A self-storage store fills one customer at a time and empties the same way. The stabilised level and the time to reach it follow from two numbers.
A self-storage store leases up until move-outs catch up with move-ins, and the occupancy it settles at is move-ins a month times average length of stay. An illustrative 600-unit store taking 38 move-ins a month from a customer base that stays 14 months stabilises at 532 units, 88.7 per cent, and takes 35 months to reach 80 per cent and 47 to reach 85. A plan that needs 90 per cent by month 36 needs 42.5 move-ins a month, not 38.
Worked in full in Self-Storage Real Estate by Julian R. Sterling, with every figure reproduced in a free workbook.See the book on Amazon →
A self-storage store has no leases to sign up for five years. It fills one customer at a time and empties one customer at a time, and every month a share of the customers it has won leave. If customers stay 14 months on average, roughly one in fourteen of them moves out each month. Occupancy rises while move-ins exceed move-outs and stops rising when they are equal. That gives the stabilised level directly:
Stabilised occupied units = move-ins per month × average length of stay (months)
= 38 × 14 = 532 units, 88.7% of 600
Monthly roll-forward: =MIN(Units, Occupied_prev*(1-1/LOS)+MoveIns)
This is Little's law, the same identity queues obey. It means a lease-up plan is really two claims, one about demand (move-ins) and one about behaviour (length of stay), and both can be checked.
| Input | Value |
|---|---|
| Units | 600 |
| Move-ins in month 1 | 20 |
| Move-ins from month 12 onward | 38 a month |
| Ramp | linear over 12 months |
| Average length of stay | 14 months |
Move-ins start low because a new store has no reviews, no search ranking and no drive-by awareness, and build over its first year as marketing and word of mouth take hold.
| Month | Move-ins | Move-outs | Occupied | Occupancy |
|---|---|---|---|---|
| 6 | 28.2 | 7.3 | 123 | 20.5% |
| 12 | 38.0 | 16.4 | 251 | 41.8% |
| 24 | 38.0 | 29.1 | 416 | 69.4% |
| 36 | 38.0 | 34.3 | 485 | 80.8% |
| 48 | 38.0 | 36.5 | 512 | 85.4% |
| 60 | 38.0 | 37.4 | 524 | 87.3% |
| Stabilised | 38.0 | 38.0 | 532 | 88.7% |
The store passes 70 per cent in month 25, 80 per cent in month 35 and 85 per cent in month 47. The slowdown is built in: by month 36 move-outs are 34.3 a month, and the store needs 38 move-ins just to stand still. Over the first three years it wins 1,260 customers and loses 775 of them.
The approach to the stabilised level is geometric. With constant move-ins, the share of the way there after n months is 1 − (1 − 1/LOS)n: 58.9 per cent after a year, 83.1 per cent after two, 93.1 per cent after three. Reaching 95 per cent of the way takes 40.4 months even with no ramp at all. A long tail is not a sign of a failing store; it is the arithmetic of customers who leave.
| Move-ins a month | LOS 12 months | LOS 14 months | LOS 18 months |
|---|---|---|---|
| 30 | 60.0% / never | 70.0% / never | 90.0% / 41 |
| 38 | 76.0% / never | 88.7% / 35 | 100% / 25 |
| 45 | 90.0% / 30 | 100% / 24 | 100% / 20 |
Two months less length of stay, from 14 to 12, means the store never reaches 80 per cent on 38 move-ins: it tops out at 76.0. Where the flow times the stay exceeds the store, occupancy hits capacity and the right response is to raise rates rather than celebrate, because the store is turning customers away.
Many lease-up plans are drawn as an S-curve to a target, say 90 per cent by month 36, with no move-in or move-out numbers beneath them. Invert the plan and test it. Holding 90 per cent at a 14-month stay needs 38.6 move-ins a month for ever. Reaching it by month 36, with the same first-year ramp, needs a stabilised flow of 42.5 a month, 12 per cent more than this catchment produces, and that flow would eventually fill the store to 99.1 per cent. If the enquiry and conversion data cannot support 42.5 move-ins, the curve is a hope. Length of stay deserves equal scepticism: read off a filling store's own move-outs, for instance as occupied units divided by monthly move-outs, it tends to overstate the true figure, because a young customer base has not yet produced its full share of departures. Use a measured stay from comparable mature stores.
The companion Lease-Up Tracker asks the same question of a real flow, and the free workbook for this case builds the store's rent roll from cohorts of move-ins decaying month by month. Once the store is stable, REVPAF is the measure that replaces occupancy, and the valuation follows from it.
Stabilised occupancy is where move-outs equal move-ins, so it equals monthly move-ins times average length of stay divided by units. On an illustrative 600-unit store, 38 move-ins and a 14-month stay give 532 units, 88.7 per cent. A store with 30 move-ins and the same stay stabilises at 70.0 per cent, however long it waits.
Divide the target occupied units by the average length of stay. Holding 90 per cent of 600 units with a 14-month stay needs 38.6 move-ins a month indefinitely. Getting there by month 36, after a first-year ramp, needs a stabilised flow of 42.5 a month.
Because move-outs grow with the customer base. With a 14-month stay, about one occupied unit in fourteen leaves each month. At month 36 the illustrative store loses 34.3 customers a month against 38 move-ins, so it gains under four net. With constant move-ins the store covers 58.9 per cent of the distance in a year and 93.1 per cent in three.
This article is one calculation from Self-Storage Real Estate. The book takes the same case from first principles to the decision, chapter by chapter, and every figure it prints is a live formula in the free companion workbooks.
Get the book on Amazon →Free companion files
Also on Amazon UK · Amazon Germany · Amazon France · Amazon Canada
Reading guide: real estate investing, finance and fund management → · All 681 articles →
If this book helped, or didn’t, a few lines on Amazon are worth more than they look: they are what the next reader goes on. Write a review. The workbook stays free either way.