# Capex Analysis > Model the cashflow impact of capital interventions and operational changes on a real estate asset. Computes Yield on Cost, Investment Spread, IRR delta, and exit value change. Requires a base cashflow model built by the cashflow-model skill. Triggers on: "what's the impact of [intervention]", "model solar for [asset]", "if we renovate [N] units", "what's the yield on cost for [upgrade]", "model EV chargers / smart HVAC / tech package / unit reno / amenity upgrade". - Skill: `soapboxbuild/capex-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add soapboxbuild/capex-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/soapboxbuild/capex-analysis/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/capex-analysis --- # Capex Analysis Model the impact of interventions on a real estate asset's cashflows. Primary outputs: **Yield on Cost** and **Investment Spread**. --- ## Step 1: Identify Asset and Load Base Model Get the asset name from context or ask. Compute the asset key (lowercase, spaces → hyphens). Load the base model: ```bash cat .cashflow-models/.json ``` If the file does not exist, say: > "I don't have a cashflow model for [asset] yet. Would you like me to build one first?" If yes, invoke the `cashflow-model` skill, then continue. Extract from the loaded model: - `dcf_output.annual` — year-by-year cashflows - `dcf_output.going_in_noi` - `dcf_output.exit_value` - `dcf_output.unlevered_irr` - `exit_cap_rate` --- ## Step 2: Elicit Intervention Details Ask what intervention(s) the user wants to model. For each, collect: ### ESG / Sustainability **Solar:** - System size (kW) or just capex if known - Gross capex ($) — use $70,000–$100,000 for a typical clubhouse system as a prompt if unknown - Annual electricity savings ($) — estimate if not known: annual_kwh × local rate ÷ 100 - Start year (default: 1) - Ask: "Do you have a tax credit (ITC)? In the US, the current ITC is 30%." If yes, note it in the scenario description (it does not affect NOI but affects investor returns) **EV Charging:** - Number of chargers, capex per charger ($) - Annual charging revenue (optional) - Start year **Smart HVAC / Thermostats:** - Capex ($/unit × units, or lump sum) - Energy savings as % of current owner utility expense, or direct $ amount - Start year **Green PPA:** - Annual savings ($ or % rate differential) - No capex **Water / Submetering:** - Capex ($) - Increase in RUBS/utility recovery ($/unit/month) - Start year ### Revenue-Enhancing **Unit Renovation:** - Capex per unit ($) - Number of units to renovate - Expected rent premium per unit per month ($) ⚠️ If user says "typical BREEAM/LEED premium" or cites a specific %: ask them to confirm the $ amount rather than using any published figure — green premium evidence is market-dependent. - Absorption pace (units renovated per month) - Vacancy during reno (days per unit) - Start year **Amenity Upgrade:** - Capex ($) - Expected annual NOI uplift ($ — user must provide this; do not estimate) - Start year **Tech Package:** - Capex per unit ($), number of units - Rent premium per unit per month ($) - Start year ### Operational **Utility Reduction (WasteX, renegotiation):** - Program cost ($) - Annual savings ($) - Start year **Management Fee Change:** - New fee % of EGI --- ## Step 3: Confirm Before Computing List all interventions with their inputs. Ask: "Ready to run the analysis?" --- ## Step 4: Run Intervention Engine For each intervention, run the engine. Find the script: ```bash SCRIPT=$(find ~/.claude/plugins -name 'intervention_engine.py' 2>/dev/null | head -1) if [ -z "$SCRIPT" ]; then SCRIPT=~/soapbox-agent/scripts/intervention_engine.py fi ``` The saved model stores cashflows under `dcf_output`. Extract the fields the intervention engine needs by flattening before passing: ```bash BASE_MODEL_JSON=$(python3 - << 'PYEOF' import json with open('.cashflow-models/.json') as f: model = json.load(f) base = { "asset_name": model["asset_name"], "annual": model["dcf_output"]["annual"], "going_in_noi": model["dcf_output"]["going_in_noi"], "exit_value": model["dcf_output"]["exit_value"], "unlevered_irr": model["dcf_output"]["unlevered_irr"], "implied_purchase": model["dcf_output"].get("implied_purchase", 0), "exit_cap_rate": model.get("exit_cap_rate", 0.05), } print(json.dumps(base)) PYEOF ) ``` Replace `` with the actual asset key (e.g. `prose-frontier`). Run for each intervention: ```bash python3 "$SCRIPT" \ --base "$BASE_MODEL_JSON" \ --intervention '' \ --market-cap-rate ``` For multiple interventions, sum the `noi_delta_by_year` arrays and re-run a combined pass through the engine with the summed deltas as a `custom` intervention type — or present them individually and then show a combined total. --- ## Step 5: Present Results Show the summary card from the engine output. Then show the full year-by-year comparison table if more than one intervention or if the user asks: ``` Year Base NOI Delta With Intervention 1 $6,947,357 +$67,064 $7,014,421 2 $7,225,251 +$98,800 $7,324,051 ... ``` For multiple interventions combined: - Show individual YOC and Investment Spread per intervention - Show combined NOI delta and combined IRR delta --- ## Step 6: Explain Investment Spread After presenting the YOC, always explain the Investment Spread: > "The Investment Spread of **[X]pp** means your intervention earns [X]pp above what > the market would pay for a stabilised asset at [cap rate]% cap. A positive spread > = value creation. A negative spread means you're paying more to create NOI than > the market will pay for it at exit." --- ## Step 7: Save Scenario Save the scenario to the model JSON. Load the existing model, append the scenario, and write it back: ```python import json, datetime with open('.cashflow-models/.json') as f: model = json.load(f) scenario_name = '-' # e.g. "solar-unit-reno-2026-06-26" model['scenarios'][scenario_name] = { 'interventions': [], 'noi_delta_by_year': [...], 'yoc': ..., 'investment_spread': ..., 'irr_delta': ..., 'exit_value_delta': ..., 'run_date': datetime.date.today().isoformat() } model['last_updated'] = datetime.date.today().isoformat() with open('.cashflow-models/.json', 'w') as f: json.dump(model, f, indent=2) ``` --- ## Rules - Always load the base model from disk — never recompute it from scratch. - Never suggest specific green premium percentages. If asked: "Green premium evidence is market- and methodology-dependent. Please provide your own assumption or use 0 for a conservative base case." - Always show the Investment Spread explanation after presenting YOC. - Always save the scenario to disk before ending the session. - If YOC is negative: "This intervention reduces NOI — the costs outweigh the savings/revenue at current assumptions. Review the inputs." - If Investment Spread is negative but YOC is positive: "This intervention creates value, but the return (YOC [X]%) is below the market cap rate ([Y]%). You're paying more to create NOI than the market will pay for it at exit."