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Realtor Property Scraper

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Realtor Property Scraper

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

AgentX

Maintained by Community

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.

For Sale For Rent Sold All 50 States 50+ Fields


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

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

ParameterTypeDescriptionExample Values
locationstringUS location to search"Austin, TX", "10001", "Texas"
listing_typestringListing status to target"for_sale", "sold", "for_rent"
max_resultsintegerMaximum number of properties to return100, 1000, 10000

Optional Configuration Fields

ParameterTypeDescriptionExample Values
listed_sincestringFilter by listing or sale date"30 days", "1 year", "2024-01-01"
property_typearrayFilter 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, not TX)

Listing Type Options

ValueDescription
for_saleActive listings currently on the market
for_rentActive rental listings
soldRecently closed transactions
pendingUnder contract, awaiting closing
off_marketNot actively listed
new_communityNew construction communities
ready_to_buildLots 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

FieldTypeDescription
processorstringApify actor URL that processed this record
processed_atstringISO 8601 UTC timestamp when scraped
property_urlstringFull URL to the listing on Realtor.com
property_idstringUnique property identifier
listing_idstringUnique listing event identifier
mls_idstringMLS board code (e.g. NWMLS, CRMLS)
mls_listing_idstringListing ID within the MLS board
statusstringListing status: for_sale, for_rent, sold, etc.
mls_statusstringExact MLS status string as reported by the board
is_new_constructionbooleanTrue if newly built and never occupied
is_contingentbooleanTrue if under contract with contingencies
is_pendingbooleanTrue if under contract pending closing

Dates

FieldTypeDescription
date_listedstringDate first listed on the market
date_pendingstringDate offer was accepted
date_soldstringDate sale was closed and recorded
date_updatedstringDate listing data was last modified
date_status_changedstringDate and time of last status change

Pricing & Valuation

FieldTypeDescription
list_pricenumberCurrent asking price in USD
list_price_minnumberMinimum price for range-priced listings
list_price_maxnumberMaximum price for range-priced listings
sold_pricenumberFinal recorded sale price in USD
price_per_sqftnumberListing price divided by living area (USD/sqft)
hoa_feenumberMonthly HOA fee in USD
estimated_valuenumberAVM best estimate in USD
estimated_highnumberUpper bound of the AVM estimate range
estimated_lownumberLower bound of the AVM estimate range

Property Details

FieldTypeDescription
property_typestringStyle: single_family, condos, land, etc.
bedsintegerNumber of bedrooms
baths_fullintegerNumber of full bathrooms
baths_halfintegerNumber of half bathrooms
sqftintegerInterior living area in square feet
lot_sqftintegerTotal lot area in square feet
year_builtintegerYear originally constructed
garage_spacesintegerEnclosed garage parking spaces
storiesintegerNumber of above-ground floors

Location

FieldTypeDescription
addressstringStreet address line
unitstringApartment or unit number
citystringCity or municipality
statestringTwo-letter US state code
zip_codestringFive-digit US postal ZIP code
countystringCounty or parish name
fips_codestringFederal FIPS county code
latitudenumberWGS 84 decimal latitude
longitudenumberWGS 84 decimal longitude
neighborhoodsarrayNeighborhood or subdivision names

Agent & Contact Data

FieldTypeDescription
agent_namestringFull name of the listing agent
agent_emailstringContact email for the listing agent
agent_phonestringContact phone for the listing agent
agent_officestringReal estate office or brokerage name
agent_brokerstringManaging broker name
co_agent_namestringCo-listing agent full name
co_agent_phonestringCo-listing agent phone

Media & Extended Data

FieldTypeDescription
primary_photostringCover photo URL from the listing gallery
photosarrayAll photo URLs from the listing gallery
open_housesarrayScheduled open house events with dates and times
detailsarrayFull property attributes grouped by category
descriptionstringFull marketing description text
tagsarrayAmenity and feature tags
pets_dogsbooleanTrue if dogs are permitted
pets_catsbooleanTrue if cats are permitted
tax_record_idstringPublic 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 ApifyClient
client = 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:

  1. Login to Make.com (Get 1000 Free Credits)
  2. Add module "Run an Actor"
  3. Turn 'Map' on - right side of the 'Actor*'
  4. Paste Actor ID - from above
  5. Click the '⟳ Refresh' - left side of Map
  6. Input JSON* - Modify the parameters as needed
  7. Set "Run synchronously" to YES
  8. Add module "Get Dataset Items" - receive the result
  9. In Dataset ID* select defaultDatasetId

🎱 N8N.io Setup:

  1. Add 'Run an Actor and get dataset' - from the apify node
  2. ActorBy IDPaste Actor ID - from above
  3. 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 TypePriceDescription
Actor Start$0.01One-time charge per Actor run
Result$0.0025Charged 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_since date filter
  • Try a different listing_type — some markets have few active for_sale but many sold records

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

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:


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Last Updated: April 21, 2026