House Flipper
Buyers, sellers and landlords in Madrid make high-stakes decisions with fragmented information. Prices and availability shift by district and property type, yet tracking those changes in real time is tedious and error-prone. Signals from multiple portals are inconsistent, making it hard to translate them into reliable price/rent benchmarks. Comparing properties (features and images included) requires hopping across tabs and spreadsheets, which slows decisions, weakens negotiation leverage, and risks margin erosion.
WhiteBox built a unified analytics layer that consolidates listings and time-series signals to quantify demand at district and typology level. On top, an AI valuation module estimates fair sale price and fair rent for individual homes or defined cohorts, enabling disciplined ask-price setting, acquisition prioritization, and portfolio-wide benchmarking. The product experience brings side-by-side property comparison (features and images), cohort analytics, and evidence-based workflows so teams can cut days-on-market and vacancy and negotiate with confidence, all in a single dashboard.
The platform replaces manual tracking with automated, decision-ready intelligence. Users set prices with discipline, identify high-yield targets faster, shorten time to sale or lease, reduce vacancy, and support negotiations with transparent evidence to protect margin and maximize yield across portfolios.
Churn Prediction (Coworking)
