Every commercial real estate decision relies on data. The question is which data, from where, and whether the sources you are using actually answer the questions your team is asking.
Most CRE professionals work with two or three familiar platforms and fill the gaps with spreadsheets, broker opinions, and intuition. That approach works until it does not — until you miss a site because your foot traffic data was stale, overpay for a lease because you lacked competitive benchmarks, or hold an underperforming asset because your market analysis was incomplete.
This guide covers the full landscape of CRE data sources, organized by data type rather than by vendor. For each category, we explain what the data tells you, where to get it, and how it fits into the decisions that retail real estate teams make every day.
The six categories of CRE data
Commercial real estate data is not one thing. It spans six distinct categories, each answering a different set of questions:
1. Transaction data — what properties have sold, at what price, at what cap rate. This is the traditional foundation of CRE analysis.
2. Listing and availability data — what is on the market, asking rents, vacancy rates, available square footage.
3. Foot traffic and visitation data — how many people visit a property or trade area, when they visit, where they come from, and how patterns change over time.
4. Demographic and psychographic data — who lives and works near a property, their income, age, education, spending habits, and lifestyle preferences.
5. Spend and transaction data — how much consumers spend in a trade area, at which merchants, and how spending patterns shift seasonally.
6. Property and ownership data — tax records, deed history, zoning, building characteristics, and current ownership.
No single platform covers all six categories well. The CRE teams that make the best decisions assemble a data stack that covers the categories most relevant to their decisions without paying for data they do not use.
Transaction and comp data
Transaction data tells you what properties have traded, at what price, and under what terms. It is the backbone of valuation, underwriting, and investment analysis.
Where to get it
CoStar Group. The dominant platform for CRE transaction data in the US. CoStar maintains the largest database of commercial property sales and lease comparables, covering office, retail, industrial, and multifamily. Their research team verifies transactions through broker outreach and public records. CoStar also owns LoopNet and Apartments.com.
- Best for: comprehensive sales and lease comps, tenant intelligence, market-level analytics
- Limitation: expensive, with enterprise contracts that can run $20,000–$50,000+ annually depending on scope
- Retail relevance: strong on retail leasing data but limited on the consumer intelligence side (no foot traffic, no spend data)
MSCI Real Capital Analytics (RCA). The standard for institutional-grade transaction data. RCA tracks commercial property transactions globally, with deep coverage of portfolio deals, entity-level transactions, and cross-border activity.
- Best for: institutional investors, REITs, and fund managers who need market-level benchmarking and transaction tracking
- Limitation: focused on large transactions; less useful for local retail leasing decisions
- Retail relevance: good for tracking retail property investment flows and cap rate trends at the national and metro level
CompStak. A crowdsourced platform that collects verified lease and sales comps from CRE professionals. The exchange model (share comps to access comps) creates a dataset that fills gaps in CoStar’s coverage, particularly for smaller and mid-market transactions.
- Best for: brokers, appraisers, and analysts who need granular lease comp data
- Limitation: coverage varies by market; strongest in major metros
- Retail relevance: useful for retail lease negotiations and valuation benchmarking
Crexi. Combines a listings marketplace with transaction intelligence. Crexi has grown quickly as a CoStar alternative, offering sales comps, lease data, ownership history, and auction capabilities on a more accessible pricing model.
- Best for: brokers and mid-market investors who want deal sourcing and data in one platform
- Limitation: database is younger than CoStar’s, so historical depth is thinner
- Retail relevance: strong retail listing coverage; growing comp database
Foot traffic and visitation data
Foot traffic data answers the questions that transaction data cannot: how many people actually visit a property, when do they come, where do they come from, and how is traffic trending over time. For retail CRE, this is often the most actionable data category because it directly measures the demand side of the equation. For a primer on what foot traffic data is and why it matters, start with our pillar guide.
Why this matters for retail CRE
A retail property with strong transaction comps and excellent demographics can still underperform if the actual visitation patterns do not support the tenant mix. Foot traffic data reveals:
- Whether a property’s traffic is growing, stable, or declining before you sign a lease or close an acquisition
- How traffic at a target property compares to competitive properties in the same trade area
- Which tenants drive traffic to a center versus which merely occupy space
- Whether the trade area has been permanently altered by remote work, new development, or competitive openings
For a deep dive on this data type, see our foot traffic data guide.
Where to get it
PassBy. Provides foot traffic analytics for 1.5 million+ US retail locations through Almanac, an in-browser market intelligence platform. PassBy uses 15+ data inputs (not just mobile GPS) to model visitation, achieving 94% correlation to ground truth when validated against in-store sensors and sales data. Also covers consumer spend data alongside traffic, which eliminates the need to source spend separately. For a comparison of how PassBy stacks up against other providers, see our foot traffic data providers guide or the Placer.ai alternatives breakdown.
- Best for: retail real estate teams that need site selection, competitive benchmarking, trade area analysis, and portfolio monitoring in a single platform
- Includes: foot traffic, trade areas, demographics, spend data, competitive benchmarking, 5+ years of history, 90-day predictive forecasting
- Access: in-browser platform (Almanac), API, cloud data feeds, AI integrations (ChatGPT, Copilot, Gemini)
To see how Almanac supports real estate decisions, visit the help center or explore the platform.
Demographic and psychographic data
Demographic data tells you who lives and works near a property. Psychographic data tells you how they behave — their interests, lifestyle preferences, and spending habits. Together, they answer whether a property’s surrounding population matches the customer profile that a retailer or restaurant concept requires.
Where to get it
US Census Bureau and American Community Survey (ACS). The foundational free source for demographic data. ACS provides income, education, age, household composition, commuting patterns, and housing data at the census tract level, updated annually. Available through data.census.gov.
- Best for: baseline demographic analysis at no cost
- Limitation: data lags 1-2 years behind real time; no psychographic or behavioral data; granularity stops at the census tract level
Esri (ArcGIS Business Analyst). The industry standard for enriched demographics in CRE. Esri combines census data with proprietary models to produce current-year estimates and 5-year projections at detailed geographic levels. Their Tapestry segmentation system classifies populations into 67 lifestyle segments.
- Best for: detailed demographic profiling, drive-time analysis, thematic mapping
- Limitation: expensive for full access; the platform’s analytical depth can be overwhelming for teams that primarily need quick demographic snapshots rather than deep GIS workflows
Precisely (formerly Pitney Bowes). Provides demographic and consumer data enrichment, often used by enterprise CRE teams and brokerages for trade area profiling.
PassBy. Includes demographic and psychographic profiling as part of the Almanac platform. Rather than requiring a separate demographic data subscription, PassBy integrates visitor demographics alongside foot traffic and spend data. This means you see who visits a location, not just who lives nearby — an important distinction for retail properties that draw from a wide trade area.
The “who lives here” vs. “who visits here” distinction
Most demographic data sources tell you about the resident population around a property. That is useful, but for retail CRE it is incomplete. A shopping center in a suburban area may draw 40% of its visitors from outside the immediate catchment area. Census demographics of the surrounding neighborhood do not capture those visitors.
Foot traffic platforms like PassBy provide visitor demographics — the actual profile of people who visit the property, regardless of where they live. For retail site selection and tenant mix decisions, visitor demographics are often more predictive than resident demographics.
Spend and transaction data
Consumer spend data shows how much money flows through a trade area, at which merchants, and in which categories. For retail CRE, this answers a fundamental question: is there enough category spend in this trade area to support the tenants you want?
Where to get it
Card transaction providers. Companies like Mastercard, Visa, and American Express license aggregated, anonymized transaction data. This data shows spending volumes by merchant category, by geography, and over time. Typically accessed through data aggregators rather than directly from the card networks.
PassBy. Includes spend and transaction data from one of the world’s largest card providers as a core part of Almanac. This integration means you can see foot traffic and spend side by side for any location or trade area — answering both “how many people visit?” and “how much do they spend?” from a single platform.
Earnest Research, Second Measure, Bloomberg Second Measure. Alternative data providers that aggregate consumer transaction data for investment research. These platforms are primarily used by hedge funds and institutional investors rather than CRE operators, but the data is relevant for retail asset valuation.
Why spend data matters for retail CRE
Consider two trade areas with identical foot traffic and similar demographics. One has $50 million in annual restaurant spending. The other has $120 million. The first cannot support a premium dining anchor. The second can. Without spend data, those two trade areas look the same on paper.
Spend data also reveals category gaps — what white space analysis relies on. If a trade area has strong total spending but low spend in a specific category relative to the demographic profile, that gap represents unmet demand — and an opportunity for the right tenant.
Property and ownership data
Property data covers the physical and legal characteristics of a building: tax records, deed history, building size, year built, zoning, current ownership, and liens. This is the transactional infrastructure that supports acquisitions, dispositions, and prospecting.
Where to get it
County assessor and recorder offices. The primary source for tax records, deed transfers, and ownership information. Data is public but fragmented across thousands of county offices, which is why aggregators exist.
ATTOM. The largest aggregator of property tax, deed, mortgage, and foreclosure data in the US. ATTOM provides bulk datasets and APIs that feed into many CRE platforms and proptech applications. Used primarily by enterprise data teams and platform builders rather than individual analysts.
Reonomy (Altus Group). Uses AI to synthesize public records, proprietary data, and third-party sources into property-level profiles with ownership intelligence and contact information. Strong for prospecting and off-market deal sourcing.
PropertyShark. Detailed property reports, ownership history, and foreclosure data. Strongest coverage in major metros, particularly New York City.
Cherre. A data management platform that connects and normalizes data from multiple CRE sources into a unified view. Cherre is not a primary data source — it is a layer that sits on top of other sources and makes them queryable together. Useful for enterprise CRE firms that subscribe to multiple data providers and need to integrate them.
Listing and availability data
Listing data shows what is currently on the market: available spaces, asking rents, vacancy rates, lease types, and landlord contacts. This is the most commoditized data category in CRE, but coverage and freshness vary significantly by provider.
Where to get it
CoStar / LoopNet. The dominant source for commercial listings. CoStar’s paid platform has the widest coverage. LoopNet, its free public listing site, shows a subset.
Crexi. Growing alternative to LoopNet for commercial listings, with strong coverage in retail and industrial properties.
CRE brokerages (CBRE, JLL, Cushman & Wakefield, Colliers). National brokerages maintain their own listing databases for properties they represent. Useful for seeing what specific brokers have on the market.
Costar alternatives for retail: For retail leasing specifically, many regional developers and REIT property management teams maintain their own availability data. These are not always syndicated to CoStar.
Building a CRE data stack for retail
For retail real estate teams, the challenge is not finding data — it is assembling the right combination of sources without overspending or creating gaps in coverage.
Here is what a well-constructed retail CRE data stack looks like:
Tier 1: Essential (covers 80% of decisions)
| Data need | Recommended source | Why |
|---|---|---|
| Foot traffic + spend | PassBy (Almanac) | Traffic, spend, trade areas, demographics, and competitive benchmarking in one platform |
| Listings + comps | CoStar or Crexi | Transaction data and market availability |
| Demographics | Esri or Census (free) | Resident population profiling |
Tier 2: Valuable additions (covers specialized needs)
| Data need | Recommended source | Why |
|---|---|---|
| Institutional transaction tracking | MSCI Real Capital Analytics | Portfolio-level deal flow and cap rate benchmarks |
| Property ownership + prospecting | Reonomy | Off-market deal sourcing and owner identification |
| Lease comp verification | CompStak | Crowdsourced, verified comps that fill CoStar gaps |
| Data integration layer | Cherre | Normalizes multiple data feeds for enterprise teams |
Tier 3: Situational
| Data need | Recommended source | Why |
|---|---|---|
| Bulk property records | ATTOM | Tax and deed data for custom models |
| Alt data for investment | Earnest Research, Bloomberg SM | Transaction-level spend data for asset valuation |
| Custom mobility analytics | SafeGraph / raw panel data | For teams with data science capability building proprietary models |
The most common mistake is subscribing to an expensive transaction data platform and trying to use it for questions it cannot answer. CoStar tells you what a property sold for. It does not tell you how many people visit it, whether traffic is growing, or what those visitors spend. For retail CRE, the demand-side data (foot traffic, spend, visitor demographics) is what separates good site selection from bad.
How the data categories connect
Each category of CRE data answers a different question. The decisions that matter most require answers from multiple categories simultaneously:
Site selection. Foot traffic + demographics + spend + listings. You need to know that people visit the area (traffic), that the visitors match the tenant’s customer profile (demographics), that there is enough category spending to support the tenant (spend), and that space is available at viable terms (listings). See our retail site selection guide for the full process, or jump to the criteria checklist if you already have a shortlist.
Acquisition underwriting. Transaction comps + foot traffic trends + trade area demographics. Comps tell you market value. Traffic trends tell you whether demand is growing or declining. Demographics tell you whether the trade area can sustain the property’s current tenant mix long-term.
Portfolio monitoring. Foot traffic trends + competitive benchmarking + spend data. Monthly traffic and spend trends across your portfolio, benchmarked against competitive properties, reveal which assets are strengthening and which need intervention. See our portfolio optimization guide for frameworks.
Tenant mix optimization. Cross-visitation data + visitor demographics + spend by category. Which tenants share customers, which demographic segments are under-represented, and which spending categories have unmet demand. See our tenant mix optimization guide.
Cannibalization analysis. Trade area overlaps + foot traffic redistribution modeling. Before opening a new location, model how much traffic the new site will pull from existing ones. See our retail cannibalization guide.
Getting started
If you are building or upgrading your CRE data stack for retail, start with the data category that matters most for your current decisions:
- Evaluating sites? Start with foot traffic and trade area data. Explore Almanac →
- Underwriting acquisitions? Add transaction data to your traffic analysis. See how PassBy supports real estate teams →
- Managing a portfolio? Layer competitive benchmarking and spend data on top of your existing view. The Test & Learn tier provides 90 days of Almanac access to evaluate fit. See pricing →
FAQ
What are the main sources of commercial real estate data? The six main categories are: transaction and comp data (CoStar, RCA, CompStak), foot traffic and visitation data (PassBy), demographic and psychographic data (Census, Esri), spend and transaction data (card providers, PassBy), property and ownership data (ATTOM, Reonomy), and listing and availability data (CoStar, Crexi). No single platform covers all categories well, so most CRE teams assemble a stack of 2-4 sources.
What is the best free source of CRE data? The US Census Bureau and American Community Survey provide free demographic data at the census tract level. County assessor offices provide free property tax and ownership data, though it is fragmented across thousands of jurisdictions. For foot traffic data, PassBy’s Test & Learn tier provides 90 days of platform access for teams that want to evaluate the data before committing to an annual plan.
What data do you need for retail site selection? Effective retail site selection requires foot traffic data (visit volumes, daypart patterns, trends), trade area demographics (who visits the area, not just who lives there), consumer spend data (category spending levels), competitive landscape data (how nearby properties and competing retailers perform), and listing data (availability, asking rents, lease terms). See our site selection guide for the complete process.
How is CRE data different from residential real estate data? CRE data focuses on investment-grade metrics: cap rates, net operating income, tenant credit quality, lease structures, and trade area performance. Residential data focuses on home sale prices, mortgage rates, and property conditions. The data sources are also different — CRE relies on specialized providers like CoStar and RCA, while residential uses MLS systems and platforms like Zillow and Redfin.
What is the difference between CRE data and CRE analytics? Data is the raw input: transaction records, visit counts, demographic tables. Analytics is the application of that data to answer specific questions: is this site worth the rent, is this asset outperforming its market, should we renew this tenant’s lease? For a deeper look at CRE analytics, see our commercial real estate data guide.