Turn my comps into a pricing recommendation for a seller
A comparative market analysis with adjusted comps, a recommended list-price range, and a plain-English explanation for the seller.
The prompt
Help me turn my comparable sales into a pricing recommendation (CMA) for a seller.
Subject property: [ADDRESS AREA, BEDS, BATHS, SQ FT, LOT, CONDITION, KEY FEATURES]
Closed comps (last 3-6 months): [FOR EACH: SALE PRICE, CLOSE DATE, SQ FT, BEDS/BATHS, CONDITION, DAYS ON MARKET, CONCESSIONS, DISTANCE]
Active and pending listings: [LIST PRICE, DOM, NOTES]
Expired or withdrawn listings: [IF ANY]
Market conditions: [MONTHS OF INVENTORY, LIST-TO-SALE RATIO, PRICE TRENDS - from my MLS data]
Seller's goal: [e.g. fastest sale, top dollar, specific net]
Steps:
1. Rank comps by similarity and say which to weight most and why. Flag weak comps (different condition, far away, older than 6 months, seller concessions).
2. Suggest adjustments for differences (size, beds/baths, condition, garage, lot, view) and show the adjusted value for each comp. Label every adjustment amount as my estimate to confirm with local data, not a fact.
3. Use actives as competition and expireds as evidence of where the market said no.
4. Recommend a list-price range and a pricing strategy (e.g. at market, just under a search price band) with reasoning.
Return: an adjustment table (Comp | Sale price | Adjustments | Adjusted value | Weight), the recommended range, a one-paragraph explanation the seller can understand, and the three biggest risks to this price. Use only the data I supply; do not invent sales. Note that this is not an appraisal.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- LIST PRICE, DOM, NOTES
- IF ANY
- e.g. fastest sale, top dollar, specific net
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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