Shovels.ai Review: AI-powered building permit data and API for commercial real estate site selection

Shovels.ai is a commercial real estate data platform whose primary use case is providing AI-powered permit data and building permit information across the United States. In an industry where site selection and development feasibility often stall during the municipal research phase, this platform provides an API-first approach to querying local construction activity. Rather than forcing analysts to manually scrape county websites or rely on static quarterly reports, the software continuously ingests records from thousands of jurisdictions. It then applies machine learning to standardize disparate municipal formats into a unified schema, making it possible to compare construction momentum in Florida directly against zoning approvals in Texas.

As of August 2026, the company has positioned itself as a critical infrastructure layer for CRE developers, investors, and geospatial analysts. By treating a building permit not just as a compliance document but as a leading indicator of market activity, the platform allows users to map emerging development zones before ground breaks. BestCRE classifies this system as a CRE-Native, Tier 1 database, reflecting its deep specialization in the built environment. While the market is crowded with legacy data brokers, this vendor differentiates itself through high-frequency updates and direct integrations with modern data warehouses. The result is a highly programmatic tool designed for technical teams that need to feed live construction signals into their proprietary underwriting models. This focus on developer-friendly infrastructure ensures that quantitative funds can maintain a significant informational advantage in highly competitive markets.

What Shovels.ai does and how it works

At its core, Shovels.ai functions as a massive, continuously updating pipeline for municipal building records. The system programmatically extracts data from over 2,750 jurisdictions, capturing everything from new commercial construction and multifamily developments to electrical upgrades and demolition approvals. Because local governments use wildly different naming conventions and classification systems, the software utilizes natural language processing to categorize each permit into standardized tags. This means a user searching for commercial HVAC upgrades will receive accurate results regardless of whether the local county labeled it as one code or another.

Beyond basic permit extraction, the platform builds relational profiles for both properties and contractors. Every permit is mapped to a specific parcel using geographic identifiers, which allows users to view the complete construction history of a single address or aggregate activity across a census tract. Furthermore, the system resolves contractor identities, linking disparate records to create a unified profile of a builder’s activity. A CRE developer can query the database to find which general contractors have pulled the most multifamily permits in a specific metropolitan statistical area over the last twelve months, effectively using the tool for vendor vetting and procurement.

The delivery mechanism is heavily weighted toward technical integration. Users interact with the data via a REST API, direct database shares into platforms like Snowflake and BigQuery, or through pre-built geospatial layers for Esri ArcGIS. While there is a web interface for manual lookups, the true mechanical advantage lies in its programmability. Analysts can set up automated alerts for specific zoning changes or construction milestones, feeding these signals directly into their firm’s site selection algorithms or market monitoring dashboards.

9AI Framework: the score, dimension by dimension

CRE Relevance — 10/10

As a Tier 1 CRE-Native database, Shovels.ai is fundamentally engineered for the built environment. Every data point it processes—from zoning decisions to contractor activity—directly impacts commercial real estate underwriting, site selection, and development feasibility. Unlike generalist data scrapers that happen to capture municipal records, this platform understands the specific metadata required by real estate professionals, such as parcel boundaries, property use codes, and valuation estimates. The tool’s ability to isolate commercial from residential activity, and to track leading indicators like municipal council decisions, proves it was designed with the developer and investor in mind. It solves a highly specific, notoriously difficult data bottleneck in the CRE lifecycle. In practice: Real estate principals use this data to identify emerging submarkets by tracking early spikes in commercial permit density long before new inventory hits the market.

Data Quality and Sources — 10/10

The platform ingests over 170 million permits, covering approximately 85 percent of the United States population. Data quality in the municipal sector is historically poor, plagued by typos, missing fields, and inconsistent formatting. Shovels.ai mitigates this by applying machine learning models to clean, standardize, and enrich the raw records. The system cross-references addresses with established parcel datasets, ensuring spatial accuracy. While no permit database is entirely free of gaps—especially in rural or technologically lagging counties—the vendor maintains a high standard of transparency, providing a coverage dashboard that explicitly shows where data is thin. The entity resolution for contractor names is particularly effective, merging variations of a company name into a single reliable profile. In practice: Data scientists at CRE firms can trust the standardized schema to run accurate time-series analyses without spending weeks cleaning the raw municipal inputs.

Ease of Adoption — 8/10

Adopting this platform requires a distinct level of technical maturity. While the vendor offers a basic web dashboard for simple lookups, the primary value is unlocked through its API and data warehouse integrations. Firms without dedicated data engineers or GIS specialists will face a steep learning curve. Setting up automated pipelines into Snowflake or configuring custom layers in ArcGIS demands specialized knowledge. However, for technical teams, the developer experience is excellent. The API documentation is comprehensive, the data dictionary is well-maintained, and the endpoints are logically structured. The company also provides command-line tools for rapid data extraction. It is not a plug-and-play application for the average broker, but rather a sophisticated infrastructure component for quantitative teams. In practice: A firm will need to allocate engineering hours to build the initial data pipelines and integrate the permit signals into their existing proprietary dashboards.

Output Accuracy — 9/10

Translating fragmented government records into a reliable dataset is a complex technical challenge. The platform’s natural language processing models demonstrate high accuracy in classifying permit types, successfully distinguishing between minor renovations and ground-up commercial construction. The geographic mapping is precise, linking permits to exact parcel boundaries rather than relying on rough zip code approximations. However, the accuracy of the output is ultimately constrained by the quality of the municipal input. If a county fails to digitize a record or inputs an incorrect valuation, the system can only do so much to correct the error. Despite these inherent limitations, the AI-driven normalization process significantly reduces false positives and ensures that cross-jurisdictional comparisons are mathematically sound. In practice: Analysts can confidently present market momentum reports to their investment committees, knowing the underlying permit classifications have been rigorously standardized across different state lines.

Integration and Workflow Fit — 10/10

The platform excels in its ability to embed itself into a modern commercial real estate technology stack. By offering native data shares for Snowflake, BigQuery, and Databricks, it bypasses the need for complex extract, transform, and load processes, allowing enterprise teams to query the data immediately. For spatial analysis, the deep integration with Esri ArcGIS is a major advantage, enabling GIS teams to overlay live permit activity onto their existing demographic and property boundary maps. The REST API is highly performant, supporting integrations with CRM systems or custom underwriting software. The vendor also partners with enrichment tools like Clay, facilitating automated workflows for lead generation and contractor outreach. In practice: An enterprise data team can connect the database to their Snowflake instance in minutes, instantly merging national construction signals with their internal portfolio metrics.

Pricing Transparency — 4/10

The vendor operates with a custom pricing model for its primary enterprise offerings, which obscures the true cost of adoption for prospective buyers. While the company provides an API and data warehouse integrations, the exact financial commitment required for nationwide access, historical data, and high-volume querying is not published on their primary marketing channels. Buyers must engage in a traditional sales cycle to obtain a quote tailored to their specific data consumption needs. This lack of upfront clarity makes it difficult for analysts to budget for the tool during the initial evaluation phase. As per BestCRE guidelines, a vendor that does not publish pricing cannot exceed a score of 5 in this dimension. In practice: Procurement teams must initiate contact with the sales department and undergo a scoping process to determine the exact annual cost for their required data coverage.

Support and Reliability — 6/10

As a venture-backed startup that has raised capital to expand its coverage, the company demonstrates strong operational stability, though it lacks the decades-long track record of legacy data providers. Customer support is highly regarded by early adopters, with the engineering team frequently assisting clients in optimizing their API queries and spatial joins. The platform’s uptime is excellent, and the semi-monthly data refresh cycles are executed consistently. The vendor maintains an active knowledge base and provides detailed release notes when API endpoints are updated. However, relying on a younger company for critical infrastructure always carries a marginal degree of risk regarding long-term corporate independence. In practice: Technical users receive rapid, highly competent assistance from the vendor’s engineering staff when troubleshooting complex database queries or spatial mapping issues.

Innovation and Roadmap — 9/10

The company maintains an aggressive and highly visible development cycle. Recent updates include the introduction of predictive decisions data, which tracks city council zoning changes and development approvals to forecast future construction before permits are even filed. The vendor has also integrated natural language querying capabilities, allowing users to interrogate the database using conversational prompts. Furthermore, their strategic acquisition of ReZone demonstrates a clear commitment to expanding beyond raw permits into comprehensive zoning and land-use intelligence. The engineering team consistently ships new features, such as enhanced contractor entity resolution and expanded geographic coverage, proving they are actively anticipating the future needs of quantitative real estate teams. In practice: Clients benefit from a continuously evolving dataset that increasingly focuses on predictive market signals rather than just historical construction records.

Market Reputation — 6/10

Within the niche of proptech developers and quantitative real estate funds, the platform has cultivated a strong reputation as a modern, reliable alternative to legacy permit scrapers. Technical leaders frequently praise the clean schema and developer-friendly documentation. Partnerships with established geospatial giants like Esri and Precisely have further validated the company’s standing in the enterprise market. However, among traditional, non-technical commercial real estate brokers, brand awareness remains relatively low. The tool is known as a specialized instrument for data scientists rather than a household name in the broader brokerage community. Despite this, its focused approach has earned it high marks from its core target audience. In practice: Data engineers evaluating permit providers frequently short-list this vendor due to its strong word-of-mouth credibility within the spatial analytics community.

Who should use Shovels.ai

This platform is highly recommended for:

• Quantitative investment funds requiring programmatic access to leading market indicators for algorithmic site selection.
• Enterprise development firms with in-house GIS teams utilizing Esri ArcGIS to map competitive construction activity.
• Commercial real estate lenders needing to monitor construction progress and contractor activity across a large, geographically diverse portfolio.
• Proptech software developers building proprietary applications that require a reliable, normalized feed of municipal data.

Who should look elsewhere

This platform is not suitable for:

• Traditional investment sales brokers looking for a simple, ready-made dashboard of property ownership contacts.
• Small development shops without dedicated data engineering or spatial analytics personnel to process API feeds.
• Firms seeking historical data in highly rural or unincorporated areas where municipal digitization remains non-existent.
• Users expecting a CRM-style interface for managing daily broker workflows and client communications.

Pricing and ROI

Because Shovels.ai employs a custom pricing model for its Enterprise Data Licenses, exact costs are not published for enterprise buyers. Organizations looking to ingest the full national dataset via Snowflake, BigQuery, or high-volume API must undergo a scoping process with the sales team. The final contract value typically depends on the geographic footprint required, the frequency of data refreshes, and the specific fields accessed, such as contractor histories or predictive zoning decisions.

For a commercial real estate firm, the return on investment math centers on time saved and opportunities captured. A mid-sized development group might currently spend hundreds of analyst hours per quarter manually checking municipal websites for zoning approvals and competitive permit filings. If a custom data feed costs a firm $25,000 annually, the expense is quickly offset by the elimination of manual data entry and the reduction of expensive third-party consulting reports. More importantly, the ROI is realized when the platform’s early signals allow a developer to secure land in an emerging corridor three months before competing firms notice the uptick in commercial activity. The cost of the data is negligible compared to the margin gained on a single successful early-market acquisition.

Integration and CRE tech stack fit

Shovels.ai is engineered specifically to integrate into modern, sophisticated commercial real estate technology stacks. For data engineering teams, the platform’s ability to deliver data directly into Snowflake, Databricks, and Google BigQuery is its most significant architectural advantage. This eliminates the need for custom scraping scripts and allows firms to immediately join the permit data with their internal property management or financial modeling datasets.

On the spatial analysis front, the integration with Esri ArcGIS is exceptional. The vendor provides hosted feature layers that allow GIS professionals to drop live permit data directly onto their existing maps, layering it over demographic trends and parcel boundaries provided by partners like Regrid. For teams building custom applications, the REST API is well-documented and supports webhooks for real-time alerts. Furthermore, the platform integrates with modern enrichment tools like Clay, enabling automated workflows where a new commercial permit triggers a sequence to find the developer’s contact information and push it into a CRM like Salesforce. This flexibility makes it a highly adaptable component for quantitative CRE teams.

Competitive landscape

The market for municipal data is highly fragmented, but Shovels.ai competes against several distinct classes of software. When evaluating CRE permitting and zoning tools, BestCRE has scored peers such as LandScout AI (87), PermitFlow (76), GreenLite (71), ReZone (70), GatherGov (70), and Snaptrude (70).

Compared to PermitFlow and GreenLite, which focus heavily on the workflow of actually acquiring permits and managing the municipal approval process, Shovels.ai is strictly a data intelligence play. It does not help you submit a permit; it helps you analyze everyone else’s permits.

For site selection and zoning intelligence, LandScout AI remains the category leader due to its comprehensive generative AI interface and broader focus on land feasibility. However, Shovels.ai offers deeper, more granular historical contractor data and superior API programmability for enterprise data teams. It is worth noting that Shovels.ai recently acquired ReZone, effectively absorbing a direct competitor in the zoning and municipal decisions space to bolster its predictive capabilities.

Other alternatives include legacy providers like BuildZoom and ConstructionMonitor. BuildZoom has a massive dataset but is heavily skewed toward residential contractors and lacks the enterprise data warehouse integrations that modern quantitative funds require. ConstructionMonitor provides reliable data but relies on older delivery methods like email reports rather than live REST APIs. Ultimately, for teams that want raw, normalized data delivered directly into Snowflake or ArcGIS, Shovels.ai offers a more modern, developer-centric architecture than the legacy alternatives.

The bottom line

Shovels.ai is a highly specialized, technically demanding platform that delivers exceptional value to the right user. It is not a casual research tool for the average broker. Instead, it is a heavy-duty data infrastructure component designed for quantitative funds, enterprise developers, and GIS teams who need programmatic access to construction signals. If your firm relies on manual municipal research or static quarterly reports, this database will drastically accelerate your market analysis. The requirement for technical expertise to fully utilize the API and data warehouse integrations is a barrier to entry, but it is also the platform’s greatest strength. For CRE organizations equipped to ingest and model large spatial datasets, Shovels.ai provides a critical informational advantage, turning fragmented local government records into actionable, predictive intelligence for site selection and market monitoring.

Frequently asked questions