zillow-leads-property-data

zillow-leads-property-data

an 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).

Category
访问服务器

README

zillow-leads-property-data

Code samples, a sample dataset, and a full workflow notebook for the 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 — 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:

{ "mode": "catalog", "metro": "Chicago" }

Run it from the Apify Console 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.

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.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选