The investment research team at a multi-fund real estate investment manager stitched together six licensed data sources by hand every quarter to compare markets, and the largest macroeconomic export was about 680 MB, far too big to open in Excel.
Acquisitions needed a one-page market snapshot for every investment committee memo, assembled each time from macro data, corporate headquarters counts and employer lists. The student-housing team had no systematic way to rank university markets. All of it was slow, hard to repeat, and lived on whoever built it last.
I built three internal sites, each hosted behind the company's single sign-on, each rebuilt automatically from a Python pipeline. One trims the 680 MB macro export to about 15 MB and shows market momentum across the 40 top markets for each of four property types on a US map. One generates a print-ready market snapshot for any of roughly 370 US metros against the national benchmark, ready to drop into an IC memo. One scores 74 university student-housing markets on nine weighted factors and ranks them. Each site has an owner on the research team and a written quarterly runbook, so they refresh the data themselves with Claude Code; a rebuild takes two to three minutes. The firm's own summary of the pilot: 700 MB of macro data visualized in minutes.
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