capex-analysis

name	capex-analysis
description	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".
version	1.0.0

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:

cat .cashflow-models/<asset-key>.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

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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:

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:

BASE_MODEL_JSON=$(python3 - << 'PYEOF'
import json
with open('.cashflow-models/<asset-key>.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 <asset-key> with the actual asset key (e.g. prose-frontier).

Run for each intervention:

python3 "$SCRIPT" \
  --base "$BASE_MODEL_JSON" \
  --intervention '<INTERVENTION_JSON>' \
  --market-cap-rate <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:

import json, datetime
with open('.cashflow-models/<asset-key>.json') as f:
    model = json.load(f)

scenario_name = '<intervention-type>-<date>'  # e.g. "solar-unit-reno-2026-06-26"
model['scenarios'][scenario_name] = {
    'interventions': [<intervention_dicts>],
    '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/<asset-key>.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."