Python for real estate — MLS scraping, valuation models, market analytics
The agent who spots the pricing gap 48 hours before Zillow updates wins the listing. Python spots it.
Real estate is a public-data + rules-of-thumb game — and public data is the exact shape pandas is built for. The agent who joins county tax records × MLS × permit filings × walkability API × school ratings and pipes it into a weekly Slack alert isn't relying on Zillow's algorithm; they're building the private-signal version their brokerage never buys. It's one weekend of scraping + two weekends of pandas.
Automation track (54 lessons): BeautifulSoup + requests for MLS/Redfin/tax-portal scraping, gspread for shared team sheets, openpyxl for the client-facing report. Data Science track (104 lessons): pandas joins across sources, scipy.stats for comp-set outlier detection, matplotlib for the CMA deck. First 15 free.