NAR Directory Member Scraper

NAR Directory Member Scraper extracts detailed realtor profile information from the National Association of Realtors directory. It helps professionals collect verified agent contact and office details efficiently without manual lookup, saving time and improving data accuracy.

     

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Introduction

This project collects structured profile data of individual real estate agents listed in the NAR member directory. It solves the challenge of manually gathering reliable agent contact information by automating profile-level data extraction. It is designed for real estate professionals, data analysts, marketers, and research teams who need accurate agent intelligence.

Realtor Profile Data Extraction

• Works on individual member profile URLs
• Retrieves both personal and office-level information
• Captures contact details without authentication
• Outputs clean, structured, analysis-ready data
• Suitable for lead generation and directory research

Features

Feature	Description
Profile-Level Scraping	Extracts detailed information from individual member profiles.
Contact Data Access	Collects business emails and phone numbers when available.
Office Information Mapping	Retrieves office name, address, and contact details.
Structured Output	Returns normalized, machine-readable data for easy reuse.
URL-Based Targeting	Scrapes only specified member profiles for precision.

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What Data This Scraper Extracts

Field Name	Field Description
personId	Unique identifier of the member profile.
firstName	Realtor’s first name.
lastName	Realtor’s last name.
memberTypeCode	Membership classification code.
memberTypeDescription	Human-readable membership type.
businessEmailAddress	Business email associated with the agent.
preferredPhone	Primary contact phone number.
localAssociationName	Name of the local realtor association.
stateAssociationName	Name of the state-level association.
primaryFieldOfBusinessName	Main business specialization.
officeBusinessName	Office or brokerage name.
officeEmailAddress	Office contact email.
officePhoneNumber	Office phone number.
streetAddressLine1	Office street address.
streetCity	Office city.
streetState	Office state.
streetZip	Office ZIP code.

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Example Output

[
      {
        "PersonId": 4575908,
        "FirstName": "Aaliyah",
        "LastName": "Aguero",
        "MiddleName": null,
        "MemberTypeCode": "R",
        "MemberTypeCodeDescription": "REALTOR®",
        "WebPageAddress": null,
        "BusinessEmailAddress": "aaliyaha.xxxxxx@gmail.com",
        "PreferredPhoneType": "O",
        "PreferredPhone": "832646xxxx",
        "PrimaryLocalAssociationId": 8055,
        "LocalAssociationName": "HOUSTON ASSOCIATION OF REALTORS® INC",
        "PrimaryStateAssociationId": 886,
        "StateAssociationName": "TEXAS ASSOCIATION OF REALTORS®",
        "PrimaryFieldOfBusinessId": 100,
        "PrimaryFieldOfBusinessName": "General Residential Sales",
        "OfficeId": 805513590,
        "OfficeBusinessName": "C.R.Realty",
        "OfficeEmailAddress": "cr@crrealtyco.com",
        "OfficePhoneNumber": "8326460512",
        "StreetAddressLine1": "5604 1st St. suite 101",
        "StreetCity": "Katy",
        "StreetState": "TX",
        "StreetZip": "77493"
      }
    ]

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Directory Structure Tree

NAR Directory Member Scraper/
├── src/
│   ├── main.py
│   ├── scraper/
│   │   ├── member_parser.py
│   │   └── profile_fetcher.py
│   ├── utils/
│   │   ├── validators.py
│   │   └── normalizers.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── input_urls.txt
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Real estate marketers use it to collect agent emails, so they can run targeted outreach campaigns.
• Brokerage analysts use it to map agent-office relationships, so they can evaluate market coverage.
• Lead generation teams use it to build verified contact lists, so they can improve conversion rates.
• Researchers use it to analyze membership distribution, so they can identify regional trends.

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FAQs

Do I need an account to use this scraper? No, it works directly with publicly accessible member profile URLs.

Can I scrape multiple profiles at once? Yes, you can provide multiple profile URLs to process them in a single run.

Is sensitive data included in the output? Only publicly available profile information is collected, and outputs can be filtered further if needed.

What happens if a profile field is missing? Missing fields are returned as null to maintain a consistent data structure.

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Performance Benchmarks and Results

Primary Metric: Processes an average member profile in under 2 seconds.

Reliability Metric: Maintains a success rate above 98% on valid profile URLs.

Efficiency Metric: Capable of handling hundreds of profiles per hour with minimal resource usage.

Quality Metric: Delivers highly complete records with consistent field normalization across profiles.

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