Real Estate Market Data Providers Compared

Commercial real estate teams routinely pay for two or three data platforms at once, and still hit a wall the moment a deal needs real underwriting. That's not sloppy vendor management, it's structural: tools in this category don't overlap cleanly across asset class and workflow, so running several subscriptions at once is closer to a rational response than a mistake. U.S. commercial and multifamily mortgage borrowing hit $498 billion in 2024, up 16% from 2023. At that scale, a gap in the data stack isn't a minor inconvenience. It's a line item on somebody's P&L.

The real question isn't which database is biggest, it's which combination of coverage, depth, and integration actually matches how a given team makes decisions. Most teams get this backwards: they shop for the platform with the most features, then spend a year discovering what it doesn't do. This guide walks through why, dimension by dimension, provider by provider.

The six dimensions that should drive any CRE data provider evaluation

Start with asset class depth. Does a provider treat office, industrial, multifamily, retail, and hospitality as genuinely separate data models, each with its own comp logic and metrics? Or does it flatten everything into one generic property layer with a few filters bolted on? That distinction alone explains why some platforms feel razor-sharp in one sector and oddly thin in another.

Then there's data type. Listings and comps are one animal. Ownership and contact records are another. Debt and CMBS performance, capital flows and transaction history, foot traffic and other alternative signals each get collected differently, sold differently, and answer a different question entirely.

Workflow integration matters just as much, maybe more, because data that doesn't land where the work happens is data nobody looks at. Does it flow into the underwriting model, the CRM, a portfolio dashboard, a document system? Geographic granularity is its own filter, too. Plenty of providers are sharp in New York, Chicago, and Los Angeles and thinner once you drop into secondary and tertiary markets.

Update cadence and sourcing transparency deserve a harder look than most teams give them. How often does the data actually refresh, and can the provider say, in plain terms, where it comes from? Then there's price, and the spread isn't small: Crexi Intelligence starts at $299 a month, Cherre runs $150,000 or more per year. Those two numbers describe practically different kinds of purchase decisions, so the useful question was never "what does this cost" but "what does this cost relative to the decision it's supporting."

Each of the six fails in its own particular way. Wrong asset class depth gives bad comps. Wrong integration gives data that technically exists but never reaches the model. Wrong update cadence gives stale numbers at exactly the moment a fast-closing deal needed fresh ones.

Full-suite database: CoStar

CoStar is the closest thing this industry has to an institutional default: broad coverage across commercial listings, lease comps, and market analytics that most large firms treat as table stakes. CoStar Group posted $3.2 billion in 2025 revenue against net income of just $7 million, a gap that reflects heavy acquisition spending (Matterport, Domain) aimed at widening the data moat rather than any weakness in its market standing.

Pricing runs high, and that's worth sitting with before signing anything. Vendr benchmark data puts the annual median around $40,000, list price closer to $71,000, with entry-level seats commonly reported between $300 and $485-plus per user per month. It sits among the priciest platforms in the category, carries a learning curve that takes real time to climb, and teams with narrow, specialized needs should evaluate whether its breadth aligns with their specific workflow before committing.

Best fit is institutional acquisitions and research teams who need one authoritative source spanning asset classes and geographies. A lot of those same teams still run Crexi alongside it: CoStar for the research and comps, Crexi for tracking live inventory, because the two tools answer different questions at different moments in the acquisition process. Paying for both isn't redundant. Paying for CoStar and expecting it to do Crexi's job is where teams waste money.

Marketplace and deal-flow platforms: LoopNet and Crexi

LoopNet is the listing marketplace most people picture when they hear "commercial real estate online." Ad tiers run $89 to $2,499 per listing per month, and its real strength is visibility and inbound flow, not analytical depth. Crexi Intelligence starts at $299 a month and positions itself as the more accessible, deal-flow-first alternative.

The pairing shows up again here. The pairing reflects a broader pattern: marketplace platforms tend to add to a stack rather than replace part of it.

Marketplace platforms surface what's already for sale. That's the limit, plainly stated. Off-market opportunity, by definition, lives outside that universe, and finding it takes a completely different tool. If live deal flow and seller-side visibility are the priority, marketplace platforms earn their keep. Treating LoopNet or Crexi as the answer for finding deals nobody's listed yet isn't a small gap, it's asking a phone book to do a detective's job. The next section is where that problem actually gets solved.

Ownership intelligence and off-market prospecting: Reonomy

Reonomy's job is mapping who actually owns a property, sometimes through layers of LLCs and holding structures that would otherwise take a title search to untangle, and surfacing contacts an acquisitions team or lender can actually reach. It's purpose-built for exactly that question: who owns this, and how does someone get them on the phone?

Pricing sits at $400 to $575 per user per month, a premium that reflects the fact that contact and ownership data has no real substitute in a comps or listings database. The fit is specific: acquisitions teams running structured off-market outreach programs, and lenders prospecting for refinance or bridge opportunities across a target market. Listings tell you what's for sale today. Ownership intelligence tells you what could be for sale next quarter, if the right person picks up the phone.

Lease comps and transaction intelligence: CompStak and MSCI RCA

CompStak built its lease comp database on a reciprocal model: brokers hand over data in exchange for access to the pool, which gives it coverage a purely proprietary collection effort would struggle to match. It's well-regarded for lease comps specifically, and the reciprocal model is the reason why.

MSCI RCA (formerly Real Capital Analytics) tracks something different: transactions and capital flows across commercial real estate, which makes it a core tool for institutional investors reading market liquidity and pricing direction. Its institutional focus and premium pricing make it most relevant at the portfolio and market-cycle level, not for pulling a one-off comp for a single deal.

The two answer different questions that tend to surface in the same underwriting conversation. CompStak tells you what the space down the hall leased for and on what terms. RCA tells you what capital is paying for buildings like this one, and whether that number is moving. A team that needs both lease-level and transaction-level comps won't find both under one roof, and pretending otherwise is how underwriting gaps happen.

Multifamily and sector-specific analytics: Yardi Matrix

Yardi Matrix leads the multifamily category: rent trends, supply pipeline, property-level financials, all built around apartment assets rather than adapted from a general commercial template. Teams already running Yardi for property management or accounting get an added integration advantage, since data doesn't need to be re-keyed or reconciled between systems.

There's a broader principle buried in here. A platform built around a single asset class surfaces signals, lease expiration clusters, concession trends, submarket absorption patterns, that a general-purpose database tends to smooth over in the name of covering everything a little bit. Teams with heavy multifamily exposure should ask directly whether a specialist tool gives them a materially sharper signal than the multifamily module bolted onto a broader platform. Sometimes the answer is yes. Sometimes the general platform is good enough and the specialist subscription just sits there unused, which is worth finding out before paying for both.

CMBS, debt performance, and credit risk: Trepp

Trepp isn't a property database at all. It's a financing-side intelligence tool built around loan-level CMBS performance, delinquency trends, and credit risk, and it is a leading tool for CMBS and debt data, focused on a lane most property-side platforms don't enter.

The user here isn't the acquisitions analyst pulling comps for an offering memo. It's the lender, the debt investor, the credit officer whose entire job depends on how the loans in a pool, or a book, are actually performing month to month. If a firm's decisions touch CMBS exposure, credit risk assessment, or loan-book surveillance, Trepp isn't a nice-to-have sitting next to a property platform, it sits in its own category entirely. That's the deeper point running through this whole piece: even inside "market data," the debt layer and the property layer are separate disciplines, and treating them as one leads to gaps that surface at exactly the worst possible moment, usually mid-deal.

Retail site selection and alternative data: Placer.ai

Placer.ai analyzes foot traffic patterns at the property and trade-area level. It's the category leader for retail site selection, the specific use case where traffic patterns help predict rent performance and whether a given tenant mix will actually work.

Fair warning: it adds close to nothing for office, industrial, or multifamily underwriting. Its relevance is tightly bounded to retail and mixed-use decisions, and that's not a flaw, it's the whole design. The broader lesson for anyone evaluating "alternative data" in general: foot traffic isn't valuable the way a comps database is valuable across the board. It's valuable for one category of decision, and close to useless outside it.

Data integration platforms: Cherre

Cherre doesn't collect its own market data so much as pull together and normalize data from whatever specialized sources a firm already subscribes to into one layer that feeds internal models and dashboards. Contracts typically run $150,000 or more a year, a serious commitment that only makes sense once a firm runs enough separate subscriptions that reconciling them by hand is genuinely slowing people down.

That's the actual problem Cherre solves. Subscribe to four specialized platforms and the data arrives in four formats with four different taxonomies. Normalizing that isn't a technical nicety, it's the precondition for doing any real portfolio-level analysis across all of it. Cherre isn't where a firm starts, it's where a firm ends up after piling on several specialized subscriptions and realizing analysts spend more time reconciling spreadsheets than actually analyzing anything. Normalized, structured data matters for one more reason too: it's the prerequisite for any AI layer downstream, document intelligence, covenant monitoring, automated spreading, none of which run reliably on inputs that don't match each other in format to begin with.

What the provider landscape leaves unaddressed for lenders and asset managers

Every provider covered so far answers questions about the market: what's this asset worth, how are deals pricing, who owns what, how are the loans in this pool performing. None of them answer questions about a firm's own book. What covenants are drifting toward breach? Which rent rolls haven't been reviewed in six months? Where's a refinance window opening across a portfolio of maturing loans?

None of that lives in a market data subscription, and no amount of additional market coverage fixes it. The gap sits in the document layer: rent rolls, operating statements, loan agreements, covenant packages, arriving in inconsistent formats that no comps database or ownership record ever touches. A market data platform can flag an opportunity in seconds. If the internal process for reviewing the relevant documents still takes weeks, the speed of that flag stops mattering, the deal moves on without you. That gap, specifically, is what purpose-built CRE document intelligence platforms exist to close, not as a replacement for market data providers, but as a layer operating below where those providers stop.

How AI-native asset intelligence platforms connect market data to internal portfolio decisions

This category takes a firm's own documents, rent rolls, operating statements, loan agreements, covenant packages, and turns them into structured, decision-ready intelligence instead of a pile of PDFs someone has to read line by line. The practical output looks like automated financial spreading, covenant monitoring that runs continuously instead of quarterly, extraction with citations back to the source page, and portfolio dashboards that surface refinance windows and maturing loans without an analyst manually checking every file.

The speed difference isn't subtle. AI-driven lease abstraction cuts processing time from four to eight hours per lease down to fifteen to thirty minutes, with accuracy in the 95 to 99% range, and a similar jump shows up in financial spreading and general document extraction. General-purpose AI tools don't treat a DSCR calculation or a covenant structure as a distinct thing to be understood, they treat it as text on a page, no different from a grocery list. Purpose-built CRE platforms are built around those specific structures, which is exactly why they extract and flag with the precision a lender actually needs.

This is where the earlier sections connect back up. Normalized market data from a Cherre or a CoStar becomes more useful once it sits next to structured internal document intelligence: market signals and portfolio signals living in the same decision instead of two separate browser tabs. Citations matter more than they sound like they should. When a covenant flag or a financial anomaly pops up, an analyst needs to see exactly where in the source document that number came from, an auditable trail rather than a confident-sounding output with nothing behind it.

The gap between piloting a tool and actually getting results from it comes down, largely, to whether the tool sits inside the real document workflow or just floats above it as a surface-layer add-on.

Building a data stack that matches how your team actually makes decisions

Start with the decision, not the database. Teams that start by asking "what's the biggest database" end up with a stack full of overlap and gaps at the same time, paying twice for some things and still missing others entirely. Map out the three or four decisions a team makes most often (comp pulls, ownership outreach, debt performance checks, covenant reviews) and match each one to the data type it actually needs. That order matters more than which vendor wins any single category above.

Multiple subscriptions aren't a symptom of bad vendor discipline, they're structural. No single provider covers every data type at the depth a specialized workflow demands, which is exactly why the two-to-three-subscription pattern shows up across the industry rather than in one sloppy corner of it.

Sequence matters, too. Market data providers, CoStar, Reonomy, CompStak, RCA, Trepp, Placer.ai, inform the front end of evaluating a deal. Document intelligence platforms inform the back end: underwriting, ongoing monitoring, portfolio surveillance. Neither replaces the other. They're complements, doing different jobs at different stages of the same decision, and the strongest data stacks get built around that division rather than around whichever platform has the biggest logo.