Build a Lease-Adjusted Valuation Comparability Desk

A valuation reviewer comparing leased and owned operating assets.

Compare lease-heavy businesses under consistent operating and financing conventions. Detect double counting between lease liabilities, rent adjustments, and cash-flow forecasts.

Key: company_id + lease_id + payment_date.

Fields: contractual_payment, discount_rate, recognized_liability, rent_expense, convention_id.

Do not infer full lease schedules from a single recognized liability.

Extract schedules and identify omitted payment detail.

Apply one consistent lease convention per comparison.

Reconcile adjusted operating metrics with the enterprise-value bridge.

Export convention differences and double-counting warnings.

Side-by-side valuation conventions with lease schedules and an inconsistency warning panel.

A lease cash-flow and convention reconciliation module.

A peer-comparison workbook with accounting-to-analytical bridges.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

Do not add lease debt while retaining an incompatible rent deduction without adjustment.

Payment timing and discount conventions are explicit.

Missing contingent payments appear as limitations.

Lease liability present value reproduces the supplied simplified schedule within tolerance.

The same payment schedule under a different discount rate changes present value, not contractual cash payments.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Use public lease footnotes and fictional contractual payment schedules. Include both operating-rent and financing-style analytical presentations with explicit assumptions.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

pandas, SciPy, openpyxl

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

Analytical normalization is not a substitute for the applicable accounting standard.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.