Industrial Real Estate AI: Smarter Site Selection for Logistics
Industrial real estate has been the strongest-performing CRE asset class for five consecutive years. According to CBRE, U.S. industrial vacancy rates remain below 5% nationally, with last-mile logistics corridors in major metros hovering near 2%. The combination of e-commerce growth, nearshoring, and supply chain diversification has created sustained demand that shows no signs of slowing.
But finding the right sites in this market has become exponentially more complex.
The New Complexity of Industrial Site Selection
Traditional industrial site selection relied on relatively simple criteria: proximity to highways, available acreage, and zoning for industrial use. Today's logistics networks demand a far more sophisticated analysis.
A modern warehouse site must be evaluated against transportation network efficiency (distance to intermodal facilities, port access, last-mile delivery radii), labor market depth (warehouse worker availability, prevailing wages, commute patterns), utility infrastructure (power capacity for automated facilities, broadband for IoT), zoning and entitlement feasibility, and increasingly, environmental and sustainability requirements.
JLL's 2024 Industrial Outlook notes that the average site selection study now evaluates 15–20 variables across 200+ potential sites — a scope that overwhelms traditional broker-driven processes.
How AI Transforms the Process
AI-powered site selection processes these variables simultaneously rather than sequentially. Where a human analyst might screen 20–50 parcels per week, an AI system can evaluate 10,000+ parcels in hours against the full matrix of criteria.
The process works in layers. First, AI agents screen large geographic areas against baseline requirements — minimum acreage, industrial zoning, highway access within a defined radius. This initial filter typically reduces the universe from thousands of parcels to hundreds.
Second, the system applies more granular analysis to surviving candidates: transportation network modeling (drive-time analysis to key distribution points), labor market scoring (worker availability, wage benchmarks, competing employers), utility capacity assessment, and environmental screening (flood zones, wetlands, contamination databases).
Finally, top candidates receive detailed financial modeling — land cost, site preparation estimates, construction budgets, and operating cost projections that produce a ranked shortlist with preliminary pro formas attached.
Nearshoring and Reshoring Amplify Demand
The reshoring trend is adding a new dimension to industrial site selection. According to the Reshoring Initiative, reshoring and foreign direct investment announcements exceeded 350,000 jobs in 2024, concentrated in semiconductor manufacturing, EV battery production, and advanced manufacturing.
These facilities require sites with specific characteristics — heavy power (often 50+ MW), rail access, large water supplies, and proximity to skilled manufacturing labor. They also often qualify for state and federal incentives (CHIPS Act, IRA tax credits) that add financial complexity to site evaluation.
AI systems can integrate incentive databases with site characteristics to identify locations where the combination of physical suitability and financial incentives creates optimal total cost of occupancy.
The Last-Mile Challenge
Last-mile logistics — the final leg of delivery to the end consumer — represents the most competitive segment of industrial real estate. Amazon, Walmart, and a growing roster of third-party logistics providers are competing for infill sites near dense population centers.
These sites are scarce by definition. They require creative sourcing — identifying obsolete retail properties, underutilized industrial parcels, or land assemblages in urban areas. AI excels at this type of pattern matching, scanning property records, zoning maps, and transaction databases to identify opportunities that traditional search methods miss.
What This Means for Industrial Developers
The industrial developers gaining competitive advantage today are those who can evaluate more sites, faster, with greater analytical rigor. Build's agentic AI platform does exactly this — our AI agents screen thousands of parcels against logistics, labor, infrastructure, and financial criteria simultaneously, producing ranked shortlists with institutional-quality analysis attached.
The result is a site selection process that takes days instead of months, evaluates 10x more candidates, and produces analysis that stands up to institutional investor scrutiny. In a market where the best sites are gone within weeks of coming available, speed and precision aren't just advantages — they're requirements.