Auditable property valuations from comps, trends, and local market data.
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.
We connect to your MLS feeds and public records sources via FastAPI ingest pipelines, storing 24+ months of local sales history in PostgreSQL with PostGIS for spatial queries. Hedonic regression models (scikit-learn) are trained per market, so adjustments reflect local price-per-sqft dynamics rather than national indices. Redis caches comp sets and trend snapshots for sub-second report generation. Setup takes 3–4 weeks: data ingestion, model calibration, and report formatting to your spec.
Every comparable is scored on proximity (PostGIS radius query), recency, and property similarity — and those scores appear in the report. No black-box selections. An underwriter can trace why each comp was included or excluded.
Price-per-sqft, bedroom count, lot size, and condition adjustments are derived from regression coefficients trained on your local sales data — not pulled from a national lookup table. The model re-calibrates as new sales hit your MLS feed.
Median DOM, list-to-sale ratio, and active inventory counts are computed at subdivision granularity using PostGIS boundary data. Valuations reflect current supply pressure, not just historical comps.
All valuations run through the same pipeline with versioned model snapshots, so acquisition comparisons across a portfolio use the same methodology and adjustment coefficients. Model version and calibration date are stamped on every report.
Anonymized work with hard metrics — NDA-bound, no client names.
2.1x
Time on Page
38%
More Qualified Leads
52%
Fewer No-Shows
“The no-show reduction was the metric our agents cared about most. The buyers who book visits after exploring the virtual tour have already self-selected — they know the property and they are serious.”
— Head of Digital, Real Estate Platform
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call
Fordel Studios