Real estate
Residential brokerage / valuations
A residential brokerage was losing listing momentum to the time its agents spent assembling comparative market analyses by hand. SDEN rolled out Real Estate's AI-assisted valuation in four months, taking 70% off the time each valuation required.
- Client
- A residential brokerage operating across a North American metro
- Sector
- Residential brokerage / valuations
- Duration
- Approximately four months from kickoff to full rollout
The premise
Manual valuation is where listing momentum goes to die. An agent pulls comparables across three portals, reconciles them in a spreadsheet, and arrives at a single number they then have to defend to the seller. The work takes the better part of a day per property, and two agents looking at the same house routinely land in different places, which erodes the seller's confidence before the listing is even live.
Real Estate treats valuation as an explainable range, not a point estimate. This case covers the rollout to a brokerage that wanted speed without giving up the agent's judgment.
A day per valuation, and no two agents agreed
Agents assembled comparables manually across multiple listing portals, then reconciled them in personal spreadsheets that never matched. A valuation took most of a working day, and because the method lived in each agent's head, the brokerage had no consistent basis to stand behind a number when a seller pushed back.
The brokerage had tried off-the-shelf automated valuation models, but a single opaque number was worse than the spreadsheet: agents could not explain it, so they did not trust it, so they did not use it.
Explainable valuation built into the agent's workflow
Real Estate's valuation produces a range with the comparables and weighting that drove it, surfaced inside the same CRM the agent already works in. The rollout prioritized adoption: the model assists the agent, it does not replace the agent's sign-off.
Phase 1: Data and comparable scoping
Two weeks. Mapped the brokerage's historical transactions and the comparable sources its agents trusted, so the model's inputs matched the way the team already reasoned about value.
Phase 2: Valuation rollout
Six weeks. Real Estate deployed with the explainable valuation engine: Python and PyTorch models surfaced through the NestJS API into the agent's listing workflow, each estimate shown as a range with the comparables and per-factor weighting behind it.
Phase 3: Agent onboarding and tuning
Four weeks. Agents ran live valuations alongside their manual method; the weighting was tuned against the cases where the two diverged until the team trusted the range.
Seventy percent off every valuation, with a number agents will defend
Time per valuation dropped by 70%. More importantly, every estimate now comes with the comparables and weighting that produced it, so an agent can walk a seller through the reasoning instead of defending a single opaque figure.
Because the valuation lives inside the CRM, the brokerage also gained a consistent, auditable record of how each listing was priced, something the personal-spreadsheet method never produced.
70%
less time per valuation
Range, not point
explainable estimate with comparables shown
99.98%
platform uptime
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