I am building a real estate “market radar” for Uruguay that identifies undervalued properties and potentially motivated sellers. The goal of this project is to create a data workflow that automatically collects property listings from real estate portals and organizes the data so it can be analyzed in Excel. Primary data sources: • MercadoLibre Inmuebles (primary source) • InfoCasas Uruguay (secondary source, optional) No public records or auction data are required at this stage. PROJECT SCOPE The system should automatically collect property listings and store the data in a structured dataset. The scraper should run once per day and collect the following fields: • listing ID or unique identifier • property price • property location or neighborhood • property size (m²) • property type • number of bedrooms (if available) • listing URL • original publication date The dataset should avoid duplicates and track listings over time. MARKET ANALYSIS FEATURES The system should allow simple market analysis including: • price per square meter (price / m²) • comparison to neighborhood median price • filtering by property type, price range and neighborhood MOTIVATED SELLER DETECTION A key feature of this project is detecting listings that may indicate motivated sellers. The system should track historical data for each listing including: • first date detected • days on market • price changes over time The dataset should allow identification of: • listings with price reductions • listings with multiple price reductions • listings active for more than 60–90 days These indicators will help detect potential negotiation opportunities. DELIVERABLES 1. A working scraper (Python or similar tool). 2. A structured dataset stored in Excel, Google Sheets, or CSV format. 3. An Excel dashboard or structured sheet that allows quick filtering and analysis of the listings. 4. Clear instructions so the scraper can be run on my computer. TECHNICAL APPROACH Preferred tools include: Python, Selenium, BeautifulSoup, Scrapy, Apify, or similar scraping tools. The developer may propose the most stable solution. ACCEPTANCE CRITERIA • The scraper can run automatically once per day. • The dataset correctly tracks listings over time and avoids duplicates. • Price changes and days on market can be calculated. • The Excel file allows quick identification of potential opportunities. BUDGET USD 400 – 800 depending on experience and implementation. QUESTION FOR APPLICANTS Please include in your proposal: 1. Examples of previous web scraping projects 2. The technology you would use 3. How you would handle website structure changes and anti-scraping measures