PropTech Product Economics
Real estate technology carries unique R&D challenges: massive data integration surfaces, legacy MLS systems, AI valuation accuracy requirements, and IoT infrastructure debt.
Property Data Integration Debt
MLS feeds, county records, tax databases — each with different formats, update frequencies, and data quality. PropTech companies maintain dozens of integrations that constantly break.
Average PropTech: 15-30 data source integrationsValuation Model Economics
AVM (Automated Valuation Models) and Zestimate-style predictions require massive training data, continuous retraining, and accuracy monitoring. Model drift in real estate can mean million-dollar errors.
Model retraining: $50K-$200K per cycleLegacy MLS Infrastructure
The real estate industry runs on MLS systems built in the 1990s-2000s. RETS/RESO standards provide some standardization but legacy integration debt is enormous.
RETS → RESO Web API migration: 6-12 monthsSmart Building IoT Debt
Commercial PropTech manages thousands of IoT sensors (HVAC, lighting, occupancy). The IoT infrastructure creates unique technical debt: firmware updates across thousands of devices, connectivity failures, and sensor drift.
10,000+ devices = massive IoT debt surfacePropTech R&D Audit
Quantify your data integration debt, valuation model economics, and IoT infrastructure costs.
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