# Realtor Properties Contact Email (`b2b_lead_generation/realtor-properties-contact-email`) Actor Realtor contact email extractor that fetches agent names, emails, phones, and office details from Realtor.com listings using property IDs or US addresses, returning one flat row per agent. - **URL**: https://apify.com/b2b\_lead\_generation/realtor-properties-contact-email.md - **Developed by:** [B2B Lead Generation](https://apify.com/b2b_lead_generation) (community) - **Categories:** Lead generation, Real estate, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 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 Properties Contact Email: collect public agent contacts from Realtor.com listings ![Realtor Properties Contact Email](https://i.imgur.com/G0FMfFy.png) Realtor Properties Contact Email collects publicly available listing agent contact details from Realtor.com property listings. Pass in US property addresses or numeric property IDs and get back agent names, emails, direct phone numbers, brokerage names, and office contact info — all in a flat dataset, one row per agent. A property listed by two agents gives you two rows. A property with three agents gives three. The property address, price, beds, baths, and square footage repeat on every row so the dataset stays self-contained. No Realtor.com account, no login, no API key. Plain addresses and numeric IDs both work, and you can mix them in the same run. ### Use cases - **Direct mail and email campaigns**: build targeted mailing lists of listing agents for new property announcements, open house invites, or market update newsletters - **Mortgage lead generation**: reach out to listing agents on recently posted or price-reduced properties to introduce lender services and referral programs - **New listing alerts**: collect agent contacts from freshly listed properties in a target zip code and trigger automated outreach the moment new inventory appears - **CRM and database enrichment**: append verified agent emails and phone numbers to existing property records using addresses or listing IDs as the lookup key - **Real estate networking**: build a structured contact database of active brokers and agents in any US market for professional outreach and partnership development - **Investor prospecting**: identify listing agents on distressed, foreclosed, or price-reduced properties for acquisition conversations - **Brand awareness campaigns**: collect agent emails to run awareness campaigns for fintech tools, property management software, inspection services, or other real estate adjacent products - **Market research**: gather brokerage and agent data across listings to map which firms dominate specific property types, neighborhoods, or price ranges - **Referral program outreach**: contact listing agents about referral partnerships, co-marketing opportunities, or preferred vendor programs - **Event and webinar promotion**: pull agent emails from active listings in a region to promote real estate events, certifications, or local networking meetups ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `searchQueries` | array of strings | (required) | US property addresses or numeric Realtor.com property IDs. Mix both in the same run. One per line. | | `maxItems` | integer | 0 (no limit) | Maximum number of properties to process. Set 0 for no cap. | | `timeoutSecs` | integer | 30000 | Overall run timeout in seconds. Increase for very large batches. | | `requestTimeoutSecs` | integer | 30 | Per-request timeout in seconds. | | `proxyConfiguration` | object | Datacenter US | Proxy type and location. US proxies are recommended. Optional. | #### Example input ```json { "searchQueries": [ "4321 Oak Ridge Dr, Austin, TX 78731", "890 Brickell Ave, Miami, FL 33131", "501 Magnolia Blvd, Nashville, TN 37211", "3291777140", "2215777890" ], "maxItems": 0, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } } ```` ### What data does this actor extract? One flat row per agent. Property header fields repeat on every agent row. A listing with two agents produces two rows with the same property details. #### Sample output — single agent listing ```json { "searchQuery": "4321 Oak Ridge Dr, Austin, TX 78731", "propertyId": "4089123456", "address": "4321 Oak Ridge Dr", "city": "Austin", "state": "TX", "postalCode": "78731", "listPrice": 875000, "beds": 4, "baths": 3, "sqft": 2850, "propertyType": "single_family", "agentName": "Sarah Mitchell", "agentEmail": "sarah.mitchell@austinrealty.com", "agentType": "seller", "agentPhone": "5124789901", "agentHref": "https://www.austinrealty.com", "agentStateLicense": "TX-789012", "officeName": "Austin Premier Realty", "officeEmail": "office@austinrealty.com", "officePhone": "5124780000", "officeCity": "Austin", "officeState": "TX", "error": "" } ``` #### Sample output — two agents on same listing (two rows) ```json [ { "searchQuery": "890 Brickell Ave, Miami, FL 33131", "propertyId": "3055678901", "address": "890 Brickell Ave", "city": "Miami", "state": "FL", "postalCode": "33131", "listPrice": 1250000, "beds": 3, "baths": 2, "sqft": 1900, "propertyType": "condo", "agentName": "Carlos Mendez", "agentEmail": "carlos@miamiproperty.com", "agentType": "seller", "agentPhone": "3055551234", "agentHref": "https://www.miamiproperty.com", "agentStateLicense": "FL-334512", "officeName": "Miami Property Group", "officeEmail": "info@miamiproperty.com", "officePhone": "3055550000", "officeCity": "Miami", "officeState": "FL", "error": "" }, { "searchQuery": "890 Brickell Ave, Miami, FL 33131", "propertyId": "3055678901", "address": "890 Brickell Ave", "city": "Miami", "state": "FL", "postalCode": "33131", "listPrice": 1250000, "beds": 3, "baths": 2, "sqft": 1900, "propertyType": "condo", "agentName": "Diana Torres", "agentEmail": "diana.torres@miamiproperty.com", "agentType": "seller", "agentPhone": "3055559876", "agentHref": "https://www.miamiproperty.com", "agentStateLicense": "FL-229803", "officeName": "Miami Property Group", "officeEmail": "info@miamiproperty.com", "officePhone": "3055550000", "officeCity": "Miami", "officeState": "FL", "error": "" } ] ``` #### Sample output — address not found or data unavailable ```json { "searchQuery": "999 Fake Street, Nowhere, TX 00000", "error": "Data unavailable" } ``` #### Output field reference | Field | Type | Description | |-------|------|-------------| | `searchQuery` | string | The original address or property ID you passed in. | | `propertyId` | string | Resolved numeric Realtor.com property ID. | | `address` | string | Full street address of the property. | | `city` | string | Property city. | | `state` | string | Property state code (e.g. TX, FL, CA). | | `postalCode` | string | Property ZIP code. | | `listPrice` | number | Current listing price. | | `beds` | number | Number of bedrooms. | | `baths` | number | Number of bathrooms. | | `sqft` | number | Square footage. | | `propertyType` | string | Property type (e.g. single\_family, condo, multi\_family). | | `agentName` | string | Listing agent full name. | | `agentEmail` | string | Listing agent public email address. | | `agentType` | string | Agent role on this listing (e.g. seller, buyer). | | `agentPhone` | string | Agent primary direct phone number. | | `agentHref` | string | Agent or brokerage website URL. | | `agentStateLicense` | string | Agent state license number. | | `officeName` | string | Brokerage office name. | | `officeEmail` | string | Brokerage office public email address. | | `officePhone` | string | Brokerage office phone number. | | `officeCity` | string | City where the brokerage office is located. | | `officeState` | string | State code of the brokerage office. | | `error` | string | Set when a property could not be retrieved. Empty on success. | ### How it works 1. The actor reads each item from `searchQueries`. 2. Numeric-only inputs are used as property IDs directly. 3. Plain text addresses are resolved to a numeric property ID via Realtor.com's geocoding service. 4. Full listing details are fetched for each resolved property ID. 5. Each agent in the listing's advertiser list becomes its own output row, with the property header fields repeated. 6. If a property has no agent data listed, the actor still writes a row with the property details and empty agent fields so nothing is silently skipped. 7. Properties that fail to resolve or return no data write an error row with the original search query so you can identify and retry them. ### Tips for best results **Use US proxies.** Realtor.com heavily prefers US-based traffic. `apifyProxyCountry: "US"` is set by default. For high error rates on large batches, switch to Residential proxies. **Mix addresses and IDs freely.** Plain addresses like `"123 Main St, Austin TX"` and numeric IDs like `"3291777140"` work in the same `searchQueries` list. The actor detects which is which automatically. **Check the error field.** Any row with a non-empty `error` value means that input failed. Export those rows and re-run them in a separate smaller batch. **Set `maxItems: 0` for no cap.** The default has no limit. Use a number only when testing a subset of a large list. **Connect to your CRM or email tool.** Use the Apify Google Sheets integration or a webhook to push agent contacts into HubSpot, Mailchimp, Airtable, or your outreach platform automatically when a run finishes. ### FAQ **Can I pass plain addresses instead of property IDs?** Yes. The actor resolves plain US addresses to property IDs automatically. Numeric IDs are also accepted. Both work in the same run. **How many rows will I get per property?** One row per listing agent. A property with two agents gives two rows, both with the same property details repeated. **What happens if a property has no agent listed?** The actor writes one row with the property details and empty agent fields. The property still appears in your dataset so you know it was processed. **What if an address or property ID returns no data?** The actor writes a row with `error: "Data unavailable"` so you can spot and retry it separately. **Do I need a Realtor.com account?** No. No login, no API key, no credentials required. All contact data returned is publicly listed on Realtor.com. **Should I use a proxy?** Yes, especially for large batches. US Datacenter proxies work for most runs. Switch to Residential if you see frequent failures. **Can I export results to CSV or Google Sheets?** Yes. From the Apify dataset view, export directly to CSV, JSON, or Excel. The Apify Google Sheets integration can push results automatically. **Is this data public?** Yes. All agent contact information returned by this actor (names, emails, phones, office details) is publicly listed on Realtor.com property pages. No private or restricted data is accessed. ### Integrations Connect Realtor Properties Contact Email with your tools using [Apify integrations](https://apify.com/integrations). Works with Make, Zapier, Slack, Airbyte, GitHub, Google Sheets, Google Drive, HubSpot, Mailchimp, and more. Use [webhooks](https://docs.apify.com/integrations/webhooks) to push agent contacts into your CRM or email platform the moment a run finishes, with no manual steps needed. # Actor input Schema ## `searchQueries` (type: `array`): List of US property addresses or numeric Realtor.com property IDs. Mix both in the same run. Enter one per line. ## `maxItems` (type: `integer`): Maximum number of properties to process per run. Set to 0 for no limit. ## `timeoutSecs` (type: `integer`): Maximum total run time in seconds. Increase for large batches. ## `requestTimeoutSecs` (type: `integer`): How long to wait for each individual HTTP request before moving on. ## `proxyConfiguration` (type: `object`): Select proxies to use for requests. Helps avoid IP blocking and rate limits. Datacenter proxies are fastest; Residential proxies are harder to detect. ## Actor input object example ```json { "searchQueries": [ "890 Brickell Ave, Miami, FL 33131", "4660981796", "39 Post Ln, Staten Island" ], "maxItems": 0, "timeoutSecs": 30000, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } } ``` # 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 = { "searchQueries": [ "890 Brickell Ave, Miami, FL 33131", "4660981796", "39 Post Ln, Staten Island" ], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("b2b_lead_generation/realtor-properties-contact-email").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 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); }); // 📚 Want to learn more 📖? Go to → 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 = { "searchQueries": [ "890 Brickell Ave, Miami, FL 33131", "4660981796", "39 Post Ln, Staten Island", ], "proxyConfiguration": { "useApifyProxy": True, "apifyProxyCountry": "US", }, } # Run the Actor and wait for it to finish run = client.actor("b2b_lead_generation/realtor-properties-contact-email").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchQueries": [ "890 Brickell Ave, Miami, FL 33131", "4660981796", "39 Post Ln, Staten Island" ], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } }' | apify call b2b_lead_generation/realtor-properties-contact-email --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=b2b_lead_generation/realtor-properties-contact-email", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Realtor Properties Contact Email", "description": "Realtor contact email extractor that fetches agent names, emails, phones, and office details from Realtor.com listings using property IDs or US addresses, returning one flat row per agent.", "version": "0.0", "x-build-id": "JcxweydzZ4Naiua8a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/b2b_lead_generation~realtor-properties-contact-email/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-b2b_lead_generation-realtor-properties-contact-email", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/b2b_lead_generation~realtor-properties-contact-email/runs": { "post": { "operationId": "runs-sync-b2b_lead_generation-realtor-properties-contact-email", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/b2b_lead_generation~realtor-properties-contact-email/run-sync": { "post": { "operationId": "run-sync-b2b_lead_generation-realtor-properties-contact-email", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQueries" ], "properties": { "searchQueries": { "title": "Addresses or Property IDs", "type": "array", "description": "List of US property addresses or numeric Realtor.com property IDs. Mix both in the same run. Enter one per line.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Maximum number of properties to process per run. Set to 0 for no limit.", "default": 0 }, "timeoutSecs": { "title": "Overall timeout (seconds)", "minimum": 30, "maximum": 86400, "type": "integer", "description": "Maximum total run time in seconds. Increase for large batches.", "default": 30000 }, "requestTimeoutSecs": { "title": "Per-request timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "How long to wait for each individual HTTP request before moving on.", "default": 30 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to use for requests. Helps avoid IP blocking and rate limits. Datacenter proxies are fastest; Residential proxies are harder to detect." } } }, "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 } } } } } } } } } } ```