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Freelance Job Description – Canadian Real Estate Data Scraping & Processing

Budget: $60.0 FIXED / ⭐ 5.00 (2) TWN

data-scraping

Job Title: Canadian Real Estate Website Data Scraping & Processing (Python / Web Scraping) Background: We need historical residential sales data for three major Canadian cities (Toronto / Vancouver / Calgary), covering Q1 2020 to Q4 2025 (24 quarters). The data source is a major Canadian real estate listings website (we'd prefer not to disclose the specific site publicly — happy to share it directly with the freelancer after hiring). Scope of Work: Scrape historical sold residential property data from the specified real estate website, covering both Condo (Strata) and Freehold properties. Retain the following fields only (all others can be discarded): Address reference (address navigation) Sold price Property area/size (used to calculate CAD/sqft) Sale/closing date (used to derive quarter) Latitude/longitude (used to derive FSA — the first 3 characters of the postal code, similar to a UK outcode) Municipality/city name Property type (to distinguish Condo vs Freehold) MLS number (to prevent duplicate records) Organize the output into Excel/CSV, grouped by FSA and quarter. Important note: property size precision varies by data source — some sources provide exact figures (e.g. 1,523 sqft), while others only give a range (e.g. 1,500–2,000 sqft). This needs to be verified item-by-item after scraping, and any range-based entries should be flagged in the final deliverable. Other fields (e.g. property tax, basement status, parking, etc.) may also have inconsistent formats across sources — please note any such discrepancies as well. Requirements: Proficiency in Python (requests / BeautifulSoup / Selenium or similar tools) or other web scraping techniques Experience handling large volumes of structured/semi-structured data (Excel, Pandas) Detail-oriented, comfortable dealing with inconsistent data sources Prior experience with real estate/property data a plus Deliverable: Excel or CSV file organized per the fields above, with brief notes on data source/precision issues.
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