Claude reads your market files.
Five jobs. Four populated markets included.
A folder of markdown files turns Claude into a residential-real-estate specialist for your city. Five jobs. About 20 minutes to set up. Free, MIT-licensed, no install, no API key, no monthly cost beyond your Claude subscription.
Start from a populated market.
Four real markets ship with v2. If yours is structurally similar to any of them, copy the region pack over and edit for your specifics. Faster than the 20-minute fill-in, and you see the format before you commit.
Path A — start from a case study pack
Open case-studies/ and pick the market closest to yours by shape — diaspora-driven (Jerusalem), universal-no-foreign-hook (Novato), foreign-buyer-dominant resort (Khao Lak), or mainstream urban European (Lisbon). Copy its region folder over the empty one and edit. You'll be running real jobs in under 30 minutes.
Path B — start from scratch with six placeholder files — works too, and is documented step-by-step in the repo. Most agents pick A.
Five jobs. One folder. That's the whole thing.
Every new chat opens with a menu. Pick a job, hand it the inputs, get back a structured artifact you can defend to a client or a broker.
| Job | You hand it... | You get back |
|---|---|---|
| Write a listing | Property specs + audience (e.g. "international investor") | A finished listing tuned to that buyer pool. Multiple languages on request. |
| Match a buyer★ most-used job | Buyer profile + 3–4 candidate listings | Ranked fit with reasoning, friction points called out, which one to show first. |
| Price a property | Property specs + target window | Defensible price band (low / mid / high), comp set, adjustment logic. |
| Capture showing notesnew in v2 | Date, buyer ID, your dictation transcript | Structured note, friction tags, agent-facing read. Cited back by future jobs. |
| Look up a servicenew in v2 | Service type + property context | Your top pick from your own curated vendor list, with the reasoning. Never invents. |
Four populated markets. Real outputs.
Same architecture, four very different markets. Same five jobs, working outputs. Here's what comes back when you actually run a job.
Listing job. 4-unit residential building, 5 minutes' walk from Jaffa Gate. ~340 m² total, fully occupied, current monthly rent ~$11,000 USD aggregate. Asking ~$3.4M USD. Audience: Chinese investor, English-speaking intermediary, yield-focused.
- An English/investor-framed listing with yield calculation, location thesis, and exit-liquidity note.
- Foreign-buyer tax considerations surfaced (Mas Rechisha, Mas Shevach) — not invented, drawn from the region pack.
- A side-by-side Hebrew local-market listing.
- Agent-facing prep notes for the showing — residency status, source-of-funds path, hold thesis, decision-pace expectations.
Matching job. Buyer: Family of 4 relocating from NYC (aliyah). Modern Orthodox; walking-distance shul + day school is critical. Budget $1.5M (stretch $1.65M). Three listings: A) Talbiya 4BR $1.7M walk-up; B) Baka 4BR $1.45M ground-floor garden; C) Old Katamon 3BR + office $1.55M elevator. Rank fit.
- Ranked table — Listing C top pick, B at "Friction" (specific reason), A at "Blocker" (specific reason).
- Detailed scoring on the top pick across Budget / Location / Type-size / Lifestyle / Friction.
- Honest tradeoff section ("they asked for 4BR, this is 3+office").
- A "don't dismiss yet" callout on Listing B with a real case for a second showing.
Pricing job. 4BR/3BA in Novato (Pleasant Valley), 2,150 sqft, 0.18 acre lot, 1978 single-story, kitchen + primary bath remodeled 2021. Owner targeting on-market within 3 weeks.
- Defensible band: Low $1,225K / Mid $1,275K / High $1,325K — with when-to-use guidance for each.
- Comp-set table — 4 properties with sqft, sale price, $/sqft, sale date, adjustments to subject.
- Adjustment logic with reasoning, drawn from the local market file you populated.
- Market context (days-on-market, season, buyer-pool dependency) and a short caveat to verify before pricing.
Showing notes job. Showing was 2026-05-04 at 142 Bel Marin Keys Blvd. Buyer: the Hendersons (empty-nesters from Tiburon, $1.5–1.8M). Here's my dictation: [paste]
- Structured note with property/buyer header, reaction summary, positive signals, friction (each line marked "from transcript" or "agent's read").
- Comparisons, questions raised, logistics — separated cleanly.
- Friction tags (
kitchen-layout,hoa-cost,flood-risk) so future matching/pricing jobs cite it back. - Agent's read: "Linda's kitchen objection is the real obstacle, not the dock — lead the second showing with that."
Four geographies. Four different proof points.
Each ships with a populated region pack you can copy and edit. The diversity is the point — the architecture has to work across very different market shapes, not just one.
Multi-language (Hebrew + English), strong international diaspora flow, complex title types (Tabu vs. Khevra Meshakenet). Proves the multilingual + foreign-buyer wedge.
English-monolingual U.S. suburban market — proves the architecture works without any foreign-buyer dependency. The "could this work in my normal American suburb?" answer.
Coastal resort, foreign-buyer-dominant, leasehold/freehold structure, multi-language (English / German / Russian / Mandarin). Proves the resort/expat market shape.
Mainstream urban European international-buyer market, post-Golden-Visa context, multilingual (Portuguese / English / French / Brazilian Portuguese), Roman-law title system.
Three places. Honest about the rest.
Most AI-for-agents marketing claims to beat ChatGPT at everything. This claims three things, and tells you where ChatGPT is fine.
1. Buyer–property matching with reasoned friction
ChatGPT keyword-matches ("4BR matches 4BR"). The copilot reasons about intensity of fit, surfaces what the buyer will compromise on for the top pick, and tells you when no listing in your candidate set actually works.
2. Price bands with citable comp logic
Not "list at $X" — a band, with the comp set you can defend on a listing presentation, with adjustment magnitudes pulled from your local market file.
3. Cross-job memory
Showing notes you capture today can be cited in a matching job next week. The friction tags carry forward. ChatGPT forgets every chat.
Where ChatGPT is fine
Short marketing blurbs, generic Q&A, idea brainstorming. The copilot doesn't compete on those — there are dedicated tools and ChatGPT itself does the job. Save the copilot for the work that needs structure.
Three files. Three jobs. That's the mechanism.
The copilot isn't a model trained on real estate. It's Claude, reading three layers of plain-text files in the right order. Your market file is the layer that makes the answers yours.
Specialist (universal)
Identity, rules, examples, frameworks. The "how this works" that's the same in every market. Already written, already loaded — you don't touch it.
Region pack (yours)
Six files describing your market: inventory, neighborhoods, regulations, contracts, glossary, services. Fill in once. Update when something changes.
Cross-job memory
Showing notes get tagged with friction terms. The next time you run a matching or pricing job for the same buyer or property, those tags are visible to the copilot.
What it won't do.
The copilot redirects out-of-scope requests on purpose. Five jobs. Anything else, it points you to the right tool.
Out of scope, by design
- Won't give binding legal or tax advice. Surfaces what to ask your lawyer or accountant about.
- Won't quote a single list price. Always a band, always with comps.
- Won't draft client-facing emails (seller updates, buyer follow-ups). That's CRM territory.
- Won't recommend vendors that aren't in your own services list. Never invents.
- Won't write blog posts, social copy, or cold-outreach campaigns. Out of scope.
- Won't try to do anything outside the five jobs.
Three steps. About 20 minutes.
No code. No install. No API key. If you can use Claude in a web browser, you can use this.
Download the repo
On the GitHub page, click Code → Download ZIP. Unzip. You'll have a folder with specialist/, case-studies/, and docs/ inside.
If you've used GitHub before, git clone works too. Either way, no install — it's just files on your computer.
Fill in six files for your market
Open specialist/reference/region/. Six files with section headers and [PLACEHOLDER] markers: market, neighborhoods, regulations, contracts, glossary, services. Replace placeholders with your local data.
Or — much faster — copy a case study pack over and edit. Partial fills are fine; the copilot will tell you when it's missing something it needs.
Drop into a Claude Project and start a chat
Go to claude.ai. Create a new Project. Drag the specialist/ folder into Project knowledge. Start a chat.
The copilot greets you with the welcome menu — five jobs to choose from. Pick one. Hand it the inputs. You're working.
Ready to set up your market?
Free. MIT-licensed. The whole thing is a folder of markdown files. About 20 minutes from download to first real answer.