Realtor Property Scraper
Pricing
from $2.20 / 1,000 results
Realtor Property Scraper
Realtor.com listings API: MLS data, AVM valuations, days-on-market, price-cut history, agent contacts, and broker IDs across 50 US states and 7 listing types. JSON output for real-estate intelligence and lead-enrichment pipelines.
Pricing
from $2.20 / 1,000 results
Rating
5.0
(2)
Developer
AgentX
Actor stats
1
Bookmarked
6
Total users
5
Monthly active users
17 hours ago
Last modified
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Realtor Property Scraper - MLS Data, Agent Contacts & AVM Valuation API
Extract 10,000+ US property listings from Realtor.com in one API call. This real estate scraper delivers 50+ data fields per property including MLS details, agent contacts, automated valuation model (AVM) estimates, photos, and open house schedules. Covers all 50 states across 7 listing types — for sale, for rent, sold, pending, and more — for underwriting, broker enrichment, and property-intelligence workflows.
Why Choose This Real Estate Scraper API
The Most Complete US Property Data Extraction Solution
🏠 7 Listing Types in One API Call Extract for_sale, for_rent, sold, pending, off_market, new_community, and ready_to_build listings simultaneously. No need to run multiple queries or manage separate tools per status.
💰 Full Pricing Intelligence Access list price, sold price, price per sqft, HOA fees, and AVM valuations (estimated value, high, and low) for every property. Essential for comps analysis, investment underwriting, and market benchmarking.
🧑💼 Agent Contact Data Each listing includes agent name, email, phone, office, and broker — plus co-agent details when available. Ready for CRM import, lead generation, and outreach automation.
📍 Flexible Location Search Query by ZIP code, city, city+state, full address, neighborhood, county, or full state name. Covers all 50 US states with no geographic restrictions.
🤖 AI-Ready JSON Output Structured data optimized for LangChain, CrewAI, and custom AI workflows. Ideal for property recommendation engines, automated valuation models, and real estate intelligence platforms.
📊 50+ Data Fields Per Property Extract comprehensive property intelligence including:
- Listing metadata (MLS ID, status, dates, flags)
- Pricing (list, sold, AVM estimate range, $/sqft, HOA)
- Physical details (beds, baths, sqft, lot, year built, garage, stories)
- Location (address, city, state, ZIP, county, FIPS, coordinates, neighborhoods)
- Agent & co-agent contacts (name, email, phone, office, broker)
- Media (primary photo, full photo gallery, open house schedule)
- Extended details (room dimensions, appliances, utilities, construction)
Why Choose This API
This actor is designed for structured output, repeatable automation, and downstream integration in monitoring, enrichment, or ingestion pipelines.
Quick Start Guide
How to Extract Property Data in 3 Steps
Step 1: Configure Your Search
Set your target location, listing type, and result volume. Use listed_since to filter by recency and property_type to narrow by property style.
Step 2: Run the Actor
Click ▷ Start and the scraper will extract all matching listings from Realtor.com with full field coverage.
Step 3: Download Your Data
Export results in JSON or CSV, or access via the Apify API. Each record includes all 50+ fields.
Input Parameters
Required Configuration Fields
| Parameter | Type | Description | Example Values |
|---|---|---|---|
location | string | US location to search | "Austin, TX", "10001", "Texas" |
listing_type | string | Listing status to target | "for_sale", "sold", "for_rent" |
max_results | integer | Maximum number of properties to return | 100, 1000, 10000 |
Optional Configuration Fields
| Parameter | Type | Description | Example Values |
|---|---|---|---|
listed_since | string | Filter by listing or sale date | "30 days", "1 year", "2024-01-01" |
property_type | array | Filter by property style (leave empty for all) | ["single_family", "condos"] |
Location Format Options
ZIP Code:
"10001"— Manhattan, New York"90210"— Beverly Hills, California
City / City + State:
"Austin"— all Austin metro listings"Austin, TX"— city with state code"Austin, Texas"— city with full state name
Full Address (single property lookup):
"350 5th Ave, New York, NY 10118"
Neighborhood / County / State:
"South Beach"— neighborhood search"Cook County"— county-level search"Texas"— full state (use full name, notTX)
Listing Type Options
| Value | Description |
|---|---|
for_sale | Active listings currently on the market |
for_rent | Active rental listings |
sold | Recently closed transactions |
pending | Under contract, awaiting closing |
off_market | Not actively listed |
new_community | New construction communities |
ready_to_build | Lots and land ready for development |
Date Format Options
Relative Timeframes (Recommended):
"7 days"— listed in the last week"30 days"— listed in the last month"1 year"— listed in the last year
Absolute Dates (YYYY-MM-DD):
"2024-06-01"— listed since June 1, 2024
Property Type Options
single_family, multi_family, apartment, condos, condo_townhome, condo_townhome_rowhome_coop, townhomes, duplex_triplex, farm, land, mobile
Example Input Configuration
{"location": "Austin, TX","listing_type": "for_sale","max_results": 1000,"listed_since": "90 days","property_type": ["single_family", "condos", "townhomes"]}
Output Data Schema
Complete Property Data Structure
Each extracted property contains 50+ fields organized into these categories:
Listing Metadata
| Field | Type | Description |
|---|---|---|
processor | string | Apify actor URL that processed this record |
processed_at | string | ISO 8601 UTC timestamp when scraped |
property_url | string | Full URL to the listing on Realtor.com |
property_id | string | Unique property identifier |
listing_id | string | Unique listing event identifier |
mls_id | string | MLS board code (e.g. NWMLS, CRMLS) |
mls_listing_id | string | Listing ID within the MLS board |
status | string | Listing status: for_sale, for_rent, sold, etc. |
mls_status | string | Exact MLS status string as reported by the board |
is_new_construction | boolean | True if newly built and never occupied |
is_contingent | boolean | True if under contract with contingencies |
is_pending | boolean | True if under contract pending closing |
Dates
| Field | Type | Description |
|---|---|---|
date_listed | string | Date first listed on the market |
date_pending | string | Date offer was accepted |
date_sold | string | Date sale was closed and recorded |
date_updated | string | Date listing data was last modified |
date_status_changed | string | Date and time of last status change |
Pricing & Valuation
| Field | Type | Description |
|---|---|---|
list_price | number | Current asking price in USD |
list_price_min | number | Minimum price for range-priced listings |
list_price_max | number | Maximum price for range-priced listings |
sold_price | number | Final recorded sale price in USD |
price_per_sqft | number | Listing price divided by living area (USD/sqft) |
hoa_fee | number | Monthly HOA fee in USD |
estimated_value | number | AVM best estimate in USD |
estimated_high | number | Upper bound of the AVM estimate range |
estimated_low | number | Lower bound of the AVM estimate range |
Property Details
| Field | Type | Description |
|---|---|---|
property_type | string | Style: single_family, condos, land, etc. |
beds | integer | Number of bedrooms |
baths_full | integer | Number of full bathrooms |
baths_half | integer | Number of half bathrooms |
sqft | integer | Interior living area in square feet |
lot_sqft | integer | Total lot area in square feet |
year_built | integer | Year originally constructed |
garage_spaces | integer | Enclosed garage parking spaces |
stories | integer | Number of above-ground floors |
Location
| Field | Type | Description |
|---|---|---|
address | string | Street address line |
unit | string | Apartment or unit number |
city | string | City or municipality |
state | string | Two-letter US state code |
zip_code | string | Five-digit US postal ZIP code |
county | string | County or parish name |
fips_code | string | Federal FIPS county code |
latitude | number | WGS 84 decimal latitude |
longitude | number | WGS 84 decimal longitude |
neighborhoods | array | Neighborhood or subdivision names |
Agent & Contact Data
| Field | Type | Description |
|---|---|---|
agent_name | string | Full name of the listing agent |
agent_email | string | Contact email for the listing agent |
agent_phone | string | Contact phone for the listing agent |
agent_office | string | Real estate office or brokerage name |
agent_broker | string | Managing broker name |
co_agent_name | string | Co-listing agent full name |
co_agent_phone | string | Co-listing agent phone |
Media & Extended Data
| Field | Type | Description |
|---|---|---|
primary_photo | string | Cover photo URL from the listing gallery |
photos | array | All photo URLs from the listing gallery |
open_houses | array | Scheduled open house events with dates and times |
details | array | Full property attributes grouped by category |
description | string | Full marketing description text |
tags | array | Amenity and feature tags |
pets_dogs | boolean | True if dogs are permitted |
pets_cats | boolean | True if cats are permitted |
tax_record_id | string | Public tax record identifier |
Example JSON Output
{"processor": "https://apify.com/agentx/realtor-property-scraper?fpr=aiagentapi","processed_at": "2026-04-07T10:30:00+00:00","property_url": "https://www.realtor.com/realestateandhomes-detail/123-Main-St_Austin_TX_78701","property_id": "M9234718495","listing_id": "2961478523","mls_id": "ABOR","mls_listing_id": "H6298471","status": "for_sale","mls_status": "Active","is_new_construction": false,"is_contingent": false,"is_pending": false,"date_listed": "2026-01-15","date_sold": null,"list_price": 875000,"sold_price": null,"price_per_sqft": 412,"hoa_fee": 250,"estimated_value": 860000,"estimated_high": 920000,"estimated_low": 800000,"property_type": "single_family","beds": 4,"baths_full": 3,"baths_half": 1,"sqft": 2124,"lot_sqft": 7200,"year_built": 2019,"garage_spaces": 2,"stories": 2,"address": "123 Main St","unit": null,"city": "Austin","state": "TX","zip_code": "78701","county": "Travis","fips_code": "48453","latitude": 30.2672,"longitude": -97.7431,"neighborhoods": ["Downtown Austin", "Rainey Street"],"agent_name": "Sarah Chen","agent_email": "sarah.chen@austinrealty.com","agent_phone": "(512) 555-0198","agent_office": "Austin Premier Realty","agent_broker": "Texas Realty Group","co_agent_name": null,"co_agent_phone": null,"primary_photo": "https://photos.rdc.com/property/M9234718495/cover.jpg","photos": ["https://photos.rdc.com/property/M9234718495/1.jpg"],"open_houses": [{ "start_date": "2026-04-12T13:00:00", "end_date": "2026-04-12T15:00:00" }],"tax_record_id": "48453-123-0042"}
Integration Examples
Python Integration Example
from apify_client import ApifyClientclient = ApifyClient("YOUR_API_TOKEN")run_input = {"location": "Austin, TX","listing_type": "for_sale","max_results": 1000,"listed_since": "90 days","property_type": ["single_family", "condos"]}run = client.actor("eou9sUeDcGLZomwXL").call(run_input=run_input)for item in client.dataset(run["defaultDatasetId"]).iterate_items():print(item)
JavaScript/Node.js Integration
import { ApifyClient } from "apify-client";const client = new ApifyClient({ token: "YOUR_API_TOKEN" });const input = {location: "Austin, TX",listing_type: "for_sale",max_results: 1000,listed_since: "90 days",property_type: ["single_family", "condos"],};const run = await client.actor("eou9sUeDcGLZomwXL").call(input);const { items } = await client.dataset(run.defaultDatasetId).listItems();items.forEach((item) => console.log(item));
Actor ID for Platform Integration
eou9sUeDcGLZomwXL
Ⓜ️ Make.com Setup:
- Login to Make.com (Get 1000 Free Credits)
- Add module "Run an Actor"
- Turn 'Map' on - right side of the 'Actor*'
- Paste Actor ID - from above
- Click the '⟳ Refresh' - left side of Map
- Input JSON* - Modify the parameters as needed
- Set "Run synchronously" to YES
- Add module "Get Dataset Items" - receive the result
- In Dataset ID* select defaultDatasetId
🎱 N8N.io Setup:
- Add 'Run an Actor and get dataset' - from the apify node
- Actor → By ID → Paste Actor ID - from above
- Input JSON - Modify the parameters as needed
JSON-LD Metadata
{"@context": "https://schema.org","@type": "SoftwareApplication","name": "Realtor Property Scraper","description": "US real estate property data extraction API for Realtor.com listings across all 50 states","applicationCategory": "Developer Tools","operatingSystem": "Cloud","url": "https://apify.com/agentx/realtor-property-scraper?fpr=aiagentapi","dateModified": "2026-04-21","offers": {"@type": "Offer","price": "0.00189","priceCurrency": "USD"}}
Pricing & Cost Calculator
Transparent Pay-Per-Use Model
| Event Type | Price | Description |
|---|---|---|
| Actor Start | $0.01 | One-time charge per Actor run |
| Result | $0.0025 | Charged per property returned — price, address, status, and listing details |
Cost Examples
Small Scale (100 properties):
- Property Data: 100 × $0.0025 = $0.25
- Actor Start: $0.01
- Total: ~$0.26
Medium Scale (1,000 properties):
- Property Data: 1,000 × $0.0025 = $2.50
- Actor Start: $0.01
- Total: ~$2.51
Large Scale (10,000 properties):
- Property Data: 10,000 × $0.0025 = $25.00
- Actor Start: $0.01
- Total: ~$25.01
Use Cases & Applications
Real Estate Investment & Analysis
Market Comps & Valuation Pull sold listings and AVM estimates to build automated comparable sales analysis. Benchmark properties by $/sqft, year built, and lot size across any US market.
Investment Pipeline Screening Filter off-market, pending, and new construction listings across multiple markets in bulk. Combine price data with agent contacts for direct outreach to listing agents.
Rental Market Intelligence Extract for_rent listings across cities and neighborhoods to map rental rates by property type, bedroom count, and zip code. Identify undersupplied markets for investment targeting.
AI & Proptech Applications
Automated Valuation Models (AVM) Train or validate AVM models using real-time list price, sold price, and Realtor.com AVM estimates. Structure training data by property type, location, and physical attributes.
Property Recommendation Engines Build AI-powered search tools that match buyers and renters to properties based on structured preference data. JSON output is compatible with LangChain, CrewAI, and vector databases.
Market Trend Forecasting Aggregate listing velocity, price changes, and days-on-market across zip codes to build predictive models for price appreciation, inventory shifts, and market timing.
Lead Generation & CRM
Agent & Broker Lead Lists Every listing includes agent name, email, phone, office, and broker. Export directly to CRM for mortgage, title, insurance, and proptech outreach campaigns.
Open House Prospecting Filter upcoming open house events by geography and property type for targeted door-knocking, direct mail, and digital ad campaigns.
Data & Research
Academic & Policy Research Compile housing market datasets for neighborhood affordability studies, zoning impact analysis, and housing supply research. FIPS codes and coordinates enable GIS integration.
Journalism & Reporting Pull real-time market snapshots for housing affordability stories, neighborhood price trend reports, and regional market comparisons.
FAQ
What data source does this scraper use?
This actor extracts data from Realtor.com, one of the largest US real estate listing platforms, covering all 50 states and thousands of MLS boards.
How many properties can I scrape at once?
Up to 10,000 properties per run. For larger datasets, run multiple queries with different locations or listing types.
Does it cover all 50 US states?
Yes. Any valid US location — ZIP code, city, county, or state — is supported across all 50 states.
What listing types are available?
Seven types: for_sale, for_rent, sold, pending, off_market, new_community, ready_to_build.
Why are some agent email fields empty?
Agent email disclosure is optional. Many agents list only a phone number. Email availability varies by MLS board and agent preference.
What is the AVM estimate?
The estimated_value field is Realtor.com's automated valuation model (AVM) — a machine learning estimate of the property's current market value. estimated_high and estimated_low define the confidence range.
Why are some fields null?
Fields like sold_price, date_sold, and hoa_fee are only populated when applicable to the listing. For example, sold_price is null for active for_sale listings.
How fresh is the data?
Data is fetched live from Realtor.com at the time of each run. Use listed_since to filter by recency.
No properties found — what should I check?
- Verify the location format (city + state is most reliable)
- Try a broader location (state instead of neighborhood)
- Expand or remove the
listed_sincedate filter - Try a different
listing_type— some markets have few activefor_salebut manysoldrecords
The result count is lower than max_results — why?
Realtor.com may have fewer active listings than your cap for the given location and listing type. The scraper returns all available results up to the limit.
SEO Keywords & Search Terms
Primary Keywords
realtor property scraper API, realtor.com data extraction, US real estate scraper, MLS property data API, real estate listing extraction, property intelligence API
Long-Tail Keywords
extract realtor.com property listings with AVM valuation agent contacts API, US real estate data structured JSON pipeline, MLS property scraper 50 fields per listing
Industry Terms
automated valuation model, MLS data extraction, property intelligence, real estate lead generation, comps analysis, listing velocity, market benchmarking, proptech data pipeline
Trust & Certifications
- ✅ Enterprise Ready - Processing 10,000+ properties per run across all 50 US states
- ✅ GDPR Compliant - EU data protection standards
- ✅ 50+ Fields Per Property - Comprehensive MLS-level data coverage
- ✅ Regular Updates - Continuous maintenance for Realtor.com platform changes
Legal & Compliance
Data Rights & Usage
All data is extracted from publicly available Realtor.com property listings. Users are responsible for ensuring their use of extracted data complies with applicable laws and Realtor.com's Terms of Service.
Privacy Compliance
- GDPR: Compliant with EU General Data Protection Regulation. No personal data is stored beyond the extraction session.
- CCPA: Compliant with California Consumer Privacy Act requirements.
Platform Terms of Service
Users must review and comply with Realtor.com's Terms of Service regarding data extraction and usage.
Enterprise Support
For enterprise licensing, custom integrations, or compliance inquiries:
- Telegram: @AiAgentApi
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Support & Community
- 👥 Community: @Apify_Actor
- 👤 Contact Team: @AiAgentApi
Last Updated: April 21, 2026
