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Challenge

Build CRE Multi-Tenant Debt Servicing Assessment Engine using CrewAI

Evaluating default risk for multi-tenant commercial office portfolios experiencing high vacancies requires dynamic collaboration between lease abstractors, cash flow modelers, and debt restructuring specialists. Developers will construct a multi-tenant debt servicing evaluation crew using CrewAI. The crew assigns distinct tasks: abstracting tenant lease expirations, forecasting debt service coverage under stress, and generating workout term sheets to prevent REIT liquidations.

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Challenge brief

What you are building

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

Construct a multi-tenant CRE debt servicing assessment engine using CrewAI to evaluate lease roll risk and debt coverage.

Delivery guide

How work is evaluated

Evaluation

Evaluates calculation precision of dynamic DSCR and identification of high-risk lease expirations.

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 Below Covenant Threshold Flag

Ensures DSCR < 1.0 triggers HIGH_RISK categorization

Binary check

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

Dimension 2

DSCR Math Accuracy

Precision of calculated DSCR • target: 0.95 • range: 0-1

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

  • Orchestrate multi-agent task workflows using CrewAI for commercial real estate risk analysis

  • Build custom lease roll analysis tools to model tenant renewal probabilities

  • Calculate dynamic monthly Debt Service Coverage Ratios (DSCR) under varying tenant loss assumptions

  • Generate automated CRE debt workout recommendations

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 30 commercial multi-tenant lease schedules and debt terms.

Challenge setup

Evaluates calculation precision of dynamic DSCR and identification of high-risk lease expirations.

Challenge input

JSON with total_sqft, leases_expiring_12m_sqft, current_noi, annual_debt_service

CrewAI

Required framework for multi-agent workflows.

Evaluated output

JSON with projected_dscr, default_risk_category, critical_tenant_expirations_pct

Evaluation checks
  • Ensures DSCR < 1.0 triggers HIGH_RISK categorization
  • Precision of calculated DSCR • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: CREDebtBench
  • DSCR Math Accuracy target: 0.95
  • 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
  • CrewAI · Required
  • crewAI · Optional
  • Aim · Optional

Evaluation contract

  • DSCR Below Covenant Threshold Flag · Weight 1
  • DSCR Math Accuracy · Weight 1

Metrics

  • DSCR Math Accuracy · Target 0.95

Benchmarks

  • CREDebtBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with total_sqft, leases_expiring_12m_sqft, current_noi, annual_debt_service

    Output: JSON with projected_dscr, default_risk_category, critical_tenant_expirations_pct

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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