Realtor.com Scraper by Location

Realtor.com Scraper by Location is a focused data extraction tool designed to collect structured real estate listing information from Realtor.com using location-based queries. It helps teams gather reliable property and seller details at scale, enabling faster analysis and outreach using clean, consistent data.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for realtor-com-scraper-by-location you've just found your team — Let’s Chat. 👆👆

Introduction

This project extracts real estate listings and associated seller contact details based on a user-defined location such as ZIP code, city, or state. It solves the challenge of manually browsing and compiling property data by delivering structured outputs ready for analysis or integration. It is built for real estate professionals, data analysts, and growth teams who need location-specific property intelligence.

Location-Based Real Estate Data Collection

• Searches listings using ZIP codes or city/state combinations
• Collects both property attributes and seller contact information
• Produces structured, analysis-ready records
• Supports scalable data collection across multiple locations

Features

Feature	Description
Location Search	Extracts listings by ZIP code, city, or state input.
Detailed Listings	Captures price, size, status, and property metadata.
Seller Contacts	Includes publicly available seller phone and email details.
Media Links	Collects primary photos and street view URLs.
Structured Output	Returns clean, consistent records for easy processing.

---

What Data This Scraper Extracts

Field Name	Field Description
property_id	Unique identifier for the property listing.
listing_price	Current listed sale price of the property.
street	Street address of the property.
city	City where the property is located.
state	State abbreviation of the listing location.
zip_code	ZIP or postal code of the property.
beds	Number of bedrooms available.
baths	Number of bathrooms available.
sqft	Interior living area in square feet.
lot_sqft	Total lot size in square feet.
year_built	Year the property was constructed, if available.
status	Current listing status such as for_sale.
listing_date	Date the property was listed.
primary_photo	URL of the main property image.
street_view_url	Map-based street view image URL.
seller_name	Name of the listing agent or seller.
office_name	Brokerage or office managing the listing.
seller_phone	Public contact phone number.
seller_email	Public contact email address.

---

Example Output

[
    {
        "Property ID": "9955518548",
        "Listing Price": 2100000,
        "Street": "121 S Palm Dr",
        "City": "Beverly Hills",
        "State": "CA",
        "ZIP Code": "90212",
        "Beds": 4,
        "Baths": "4",
        "Sqft": 2113,
        "Lot Sqft": 18215,
        "Year Built": "",
        "Status": "for_sale",
        "Listing Date": "2024-07-17T20:03:18.000000Z",
        "Primary Photo": "http://ap.rdcpix.com/c2949b2276976fd2b76d92a78bb7166al-m164859860s.jpg",
        "Street View URL": "https://maps.googleapis.com/maps/api/streetview?location=121%20S%20Palm%20Dr%2C%20Beverly%20Hills%2C%20CA%2090212",
        "Seller Name": "Stex Z",
        "Office Name": "Nest Seekers Beverly Hills",
        "Seller Phone": "(323) 354-7913",
        "Seller Email": "sx@x.com"
    }
]

---

Directory Structure Tree

Realtor.com Scraper by Location (IMPORTANT :!! always keep this name as the name of the apify actor !!! Realtor.com Scraper by Location )/
├── src/
│   ├── main.py
│   ├── collectors/
│   │   ├── listings_parser.py
│   │   └── contact_extractor.py
│   ├── utils/
│   │   ├── location_normalizer.py
│   │   └── validators.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── input.sample.json
│   └── output.sample.json
├── requirements.txt
└── README.md

---

Use Cases

• Real estate investors use it to analyze listings by location, so they can identify high-value opportunities faster.
• Brokerage teams use it to collect seller contact details, so they can streamline outreach campaigns.
• Market analysts use it to study pricing trends, so they can generate accurate market insights.
• Lead generation teams use it to build targeted property datasets, so they can improve conversion rates.

---

FAQs

What locations are supported for searches? You can search using ZIP codes, city names, or city and state combinations, allowing flexible geographic targeting.

Does it include seller contact information? Yes, it extracts publicly available seller phone numbers and email addresses when present in the listing.

Is the data structured for easy integration? All outputs follow a consistent structure, making them suitable for databases, CRMs, or analytics pipelines.

Are there limits on the number of results? Trial usage may return a limited number of records, while full usage supports larger datasets depending on configuration.

---

Performance Benchmarks and Results

Primary Metric: Processes hundreds of listings per location query with consistent field coverage.

Reliability Metric: Maintains a high success rate across common residential listing pages.

Efficiency Metric: Optimized location-based queries reduce redundant requests and improve throughput.

Quality Metric: Delivers high data completeness with accurate property and seller details across listings.

"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington Marketer ★★★★★

"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza SEO Affiliate Expert ★★★★★

"Exceptional results, clear communication, and flawless delivery. Bitbash nailed it." Syed Digital Strategist ★★★★★