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Apers for Student Housing
Student housing underwriting that thinks in beds, not units.
Apers is the AI system for student housing pro forma modeling — per-bed economics, pre-lease velocity, and university demand drivers — because per-unit math misses half the story.
Your multifamily model doesn't work here
You're underwriting a 200-unit, 600-bed student housing property adjacent to a state university with 35,000 enrollment. Your multifamily model doesn't work here. Revenue is per bed, not per unit. A 4BR/4BA unit doesn't rent for $2,400/month — it rents for $600/bed/month. Occupancy isn't measured by unit — it's measured by bed, and an empty bed in an occupied unit is lost revenue that no lease abstraction captures correctly.
By February, your property needs 70% of beds pre-leased for the August move-in. If you're at 50% in February, you're cutting rates. If you're at 85%, you're raising them. Pre-lease velocity — the pace at which beds fill from October through August — determines your revenue for the entire academic year. Multifamily lease-up curves don't apply. Student housing has a single, annual, high-stakes lease-up window.
Enrollment trends, on-campus housing capacity, freshman live-on requirements, Greek life participation — these university-specific factors determine your capture rate. A 3% enrollment decline doesn't reduce your occupancy by 3% — it might reduce it by 8% if you're competing for the marginal student who now chooses to live at home. The demand model is university-specific, not market-level.
Student housing is a hospitality business disguised as real estate. The product is beds, the demand driver is the university, and the lease-up window is one shot per year. Apers models student housing with the demand drivers and economics that actually determine returns.
What changes with Apers
Revenue by the bed, not the unit
Per-bed rent, per-bed occupancy, per-bed concession. The model thinks the way the property operates — individual leases per bed, not per unit. 4BR/4BA, 2BR/2BA, studio — each bed type has its own economics.
Track the only lease-up that matters
Pre-lease pace modeling from October through August. Velocity curves by bed type. Rate adjustment triggers based on pre-lease attainment. The model shows whether you'll fill by August — or whether you'll be cutting rates in March.
Enrollment, capture rate, competitive supply
Demand model tied to university enrollment, on-campus capacity, competitive purpose-built supply, and capture rate assumptions. Not a generic vacancy rate — a university-specific demand framework.
Rent rolls with bed-level detail
Upload the rent roll — Apers extracts bed-level lease data: rent per bed, lease start/end (academic year), roommate matching status, and concession by bed. The per-bed model builds from actual lease data.
A deal, start to finish
A 200-unit, 600-bed purpose-built student housing property. 0.5 miles from a 35,000-student state university. Mix of 4BR/4BA, 2BR/2BA, and studios. $48M acquisition.
Upload rent roll and market data
Bed-level rent roll, T-12, and university enrollment data. Apers extracts per-bed lease details — rent, lease term, concession — and categorizes by unit configuration and bed type.
Per-bed model built
600 beds across 3 unit types. 4BR/4BA at $600/bed, 2BR/2BA at $750/bed, studios at $1,100/bed. Occupancy and concessions modeled per bed, not per unit. Revenue reflects the actual lease structure — individual bed leases with parent guarantees.
Pre-lease velocity modeled
October-August lease-up curve by bed type. Current velocity: 45% pre-leased by January. Rate adjustment triggers — if below 60% by February, model concession scenarios. If above 75%, model rate increase scenarios. Revenue for the full academic year projected from the velocity curve.
University demand analysis
35,000 enrollment with 1.2% annual growth. On-campus housing at capacity (4,500 beds). Competitive purpose-built supply: 2 new properties (800 beds) delivering in 18 months. Capture rate sensitivity — what happens if enrollment grows 0.5% instead of 1.2%?
IC-ready output
Excel model with per-bed revenue, pre-lease velocity projections, university demand sensitivity, renovation scenarios by unit type, and return analysis. Every demand assumption tied to university enrollment data.
Models built for student housing
Specialized models for purpose-built student housing — per-bed economics, academic calendars, and university demand drivers.
Student Housing Model
Purpose-built student housing with bed count, academic calendar, pre-leasing timeline, parent guarantees, and university demand analysis.
Ground-Up Development Pro Forma
Development model for ground-up student housing with construction, bed count optimization, and pre-lease projections.
Rent Roll Analysis & Market Positioning
Rent roll analysis comparing in-place rents to market — applicable to per-bed student housing rent positioning and comp analysis.
Frequently Asked Questions
Does Apers model per-bed economics instead of per-unit?
Yes. Apers models revenue per bed, not per unit. A 4BR/4BA unit is four individual revenue streams at $600/bed/month, not one unit at $2,400. Occupancy is tracked by bed, and an empty bed in an occupied unit shows as lost revenue — because that's how student housing actually works.
How does Apers handle pre-lease velocity and the annual lease-up window?
Apers models the pre-lease cycle from October through August with velocity milestones. You set targets — 70% by February, 90% by May — and the model shows how falling behind on pre-leasing cascading into rate concessions and revenue shortfalls for the full academic year.
Can Apers incorporate university enrollment and demand drivers?
Yes. The models factor in enrollment trends, on-campus housing capacity, freshman live-on requirements, and capture rate analysis. A 3% enrollment decline doesn't reduce occupancy by 3% — the model reflects the amplified impact on off-campus demand for marginal students.
Does Apers support by-the-bed and by-the-unit lease structures?
Yes. Apers models both individual lease (by-the-bed) structures where each resident signs separately and unit-level leases where one party is responsible for the full unit rent. The model handles the different vacancy risk profiles and concession mechanics of each structure.