Real Estate — Pricing & Price-Reduction Decision

Activate when: a listing isn't selling and the agent must decide list price or a price reduction; 'should we drop the price', 'how much and when', days-on-market rising, showings without offers. Do NOT activate when: the property is priced right and getting offers (no decision).

Install

openclaw skills install @deciqai/realtor-price-reduction-decision

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 — 223 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/c/realtor-price-reduction-decision · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.