TerraFirm REIT — Asset Sales & Debt Covenants
A constrained optimization benchmark that tests whether AI models can navigate commercial real estate asset disposition under debt covenant pressure.
The Problem
TerraFirm REIT holds 12 office properties ($1.95B book value) with $1.42B in debt. Projected appraisal declines of 27% will leave near-zero equity and trip debt covenants. The model must identify which assets to sell NOW at clearing prices to ensure the REMAINING portfolio passes 5 simultaneous constraints at projected future values.
Why This Is Hard
The core trap: assets with the deepest projected declines (Prince 40%, CenterSquare 55%) have even steeper clearing discounts (63%, 70%). Selling them is value-destructive — the clearing price is below the projected future value. Models that naively sell "most distressed" assets will fail every time.
The correct reasoning requires computing net benefit (clearing price − implied future value) per asset, which ranges from −$48.5M (Prince) to +$50.4M (Phillips). This is a domain-specific financial concept that models must discover from the problem structure.
Constraints (all 5 must pass)
| # | Constraint | Notes |
|---|---|---|
| 1 | LTV ≤ 85% on remaining portfolio | Primary binding constraint |
| 2 | Equity ≥ $85M on remaining portfolio | Secondary |
| 3 | Must retain ≥1 of {Prince, CenterSquare} | Structural — traps naive sellers |
| 4 | Must retain ≥1 of {Phillips, Chambers} | Structural |
| 5 | Must retain ≥5 properties | Loose |
Solution Space
- 649 valid scenarios (sell 4–7 of 12 assets)
- 25 valid 4-sale combos (minimum required)
- 0 valid 3-sale combos (LTV too tight)
- Prince and CenterSquare should never be sold (negative net benefit)
Scoring
| Reward Function | Weight | Condition |
|---|---|---|
terrafirm_reward | 1.0 | ≥1 parsed scenario passes all 5 constraints |
both_pass_reward | 0.5 | Both parsed scenarios pass |
avoided_trap_reward | 0.25 | Model never sold CenterSquare or Prince |
Partial credit (0.5) is awarded if the model produces parseable scenarios that fail constraints, vs. 0.0 for unparseable output.
Benchmark Results (10 iterations each)
| Model | Pass Rate (≥1 scenario) | Sold CenterSquare/Prince |
|---|---|---|
| o4-mini | 80% | 2x / 2x |
| o3 | 50% | 3x / 4x |
| Claude Opus 4.6 | 20% | 6x / 8x |
| GPT-4.1 | 10% | 6x / 3x |
| Claude Sonnet 4.5 | 0% | 6x / 9x |
| GPT-4o | 0% | 6x / 9x |
| Claude Haiku 4.5 | 0% | 8x / 9x |
Domain
Commercial Real Estate · Financial Reasoning · Constrained Optimization · Debt Covenants
Author
Patrick Browne — CRE finance professional building domain-specific AI evaluation benchmarks.