# Realtor.com Agents Scraper | Contacts & Reviews (`fatihtahta/realtor-agents-scraper`) Actor Extract Realtor.com agent profiles across the US with rich contact data, reviews, ratings, brokerage details, social links, service areas and more. Built for enterprise-grade real estate lead generation, agent intelligence, recruiting, and automated analytics pipelines. - **URL**: https://apify.com/fatihtahta/realtor-agents-scraper.md - **Developed by:** [Fatih Tahta](https://apify.com/fatihtahta) (community) - **Categories:** Real estate, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $0.70 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Realtor.com Agents Scraper Collect enriched real estate agent records from Realtor.com agent search. The actor starts from one or more location inputs, resolves each location through Realtor.com's agent location search, paginates the agent search API for the resolved market, then enriches each saved agent from the Realtor.com branding profile API before writing the dataset. This initial adaptation keeps the public contract focused on agent search. Price and property-listing filters are not exposed. ### Input | Parameter | Type | Description | Default | |---|---:|---|---| | `location` | array of strings | Cities or ZIP codes to search, for example `Los Angeles`, `San Francisco`, `10001`, or `Austin, TX`. Each value is resolved by Realtor.com before agent collection starts. | Required | | `deal_type` | string | Agent intent filter. Use `sell` for seller agents, `buy` for buyer agents, or `both` to keep Realtor.com's default mixed intent search. | `both` | | `agent_name` | string | Optional agent name or surname filter. Spaces are sent to Realtor.com in its agent-name query format, such as `john smith` -> `john-smith`. | Empty | | `realtor_com_member` | boolean | When enabled, restricts the GraphQL agent search to agents that Realtor.com identifies with a member record. | `false` | | `get_reviews` | boolean | When enabled, also collects all available reviews and testimonials from each saved agent profile and writes them as separate linked records. | `false` | | `max_reviews` | integer | Maximum total number of review and testimonial records to save per agent profile when `get_reviews` is enabled. Leave empty to save all available reviews and testimonials. | Empty | | `limit` | integer | Maximum number of agents to save per location. Leave empty to collect as many matching agents as the run can retrieve. | `100` in the UI | ### Example Input ```json { "location": ["Los Angeles"], "deal_type": "both", "agent_name": "jane parker", "realtor_com_member": true, "get_reviews": true, "max_reviews": 10, "limit": 50 } ```` ### Output Each dataset item is a Realtor.com agent profile record. The saved row starts from the search result, then adds profile-page details such as biography, website, languages, specializations, served areas, MLS and license data, phones, social links, Q\&A, and richer office or brokerage data when Realtor.com returns it. When `get_reviews` is enabled, the dataset also includes separate `agent_review` and `agent_testimonial` records. Each related record includes the agent ID, fulfillment ID, agent name, profile URL, and broker or office context so it can be joined back to the source profile. Use `max_reviews` to cap the combined number of review and testimonial records saved for each profile. ```json { "id": "example-agent-5deeda36774fb40013d1af17", "agent_id": "example-agent-5deeda36774fb40013d1af17", "fulfillment_id": "9001234", "url": "https://www.realtor.com/realestateagents/Jane-Parker-9001234", "name": "Jane Parker", "is_paid": true, "is_realtor": true, "display_ratings": true, "has_rps": false, "avatar": { "url": "https://example.com/images/agent-jane-parker.jpg", "initials": "JP" }, "broker": { "name": "Example Realty Partners", "fulfillment_id": "9001200" }, "office": { "name": "Example Realty Partners", "website": "https://www.example-realty-partners.com/", "phones": [ { "type": "office", "value": "(323) 555-0142" } ], "address": { "line_1": "123 Market View Ave", "line_2": "Suite 400", "city": "MONTEBELLO", "postal_code": "90640", "state_code": "CA" } }, "ratings_reviews": { "average_rating": 5.0, "recommendations_count": 2, "reviews_count": 11 }, "listing_stats": { "combined_annual": { "min": 370000, "max": 1350000 }, "for_sale": { "count": 1, "last_listing_date": "2026-04-30T23:23:53Z", "min": 819000, "max": 819000 }, "recently_sold_annual": { "count": 18 }, "recently_sold_listing_details": { "listings": [ { "beds": 3.0, "baths": 1.0, "city": "Compton", "state_code": "CA", "photo": "https://example.com/images/recent-sale-compton.jpg" }, { "beds": 4.0, "baths": 3.0, "city": "Riverside", "state_code": "CA", "photo": "https://example.com/images/recent-sale-riverside-1.jpg" }, { "beds": 4.0, "baths": 2.0, "city": "Riverside", "state_code": "CA", "photo": "https://example.com/images/recent-sale-riverside-2.jpg" } ], "show_additional": true }, "recently_sold": { "count": 29, "last_sold_date": "2026-05-01", "min": 370000, "max": 1353000 } }, "services": { "buyer": [ "Personalized neighborhood guidance", "Early access to new listings", "Lender referrals", "Other professional network referrals" ], "seller": [ "Open houses", "Professional photography", "Home staging", "Postcards and flyers", "Comparative market analysis" ] }, "sorting_weight": 70, "source_context": { "seed_id": "example-los-angeles-seed", "seed_type": "query", "seed_value": "Los Angeles", "page_index": 1, "enriched": true, "enrichment_source": "agent_branding_profile", "detail_url": "https://www.realtor.com/realestateagents/Jane-Parker-9001234" }, "bio": "Jane Parker is a Los Angeles County real estate professional with more than 20 years of experience helping buyers, sellers, and investors navigate residential transactions. She focuses on clear communication, local market guidance, pricing strategy, and organized transaction support across the greater Los Angeles area.", "website": "https://www.example-agent-site.com", "designations": ["SFR"], "languages": ["Spanish"], "intent_type": ["BUYER", "SELLER"], "specializations": [ "Divorce", "probate sales", "life events sales", "foreclosures", "short sales", "first time buyers", "investors", "Buyer's agent", "Seller's agent" ], "experience": { "first_year": "2006", "first_month": "June", "label": "19 years 11 months of experience" }, "served_areas": [ { "name": "Corona", "state_code": "CA" }, { "name": "Downey", "state_code": "CA" }, { "name": "La Mirada", "state_code": "CA" }, { "name": "Lakewood", "state_code": "CA" }, { "name": "Long Beach", "state_code": "CA" }, { "name": "Los Angeles", "state_code": "CA" }, { "name": "Montebello", "state_code": "CA" }, { "name": "Norwalk", "state_code": "CA" }, { "name": "Ontario", "state_code": "CA" }, { "name": "Pico Rivera", "state_code": "CA" }, { "name": "Riverside", "state_code": "CA" }, { "name": "San Bernardino", "state_code": "CA" }, { "name": "Santa Fe Springs", "state_code": "CA" }, { "name": "West Covina", "state_code": "CA" }, { "name": "Whittier", "state_code": "CA" } ], "mls": [ { "mls_set": "A-EXPL-11111111-2222-3333-4444-555555555555", "agent_id": "11111111-2222-3333-4444-555555555555", "id": "CENT", "primary": false, "status": "A" }, { "mls_set": "A-MRCA-M900123", "agent_id": "M900123", "id": "MRCA", "primary": true, "license_number": "01900123", "status": "A" } ], "phones": [ { "type": "office", "value": "(323) 555-0142" }, { "type": "mobile", "value": "(562) 555-0188" } ], "license_number": "01900123", "license_state": "CA", "is_empty_profile": false, "social_media": { "facebook": "https://www.facebook.com/exampleagent", "instagram": "https://www.instagram.com/example_agent", "linkedin": "https://www.linkedin.com/in/example-agent", "youtube": "https://www.youtube.com/@ExampleAgent" }, "about_video": { "id": "example-video-id", "source": "YOUTUBE", "url": "https://youtu.be/example-video-id" }, "questions_and_answers": [ { "question": "What’s the top reason to hire you?", "answer": "With over 20 years of real estate experience and more than 1,000 homes successfully sold, I bring proven results, strategic marketing, and strong negotiation skills to every transaction." }, { "question": "What makes you stand apart from other agents?", "answer": "What sets me apart is a powerful combination of experience, results, and a client-first approach that consistently delivers success in today’s competitive market." }, { "question": "What is the latest market trend in your service area?", "answer": "The latest real estate trends in Los Angeles County and surrounding areas show a shifting, more balanced market that is creating new opportunities for both buyers and sellers in 2026." }, { "question": "What is your online and offline marketing strategy for sellers?", "answer": "My marketing strategy is designed to deliver maximum exposure, strong buyer demand, and top-dollar results by combining cutting-edge online marketing with proven offline techniques." } ] } ``` Example related review record: ```json { "id": "example-review-1e14fbbef27d8df66866", "record_type": "agent_review", "review_id": "example-review-2668854b-2389-4da5-9e59-ca61290b6ac8", "agent_id": "example-agent-598510673a857f0011ac6802", "fulfillment_id": "9005678", "agent_name": "Alex Morgan", "agent_url": "https://www.realtor.com/realestateagents/Alex-Morgan-9005678", "broker": { "name": "Example Estate Group", "fulfillment_id": "9005600" }, "source_id": "RDC", "reviewer_name": "Sample Client", "reviewer_location": "Riverside, CA", "rating": 5, "comment": "The agent stayed on top of every step and communicated clearly throughout the process.", "relationship": "SELLER", "started_timestamp": "2026-05-04T16:56:35.962Z", "review_date_display": "May 4, 2026", "agent": { "id": "example-agent-598510673a857f0011ac6802", "agent_id": "example-agent-598510673a857f0011ac6802", "fulfillment_id": "9005678", "name": "Alex Morgan", "url": "https://www.realtor.com/realestateagents/Alex-Morgan-9005678" } } ``` ### Notes - Location resolution uses Realtor.com's `AgentLocationSearch` GraphQL operation. - Agent collection uses Realtor.com's `SearchAgents` GraphQL operation. - Each saved record is enriched with Realtor.com's `AgentBrandingProfile` GraphQL operation before it is written to the dataset. - Review collection uses Realtor.com's `AgentBrandingReviews` and `AgentBrandingRecommendations` GraphQL operations when `get_reviews` is enabled. - The actor currently supports city and ZIP-code style resolver results. - Output fields depend on what Realtor.com returns for each agent at run time. # Actor input Schema ## `location` (type: `array`): Enter one U.S. city or ZIP code per item, such as "Los Angeles", "San Francisco", "10001", or "Austin, TX". City plus state is recommended when a place name may be ambiguous. ## `deal_type` (type: `string`): Choose whether to focus on agents presented for sellers, agents presented for buyers, or both. Select Both for the broadest default agent search. ## `agent_name` (type: `string`): Enter a full or partial agent name when you want to focus on a specific professional within the selected location. Leave blank to include any matching agent. ## `realtor_com_member` (type: `boolean`): Enable this to return only agents with Realtor.com member record data. Leave it off for the standard broader agent search. ## `get_reviews` (type: `boolean`): Turn this on to save available reviews and testimonials as separate records linked to each agent profile. Leave it off when you only need the core agent profile dataset. ## `max_reviews` (type: `integer`): Set a per-agent cap for combined review and testimonial records when review collection is enabled. Leave blank to keep all available related records. ## `limit` (type: `integer`): Enter the maximum number of agent profile records to save for each location. Leave blank to collect as many matching profiles as the run can retrieve. ## Actor input object example ```json { "location": [ "Los Angeles", "10001" ], "deal_type": "both", "realtor_com_member": false, "get_reviews": false, "limit": 100 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "location": [ "Los Angeles", "10001" ], "limit": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("fatihtahta/realtor-agents-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "location": [ "Los Angeles", "10001", ], "limit": 100, } # Run the Actor and wait for it to finish run = client.actor("fatihtahta/realtor-agents-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "location": [ "Los Angeles", "10001" ], "limit": 100 }' | apify call fatihtahta/realtor-agents-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fatihtahta/realtor-agents-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Realtor.com Agents Scraper | Contacts & Reviews", "description": "Extract Realtor.com agent profiles across the US with rich contact data, reviews, ratings, brokerage details, social links, service areas and more. Built for enterprise-grade real estate lead generation, agent intelligence, recruiting, and automated analytics pipelines.", "version": "0.0", "x-build-id": "8jt7cc9k4bFWdVTSl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fatihtahta~realtor-agents-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fatihtahta-realtor-agents-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fatihtahta~realtor-agents-scraper/runs": { "post": { "operationId": "runs-sync-fatihtahta-realtor-agents-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fatihtahta~realtor-agents-scraper/run-sync": { "post": { "operationId": "run-sync-fatihtahta-realtor-agents-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "location" ], "properties": { "location": { "title": "Add Search Locations (City or ZIP code)", "minItems": 1, "type": "array", "description": "Enter one U.S. city or ZIP code per item, such as \"Los Angeles\", \"San Francisco\", \"10001\", or \"Austin, TX\". City plus state is recommended when a place name may be ambiguous.", "items": { "type": "string" } }, "deal_type": { "title": "Filter by Client Intent", "enum": [ "sell", "buy", "both" ], "type": "string", "description": "Choose whether to focus on agents presented for sellers, agents presented for buyers, or both. Select Both for the broadest default agent search.", "default": "both" }, "agent_name": { "title": "Filter by Agent Name", "type": "string", "description": "Enter a full or partial agent name when you want to focus on a specific professional within the selected location. Leave blank to include any matching agent." }, "realtor_com_member": { "title": "Filter to Realtor.com Members", "type": "boolean", "description": "Enable this to return only agents with Realtor.com member record data. Leave it off for the standard broader agent search.", "default": false }, "get_reviews": { "title": "Include Reviews and Testimonials (saved seperately)", "type": "boolean", "description": "Turn this on to save available reviews and testimonials as separate records linked to each agent profile. Leave it off when you only need the core agent profile dataset.", "default": false }, "max_reviews": { "title": "Set Maximum Reviews per Agent", "minimum": 1, "type": "integer", "description": "Set a per-agent cap for combined review and testimonial records when review collection is enabled. Leave blank to keep all available related records." }, "limit": { "title": "Set Maximum Agents per Location", "minimum": 1, "type": "integer", "description": "Enter the maximum number of agent profile records to save for each location. Leave blank to collect as many matching profiles as the run can retrieve." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```