About

We are looking for a skilled Python developer to create a script that will scrape current MLS (Multiple Listing Service) property listings. The script should efficiently extract all relevant property details and output them into a well-structured CSV file for easy analysis and usage. Familiarity with web scraping libraries such as Beautiful Soup or Scrapy, as well as experience working with APIs, is essential. If you have a strong attention to detail and a passion for data extraction, we would love to hear from you! Project Type: One-time project (with potential for future enhancements) Skills Required: Python, Web Scraping, BeautifulSoup / Selenium / Scrapy, Data Parsing, CSV generation, API integration (optional), Real Estate data familiarity ⸻ Project Overview I’m looking for a developer who can build a Python-based web scraper that collects all current MLS real estate listings from a specified region or MLS-accessible website and compiles the data into a clean, structured CSV file. This script will be run manually from a test environment (local machine or cloud VM). It doesn’t need to be deployed as a production system—just a reliable script that outputs complete listing data on demand. ⸻ What the Script Should Do • Scrape all active / current MLS listings from the target source. • Collect all key data fields from each listing, including but not limited to: • Listing address • Listing description • Days on Market (DOM) • Number of units (if multi-family) • Lot size • Building square footage • Beds & baths • Year built • Listing price • Property type • Any agent/office fields • Any additional structured data included on the listing page • Save all results into a CSV file with one row per property. ⸻ Technical Requirements • Script must be written in Python. • Use a reputable scraping framework such as: • Scrapy • BeautifulSoup + Requests • Selenium (only if necessary for dynamic content) • Output format must be a CSV with consistent column headings. • Code should be well-structured, commented, and easy for a non-developer to run (e.g., python scraper.py). • Script should gracefully handle pagination, rate limits, dynamic content, and errors. • Should include a configuration section for: • Target URL(s) • Any login credentials or tokens, if applicable • File output name ⸻ Important Notes • Developer must ensure scraping follows terms of service, uses allowed endpoints, or utilizes any available APIs. If certain MLS data cannot legally be scraped, I’m open to: • API-based retrieval • RETS/RESO Web API integration • A hybrid approach (Please mention your familiarity with MLS data access.) ⸻ What You Should Include in Your Proposal • Past experience with web scraping (especially real estate data is a big plus). • Your preferred scraping framework and why. • Estimated timeline for delivering: • First working prototype • Final polished script • Any assumptions or data access requirements you foresee. ⸻ Deliverables 1. Fully functional Python scraper script. 2. Clean CSV output containing all listing fields. 3. Brief readme or usage notes on how to run the script. 4. Optional: virtual environment or requirements.txt file.