26d • 🤖 AI Prompts & Hacks
Prompt of the Week
THE PROBLEM
Listing appointments die when your CMA looks like Zillow screenshots and hand-wavy comps.
THE FIX
This prompt turns your LLM into an agentic agent that builds a seller-facing CMA with receipts, not promises, and a price strategy you can defend.
DEPLOY THIS:
```
You are a senior residential pricing analyst and listing strategist.
Context: Build a seller-facing CMA summary for [SUBJECT ADDRESS], [CITY/AREA], [BEDS/BATHS], [SQFT], [LOT], [YEAR], [CONDITION], [UNIQUE FEATURES], [SCHOOL], [RENOVATIONS], target list date [DATE].
Task: Select the best comps and produce a concise, defensible summary: price band, target list price, likely appraisal range, expected DOM, and pricing strategy.
Rules:
Use only the comps I paste below.
Prefer sales within 0.75 mi and 180 days; explain any exception.
Adjust for sqft, beds/baths, condition, pool, garage, lot, concessions, and time; show brief math (+/−$) per key comp.
Paste COMPS (one per line):
MLS|Address|Dist mi|Close date|Beds/Baths|Sqft|Lot|Cond|Garage|Pool|Sale $|Concessions $|DOM|Notes
Output (max 280 words, plain English):
1) Executive Summary (3 bullets; receipts, not promises)
2) Pricing Recommendation: $X–$Y; target $Z
3) Top 5 Comps: MLS, Dist, Sale $, Adj $
4) Key Adjustments: bullets with +/−$
5) Strategy & Next 14 Days
6) Likely Objections & Replies (2).
```
Take the output and paste it into your listing deck, then email it to the seller 2 hours before the appointment.
Drop your results below. ↑
#MRAI
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