Property Valuation Engine
An automated valuation model that ingests comparable sales, rents, macro-data, and property features — produces instant valuations with documented uncertainty, plus sensitivity scenarios (cap-rate moves, rent growth assumptions, expense trajectory) the deal team uses for underwriting.
- Engagement
- 8–14 week build · ongoing model refresh
- Built for
- Investment teams · REIT acquisitions · Underwriters · Brokers
Underwriting at scale requires consistent valuations across a pipeline of dozens of properties — but the comparable-sales analysis, the rent-roll modeling, the cap-rate sensitivity scenarios all happen on a per-property basis in spreadsheets, the analyst time scales linearly with deal flow.
What this is
A valuation platform for deal teams running real underwriting volume. Three layers:
- Data ingestion. Comparable sales (MLS, public records, syndicated commercial feeds), rent rolls and rent comparables, macro-data (Treasury rates, regional employment, CPI), per-property features (square footage, unit count, age, amenity scoring).
- Modeling. Ensemble valuation model (LightGBM + XGBoost + GWR) tuned per property type. Per-market calibration. Documented uncertainty per estimate.
- Scenario layer. Sensitivity analysis across configurable axes — cap-rate, rent growth, expense trajectory, exit assumptions. Surface visualization for the deal team.
How it's built
Modeling stack as referenced (LightGBM, XGBoost, GWR via PySAL). Postgres or Snowflake for canonical storage. Per-market calibration runs quarterly; per-property-type ensembles tune on engagement and on retrain. PDF report generation for broker- and investor-facing valuations where the workflow needs them.
What you get
- The ensemble model, calibrated to your portfolio's markets and property types.
- The sensitivity-scenario engine.
- PDF report generation for outbound deliverables.
- Per-market uncertainty documentation.
- Quarterly model refresh.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 8–14 week build · ongoing model refresh
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
What buyers ask about this one.
How is this different from HouseCanary, Reonomy, Quantarium?
The single-point AVM is a commodity at this point. What's not commodity is the sensitivity layer — the deal team rarely cares about the point estimate alone; they care about valuation under different cap-rate, growth, and expense scenarios. The engine produces the full scenario surface, not just the point. Plus the model is custom-trained against your portfolio's geographies and property types, not a one-size-fits-all national model.
What's the modeling stack?
Ensemble of LightGBM, XGBoost, and a geographically-weighted regression (GWR) layer for spatial correction — the same stack OpenAVMKit uses on the assessor side, applied to investor-side workflows. Where the portfolio has specific property types (multifamily, industrial, mixed-use) we tune the ensemble per type rather than running one general model.
Does it handle commercial as well as residential?
Yes — multifamily, single-tenant industrial, retail, mixed-use. Office is supported but model quality varies with how distressed and re-pricing-heavy the local market is; we document the per-market uncertainty explicitly.
How are the sensitivity scenarios structured?
Per-deal, the deal team configures the scenarios that matter — typically cap-rate moves of +/- 50–150 bps, rent growth ranges from -2% to +6% per year, expense trajectory variations. The engine produces the valuation surface across all combinations; the deal team focuses on the cells that matter for their underwriting bar.
Pricing?
Scoped to portfolio scope, property-type breadth, and model-refresh cadence. Discovery call covers all three.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026