# Loopnet Agents Email (`b2b_lead_generation/loopnet-agents-email`) Actor LoopNet agent email extractor that fetches broker contacts and listing details from LoopNet listing IDs, returning a clean flat dataset with names, emails, phones, and titles in one row per listing. - **URL**: https://apify.com/b2b\_lead\_generation/loopnet-agents-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 $19.90 / 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 ## LoopNet Agents Email: Extract B2B Broker Contacts and Property Details from LoopNet ![LoopNet Agents Email cover](https://i.imgur.com/EO3oIKS.png) LoopNet Agents Email pulls B2B broker contacts from LoopNet commercial real estate listings using listing IDs. Pass in one ID or a few thousand. You get broker names, emails, phone numbers, job titles, brokerage companies, and property address info. A listing with multiple agents produces multiple rows, one per broker, with the property address repeated on each. Two agents on one listing means two rows of full contact data. Three agents means three rows. That's it. No LoopNet account or API key needed. ### Output preview **Table view — one row per broker, listing header repeated across all brokers for that property:** ![Apify dataset table view showing LoopNet broker contacts](https://i.imgur.com/WiSYwTP.png) **JSON view — all fields flat, no nested objects:** ![Apify dataset JSON view showing flat broker contact fields](https://i.imgur.com/KGFxEWg.png) ### Use cases - **B2B lead generation**: build targeted LoopNet broker email lists for cold outreach campaigns without manual searching - **CRM enrichment**: append verified commercial real estate agent contacts to existing records using listing IDs as the join key - **CRE market research**: pull listing headers and full broker contact info for hundreds of properties in one run - **Market mapping**: collect broker and brokerage data across listings to identify the most active commercial agents in any city or submarket - **Broker contact database**: build a structured CRE contact database from LoopNet listing IDs collected from search results or other sources - **Competitor analysis**: see which agents and brokerages dominate specific property types, markets, or zip codes - **Outreach automation**: feed broker emails into Make, Zapier, or n8n to trigger personalized email sequences as soon as data lands - **Investment prospecting**: find listing agents on newly posted or off-market commercial properties for acquisition outreach ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `listing_ids` | array of strings | (required) | One or more LoopNet listing IDs to fetch broker and property data for. | | `maxItems` | integer | 100 | Maximum number of listings to process per run. Up to 10,000. | | `timeoutSecs` | integer | 30000 | Overall actor run timeout in seconds. Increase for very large batches. | | `requestTimeoutSecs` | integer | 30 | Per-request timeout in seconds. | | `proxyConfiguration` | object | Datacenter (Anywhere) | Proxy type and location. Supports Datacenter, Residential, Special, and custom proxies. Optional. | #### Example input ```json { "listing_ids": ["31600674", "12345678", "98765432"], "maxItems": 500, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true } } ```` ### What data does this actor extract? One flat row per broker. Listing header fields repeat on every broker row. A property with two agents gives two rows; three agents gives three rows. #### Sample output (two brokers on one listing) ```json [ { "query": "31600674", "headerAddress": "1512 Curtis St", "listingName": "Candy Factory Coworking", "subtext": "Coworking Space Available", "header_location": "Denver, CO 80202", "name": "Blake Ham", "tagLine": "Leasing Manager at KEW Realty Corporation", "company": "KEW Realty Corporation", "companyLogo": "https://images1.loopnet.com/i2/Kh6cu2g.../image.jpg", "brokerImage": "https://images1.loopnet.com/i2/lpdT_SI.../image.jpg", "location": "Denver, CO", "phone": "720-308-2230", "email": "bham@kewrealty.com", "fieldTitle": "Leasing Manager", "phoneNumber": "7203082230", "phoneInfo_countryCode": "USA", "phoneInfo_nationalPrefix": "1", "city": "Denver", "sc": "CO", "cc": "USA", "listingPhone": "7208071332", "isCTN": true, "phoneCountryCode": "USA", "companyLocationId": 129024, "isCoStarSubscriber": true, "isPowerBroker": false, "roleId": 11 }, { "query": "31600674", "headerAddress": "1512 Curtis St", "listingName": "Candy Factory Coworking", "subtext": "Coworking Space Available", "header_location": "Denver, CO 80202", "name": "Jeremy Spira", "tagLine": "Vice President of Leasing and Development for KEW Realty.", "company": "KEW Realty Corporation", "companyLogo": "https://images1.loopnet.com/i2/Kh6cu2g.../image.jpg", "brokerImage": "https://images1.loopnet.com/i2/a92-2gu.../image.jpg", "location": "Denver, CO", "phone": "303-468-8370", "email": "jspira@kewrealty.com", "fieldTitle": "Vice President of Leasing & Development", "phoneNumber": "3034688370", "phoneInfo_countryCode": "USA", "phoneInfo_nationalPrefix": "1", "city": "Denver", "sc": "CO", "cc": "USA", "listingPhone": "7208071332", "isCTN": true, "phoneCountryCode": "USA", "companyLocationId": 129024, "isCoStarSubscriber": true, "isPowerBroker": false, "roleId": 11 } ] ``` #### Output field reference | Field | Type | Description | |-------|------|-------------| | `query` | string | LoopNet listing ID. Repeats on every broker row for this listing. | | `headerAddress` | string | Street address of the commercial property. | | `listingName` | string | Property or listing name. | | `subtext` | string | Availability note from the listing header (e.g. "Office Space Available"). | | `header_location` | string | City, state, and ZIP of the property. | | `name` | string | Broker full name. | | `tagLine` | string | Broker professional tagline or bio line. | | `company` | string | Brokerage company name. | | `companyLogo` | string | URL of the brokerage company logo image. | | `brokerImage` | string | URL of the broker profile photo. | | `location` | string | Broker city and state. | | `phone` | string | Broker direct phone number (formatted). | | `email` | string | Broker B2B email address. | | `fieldTitle` | string | Broker job title (e.g. "Leasing Manager", "VP of Investments"). | | `phoneNumber` | string | Phone number digits only, from phoneInfo. | | `phoneInfo_countryCode` | string | Phone country code from phoneInfo (e.g. USA). | | `phoneInfo_nationalPrefix` | string | National dialing prefix (e.g. 1 for US). | | `city` | string | Broker city. | | `sc` | string | Broker state code (e.g. CO, TX, CA). | | `cc` | string | Broker country code (e.g. USA). | | `listingPhone` | string | Listing contact phone digits. | | `isCTN` | boolean | Whether click-to-call is enabled for this broker. | | `phoneCountryCode` | string | Top-level phone country code. | | `companyLocationId` | integer | Internal brokerage location ID. | | `isCoStarSubscriber` | boolean | Whether the broker is a CoStar subscriber. | | `isPowerBroker` | boolean | Whether the broker holds LoopNet PowerBroker status. | | `roleId` | integer | Broker role ID. | | `error` | string | Populated when a listing could not be fetched. Empty on success. | ### Tips for getting the most out of this actor **Build your listing ID list first.** This actor takes listing IDs as input — it does not search LoopNet for you. Collect listing IDs from LoopNet search URLs, your own CRM, or a separate LoopNet search actor, then feed them here in bulk. **Use proxies for large batches.** For runs over 500 listings, enable Datacenter proxies in the proxy configuration. Switch to Residential proxies if you see a high rate of failed rows. **Check the error field.** Any row with a non-empty `error` field means that listing ID failed. Export those IDs, investigate, and re-run them in a separate smaller batch. **Connect to Google Sheets or a CRM.** Use the Apify Google Sheets integration or a webhook to push broker emails directly into your outreach tool the moment a run finishes. No manual CSV exports needed. **Filter by `isPowerBroker` or `isCoStarSubscriber`.** These boolean fields let you prioritize the most active or established brokers in your downstream outreach workflow. ### FAQ **Can I pass a single listing ID?** Yes. Add one ID to the `listing_ids` array. The actor works for one or ten thousand. **How many rows will I get per listing?** One row per broker on that listing. A property listed by two agents gives two rows, each with the same property address and listing name repeated. **What happens if a listing has no broker details?** The actor writes `error: "Data unavailable"` for that listing ID. The property header fields stay empty and the row is easy to filter out. **How many listings can I process per run?** Up to 10,000 per run. For very large datasets, split IDs across multiple runs and use the Apify scheduler to chain them. **Do I need a LoopNet account?** No. No login, no API key, no credentials of any kind. **Should I use a proxy?** For batches over a few hundred, yes. Datacenter proxies are fast and cheap. Residential proxies are slower but more reliable if you see lots of failures. **Can I export the results to CSV or Google Sheets?** Yes. From the Apify dataset view, export directly to CSV, JSON, Excel, or connect to Google Sheets via the Apify integration. ### Integrations Connect LoopNet Agents Email with your existing tools using [Apify integrations](https://apify.com/integrations). Works with Make, Zapier, Slack, Airbyte, GitHub, Google Sheets, Google Drive, HubSpot, and more. Use [webhooks](https://docs.apify.com/integrations/webhooks) to push broker contact data into your CRM or outreach platform the moment a run finishes, with no manual steps in between. # Actor input Schema ## `listing_ids` (type: `array`): One or more LoopNet listing IDs to fetch. Enter one ID per line. ## `maxItems` (type: `integer`): Maximum number of listings to process per run. Useful to cap costs on large batches. ## `timeoutSecs` (type: `integer`): Overall actor timeout in seconds. Increase for very large batches. ## `requestTimeoutSecs` (type: `integer`): Per-request timeout in seconds. Increase if you experience frequent timeouts. ## `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 { "listing_ids": [ "31600674", "12345678" ], "maxItems": 100, "timeoutSecs": 30000, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "listing_ids": [ "31600674" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("b2b_lead_generation/loopnet-agents-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 = { "listing_ids": ["31600674"], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("b2b_lead_generation/loopnet-agents-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 '{ "listing_ids": [ "31600674" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call b2b_lead_generation/loopnet-agents-email --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=b2b_lead_generation/loopnet-agents-email", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Loopnet Agents Email", "description": "LoopNet agent email extractor that fetches broker contacts and listing details from LoopNet listing IDs, returning a clean flat dataset with names, emails, phones, and titles in one row per listing.", "version": "0.0", "x-build-id": "GewNay2kfgoef9XK7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/b2b_lead_generation~loopnet-agents-email/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-b2b_lead_generation-loopnet-agents-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/b2b_lead_generation~loopnet-agents-email/runs": { "post": { "operationId": "runs-sync-b2b_lead_generation-loopnet-agents-email", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/b2b_lead_generation~loopnet-agents-email/run-sync": { "post": { "operationId": "run-sync-b2b_lead_generation-loopnet-agents-email", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "listing_ids" ], "properties": { "listing_ids": { "title": "Listing IDs", "type": "array", "description": "One or more LoopNet listing IDs to fetch. Enter one ID per line.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "maximum": 10000, "type": "integer", "description": "Maximum number of listings to process per run. Useful to cap costs on large batches.", "default": 100 }, "timeoutSecs": { "title": "Run timeout (seconds)", "maximum": 86400, "type": "integer", "description": "Overall actor timeout in seconds. Increase for very large batches.", "default": 30000 }, "requestTimeoutSecs": { "title": "Request timeout (seconds)", "maximum": 120, "type": "integer", "description": "Per-request timeout in seconds. Increase if you experience frequent timeouts.", "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 } } } } } } } } } } ```