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Textbook examples aren’t deal data.
Students learn a market that was cleaned up for the textbook.
For Universities · CompStak One Connect
Textbook examples age fast. CompStak gives faculty and students the same lease and sale records investment firms use, going back more than a decade.
Trusted by the leaders in commercial real estate
Students learn a market that was cleaned up for the textbook.
Research that can’t be sourced can’t be published.
The first month of an analyst’s job is spent on tools school skipped.
CompStak One Connect answers each of these with verified, analyst-reviewed comps.
Get StartedCreate case studies focused on valuation, lease rollover, market analysis, and risk mitigation using live comp data.
Get StartedCase study · lease comps
Gotham CBD office · Class A
Students describe a space in Rent Predictor and get the Estimated Starting Rent Today with the comps behind it, so the estimate can be checked against the evidence.
Get StartedRent Predictor · model input
Subject space · comps behind the estimate
Query CompStak comps from ChatGPT, Claude, Gemini, or Copilot through the Agent Connect MCP server, and join them against your own records. Statistics and trends are free; pulling the underlying records counts toward your weekly comp limit.
Get Started“Build a teaching dataset of Class A office lease comps in Gotham CBD signed since 2024, with NER and concessions.”
28 comps. Median NER $75.50. Free rent 8–12 months. Dataset ready to join to a course workbook.
Rent Predictor and Agent Connect are available on CompStak One Connect.
Agent Connect puts CompStak inside Claude, ChatGPT, Gemini, and Copilot. Ask the way you’d ask an analyst, and the work comes back with its source comps attached.
“Build a teaching dataset: Manhattan office leases since 2015 with starting rent, effective rent, and term, summarized by year.”
“Test whether free rent on Chicago office leases over 10,000 SF rose after 2020, with the distribution before and after.”
“Prepare a case study on 1 Vanderbilt Avenue: its leases, its comp set, and how Grand Central rents moved around each signing.”
“Summarize average lease term by space type in Chicago over the last five years, for a lecture on lease structure.”
Access verified lease, sale, loan, and property data across major CRE asset types in 105 markets, with the details to analyze markets, value assets, and underwrite opportunities.
Across the asset types you work with:
*Multifamily data powered by RealPage
Each comp goes through a multi-step data verification process.
40,000+ verified CRE professionals at brokerage and appraisal firms submit comps daily. Transaction data is received and reviewed multiple times.
Machine learning and statistical anomaly detection flag figures outside the expected range for the market and building. CRE Ontology resolves the address to one building and matches every party on the deal to a verified entity. Flagged records are held for review.
Our team of CRE research analysts review and verify data daily. They cross-check each transaction against deal participants and property records. Data is never estimated or blindly added to the system.
Yes. Lease and sale records carry documented sourcing and verification, with history deep enough for longitudinal study.
Case studies and coursework on actual leases and sales, using the same fields practitioners work from: net effective rent, free rent, landlord work, cap rate, and NOI.
Every comp is processed by a multi-step data verification system, including machine learning algorithms, statistical anomaly detection, and a team of CRE data analysts. Read more about our data standard.
Rent Predictor, which estimates starting rent for any space, and Agent Connect, which pulls a teaching dataset from a question in your own words inside Claude, ChatGPT, Gemini, or Copilot.
CompStak has collected 4M+ versions of lease and sale comps across 105 US markets, contributed by more than 40,000 verified CRE professionals and reviewed by CompStak’s analyst team.