# Cashflow Model > Build a multi-year DCF cashflow model for a real estate asset from natural language inputs. Supports multifamily, office, industrial, retail, and hotel across US and international markets. Saves the model to the workspace for use by capex-analysis. Triggers on: "build a model for [asset]", "what are the financials on [asset]", "model [asset]'s cashflows", "what's the IRR on [asset]", or when capex-analysis finds no base model for the asset. - Skill: `soapboxbuild/cashflow-model` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add soapboxbuild/cashflow-model` - Raw SKILL.md: https://api.skillmd.com/api/skills/soapboxbuild/cashflow-model/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: soapboxbuild (https://skillmd.com/u/soapboxbuild) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/soapboxbuild/cashflow-model --- # Cashflow Model Build a multi-year DCF for a real estate asset. Saves the model to `.cashflow-models/.json` in the workspace. --- ## Step 1: Identify the Asset Ask for the asset name if not already clear from context. The asset name becomes the memory key (e.g. "Prose Frontier" → key `prose-frontier`). Check whether `.cashflow-models/.json` already exists: ```bash ls .cashflow-models/.json 2>/dev/null && echo "EXISTS" || echo "NOT FOUND" ``` If it exists, ask: > "I have an existing model for [asset]. Would you like to update it, or use it as-is?" If "use as-is", stop here. The model is ready for `capex-analysis`. --- ## Step 2: Establish Asset Profile Ask three questions (one at a time): **Q1 — Asset type:** > "What type of asset is [name]?" > 1. Multifamily / Residential > 2. Office > 3. Industrial > 4. Retail > 5. Hotel > 6. Mixed-use **Q2 — Region:** > "Where is the asset located?" > 1. United States > 2. United Kingdom > 3. Germany > 4. France > 5. Australia > 6. Other Europe > 7. Asia-Pacific / Other **Q3 — Hold convention:** > "What level of analysis do you want?" > 1. Unlevered (NOI and IRR only — no debt) > 2. Levered (add debt terms) > 3. Full waterfall (debt + LP/GP splits) Store the profile: ```json { "asset_type": "", "region": "", "currency": "", "hold_convention": "" } ``` --- ## Step 3: Check for Uploaded Documents Before eliciting inputs, check whether the user has uploaded an Excel model or PDF. If yes, extract the relevant figures from it: - For Excel: identify sheets named "Summary", "Cash Flow", "Operating Budget", or similar. Read key fields: unit mix, market rents, vacancy %, opex line items, hold period, exit cap rate. - For PDF (OM): read the "Financial Analysis" / "Pro Forma" section for stabilized NOI, unit mix, and operating expenses. Pre-populate inputs from documents and skip elicitation questions for fields already found. Note which fields came from the document vs. were assumed. --- ## Step 4: Elicit Inputs Ask only for fields not already extracted from documents. Group by template: ### Multifamily inputs: - Unit mix: for each unit type — count, avg SF, current market rent/unit/month - Going-in occupancy % and loss-to-lease % - Vacancy %, concessions %, bad debt % - Other income per unit per year (RUBS, parking, fees, pet — can use $2,000–$2,500 as a starting benchmark for US suburban Class A) - Opex: payroll $/unit, O&M $/unit, marketing $/unit, G&A $/unit, utilities $/unit, mgmt fee % of EGI, insurance $/unit, taxes $/unit, reserves $/unit - Hold period (years), exit cap rate, sale costs % ### Office / Industrial / Retail inputs: - Total rentable SF, current occupancy % - Passing rent $/SF/year, market (ERV) rent $/SF/year - Lease structure: gross / NNN / net - Opex $/SF (gross leases), capex $/SF/year, TI at renewal $/SF, LC % of lease value - Avg lease term, renewal probability - Hold period (years), exit cap rate, sale costs % ### Hotel inputs: - Number of rooms - ADR (Average Daily Rate), going-in occupancy % - Rooms expense %, undistributed expense %, management fee % of total revenue - FF&E reserve % of total revenue (typically 3–5%) - Insurance $/room/year, taxes $/room/year - Other revenue % of rooms revenue (F&B, spa, etc.) - Hold period (years), exit cap rate, sale costs % ### Regional rent growth defaults (suggest to user, let them override): | Region | Rent growth | Expense growth | |---|---|---| | US (Yr 1–2) | 4.0% | 3.0% | | US (Yr 3+) | 3.0% | 3.0% | | UK | ERV growth per MSCI/JLL submarket data | 3.0% | | Germany | CPI (10-point trigger) | 3.0% | | France | INSEE ILC/ILAT statutory | 3.0% | If the user does not provide growth rates, use the regional defaults above and note them explicitly. --- ## Step 5: Confirm Before Computing Present a confirmation table of all inputs. Note which came from documents vs. assumed. Ask: "Ready to build the model with these inputs?" --- ## Step 6: Run DCF Engine Find and execute the DCF engine: ```bash SCRIPT=$(find ~/.claude/plugins -name 'dcf_engine.py' 2>/dev/null | head -1) if [ -z "$SCRIPT" ]; then SCRIPT=$(find ~/audette-skills -name 'dcf_engine.py' 2>/dev/null | head -1) fi python3 "$SCRIPT" --inputs '' ``` Replace `` with the full inputs object including `asset_name` and `profile`. Parse the JSON stdout. If the script fails, show the stderr to the user and stop. --- ## Step 7: Handle Levered / Waterfall (if requested) If hold_convention is `levered` or `levered-with-waterfall`, ask for: - Loan amount or LTC %, interest rate, I/O period (months), amortization (years), closing costs % Compute levered cashflows: - Annual debt service = compute from loan terms (I/O: rate × principal; amortizing: standard mortgage formula) - Levered CF = Unlevered CF − Debt Service - DSCR Year 1 = NOI Year 1 / Annual Debt Service (flag if < 1.20) If waterfall: - Ask for: LP % equity, GP % equity, hurdle tiers (rate, promote %) (Common US structure: 10%/13%/16% hurdles, 20%/30%/40% promotes) - Compute tiered distributions across the hold period Present levered IRR and LP/GP IRRs in the summary card. --- ## Step 8: Save Model Create the `.cashflow-models/` directory if it does not exist: ```bash mkdir -p .cashflow-models ``` Write the model JSON: ```json { "asset_name": "", "asset_key": "", "profile": { ... }, "unit_mix": [ ... ], "going_in_occupancy": 0.95, "loss_to_lease_pct": 0.02, "vacancy_pct": 0.05, "concessions_pct": 0.01, "bad_debt_pct": 0.0015, "other_income_per_unit": 2049, "opex": { ... }, "growth": { "rent": [...], "expense": [...] }, "hold_period_years": 5, "exit_cap_rate": 0.05, "sale_costs_pct": 0.015, "debt": null, "waterfall": null, "dcf_output": { "annual": [...], "going_in_noi": ..., "stabilized_noi": ..., "exit_value": ..., "unlevered_irr": ... }, "scenarios": {}, "last_updated": "" } ``` --- ## Step 9: Present Summary Card Show the summary card from the DCF engine output. Then say: > "Model saved for [asset name]. Use `capex-analysis` to model the impact of any interventions — solar, unit renovations, EV chargers, operational changes, etc." --- ## Rules - Never fabricate inputs — ask if missing. The only exception is regional rent growth defaults which must be stated explicitly. - Always note which inputs came from uploaded documents vs. user-provided vs. defaults. - Never assert specific green premium percentages — these must be user-provided. - Always confirm inputs before running the engine. - Always write the model JSON to disk before presenting the summary card. - If the engine produces an IRR outside 0–30%, flag it: "The IRR of X% is outside typical range — please review the inputs." - Hotel assets: remind the user this is an investor-level model, not a hotel management system.