How AI property valuation actually works (and where it falls short)

“AI valuation” sounds like a black box, but the model underneath is closer to what an appraiser already does — automated, and applied consistently at scale.

Step one: find comparable sales

The model starts with a pool of recent transactions in the same building, then the same community, filtered by property type, size band and bedroom count. Recency matters most.

Step two: adjust for differences

A comparable-sales model applies adjustments for the differences that actually move price: floor level, view, finish quality, layout efficiency, and handover status.

Step three: produce a range, not a single number

Real markets clear within a band, shaped by negotiation and buyer competition. A responsible model outputs a fair-value range (typically ±5–6%) rather than false precision.

Where automated valuation genuinely falls short

• Unique or heavily renovated units with few true comparables.
• Thin markets — newly launched buildings with only a handful of resales.
• Legal, mortgage and court purposes, which require a licensed, RERA/DLD-registered valuer.
• Off-market context a model can't see, like a seller needing to close in ten days.

Try the valuation calculator, or explore the API.