB-rated private auto deal prices at SOFR+175, the 50 bps difference is liquidity and complexity.
Rating-implied vs. market-implied credit spreads
Rating agencies publish idealized default rates by rating category. You can translate these into implied spreads:
| Rating | 5-Year Cumulative Default Rate (Idealized) | Implied Annual Credit Spread (40% severity) |
|---|---|---|
| AAA | 0.10% | ~1-2 bps |
| AA | 0.30% | ~3-5 bps |
| A | 0.60% | ~6-10 bps |
| BBB | 2.00% | ~20-30 bps |
| BB | 8.00% | ~80-100 bps |
Illustrative pricing. See pricing disclaimer.
Market spreads are wider than these credit-only numbers because they include liquidity, complexity, and risk premia beyond expected loss.
Liquidity premium
Private ABF prices wider than public ABS because there’s no secondary market. You can’t sell easily, so you demand compensation for being locked in.
Typical liquidity premiums:
| Structure | Liquidity Premium vs. Public ABS |
|---|---|
| Public rated term ABS | Baseline (0) |
| 144A private placement | 10-30 bps |
| Unrated term deal | 50-100 bps |
| Warehouse facility | 75-150 bps |
| Bespoke bilateral | 100-200+ bps |
These premiums vary with market conditions. In a risk-off environment, liquidity premiums widen significantly.
Complexity premium
Bespoke structures require more work to analyze. Investors charge for that effort.
Vanilla auto ABS prices tighter than a bespoke container lease securitization, even at the same rating, because:
- More investors can analyze it
- Historical data is more robust
- Legal and structural precedent exists
If you’re bringing a novel asset class or unusual structure, expect to pay 25-50+ bps for the analytical burden you’re imposing.
Worked example: decomposing a 350 bps spread
A consumer unsecured warehouse prices at SOFR+350. Here’s a rough decomposition:
| Component | Estimate | Rationale |
|---|---|---|
| Credit spread | 150 bps | 3.5% expected CNL over 2-year WAL, plus unexpected loss buffer |
| Liquidity premium | 125 bps | Warehouse vs. public ABS benchmark spread |
| Complexity premium | 25 bps | Standard consumer unsecured, minimal structural complexity |
| Risk premium / margin | 50 bps | Lender’s required return over costs |
| Total | 350 bps |
This decomposition helps you negotiate. If you have exceptional collateral quality (1.5% expected CNL instead of 3.5%), you should argue for 75-100 bps tighter on the credit component.
Relative value framework
The fundamental question in pricing is: compared to what? Relative value analysis lets you determine if a deal is cheap, fair, or expensive.
Building a relative value view
Compare your deal across three dimensions:
Same asset class, different structures. How does a consumer unsecured warehouse compare to:
- Forward flow at a fixed spread
- Term ABS (rated, public)
- Private placement (rated, bilateral)
Each structure has different cost, but also different advance rates, flexibility, and operational requirements. You need an apples-to-apples comparison.
Same structure, different asset classes. How does a consumer unsecured warehouse compare to:
- Auto warehouse
- Equipment warehouse
- BNPL warehouse
Same structure, same capital provider type, but different credit profiles. Spreads should reflect loss expectations.
Same risk profile, different capital structures. How does senior warehouse pricing compare to:
- Mezzanine tranche at the same facility
- Equity return requirement
- Alternative senior provider
Your cost of capital is the blended stack, not just the senior spread.
Adjusting for structural differences
Raw spread comparisons are misleading without adjustment.
Advance rate normalization. A facility at SOFR+200 with an 85% advance rate is NOT cheaper than SOFR+150 at 75% advance rate if you need to fund the remaining equity at 15%+ returns.
Calculate effective cost:
- Deal A: 85% at SOFR+200, 15% equity at 15% = (0.85 x 2.00%) + (0.15 x 15%) = 1.70% + 2.25% = 3.95% blended
- Deal B: 75% at SOFR+150, 25% equity at 15% = (0.75 x 1.50%) + (0.25 x 15%) = 1.13% + 3.75% = 4.88% blended
Deal A is cheaper despite the wider spread.
Tenor adjustment. Shorter WAL deals should price tighter. A 1-year WAL warehouse should be inside a 3-year WAL term deal, all else equal.
Enhancement adjustment. More subordination should mean lower spreads. If Deal A has 15% enhancement and Deal B has 10%, Deal A’s senior tranche should price tighter.
When comparisons don’t work
Some deals are truly bespoke. A first-of-kind litigation finance securitization has no comps. In those cases:
- Decompose the spread into components and price each separately
- Use risk-adjusted return metrics rather than spread comparisons
- Acknowledge the uncertainty and size your position accordingly
Risk-adjusted return metrics
Spread alone doesn’t tell you if a deal is good. You need to adjust for risk.
ROE (return on equity)
For originators, ROE measures return on your equity contribution to the facility:
ROE = (Excess Spread - Losses - Overhead) / Equity Contribution
Example:
- $100M facility, 80% advance rate, you contribute $20M equity
- Gross yield on assets: 18%
- Cost of funds: SOFR+250 (assume 8%)
- Operating costs: 4% of portfolio
- Expected losses: 3%
ROE = (18% - 8% - 4% - 3%) / 20% = 3% / 20% = 15% ROE
For capital providers, ROE measures return on economic capital allocated to the position.
ROA (return on assets)
ROA = Net yield after credit costs, before leverage.
When leverage is constrained (as with many insurance investors), ROA matters more than ROE. A 1.5% ROA on a A-rated tranche may be attractive even if ROE is modest.
IRR (internal rate of return)
Cash flow timing matters. A deal that returns cash early has better IRR than one that back-loads returns, even at the same nominal yield.
Compare:
- Deal A: 12% yield, 2-year WAL, cash flows evenly
- Deal B: 12% yield, 2-year WAL, back-loaded (80% of cash flows in year 2)
Deal A’s IRR is higher because you reinvest earlier.
Caution: IRR can mislead when comparing deals of different sizes or durations. Use multiple metrics.
Sharpe ratio
Spread per unit of volatility. Useful for portfolio construction.
Sharpe Ratio = (Portfolio Return - Risk-Free Rate) / Return Volatility
A BBB-rated consumer ABS at 200 bps spread with 50 bps return volatility has a Sharpe of 4.0. A BB-rated deal at 400 bps with 200 bps volatility has a Sharpe of 2.0. The BBB is a better risk-adjusted investment.
RAROC (risk-adjusted return on capital)
Banks use RAROC to evaluate ABF economics:
RAROC = Net Income / Economic Capital
Where economic capital = regulatory capital allocation + internal buffers.
A deal that looks attractive on spread alone may have poor RAROC if it consumes significant capital under Basel rules.
Worked example: comparing two deals
| Metric | Deal A | Deal B |
|---|---|---|
| Asset class | Consumer unsecured | Auto loans |
| Spread | SOFR+300 | SOFR+200 |
| Advance rate | 80% | 90% |
| Expected loss | 3.0% | 1.0% |
| WAL | 2 years | 2.5 years |
| Enhancement | 20% | 10% |
At first glance, Deal A pays 100 bps more. But:
Net spread after expected loss:
- Deal A: 300 bps - 150 bps (3% loss over 2 years) = 150 bps net
- Deal B: 200 bps - 40 bps (1% loss over 2.5 years) = 160 bps net
Cost of capital comparison (assuming 15% equity return):
- Deal A: (0.80 x 3.00%) + (0.20 x 15%) = 5.40%
- Deal B: (0.90 x 2.00%) + (0.10 x 15%) = 3.30%
Conclusion: Deal B is cheaper on a blended cost basis despite the lower spread, and has similar risk-adjusted returns after losses.
Comparable transaction analysis
Building a comps table is the core skill in ABF pricing. Here’s how to do it properly.
What to include
A useful comps table contains:
| Field | Why It Matters |
|---|---|
| Issuer/Originator | Name recognition affects pricing |
| Asset class | Apples to apples |
| Structure | Warehouse vs. term vs. forward flow |
| Rating (if any) | Primary driver of pricing |
| Size | Larger deals often price tighter |
| Pricing (spread) | The number you’re benchmarking |
| Date | Market conditions change |
| Advance rate | Normalize for fair comparison |
| WAL | Shorter should price tighter |
| Enhancement | More protection = tighter pricing |
Where to find data
Public sources:
- Bloomberg NI ABS screen: New issue data for rated deals
- TRACE: Secondary trading levels
- EDGAR 10-D filings: Performance data that helps infer pricing
- Rating agency presale reports: Structural details and pricing context
Private sources:
- Dealer research: Market updates, indicative pricing, deal commentary
- Industry networks: SFIG, conferences, relationships
- Your own deal database: Build it over time
Adjusting for differences
Raw comps require adjustment. A deal that priced 6 months ago in a different rate environment isn’t directly comparable to today.
Vintage adjustment: Market spreads move. Look at how indices (if available) have moved since the comp priced.
Size adjustment: Larger deals access more investors and often price 10-25 bps tighter.
Structure adjustment: Normalize for advance rate, WAL, and enhancement differences.
Sample comps table
| Issuer | Asset Class | Structure | Rating | Size ($M) | Spread | Date | Advance Rate | WAL | Enhancement |
|---|---|---|---|---|---|---|---|---|---|
| ABC Finance | Consumer unsecured | Warehouse | NR | 150 | S+325 | Mar 2026 | 82% | 2.0 | 18% |
| XYZ Lending | Consumer unsecured | Warehouse | NR | 200 | S+300 | Feb 2026 | 85% | 1.8 | 15% |
| LMN Capital | Consumer unsecured | Term ABS | BBB | 350 | S+225 | Mar 2026 | N/A | 2.5 | 10% sub |
| PQR Finance | Consumer unsecured | Warehouse | NR | 100 | S+375 | Jan 2026 | 78% | 2.2 | 22% |
Illustrative pricing. See pricing disclaimer.
Analysis: Your deal at $175M, 80% advance rate should price in the S+315-340 range based on these comps, adjusting for size (between ABC and XYZ) and advance rate (between XYZ and PQR).
Presenting comps to IC
Your comps table tells a story. Frame it clearly:
-
Lead with your conclusion: “Based on comparable transactions, we believe fair value is S+320, +/- 15 bps.”
-
Highlight the most relevant comps: Not every deal in your table is equally useful. Call out the 2-3 most comparable.
-
Explain adjustments: “XYZ priced tighter due to larger size and repeat issuer premium. We adjust +15 bps for these factors.”
-
Acknowledge limitations: “Limited recent comps in this asset class; we supplemented with adjacent deals and historical spread relationships.”
Market data sources and limitations
You can’t price without data. But all data sources have limitations.
Public ABS data
Bloomberg NI ABS: New issue screen showing recent rated deals. Good for term ABS comps, less useful for warehouse and private deals.
TRACE: Secondary trading data. Useful for seeing where deals trade post-issuance, but coverage is limited for ABF.
EDGAR: 10-D filings show monthly performance data. Useful for deriving implied pricing from deal economics. ABS-EE provides loan-level data for some deals.
Rating agency reports: Presale reports include structural details. Surveillance reports track performance. New issue reports often include pricing context.
Private market data
Dealer research: Most useful for current indicative pricing. Banks publish market updates, spread grids, and deal commentary. Build relationships with coverage bankers.
Industry conferences: SFIG Vegas, regional events. Valuable for calibrating expectations and hearing about deals that aren’t public.
Internal databases: Over time, your own deal history becomes your best data source. Track every deal you see or do.
Limitations to understand
Public ABS is not private ABF. A public auto ABS deal prices inside where a private auto warehouse will. Don’t use public comps without adjusting for the liquidity premium.
New issue vs. secondary. Primary spreads are often tighter than secondary because new issue includes placement fees and allocations to key accounts. Secondary levels reflect true clearing prices.
Selection bias. You only see deals that got done. The deals that couldn’t get done (at any price) aren’t in your data set.
Timing. By the time you see a data point, the market may have moved. A 3-month-old comp is ancient history in volatile markets.
Making data actionable
Triangulate. No single source tells the whole story.
- Check new issue data (Bloomberg NI ABS)
- Cross-reference with dealer indicative levels
- Adjust for time and structural differences
- Validate with industry contacts
Build your own database. Every deal you evaluate, every indicative term sheet, every market data point goes into a spreadsheet you maintain. In 2-3 years, this becomes invaluable.
When “cheap” isn’t cheap: hidden pricing risks
High spreads exist for a reason. Your job is to understand whether that reason still applies to you.
Yield chasing traps
A 500 bps spread looks attractive until you understand what it’s compensating for:
- Severely subordinate position: That “senior” tranche might be first-loss in disguise
- Untested asset class: No performance history means no reliable loss forecast
- Distressed originator: The spread reflects originator survival risk, not just collateral risk
- Structural traps: Triggers that trap cash, mark-to-market provisions, extension risk
The spread is not alpha if it compensates exactly for the risk you’re taking.
Hidden risks that blow up economics
Structurally subordinate positions. “Senior” in a deeply subordinated structure might still take losses in a moderate stress. Read the waterfall, not just the label.
Excessive triggers. Tight triggers that trap cash improve credit quality but destroy your returns. A deal that traps all excess spread at 3% delinquency might leave you with no current yield.
Mark-to-market provisions. Some facilities require mark-to-market valuations that can trigger margin calls. In a liquidity crunch, you’re forced to post collateral precisely when you can least afford it.
Extension risk. Bullet structures rely on refinancing. If you can’t refinance at maturity, you’re stuck in a deal that may convert to amortizing at a punitive rate.
Servicer quality. The best collateral performs poorly with a bad servicer. That wide spread might reflect servicing risk you can’t easily assess.
The “new asset class” premium
Emerging asset classes (litigation finance, pharma royalties, novel fintech products) price wider. The premium reflects:
- No historical performance data
- Untested legal and structural frameworks
- Smaller, concentrated investor base
- Higher analytical burden
Is the premium sufficient? Often, no. The first movers into new asset classes frequently experience losses that exceed their spread premium. Be skeptical until track records develop.
Illiquidity discount vs. illiquidity trap
Getting paid for illiquidity is fine. You earn a premium for committing capital to positions you can’t easily exit.
Getting stuck in an illiquid position you need to exit is a trap. If your fund faces redemptions, or your balance sheet needs rebalancing, an illiquid position becomes a problem.
Size positions appropriately:
- Match illiquidity to your liability structure
- Don’t overweight positions you might need to exit
- Understand the secondary market (if any) before committing
Due diligence shortcuts that cost you
Wide spreads sometimes reflect what you’ll find in diligence. Skipping that diligence doesn’t make the risk go away:
- Skipped site visits: Operations that look fine on paper can be a mess in practice
- Stale performance data: A tape that’s 6 months old may not reflect current underwriting
- Unvalidated third-party diligence: The DD firm’s sample may not represent the portfolio
Worked example: the “cheap” deal that wasn’t
A capital provider evaluated a consumer unsecured deal at SOFR+400, 75 bps wider than comparable transactions. The spread looked attractive.
What the pricing missed:
- Originator had loosened underwriting 9 months prior (no performance data on new credit box)
- Geographic concentration of 40% in a single state with rising unemployment
- Servicer was thinly staffed and had no workout capability
- Trigger calibration was tight (spread trapping at 2.5% DQ)
Outcome: Losses came in at 3x initial expectations (12% CNL vs. 4% base case). The spread trap activated in month 8, eliminating current yield. After 18 months, the position was worth 70 cents on the dollar.
The 75 bps “premium” compensated for maybe 25 bps of the actual incremental risk.
The advance rate / spread trade-off
Your total cost of capital matters more than spread alone. A lower spread at a lower advance rate can be more expensive.
Total cost of capital calculation
Blended Cost = (Advance Rate x Debt Cost) + ((1 - Advance Rate) x Equity Cost)
Example:
| Scenario | Advance Rate | Spread | Equity Cost | Blended Cost |
|---|---|---|---|---|
| Deal A | 75% | S+150 (6.50%) | 15% | 8.63% |
| Deal B | 85% | S+225 (7.25%) | 15% | 8.41% |
| Deal C | 90% | S+275 (7.75%) | 15% | 8.48% |
Illustrative pricing. See pricing disclaimer.
Deal B is cheapest despite 75 bps wider spread than Deal A, because the higher advance rate reduces expensive equity.
When to push for higher advance rate
Optimize for higher advance rate when:
- Your equity cost is high (early-stage originator, limited capital)
- You have confidence in collateral performance (can support the leverage)
- The spread differential is modest (<100 bps for 10% more advance)
When to accept lower advance rate
Accept lower advance rate when:
- You have cheap equity (family office capital, patient investors)
- Collateral volatility is high (you need the cushion)
- The spread savings are significant
- Covenant and trigger headroom is better at lower leverage
Relationship to triggers
Higher advance rates typically come with tighter triggers. At 90% advance rate, you might have:
- Tighter DQ triggers (2.5% vs. 3.5%)
- Less OC cushion before spread trapping
- More restrictive concentration limits
Model the scenarios. What happens to your economics if you trip a trigger? Sometimes the lower advance rate with looser triggers produces better risk-adjusted returns.
Cross-reference: applying this framework
This guide gives you the analytical framework. To apply it to your specific deal:
How to Benchmark Your Deal Against the Market is the hands-on execution guide. It walks through:
- Step-by-step process for building your comps table
- Where to source market data for your specific asset class
- How to present pricing analysis to your IC or capital provider
- Template spreadsheets and sample deliverables
Related topics:
- Collateral Analysis for the tape analytics that feed your loss assumptions
- Cash Flow Modeling for yield and return calculations
- Credit Analysis for the risk assessment that informs risk-adjusted metrics
- Market Spreads Guide for current indicative pricing by asset class and structure
- Economics of ABF for Originators for the originator perspective on cost of capital