Real Estate · CRE AIAI in Commercial Real Estate
Commercial real estate AI for owners, investors and asset managers — faster deal screening, underwriting figures pulled from offering memoranda and rent rolls, and portfolio signals caught early.
Message on WhatsApp
TRUSTED BY TEAMS
AI in commercial real estate is mostly about volume. Deal teams read hundreds of offering memoranda to close a handful of deals, and asset managers track portfolios across dozens of rent rolls and reports.
Sodio builds AI for commercial real estate that does the first pass — extracting figures, screening deals against your criteria, flagging portfolio changes and drafting reports — so analysts spend their time on judgement rather than retyping. Commercial real estate AI should speed decisions up, not make them.
/// WHAT WORKS, AND WHAT IS HYPE
What AI Does Well in CRE — and What It Does Not
AI does not find the deal. It clears the pile so your analysts can.
Works today
- Extracting rent roll, T-12 and offering memorandum figures into an underwriting model
- First-pass screening of deals against your investment criteria
- Flagging arrears, cost spikes and occupancy changes across a portfolio
- Drafting investment committee memos and investor reports for review
- Surfacing lease expiries and tenant risk early
Still more promise than practice
- Predicting cap rates or timing the market
- AI making investment decisions
- Replacing a valuer or an ARGUS model
- Autonomous asset management
- Anything decided without a person checking it
/// AI APPLICATIONS
AI Tools for Commercial Real Estate Real Estate
Deal screening
Underwriting inputs
Lease abstraction
Portfolio anomaly detection
Tenant risk and renewal
Valuation between valuations
Investor and committee drafts
Operating cost and energy
Market research
/// WHERE AI FITS IN A DEAL
From Offering Memorandum to Investor Report
Offering memoranda arrive in volume
Reading the pile and deciding which deals are worth pursuing
Each deal scored against your criteria
Choosing which deals to take forward
Rent roll and T-12 figures extracted into your model
Setting the assumptions and judging the returns
Leases abstracted and checked against estoppels
Negotiating and deciding to close
Portfolio anomalies and tenant risk flagged
Deciding what to do about them
Committee and investor reports drafted
Reviewing, editing and signing off
/// THE DOCUMENTS BEHIND CRE AI
The Documents Behind CRE AI
Machine learning in commercial real estate succeeds or fails on documents that were never designed to be read by a machine.
/// AI IN UNDERWRITING
AI in Commercial Real Estate Underwriting
Most of an analyst’s time on a new deal goes on getting the numbers into the model, not on judging them. That is the part AI is good at.
/// COMPARE
Three Ways to Bring AI Into CRE
/// COST AND TIMELINE
How a CRE AI Project Runs, and What Drives the Cost
A pilot on a batch of deals you have already underwritten typically runs within 30 days, compared figure by figure with your analysts’ work. If it holds up, a production version follows within 90.
The cost moves with six things: how varied your documents are; how closely it must work with your underwriting model; how many portfolio data sources are involved; the review workflow; private hosting for confidential deal data; and how many applications are built.
/// FAQ
Frequently Asked Questions
Mostly to clear the volume of reading that slows deal and asset teams down. It extracts figures from offering memoranda, rent rolls and operating statements, screens deals against investment criteria, abstracts leases, flags changes across a portfolio and drafts reports. The decisions stay with people; AI gives them more time to make them.
It can do the first pass. It can pull the rent roll and operating figures into your model, check them for inconsistencies and set out a first view against your criteria, in minutes rather than hours. Setting assumptions, judging risk and deciding to proceed remain the analyst's and the committee's job — and should.
Only if it is built for it. Offering memoranda usually arrive under a confidentiality agreement, and uploading them to a public AI tool may breach it. Models for this work should be hosted privately, so documents are never used to train anyone else's model and never leave the environment you control.
Yes. Extracted figures are written into your existing template rather than replacing it. The model stays yours and your team keeps working the way it does now — with the retyping removed.
Not reliably, and it is worth being wary of anyone who says otherwise. AI is good at reading documents, spotting patterns in your own portfolio and summarising market research. Forecasting where yields are heading is a different problem, and no model has solved it.
By testing it on deals you have already underwritten. A pilot runs a batch of past offering memoranda and rent rolls through the system and compares the results with what your analysts produced — figure by figure, and in time taken.
/// RELATED SERVICES
Related Industries and Services
Routes where scope overlaps with real estate software.
/// GET STARTED
Start With One Deal Your Analysts Already Underwrote
Send us a batch of past offering memoranda and rent rolls. We will benchmark AI extraction against your team's numbers and show you, free, where it matches, where it disagrees, and what it would take to integrate into your model.