Real Estate Web Scraper Required
Budget: $30 – $250 USD
Web Scraper Needed – Extract Realtor Contact Info from Realtor.com
Job Description:
I’m looking for an experienced web scraper to extract real estate agent contact information from Realtor.com and compile it into a clean, structured CSV file. This will be used for a high-volume outreach campaign, so accuracy and efficiency are key.
Data Required:
Agent Name
Phone Number
Email (if available)
Company Name
City/State
Project Scope:
Extract at least 10,000 realtor contacts
Deliver data in CSV format
Ensure deduplication (no repeated contacts)
Scrape efficiently without getting blocked
Ideal Candidate:
Expert in web scraping (Python, Selenium, BeautifulSoup, Scrapy, or similar tools)
Experience with Realtor.com or similar real estate platforms
Has completed similar projects at scale
Delivers clean, organized data
Deadline: ASAP (within a few days preferred)
To apply, please provide:
1. Examples of past web scraping projects
2. Your approach to bypassing rate limits (if applicable)
3. Estimated turnaround time
If you do this well, there will be ongoing work for even larger datasets.
Job Description:
I’m looking for an experienced web scraper to extract real estate agent contact information from Realtor.com and compile it into a clean, structured CSV file. This will be used for a high-volume outreach campaign, so accuracy and efficiency are key.
Data Required:
Agent Name
Phone Number
Email (if available)
Company Name
City/State
Project Scope:
Extract at least 10,000 realtor contacts
Deliver data in CSV format
Ensure deduplication (no repeated contacts)
Scrape efficiently without getting blocked
Ideal Candidate:
Expert in web scraping (Python, Selenium, BeautifulSoup, Scrapy, or similar tools)
Experience with Realtor.com or similar real estate platforms
Has completed similar projects at scale
Delivers clean, organized data
Deadline: ASAP (within a few days preferred)
To apply, please provide:
1. Examples of past web scraping projects
2. Your approach to bypassing rate limits (if applicable)
3. Estimated turnaround time
If you do this well, there will be ongoing work for even larger datasets.