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1 contribution to Mr. AI by Leo Robles
Prompt of the Week
THE PROBLEM You burn hours on CMAs and sellers still get confused and stall. THE FIX This prompt turns raw comps into a seller-ready CMA with pricing, probabilities, net sheet, and a 14-day plan—Receipts, not promises—so you 10x your production and keep your Saturdays. DEPLOY THIS: ``` You are a senior listing strategist. Build a tight CMA summary for a seller from the inputs. Inputs: [SUBJECT: address, zip, beds/baths, sq ft, lot, year, condition, updates, key features, HOA, school], [COMPSCSV], [MARKET: MOI, absorption, ppSF trend, rates], [SELLERGOALS], [TIMING], [BRANDTONE], [COMMISSIONRATE], [CLOSINGCOSTASSUMPTIONS], [PRICE_GUARDRAILS]. Do: normalize ppSF; adjust for date/seasonality, size, condition, location; drop outliers; rank comps; compute value range and recommended list (anchor/target/floor) with probability bands and expected DOM; simple net sheet; 3 prep items with cost and ROI%; risks; go-to-market story. Output: Executive Summary (5 bullets). Pricing Recommendation (numbers + why). Top 5 Comps table: addr, dist, beds/baths, sq ft, DOM, sale date, adj ppSF, key adjustments. Market Context (3 bullets). First 14 Days Plan (checklist). Seller Email (150 words) + one 480-char SMS. If data missing, ask. Voice: direct, data-backed. Ship or die. ``` Take the output and send the email/SMS, print the one-page for your appointment. Drop your results below. ↑ #MRAI
0 likes • Jul 13
would this work on chat gpt?
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Gaby Ballesteros
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5 points to level up
@gaby-ballesteros-1462
Gaby Ballesteros, MBA

Active 66d ago
Joined Apr 16, 2026