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Challenge

Distressed Commercial Real Estate Debt-for-Equity Underwriter with OpenAI Agents SDK

Higher interest rates have driven commercial property debt restructurings, requiring rapid financial modeling of debt-for-equity swaps. Build an autonomous underwriting agent using the OpenAI Agents SDK to analyze debt schedules, cash flows, and property valuations. Achieve high accuracy in predicting optimal equity conversion percentages.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an automated CRE debt restructuring platform using OpenAI Agents SDK to model debt-for-equity swap valuations.

Delivery guide

How work is evaluated

Evaluation

Evaluates equity conversion percentage calculations and DSCR post-restructuring projections.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

DSCR Target Verification

Calculated remaining debt must meet or exceed target DSCR

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Swap Calculation Variance

Percentage error from exact closed-form financial solution • target: 0.1 • range: 0-2

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build financial modeling tools in Python for debt-for-equity calculations

  • Orchestrate multi-step restructuring logic using OpenAI Agents SDK functions

  • Assess debt service coverage ratio (DSCR) before and after proposed recapitalization

  • Export standardized term sheets with optimal conversion splits

Resources and assets

Reference links and supporting material

Dataset notes

Commercial property financial statements and loan terms undergoing debt-for-equity restructuring.

Challenge setup

Evaluates equity conversion percentage calculations and DSCR post-restructuring projections.

Challenge input

JSON with total_debt, interest_rate, noi, target_dscr

OpenAI Agents SDK

Provides robust function calling and structured agent workflows

Evaluated output

JSON with converted_equity_amount, remaining_debt, post_swap_dscr

Evaluation checks
  • Calculated remaining debt must meet or exceed target DSCR
  • Percentage error from exact closed-form financial solution • target: 0.1 • range: 0-2
Configured evaluation evidence
  • Benchmark: CREUnderwritingBench
  • Swap Calculation Variance target: 0.1
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Writer · Optional
  • Zed · Optional

Evaluation contract

  • DSCR Target Verification · Weight 1
  • Swap Calculation Variance · Weight 1

Metrics

  • Swap Calculation Variance · Target 0.1

Benchmarks

  • CREUnderwritingBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with total_debt, interest_rate, noi, target_dscr

    Output: JSON with converted_equity_amount, remaining_debt, post_swap_dscr

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Distressed Commercial Real Estate Debt-for-Equity Underwriter with OpenAI Agents SDK