upcoming
Apers for Self-Storage
Self-storage underwriting for the operator you'll be, not the one you're replacing.
Apers is the AI system that models your self-storage revenue management program — ECRI, web rates, unit mix — not the seller's trailing numbers.
The trailing NOI tells you what the seller did, not what you'll do
You're underwriting a 650-unit self-storage facility — 10x10s, 10x20s, 5x10s, climate-controlled and drive-up. The trailing NOI says one thing. The revenue management opportunity says another. The current operator runs flat pricing with no ECRI (existing customer rate increases). An institutional operator would implement a revenue management program on day one — pushing ECRI every 6-9 months, optimizing web rates by unit type, and adjusting move-in specials seasonally. The model needs to reflect the operator's strategy, not the seller's.
A 10x10 climate-controlled unit rents for $180/month. A 10x20 drive-up rents for $140. Same square footage economics? Not even close — the climate-controlled unit has higher CapEx, higher insurance, and higher utility cost per square foot. But it also has lower turnover and higher ECRI tolerance. Your model blends these into an average rent per SF and misses the mix story entirely.
Half the self-storage deals in the market include an expansion component — add 200 units on the unused land parcel. The expansion economics are fundamentally different from the existing facility: construction cost, new unit lease-up, blended occupancy during construction, and the impact on existing unit demand. Two models in one deal, but most analysts build them separately and never connect the cash flows.
Self-storage looks simple from the outside — rent boxes, collect rent. Institutional operators know the reality: revenue management is the business model, unit mix is the strategy, and expansion is the value-add thesis. Apers models self-storage the way operators run it.
What changes with Apers
ECRI, web rates, and move-in specials — modeled
Existing customer rate increase modeling by cohort. Web rate optimization by unit type. Seasonal move-in special impact on effective rent. The revenue management program is the underwriting thesis — Apers models it explicitly, not as a blended rent growth assumption.
Every unit type, individually priced
Revenue by unit type: climate-controlled vs. drive-up, small vs. large. Occupancy by type. Turnover by type. The unit mix determines the facility's revenue quality — not just its revenue quantity.
Add 200 units and model the impact
Expansion CapEx, construction timeline, new unit lease-up curve, and the impact on existing facility demand. Connected cash flow that shows the blended return — existing plus expansion — not two disconnected models.
Rent rolls with unit types extracted
Upload the rent roll — Apers extracts unit number, size, type (climate/drive-up), current rate, move-in date, and rate increase history. The revenue management analysis starts from actual tenant data, not assumptions.
A deal, start to finish
A 650-unit self-storage facility, mix of climate-controlled and drive-up. Current operator runs flat pricing. 1.5-acre expansion parcel. $18M acquisition.
Upload rent roll and financials
Rent roll with 650 units and the T-12. Apers extracts unit-level data — size, type, current rate, move-in date, rate increase history — and categorizes by unit type for revenue management analysis.
Unit-level model built
650 units across 8 unit types. Current rates, market rates, and occupancy modeled by type. Climate-controlled units at 94% occupancy with $180/month average. Drive-up at 88% with $125/month average. The unit mix tells the revenue story.
Revenue management modeled
ECRI program: 8-10% increases every 9 months for tenants with 6+ months tenure. Web rate optimization by unit type — climate-controlled 5x10s priced $15 above current street rate. Seasonal move-in specials modeled with burn-off timing.
Expansion underwriting
200 new climate-controlled units on the 1.5-acre parcel. $3.2M construction cost. 18-month lease-up to 90% occupancy. Impact on existing facility demand modeled — are you cannibalizing your own units or capturing new market share?
Combined output
Excel model with unit mix analysis, revenue management projections, expansion feasibility, blended facility cash flow, and combined return analysis. One model, not two disconnected workbooks.
Models built for self-storage
A growing collection for self-storage operators and investors — from stabilized acquisitions to ground-up development.
Pocket Model: Self-Storage Screener
Single-sheet screener with unit mix, occupancy, and rate per SF inputs for rapid pipeline screening.
Self-Storage Facility Model
Full pro forma with unit mix, occupancy ramp, rate optimization, operating expenses, and expansion analysis.
Ground-Up Development Pro Forma
Development model for ground-up self-storage — construction, phased unit delivery, lease-up to stabilization.
Frequently Asked Questions
Does Apers model ECRI (existing customer rate increases)?
Yes. Apers models ECRI programs by unit type — push frequency, rate increase percentage, and tenant turnover impact. The model projects revenue based on your operating strategy, not the seller's trailing numbers, so you underwrite the operator you'll be.
How does Apers handle different self-storage unit types?
Apers models each unit type independently — climate-controlled vs. drive-up, 5x10 through 10x30 — with individual rents, occupancy rates, operating costs, and ECRI tolerance. Climate-controlled units have different CapEx, insurance, and utility profiles that affect per-SF economics.
Can Apers model self-storage expansion on adjacent land?
Yes. Apers separates existing facility economics from expansion economics — construction cost, new unit lease-up curve, and incremental operating expenses. The model shows blended returns and the timeline for expansion units to reach stabilized occupancy.
Does Apers support web rate optimization and seasonal pricing?
The models incorporate web rate assumptions by unit type with seasonal adjustments and move-in special structures. You can model different pricing strategies — aggressive web rates with concessions vs. higher asking rents with longer lease-up — and compare the revenue outcomes.