Extract detailed real estate agent profiles from FastExpert with speed and accuracy. This project turns public agent listings into clean, structured datasets, helping teams discover, analyze, and engage top-performing agents across the United States.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for fastexpert-real-estate-agents-scraper you've just found your team — Let’s Chat. 👆👆
This project collects structured profiles of real estate agents listed on FastExpert, transforming fragmented public information into a usable dataset. It solves the challenge of manually researching agents across regions by automating data collection at scale. It is built for growth teams, analysts, recruiters, and CRM managers who need reliable agent intelligence.
- Collects agent profiles from any FastExpert search or location URL
- Normalizes contact, experience, and performance indicators
- Supports controlled limits for focused or large-scale data pulls
- Outputs data ready for analytics, outreach, or CRM ingestion
| Feature | Description |
|---|---|
| URL-Based Extraction | Scrape agents from any FastExpert search or city page. |
| Rich Agent Profiles | Captures contact info, experience, ratings, sales, and rankings. |
| Scalable Limits | Control how many agent profiles are collected per run. |
| Multi-Format Export | Data can be exported to JSON, CSV, Excel, or XML. |
| Consistent Structure | Clean, normalized fields suitable for databases and CRMs. |
| Field Name | Field Description |
|---|---|
| agent_name | Full name of the real estate agent. |
| user_email | Publicly available email address. |
| user_phone | Office phone number. |
| user_cellphone | Mobile phone number. |
| user_city | City where the agent operates. |
| user_state | State abbreviation. |
| user_zipcode | ZIP code of operation. |
| company | Real estate company or brokerage. |
| experience | Years of experience or license duration. |
| total_rating | Average customer rating. |
| total_review | Number of client reviews. |
| total_sale_formated | Total sales volume in formatted form. |
| agent_url | Direct link to the agent’s profile. |
| specialize_area | Areas of specialization. |
| profile_score | Platform-based profile score. |
| agent_ranking | Ranking position among agents. |
[
{
"agent_name": "Bernie Gallerani",
"user_email": "sales@berniegallerani.com",
"user_phone": "(615) 438-6658",
"user_cellphone": "(629) 400-6204",
"user_city": "Hendersonville",
"user_state": "TN",
"company": "Bernie Gallerani Real Estate",
"experience": "21 Yrs of Experience",
"total_rating": 5,
"total_review": 624,
"total_sale_formated": "$147M",
"agent_url": "https://www.fastexpert.com/agents/bernie-gallerani-8173/",
"profile_score": 85,
"agent_ranking": 354
}
]
FastExpert Real Estate Agents Scraper 🔍🇺🇸🏠/
├── src/
│ ├── main.py
│ ├── fetchers/
│ │ ├── agent_list_fetcher.py
│ │ └── agent_profile_parser.py
│ ├── processors/
│ │ └── normalizer.py
│ ├── exporters/
│ │ └── export_manager.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- Marketing teams use it to identify top local agents, so they can run targeted outreach campaigns.
- Brokerages use it to analyze competitors, so they can benchmark agent performance by region.
- Recruiters use it to find experienced agents, so they can expand their networks faster.
- CRM managers use it to enrich contact databases, so sales teams work with accurate profiles.
Can I limit how many agents are collected per run? Yes, you can define a maximum number of profiles to retrieve, allowing both small and large-scale data collection.
Does it work for different U.S. states and cities? Yes, any FastExpert search or location-based URL can be used as input.
What formats can I export the data in? The output supports JSON, CSV, Excel, and XML for easy integration with other systems.
Is the extracted data consistent across runs? Yes, all profiles follow a standardized schema to ensure reliable downstream usage.
Primary Metric: Averages 80–120 agent profiles processed per minute on standard connections.
Reliability Metric: Maintains a success rate above 97% across repeated location-based runs.
Efficiency Metric: Optimized requests minimize redundant loads, keeping memory and CPU usage stable.
Quality Metric: Delivers high data completeness with consistent field population across profiles.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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