AI for Asset Management

AI agents automate the document-heavy parts of institutional CRE dealmaking, reading deal materials and populating underwriting models with every figure cited back to its source.

Agentic AI runs five domain-trained agents that detect financial variances, diagnose root causes, and write investor-grade summaries, pulling context straight from on-site teams.

AI reads unstructured real estate documents and public listing data to automate underwriting, document abstraction, and rent comps for multifamily investors.

Cherre builds a real estate knowledge graph that connects properties, addresses, people, and companies, then runs machine learning to resolve which records across many sources describe the same property. Its Agent.STUDIO platform lets clients build custom AI agents on that unified data.

Discovery algorithms scan single-family inventory every 15 minutes across 100+ on and off-market sources, then match each property to an investor's buy box and predict its financial returns.

Machine learning ties fragmented public records into one clean record per property, then surfaces owners and likely sellers across 53M+ commercial properties and 3,100+ counties.

Machine learning turns existing WiFi network data into real-time occupancy and space utilization metrics, no hardware sensors required, and a conversational AI assistant lets workplace teams query that data in plain language.

Machine learning ingests rent roll data from any format, language, or market, then standardizes, validates, and tracks changes against an 800-point data model.

Digital coworkers run commercial real estate workflows through a central orchestrating agent that delegates tasks to specialized, CRE-trained agents.

AI collectors crawl property websites nightly, then machine learning cleans, verifies, and classifies the listings into daily unit-level rent, availability, and concession data.

Machine learning models clean and debias anonymized mobile location signals from a panel of tens of millions of devices, then extrapolate them to population-level estimates of where people go.

Agentic AI and large language models read unstructured building data, utility bills, energy audits, and architectural drawings, then surface ROI-ranked retrofit opportunities, generate capex strategies, and automate compliance reporting for commercial real estate owners.

HouseCanary runs machine-learning valuation models, image recognition, and a generative AI assistant across data on more than 136 million US residential properties.

Runs AI agents that integrate with property management systems to audit leases, automate acquisition due diligence, and manage delinquency workflows for multifamily operators.

Computer vision and deep learning analyze property photos to detect features, classify rooms, and score condition, turning listing imagery into structured property data at scale.

Two AI systems run inside the underwriting model. A machine-learning forecaster assigns each lease a renewal probability and blends those odds with the property's own payment history to project income, recalculating automatically as the deal timeline advances. Saga, a natural-language assistant, answers questions about the model's assumptions, outputs, and scenario changes in plain English, grounded in the specific property.

CityBldr applies machine learning to public and proprietary real estate data to surface, value, and analyze underutilized development sites and parcel assemblages across roughly 155 US and Canadian cities.

Leni runs an agentic AI analyst for commercial real estate and private equity teams, automating underwriting, portfolio reporting, market research, and memo generation across data pulled from property and finance systems.

Pecan is a predictive AI agent that connects to a cloud data warehouse, auto-generates SQL training sets, engineers features, and builds and benchmarks machine-learning models from plain-language business questions. For property operators and REIT analysts this supports tenant churn prediction, rent and demand forecasting, and tenant lifetime-value scoring without an in-house data science team, with predictions pushed back to CRMs and BI dashboards.

Areii runs AI-guided deal analysis that pulls hundreds of property and owner data points to estimate ARV, rents, and closing and holding costs, then scores fix-and-flip or rental viability. When a deal clears, it matches the investor to a tracked network of lenders and manages the loan request and closing steps.