Real estate:
Residential brokerage / valuations
A residential brokerage was losing listing momentum to the hours its agents spent assembling comparative market analyses by hand. Over four months we rolled out Real Estate's valuation agent, which assembles the comparables and proposes an explainable range, 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 runs an AI valuation agent that assembles the comparables and returns an explainable range, not a point estimate, with the agent's sign-off kept in the loop. This case covers the rollout to a brokerage that wanted speed without giving up the human agent's judgment.
How this engagement ran
From the problem to a system the team now runs themselves.
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.
An explainable valuation agent inside the existing workflow
The valuation agent assembles the comparables and returns a range with the weighting that drove it, surfaced inside the same CRM the brokerage's agents already work in. The rollout prioritized adoption: the agent assists the human, it does not replace the human'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
Illustrative scenario. The figures model the engineering shape of real SDEN work, not a specific client's measured results.
Before and after
What changed.
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