How We Built an AI-Powered CMA & Price Prediction Engine for Real Estate

Real estate teams waste hours assembling CMAs, estimating prices, and polishing listing copy. We built Dual AI to automate those steps so agents get reliable, data-backed insights in minutes and spend their time with clients—not spreadsheets.

The business problem

Consistent CMAs
Trustworthy price predictions, and
Listing descriptions that don’t require manual writing

Existing tools were fragmented and slow, and didn’t scale to brokerage-level usage. Dual AI set the bar higher: automate CMA, deliver real-time price guidance, and generate polished descriptions—securely and at scale.

What we delivered

AI-Powered CMA: in-depth reports built from real-time market data and advanced models.
Price Prediction: an engine validated to 98.2% accuracy, giving agents confidence at the offer table.
Listing Descriptions: photo-aware copy drafted in seconds—quality and consistency without the manual lift.
Built to scale: infrastructure designed from day one to support brokerage-wide usage (capacity planned for up to 1.8M users).

How we approached it (playbook you can reuse)

1) Start from workflows, not models

We mapped the end-to-end agent journey: prospect → property research → pricing → listing → client comms. That told us where AI should sit (CMA, pricing, copy) and what “good” looks like (speed, accuracy, explainability).

2) Design a data pipeline agents can trust

Ingestion & normalization: consolidate market data into a consistent schema (properties, comps, temporal trends).
Feature signals: location context, comp similarity, time-on-market dynamics, listing attributes, and qualitative cues tied to photos/descriptions.
Governance: access controls, auditability, and clear data lineage so results can be explained to clients and managers.

3) Model the decision, not just the number

CMA assistant: retrieve and rank comps with similarity scoring, then render a report that mirrors how top agents reason.
Pricing: combine statistical baselines with ML ensembles; tune for calibration so 98% “confidence” means what it says in the field.
Listing generator: ground the LLM on property facts and images; add guardrails for fair-housing sensitive terms and style guidelines.

4) Make results usable in real life

Speed: answers in minutes, not hours. Agents reported saving multiple hours per week once CMA and descriptions were automated.
Explainability: show “why these comps,” confidence ranges, and editable narratives so agents stay in control.
Distribution: one-click exports for client-ready PDFs and shareable links; easy handoff to CRMs and transaction tools.

5) Build for reliability and growth

Observability: monitor data drift and model performance; re-train or re-weight features when neighborhoods shift.
Security: role-based access and data separation by office/team.
Scalability: multi-tenant design so a single brokerage—or many—can run thousands of CMAs without slowdown. (Dual AI’s launch architecture was planned for brokerage-scale loads from day one.)

Results that matter

Accuracy: price predictions measured at 98.2%—turning estimates into defensible guidance.
Throughput: tasks that took hours now complete in minutes; agents highlighted the time savings immediately after rollout.
Consistency: every agent can produce a high-quality CMA and listing package, even on busy days.
Adoption: designed around the agent workflow, so the tool becomes the fastest path to a client-ready deliverable.

Who this is for

Broker-owners, team leads, and proptech founders who need:

faster, more consistent CMAs;
price guidance you can stand behind;
listing copy that’s on-brand and compliant;
a platform ready to scale across offices.

What’s next

If you’re exploring Real Estate × AI, start with three questions:

What data do you already trust for pricing?
How will agents verify and edit AI outputs?
What KPI will prove the pilot paid for itself (time saved per CMA, list-to-close accuracy, conversion from listing views)?