# SRES Realtor Directory Scraper (`epicscrapers/sres-realtor-scraper`) Actor
Extract every listing from the SRES® member directory — names, contact info and locations of REALTORS® specializing in the 50+ seniors market.
- **URL**: https://apify.com/epicscrapers/sres-realtor-scraper.md
- **Developed by:** [Epic Scrapers](https://apify.com/epicscrapers) (community)
- **Categories:** Lead generation, Real estate, Automation
- **Stats:** 2 total users, 1 monthly users, 75.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet
## Pricing
from $4.00 / 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
## SRES Realtor Directory Scraper — Senior Real Estate Specialist (SRES®) Leads
Scrape the **official [sres.realtor](https://www.sres.realtor) directory** to build a complete, clean, ready-to-use dataset of every **Seniors Real Estate Specialist® (SRES®)** designee across the United States and Canada.
The SRES® designation is awarded by the National Association of REALTORS® to agents who specialize in helping clients aged 50+ buy, sell, or relocate. This is a small, high-intent, high-value niche — which makes it one of the best targeted lead sources available for anyone selling into the senior housing and real estate space.
This actor turns that public directory into a structured dataset in minutes, with no manual searching, copy-pasting, or clicking through hundreds of agent profiles.
---
### Who is this for?
- **Real estate marketing agencies** running targeted outreach or ad campaigns to senior-focused agents
- **SaaS & martech companies** building tools, CRMs, or lead-gen products for the senior housing market
- **Data analysts & researchers** studying the seniors real estate niche or agent distribution by region
- **Recruiters & brokerages** identifying and reaching out to agents with the SRES® designation
- **Senior living communities & service providers** looking to partner with locally trusted agents
### Why scrape this directory instead of doing it manually?
The official directory is easy to browse one agent at a time, but there's no built-in way to export the full list. Doing this by hand across thousands of agents in every US state and Canadian province would take days. This actor automates the entire process — crawling the directory, visiting each listing, and extracting standardized fields — so you get a complete dataset in a single run.
---
### What data you get per listing
Each row in the output dataset includes:
| Field | Description |
|---|---|
| **Full Name** | Agent's first and last name |
| **Email Address** | Direct contact email (where publicly listed) |
| **Phone Number** | Direct or office phone number |
| **Office / Agency Name** | The brokerage or agency the agent is affiliated with |
| **Street Address** | Office street address |
| **City** | City of the office/agency |
| **State / Province** | State (US) or province (Canada) |
| **ZIP / Postal Code** | ZIP or postal code |
| **Country** | US or Canada |
| **SRES® Designation Status** | Confirms active SRES® designation |
The result is a clean, deduplicated table you can filter by state, city, or country, and plug directly into a CRM, spreadsheet, or outreach tool.
---
### Sample output
```json
{
"fullName": "Jane Smith",
"email": "jane.smith@example-realty.com",
"phone": "+1 (555) 123-4567",
"officeName": "Example Realty Group",
"streetAddress": "123 Main St",
"city": "Scottsdale",
"state": "AZ",
"zip": "85251",
"country": "United States",
"sresDesignation": "Active"
}
````
***
### How it works
1. The actor crawls the sres.realtor directory across all available states/provinces.
2. It visits each individual agent listing page.
3. It extracts and normalizes the contact and location fields listed above.
4. Results are streamed into a structured dataset as the run progresses, so you can export partial results at any time.
5. When the run completes, you get one unified dataset covering the entire directory.
### Output formats
Export your results in whichever format fits your workflow:
- **JSON / JSON Lines**
- **CSV**
- **Excel (XLSX)**
- **HTML table**
- Direct integration via API for automated pipelines
***
### Use case examples
- **Targeted email/direct mail campaigns** to SRES® agents in specific ZIP codes or states
- **Building a partner network** for a senior relocation, downsizing, or estate-sale service
- **Market research** on the geographic density of SRES®-certified agents
- **Lead lists for CRM import** segmented by region for a sales team
- **Competitive analysis** for companies already selling tools to this niche
***
### Frequently asked questions
**Is this data public?**
Yes — all information comes from the publicly accessible sres.realtor directory. This actor only automates collection of data that agents have already chosen to list publicly.
**How often is the directory updated?**
The source directory is updated by NAR/SRES as agents earn or renew their designation. Re-run this actor periodically to keep your dataset current.
**Does this cover both the US and Canada?**
Yes, the actor covers all states and Canadian provinces represented in the directory.
**Can I filter results by state before running?**
Check the actor's input schema — depending on configuration, you may be able to scope the run to specific states/provinces to save time and compute units.
***
### Get started
Click **Run** to start scraping, or use the API/CLI to integrate this actor into your own automated lead-generation pipeline. Results are ready to download or push directly into your existing tools as soon as the run finishes.
# Actor input Schema
## `fname` (type: `string`):
Filter results by the agent's first name.
## `lname` (type: `string`):
Filter results by the agent's last name.
## `country` (type: `string`):
Filter by country. Must match the exact name from the SRES directory (e.g. United States, Canada, Mexico).
## `state` (type: `string`):
Select a US state to filter by. Only applies when Country is set to United States. Leave empty for all states.
## `city` (type: `string`):
Filter by city name.
## `zip` (type: `string`):
Filter by zip or postal code.
## `maxPages` (type: `integer`):
Number of search result pages to scrape (up to 10 members per page). Set to 0 for all pages.
💡 Keep this low for a quick test — 5 pages returns ~50 members.
## `fetchProfiles` (type: `boolean`):
Fetch individual profile details from the CRD API for each member (structured address, bio, certifications, headshot URLs).
ℹ️ Disable to scrape faster and save on compute, but you'll only get basic info from search results.
## Actor input object example
```json
{
"country": "United States",
"maxPages": 5,
"fetchProfiles": true
}
```
# Actor output Schema
## `results` (type: `string`):
Link to the dataset of scraped records
# 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 = {
"country": "United States",
"maxPages": 5,
"fetchProfiles": true
};
// Run the Actor and wait for it to finish
const run = await client.actor("epicscrapers/sres-realtor-scraper").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 = {
"country": "United States",
"maxPages": 5,
"fetchProfiles": True,
}
# Run the Actor and wait for it to finish
run = client.actor("epicscrapers/sres-realtor-scraper").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 '{
"country": "United States",
"maxPages": 5,
"fetchProfiles": true
}' |
apify call epicscrapers/sres-realtor-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=epicscrapers/sres-realtor-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "SRES Realtor Directory Scraper",
"description": "Extract every listing from the SRES® member directory — names, contact info and locations of REALTORS® specializing in the 50+ seniors market.",
"version": "0.0",
"x-build-id": "yXonD0EPx8eoIgcFN"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/epicscrapers~sres-realtor-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-epicscrapers-sres-realtor-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/epicscrapers~sres-realtor-scraper/runs": {
"post": {
"operationId": "runs-sync-epicscrapers-sres-realtor-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/epicscrapers~sres-realtor-scraper/run-sync": {
"post": {
"operationId": "run-sync-epicscrapers-sres-realtor-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"fname": {
"title": "First Name",
"type": "string",
"description": "Filter results by the agent's first name."
},
"lname": {
"title": "Last Name",
"type": "string",
"description": "Filter results by the agent's last name."
},
"country": {
"title": "🌍 Country",
"type": "string",
"description": "Filter by country. Must match the exact name from the SRES directory (e.g. United States, Canada, Mexico).",
"default": "United States"
},
"state": {
"title": "📍 State (US only)",
"enum": [
"",
"AL",
"AK",
"AS",
"AZ",
"AR",
"AA",
"AE",
"AP",
"CA",
"CO",
"CT",
"DE",
"DC",
"FL",
"GA",
"GU",
"HI",
"ID",
"IL",
"IN",
"IA",
"KS",
"KY",
"LA",
"ME",
"MH",
"MD",
"MA",
"MI",
"FM",
"MN",
"MS",
"MO",
"MT",
"NE",
"NV",
"NH",
"NJ",
"NM",
"NY",
"NC",
"ND",
"MP",
"OH",
"OK",
"OR",
"PW",
"PA",
"PR",
"RI",
"SC",
"SD",
"TN",
"TX",
"UT",
"VT",
"VI",
"VA",
"WA",
"WV",
"WI",
"WY"
],
"type": "string",
"description": "Select a US state to filter by. Only applies when Country is set to United States. Leave empty for all states."
},
"city": {
"title": "🏙️ City",
"type": "string",
"description": "Filter by city name."
},
"zip": {
"title": "📮 Zip / Postal Code",
"type": "string",
"description": "Filter by zip or postal code."
},
"maxPages": {
"title": "📄 Max Pages",
"type": "integer",
"description": "Number of search result pages to scrape (up to 10 members per page). Set to 0 for all pages.
💡 Keep this low for a quick test — 5 pages returns ~50 members.",
"default": 5
},
"fetchProfiles": {
"title": "👤 Detailed Profiles",
"type": "boolean",
"description": "Fetch individual profile details from the CRD API for each member (structured address, bio, certifications, headshot URLs).
ℹ️ Disable to scrape faster and save on compute, but you'll only get basic info from search results.",
"default": true
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```