A real-world experiment in AI-assisted home selling.
Realaitor documents what happens when modern AI tools are used to support a real home sale: market research, pricing logic, listing narrative, workflow planning, offer analysis, and decision support. The point is not to prove that AI can replace real estate professionals. The point is to test where AI is genuinely useful, where it is unreliable, and where human judgment still carries the work.
This is not a demo. It is not a hypothetical. It is a live experiment with a real property, real money, and real tradeoffs.
Can AI meaningfully assist in selling a home without pretending to be a realtor, attorney, appraiser, inspector, photographer, lender, or negotiator?
Realaitor is built around a practical answer:
- Use AI for research, synthesis, drafting, scenario planning, and operational leverage.
- Use human experts for licensed judgment, market nuance, negotiation, compliance, and final decisions.
- Document the difference honestly.
Realaitor follows the home-sale lifecycle from prep to close:
| Phase | What AI Can Support | What Still Needs Human Review |
|---|---|---|
| Market research | Comps, trends, pricing ranges, data synthesis | Local market judgment, stale or incomplete data checks |
| Positioning | Buyer profile, value narrative, listing angles | Agent feedback, legal claims, property-specific nuance |
| Listing prep | Copy drafts, photo shot lists, staging checklists | Professional photography, disclosures, MLS compliance |
| Go-to-market | Social posts, outreach copy, launch plan | Timing, buyer-agent relationships, showings |
| Offer review | Comparison tables, net proceeds scenarios, negotiation prompts | Legal review, final acceptance strategy, risk tolerance |
| Post-close | Scorecard, lessons learned, reusable workflow | What actually mattered in the transaction |
- Model-agnostic: The workflow is not tied to one AI vendor, model, or interface.
- Human-in-the-loop: AI can recommend; people decide.
- Evidence over hype: Every claim should be tied to observable output, market data, or transaction results.
- Reusable but contextual: The methods may transfer, but every property and market is different.
- No private financial leakage: Sensitive financial details, exact offers, and closing documents stay private.
/skills
Reusable workflow instructions for market research and sale-related content.
/social
Public content drafts documenting the experiment.
/research
Intended home for sanitized market research and methodology artifacts.
skills/researching-philadelphia-real-estate/
Used for comps analysis, neighborhood trend checks, days-on-market research, price-per-square-foot benchmarking, offer validation, and market update summaries.
skills/selling-house-marketing-content/
Used for listing copy, buyer-facing positioning, staging briefs, social posts, video outlines, and recap content.
social/linkedin-post-01-launch.md
The first public post framing the experiment as a grounded test of AI usefulness versus AI hype.
The end goal is not just "did the house sell?" The project should answer:
- What work did AI materially accelerate?
- Where did AI produce useful first drafts but need heavy editing?
- Where did AI make confident but wrong assumptions?
- Which human experts were still essential?
- Did AI-supported research align with appraisal, agent guidance, buyer feedback, and final sale outcome?
- What parts of the workflow are reusable for another seller?
Realaitor is not:
- Real estate advice
- Legal advice
- A recommendation to sell without an agent
- A claim that AI replaces licensed professionals
- A complete public record of the transaction
It is a documented operating experiment.
Public:
- Methodology
- Reusable workflows
- Sanitized research patterns
- Lessons learned
- Public content drafts
Private:
- Sensitive financial details
- Exact offer terms
- Closing documents
- Personal contact details
- Anything that would compromise negotiation leverage or privacy
Active experiment. The README and workflows will evolve as the sale progresses and the scorecard becomes more concrete.
If this helps you think through your own AI-assisted workflow, fork it and adapt it. Just do the obvious adult thing: validate outputs, involve qualified professionals where required, and do not treat a repo as a substitute for real advice.