Realtor Agents Scraper USA

Realtor Agents Scraper USA (Apify Actor) 🇺🇸

Effortlessly discover and export real estate agent data across the United States. This Apify actor searches agents, retrieves full agent profiles, reviews, and recommendations, and saves clean JSON to your Apify dataset for instant download as JSON/CSV/Excel. ⚡

— Perfect for lead generation, market research, enrichment, and analytics.

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Table of Contents 📚

• Overview
• Features
• How It Works
• Endpoints Supported
• Input Parameters
• Output Schema
• Quick Start
• Output Examples
• Usage Recipes
• Best Practices
• Limits & Notes
• FAQ
• Support
• SEO Keywords

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Overview 🔎

Realtor Agents Scraper USA searches realtor agents by ZIP/postal code and lets you fetch rich details like profiles, reviews, and recommendations. Each result is pushed to the default dataset so you can export it in one click from the Apify console.

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Features ✅

• Powerful search by ZIP/postal code
• Full agent profile retrieval
• Reviews and recommendations collection
• Friendly input UI with sections and validation
• Robust logging, retries with backoff, and rate-limiting controls
• Clean, structured output ready for BI tools and spreadsheets

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How It Works ⚙️

1. Run List Agents first: provide postal_code and optional filters (languages, sort, etc.).
2. From the results, copy profile_id and/or fulfillment_id.
3. Run again with either profile_id (to get profile or reviews) or fulfillment_id (to get recommendations).
4. Download results in JSON/CSV/Excel from the dataset tab.

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Endpoints (auto-detected) 🧩

• list_agents → Search agents (supports postal_code, filters, sort, limit)
• get_profile → Detailed agent profile by profile_id
• get_reviews → Agent reviews by profile_id
• get_recommendations → Agent recommendations by fulfillment_id

Tip: The actor auto-detects which calls to make based on fields you provide. Run list_agents first to discover profile_id and fulfillment_id.

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Input Parameters 🧰

Configured via input_schema with helpful sections in the Apify UI.

Field	Type	Required	Description
postal_code	string	For search	ZIP/postal code to list agents.
agent_type	enum	Optional	Filter by BUYER/SELLER (or leave blank).
agent_filter_criteria	enum	Optional	Advanced filter criteria.
languages	array	Optional	One or more languages, e.g., ["english","spanish"].
sort	enum	Optional	RATINGS_REVIEWS, RELEVANT_AGENTS, MOST_SALES, TESTIMONIALS_RECOMMENDATIONS, MOST_RECENT_ACTIVITY.
limit	integer	Optional	Page size (max 20).
profile_id	string	For profile/reviews	Use an id returned by list_agents.
fulfillment_id	string	For recommendations	Use an id returned by list_agents.

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Output Schema (Overview) 📄

Each dataset item represents either an agent summary (from search), a full profile, a review, or a recommendation, depending on mode.

Mode	Key fields in item
list_agents	agent_summary (id, fullname, is_realtor, broker_name, ratings_reviews, listing_stats), full agent, and a meta item with matching_rows
get_profile	profile with rich agent details, plus raw payload
get_reviews	One review per item (display_name, comment, rating...) plus a meta item with count
get_recommendations	One recommendation per item (display_name, comment...) plus a meta item with count

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Quick Start 🚀

1. Open the actor on Apify and click Run.
2. Step 1 (List Agents): Enter a postal_code and optional filters. Run to get agent results.
3. Copy profile_id and/or fulfillment_id from results.
4. Step 2 (Profiles/Reviews/Recommendations): Run again with profile_id (profile/reviews) or fulfillment_id (recommendations).
5. Export results from the Dataset.

Example A — Search by postal code

{
  "postal_code": "10019",
  "limit": 5,
  "sort": "RELEVANT_AGENTS"
}

Example B — Agent profile

{
  "profile_id": "5732a184a4623c01005318d9"
}

Example C — Reviews

{
  "profile_id": "5732a184a4623c01005318d9"
}

Example D — Recommendations

{
  "fulfillment_id": "2278521"
}

Output Examples 📄

list_agents item (summary + raw)

{
  "success": true,
  "endpoint": "list_agents",
  "query": { "postal_code": "10019", "limit": 5, "sort": "RELEVANT_AGENTS" },
  "agent_summary": {
    "id": "5732a184a4623c01005318d9",
    "fullname": "Brian Phillips",
    "is_realtor": true,
    "broker_name": "Douglas Elliman Real Estate",
    "ratings_reviews": { "average_rating": 5, "reviews_count": 22 },
    "listing_stats": { "for_sale": { "count": 14 } }
  },
  "agent": { "id": "5732a184a4623c01005318d9", "fullname": "Brian Phillips", "...": "..." }
}

Additionally, a meta item is saved with matching_rows and the count returned.

get_profile item

{
  "success": true,
  "endpoint": "get_profile",
  "query": { "profile_id": "5732a184a4623c01005318d9" },
  "profile": { "id": "5732a184a4623c01005318d9", "fullname": "Brian Phillips", "...": "..." },
  "raw": { "data": { "agent_branding": { "branding": { "id": "..." } } } }
}

get_reviews items

Each review is a separate dataset item plus a meta item with the total count.

{
  "success": true,
  "endpoint": "get_reviews",
  "query": { "profile_id": "5732a184a4623c01005318d9" },
  "review": {
    "source_id": "RDC",
    "display_name": "R.E.",
    "rating": 5,
    "comment": "Brian Phillips is a consummate real estate professional..."
  }
}

get_recommendations items

Each recommendation is a separate dataset item plus a meta item with the total count.

{
  "success": true,
  "endpoint": "get_recommendations",
  "query": { "fulfillment_id": "2278521" },
  "recommendation": {
    "display_name": "Todd Fisch",
    "comment": "We were looking for a home in NYC and could only view them remotely from abroad..."
  }
}

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Usage Recipes 🍳

• Lead lists by ZIP: Run list_agents with postal_code and export to CSV.
• Deep dive a specific agent: Use get_profile with profile_id from search results.
• Sentiment snapshots: Pull recent get_reviews for selected agents.
• Trust signals: Fetch get_recommendations to showcase testimonials.

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Best Practices 🧠

• Keep limit ≤ 20 per request for reliable paging.
• Start with broader searches (e.g., only postal_code) and narrow as needed.
• Save dataset exports regularly for your reporting workflows.
• Use RATE_LIMIT_PER_HOUR to align with your plan and avoid throttling.

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Limits & Notes 📌

• Not all agents will have reviews or recommendations.
• Availability of fields can vary by region or agent profile completeness.
• Timestamps and counts may reflect the most recent available data.

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FAQ ❓

• What formats can I export?
  JSON, CSV, and Excel — directly from the Apify dataset UI.

• Can I run all modes in one go?
  This actor is mode-based for clarity. If you want a chained workflow (search → profile → reviews → recommendations), contact us — we can add an automation flow.

• Why do I see fewer items than matching_rows?
  limit and offset control paging. Increase pages to cover more results.

• Do I need special setup?
  No. Just fill the input form, run, and export results.

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Support 🙌

If you need help, feature requests, or a custom workflow, open an issue in your tracker or contact the actor author.

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SEO Keywords 🔎

realtor agents scraper, real estate agent scraper, realtor profile extractor, realtor reviews scraper, agent recommendations scraper, USA realtor database, real estate lead generation, real estate data enrichment, Apify actor real estate, export agents CSV, agents API, agents dataset, real estate market research, realtor contact data, realtor analytics, real estate intelligence