Commercial Real Estate & Investment Management · Real Estate & Construction
Should you build or buy Commercial Real Estate Market Data & Analytics?
Commercial real estate market data and analytics platforms aggregate property sales records, lease transactions, ownership data, and market intelligence to give CRE professionals access to comparable transaction data, property histories, and market trend reporting for investment, leasing, and research decisions.
The build-vs-buy decision for CRE market data and analytics turns on whether your organization can assemble a competing transaction dataset versus whether you're renting access to a commodity data network, and what kind of AI analytics layer you could build on top of vendor data feeds; the trajectory here is shifting as AI analytics become more accessible.
Build it, buy it, or bridge?
When building makes sense
Building meaningful market data coverage is effectively off the table for most organizations. The data moat, decades of sales records, lease transactions, and ownership histories that CoStar and similar platforms have assembled across markets and property types, is vendor-owned and not replicable through an independent effort. What is buildable is the analytics layer. Proptech startups are actively building property intelligence tools, AI-driven comp selection workflows, and market narrative generation on top of data feeds. For organizations with proprietary transaction data of their own, a custom analytics layer that combines vendor market data with internal deal history, loan performance records, or portfolio outcomes creates something genuinely differentiated. That's where building adds value: on top of, not in place of, the data layer.
When buying makes sense
Buying makes sense when market coverage is what you need. The comp database, property records, and ownership histories that CoStar and MSCI Real Assets provide reflect data collection that took decades and scale that no buyer can replicate. For commercial real estate professionals who need accurate market intelligence to price deals, evaluate markets, and underwrite acquisitions, licensed access to those networks is the practical path. The more productive decision is often vendor selection and subscription right-sizing: CoStar at $350-$1,000 per user per month covers broad market analysis, while alternatives like Reonomy or Cherre may fit specific use cases at lower cost. Over-buying is the common trap, paying for full-platform access when only specific data queries drive actual decisions.
The desk read
CRE market data is fundamentally a data network problem. The comps databases, sales records, lease transactions, and ownership histories that vendors like CoStar and Reonomy have assembled reflect decades of data collection across markets and property types. The analytics layer on top of that data, including AI-driven comp selection, market narratives, and investment signals, is technically buildable and increasingly being built by proptech startups. But the data moat is vendor-owned.
Buying earns its keep when you need broad market coverage, because the alternative for most buyers isn't building a competing data network, it's choosing between licensed access points. The build conversation is more relevant for organizations with proprietary transaction data or specific analytics requirements that vendor platforms don't support. For most commercial real estate professionals, the decision is less build-vs-buy and more which vendor's coverage depth, data freshness, and subscription structure fits how the team actually uses market intelligence day to day.
Frequently asked
What is commercial real estate market data and analytics software?
Commercial real estate market data and analytics platforms aggregate property sales records, lease transactions, ownership data, and market intelligence to give CRE professionals access to comparable transaction data, property histories, and market trend reporting for investment, leasing, and research decisions.
When does building CRE market data analytics make sense?
Building the data layer is not viable for most organizations. The analytics layer on top of vendor data feeds is buildable, and proptech teams are actively doing it for AI-driven comp selection and market intelligence workflows.
When does buying CRE market data analytics make sense?
Buying makes sense for any CRE professional who needs market coverage, because the underlying transaction data moat is vendor-controlled and decades in the making. The decision is less build-vs-buy and more which vendor's coverage and pricing structure fits actual usage patterns.
What are the main CRE market data vendors?
Representative vendors include CoStar, MSCI Real Assets (Real Capital Analytics), Reonomy (Altus), Cherre. B4 Pro scores the full set.