CRE Data Platforms Compared: Reonomy, Cherre, 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 categories — property intelligence platforms, data integration layers, and the emerging AI-native alternatives.

The Legacy Model: Data Without Workflow

Traditional CRE data platforms share a common limitation: they provide access to data, but the analytical work still falls to the analyst. A platform can surface comparable transactions, market reports and property records, but turning that raw data into a completed underwriting model, site selection brief or market study requires hours of manual work.

This is the core problem the newer generation of platforms is designed to solve — not just better data, but completed work product.

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. Accessible price point for targeted use cases.

Limitations: Narrower data coverage for market-level analytics, lease comps and transaction data. Less comprehensive across all asset classes. Analytical tools are improving but remain less mature than integrated workflow platforms.

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 generation with emerging track records. Require trust in AI-generated analysis, mitigated by human expert review. Coverage and depth continue to expand rapidly.

The Stack Decision

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

• Deal origination: Property intelligence platforms like Reonomy for ownership data and off-market deal sourcing.

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

• Workflow execution: AI-native platforms 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 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.