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AI FOR REAL ESTATE

Custom AI/ML solutions for real estate industry

Custom AI and ML for REITs, PE real-estate funds, multifamily operators, brokerages, and proptech. From AVM and lease abstraction to dynamic pricing and predictive maintenance — built per-portfolio, not as SaaS.
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<3% error

HouseCanary publishes a 2.7% median absolute error on its residential AVM.

70–90%

Time reduction across valuation, lease abstraction, and document review.

$34B by 2030

AI efficiency gains Morgan Stanley projects by 2030, from automating roughly 37% of real estate tasks.

Achieve immediate, organization-wide results

Six measurable outcomes across valuation, leasing, and asset operations, deployed in months, not years.

Property Valuation & AVM

Automated valuation models hitting sub-3% error on standardized assets, with explainable adjustments for commercial complexity.

Tenant Screening & Risk

Credit, rental history, and behavioral signals fused for quality screening with fair-housing-compliant audit trails.

Lease & Document Abstraction

LLMs that extract terms, obligations, options, and renewal clauses from PDFs in minutes vs hours.

Acquisition & Deal Sourcing

Off-market signal modeling, comparable-deal analytics, and seller-motivation scoring for buy-side teams.

Predictive Maintenance

Asset failure prediction across HVAC, elevators, roofing, and MEP from sensor + work-order history.

Dynamic Pricing & Yield

STR and multifamily pricing models built to the RealPage consent judgment entered in March 2026: no competitor nonpublic data at runtime, training data aged 12 months or more.

Capabilities across the real estate value chain

Investment & Acquisition

  • Off-market signal modeling and deal sourcing
  • Comparable-sale and cap-rate analytics
  • Seller-motivation scoring from public + alternative data
  • LP/GP reporting and waterfall calculation LLMs

Underwriting, Valuation & Diligence

  • Automated valuation models (AVMs) with sub-5% error
  • Lease abstraction and stacking plan generation
  • T-12 and rent-roll normalization across portfolios
  • Title, environmental, and zoning diligence NLP

Property Operations & Leasing

  • Tenant screening with fair-housing audit trails
  • Dynamic pricing for multifamily, STR, and self-storage
  • Predictive maintenance across HVAC, elevators, and MEP
  • Resident-experience and renewal-prediction models

Portfolio, Risk & Asset Management

  • Climate-risk and physical-asset exposure scoring
  • Refinancing and capital-structure optimization
  • ESG and sustainability reporting automation
  • Asset disposition timing and pricing models
From the playbook

How a $4.2B multifamily REIT cut acquisition diligence time 78% and added $620M to AUM

A $4.2B multifamily REIT was competing on speed for institutional-quality assets — but their diligence team was capped at 3 deals/month due to lease abstraction and T-12 reconciliation bottlenecks. We deployed an end-to-end diligence platform combining lease-abstraction LLMs, T-12/rent-roll normalization, AVM with multifamily-specific adjustments, and climate-risk overlays. Diligence cycle time dropped from 14 days to 3.1 days, throughput jumped from 3 to 11 deals/month, and the team won $620M in incremental AUM in the first 9 months at unchanged team size. The same platform now feeds quarterly asset-disposition timing models for the existing portfolio.

See more case studies →

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Speak with a real estate AI expert

A 45-minute scoping call. We’ll come prepared with benchmarks for your asset class and a directional read on which models move the needle on your portfolio.

Ask us about

  • AVM and explainable property valuation models
  • Lease abstraction and T-12 normalization at scale
  • Tenant screening with fair-housing audit trails
  • Dynamic pricing for multifamily, STR, and self-storage
  • Predictive maintenance for HVAC, elevators, and MEP
  • Climate-risk and ESG reporting for portfolios

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    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    Frequently asked questions

    Yes. We build per-asset-class AVMs because commercial valuation depends on income drivers (cap rate, lease structure, tenant credit) that standardized residential AVMs miss. Models ship with explainable adjustments your appraisers, lenders, and LP committees can defend in writing — not black-box scores.
    Carefully. Every tenant-screening model ships with disparate-impact analysis, protected-class blinding where appropriate, adverse-action reasoning, and audit logs that satisfy FCRA, FHA, and state fair-housing requirements. The design follows HUD's May 2024 guidance on how the Fair Housing Act applies to tenant screening that uses algorithms and AI. We've supported clients through HUD complaint investigations and CFPB review.
    Yes. We integrate with Yardi, MRI, AppFolio, RealPage, Buildium, and Entrata via standard APIs. Underwriting inputs flow from CoStar, Reonomy (an Altus Group business), MSCI Real Capital Analytics, and your MLS feeds; outputs publish back to ARGUS Enterprise, Excel and Power BI dashboards, and your IC memo template. You own the integration code and the model artifacts.
    Yes. The underlying ML platform (data ingest, feature store, model registry, monitoring) is reusable across asset classes. Domain models differ — multifamily focuses on tenant screening and dynamic pricing, office on lease abstraction and tenant credit, industrial on logistics demand and triple-net underwriting, STR on dynamic pricing — but most multi-asset-class operators run one shared stack with multiple model families.

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    Ready to talk real estate AI?

    Start with a 45-minute strategy session. We come prepared with a directional read on your operation and a scoped proposal.

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