We checked 74,000 leases. The short answer: mostly no, with one clear exception.
The Short Version
A reasonable first-year rate increase, communicated in advance, causes a negligible bump in move-outs.
Five operators that raise rents at different points in a tenant's first year show no dip in retention at their own increase month, and a formal difference-in-differences test confirms it.
The exception: very large increases — above roughly 50%, and sharply above 100% — do push tenants out.
The Fear and the Question
An Existing Customer Rate Increase (ECRI) is one of the most dependable levers a self-storage operator has for growing revenue. Yet many operators hesitate to use it, worried that raising the rent on a sitting tenant is an invitation to move out. The concern is intuitive: nobody likes a price hike, and a storage unit is easy to leave.
But intuition and evidence aren't the same thing. So instead of arguing from anecdote, we went to the data: rate increase events stored in the Cubby data warehouse across various operators in various markets, matched to whether, and when, tenants moved out. This post walks through our findings.
What We Measured and What We Excluded
We focused on the first increase a tenant receives within their first 12 months, and we tracked retention month by month afterward. We restricted the analysis to tenants whose full first year we could observe, so no retention number is a partial count masquerading as a complete one.
Standard practice (often required by law) is to give 30 days' notice before an increase takes effect, so a tenant's reaction, if there is one, shows up within the first month or two after notice, comfortably inside our measurement window.
One caveat up front: this is observational data, not a randomized experiment; tenants weren’t randomly assigned to test or control. However, different operators start raising rents at different points in a tenant's tenure, which gives us something close to a controlled comparison.
The Finding: Retention Doesn't Flinch
Consider five operators on the platform — we'll call them A through E — that together had roughly 8,000 first-year rate increases in the observation window. They range in scale, from Operator E's ~330 first-year increases to Operator A's ~3,350, and each has a settled policy for when a tenant's first increase goes out. If a rate increase drove tenants away, each operator's retention curve should bend downward right around its own increase month.
Visually, there is no dip in retention at the point of first ECRI. Neither is there a noticeable dip one month before the ECRI effective date, when the notice letters hit. The curves are separated from one another — some operators simply retain better than others — but within each curve, the month the increase lands (marked with a diamond) passes without a ripple. First-year attrition looks the same whether an operator raises at month 4 or month 8.
Putting Statistics Behind It
“We don't see anything” is easy to say and hard to trust, so we tested it formally. Because these operators (and dozens more) raise rents at different tenure months, we can run a difference-in-differences model. In plain terms, it asks whether a tenant's monthly move-out rate rises during their operator's increase window, after accounting for both that operator's baseline churn and the normal shape of early-tenure attrition.
Across 68 operators, the answer is no. Being in the increase window changes the monthly move-out rate by +0.18 percentage points, against a baseline of 9.2% — a relative change of about two percent, and statistically indistinguishable from zero (95% confidence interval of −0.24 to +0.60 points; p = 0.40). An event-study version tells the same story: no rise in move-outs in the months leading up to the increase, and no spike when it lands. Even the optimistic edge of that confidence interval would amount to only a couple of points of added move-out probability over the following quarter.
For retention purposes, a reasonably sized first-year increase, communicated in advance, is essentially a non-event.
The Exception: How Big Is Too Big
That conclusion holds across the range of increases operators typically use, but it has a ceiling. When we sort first-year increases by size and look at 90-day retention, the picture stays flat until the increases become extreme, and then it doesn't:
Up to about 30%, which covers the large majority of increases, retention barely moves. Past 50% it begins to erode, and above 100% it drops sharply: those tenants retain about 16 points worse than the baseline. This isn't just an artifact of which operators make big increases; the pattern holds even when we compare tenants within the same operator. There is a point where an increase stops being routine and starts costing you the tenant.
One note: the most extreme percentage increases tend to land on units that started at very low or promotional rates, so part of the effect reflects the kind of tenant who takes a cheap unit and leaves once it's repriced. That has a practical implication: a deep move-in discount isn't free money you recover later. The further you discount to win a tenant, the larger the first increase needed to close the gap — and large first increases are exactly the ones that drive move-outs. The discount that wins the customer and the increase that recovers it are two ends of the same tradeoff, not independent wins.
What This Means for Operators
Don't fear the routine increase. A reasonable first-year ECRI, given proper notice, costs you essentially no additional move-outs. Operators who hold back are leaving revenue on the table to avoid a risk that, in the data, doesn't materialize.
Keep it proportionate. The damage is concentrated at the extreme. Increases up to about 30% behave, for retention, like no increase at all; the risk climbs only when you get aggressive, and it climbs steeply past 100%.
Price the discount and the increase together. If you lean on deep move-in promotions to juice occupancy, don't assume you'll quietly earn it back at the first ECRI — that recovery is a large increase, and large increases are the one place move-outs climb. Weigh the promotion and the eventual increase as a single decision. (Cubby's ECRI recommendations already account for this, factoring churn risk into the increase they suggest.)
A Note on Data
The data used in this analysis came from the Cubby Data Warehouse, the same live copy of operating data that every operator on Cubby can query. Analysis was done by asking plain-text questions to a connected AI assistant. For Cubby customers, questions like “are my rate increases costing me tenants?” are answerable with just a few thoughtful prompts.
A Note on Regulation
Rate increases on existing storage tenants are governed by the lease and by state law. What jurisdictions tend to share is a notice requirement: commonly around 30 days' written notice before an increase takes effect. Some jurisdictions also have price-gouging rules or increase caps.
None of this cuts against the approach in this post; if anything, it runs the same direction. The routine, well-communicated increases that our analysis shows don't move churn are rarely the ones that draw regulatory scrutiny. Where both the data and the law get uncomfortable is the same spot: extreme and ill-communicated increases.
This isn't legal advice, and requirements vary by state and locality and change over time. Confirm your state's rules (or check with your self-storage association) before setting policy.
Methodology and Limitations
The analysis covers first-year first increases across operators on the Cubby platform, drawn from lease-level rate-change records. We measured only tenancies we could follow from their move-in date, and restricted them to cohorts old enough to observe a complete 12 months, which removes the selection and censoring effects that otherwise distort retention figures. Retention is measured from the lease start date (for the survival curves) and from the increase notice date (for the size comparison). The difference-in-differences model is a two-way fixed-effects specification — operator and tenure-month — with treatment defined as the increase-notice month and the two months following; standard errors are clustered by operator, across 68 operators (42 that raise within the first year and 26 that serve as never-treated-in-year-one controls).
Limitations: the study is observational; increase size is not randomly assigned, so the size comparison in particular reflects operator and unit selection alongside the increase itself.
