Industry · Real Estate & PropTech

Data Engineering for Real Estate

Geospatial lakehouses and AI-ready property data platforms — high-cardinality spatial data, valuation model pipelines, and portfolio analytics built on Databricks, AWS, and GCP.

Problems We Solve

Industry Challenges

Property data is fragmented

Listings, transactions, tax records, GIS layers, and IoT building telemetry live in incompatible silos. We unify them into a single governed lakehouse keyed on standardised parcel and geography identifiers.

Geospatial workloads break warehouses

High-cardinality spatial joins — parcels x flood zones x school districts — overwhelm conventional warehouses. We engineer Databricks geospatial architectures (H3 indexing, Delta Lake) built for exactly this shape of data.

AVMs need fresh, clean features

Automated valuation models are only as good as their feature pipelines. We build versioned, monitored feature engineering pipelines so your data science team ships models instead of fixing data.

Portfolio decisions need one view

Acquisitions, asset management, and finance each keep their own spreadsheets. We deliver governed portfolio analytics with a single source of truth for NOI, occupancy, and valuation metrics.

Proven In Production

Measured Results

Clouds, one lakehouse

aws + gcp hybrid

AI-ready

Spatial feature store

high-cardinality geodata

30+

Projects delivered

across 3 continents

Evidence

Related Case Studies

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Questions, Answered

Real Estate FAQ

Can you handle parcel-level geospatial data at scale?

Yes — our real-estate AI lakehouse engagement was built precisely for high-cardinality spatial data: H3 spatial indexing on Databricks Delta Lake, hybrid across AWS and GCP, serving model training and interactive map analytics from the same governed tables.

Which property data sources can you integrate?

MLS and listing feeds, county assessor and tax rolls, deed and transaction records, GIS layers (zoning, flood, schools), building IoT/BMS telemetry, and commercial sources like CoStar-style exports — normalised onto standard parcel and geography keys.

Do you build pipelines for automated valuation models (AVMs)?

Yes — we build the data side of AVMs: versioned feature pipelines, point-in-time-correct training datasets, drift monitoring, and low-latency feature serving, so models are reproducible and auditable.

Can asset managers get self-service dashboards?

Yes — we deliver governed semantic layers (dbt + Looker/Power BI) over the lakehouse so acquisitions, asset management, and finance see consistent NOI, occupancy, and valuation metrics without exporting to spreadsheets.

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