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). More: deciqai.com/s/realtor-price-reduction-decision
Scanned 9/3/2026
Install to Claude Code
npx -y skills add deciqAI/knowledge-skills --skill realtor-price-reduction-decision --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: realtor-price-reduction-decision
description: "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). More: deciqai.com/s/realtor-price-reduction-decision"
---
# Real Estate — Pricing & Price-Reduction Decision
> **Industry front door for [decision-tree](../decision-tree/SKILL.md).** 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](../decision-tree/SKILL.md) 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*
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