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Distressed CRE Capital Stack Recovery Forecasting Agent

Debt-for-equity conversions require detailed financial recovery forecasting under dynamic interest rate scenarios. Build a time-series forecasting engine using Prophet and PyMC to model asset net operating income (NOI) recovery trajectories across a 5-year investment horizon. Predict recovery yield within 5% error bounds.

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What you are building

Construct a time-series and probabilistic cash flow forecasting model for distressed commercial real estate recapitalizations.

How work is evaluated

Evaluation

Evaluates accuracy of projected 5-year NOI CAGR against historical recovery benchmarks.

Datasets

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

How submissions are scored

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

Positive Recovery Trend

Projected CAGR must be positive for distressed asset turnaround scenario

Binary check

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

Dimension 2

IRR Prediction Variance

Absolute percentage point error in predicted IRR • target: 0.5 • range: 0-3

Binary check

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

Learning goals

  • Fit time-series forecasting models (Prophet) on historical property occupancy and rent trends

  • Incorporate macroeconomic interest rate and inflation covariates

  • Simulate Monte Carlo cash flow outcomes using Bayesian inference (PyMC)

  • Output net present value (NPV) and internal rate of return (IRR) distribution curves

Resources

Dataset notes

Quarterly operating statements and rent roll trajectories of distressed commercial office assets.

Challenge setup

Evaluates accuracy of projected 5-year NOI CAGR against historical recovery benchmarks.

Challenge input

JSON with initial_noi, occupancy_pct, market_rate_trend_pct

Prophet

Handles non-linear growth curves and seasonal occupancy variations

PyMC

Simulates probabilistic cash flow distributions for debt recapitalizations

Evaluated output

JSON with projected_5yr_noi_cagr, expected_irr_pct

Evaluation checks
  • Projected CAGR must be positive for distressed asset turnaround scenario
  • Absolute percentage point error in predicted IRR • target: 0.5 • range: 0-3
Configured evaluation evidence
  • Benchmark: FinForecastBench
  • IRR Prediction Variance target: 0.5
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Prophet · Required
  • Retell AI · Optional
  • Arcee AI · Optional

Evaluation contract

  • Positive Recovery Trend · Weight 1
  • IRR Prediction Variance · Weight 1

Metrics

  • IRR Prediction Variance · Target 0.5

Benchmarks

  • FinForecastBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with initial_noi, occupancy_pct, market_rate_trend_pct

    Output: JSON with projected_5yr_noi_cagr, expected_irr_pct

    Execution harness: Python

Evaluation tools

  • Prophet · Time-Series Forecasting
  • PyMC · Bayesian Modeling

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Distressed CRE Capital Stack Recovery Forecasting Agent