Three definitions of length of stay run on the same illustrative store, and why the one most rent rolls report is the wrong one.
Average length of stay in self-storage is occupied customers divided by move-outs per month, measured over a stabilised year. An illustrative store holding 518 customers and losing 36 a month has an average stay of 14.4 months, the same figure as the average completed stay of the customers who left. The number most rent rolls report, the average tenure of the customers in place today, is 25.7 months: 78 per cent too long, because long stayers dominate any snapshot.
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 stabilised store takes 36 new customers a month and has done so for years, so move-outs also run at 36 a month, 432 a year. Its customers are of two kinds, as in most stores: 60 per cent are short stayers, moving house or decluttering, who stay four months on average, and 40 per cent are long stayers, businesses and people storing indefinitely, who stay 30 months on average. Each kind leaves at a constant monthly rate. The mix is illustrative; the arithmetic that follows holds for any mix.
| Item | Value |
|---|---|
| Move-ins a month (steady) | 36 |
| Short stayers: share of arrivals / mean stay | 60% / 4.0 months |
| Long stayers: share of arrivals / mean stay | 40% / 30.0 months |
| Average rent per customer | £95.00 a month |
Weight each group's mean stay by its share of arrivals.
Average stay = 60% × 4.0 + 40% × 30.0 = 2.4 + 12.0 = 14.4 months
That is what a new customer will stay on average, and it is the figure that drives revenue per customer, the value of a move-in and the occupancy a given flow of enquiries can hold.
1. Stock over flow. In steady state, customers in place equal arrivals a month times the average stay. Turn it round: average stay = customers in place / move-outs a month.
Customers in place = 36 × 14.4 = 518.4
Average stay = 518.4 / 36 = 14.4 months
Excel, over a trailing twelve months: =AVERAGE(Occupied_units)/(SUM(Move_outs)/12)
2. Average completed stay. Take every customer who moved out in the last twelve months and average the months each one stayed. In a stabilised store this also gives 14.4 months. It needs move-in dates on the move-out records, which most management systems hold.
3. Average tenure of current customers. Take today's rent roll and average how long each customer has been in. This is the easiest to produce, and it is wrong.
Short stayers in place = 36 × 60% × 4.0 = 86.4; long stayers in place = 36 × 40% × 30.0 = 432.0
Long stayers are 40% of arrivals but 83.3% of customers in place
Average tenure in place = (86.4 × 4.0 + 432.0 × 30.0) / 518.4 = 25.7 months
| Measure | Data needed | Months |
|---|---|---|
| Customers in place / monthly move-outs | Occupancy and move-out counts | 14.4 |
| Average completed stay of leavers | Move-in and move-out dates of leavers | 14.4 |
| Average tenure of current customers | Today's rent roll | 25.7 |
| Median stay of a new customer | Survival curve by cohort | 5.2 |
The snapshot over-samples whoever stays longest: a customer who stays 30 months is in the store on thirty month-ends, one who stays four is there on four. The same bias appears in any business where customers can leave at will. The median shows the other side of the mix: half of all new customers have gone within 5.2 months, 35.5 per cent within three months and 70.2 per cent within a year.
None of the three measures works in a lease-up, because the store is not in steady state. At month 12 after opening, with the same 36 arrivals a month and the same mix, the store holds 225 customers and is losing 25.3 a month.
| Measure | Months | Error against 14.4 |
|---|---|---|
| Customers in place / monthly move-outs | 8.9 | too short |
| Average completed stay of leavers | 2.6 | too short |
Only short stays can have been completed in a store that has been open a year, so the leavers' average says 2.6 months. The direction and size of the error depend on the mix and on how quickly customers leave in their first months; the lease-up article warns that the ratio usually reads long, because a young customer base has not yet produced its departures. Both happen: where few customers leave in their first months it overstates, and where, as here, a large group of short stayers leaves fast it understates. Measure it by cohort: follow each month's arrivals and record the share still in place one, three, six and twelve months later. That survival curve is also what the lease-up calculation needs.
| Length of stay used | Months | Rent per customer £, undiscounted | Customers held by 36 a month |
|---|---|---|---|
| True average | 14.4 | 1,368 | 518 |
| Rent roll tenure | 25.7 | 2,438 | 924 |
Read off the rent roll, a customer looks worth £2,438 of lifetime rent rather than £1,368, both undiscounted and before any rate increases, which makes move-in discounts and marketing look cheap. And a lease-up plan built on 25.7 months would expect 36 move-ins a month to fill 924 units; they hold 518. The value of a customer is worked with the same logic in what a half-price move-in offer actually costs.
The common mistake is reporting the average tenure of the current rent roll as the length of stay. It is a correct answer to a different question, how long today's customers have been here, and it overstates the stay by 78 per cent in this store. The second mistake is quoting one average without the distribution: the 40 per cent of long stayers are the customers who absorb existing customer rate increases, and the 60 per cent who leave within months are the ones a move-in offer is paying for.
The book builds its rent roll from cohorts of arrivals decaying against a hazard of departure, and the free workbook for this case holds that cohort engine, with a working document that measures length of stay from the two raw series and sets the three definitions side by side.
Because a snapshot of current customers over-samples long stayers: short stayers leave quickly and are rarely in the store on any given day. In this illustrative store 40 per cent of arrivals are long stayers but they make up 83.3 per cent of customers in place, so the average tenure on the rent roll is 25.7 months against a true average stay of 14.4.
The average is pulled up by a minority of very long stays; the median is the stay that half of customers do not reach. In this illustrative mix the average is 14.4 months but the median is 5.2, and 35.5 per cent of new customers have gone within three months. Revenue per customer follows the average; marketing payback follows the early leavers.
In steady state, customers in place equal move-ins a month times average length of stay. With 36 move-ins a month and a 14.4-month stay the store holds about 518 customers. Using the rent roll's 25.7-month tenure instead would imply 924, and a lease-up plan built on it would never be reached.
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.
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