CRE Data Platforms Compared: CoStar, Reonomy, and AI-Native Tools

Commercial real estate data has historically been fragmented, expensive, and difficult to operationalize. A proliferation of platforms now promise to solve this problem — but they do so in fundamentally different ways. Understanding the landscape is essential for CRE professionals evaluating their technology stack.

Here is a practitioner's comparison of the major platforms and the emerging AI-native alternatives.

CoStar Group: The Incumbent

CoStar is the dominant CRE data platform, with an estimated 70%+ market share among institutional users. Its portfolio includes CoStar (commercial property data), LoopNet (listings), Apartments.com (multifamily), STR (hospitality), and Ten-X (auction platform).

Strengths: Unmatched breadth of property data across all CRE asset classes. Comprehensive comparable transaction database. Deep historical data. Strong research team producing regular market reports. Integration with most major CRE software platforms.

Limitations: Cost — enterprise subscriptions typically start at $30,000+ annually and can exceed $100,000 for full platform access. The platform is primarily a data tool, not a workflow tool — users must extract data and apply it manually to their analysis. The interface, while improved in recent years, remains optimized for search and retrieval rather than analytical workflows.

Best for: Large institutional users who need comprehensive data access across all asset classes and can afford the subscription cost.

Reonomy: Property Intelligence

Reonomy positions itself as a property intelligence platform, focused on ownership data, property characteristics, and deal-finding capabilities. Acquired by Altus Group in 2023, the platform emphasizes property-level data enrichment and owner identification.

Strengths: Strong ownership and entity data — useful for identifying decision-makers and off-market opportunities. Property-level data linking across multiple public record sources. More accessible price point than CoStar for targeted use cases.

Limitations: Narrower data coverage than CoStar — particularly for market-level analytics, lease comps, and transaction data. Less comprehensive in asset classes outside office and industrial. The analytical tools are improving but remain less mature than CoStar's.

Best for: Brokers, investors, and developers focused on deal origination and owner identification rather than comprehensive market analytics.

Cherre: Data Integration Layer

Cherre takes a different approach — rather than building a proprietary database, it serves as a data integration and normalization layer that connects disparate CRE data sources into a unified platform.

Strengths: Connects 100+ data sources into a single API. Strong data normalization and entity resolution capabilities. Flexible — users can integrate their own proprietary data alongside third-party sources. Increasingly AI-capable for predictive analytics.

Limitations: Requires existing data subscriptions — Cherre integrates data you already have access to, rather than providing its own comprehensive database. Implementation complexity is higher than plug-and-play platforms. Best suited for organizations with technical teams capable of building on API infrastructure.

Best for: Large institutional investors and operators who want to unify multiple data sources into a single analytical layer.

AI-Native Platforms: Beyond Data to Workflow

The most significant shift in CRE technology is the emergence of platforms that go beyond data provision to workflow execution. These AI-native platforms don't just provide data for human analysts to process — they process the data themselves, producing completed analytical work product.

The distinction is fundamental. A traditional CRE data platform gives you access to zoning data. An AI-native platform takes a development brief and returns a complete zoning feasibility analysis. A traditional platform provides market comparables. An AI-native platform produces a finished market study.

Strengths: Dramatically faster time-to-insight. Institutional-quality outputs without proportional analyst headcount. Ability to evaluate more opportunities in parallel. Consistent analytical rigor across every engagement.

Limitations: Newer platforms with less historical data depth than CoStar. Require trust in AI-generated analysis (mitigated by human expert review). May not cover every niche data need.

The Stack Decision

Most sophisticated CRE operators will use a combination of platforms. The decision framework:

• Comprehensive data access: CoStar remains the default for broad market data and comparable transactions.

• Deal origination: Reonomy or similar platforms for ownership data and off-market deal sourcing.

• Data integration: Cherre or custom solutions for organizations with multiple data subscriptions.

• Workflow execution: AI-native platforms like Build for automated analysis, due diligence, and deliverable production.

The key insight is that data platforms and workflow platforms serve different functions. Data tells you what exists. Workflow platforms tell you what to do about it.

Build's Position

Build is not a data platform — we're an AI-native operating partner. We integrate data from multiple sources (including the platforms described above) and apply agentic AI to execute development workflows end-to-end: site selection, due diligence, underwriting, and market analysis.

The value proposition is not better data access — it's completed work product delivered at AI speed with institutional quality. For development firms and investors, Build doesn't replace your data subscriptions. It replaces the weeks of analyst time required to turn that data into actionable intelligence.