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wholesale deal analyzer pro
Production-grade real estate underwriting for wholesale deals, assignment economics, and end-buyer ROI modeling.
$43.99
wholesale deal analyzer pro
Example session with this skill installed
Underwrite 123 Maple St. ARV is 350k. Purchase price 200k. Repairs estimated at 45k. Target assignment fee 15k. End-buyer wants 15% ROI. Double close. Give me the full breakdown.
- Read your context and instructions
- Compiled the wholesale deal analyzer
I have completed the underwriting for 123 Maple St based on a double-close scenario. The model assumes standard closing costs and a 6-month hold period for the end-buyer.
Wholesale Deal Analysis: 123 Maple St
| Category | Wholesaler (You) | End-Buyer (Investor) |
|---|---|---|
| Purchase Price | $200,000 | $215,000 |
| Assignment/Spread | $15,000 | - |
| Estimated Repairs | - | $45,000 |
| Buying Costs (3%) | $6,000 | $6,450 |
| Holding/Finance (est) | - | $12,500 |
| Selling Costs (6%) | - | $21,000 |
| Total Capital Outlay | $206,000 | $299,950 |
| Projected ARV | - | $350,000 |
| Net Profit | $9,000 (Net) | $50,050 |
| ROI / Margin | - | 16.69% |
Underwriting Notes
- Wholesaler Net: The $15k spread is reduced by estimated double-close costs ($6k).
- End-Buyer ROI: At 16.69%, the deal exceeds the buyer's 15% target.
- MAO Limit: The maximum price the buyer can pay to hit exactly 15% ROI is $221,500.
- Sensitivity: If repairs overrun by 20% ($9k), buyer ROI drops to 13.6%.
Next steps
- Verify the $45k repair estimate with a line-item contractor walkthrough.
- Confirm the $350k ARV by pulling the three closest comparable sales from the last 90 days.
- Draft the purchase agreement with the specific double-close escrow instructions.
Connects securely to your tools. The creator never sees your data.
About this skill
Wholesale real estate deals fail when underwriting relies on vague heuristics like the 70% rule instead of hard numbers. This skill provides a production-grade underwriting engine that decomposes every cost layer of a wholesale transaction to ensure assignment economics actually work for the end-buyer. It moves beyond simple math to deliver auditable decision packages based on trade-level repair budgets, funding stacks, and exit scenarios.
What it does
- ARV reconstruction provides a range-based valuation backed by a comparable sales evidence table.
- Cost layering calculates trade-level repair budgets with built-in contingency tiers.
- Wholesaler economics isolates the net assignment fee after accounting for all marketing and closing costs.
- End-buyer modeling solves for maximum allowable offer (MAO) based on specific investor return targets.
- Exit sensitivity compares outcomes for assignment, double-closing, renovation flips, and long-term holds.
How it works
- Data ingestion where you provide property details, repair scope, comparable sales, and financing terms.
- Economic modeling where the engine calculates the full stack from purchase price to net profit.
- Stress testing where the deal is run through ±10/20% ARV shifts and repair overruns.
- Memo generation delivering a structured deal package with sources, uses, and diligence checklists.
Frameworks & tools
The skill is designed for execution within environments like Claude Code or Cursor. It integrates with document readers for contracts, spreadsheet tools for financial modeling, and web search for verifying public records.
Why this beats prompting it yourself
Generic prompts often double-count assignment fees or ignore hold costs and financing points. This skill uses a strict calculation architecture that ensures every dollar is accounted for in the end-buyer's stack, preventing deals that look good on paper but die in due diligence.
Use cases
- Underwriting off-market properties to determine a realistic maximum allowable offer.
- Comparing assignment vs. double-closing scenarios for high-spread deals.
- Preparing professional deal memos for JV partners or cash buyer lists.
- Auditing acquisition manager submissions to ensure consistent underwriting standards.
Known limitations
Does not provide legal advice or verify licensing requirements for equitable interest advertising. Users must verify jurisdiction-specific rules regarding assignment and disclosures with counsel.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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