AI Real Estate Arbitrage Analyzer

I want to bring together the scattered information on U-S residential, commercial, and land listings into a single, smart application that flags instant profit opportunities. The core of the build is an AI engine that cross-checks fresh data from MLS databases, leading real-estate websites, and relevant public records, then benchmarks each listing against the average market price, historical sale prices, and true comparable property prices. Here is what I need delivered: • A data-ingestion pipeline that pulls and normalizes feeds or scraped data from the three sources above, updating at least daily. • An algorithm (Python preferred, but I’m open) that scores each property for potential arbitrage by measuring the price gap between its asking price and the composite “fair value” you derive from the three key metrics. • A lightweight web dashboard that lets me filter by location, asset class, and gap size, and then view supporting comps and historical charts. • Clear documentation of data sources, model assumptions, and how to retrain or fine-tune the model as additional data comes in. Acceptance will be based on the dashboard correctly surfacing at least ten demonstrable price-gap opportunities in a pilot market of my choosing and showing the calculation steps behind each score.