Industry: Real Estate

Knowledge graphs for real estate.

Property ownership traversal, portfolio 360, valuation model context, tenant screening, and grounded PropTech copilots: for brokers, investors, asset managers, and PropTech vendors who need answers that walk real ownership chains and cite real transactions.

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Why real estate has been waiting for the graph moment

Property Data Is a Graph Problem

Every important real estate question is a traversal. Who really owns this property. What else do they own. How is this asset connected to that comp. Which of my portfolio's leases mature next quarter and share the same tenant guarantor. Spreadsheets and warehouses die on these; graphs answer them in one query.

Regulatory Pressure Is Catching Up

FinCEN beneficial ownership reporting, tighter foreign buyer disclosures, and ESG regulatory pressure are all forcing real estate businesses to answer questions their current data infrastructure cannot. A graph is the shortest path to compliance without a multi year system overhaul.

PropTech AI Needs a Grounded Substrate

Every PropTech pitch now includes AI. The ones that will survive are the ones grounded in a real property/ownership graph: because the fabricated address, fabricated comp failures of pure LLM approaches will burn the category otherwise.

Six proven real estate use cases

Property ownership traversal and portfolio 360

Model properties, LLCs, holding entities, parent companies, and beneficial owners as a graph. Answer "who really owns this property, and what else do they own" in one query: critical for institutional investors, brokers, and compliance.

•Cross portfolio exposure analysis without spreadsheets
•Detection of related party transactions and undisclosed relationships
•One hop lookup of ultimate beneficial owners across shell entities

Valuation and comparables with real context

Instead of nearest neighbour comps, model the graph of properties, transactions, tenants, and neighbourhood attributes. AVMs improve when they see actual relationships (same landlord, same submarket, comparable lease structure), not just distance.

•Tighter valuation ranges on unique or thin market assets
•Explainable comp selection auditable by underwriters and regulators
•Continuous refresh as transactions and tenant events roll in

Tenant and counterparty screening

Applications, prior tenancies, sanctions, adverse media, and corporate relationships as a graph. Screen new tenants and counterparties transitively, not just by direct match.

•Catch related party tenants that name only screens miss
•Explainable decisions with the exact path that triggered a flag
•Continuous re screening as sanctions and adverse media evolve

Lease, contract, and obligation graphs

Every executed lease and service contract becomes a graph of clauses, obligations, renewal dates, escalations, and options. Answer "which leases have CAM caps expiring next quarter" in one query.

•Never miss a renewal, option, or reset trigger
•Portfolio wide clause search for playbook deviations
•Foundation for grounded lease analysis AI

Grounded copilots for brokers, investors, and asset managers

GraphRAG assistants over your deal pipeline, market data, and internal research. Every answer cites the specific listing, comp, or memo it came from: no fabricated addresses or transactions.

•Broker research time reduced with cite checked deal brief drafting
•Asset manager copilots that pull real portfolio data in seconds
•PropTech products that ship with a defensible 'why we recommended this'

Regulatory, tax, and ESG reporting

Ownership structures, transactions, and property level ESG metrics rolled up through the graph with lineage. Defensible reporting under evolving disclosure regimes without spreadsheet rework each period.

•Faster ownership change reporting under BOI/FinCEN and equivalents
•Portfolio level ESG rollups with fact level source references
•Reduced audit and tax prep effort at year end

Frequently asked questions

Where does the property data come from?+

A mix of your internal systems (deal pipeline, portfolio management), public records (county assessor, deed, mortgage filings), aggregators (CoreLogic, Attom, Real Capital Analytics, CoStar, Reonomy), and (for institutional clients) your existing DMS and CRM. The graph resolves entities across these sources and tracks provenance.

How does this help with FinCEN beneficial ownership requirements?+

The graph maintains a continuously refreshed ownership tree. Answering "who ultimately owns this entity" becomes a single query with full lineage, not a multi week research project. Ownership changes trigger update notifications automatically.

Can this integrate with our AVM or valuation models?+

Yes. Your existing AVM stays; the graph provides richer context features (related party transactions, tenant network, landlord portfolio, submarket clusters). We've seen valuation accuracy improve materially on unique or thin market assets where nearest neighbour comps break down.

What about privacy and data licensing constraints?+

Property data has complex licensing. We design ingestion with per source usage constraints tracked at the node level, so downstream queries respect "this data can only be used for X purpose". Encryption and access control at the property, entity, and portfolio level.

What's a realistic first engagement?+

Two common starts: (1) an 8 week ownership graph pilot combining public records with your internal systems for portfolio 360 queries, or (2) a 10 to 12 week valuation context graph for AVM enhancement over one asset class.

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