Code samples, sample dataset, and workflow notebook for the Zillow Leads & Property Data Apify actor
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Documented (README)
git clone https://github.com/kmatata/zillow-leads-property-data{
"mcpServers": {
"zillow-leads-property-data": {
"command": "node",
"args": ["/path/to/zillow-leads-property-data/dist/index.js"]
}
}
}Resumen de MCP Servers
# zillow-leads-property-data
Code samples, a sample dataset, and a full workflow notebook for the
[Zillow Leads & Property Data](https://apify.com/germane_binoculars/zillow-leads-property-data)
Apify actor.
The actor pulls Zillow listings enriched with agent/broker contact info,
full price-history timelines, 20yr+ tax history, foreclosure/distress flags,
schools, and the resoFacts long tail (heating/cooling/construction). Pay-per-event
pricing, no subscription: **$0.70 per 1,000 bare rows, $1.20 per
1,000 enriched rows.**
- **Actor:** https://apify.com/germane_binoculars/zillow-leads-property-data
- **Architecture writeup:** [Predictable web scraping with web extensions](https://dev.to/kinuthia_matata_842726bc3/predictable-web-scraping-with-web-extensions-a-more-localized-apify-approach-12jn) — why this actor runs on a real browser session instead of a proxy farm, and what that buys you against Zillow's PerimeterX + AWS WAF.
## Try it in 30 seconds, no signup beyond an Apify account
The actor's `catalog` mode returns a cached, already-enriched snapshot
instantly, no bounding box, no wait:
```json
{ "mode": "catalog", "metro": "Chicago" }
```
Run it from the [Apify Console](https://apify.com/germane_binoculars/zillow-leads-property-data)
or via `apify-client` (see `examples/` below).
## What's in this repo
| Path | What it is |
|---|---|
| `sample/sample_listings.csv` | 20 real enriched rows (Chicago/Houston/Phoenix) — flat, spreadsheet-ready |
| `sample/sample_listings.jsonl` | Same rows, one JSON object per line, with a nested `price_history` array per listing |
| `examples/python_quickstart.py` | Call the actor from Python via `apify-client`, wait for results, print a summary |
| `examples/node_quickstart.js` | Same, in Node, via `apify-client` |
| `mcp-server/` | One-tool MCP server for Claude Desktop, Cursor and any MCP client; see `mcp-server/README.md` |
| `notebook/zillow_leads_workflow.ipynb` | A realistic lead-gen workflow: run the actor, dedupe against a prior export, filter to rows with agent phone numbers, export a clean CSV |
## Field reference
Two depths, `listings` (bare) and `enriched` (the default):
- **`listings`**: address, price, beds/baths, status, lot/living area, listing-type flags — whatever Zillow's own search API returns, no detail-page fetch involved. See `sample_listings.csv` for the shape (a subset of the columns shown there — the sample above is enriched-depth, listings-depth rows omit everything past the bare fields).
- **`enriched`** (default): everything in `listings`, plus agent/broker contact (name, phone, email, brokerage, MLS attribution), full price-history and 20yr+ tax-history timelines, foreclosure/distress signals, assigned + nearby schools, HOA fees, and the resoFacts long tail (heating/cooling/parking/construction).
Full field list, pricing table, and every input mode (`catalog` / `custom_search` / `recent_activity`) are documented on the [actor's own README](https://apify.com/germane_binoculars/zillow-leads-property-data).
## Dedup across repeat orders
Every delivery writes a `DEDUP_UPDATE` record to the run's key-value store:
the union of the zpids and MLS IDs you already had plus everything just shipped.
Feed those two arrays back into your next order's `dedupZpids`/`dedupMlsIds`
fields and you're never charged for the same row twice. See
`notebook/zillow_leads_workflow.ipynb` for a worked example.
## Use it from an AI agent (MCP)
This actor is callable as a tool via Apify's hosted MCP server
(`https://mcp.apify.com`) or the local `@apify/actors-mcp-server`, no manual
input-form filling required — `catalog` mode (the default) is a good agent
smoke test since it returns cached rows in seconds with no polling loop
needed. See the actor's own README for the full agent-facing input schema
and the fire/poll/fetch pattern for a live-collection order.
## Questions / issues
This repo is documentation and samples only — it doesn't run the actor
itself. For a specific order that isn't behaving as expected, check that
run's key-value store `STATUS` record first (the actor writes a plain-English
reason there for any zero-row or partial result), then open an issue here or
reach out via the actor's Apify Store page.
Lo que la gente pregunta sobre zillow-leads-property-data
¿Qué es kmatata/zillow-leads-property-data?
+
kmatata/zillow-leads-property-data es mcp servers para el ecosistema de Claude AI. Code samples, sample dataset, and workflow notebook for the Zillow Leads & Property Data Apify actor Tiene 0 estrellas en GitHub y su última actualización registrada es del 2026-08-23.
¿Cómo se instala zillow-leads-property-data?
+
Puedes instalar zillow-leads-property-data clonando el repositorio (https://github.com/kmatata/zillow-leads-property-data) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar kmatata/zillow-leads-property-data?
+
Nuestro agente de seguridad ha analizado kmatata/zillow-leads-property-data y le ha asignado un Trust Score de 87/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene kmatata/zillow-leads-property-data?
+
kmatata/zillow-leads-property-data es mantenido por kmatata. La última actividad registrada en GitHub es del 2026-08-23, con 0 issues abiertos.
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+
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