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Data Science AnalyticsTop 10 Best Self Storage Data Services of 2026
Compare self storage data providers for property research, using CBRE Research, Cushman & Wakefield, and Northmarq to match needs and tradeoffs.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
CBRE Research is the best pick for relocation planners and analysts because its regional storage market census and trend framing turns facility context into planning-ready intelligence, while SkyView Advisors suits storage teams that need facility-grade benchmarking datasets packaged for recurring operator reporting.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
CBRE Research
Regional storage market census and trend framing delivered as planning-ready intelligence, not raw unit feeds.
Best for: Fits when relocation planners and analysts need consistent multi-metro market research context.
Cushman & Wakefield
Editor pickResearch-led facility intelligence supports market-to-portfolio benchmarking with repeatable methodology across recurring scopes.
Best for: Fits when teams need facility and market benchmarking for planning cycles, not real-time API ingestion.
Northmarq
Editor pickFacility census outputs tailored for storage underwriting and competitor comp set build workflows, not just generic market reporting.
Best for: Fits when investment, underwriting, and benchmarking teams need facility-level self-storage inputs.
Comparison Table
CBRE Research
enterprise_vendorCBRE Research publishes commercial property market reports, forecasts, investment analysis, and sector benchmarks.
Regional storage market census and trend framing delivered as planning-ready intelligence, not raw unit feeds.
CBRE Research supports storage moving relocation decisions by pairing storage market context with broader economic signals that influence move-in and move-out patterns. The provider’s deliverables focus on market definition, demand and supply dynamics, and benchmarking context for property teams and planners. Fit is strongest when teams need research continuity across multiple geographies and want consistent definitions across reports.
A key tradeoff is that CBRE Research is less suited for high-granularity unit inventory, unit mix, or delinquency modeling that requires raw unit-level inputs. It works best for relocation and expansion planning where a facility catchment area view and trend context matter more than automated provisioning of datasets into an operational stack.
- +Strong facility census context for metro and regional market sizing
- +Research deliverables align with planning workflows for relocation and expansion
- +Consistent definitions across geographies for multi-region comparisons
- +Useful for executive-ready benchmarking narratives
- –No unit-level inventory feed for direct revenue management pipelines
- –Automation and API surface are not the primary channel for delivery
Real estate strategy teams
Metro sizing for relocation expansion
Faster market screening
Corporate planning analysts
Demand-supply scenario planning
Clear scenario assumptions
Show 1 more scenario
Investor relations teams
Portfolio-level market benchmarking
More consistent reporting
Teams translate market research into comparable storylines for tenant mix and demand context.
Best for: Fits when relocation planners and analysts need consistent multi-metro market research context.
Cushman & Wakefield
enterprise_vendorCushman & Wakefield provides self-storage market research, valuation, consulting, and investment services.
Research-led facility intelligence supports market-to-portfolio benchmarking with repeatable methodology across recurring scopes.
Cushman & Wakefield is a fit for teams that want storage-specific market intelligence anchored to broader commercial real estate research processes, not just unit-level data scraping. Facility-level reporting is commonly used for trade-area interpretation, competitive comp set context, and move and occupancy trend analysis for planning cycles. Governance is typically delivered through vetted research outputs and repeatable methodology, which reduces variation between analyst runs when the same market scope is reused.
A tradeoff is that data access patterns often depend on engagement-based delivery rather than standardized API provisioning for high-throughput pipelines. Teams planning quarterly planning updates work best when they can align to recurring market packs and benchmarking cycles, and they can ingest files into their own data warehouse.
- +Facility and market intelligence anchored to commercial real estate research methods
- +Benchmarking support supports underwriting and portfolio planning workflows
- +Recurring market updates align with quarterly business planning rhythms
- +Methodology-driven outputs reduce analyst variance across repeated scopes
- –Automation depends on engagement delivery patterns rather than a standardized API surface
- –Facility refresh timing may lag behind real-time operational changes
- –Data granularity for unit-level modeling can require extra scope definition
- –Integration effort increases when internal schemas need normalization
Real estate investment teams
Storage underwriting using market benchmarks
More consistent underwriting decisions
Revenue management leaders
Competitive positioning within trade areas
Sharper competitive framing
Show 2 more scenarios
Portfolio strategy teams
Same-store performance planning
Clearer performance gap focus
Facility-focused reporting supports cohort comparisons and planning targets across properties.
Development and acquisitions
Supply demand planning for catchment
Better timing of decisions
Market indicators help assess pipeline impacts and absorption expectations by area.
Best for: Fits when teams need facility and market benchmarking for planning cycles, not real-time API ingestion.
Northmarq
enterprise_vendorNorthmarq provides self-storage investment sales, debt placement, valuation, and market analysis.
Facility census outputs tailored for storage underwriting and competitor comp set build workflows, not just generic market reporting.
Northmarq delivers self storage market data used for facility census and unit inventory planning, with an emphasis on investor and operator workflows. The service is structured around facility-level coverage and performance indicators, which supports supply and demand comparisons across specific trade areas.
Northmarq also supports operational planning needs like assessing occupancy conditions and rental rate benchmarks tied to geography and market segmentation. Automation depends on the available export and integration methods, so teams typically use it through analyst workflows or data feeds rather than ad hoc charting.
- +Facility-level coverage supports census building and unit mix modeling
- +Market segmentation enables street, trade-area, and competitor comp work
- +Operational metrics support occupancy and rent benchmark comparisons
- +Analyst-friendly exports fit spreadsheet and reporting pipelines
- –Integration depth and API surface are limited for automated ingestion
- –Data model transparency is thinner than schema-first data providers
- –Turnkey governance controls for RBAC and audit logs are not a clear strength
- –Requires analyst review for mapping issues when stitching custom geographies
Investor portfolio analysts
Track trade area occupancy trends
Improves investment thesis accuracy
Self storage acquisition teams
Benchmark rental rates by geography
Strengthens acquisition model assumptions
Show 2 more scenarios
Operator revenue analysts
Plan unit inventory and pricing
Reduces planning guesswork
Enables analyst workflows using facility-level indicators tied to specific trade areas.
Asset management operations
Monitor performance against local benchmarks
Standardizes performance monitoring
Feeds automated reporting on census and inventory conditions to support ongoing portfolio reviews.
Best for: Fits when investment, underwriting, and benchmarking teams need facility-level self-storage inputs.
JLL
enterprise_vendorJLL provides self-storage market research, valuation, transaction advisory, and development consulting.
JLL packages facility and market intelligence into commercial real-estate decision workflows that match underwriting and portfolio review needs.
JLL delivers self-storage market data with an established property-data footprint and built-for-commercial-real-estate workflows. Its core capability is market and facility intelligence tied to underwriting and portfolio analysis use cases, with data products intended for recurring reporting and decision cycles.
- +Commercial real-estate data context supports market sizing and underwriting workflows
- +Facility-level analytics support occupancy and rate diagnostics for portfolio decisions
- +Recurring reporting use cases fit teams running monthly or quarterly review cycles
- +Integration into enterprise reporting workflows is supported through delivery artifacts
- –Unit-level extracts and API-first access are not the primary delivery shape
- –Turnaround and customization require engagement rather than fully self-serve configuration
- –Governance controls are less transparent than API-native self-service data platforms
- –Depth across every unit-mix and delinquency workflow may need tailored packaging
Commercial real estate lenders
Prepare self storage market sizing checks
Faster underwriting assumptions
Portfolio asset managers
Run occupancy and unit inventory reporting
Cleaner market comparison notes
Best for: Fits when portfolio analysts need integrated market and facility intelligence for recurring underwriting and reporting cycles.
Berkadia
enterprise_vendorBerkadia provides self-storage investment sales, debt advisory, valuation, and market research.
Berkadia’s institutional market-research workflow packages storage facility intelligence for repeatable underwriting decisions.
Berkadia’s self-storage data coverage is oriented toward facility census and property screening needs used in underwriting and ongoing portfolio monitoring. Its research output is aligned to common storage decision workflows such as competitive set framing, trade-area style analysis, and performance benchmarking across a market footprint. Teams get stronger traction when the data is consumed inside established investment or asset management processes that already standardize assumptions and reporting templates.
- +Facility-focused market research supports underwriting and portfolio monitoring workflows
- +Outputs align with real estate decision cycles used by investment and asset teams
- +Competitive framing supports repeatable market comparisons across target geographies
- +Consistent reporting structure fits ongoing refresh needs
- –Less suited for pure self-serve exports and raw feed ingestion
- –Automation depth depends on the delivery pattern agreed for the engagement
- –Governance controls are not the primary strength compared with data-first vendors
- –Unit-level granularity may not match needs that require immediate self-serve drilldowns
Investment research teams
Screen storage targets with facility context
Faster target shortlists
Asset management teams
Benchmark performance across markets
Clearer variance attribution
Show 2 more scenarios
Revenue management analysts
Support pricing and leasing assumptions
Tighter underwriting assumptions
Research-driven market inputs inform rent assumption work used in internal models.
Due diligence teams
Validate competitive set for deals
More defensible deal memos
Competitive framing supports diligence narratives built around market comparables and trade-area logic.
Best for: Fits when investment and asset teams need consistent market refreshes for storage underwriting.
SkyView Advisors
specialistSkyView Advisors provides self-storage brokerage, valuation, feasibility studies, and market analysis.
Facility-focused dataset preparation for storage benchmarking outputs that map to operator reporting workflows.
SkyView Advisors differentiates through a data services focus aimed at self-storage operators and analysts that need facility-level market inputs and reporting outputs. The service centers on pulling and structuring market and facility intelligence for workflows like benchmarking and performance comparisons across geographic or competitive sets.
- +Facility-level market and inventory oriented deliverables for storage operators
- +Structured outputs for benchmarking workflows across markets and competitive sets
- +Repeatable data pulls suited to ongoing reporting cycles
- +Analyst-friendly packaging for combining market inputs with internal metrics
- –Limited public detail on API surface and automated provisioning workflows
- –Data governance and role-based access controls are not clearly documented
- –Setup and configuration may require dedicated internal coordination for pipelines
- –Less suited to fully self-serve exploration without analyst support
Self storage operators
Benchmark rent and occupancy trends
More consistent reporting sets
Market analysts
Compare facilities across competitive sets
Repeatable facility comparisons
Best for: Fits when storage teams need facility-grade market datasets packaged for benchmarking and recurring reporting.
Newmark
enterprise_vendorNewmark provides self-storage research, valuation, investment sales, and consulting services.
Curated facility and unit inventory inputs designed for property-level benchmarking workflows, not ad hoc analytics exports.
Newmark provides self-storage market data through a set of storage-focused datasets tied to its real estate research and advisory operations. The coverage is oriented to facility census and unit inventory style inputs that feed property-level benchmarking, competitive comp sets, and rent and occupancy analysis.
- +Facility-level dataset inputs support benchmarking across like-for-like comparisons
- +Market research framing helps connect supply and demand signals to operating outcomes
- +Unit inventory and unit mix inputs support operational reporting and cohort slicing
- +Outputs fit common revenue management and trade-area analysis workflows
- –API surface is narrower than competitors that offer automated provisioning for new extracts
- –Requires identifier mapping work to keep facility joins consistent across feeds
- –Automation for frequent refresh cycles is less explicit than data vendors focused on event updates
- –Limited visibility into unit-level achieved-rate constructs compared with specialized providers
Self-storage operators
Support asset management governance reporting
Recurring performance governance
Real estate investment teams
Prepare market-supported investment packets
More defensible investment memos
Show 1 more scenario
Investors planning acquisitions
Source properties using market intelligence
Wider acquisition target list
Applies brokerage-led self storage market insights to expand acquisition pipelines.
Best for: Fits when planning, underwriting, and benchmarking teams need curated facility and unit-level inputs.
Marcus & Millichap
enterprise_vendorMarcus & Millichap provides self-storage investment sales, market research, valuation, and advisory services.
Research deliverables shaped around brokerage and investment decision workflows, not unit inventory feed automation.
Marcus & Millichap delivers self-storage market data through its real estate intelligence and analytics services tied to investment research and brokerage workflows. It is distinctive for combining facility and market intelligence with an operator-oriented perspective used for underwriting, comp set building, and decision support.
- +Investment research context helps translate market signals into underwriting narratives
- +Market and facility intelligence supports faster competitor comp set creation
- +Region-focused research fit aligns with trade-area and catchment analysis workflows
- +Analytics outputs map well to property-level benchmarking for internal decisioning
- –API and automation surface are not positioned for unit-level data provisioning
- –Governance controls for data access and audit trails are not framed for enterprise admins
- –Unit-mix depth is less explicit than providers built for unit-level performance extraction
- –Automation into internal revenue management systems requires manual integration work
Self-storage developers
Assess feasibility with supply and demand evidence
Benchmark-backed feasibility view
Investment committees
Create decision-ready investment materials
Market-grounded decision packets
Best for: Fits when investment teams need research-grade market and facility comparisons for underwriting and benchmarking.
Radius+
specialistRadius+ provides self-storage market intelligence, facility data, and feasibility analysis.
Facility-centered coverage designed for comp-set driven benchmarking across portfolios and market trade areas.
Radius+ is a self storage data service provider focused on operational and competitive inputs for storage operators and analytics teams. Core capabilities center on facility and inventory coverage used for portfolio and market analysis, with outputs that support unit-level performance comparisons.
- +Facility and inventory coverage supports repeated benchmarking cycles
- +Competitive market inputs help construct comp sets for trade-area analysis
- +Data outputs align with operational reporting workflows for occupancy and rate tracking
- +Service packaging supports batch analytics for portfolios and rollups
- –Tuning output granularity for unit-mix style reporting takes governance discipline
- –Automation and API surface depth can be limiting for real-time ingestion targets
Self-storage operators
Benchmark units against nearby competitors
Improved unit-level pricing decisions
Revenue operations teams
Build occupancy inputs for forecasts
More reliable occupancy forecasting
Show 2 more scenarios
Storage analytics teams
Track facility and inventory coverage trends
Faster market insight cycles
Analytics teams refresh portfolio datasets with inventory and facility coverage for ongoing market analysis.
Corporate development teams
Assess acquisitions with competitive signals
Better acquisition target evaluation
Corporate development uses operational inputs and performance-style signals to compare target sites and nearby markets.
Best for: Fits when storage operators need recurring market benchmarking and operational reporting from facility-level inputs.
Self Storage Association
otherThe Self Storage Association provides industry research, operating benchmarks, market reports, and sector publications.
Association research outputs package self storage market benchmarking into member-friendly reports rather than a developer data product.
Self Storage Association aggregates industry-facing self storage market information through its education, research publications, and policy work tied to the self-storage operator community. It is distinct in how it publishes occupancy and rental related benchmarking content as member-oriented industry resources rather than a contract-grade unit level data API.
- +Research publications reflect operator and association workflows
- +Benchmarking content supports internal market discussion and planning
- +Documentation style fits analysts who work from reports and citations
- +Community orientation improves interpretability of market narratives
- –No clearly defined data API or automation surface for programmatic ingestion
- –Unit-level performance datasets are not the primary delivery format
- –Coverage is oriented to industry publishing instead of transactional refresh
- –Governance controls for data access and audit trails are not productized
Operator benchmarking teams
Use occupancy and rental research in planning
Better informed planning conversations
Industry policy groups
Inform guidance using benchmark context
Stronger evidence for guidance
Best for: Fits when research-first teams need association-backed benchmarking context for market planning.
Conclusion
After evaluating 10 self storage data, CBRE Research stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Self Storage Data
How do the major self-storage data providers handle API access and integrations?
Which service providers integrate data with BI tools and internal analytics pipelines using automation?
What data model and schema details should be expected for facility census and unit inventory fields?
How is tenant turnover and move activity handled across providers?
When do teams choose facility census workflows over unit-level performance feeds?
What breaks if a self-storage data service cannot align records to consistent facility identifiers?
How do onboarding and provisioning models differ across self-storage data services?
Which providers are best suited for underwriting and asset management refresh cycles?
Where does integration trade off against self-serve analytics in the self-storage data market?
What governance controls and audit capabilities are commonly needed for enterprise self-storage data use?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Self Storage Data
What separates self storage data tools is how consistently they report operational performance and how quickly they refresh unit and tenancy signals. The biggest difference you can feel in a demo is update frequency and whether occupancy rate and rental rate data match what the tool sources and documents.
Update frequency and timestamp clarity
Why it matters
Update frequency and timestamp clarity decide whether the self storage data you act on is current or stale. For occupancy rate and rental rate decisions, unclear dates create wrong comparisons.
How to check
Ask for a sample record export that includes timestamps for occupancy rate and rental rate data fields. Compare the newest timestamp shown in the UI with the last-updated date in documentation or the changelog.
Source transparency for each metric
Why it matters
Source transparency for each metric keeps self storage data auditable when analysts challenge occupancy rate or delinquency rate trends. If sources are vague, you can’t reproduce the story behind changes.
How to check
Pick three fields from the screen—at least occupancy rate, rental rate data, and delinquency rate—and ask what drives each one. Request a sample citation list or field-level notes that map each metric to a source.
Unit and facility granularity depth
Why it matters
Granularity depth determines whether the self storage data supports unit mix analysis or only coarse benchmarking. More granularity helps distinguish move-in volume patterns from market averages.
How to check
Filter to a single facility you expect to be in the dataset and see what breakdowns are available (facility-level vs unit-level vs time series). Ask whether unit mix and unit-level performance are accessible, then export one facility to verify.
Missing data handling and revisions
Why it matters
Missing data handling and revisions affect whether self storage data gaps are meaningful or just artifacts. With rental rate data and occupancy rate, quiet backfills can rewrite earlier conclusions.
How to check
Request the tool’s approach to gaps (how it flags missing periods) and show a month where data appears incomplete. Check whether prior months were revised by comparing two exports taken at different times or by viewing revision notes.
Export workflow and API availability
Why it matters
Export workflow and API availability determine how reliably analysts can repeat self storage data pulls in reports. If exports are manual-only, the data becomes harder to keep consistent across stakeholders.
How to check
Ask for an example API request or automated export workflow that returns the same fields used on the dashboard. Use a small sample payload and confirm it includes metric names, timestamps, and facility identifiers.
Red flags
- Timestamps are present but not explained, so update frequency looks ambiguous
- Occupancy rate and rental rate data change without revision history
- Unit mix appears promised, but exports only return facility-level aggregates
Fit by situation
- If we refresh reports monthly and need defensible comparisons, prioritize update frequency and timestamp clarity.
- If we write memos that cite where numbers come from, prioritize source transparency for each metric.
- If we test assumptions with unit mix details instead of market averages, prioritize unit and facility granularity depth.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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