How AI Is Helping Build
Revenue-Optimized Models for Better ROI
Mallbytes ingests footfall, POS, lease and market data every month, and layers computer vision, hybrid hedonic + gradient-boosting pricing, and digital-twin simulation to power a live management dashboard — moving teams from intuition-based decisions to automatic, self-improving value models for every floor, zone and tenant.
From Portfolio Strategy to Unit-Level Deep Dive
Portfolio Strategy — total NOI, footfall-vs-rent efficiency frontier, and AI-recommended optimization scenarios across the portfolio.
Asset Optimization — floor-plan level drilldown with unit deep-dives, underperforming zones, and AI-suggested renewal rents.
A Self-Improving Value Model
Five stages turn raw footfall, POS, lease and market data into pricing and valuation predictions that keep getting sharper every month.
Ingest
Monthly batch + real-time streams pull footfall (video/3D/Wi-Fi), POS sales, lease contracts and market comps into one pipeline.
Structure
A vector database indexes comparable properties; a feature store serves live capture-rate and dwell-time metrics.
Model
The AI/ML core (XGBoost, Random Forest, hedonic regression, computer vision, digital twin) turns raw data into pricing and valuation predictions.
Decide
One unified dashboard gives Asset Managers and Leasing Teams the same model output — rent recommendations, tenant-mix simulations.
Retrain
Actual leased rents and real sales feed back monthly, so the value model keeps improving itself automatically.
Ready to Optimize Your Mall Portfolio?
See how Mallbytes turns footfall, POS, and lease data into self-improving pricing models for your assets.