What Is Commercial Real Estate Data?
Retail vacancy in the US hit 4.2% in 2025—the lowest since 2007. Available space is scarce, competition for quality locations is fierce, and the margin for error on a new lease is razor-thin. In this market, the retailers and investors who win are the ones making decisions from data, not instinct.
Commercial real estate data is the collection of information about properties used for business purposes—office buildings, retail centers, industrial warehouses, multifamily housing. It spans everything from lease rates and vacancy levels to transaction history, tenant details, foot traffic patterns, and demographic profiles of surrounding trade areas.
What separates data from intelligence is what you do with it. Raw CRE data tells you what a property is. Layered analysis tells you whether it's worth pursuing, what it will likely produce, and what risks it carries. The gap between those two capabilities is where most real estate teams lose time and money—piecing together spreadsheets from multiple sources, manually reconciling conflicting numbers, and presenting recommendations they can't fully defend.
This guide covers the data types, metrics, sources, and analytical approaches that CRE professionals—especially retail site selection teams—use to make better location decisions in 2026.
Types of Commercial Real Estate Data
Every CRE decision draws on multiple data layers. Understanding what each layer reveals—and where it falls short—is the foundation of effective analysis.
| Data Type | What It Includes | What It Tells You |
|---|---|---|
| Property data | Size, age, condition, classification (Class A/B/C), zoning, parking, visibility | Whether the physical space fits your requirements before you visit |
| Transaction data | Sales prices, lease agreements, ownership transfers, TI allowances, free rent periods | What similar properties actually traded for and whether the market is heating or cooling |
| Market data | Vacancy rates, absorption rates, new construction pipeline, rental trends | Whether supply and demand favor tenants or landlords in a given submarket |
| Tenant data | Current occupants, lease terms, rent rolls, co-tenancy clauses, credit quality | Income stability and tenant mix risk—a grocery anchor vs. competing retailers changes the risk profile entirely |
| Demographic data | Population density, age distribution, household income, education, growth trends | Whether the surrounding population matches your target customer profile |
| Foot traffic / Mobility data | Pedestrian and device-level movement patterns, visit frequency, dwell time, origin points | How many real customers pass through or visit an area—and where they come from |
| Psychographic data | Lifestyle segments, consumer values, spending preferences, behavioral patterns | Why two neighborhoods with identical incomes produce different sales—lifestyle and values matter |
| Mortgage / Lender data | CMBS performance, debt maturity schedules, foreclosure signals, lending terms | Financial stress indicators that may create acquisition opportunities or signal distress |
Property and Transaction Data
Property data covers the physical characteristics—square footage, building age, condition, classification, parking ratio, and zoning designation. Transaction data adds the financial layer: what the property sold for, what tenants are paying, and what concessions (tenant improvement allowances, free rent periods) were negotiated.
Together, these layers establish comparable benchmarks. A Class A retail pad in a grocery-anchored center trades at a fundamentally different cap rate than a standalone strip center in a secondary market. Without accurate comp data, valuations become guesswork.
Market Data: Vacancy, Absorption, and Construction Pipeline
Market-level data reveals the supply-demand dynamics that determine pricing power. The key metrics:
- Vacancy rate — Percentage of available space that's empty. US retail vacancy at 4.2% means the market heavily favors landlords.
- Absorption rate — Net change in occupied space over a period. Positive absorption signals demand outpacing supply.
- Construction pipeline — New space under development. Minimal new retail construction (the current environment) means today's tight conditions will persist.
National trends set context, but CRE decisions happen at the submarket level. A city with 5% average retail vacancy may have individual corridors at 2% and others at 12%. Regional and submarket reports from firms like CBRE, JLL, and Cushman & Wakefield provide the local granularity that national figures mask.
Demographic and Behavioral Data
For retail site selection, demographic and behavioral data often matter more than property-level financials. The building is the container; the surrounding population is the revenue engine.
Demographic data (population density, income distribution, age, education) tells you who lives in a trade area. Psychographic data tells you why they buy—their lifestyle segments, category preferences, and spending patterns. Foot traffic data tells you how many potential customers actually move through the area and when.
These layers are where CRE data intersects with location intelligence—the applied discipline of turning geographic data into site-level business decisions.
Key CRE Metrics by Property Sector
The metrics that matter shift dramatically depending on the asset class. A retail investor cares about foot traffic and co-tenancy. An industrial investor cares about clear height and proximity to distribution corridors. Here's what the US market looked like heading into 2026.
| Sector | Vacancy (2025) | Key Trend | Cap Rate Range |
|---|---|---|---|
| Retail | 4.2% (lowest since 2007) | Minimal new construction; rent growth 2-3% YoY; grocery-anchored centers outperforming | 4.5%–7.3% (varies by credit and location) |
| Industrial | 6.8% | Recalibrating after years of explosive growth; leasing up 12% YoY in 2025 | 5.0%–7.0% |
| Office | 19.6% (record) | Class A commanding premium; hybrid work reshaping demand; sublease overhang persisting | 6.0%–8.5% |
| Multifamily | ~6.5% | Affordability crisis supporting demand; new supply wave moderating rent growth | 4.5%–6.5% |
Sources: CBRE US Real Estate Market Outlook 2026, Kaplan Group CRE Statistics 2025, Commercial Property Executive
The Six Metrics Every CRE Professional Should Track
- Vacancy rate — Available space as a percentage of total. Rising vacancy can signal oversupply or weakening demand.
- Net absorption — Net change in occupied space. US retail posted 11.3 million SF of positive absorption in Q4 2025 alone.
- Net operating income (NOI) — Revenue minus operating expenses. The truest measure of a property's profitability.
- Capitalization (cap) rate — NOI divided by market value. Lower cap rates signal lower perceived risk (and higher prices). Retail net lease cap rates averaged 6.96% in Q1 2025, up 58 bps year-over-year.
- Average lease rate — What tenants pay per square foot. Grocery-anchored centers saw 4.5% rent growth YoY—the strongest retail subcategory.
- Sales volume — Total transaction value in a market. US CRE investment hit $171.6B in Q4 2025, up 29% year-over-year, signaling returning confidence.
What CRE Data Actually Matters for Retail Site Selection
Generic CRE data guides treat all buyers the same—investors, brokers, developers, lenders. But if you're a retail real estate team evaluating where to open your next store, the data hierarchy looks different. Some layers that investors obsess over (debt maturity, CMBS performance) matter less. Other layers that investors barely consider (foot traffic patterns, co-tenancy dynamics, psychographic fit) become the entire decision.
The Retail Site Selection Data Stack
For multi-unit retailers evaluating expansion opportunities, these data inputs drive the decision—roughly in priority order:
- Trade area demographics — Does the surrounding population match your proven customer profile? Drive-time modeling based on actual mobility data defines the real catchment zone.
- Foot traffic quality — Not just volume, but composition. A site with 50,000 daily passers-by is worthless if none match your target customer. Visit frequency and dwell time matter as much as raw counts.
- Competitive density — Is the trade area saturated for your category? Are complementary businesses (traffic generators like grocery anchors) nearby?
- Analog store matching — How does this candidate site compare to your existing top performers across all measurable variables? Analog analysis grounds projections in your actual portfolio data.
- Cannibalization risk — Will this new location pull revenue from your existing stores? Quantified overlap analysis with dollar-estimated impact prevents network damage.
- Zoning compatibility — Is the property legally permitted for your use? Integrated zoning data filters out incompatible sites before your team invests in diligence.
- Revenue forecast — What will this site likely produce? Custom predictive models trained on your own store data—not industry averages—provide projections you can defend in committee.
The NAIOP Research Foundation found that data analytics "are not yet being widely used to identify building locations or influence design"—meaning the teams that do adopt data-driven site selection operate with a structural advantage over the majority still relying on manual methods.
The outcomes speak for themselves. Cavender's Western Wear went from opening 9 stores in 2024 to 27 in 2025 after adopting a data-driven approach. TNT Fireworks increased sites reviewed in committee by 10x, opening 150+ locations in under six months. Books-A-Million saved 25 hours per week per user by consolidating fragmented data workflows into a single platform.
Where to Get Commercial Real Estate Data
CRE data comes from five primary channels. Understanding what each provides—and what it doesn't—prevents overpaying for data you can get free and underinvesting in data that actually drives decisions.
Public Records and Government Sources
The foundation layer. Property deeds, tax assessments, zoning maps, and building permits are available through county assessor websites and municipal databases. Federal sources like FRED (Federal Reserve Economic Data), the US Census Bureau, and the Bureau of Labor Statistics provide macroeconomic context, demographic data, and employment figures.
The strength: free and authoritative. The limitation: scattered across thousands of jurisdictions with no standardization, requiring significant manual effort to aggregate.
Brokerage Quarterly Reports
Major firms (CBRE, JLL, Cushman & Wakefield, Colliers) publish quarterly market reports with vacancy rates, absorption figures, lease rate trends, and investment volumes by sector and submarket. These are often free and provide the most current market benchmarks available.
The limitation: they reflect the brokerage's transaction set, which may skew toward certain property types or geographies.
Subscription Data Platforms
Commercial databases aggregate property records, transaction data, tenant information, and analytics into searchable platforms. These are the workhorses for teams doing regular analysis. Pricing ranges from a few hundred dollars per month for basic access to institutional-grade subscriptions costing thousands per seat.
Alternative and Behavioral Data Sources
The fastest-growing category. The global alternative data market was valued at $11.65 billion in 2024 and is projected to reach $135.72 billion by 2030 at a 63.4% CAGR, according to Grand View Research. For CRE, the key alternative sources include:
- Foot traffic / mobility data — Aggregated, anonymized mobile device signals showing visit patterns, origin points, and cross-shopping behavior
- Satellite and aerial imagery — Parking lot occupancy, construction activity, and environmental change detection
- Consumer sentiment and web traffic — Online reviews, search trends, and social media signals as demand proxies
- Credit card and transaction data — Anonymized spending patterns by category and geography
Research combining satellite radar data with news sentiment has shown approximately 33% reduction in CRE price forecast error compared to price-only models—evidence that alternative data isn't a novelty but a genuine accuracy improvement.
Integrated Analytics Platforms
The newest category consolidates multiple data sources—demographics, foot traffic, competitors, zoning, market data—into a single analytical workspace. Instead of exporting from one tool, VLOOKUPing in a spreadsheet, and presenting from a third, integrated platforms let teams evaluate and compare sites without manual data assembly.
For retail teams juggling five or more data subscriptions, consolidation isn't just convenient—it eliminates the reconciliation errors and time waste that compound across every site evaluation.
How to Evaluate CRE Data Quality
Access to data doesn't guarantee better decisions. Bad data is worse than no data because it creates false confidence. These four criteria separate reliable inputs from expensive noise.
Recency and Update Frequency
CRE markets move fast. Data that's six months old may reflect a different market entirely—especially in sectors like industrial and retail where absorption rates shift quarterly. Ask any data provider how frequently their datasets are refreshed and what the lag time is between a transaction occurring and appearing in the platform.
Source Transparency and Methodology
How was the data collected? What's the sample size for foot traffic estimates? How are lease comps verified? Providers that can't explain their methodology clearly are asking you to trust a black box—which becomes a problem when you need to defend your analysis in front of a committee or investment partner.
One practitioner described back-checking a major foot traffic provider against internal store data and finding accuracy "like 40 to 60%, which is a coin flip." Not all providers are created equal, and transparency about methodology is the fastest way to separate reliable sources from unreliable ones.
Reconciling Conflicting Sources
Different providers will report different vacancy rates, lease comps, and traffic figures for the same submarket. This is normal—each source has different collection methodology, geographic boundaries, and reporting timelines. The best practice is to triangulate: use multiple sources, understand why they differ, and weight the most methodologically transparent source most heavily.
Acknowledging Coverage Gaps
No dataset covers everything. Niche markets, emerging property types, and smaller metropolitan areas often have sparse data. Acknowledging gaps honestly—rather than filling them with assumptions—prevents the most expensive analytical mistakes.
How AI Is Changing Commercial Real Estate Data Analysis
AI in CRE has moved from pilot to production. According to Deloitte's 2026 CRE Outlook, 76% of CRE firms are now exploring or implementing AI solutions. PropTech funding hit $16.7 billion in 2025—a 67.9% year-over-year increase—with AI-centered companies growing investment at 42% annually versus 24% for non-AI companies.
AI-Powered Site Scoring and Screening
The highest-impact AI application for retail CRE is automated site evaluation. Instead of an analyst manually pulling demographics, checking zoning, mapping competitors, and building a presentation for each candidate, AI can screen hundreds of sites against brand-specific criteria in minutes—surfacing only the candidates worth human evaluation.
The key differentiator between AI tools is transparency. A black-box score that says "78 out of 100" is useless if you can't explain what drives it. The most effective platforms show every variable and weighting, letting teams understand and challenge the model rather than blindly trusting it.
Predictive Analytics and Revenue Forecasting
Modern forecasting uses multiple model types—linear regression, decision trees, XGBoost, neural networks—selected based on how a specific brand's data behaves. The shift from one-size-fits-all models to custom-built approaches means forecasts can adapt to any business driver: membership counts for gyms, covers for restaurants, category-specific sales for specialty retailers.
The practical test: can the team presenting the forecast explain what drives the number? If the answer is "the vendor's algorithm produced it," the forecast will stall in committee. Explainable models that show their work—what GrowthFactor calls the Glass Box approach—give expansion teams the confidence to defend projections under scrutiny.
Emerging Capabilities: Spatial AI and Digital Twins
PwC and ULI identify "spatial AI"—systems trained on images, video, and geographic data to understand the physical world—as the next frontier for CRE. Applications include automated construction monitoring, satellite-based site suitability scoring, and portfolio-level geographic optimization.
Digital twins—virtual replicas of physical spaces—allow teams to model scenarios (new competitor entry, road closure, format change) before committing capital. While still emerging, these capabilities signal where CRE data analysis is heading: from retrospective reporting to real-time simulation.
Challenges in CRE Data (and How to Overcome Them)
Data Fragmentation
The single biggest pain point for CRE professionals: data lives in too many places. Demographics from one source, foot traffic from another, comps from a third, zoning from the municipality, and internal performance data in a spreadsheet. Each tool has its own format, login, and export process.
The result: teams spend more time assembling data than analyzing it. One retail analytics team described their workflow as "an ugly Google form into a Google sheet" where they had to "VLOOKUP and match everything to a unique identifying number." Integrated platforms that consolidate data sources eliminate this friction.
Cost of Access
Premium CRE data subscriptions can be prohibitive for smaller firms. Enterprise listing databases charge per seat, creating a perverse incentive to limit access. The opportunity: modern platforms with tiered pricing and unlimited seats make sophisticated data accessible to growing teams—not just to firms with six-figure data budgets.
Standardization
Data from different sources uses different geographic boundaries, reporting periods, classification systems, and measurement standards. Automated normalization and integration—where a platform handles the reconciliation layer—lets analysts focus on interpretation rather than data engineering.
Privacy and Compliance
Foot traffic and mobility data face increasing regulatory scrutiny. Multiple new US state privacy laws took effect in 2025, and FTC enforcement on geolocation data intensified. Responsible platforms use aggregated, anonymized signals and can document their data sourcing and compliance posture. This is now a vendor evaluation criterion, not just a legal footnote.
Frequently Asked Questions
What is commercial real estate data?
Commercial real estate data is the collection of information about properties used for business purposes—including property characteristics, transaction records, lease terms, market trends, demographics, and behavioral data like foot traffic. CRE professionals use it for property valuation, investment analysis, site selection, and portfolio management.
What are the most important metrics in commercial real estate?
The six core metrics are: vacancy rate (available vs. total space), net absorption (change in occupied space), net operating income (revenue minus expenses), capitalization rate (NOI divided by market value), average lease rate (per-square-foot tenant cost), and sales volume (total transactions in a market). For retail, add foot traffic volume, trade area demographics, and co-tenancy composition.
What is the difference between commercial real estate data and location intelligence?
CRE data is the raw information—property records, transactions, market statistics. Location intelligence is the applied discipline of layering CRE data with geographic, demographic, and behavioral data to make specific site-level decisions. CRE data tells you what exists. Location intelligence tells you what to do about it.
How do retailers use CRE data for site selection?
Retail teams layer trade area demographics, foot traffic patterns, competitive density, zoning compatibility, and analog store comparisons to evaluate candidate sites. The best-performing teams screen 30-50 candidates per opening—using data to narrow the funnel—then apply human judgment to the shortlist. The shift from manual evaluation (5-10 sites) to data-driven screening (50-200+ sites) dramatically improves the quality of the final selection.
What are the best sources for commercial real estate data in 2026?
Five primary channels: public records and government databases (FRED, Census Bureau, county assessors) for foundational data, brokerage quarterly reports (CBRE, JLL, Cushman & Wakefield) for market benchmarks, subscription databases for transaction comps and property records, alternative data providers for foot traffic and behavioral signals, and integrated analytics platforms that consolidate multiple sources into a single workspace.
How accurate is commercial real estate data and what affects quality?
Accuracy varies significantly by provider and data type. Transaction data from public records is generally reliable but may lag. Foot traffic estimates depend on mobile device panel size and validation methodology—quality varies widely between providers. The best practice is to triangulate across multiple sources and prioritize providers that are transparent about their methodology, sample sizes, and update frequency.
What CRE data do investors need to evaluate a property?
At minimum: comparable sales data (recent transactions for similar properties), current lease terms and tenant credit quality, market vacancy and absorption trends, the property's operating statement (NOI), and cap rate benchmarks for the asset class and submarket. For retail properties, add trade area demographics and foot traffic analysis to assess revenue sustainability.
How is AI being used in commercial real estate data analysis?
AI is deployed in four primary areas: automated site screening (evaluating candidates against brand criteria in seconds), predictive revenue forecasting (custom models trained on a brand's own store data), market trend analysis (processing thousands of data points to identify emerging patterns), and data integration (normalizing and reconciling information from disparate sources). According to Deloitte, 76% of CRE firms are now exploring or implementing AI solutions.
How much do commercial real estate data platforms cost?
Pricing spans a wide range. Basic listing database access may cost $200-500 per month. Specialized analytics platforms with foot traffic, demographics, and scoring typically run $500-3,000 per month depending on features and market coverage. Enterprise solutions with custom forecasting models and dedicated analyst support range higher. Per-seat pricing is common among legacy platforms; newer platforms increasingly offer team-based or unlimited-seat models.
How does CRE data differ for retail versus office or industrial properties?
Each sector emphasizes different data layers. Retail prioritizes foot traffic, consumer behavior, trade area demographics, and co-tenancy dynamics. Office focuses on employment density, commuting patterns, sublease inventory, and amenity quality. Industrial emphasizes proximity to distribution corridors, clear height, dock configuration, and last-mile logistics efficiency. The metrics overlap (vacancy, NOI, cap rates), but the story behind those metrics is sector-specific.
Ready to consolidate your CRE data workflow? See how GrowthFactor replaces 10+ tools for retail site selection teams.