Life Sciences Real Estate: AI-Powered Lab Site Selection
The life sciences real estate sector is navigating its first meaningful correction in a decade. After years of speculative development driven by record biotech funding, the market now carries approximately 18.7 million square feet of surplus lab space nationally, concentrated in Boston/Cambridge, the San Francisco Bay Area, and San Diego.
For developers and investors, this correction creates both risk and opportunity. The risk: poorly located or poorly specified lab projects face extended lease-up timelines and diminished returns. The opportunity: the underlying demand drivers — NIH funding, pharmaceutical R&D spending, biotech venture capital — remain structurally strong.
The differentiator between the two outcomes is site selection precision.
Why Lab Site Selection Is Uniquely Complex
Life sciences facilities have requirements that make site selection far more complex than conventional office or industrial development.
Talent proximity. Life sciences companies cluster near research universities and medical centers because they need access to PhD-level talent. A lab building 30 minutes from a major research institution is fundamentally less valuable than one 10 minutes away. Site selection must model commute patterns, housing costs, and talent pool depth at a granular level.
Infrastructure requirements. Lab buildings require significantly more power (50-80 watts per square foot vs. 5-8 for office), robust water and sewer capacity, specialized waste handling, and often proximity to vivarium facilities or clinical trial sites. Not every site can physically support these requirements.
Funding ecosystem proximity. Biotech startups — the primary demand driver for spec lab space — locate near their venture capital investors. The concentration of life sciences VC in Boston, San Francisco, and San Diego is not coincidental. Emerging clusters in Research Triangle, Maryland, and Philadelphia are growing as VC presence expands.
Regulatory environment. Local permitting for lab facilities varies dramatically. Some municipalities actively court life sciences development with streamlined approvals and tax incentives. Others impose requirements (environmental review, community benefit agreements, use restrictions) that extend timelines and increase costs.
Traditional vs. AI-Powered Site Selection
Traditional life sciences site selection relies on broker knowledge, market reports, and manual analysis of the factors described above. A typical site search takes 3-6 months and evaluates 5-15 potential locations.
AI-powered site selection fundamentally changes this process. Machine learning models can simultaneously evaluate hundreds of potential sites against dozens of weighted criteria: talent pool density and quality (using university output data, LinkedIn workforce data, and H1-B filing patterns), infrastructure capacity (using utility data, municipal planning documents, and environmental databases), competitive landscape (tracking every lab project under construction, in planning, or recently delivered), funding ecosystem strength (analyzing VC deal flow, NIH grant awards, and pharmaceutical company expansion patterns), and regulatory favorability (scoring municipalities based on historical permitting timelines and incentive availability).
The result: a comprehensive site evaluation that would take a human team months is produced in days, with quantitative scoring that enables apples-to-apples comparison across markets.
Emerging Markets to Watch
AI-driven analysis of life sciences real estate fundamentals points to several emerging markets poised for growth as the sector recovers.
Research Triangle, NC. Duke, UNC, and NC State provide a deep talent pool. Operating costs are 40-50% below Boston. State incentives are aggressive. Lab vacancy remains below the national average.
Greater Philadelphia. The University of Pennsylvania, Drexel, and a cluster of pharmaceutical headquarters (GSK, Merck nearby) create strong demand fundamentals. The Navy Yard and University City submarkets are attracting institutional capital.
Maryland/DC corridor. NIH proximity, Johns Hopkins, and a growing biodefense sector drive demand. The state offers significant tax credits for life sciences companies.
Build's Life Sciences Capability
At Build, our agentic AI platform evaluates life sciences sites with a depth and speed that traditional methods cannot match. We integrate talent data, infrastructure analysis, competitive tracking, and regulatory assessment into a single workflow that delivers institutional-quality site selection reports.
For life sciences developers and investors navigating the current correction, precision in site selection is not optional — it is the difference between projects that lease and projects that sit vacant. AI makes that precision achievable at scale.