How We Built an AI Property Valuation MVP in 8 Weeks

A Proptech startup came to us with a seed-round deadline and a hypothesis: automated property valuation could differentiate their platform if it worked on real Dutch housing data. They had domain experts but no ML engineering capacity. We scoped an 8-week agency engagement with a fixed milestone plan.

Week 1–2: Discovery and data audit

We started by validating data availability — cadastral references, transaction history, and property attributes. The Fractional CTO on the engagement mapped a minimal feature set: batch valuation for residential units, confidence intervals, and an admin review queue for edge cases. We killed nice-to-haves early (computer vision on photos, multi-country support) to protect the timeline.

Team composition

• 1 ML engineer (PyTorch, feature engineering)
• 1 backend engineer (FastAPI, PostgreSQL)
• 1 frontend engineer (React admin UI)
• Fractional CTO (architecture + weekly reviews)

Week 3–5: Model v1 and API

The ML engineer trained a gradient-boosted baseline before any deep learning — faster iteration, explainable features for compliance conversations. We exposed predictions via a versioned REST API with audit logging. The backend engineer built idempotent ingestion jobs so the client could refresh training data weekly without manual ops.

Week 6–8: Production hardening

We added monitoring (latency, drift indicators, error budgets), documentation for model retraining, and a handover session with the client's internal team. The MVP launched with 92% accuracy on held-out Amsterdam postcodes — sufficient for investor demos and five pilot customers.

Lessons learned

Fixed scope worked because discovery was ruthlessly prioritized. A Fractional CTO prevented over-engineering. If we repeated the project, we'd start drift monitoring one week earlier — the client asked for it within days of launch.