# Real Estate Agent Lead Capture Audit (`digital-ai-lab.de/real-estate-agent-lead-capture-audit`) Actor Audit real estate agent websites for seller-valuation CTAs, buyer/seller lead capture, listing visibility, neighborhood content, and nurture automation opportunities. - **URL**: https://apify.com/digital-ai-lab.de/real-estate-agent-lead-capture-audit.md - **Developed by:** [Digital AI Lab](https://apify.com/digital-ai-lab.de) (community) - **Categories:** Real estate, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $50.00 / 1,000 real estate audits This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Real Estate Agent Lead Capture Audit Audit real estate agent websites for valuation CTAs, buyer/seller lead capture, listing visibility, and nurture opportunities. This Actor is built for real-estate marketing agencies, brokerages, agent-growth consultants, and CRM automation providers who need fast website audits before outreach or sales calls. It crawls real estate agent websites and returns one dataset item per website with an opportunity score, grade, issues, strengths, top fixes, automation opportunities, pitch angle, optional outreach email, and Markdown audit. Typical workflow: run 5-50 agent or brokerage URLs, export the dataset to a CRM or Google Sheet, sort by seller valuation and buyer/seller lead-capture gaps, and use the Markdown audit as sales-call preparation. ### Best use cases - Help real estate marketing agencies identify website improvement opportunities. - Help agent growth consultants identify website improvement opportunities. - Help brokerage marketing teams identify website improvement opportunities. - Help local SEO consultants identify website improvement opportunities. - Help CRM automation providers identify website improvement opportunities. - Score prospect websites before outreach or sales calls. - Generate quick audit notes for CRMs, spreadsheets, and AI-agent workflows. ### Input ```json { "startUrls": [ "https://www.compass.com", "https://example.com" ], "auditGoal": "lead-generation", "targetIndustry": "real estate agents", "maxPagesPerSite": 3, "includePitchEmail": true, "includeRawSignals": false } ```` ### What it checks - Seller valuation or consultation CTA - Buyer and seller lead-capture paths - Listings and neighborhood content - Agent/team trust and review cues - Market report, guide, or lead magnet cues - CRM and nurture automation opportunities - Primary CTA visibility - Contact form or booking path - Analytics and conversion measurement - Trust, legal, customer, or proof content - Structured data signal - Thin content and noindex signals ### Output Each result is pushed to the default dataset: ```json { "url": "https://example.com/", "status": "ok", "companyName": "Example Business", "opportunityScore": 82, "grade": "B", "primaryOpportunity": "Lead capture path is weak or missing", "issues": ["No visible contact form or booking path found"], "strengths": ["Primary call to action is visible"], "topFixes": ["Add a low-friction lead capture path"], "automationOpportunities": ["Lead capture workflow routed to CRM and email follow-up"], "agencyPitchAngle": "Example Business already has enough public website signal to start a useful audit conversation...", "pitchEmail": { "subject": "Example Business website - quick opportunity", "body": "Hi,\n\nI reviewed Example Business's public website..." }, "auditMarkdown": "## Example Business - Website Opportunity Audit\n\n...", "confidence": 0.68 } ``` ### Pricing This Actor is designed for pay-per-event pricing. One charged event equals one real estate agents website audit written to the default dataset. The primary event is `Real estate audit`. ### Tips for better results - Use canonical homepage URLs. - Set `maxPagesPerSite` to 3-5 for quick prospect audits. - Increase `maxPagesPerSite` when auditing larger websites. - Review generated pitch emails before sending. - Works well with Google Sheets, Airtable, n8n, Make, Zapier, CRMs, and AI-agent workflows. ### Search keywords Real estate website audit, real estate lead capture, seller valuation CTA audit, realtor website audit, agent website conversion, brokerage automation audit. ### Limitations - This Actor uses public website signals only. - JavaScript-heavy websites may expose limited static HTML without browser rendering. - Audit scores are deterministic heuristics, not a full legal, financial, medical, UX, or SEO review. - This Actor should not be used for spam. It is designed for legitimate sales preparation, website auditing, and agency workflows. # Actor input Schema ## `startUrls` (type: `array`): One or more real estate agents websites to audit. Use canonical homepage URLs when possible. ## `auditGoal` (type: `string`): Choose the primary audit or sales use case. ## `targetIndustry` (type: `string`): Industry context used in recommendations and pitch language. ## `maxPagesPerSite` (type: `integer`): Maximum number of same-domain pages to fetch for each website. ## `includePitchEmail` (type: `boolean`): Generate a short outreach email based on detected website gaps. ## `includeRawSignals` (type: `boolean`): Include sampled page text and extracted technical signals in each dataset item. ## Actor input object example ```json { "startUrls": [ "https://www.compass.com" ], "auditGoal": "lead-generation", "targetIndustry": "real estate agents", "maxPagesPerSite": 3, "includePitchEmail": true, "includeRawSignals": false } ``` # 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 = { "startUrls": [ "https://www.compass.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("digital-ai-lab.de/real-estate-agent-lead-capture-audit").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 = { "startUrls": ["https://www.compass.com"] } # Run the Actor and wait for it to finish run = client.actor("digital-ai-lab.de/real-estate-agent-lead-capture-audit").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 '{ "startUrls": [ "https://www.compass.com" ] }' | apify call digital-ai-lab.de/real-estate-agent-lead-capture-audit --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=digital-ai-lab.de/real-estate-agent-lead-capture-audit", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Real Estate Agent Lead Capture Audit", "description": "Audit real estate agent websites for seller-valuation CTAs, buyer/seller lead capture, listing visibility, neighborhood content, and nurture automation opportunities.", "version": "0.1", "x-build-id": "Me8YNEORdd7URl0UF" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/digital-ai-lab.de~real-estate-agent-lead-capture-audit/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-digital-ai-lab.de-real-estate-agent-lead-capture-audit", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/digital-ai-lab.de~real-estate-agent-lead-capture-audit/runs": { "post": { "operationId": "runs-sync-digital-ai-lab.de-real-estate-agent-lead-capture-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/digital-ai-lab.de~real-estate-agent-lead-capture-audit/run-sync": { "post": { "operationId": "run-sync-digital-ai-lab.de-real-estate-agent-lead-capture-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "Website URLs", "minItems": 1, "maxItems": 50, "type": "array", "description": "One or more real estate agents websites to audit. Use canonical homepage URLs when possible.", "items": { "type": "string" } }, "auditGoal": { "title": "Audit goal", "enum": [ "lead-generation", "conversion-optimization", "automation-opportunities", "sales-prep" ], "type": "string", "description": "Choose the primary audit or sales use case.", "default": "lead-generation" }, "targetIndustry": { "title": "Target industry", "type": "string", "description": "Industry context used in recommendations and pitch language.", "default": "real estate agents" }, "maxPagesPerSite": { "title": "Max pages per site", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of same-domain pages to fetch for each website.", "default": 3 }, "includePitchEmail": { "title": "Include pitch email", "type": "boolean", "description": "Generate a short outreach email based on detected website gaps.", "default": true }, "includeRawSignals": { "title": "Include raw signals", "type": "boolean", "description": "Include sampled page text and extracted technical signals in each dataset item.", "default": false } } }, "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 } } } } } } } } } } ```