Data in real estate
real-estate data: property and market data, valuation models, occupancy and building-performance data, and portfolio analytics.
Real estate decisions rest on data that is fragmented, slow-moving, and inconsistently standardized across jurisdictions, asset classes, and vendors. This block builds fluency in the specific data ecosystem of the sector: public records, MLS feeds, appraisal and valuation datasets, IoT and building-management sensors, and tenant or transaction data. You will learn where this data originates, how to judge its quality and lineage, and which benchmarks analysts and investors actually use to compare markets, assets, and portfolios. The block also covers the governance and privacy obligations tied to property, tenant, and financial data, plus the practical audits needed to catch errors, staleness, or bias before they distort valuations, underwriting, or investment decisions.
Ce que vous allez maîtriser
- Identify and evaluate the core data sources (public records, MLS, appraisal, IoT, CRM) used across acquisition, leasing, and asset management workflows
- Assess data quality issues specific to real estate datasets, such as valuation lag, sparse comparables, and inconsistent property classification
- Apply sector-relevant analytics benchmarks (cap rate spreads, occupancy metrics, absorption rates) to interpret market and asset-level data correctly
- Design and run practical data governance checks and privacy compliance audits for tenant, transaction, and building-sensor data