CompVista

Property-level student housing sales comps intelligence for comparative market studies, pricing, underwriting, and transaction decisions.

The Problem

“Fragmented student housing sales comps slow pricing, underwriting, and transaction decisions”

Organizations face these key challenges:

1

Comparable sales data is fragmented across PDFs, emails, broker notes, and public records

2

Property names, addresses, and ownership entities are inconsistent across sources

3

Important transaction details are buried in unstructured documents

4

Manual spreadsheet workflows are slow and hard to audit

Impact When Solved

Reduce analyst time spent collecting and normalizing compsAccelerate comparative market studies and underwriting workflowsImprove consistency of property-level sales comp selectionIncrease confidence in pricing recommendations with traceable evidence

The Shift

Before AI~85% Manual

Human Does

  • •Gather sales comps from broker emails, offering memoranda, public records, market reports, and internal deal logs
  • •Standardize property and transaction fields in spreadsheets and reconcile inconsistent names, addresses, and ownership entities
  • •Compare subject property attributes against selected comps and prepare pricing or underwriting analyses
  • •Review assumptions with senior team members, fill data gaps from calls or memory, and revise comp sets as new information appears

Automation

  • •No meaningful AI support in the legacy workflow
  • •Search and comparison depend on manual spreadsheet filtering
  • •Document review and field extraction are performed by analysts
  • •Updates to comp sets are tracked through manual follow-up and versioning
With AI~75% Automated

Human Does

  • •Review low-confidence extracted fields and approve corrections to comp records
  • •Validate the final comparable set and decide which comps should influence pricing or underwriting
  • •Handle exceptions such as conflicting transaction details, unusual assets, or missing source evidence

AI Handles

  • •Ingest sales materials and public records, extract property and transaction details, and normalize comp records
  • •Resolve inconsistent property, address, and ownership references across sources and maintain traceable evidence links
  • •Retrieve and rank the most relevant student housing comps for a subject property based on key similarity factors
  • •Generate comparative insights, pricing ranges, difference summaries, and draft market study narratives with citations

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence95%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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