Document AI · CRE Lending

AI Rent Comps Extraction & Financial Analysis

Uptiq's Document AI extracts asking rent per square foot, effective rent net of concessions, free-rent months, tenant improvement allowance, and lease term from each rent comp, feeding market rent into the income approach and flagging in-place rents that sit off market.

95%+
Extraction accuracy
41%
Faster underwriting cycle time
100+
Native integrations

Trusted by financial institutions across banking, lending & credit

CRE LendersCommunity BanksCredit UnionsCommercial LendersNon-Bank Lenders

What is a Rent Comps?

A rent comparables schedule reports what competing properties ask and actually collect after concessions, and is used to validate the subject's in-place rents and support the market rent assumption in the income approach.

Uptiq's Document AI extracts asking rent per square foot, effective rent net of concessions, free-rent months, tenant improvement allowance, and lease term from each rent comp, feeding market rent into the income approach and flagging in-place rents that sit off market.

Concessions — free rent, moving allowances, oversized TI packages — hide the gap between asking rent and effective rent

Intake / ApplicationUnderwritingClosing / DocumentationServicing / Monitoring
Concessions — free rent, moving allowances, oversized TI packages — hide the gap between asking rent and effective rent
Quoted rents switch between per square foot per year, per square foot per month, and per unit within the same schedule
Gross, modified gross, and triple-net quotes are not comparable until expense loads are normalized to one basis
Asking rents on a marketing sheet reflect availability, not signed deals, so the comp may never have transacted at that number
Escalation structure and lease term differ across comps, so a headline rent understates or overstates total lease value

AI agents built for rent comps processing

Uptiq's Document AI extracts asking rent per square foot, effective rent net of concessions, free-rent months, tenant improvement allowance, and lease term from each rent comp, feeding market rent into the income approach and flagging in-place rents that sit off market.

1
Classification
Rent Comps documents are classified and matched to the correct extraction schema automatically.
2
95%+ accuracy extraction
Every field is extracted with full data lineage back to the source page.
3
Automatic ratio & metric calculation
Key financial metrics are calculated automatically from the extracted figures.
4
Delivery into your LOS
Structured output flows into your existing LOS — 100+ native integrations, no rip-and-replace.

The result: your team spends time on judgment calls, not re-keying rent comps data — measured across production deployments.

36%
Less spreading & extraction time
41%
Faster underwriting cycle time
95%+
Extraction accuracy
5 days
To first production deployment

Fields Uptiq extracts from a Rent Comps

A sample of the structured fields Uptiq's Document AI captures from this document type.

FieldExample
Rent Comp Property2200 Harbor Point
Asking Rent$32.00/SF/yr
Effective Rent$28.75/SF/yr
Free Rent Concession4 months
TI Allowance$45.00/SF
Lease Term Quoted7 years
Expense StructureTriple Net (NNN)
Rent Escalation3.0% annually
Sample structured output
{
  "rent_comp_property": "2200 Harbor Point",
  "asking_rent": "$32.00/SF/yr",
  "effective_rent": "$28.75/SF/yr",
  "free_rent_concession": "4 months",
  "ti_allowance": "$45.00/SF",
  "lease_term_quoted": "7 years"
}

What changes when this runs automatically

Rent Comps Extraction, Not Just Storage

Uptiq's Document AI extracts asking rent per square foot, effective rent net of concessions, free-rent months, tenant improvement allowance, and lease term from each rent comp, feeding market rent into the income approach and flagging in-place rents that sit off market.

Handles the Real-World Complexity

Concessions — free rent, moving allowances, oversized TI packages — hide the gap between asking rent and effective rent

Consistent, Structured Output

Gross, modified gross, and triple-net quotes are not comparable until expense loads are normalized to one basis

95%+ Accuracy, Full Audit Trail

Every extracted field traces back to its source page — examiner-ready documentation, every time.

Integrates with Your LOS

100+ native integrations across loan origination systems, cores, and workflows. No new infrastructure.

Scales Without Adding Headcount

Process more rent comps volume with your existing team as application volume grows.

Results from 150+ financial institutions in production

36%
Less spreading & extraction time
41%
Faster underwriting cycle time
95%+
Extraction accuracy
5 days
To first production deployment

Results represent aggregate outcomes across production deployments. Individual results may vary.

From rent comps receipt to structured output

Rent Comps arrives at intake

The rent comps arrives via email, portal, or upload. AI classifies it automatically.

AI extracts key fields

Fields are extracted at 95%+ accuracy with full data lineage back to the source page.

AI calculates key metrics

Derived metrics and ratios are calculated automatically from extracted figures.

Delivery into your LOS

Structured, extracted data lands in your existing loan origination or servicing system.

What teams ask before they start

Rent Comps AI is Uptiq's Document AI capability for classifying and extracting rent comps documents, turning them into structured, underwriting-ready data instead of a PDF to read manually.
Uptiq's Document AI is certified to 95%+ extraction accuracy by a Knowledge Team of former underwriters, bankers, and analysts, with every extracted field tracing back to its source page for audit purposes.
Yes. Where free rent months, TI allowance, and lease term are stated, Uptiq computes effective rent on a straight-line basis alongside the quoted asking rent, so a comp advertising $32 per square foot with four months free is not treated as a $32 comp.
Yes. Extracted market rents are matched to the rent roll by unit type or suite size, so units renting meaningfully below or above the comp set are flagged. That gives an underwriter a documented basis for accepting or trimming a mark-to-market assumption.
Yes. NNN, modified gross, and full-service quotes are captured with their expense basis, and Uptiq converts them to a single basis when the schedule states the expense load. Where the load is not disclosed, the comp is flagged rather than converted on a guess.
Yes. Uptiq connects to 100+ integrations across loan origination systems, core banking platforms, and underwriting workflows — the agent layer sits above your existing stack with no rip-and-replace required.

Explore related document types

See Rent Comps AI in Action

Book a 30-minute session. We'll run a live extraction demo on your own rent comps documents.

Rent Comps AI, done right.

150+ financial institutions run Uptiq's AI agents to process financial documents and accelerate underwriting.