Real Estate & Construction · Property valuation models
Your valuations diverge by analyst and take a week to arrive
Banao builds machine-learning valuation models trained on your transaction history, comparable sales, location signals, and asset attributes — producing a defensible number on a residential unit, a commercial asset, or an entire portfolio inside hours rather than days.
Each output names the factors that drove the estimate, so a credit committee, fund manager, or acquisition team can interrogate the result and override it with reason, rather than accepting a single analyst opinion.
The first call is free · 45 minutes · no obligation
What we build
What a Banao valuation model includes
A valuation model is only as useful as its inputs, its explainability, and where its output actually goes. We build all three.
A model trained on your transaction history
We train on your portfolio's own transaction records — not on a generic listings dataset that doesn't match your asset mix, geography, or buyer profile.
Comparable selection at scale
Comps are pulled programmatically across geography, asset class, and time window — consistent criteria applied across thousands of assets, not picked case by case by an individual analyst.
Location and micro-market signals
Transport access, amenity proximity, zoning changes, and planning notices are baked in as features, so a value shift from a new transit link or a re-zoning shows in the model without a manual refresh.
Factor attribution per valuation
Every output shows which drivers moved the estimate and by how much — floor level, catchment, distance to metro, recent comparable — so the number is auditable, not a black box.
Portfolio-level sweep with outlier flags
Run the model across hundreds of assets in one pass and surface the units furthest from current market — the assets where pricing or acquisition assumptions most need revisiting.
Output integration into your existing tools
Valuation outputs feed back into your pricing sheets, ERP, credit origination system, or fund model directly — not a separate dashboard the team has to remember to open.
Dogfooding
We run our own company on the AI we sell
Banao operates a ~300-person engineering firm on its own AI products. InterviewGod screens our own hires. Vikaas runs our own demand generation. A system that has to survive our internal operation is tested against real consequences before it reaches a client portfolio.
We are not describing AI valuation from a distance. The standard we hold a client model to is the same standard we hold our own operational systems to — which means the model that arrives at your desk has already had to earn its keep.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When a valuation model is not the right answer
A statistical model needs volume, consistency, and comparables. Without them it produces a precise number that is not accurate — and that is worse than an informed estimate.
- Thin transaction history: below a meaningful volume of comparable sales in the relevant geography and asset class, the model lacks signal. We will tell you how much data is needed before you commission a build.
- Bespoke trophy assets: a one-of-a-kind asset with no genuine comparables needs a qualified surveyor, not a statistical model. We will say so rather than force-fit the problem.
- Rapidly shifting markets: in a market moving faster than the underlying data refreshes, automated outputs lag. The Discovery Sprint identifies whether your update cadence is adequate before any build starts.
How we start
How we start — fixed price, without the risk
We do not quote a valuation model off a conversation. We look at your actual data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your transaction data, comparable supply, and location signal availability — and return a feasibility assessment, baseline accuracy estimate, and ROI maths. Yours to keep whether or not you proceed. If you do proceed, the Sprint cost is credited against the build.
- 02
Build
Data pipeline, feature engineering, model training, and attribution layer built as a deliverable and integrated into your existing systems. We hand over a production model, not a notebook.
- 03
Production & continuous improvement
Deployed with a monitoring layer that flags model drift as market conditions shift, and a retraining cadence as new transaction data lands. The model keeps improving as your portfolio grows.
FAQ
Frequently asked questions
How much transaction data do you need to build a useful model?
It depends on the asset class and geography. The Discovery Sprint quantifies this before any build — we audit what you have, identify gaps, and tell you whether augmentation with external data closes them or whether the signal is not there yet.
Can the same model handle both residential and commercial assets?
Usually no — the value drivers differ enough that a single model performs poorly across both. We typically build separate models per asset class and feed outputs into a common portfolio view.
How does the model handle unusual or distressed properties?
It flags them. Assets where the model's confidence is low — too few comparables, anomalous attributes, or thin local data — are surfaced as outliers for manual review rather than given a confident estimate the model cannot support.
How long does the first model take to build?
Two weeks for the Discovery Sprint, then six to eight weeks for the build. Banao's bench of ~300 engineers means delivery starts in weeks rather than the months a local hire or a large systems integrator would take.
How do valuation outputs reach our existing systems?
Integration is part of the build deliverable — outputs feed into your pricing sheets, ERP, fund model, or credit origination system directly. We assess your stack during the Discovery Sprint so integration scope is costed from the start.
Get started
Get a defensible number on your portfolio, not a spreadsheet opinion
Bring your transaction data and your worst valuation bottleneck. In 45 minutes we will map what a model can do and what it will cost to build.
Book a Discovery Sprint