deciqAI Real Estate — Pricing & Price-Reduction Decision
Industry front door for decision-tree. Adds domain triggers, example, packs. Parent Process unchanged. Not appraisal advice.
Activate when: setting a list price; a listing stalls (DOM up, showings without offers); deciding reduction timing/size; managing seller expectations. Do NOT activate when: priced correctly with active offers.
Why this variant
The parent decision-tree maps sequential choices under uncertainty. Pricing and reductions are a decision tree: hold vs reduce, by how much, when — against showing/offer feedback, carrying cost, and market trend, rolling back to expected net proceeds and time-to-sell.
Domain inputs → the tree
- Read the signals: showings-to-offer ratio, DOM vs market median, feedback themes, comparable adjustments.
- Branch: hold (if fresh/undersampled), small reduction (nudge into a search bracket), meaningful reduction (reset if far off).
- Value the branches by expected net proceeds × probability × carrying cost of extra DOM. Gate: reductions below a portal price bracket (e.g. $505k→$499k) capture a new buyer pool — size to brackets, not round guesses.
Worked example
30 showings, no offers, DOM 2× median, feedback "overpriced vs the one down the street." → Tree: this is a pricing (not marketing/condition) problem; a token cut won't fix a bracket miss. Reduce into the correct search bracket in one decisive move; slow drip prolongs DOM and signals weakness.
Packs
- Solo agent: showings-to-offer + DOM decision card; bracket-aware reduction sizing.
- Team: weekly stale-listing review triggering the decision.
Red flags
- Blaming marketing when the data says price.
- Tiny drip reductions that prolong DOM.
- Reductions not aligned to portal search brackets.
Verification
- Showing/offer + DOM signals reviewed vs comps
- Problem diagnosed (price vs condition vs marketing)
- Reduction sized to search brackets, not round numbers
- Expected net proceeds vs carrying cost weighed
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/realtor-price-reduction-decision · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/realtor-price-reduction-decision.json