Realtor Property Scraper - US Real Estate Data API for MLS, Agent Contacts & AVM Valuations

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.

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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)

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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.

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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, not TX)

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"]
}

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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",
  "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"
}

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Integration Examples

Python Integration

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("agentx/realtor-property-scraper").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("agentx/realtor-property-scraper").call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

items.forEach((item) => console.log(item));

Make.com Integration (No-Code)

1. Add Module: "Run an Actor"
2. Enable Map: Turn on 'Map' next to 'Actor'
3. Enter: agentx/realtor-property-scraper
4. Refresh: Click '⟳ Refresh'
5. Configure Input: Set location, listing_type, max_results
6. Set Synchronous: "Run synchronously" = YES
7. Add Output Module: "Get Dataset Items" → select defaultDatasetId

n8n Integration

1. Add Node: 'Run an Actor and get dataset' from Apify node
2. Select By ID: Actor → By ID
3. Enter: agentx/realtor-property-scraper
4. Configure Input: Set your search parameters

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Pricing & Cost Calculator

Transparent Pay-Per-Use Model

Event Type	Price	Description
Actor Usage	$0.00001	Charged for Actor runtime based on resource consumption
Property	$0.00189	Charged per property returned — price, address, status, and listing details

Cost Examples

Small Scale (100 properties):

• Property Data: 100 × $0.00189 = $0.19
• Actor Usage: ~$0.01
• Total: ~$0.20

Medium Scale (1,000 properties):

• Property Data: 1,000 × $0.00189 = $1.89
• Actor Usage: ~$0.03
• Total: ~$1.92

Large Scale (10,000 properties):

• Property Data: 10,000 × $0.00189 = $18.90
• Actor Usage: ~$0.15
• Total: ~$19.05

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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.

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FAQ

General Questions

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.

Data Questions

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.

Troubleshooting

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.

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Support & Community

• 👥 Community: @Apify_Actor
• 👤 Contact Team: @AiAgentApi

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Version: 1.0.0 | Coverage: All 50 US States | Listing Types: 7 | Fields Per Property: 50+