(A conversation with campus space strategist Chris Morett distilled for investors, developers, and occupiers.)
Higher ed real estate rarely makes front-page headlines, but it quietly moves billions of dollars in construction, leasing, operations, and community impact. In this episode of The Real Finds Podcast, I sat down with Chris Morett, a longtime university space leader (Rutgers) turned consultant, to unpack how campuses actually plan, schedule, and adapt their space and what that means for commercial real estate (CRE) in the decade ahead. Below is a field guide drawn from our conversation: the incentives, frictions, and opportunities shaping academic real estate and the signals private-sector players should track now.
Chris didn’t set out to become a facilities strategist. He taught, applied for a couple of university roles he didn’t get, then received a handwritten note asking him to consider a third: Director of Scheduling & Space Management at Rutgers. He took it and stayed ten years. Because he had an excellent team handling the daily fires, he could zoom out: learn the institution, attend talks, build relationships, and translate mission into space decisions.
“Mission-driven spaces are important. As we know, they often aren’t produced by default through the traditional design process.”
That perspective begins with purpose, then fits the space around it, and runs through everything that follows.
Chris splits underutilization into valid and fixable causes.
Valid reasons:
Inventory mismatch: Section sizes don’t perfectly match room sizes. Sometimes a 20-person seminar ends up in a 50-seat classroom because the 25-seat rooms are full.
Technology constraints: A smaller class might need the only room with the right AV.
Human constraints: Adjuncts teaching across multiple campuses, caregiving schedules, or faculty needs that limit time-of-day flexibility.
Fixable reasons:
Cultural scheduling habits: “We don’t teach before 10 or after 3.” That may reflect a subset of preferences, but it starves capacity at the edges of the day and week.
Department-controlled rooms: Centrally scheduled classrooms are consistently utilized more efficiently. Systems that pool space beat siloed control.
Takeaway for CRE: Most underutilization isn’t a “build less” problem; it’s a governance and scheduling problem. In office terms, think portfolio-wide booking, shared amenities, and data-driven allocations rather than departmental fiefdoms.
Comparing public and private universities is messy—privates skew smaller and bifurcate into ultra-wealthy and not-so-wealthy institutions. But two differences matter for space behavior:
Ownership vs. leasing: Universities typically own their space. Without an expiring lease to force a decision, the short-term financial payoff from squeezing more efficiency is weaker than in a corporate HQ with a five-to-ten-year lease horizon.
Information asymmetry: A provost can’t perfectly know each lab’s needs. Data helps; relationships help. But the work is diverse and specialized. That asymmetry slows top-down optimization—again, more a governance challenge than a construction problem.
CRE parallel: Companies with long, fully owned campuses behave differently from tenants with ticking leases. When you’re underwriting university-adjacent strategies, assume slower decision cycles—then bring tools that shrink them (data, dashboards, shared-use models).
Even in an ownership culture, leasing sometimes makes sense for universities:
Speed: Swing space during renovation or program launches.
Bundled services: Custodial, TI, and reconfiguration embedded in the deal.
Decentralized budgets: A college within a university may rent because it can control that line item faster than drawing on central capital.
On the flip side, universities can (and increasingly do) lease out surplus space, especially in urban markets with nearby corporate or startup tenants. Add in incubators near research cores (Chicago’s UIC and UChicago; Rosalind Franklin’s ecosystem), and the line between campus and city keeps blurring.
Play for owners/developers: Think program-ready suites (labs, flex classrooms, training space) and mission-adjacent uses that universities value, workforce training, community clinics, startup wet labs, or partner offices tied to sponsored research.
In the city, “town and gown” are inseparable. Alumni work nearby, executives can hop a train to guest-lecture, and third-party partnerships are easier to stand up. Rural or regional campuses face more distance friction; they may need bigger, bolder redevelopment moves to stay relevant, fewer incremental tweaks, more catalytic projects.
Investor lens: In urban settings, expect incremental mixed-use layers (research + startup + housing + retail). In rural markets, expect episodic transformations led by a developer/operator with a pipeline of tenants and the ability to program the campus like a district.
Birth trends are flattening new-frosh cohorts. Chris sees a partial offset in the “some college, no degree” population and workforce reskilling. But the housing math shifts:
Flagship publics can still grow (or hold steady) and backfill beds.
Smaller publics and tuition-dependent privates carry the most risk.
Creative partnerships—like community college students living in a private college’s dorms—foreshadow new revenue plays.
Signal list for investors:
Multi-year enrollment trajectory
Net tuition dependency vs. endowment support
Housing reliance in the operating budget
Early moves toward mergers, affiliations, or program consolidation
Move sooner, and options are plentiful; move late, and the playbook narrows.
Regardless of political lean, funding instability and short lead times ripple through capital planning:
Proposed changes to research cost reimbursements can chill lab projects.
International student visa shifts can dent enrollment (and TA/RA pipelines), with effects that show up a cycle later.
All of this lands atop the evergreen burden of deferred maintenance, which competes with new construction for limited dollars.
CRE translation: Expect a stop-start cadence in lab and academic capital. Developers who can deliver speed, specialized buildout, and operations depth (FM/commissioning/EDD) become constraint-removers—and therefore indispensable.
We compared labs and AI/data centers through a developer’s lens. My take: most developers can’t play in hyperscale AI; labs are high-barrier but still accessible to the top 20% of capable sponsors. Chris’s view: the mix of facilities is changing fast; winners will add services (analytics, ops, program management) around the core development engine to stay aligned with shifting demand.
Practical lane for mid-market sponsors:
Spec-ready lab conversion kits (MEP, vibration, exhaust zoning) for quick change-of-use
Shared instrumentation and managed services to reduce tenant capex
Program partnerships with universities to pre-align users and grants
Urban institutions can’t just “add a quad.” Chris sees a rise in activist space planning:
Build university-controlled, high-performance research buildings with flexible labs and allocate space by productivity and mission, not departmental tradition.
Leverage adaptive reuse where bones allow (structure, vibration, shafts). Newer academic buildings with straightforward systems convert more readily than ornate legacy halls.
For private owners nearby: If you control buildings that can meet lab or learning specs with rational capex, you’re positioned for long-term, credit-worthy demand especially when paired with university programs or workforce initiatives.
Chris’s single strongest recommendation: Don’t wait to approach campuses until they’re distressed. Build relationships with healthy institutions and at-risk ones alike. Many colleges lack the internal staff to monetize or right-size their space. Bring a package:
Utilization and scheduling analytics
Portfolio scenarios (consolidate, vacate, convert, lease out)
Capital stack creativity (P3, ground leases, credit tenant structures)
Community alignment (town-gown wins baked into entitlements)
“Look at yourself as the remover of constraints for a willing partner.”
That’s the role universities need from capable private-sector players.
Here’s Chris’s forecast, compressed:
More developer involvement across planning, delivery, and operations.
Faster iteration in research facilities driven by science, not tradition.
Data-rich decision-making (proptech + scheduling + utilization) to fight information asymmetry.
Revenue creativity (incubators, third-party occupancy, continuing ed, community partnerships) to buffer enrollment volatility.
The institutions that pair mission clarity with portfolio agility will thrive—and the developers who help remove roadblocks will be at the table for a long time.
Underwrite governance, not just bricks. Ask who controls rooms, how schedules are made, what data exists, and who acts on it.
Chase mission-adjacent demand. Incubators, workforce training, and partner offices tie square footage to outcomes universities value.
Package the “how,” not just the “what.” Analytics + delivery + FM beats a lone development pitch.
Move first in at-risk markets. Early partnerships preserve options (and speed approvals) before enrollment drops force fire drills.
Aim for flexible labs and adaptive reuse. Design systems so space can pivot between teaching, dry lab, and wet lab with manageable capex.
Peter Drucker, The Effective Executive — Not for nostalgia, but for the timeless lesson: technology is useless without the right questions. In an AI era, criteria of relevance still starts with humans.
Hemingway, The Old Man and the Sea — A reminder to read beyond “business books.” Endurance, craft, and meaning are leadership muscles, too.
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