Property Valuation Engine

A real estate investment firm evaluating 50+ properties monthly relies on broker opinions and manual comp analysis. An analyst spends 2-3 hours per property: pulling comps, adjusting for differences, analyzing trends, producing a memo.

HouseCanary's CanaryAI provides automated valuations with sub-3% error rates across 136 million properties. But AVMs are black boxes — they produce a number without the transparent comparable analysis investors need for underwriting and lender presentations.

The gap is a defensible valuation narrative: comps selected with rationale, adjustments with basis, and market trend impact on the conclusion.

The agent generates valuations using transparent methodology. It pulls comparable sales from MLS and public records, selecting based on proximity, recency, similarity, and market relevance. Every selection is explained: why this comp, what adjustments, and the basis for each.

Adjustments use hedonic pricing models trained on local data: per-square-foot, pool premium, age discount, condition — all from local sales, not national averages. Market trends via time-series analysis at subdivision/neighborhood level.

Output: valuation report for underwriting with subject analysis, comp rationale, adjustment grid, trend analysis, and confidence range. Portfolio mode produces consistent valuations across dozens of properties.