The Challenge
The most valuable real-estate signals are buried in the messiest data. Building permits, zoning changes, and code-enforcement records are filed across dozens of municipal systems — each with its own format, portal, and lag — and almost never connected to live listing data. By the time an off-market teardown or value-add opportunity shows up in a normal feed, the developers who watch permits already know.
The hard part isn't the model; it's making the underlying data usable at all: fragmented, civic, unstructured, and constantly changing.
What We Built
AddressIntel is a live real-estate intelligence platform covering the San Francisco Peninsula and Nantucket Island. A Python ingestion fleet, orchestrated on GitHub Actions, continuously pulls municipal permits and public listing data; the data is normalized, fused, and scored, then served through a B2B GraphQL API. It runs on a hybrid public-benefit model — a free public transparency layer over neighborhood development trends, funded by a monetized enterprise API for developers and institutional investors.
Predictive scoring & multimodal AI
- Teardown & flippability models: proprietary probability scores for teardown, flippability, and bidding-war likelihood, trained on fused permit + sales history.
- Vision-based condition scoring: Google Gemini reads listing photos to score interior/exterior property condition automatically — turning unstructured images into a model feature.
- Permit alpha: joining civic permit filings to listings surfaces off-market developer activity before it becomes visible in standard feeds.
Architecture
- Pipeline: a Python ingestion fleet on GitHub Actions cron, normalizing multi-source civic and listing data, with Gemini handling unstructured vision and NLP.
- Backend: Firebase Data Connect (GraphQL over Cloud SQL Postgres) with Firebase Auth; Stripe-metered access for enterprise clients.
- Frontend: Next.js (App Router) + React on Firebase App Hosting, with Sentry edge error tracking.
What It Shows
Turning messy, fragmented public records into a predictive product — and crossing domains, from data science into real estate, with point-in-time ML discipline so the scores reflect what was knowable at the time, not hindsight. It's the same through-line as ClearTrace, applied to civic rather than on-chain data.
What It Proves to a Client
That we can find signal in a data-rich space incumbents underuse, and turn it into a monetizable product — pipeline, models, API, and billing included. The same capability transfers to any business sitting on fragmented or underused data: marketplaces, fintech, insurance, logistics.