Did anyone model what happens to a storage facility's tenant base when a nearby apartment complex goes condo conversion

The 15 percent figure is probably not conservative enough, and here is the mechanism that makes me say that.

Condo conversions do not just remove renters gradually over 18 months. They tend to cluster move-outs in two sharp windows: the first when conversion notices go out and tenants decide whether to buy or leave, and the second at the actual closing deadline. If the complex two blocks over has 214 units and even 40 percent of those households are storage users at any occupancy rate consistent with a dense coastal New Jersey submarket, the demand pool shrinks faster than a straight-line 15 percent assumption captures. The behavioral piece you cannot find data on is that storage leases are month-to-month for most unit types, which means the conversion timeline and the storage vacancy timeline are nearly identical, not offset by any lease friction.

The assumption doing the most work in your model is the replacement tenant. Storage underwriting in apartment-dense submarkets usually assumes that turnover refills from the same demand pool. Once that pool is either displaced outward or absorbed into larger owned units, the replacement tenant has to come from somewhere else, and that somewhere else has a longer drive time, which is the variable operators almost never stress test. Facilities in markets like Toms River with drive-time catchments that are already saturated by other operators do not recover occupancy at the same rate they would in a supply-constrained exurban location.

A sharper sensitivity to run: hold your 15 percent move-out acceleration but model the replacement lease-up at 18 months instead of 6, and see what that does to your equity multiple rather than just the stabilized NOI, because the timing drag hits distributions and your promote math before it hits the trailing cap rate.

What is the average unit mix at that facility, and do you know what share of tenants

The displacement modeling you ran is directionally sound, but 15 percent over 12 months might actually be the easy case, depending on what share of that facility's tenant base is apartment-sourced versus business or life-event tenants.

The assumption doing the most work in your model is the composition of existing tenants, not the conversion timeline itself. If you have no breakout between residential-overflow renters (small-apartment households using storage as a substitute for space they do not have) and the stickier categories, you are applying a single displacement rate to a mixed population. Business users, estate tenants, and households in long-duration life transitions are materially less sensitive to what happens two blocks away. The Yardi and sector data showing roughly 60 percent of current tenants expecting to stay more than a year suggests the tenant base has gotten stickier on average, but that average can mask a skewed distribution at a specific facility with heavy apartment-market exposure.

On the condo-conversion mechanics specifically: the priced-out scenario you described is probably faster-moving than the upgrade scenario. A renter who buys a unit in that same complex may not need storage immediately, but the move itself generates a transition period where storage demand holds or spikes briefly before it drops. The displacement you should model is the trailing edge, 18 to 30 months post-conversion, when the neighborhood tenure has fully reshuffled, not just the first 12.

The risk you did not mention is the replacement demand assumption. Your model appears to treat the lost tenants as a straight vacancy hit, with no offset from the conversion itself generating short-term demand from residents staging belongings during the transaction. That does not change the long-run NOI picture, but it affects the timing of when the facility actually feels the hit versus when it appears in trailing numbers at the time of a sale.

No clean comparable exists in the public literature that I have found either. This is genuinely undertracked.

What does the operator's disclosed tenant-mix data show, if anything, on how many units are month-to-month versus longer lease terms, and do you have any sense of the business-tenant percentage from their rent roll?

The displacement rate question is genuinely hard to benchmark because almost nobody publishes move-out cohort data at the facility level, and the residential conversion literature does not track downstream storage behavior. So the honest position is that your 15 percent figure is a reasoned estimate, and I would say the same about anyone else's.

A few things that would push that number in one direction or the other, based on how self-storage demand is generally understood:

The conversion timeline matters more than the headline percentage. Condo conversions in New Jersey routinely run 18 to 36 months from filing to final closings, and tenant displacement in a 214-unit building does not happen in one wave. If the conversion drags, your 12-month acceleration window may be compressing something that actually spreads across two or three lease cycles at the storage facility.

The price-out scenario and the upgrade scenario affect your model differently. A renter who buys a larger unit in the same complex and gains a garage is likely a fast exit from storage. A renter who gets priced out and relocates farther into Ocean County may actually extend the lease while they settle, then churn. Those two paths produce different timing distributions on the same 15 percent.

The pitch leaning on apartment density as a demand driver without flagging the conversion filing is a material omission, and you were right to catch it. Whether it changes the underwriting enough depends on what the ask implies about stabilized occupancy. If the pro forma bakes in demand from that specific complex, that is a different exposure than if the density argument was ambient color.

I do not know of a published comparable tracking this specific dynamic. If one exists, the Self Storage Association's research arm or a Yardi Matrix custom pull would be the places to ask.

What was the occupancy assumption baked into the seller's NOI, and how far below that does your stress case land?

The assumption doing the most work is that storage renters near that complex are primarily apartment renters, not homeowners already using it for overflow.

What was the unit mix at the storage facility, and did the operator break out any length-of-tenancy data by unit size?

The direction of your stress test feels right but I think the input variable that matters more than the move-out rate is the timeline spread on the conversion itself. Condo conversions in Jersey have been running 18 to 36 months from filing to close-out, and tenant churn during that window is lumpy, not linear, so a straight 12-month acceleration curve probably underweights the drag in months 14 through 24 when the last hold-outs finally settle or scatter. What I would want to know is how many of those 214 nearby units were already owner-occupied under a rental-by-owner arrangement, because those tenants behave differently from institutional renters when displacement comes. The pitch leaning on apartment density as a demand driver without flagging the conversion filing is the thing that would have made me stop reading the deck.

The behavioral split you are describing actually has two different move-out curves baked into one number, and separating them changes the math. Renters who buy a converted unit are on a roughly 60 to 90 day lease-up timeline tied to closing, so their move-out is concentrated and predictable. Renters who get priced out and relocate farther away are more diffuse, some stay in the area temporarily, some sign month-to-month at the storage facility before finally vacating, and attrition can drag across 24 months rather than 12. If your 15 percent is spread evenly across 12 months, you are probably underweighting the early spike from the buyer cohort and overweighting how fast the displaced renter cohort clears.

The number I would want before finalizing that model is the conversion's unit mix. A 214-unit complex converting to condo is not a uniform demand shock: studios and one-bedrooms are the units most likely occupied by storage-dependent renters, and if the conversion is weighted toward two- and three-bedrooms targeting owner-occupants, the storage-using cohort is smaller than the headline unit count implies. That changes the denominator on your displacement estimate before you even get to the rate.

What was the unit mix on the nearby complex, and did the conversion filing include a pricing schedule that would let you estimate how many of the current renters could realistically buy versus getting pushed out?

What was the average unit size in that complex, because that is the number that tells you which way the displaced tenant moves.

The number I would want before anything else is the share of the storage facility's existing tenant base that self-reported a local address within roughly a half-mile radius at lease signing, because that is the only figure that tells you how much of the book is actually exposed to the conversion. Operators who use digital rentals often capture a home address, and some will share that in diligence if you ask for a zip code distribution rather than individual records. If 40 percent of tenants cluster within walking distance of that complex, your 15 percent move-out acceleration might be too light. If most of the book is people driving in from Brick or Bayville who happen to rent nearby, the conversion barely touches them and your model is too punishing.

The piece that tends to get skipped in this kind of stress test is the timing mismatch between when a condo conversion is filed and when tenants actually move. A January filing in New Jersey does not mean unit transfers close in 12 months. The conversion process there runs through DCA registration, a 60-day tenant purchase offer window, and then individual closings that stretch over another 12 to 18 months after that, so your move-out acceleration probably needs to be spread across a 24 to 30 month window rather than compressed into 12. That changes the NOI trajectory considerably, not the total damage but when it lands, and it can be the difference between a deal that clears a return threshold with a later refi and one that does not clear it at all. The renters who get priced out and leave the submarket are the faster-moving cohort, and they tend to go in the first six months after purchase offers are issued. The buyers who upgrade their space needs bleed out over the back half of the conversion timeline. Running those as two separate tranches with different timing gives you a more defensible

The conversion-to-move-out lag is probably longer than your 12-month window, which is what I'd push back on. Condo conversions in Jersey drag, permitting alone adds six to nine months past filing, and then actual closings stagger out over another year after that. Your 15 percent move-out acceleration might be hitting the model too early and too clean, which makes it look scarier than it plays out in real time, but also means the back half of your hold catches it right when you'd be trying to exit.

The piece nobody talks about is the replacement tenant mix. The people who get priced out don't disappear, they compress into rentals two or three miles out and sometimes they start a storage lease closer to where they landed. Doesn't help your specific facility but it means the demand doesn't evaporate, it migrates. Whether that migration lands within your facility's draw radius is the actual question.

I'm down in central Georgia messing with a 12-acre parcel and the residential-to-storage demand relationship here is basically the inverse of what you're seeing in Toms River. Dense coastal markets have that tight apartment-to-storage chain, and when it breaks it breaks fast. Down here people store because they have land and still want more room, which is a different animal.

What was the operator's existing tenant turnover rate before you layered in the conversion stress? If they were already running 40-plus percent annual churn that 15 percent acceleration is a rounding error, but if they were sitting at 20 percent it's a real problem.