The analyst seat,
on your laptop.
Real estate financial modeling the way institutional sponsor shops actually do it — correlated Monte Carlo, three-tier waterfalls with clawback, cost-seg overlays, IC memos generated from the model. Built in six sessions over one weekend. Reusable on every deal you underwrite for the rest of your career.
For acquisitions analysts at sponsor shops, aspiring LPs doing their first $5M–$30M deal, and anyone tired of looking at a broker’s proforma and not knowing where to push back.
What you build
Analyst Workstation
Claude Code + Python analyst stack (openpyxl, pandas, scipy) + a persistent persona CLAUDE.md that turns every session into a senior acquisitions analyst.
Ships: Workspace scaffold + CLAUDE.md persona
Ingestion & Normalization
Four prompts that turn a 60-page OM, a messy T12, a property-management rent roll, and CoStar comps into one clean deal_intake.xlsx with named ranges.
Ships: deal_intake.xlsx (7 sheets) + OM extract + puffery flags
Value-Add Acquisition Model
10-year DCF with bridge-to-perm debt, unit-level reno schedule, LTV/DSCR/DY-binding refi, sensitivity tornado, and a 10,000-scenario correlated Monte Carlo (rent growth ↔ exit cap at ρ = −0.45).
Ships: acquisition-model.xlsx + mc-runner.py + sensitivity + MC histogram
Institutional Waterfall
Three-tier pref + catch-up + promote with compound pref, American vs European toggle, end-of-deal clawback, and an LP quarterly update generator that writes in the sponsor’s voice.
Ships: waterfall.xlsx + LP update email template
IC Memo + LP Deck
The five-page IC memo, 18-slide pitch deck outline with chart specs, market-standard LOI, and a broker-call cheat sheet keyed to the deal’s specific red flags.
Ships: ic-memo.md + deck outline + LOI + call prep
Tax Optimization Overlay
After-tax IRR delta of cost seg, bonus depreciation, §1031 exchange, and refi-out-of-basis — the 300-600 bps most amateur syndicators leave on the table.
Ships: tax-optimizer.xlsx + cost seg scope doc + 1031 calendar
Built for
- · Acquisitions analysts at sponsor shops
- · Aspiring LPs / syndicators doing their first deal
- · Mid-career RE pros who never built a MC harness
- · GPs preparing their first institutional pitch
- · Family office RE analysts doing direct underwriting
Not for
- · Complete beginners to RE finance (take Quick Start first)
- · Retail single-family-home investors
- · Passive LPs who will never underwrite a deal themselves
- · Commercial office / industrial / retail (separate package)
$ python model/mc-runner.py Running 10,000 scenarios... Correlation matrix (rent_growth × exit_cap): -0.45 Constructed via Cholesky decomposition. Base case levered IRR: 18.4% Base case equity multiple: 2.1x --- Monte Carlo Results --- P10 IRR: 8.2% (pessimistic case) P50 IRR: 17.9% (median; note base case > median — rosy UW) P90 IRR: 27.3% (optimistic) Probability LP loses principal: 4.1% Probability of hitting 15% target: 61.2% Probability of hitting 20% target: 40.7% WARNING: P50 IRR (17.9%) is 50 bps below base case (18.4%). Base case is more optimistic than the median outcome. Recommend re-checking assumptions before IC.
The module is fully drafted. We’re opening it to a limited first cohort for feedback before general launch. If you join the waitlist, you’ll get (1) first access at the waitlist price, (2) direct input into which edge-case deals get covered, and (3) a 30-min office-hours call with the author after you finish.
Sign in to join the waitlist →Prerequisite comfort: You should know what a cap rate is, have underwritten at least one deal before, and be comfortable installing a Python package from the terminal. You do not need to be a coder.
New to Claude entirely? Quick Start Package is the right first step. Launch Package covers the local-dev workflow this module assumes.