Judgment drift
Consistent commercial rules depended on who was in the room.
✦ AI-POWERED
Decision & Commercial Optimization
Automated Value-Based Pricing for a Luxury Residential Tower
How a residential developer replaced weeks of manual price-setting meetings with a constrained optimization model that prices every unit in a tower coherently.
80%less pricing-cycle time
15.2%revenue upside
65%fewer review hours
Manual pricing made every reprice a new negotiation
The team had commercial knowledge but no shared mechanism for applying it consistently. Demand, inventory, or revenue-target changes reopened the spreadsheet and the discussion. Similar units could get inconsistent premiums, and the tower could drift from its revenue objective.
Client brief: Reduce repricing time, preserve commercial rules, and generate a price list that leadership and sales can review without rebuilding the logic manually.
Revenue-target drift
Individual unit prices could drift from the tower's revenue objective.
Slow repricing
Any change in demand, inventory, or targets meant manual recalculation from scratch.
Maximize expected tower revenue while preserving a defensible price architecture
The model optimizes each unit's price inside a tower-level revenue envelope.
- Objective — maximize expected sell-out revenue, adjusted for demand response and inventory exposure.
- Decision variables — unit price, floor premium, view premium, layout, area, and release-stage adjustments.
- Constraints — monotonic floor and unit-band rules; min/max price boundaries; tower revenue target; inventory and absorption assumptions; pricing-owner-approved overrides.
Constrained genetic algorithm with a two-level pricing structure
Tower-envelope optimization
Defines allowable total revenue and market-position range.
Unit-level optimization
Assigns prices from floor, view, layout, area, inventory, and demand signals.
Constraint validation
Rejects any candidate list violating approved commercial rules.
Two steps close the loop: scenario analysis weighs revenue, absorption, and inventory trade-offs, then human approval gates release.
The gains show up in pricing speed, revenue, and review effort.
Pricing-cycle time80% reduction
Replaces repeated manual recalculation and committee iteration with model reruns and exception review.
Expected sell-out revenue15.2% upside
Optimized allocation of unit premiums, price ladders, demand sensitivity, and tower-level revenue constraints.
Commercial review effort65% reduction
Review shifts from unit-by-unit construction to exceptions, assumptions, and final approval.
The business keeps final control of every price.
Flexible, traceable inputs
Supports spreadsheet, CRM, or ERP input; every price keeps a traceable set of value drivers.
Logged overrides, visible trade-offs
Commercial overrides are logged, not hidden, and scenario outputs show trade-offs before release.
Business owns the release
The business owns the final price list and can rerun repricing whenever demand, inventory, or positioning changes.
A pricing committee is often a constrained optimization problem wearing a spreadsheet's clothes.
The buyer isn't purchasing an algorithm in isolation. They're buying faster repricing, consistent commercial rules, a defensible price ladder, and the ability to test scenarios before committing inventory to market.
Best fitMulti-unit developments with recurring repricing, complex unit premiums, or revenue targets balanced across the full inventory.
Test the model on one tower and one pricing cycle
Bring the current price list, unit attributes, commercial rules, and revenue target. The diagnostic will identify the variables, constraints, data gaps, and validation plan required for a controlled pricing pilot.