Student Housing Data Rooms in 2026: Prove the Prelease Machine, Protect the Guarantor File

Last updated: July 2026

Quick answer: A student housing data room is a deal room for selling, recapitalizing, or financing purpose-built student housing (PBSH) — and two things make it different from any other commercial real estate room. First, the asset is priced on two curves the room has to prove: preleasing velocity (signed leases as a percentage of beds, plotted same-date against last year) and distance-to-campus (pedestrian assets within half a mile carry a rent premium; assets more than a mile out trade at a discount). Second, it holds the most PII-dense tenant file in CRE, because every bed drags a second adult — the parental guarantor, with income documents and a Social Security number — into the room. So the room has one job with two halves: prove the lease-up machine is running in a market that is normalizing (fall-2026 preleasing hit 78% across the Yardi 200 in May, up 140 basis points year over year but below the 79.8% pace of 2022-2024, at $933/bed), and treat the guarantor file like the regulated asset it actually is. When the deal involves multiple buyer groups, a lender, and thousands of pages of confidential tenant data, that room is a commercial real estate data room — not a Dropbox folder.

I'm Sean Yu, co-founder of Peony, a data room company serving 6,800+ customers across M&A, fundraising, and real estate. I don't own student housing for a living — but I've watched hundreds of CRE deals move through data rooms, and the student-housing ones do not behave like the others. In an office or industrial deal, the room is organized leases-first and the income is the whole story. In a student-housing deal, the income is a machine — a preleasing curve that has to be re-proven every single year — and the tenant file is a compliance problem, because a normal apartment has one adult on the lease and a student bed has two: the student, and a parent who signed a guaranty and handed over a Social Security number, an income document, and a credit-pull consent. Build the room to prove the machine and wall off that second file, and the deal reads correctly to every underwriter. Build it like a generic property folder and you either bury the velocity story or leak parental PII to twelve funds that never needed it.

That is the thesis of this post: purpose-built student housing sells on preleasing velocity and distance-to-campus, and carries the most PII-dense tenant file in commercial real estate. The room has to prove the lease-up machine in a normalizing market — which makes proven velocity worth more, not less — while treating the tenant file, and especially the guarantor file, like the regulated asset it is.

Here's the carve-out, because Peony has a family of CRE guides and this one owns a specific lane. If you want the generic CRE process and clock — caveat emptor, the contingency period, re-trades — read commercial property due diligence; this post assumes that and goes student-housing-deep. If you want provider and archetype selection for real estate broadly, that's data room for real estate. Student housing is often mistaken for conventional apartments, so the closest apartment sibling is the multifamily acquisition data room — same rent-roll-and-PII DNA, but student housing adds the prelease curve, the distance premium, the guarantor file, and the campus. And two operator-flavored housing siblings sit right next door: the senior housing data room, which is organized around an operations transfer rather than a property, and the skilled nursing facility data room at the clinical, government-reimbursed end. Student housing shares their "you're underwriting an operation, not just an address" instinct — the operation here is the annual lease-up.

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Why does student housing sell on a curve instead of a snapshot?

Because the income resets every year, so a buyer is not buying occupancy — they're buying the reliability of the machine that rebuilds occupancy each August. A conventional apartment building leases up once and then turns a fraction of its units annually; a purpose-built student housing asset empties almost entirely each summer and has to re-lease nearly every bed before the fall term. That makes the preleasing curve — signed leases as a percentage of total beds, tracked against the calendar — the single most important number in the deal, and it makes the same-date-last-year comparison the way buyers read it. "94% preleased" means nothing without the date and the prior-year figure at that same date; "94% on July 1, versus 88% on July 1 last year" is the machine visibly accelerating.

The market context makes proving the curve more valuable in 2026, not less. Preleasing across the Yardi 200 investment-grade universe reached 78% in May 2026 for the fall-2026 academic year — up 140 basis points year over year, and healthy — but still below the 79.8% May average recorded from 2022 through 2024. Average rent per bed rose to $933 (up 1.7% annually), and leasing-season rent growth has decelerated across three seasons: 5.9%, then 2.6%, then 0.9%. The market is not falling; it is normalizing off a super-cycle. And normalization is exactly what makes a proven lease-up curve scarce and valuable — when rent growth was 5.9% and every asset filled, velocity was assumed; when it's 0.9% and preleasing dispersion is wide (the 50 highest-preleased markets averaged 92.1% while the 50 lowest averaged 54%), a buyer pays up for the asset that can document its machine running ahead of last year. The room's job is to turn the curve into something a buyer verifies rather than trusts.

The second curve is spatial, and it is unusually clean.

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How much does distance to campus actually change the rent?

A lot — and it moves in bands, not a smooth per-tenth-of-a-mile gradient. RealPage's analysis across 175 universities found that pedestrian properties (within half a mile of campus) commanded roughly a 9% premium to total rents, at about $993 per bed, while properties more than a mile out carried roughly a 19% discount, at about $741 per bed — with the half-mile-to-one-mile band in between, against an overall average near $913 (July 2024 vintage). That is a real, sizable spread driven by the one thing students and parents pay for: the ability to walk to class.

The critical discipline here is to present distance as bands, not a linear formula. There is no published, defensible "rent premium per 0.1 mile" figure, and inventing one is the fastest way to get a valuation argument thrown out in diligence. The honest, citable framing is the three-band structure: pedestrian (under half a mile) at a premium, an intermediate band, and over-a-mile at a discount. For an asset like a 612-bed property sitting 0.3 miles from a 34,000-student flagship, the point is simply that it lands squarely inside the pedestrian band — the premium tier — and the room should prove it: a campus-proximity map, the walk time, and the distance stated in the band ("pedestrian, under half a mile"), not as a spurious precise multiplier. Buyers underwrite the band; give them the evidence for which band you're in and let the comps do the rest.

Together, the two curves are the whole pricing thesis. Velocity says the machine works; distance says demand is durable. Everything else in the room supports one of those two claims — or protects the file that proving them exposes.

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Why is the enrollment cliff a valuation risk, and how does the room defend against it?

Because it is the one macro story every buyer has read — and most have read it wrong, so an undefended room lets them price a national doom headline onto your growing flagship. The "demographic cliff" is real but widely misquoted. What WICHE actually projects (Knocking at the College Door, 11th edition, December 2024) is a roughly 13% decline in the number of U.S. high-school graduates between the 2025 peak (about 3.9 million) and 2041 (about 3.4 million) — a projection about the supply of traditional freshmen, not a forecast of college enrollment. That distinction is the whole ballgame, and the wire version ("college enrollment will fall 13%") gets it wrong.

It also genuinely conflicts with other authoritative data, and honesty about that conflict is what makes the defense credible. The National Center for Education Statistics has projected total undergraduate enrollment up by roughly 9% over a comparable horizon — the opposite direction. Both can be true at once because enrollment is not the same as the 18-year-old population (graduate students, older students, and immigration partly offset the birth decline), and because the pain is not evenly distributed. The declines concentrate at community colleges and less-selective regional publics, while flagships and elite privates are growing — the Urban Institute documents this flagship-enrollment divergence directly. Regional systems have merged campuses and closed satellites; flagships have seen applications surge.

So the risk is asset-specific, and so is the defense. A bed at a growing 34,000-student flagship carries a fundamentally different demographic exposure than a bed at a shrinking regional, and the room's job is to prove which side of the divergence your university sits on — in documents, not adjectives. That is the enrollment-evidence tab, and it holds four things: the university's 10-year enrollment trend (a decade of growth is the strongest rebuttal to a national-decline narrative), its acceptance-rate trend (a falling acceptance rate at rising applications is the flagship-demand fingerprint), its first-year live-on policy (a mandate is competition for your beds if it expands and a tailwind if it relaxes — and these policies genuinely move; Cal Poly Pomona paused its first-year requirement while Cal Poly Humboldt expanded a live-on mandate), and the university housing master plan and any public-private-partnership pipeline (planned on-campus beds are future supply you want a buyer to see you've accounted for). Pair the aggregate cliff data (WICHE on high-school graduates) with your asset-level flagship data (enrollment up, acceptance rate down, live-on policy stable), and the cliff stops being a discount lever and becomes the reason your specific asset is defensible while the sector's weaker beds are not.

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Why is the guarantor file the most sensitive document in the deal — and does FERPA protect it?

Because every student bed drags a second adult into the room — the parental guarantor — and that person's file is a complete consumer-financial profile: name, Social Security number, income verification, and credit-pull results. A conventional apartment has one adult on the lease; a by-the-bed student lease adds a parent who signed a guaranty and handed over PII that is more dangerous to leak than anything else in the transaction. That is what makes student housing carry the most PII-dense tenant file in commercial real estate, and it is the wedge that separates a credible seller from one about to create a data-breach exposure.

Here is the correction that matters most, because getting it wrong leads people to under-protect the exact data that is most dangerous: "FERPA protects the tenant file" is false for a private landlord. FERPA — the Family Educational Rights and Privacy Act — binds the education records held by a university that receives federal funds. It follows the university, not the landlord. A private purpose-built student housing owner is not a school; your lease files, your rent roll, your tenants' Social Security numbers, and your parental-guaranty packets are not FERPA records. Assuming FERPA covers them is a category error.

The regime that actually governs the guarantor file is the ordinary — and stringent — apparatus for consumer financial PII:

• State PII and breach-notification law. All 50 states have data-breach-notification statutes, and a growing set of comprehensive state privacy acts apply to businesses over certain thresholds. Guarantor Social Security numbers and income data are precisely the categories that trigger notification and credit-monitoring obligations if they are breached. This is the primary regime for a private landlord's tenant file.
• FCRA. The Fair Credit Reporting Act governs the tenant-screening and credit reports you pull on students and their guarantors — how they're obtained, used, and stored.
• GLBA-adjacent duties. To the extent a landlord or manager handles "financial information" in a creditor-like capacity, Gramm-Leach-Bliley-style obligations can attach, and they flow to the consumer-financial-data vendors in your stack; notably, GLBA does not preempt stricter state law.

The one place FERPA can reach is the on-campus, ground-leased asset — where a university (or its affiliate) holds housing and roster records, FERPA can touch that data, which is another reason the on-campus asset is a different animal (covered below). The plain-language rule to carry: FERPA follows the university; the guarantor file follows state PII law, FCRA, and GLBA-adjacent duties. Getting that regime right is the credibility test for this asset class, and it is why the redaction protocol in the next section is not optional polish — it's how you meet a genuine legal duty. To be clear about my lane: Peony is a data room company, not a privacy-law firm, and the redaction protocol described here is a workflow, not legal advice — have privacy counsel set your redaction standard.

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What is the guarantor-file protocol: redact, stage, watermark, log?

The protocol is four moves: redact guarantor PII out of the diligence rent roll, stage the unredacted file to the winning buyer only, watermark everything, and log every view. It exists because the two files in a student-housing deal carry opposite risk profiles — the bed-level economics need to reach every buyer, and the raw human identities need to reach almost none — and the way you reconcile that is to separate them physically in the room.

Redact at the document level. A diligence buyer needs to underwrite the bed, not the person standing behind it. On the rent roll and lease schedule, keep the load-bearing economic fields — unit and bed, lease term and in-place rate, lease-up and renewal status, whether a guaranty exists, and the guaranty type — and permanently mask the personally identifiable fields: guarantor and tenant names, Social Security numbers, dates of birth, home addresses, income figures, and credit scores. True redaction burns the underlying text out of the file so it cannot be copied out or recovered from the document layer; a black box drawn over a PDF that still carries the text underneath is not redaction.

One design note that student housing forces and conventional multifamily does not: the guaranty type is itself an economic field, so it stays in the redacted rent roll even though the guarantor's identity comes out. A by-the-bed lease with an individual parental guaranty isolates default to one bed and one parent; a joint-and-several structure with a general guaranty means a single parent can be pursued for the entire unit's rent — all four beds in a 4-bedroom, not just their child's share. Buyers underwrite those two structures very differently, so "guaranty: individual" versus "guaranty: joint-and-several" is load-bearing economics that belongs in the open file; the name and Social Security number behind it do not.

Stage the raw file to the winner only. The redacted rent roll lives in the open diligence folder; the unredacted guaranty packets sit in a separate, NDA-gated, view-only folder that stays dark until you grant the winning buyer access late in the process. All twelve groups underwrite the same redacted economics on a level field; the raw personal financial data is exposed to exactly one counterparty at the point it is actually needed. Watermark everything with a dynamic per-viewer watermark so a leaked page points back to one viewer, disable download on the raw guaranty folder, and gate it behind an NDA. Log every view with page-level analytics, so any access to the sensitive material is tied to a named person. Sharing bed-level economics with twelve groups is normal deal-making; sharing raw guarantor Social Security numbers with twelve groups is an exposure you never have to create.

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How do buyers verify my 94% prelease number is real?

They verify it by reconciling your headline to the system of record — which is why the velocity-evidence package, not a summary slide, is what actually closes the credibility gap. A serious buyer's analyst will not accept "94% preleased" as a number; they will want to see the machine that produced it and tie it to countable documents. Give them three layers, and the 94% stops being your claim and becomes their finding.

• The same-date year-over-year curve. Signed leases as a percentage of beds at today's date, and at the identical calendar date in each of the prior two years. This is the reading that matters in a normalizing market: the sector's May preleasing across the Yardi 200 was 78% and healthy but below the 2022-2024 pace, so a buyer wants to see your curve running ahead of its own history, not just above a national average.
• The property-management-system export. Pull the lease-up and rent-roll data straight from Entrata, StarRez, or RealPage rather than retyping it into a deck. A buyer's analyst reconciles the headline to the PMS export because the export is the system of record; a hand-built summary invites a re-audit.
• The countable audit trail. Make the signed leases and the parental guaranties individually countable in the room (redacted for PII), so the diligence team can tie the 94% to a specific set of executed documents — signed leases plus guaranties, counted — rather than a summary cell. This is the difference between a number you assert and a number they confirm.

Round it out with the turn-cost history — the summer turn is student housing's signature operating event, when the asset empties and re-leases between roughly May and August, and operators report meaningful per-bed turn costs, so a buyer wants several years of turn-cost history to model the real net operating income rather than a gross-rent fantasy. Show the renewal-versus-new-lease split too, because a machine that re-leases mostly to returning students behaves differently from one that rebuilds from scratch each year. And be explicit about the lease structure — a 12-month lease versus a 9-month-plus-summer structure changes the annualized revenue per bed and the summer-vacancy exposure — and about by-the-bed underwriting, since a buyer models revenue bed-by-bed, not unit-by-unit. Presented this way, the velocity package answers the buyer's real question — "is the machine repeatable?" — with evidence, not adjectives.

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How do I handle a university ground lease without tipping off the university?

You stage the leasehold documents you already hold early, and you sequence the consent-and-estoppel outreach to the university deliberately — late, and only for a buyer serious enough to justify it. One of the two assets in a typical student-housing package sits on a university ground lease, which makes the university a counterparty to your diligence and a source of documents buyers will demand: the ground lease itself, the affiliation agreement, and ideally an estoppel confirming the lease is in good standing plus a consent to assignment. The problem is that formally requesting an estoppel or a consent is precisely the act that tells the university a sale is underway — so the choreography matters.

Run it in stages inside the room:

1. Early — self-held documents. Stage the executed ground lease, the affiliation agreement, and the ground-rent schedule (all of which you already possess) so every buyer can underwrite the leasehold economics, the term, and the reversion without anyone contacting the university.
2. Mid — qualify the field. Let buyers price the leasehold off those documents and submit bids. Keep the university entirely out of the loop while a dozen groups are still evaluating.
3. Late — the single, deliberate outreach. Trigger the formal estoppel and consent-to-assignment process only for the winning buyer or a short list, so you approach the university once, at the right hour, rather than signaling a transfer to every tire-kicker.

On the economics: a ground-leased asset trades differently from fee-simple, because you're selling a leasehold interest, not the land — a wasting asset with a defined term, ground rent, reversion of the improvements to the university at term end, and consent restrictions baked in. The discount to an equivalent fee-simple asset is real but deal-specific, driven by the remaining term (a fresh 55-to-99-year P3 ground lease behaves very differently from one with 20 years left), the rent structure, the reversion and extension options, and how restrictive the university's covenants are — and leasehold debt is available but on tighter terms, which compresses what a leveraged buyer pays. The honest framing to give a buyer is qualitative: a long, clean, lender-friendly ground lease discounts modestly, a short or restrictive one materially, and a well-documented leasehold is discounted less than an ambiguous one. Because on-campus ground-leased assets are also where FERPA can reach (the university may hold housing and roster records), the confidentiality discipline is doubly warranted: use folder-level permissions and an NDA gate on the leasehold materials, per-viewer watermarks so any leaked page is traceable, and page-level analytics so you know who's been in the ground-lease folder. The room cannot make the university's consent unnecessary — but it lets you keep the process quiet until the moment the university genuinely has to be brought in. Coordinate the exact timing and language of that outreach with your counsel and broker.

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How do I give 12 buyer groups access without them seeing each other?

You put each buyer group in its own permission group, gate the sensitive folders behind an NDA, and stage the guarantor file so it stays dark until the winner is chosen — so twelve funds and REITs can all diligence the same asset in the same room without ever seeing each other or the raw parental PII. This is the everyday reality of a competitive student-housing process: a typical disposition expects 12 buyer groups — student-housing funds and REITs — and each group brings its own analysts, its own lender, and its own counsel. The room has to hold all of them on a level playing field and leak nothing sideways.

The mechanics that make it routine:

• Separate permission groups per buyer. Each group sees the diligence folders and nothing about the other bidders — no viewer lists, no activity, no cross-contamination. Twelve groups, twelve sightlines into the same documents.
• An NDA gate in front of the room (or in front of the sensitive folders), so a viewer agrees to confidentiality before they see a single lease or a single guaranty, and you hold the record that they did.
• Dynamic per-viewer watermarks that burn each viewer's name, email, and timestamp into every page. Twelve groups times their teams means dozens of viewers touching PII-laden documents; the watermark makes every page traceable to exactly one person.
• The staged guarantor file. The redacted rent roll is open to all twelve; the unredacted guaranty packets live in a separate, view-only, download-disabled folder that stays closed until you grant the winning buyer access late in the process.
• Page-level analytics so you see who opened the prelease package, who actually read the ground lease, and who never got past the teaser — read as engagement signal, who-viewed-what-and-how-long, not keystroke capture — which tells you where the real bidders are before the calls.

And here is the seat-math wedge that makes this affordable at twelve-buyer scale: on a flat, unlimited-viewer model you invite all twelve groups and every one of their analysts, lenders, and lawyers for free, because you pay per admin (your side of the table), not per viewer. On a per-user vendor, a competitive student-housing process — where the viewer list balloons precisely because you want maximum qualified competition — becomes a variable cost that punishes you for running a good auction.

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What if a losing bidder walks away with my rent-roll data?

They shouldn't be able to, and a room built for a competitive process is specifically designed to prevent it — through watermarking, revocation, and view-only access. This is the fear that keeps sellers up during a twelve-bidder auction: eleven groups will lose, and every one of them has spent the process inside your rent roll, your prelease data, and (if you were careless) your tenant file. The defense is layered, and it is the reason you run a competitive student-housing sale in a real data room rather than by sending files.

• Watermark everything. A dynamic per-viewer watermark burns the viewer's identity into every page, so a document that surfaces where it shouldn't points straight back to the group that leaked it. Deterrence first: people handle differently what is stamped with their own name. See the dynamic watermarking guide for how per-viewer stamping works in practice.
• Revoke on the day they lose. The instant a bidder is out, you cut their access — and because the documents live in the room rather than in their inbox, revocation is real, not theater. A losing bidder walks away with their notes, not your files.
• View-only on the sensitive material. The raw guaranty file (and, if you choose, the full rent roll) is served view-only with download disabled, so a bidder reads it in the room without ever holding a local copy to walk away with. Screenshot protection raises the friction on the crude workaround.

No control makes data physically impossible to exfiltrate — a determined viewer can photograph a screen — but the combination of a name-stamped page, instant revocation, view-only serving, and a full access log turns "a losing bidder quietly keeps my rent roll" from an easy accident into a traceable, deterred, deliberate act. For a seller handing bed-level economics to eleven eventual losers, that is the difference between a competitive auction and an unacceptable risk.

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Who buys purpose-built student housing, and how big are the trades?

The buyer pool for institutional-quality PBSH is dominated by dedicated student-housing funds, diversified real estate private equity, and REITs — and the last few years have seen some of the largest single trades in the sector's history, which tells you the depth of capital behind your process. The landmark deal set the tone: in 2022, Blackstone acquired American Campus Communities (ACC) — then the largest U.S. student-housing owner, developer, and manager — for approximately $12.8 billion including debt, at $65.47 per fully diluted share, all cash, closing August 9, 2022 across a portfolio of 166 properties. (Blackstone's own release rounds the headline to roughly $13 billion; the $12.8 billion including-debt figure and the $65.47 share price are the same transaction, answering different questions — enterprise value versus equity price.)

The trades since then show the same institutions actively rotating the asset class:

• KKR bought 19 purpose-built student housing properties (more than 10,000 beds) from BREIT for roughly $1.64 billion in 2024, anchored to leading four-year public universities and managed post-close by an operating partner. Note the clean round-trip: Blackstone took ACC private in 2022, then sold a slice of student housing to KKR in 2024 — a textbook "portfolio changes hands between institutions" data point.
• The Scion Group, with an institutional partner, acquired 8,724 beds across 14 communities for $893 million from Harrison Street in 2024, part of Harrison Street clearing more than $4 billion of dispositions that year.

On overall transaction volume, be careful: industry estimates put 2025 U.S. student-housing volume somewhere in the neighborhood of $12-15 billion, but that range comes from secondary market commentary rather than a single primary broker figure, so treat it as an estimate rather than a hard number. The durable takeaway for a seller is not the exact volume — it's that the institutions writing the biggest checks in the sector (Blackstone, KKR, Scion, and the dedicated funds and REITs alongside them) are active buyers, which is precisely why a competitive twelve-group process is realistic for a well-located, well-documented flagship asset. Your job is to make your room the one that reads cleanest to that pool.

Two practical notes on reaching that pool. On cap rates, be careful with any single number — industry commentary puts core student-housing cap rates in a soft range that varies with asset quality and campus proximity, so a bed at a growing flagship prices tighter than the sector average; verify the current range with your broker's capital-markets desk rather than anchoring to a blog figure. And on the broker-versus-direct question: for an institutional-quality flagship asset with a dozen likely bidders, a dedicated student-housing capital-markets broker almost always earns the fee by widening and managing the field — going direct to funds makes sense only for a small, single-relationship trade.

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What documents belong in a student-housing data room, and where do they go wrong?

The room is organized around the two pricing curves and the tenant-file wall — and each workstream has a signature failure mode that a buyer will hunt for. Here is the buyer's-eye document map for this asset class, with the one thing that goes wrong in each.

A few of these deserve their own paragraph, because the standard and the liability live there.

The prelease velocity package is the argument, not an appendix. Everything a buyer believes about the income flows from whether they trust the lease-up curve, so this workstream leads the index and carries the PMS exports that let an analyst reconcile the headline to the system of record. Get it right and the rest of diligence is confirmatory; get it wrong — a bare percentage on a slide — and you invite a full re-audit of the number the whole valuation rests on.

The guarantor-file wall is the compliance line. This is the workstream where a seller most often creates a legal problem out of carelessness, by treating a student rent roll like an office rent roll and circulating raw PII. The redacted-economics-for-all, raw-file-for-the-winner-only protocol isn't caution for its own sake — it's how you meet a genuine state-PII-law, FCRA, and GLBA-adjacent duty on the second adult every bed drags into the room. The multifamily acquisition data room faces a version of this with resident SSNs on leases; student housing doubles it, because the guarantor is a whole additional financial profile.

The municipal file caps the asset. College towns commonly restrict how many "unrelated adults" may share a dwelling — a constitutionally sanctioned power (the Supreme Court upheld such limits in Village of Belle Terre v. Boraas) — and those caps directly limit legal bed count and therefore NOI. Some cities are loosening them and some tightening; either way, the ordinance belongs in the room, because a buyer who discovers an occupancy cap you didn't disclose treats it as a hidden value impairment. The generic version of this title-and-entitlement workstream is in commercial property due diligence; here, make sure the licensing and occupancy documents are actually present.

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How does the data room actually run a student-housing sale?

It runs it as a permissioned, two-file workflow where the prelease machine is legible at the top, the guarantor file is walled off and staged, and every one of a dozen buyer groups gets its own sightline into the same asset. A competitive student-housing disposition moves the prelease package, the rent roll, dozens-to-hundreds of leases and guaranties, the T-12 and general ledger, the turn-cost history, the enrollment tab, the physical and title file, the municipal license, and — on the on-campus asset — the ground lease, among a seller, up to 12 buyer groups, their lenders and counsel, and a broker. Email and consumer file-sharing cannot gate, watermark, redact, revoke, or track that. Here's how a room like Peony maps to the specific risks of this asset class, at a flat $52/admin/month with no per-page or per-user surcharge — which is exactly the right shape when the viewer list balloons on purpose and the tenant file is PII-dense.

• NDA gate. The rent roll and guaranty file are the most confidential items in the deal. A click-through NDA in front of the room — or in front of the specific rent-roll or guaranty folder — means a viewer agrees before they see a single lease, and you hold the record that they did.
• Dynamic per-viewer watermarks. The same rent roll opens for a dozen buyer groups with each viewer's identity burned into the page, so a leaked PII-laden document points straight back to the group that leaked it — a real deterrent when you're circulating tenant financial data.
• Data room redaction. Guarantor names, Social Security numbers, income figures, and credit scores are redacted out of the diligence rent roll at the document level, so buyers see the bed-level economics without the identities — the practical mechanism for meeting the PII duty while still moving the deal.
• Page-level analytics. You see who viewed what and for how long — did this fund actually open the prelease curve and the ground lease, or just skim the teaser? That tells you which of the twelve groups is a real bidder before the diligence call. (This is view-and-dwell analytics — who-viewed-what-and-how-long — not keystroke capture.)
• Screenshot protection and view-only serving. The raw guaranty file is served view-only with download disabled, and screenshot protection raises the friction on the crude workaround — so a losing bidder walks away with notes, not your tenant file.
• Role-based permission groups. Twelve buyer groups, each in its own group seeing the diligence folders and nothing about the others; the winner's team gets the unredacted guaranty file late; the broker and counsel sit across the deal. One room, many sightlines, no cross-contamination.

For a flat-rate room serving 6,800+ customers, the structural fit is the single-asset or two-asset student-housing disposition, the small-portfolio sale, the recapitalization, and the agency or bank financing — where the document set is large, PII-dense, and worked by a dozen competing groups, but the deal doesn't warrant a six-figure VDR procurement.

Where you don't need any of this. Be honest about the floor. A single small student rental trading all-cash between two parties with one attorney can run on a handful of emailed PDFs — a data room is overkill, as long as the guarantor PII is handled carefully. And at the opposite extreme, a multi-billion-dollar national portfolio take-private with a full banking syndicate — the ACC-scale trade — will default to Datasite or Intralinks; that's their lane and I won't pretend otherwise. The room earns its place in the wide middle: any student-housing deal with a lender, a PII-dense tenant file, a competitive bidder field, and a document set too large and too sensitive for email — which is most PBSH sales that aren't either a two-party single-building trade or a mega-cap portfolio.

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What does the room cost, and what do brokers and vendors charge?

The data room is the cheapest moving part of the whole disposition, and on a flat-rate platform it should never scale with the deal size or the number of buyers — which is exactly the wrong way for a many-bidder student-housing process to be priced. On Peony, pricing is flat per admin per month: Free at $0, Business at $30/admin/month, the Data Room plan at $52/admin/month (the most popular tier — dynamic watermarking, screenshot protection, NDA gating, and page-level analytics), Deal Team at $64/admin/month (minimum four admins), and Enterprise for custom needs — all flat, with unlimited viewers, unlimited rooms, and unlimited storage, and no per-page fees. A three-month sale process on the Data Room tier totals a couple hundred dollars.

The unlimited-viewer model is the point for student housing specifically. When you run a competitive process with 12 buyer groups and each group adds its own analysts, lenders, and counsel, you invite all of them for free, because you pay per admin — your side of the table — not per viewer. That is the seat-math wedge: a per-user vendor punishes you for running the wide, competitive auction that actually maximizes your price. For the underlying line items on the diligence side (Phase I, PCA, survey, appraisal), see the due diligence cost breakdown; the data room is not one of the variable ones.

For contrast on the vendor landscape: Datasite averages roughly $68,000 a year on buyer-reported data and prices per page — the kind of meter that turns a document-heavy, many-bidder student-housing room into a five-figure variable cost — and iDeals lists in roughly the $500-1,000 per month range on a per-project model. Those are the two other vendors whose prices are worth naming; the rest either publish nothing or sit far above the flat-rate tier. And on the broker side, a full-service investment-sales commission on an institutional-quality asset near $68 million typically lands in the neighborhood of 1% of the sale price (smaller deals run higher, 2-4% on sub-$10-million assets), for a full marketing process — offering memorandum, a targeted campaign to the funds and REITs that buy beds, and management of the bidder field to close. The room doesn't replace the broker; a room that already proves the prelease machine and walls off the guarantor file makes the broker's assignment easier and gives you leverage on the fee. If you're new to the tool itself, start with what is a virtual data room.

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Is a student-housing data room different from a multifamily or senior-housing room?

Yes — same rent-roll DNA, but student housing adds a prelease machine, a distance premium, a guarantor file, and a campus, which none of the siblings carry all at once. Conventional multifamily shares the rent-roll-and-resident-PII structure, but its income turns slowly and its tenant is a single adult; student housing empties and re-leases almost entirely each year (so velocity, not occupancy, is the metric) and drags a second adult — the parental guarantor — into the file, which roughly doubles the PII problem. The senior housing data room and the skilled nursing facility data room share the "you're underwriting an operation, not an address" instinct — for student housing, the operation is the annual lease-up machine — but they turn on operating licenses, a change-of-ownership process, and protected health information, where student housing turns on the prelease curve and consumer-financial PII. And on the debt side, if you're trading the loan against one of these assets rather than the asset itself, that's the note sale data room. What makes the student-housing room its own lane is the combination no other asset carries: prove a machine that resets every August, price a demand story that lives in distance-to-campus bands, rebut a demographic-cliff narrative at the asset level, and protect the most PII-dense tenant file in commercial real estate. Build the room for those four at once and it reads correctly to every fund and REIT that buys beds.

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The bottom line: which student-housing data room do you actually need?

You're selling two curves and protecting one file — so the right room is the one that proves preleasing velocity and distance-to-campus to as many qualified buyers as possible while treating the guarantor file like the regulated asset it is. Here's the segmented recommendation from someone who watches these rooms run.

• Single small student rental, all-cash, two parties, one attorney: You may not need a data room at all. A clean set of PDFs — leases, rent roll, recent financials, title — can be enough, as long as the guarantor PII is handled carefully. Don't over-tool a two-party trade.
• Owner/GP selling one or two purpose-built assets to a competitive field of funds and REITs (the typical institutional disposition): This is the sweet spot for a flat-rate room. You have up to 12 buyer groups plus their lenders and counsel, a prelease machine to prove, a distance story to document, an enrollment narrative to rebut, and a PII-dense guarantor file to wall off — a document set email cannot gate, redact, watermark, revoke, or track. A room like Peony — NDA gate, per-viewer watermarks, page-level analytics, redaction, screenshot protection, role-based groups, flat $52/admin/month with unlimited free viewers — fits the many-bidder shape and the two-file discipline without a per-page or per-user meter. With 6,800+ customers across M&A, fundraising, and real estate, this is the lane it's built for.
• Multi-billion-dollar national portfolio take-private (the ACC-scale trade): Default to Datasite or Intralinks. A full banking syndicate and a global bidder list are their procurement reality, and that's the honest recommendation at that altitude.

Whichever tier you're in, the discipline is the same: prove the prelease machine with same-date, PMS-sourced, countable evidence; document the distance premium in bands, not a fake gradient; rebut the enrollment cliff at the asset level with a real evidence tab; sequence the ground-lease outreach so the university learns last; and protect the guarantor file — redacted economics for everyone, raw PII for the winner only. Build the room around the two curves and the two files, and the deal reads correctly to every underwriter who has to price it. For the process and clock underneath it, that's commercial property due diligence; for archetype selection across the asset classes, that's data room for real estate. The nearest neighbors — multifamily, senior housing, and skilled nursing — each carry their own operation-first index under the same commercial real estate hub.

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Frequently asked questions

Should I sell before or after fall prelease numbers come in?

It depends on how confident you are in the curve, but the default for a strong asset is: sell into a proven, same-date-ahead-of-last-year prelease number, not on a promise. Purpose-built student housing is priced on the lease-up machine, and buyers underwrite the velocity — signed leases as a percentage of beds, plotted against the same calendar date a year ago — more than any single snapshot. In a normalizing market that matters more, not less: fall-2026 preleasing across the Yardi 200 hit 78% in May 2026 (up 140 basis points year over year) but still sat below the 79.8% May average of 2022-2024, and average rent per bed was $933 (up 1.7%), with leasing-season rent growth decelerating from 5.9% to 2.6% to 0.9% over the last three seasons. When the tide is going out, a proven lease-up curve is worth a premium because it is scarce. If your same-date prelease is comfortably ahead of last year and ahead of your submarket, marketing before the season fully locks lets buyers watch the number climb inside the room in real time — a live proof no static teaser can match. If your curve is soft or trailing last year, you are better off waiting until the number is defensible, because a weak mid-season figure discovered in diligence forces a re-trade. Either way, the deciding factor is the evidence in the room, not the calendar: same-date year-over-year prelease reports pulled straight from the property-management system, so a buyer sees the machine running rather than a claim about it.

Should I sell the two assets as a portfolio or separately?

Portfolio if the two beds tell one story and the buyer pool is institutional; separately if the assets have different demand profiles or you want to widen the bidder set. A portfolio of purpose-built student housing near a single growing flagship is a clean institutional bite — the kind of package that KKR bought from BREIT (19 assets, more than 10,000 beds, roughly $1.64 billion in 2024) or that Scion assembled from Harrison Street (8,724 beds across 14 communities for $893 million in 2024). Bundling can command a portfolio premium, cut the buyer's transaction friction, and attract funds and REITs that will not chase a single small asset. But bundling also narrows the field to buyers who can write the larger check, and it lets one weaker asset drag the blended cap rate. Selling separately widens the pool — a local operator or a smaller fund can bid on one asset — and lets each property be priced on its own prelease curve and distance band, but it multiplies your process cost and your PII exposure across two diligence tracks. The data room makes the choice reversible: stage both assets in one room with folder-level permissions so you can run a portfolio process and a single-asset process at the same time, show a bidder the combined package or just the asset they want, and let the market tell you which framing prices higher before you commit. Run the bid both ways and let the number decide.

Will the enrollment cliff dent my valuation even though my flagship is growing?

It can dent it if you let the buyer price the headline instead of your asset — which is exactly why the room needs an enrollment-evidence tab. The 'demographic cliff' is real but widely misquoted. What WICHE actually projects (Knocking at the College Door, 11th edition, December 2024) is a roughly 13% decline in the number of U.S. high-school graduates between the 2025 peak and 2041 — a projection about the SUPPLY of traditional freshmen, not a forecast of college enrollment. And it genuinely conflicts with other data: the National Center for Education Statistics has projected total undergraduate enrollment UP by roughly 9% over a comparable horizon. Both can be true because the pain is not evenly distributed: the declines concentrate at community colleges and less-selective regional publics, while flagships and elite privates are growing — the Urban Institute documents that flagship-enrollment divergence directly. So a bed at a growing 34,000-student flagship carries a fundamentally different demographic exposure than a bed at a shrinking regional, and a sophisticated buyer knows it. Your job is to prove which side of the divergence your university sits on, in documents, not adjectives. That means an evidence tab with the university's 10-year enrollment trend, its acceptance-rate trend (a falling acceptance rate at rising applications is the flagship-demand fingerprint), its first-year live-on policy, and its housing master plan. Presented that way, the cliff narrative stops being a discount lever and becomes a reason your specific asset is defensible while the sector's weaker beds are not.

How do I prove my university is on the right side of the demographic cliff?

You prove it with an enrollment-evidence tab in the data room that turns the cliff from a vibe into a documented, asset-specific fact. Buyers who have read the headlines arrive worried; the room's job is to show them the divergence runs in your favor. Put four things in that tab. First, the university's 10-year enrollment trend — total, and ideally first-year — because a decade of growth at a flagship is the single most powerful rebuttal to a national-decline narrative. Second, the acceptance-rate trend: a flagship pulling more applications and admitting a smaller share is absorbing demand as the overall pie shrinks (the Urban Institute has documented this flagship concentration; the University of Illinois, for instance, saw applications rise more than 50% in a few recent years). Third, the first-year live-on requirement and any recent changes to it — a mandate that first-years live on campus is direct competition for your beds if it expands and a demand tailwind for off-campus beds if it relaxes, and these policies genuinely move (Cal Poly Pomona paused its first-year requirement while Cal Poly Humboldt expanded a live-on mandate). Fourth, the university's housing master plan and any public-private-partnership pipeline, because planned on-campus beds are future supply competition you want a buyer to see you have already accounted for. The move is to pair the aggregate cliff data (WICHE on high-school graduates) with your asset-level flagship data (enrollment up, acceptance rate down, live-on policy stable) so the buyer underwrites your bed, not the sector average.

Is it safe to share bed-level rent rolls and parental-guaranty files with 12 buyer groups?

Yes — but only if you separate the two files and stage them differently, because they carry very different risk. The bed-level rent roll (unit, bed, lease term, in-place rate, lease-up status) is the economic core of the deal, and every one of your buyer groups needs it. The parental-guaranty file — parent names, Social Security numbers, income verification, and credit-pull results — is the most sensitive personal data in the transaction, and almost no buyer needs the identities to underwrite. So the protocol is: redact guarantor personally identifiable information out of the diligence rent roll so buyers see the bed-level economics (rate, term, guaranty present yes/no, guaranty type) without the human identities, and keep the unredacted guaranty file staged separately, released only to the winning buyer under controlled, view-only, watermarked access late in the process. That way all 12 groups underwrite the same redacted economics on a level field, and the raw personal financial data is exposed to exactly one counterparty at the point it is actually needed. A room built for this makes the mechanics routine: an NDA gate in front of the sensitive folders, document-level redaction on the rent roll, a dynamic per-viewer watermark that burns each viewer's identity into every page, view-only access that blocks download on the raw guaranty file, and page-level analytics so you know who opened what. Sharing bed-level economics with 12 groups is normal deal-making; sharing raw guarantor Social Security numbers with 12 groups is a data-breach exposure you never have to create.

How do I redact guarantor PII before opening diligence?

You redact at the document level so the buyer keeps the economics and loses the identities, and you do it before the room opens to a single outside viewer. Start from the principle that a diligence buyer needs to underwrite the bed, not the person standing behind it. On the rent roll and lease schedule, that means keeping the load-bearing economic fields — unit and bed, lease term and in-place rate, lease-up and renewal status, whether a guaranty exists, and the guaranty type (individual versus joint-and-several) — and permanently masking the personally identifiable fields: guarantor and tenant names, Social Security numbers, dates of birth, home addresses, income-document figures, and credit-pull scores. True redaction burns the underlying text out of the file, so it cannot be copied out or recovered from the document layer — a black box drawn over a PDF that still carries the text underneath is not redaction. Then stage the file structure so the redacted rent roll lives in the open diligence folder while the unredacted guaranty packets sit in a separate, NDA-gated, view-only folder that stays dark until you grant the winning buyer access. Layer a per-viewer watermark on everything, disable download on the raw guaranty folder, and turn on page-level analytics so any access to the sensitive material is logged to a named viewer. The discipline is simple to state and easy to get wrong: bed-level economics for everyone, human identities for no one until the very end. This is a workflow protocol, not legal advice — where the stakes are high, have privacy counsel review your redaction standard.

What documents do institutional buyers expect in a student-housing data room?

Institutional buyers expect a room organized around the two things that price purpose-built student housing — the prelease machine and the distance-to-campus demand story — with the personally identifiable tenant data cleanly walled off. Concretely, the index should carry: the prelease velocity package (same-date year-over-year lease-up curves, current signed-lease count and percentage of beds, the renewal versus new-lease split, and the source exports from the property-management system such as Entrata, StarRez, or RealPage that let a buyer verify the number); the bed-level rent roll with guarantor PII redacted (unit, bed, term, rate, lease-up status, guaranty present/type); the lease and guaranty documents themselves (the standard by-the-bed lease, the parental-guaranty form, and the raw guaranty packets staged separately for the winner only); trailing and trended financials (T-12, T-3, general ledger, budget) with the summer-turn cost history called out; the enrollment-evidence tab (university 10-year enrollment trend, acceptance-rate trend, first-year live-on policy, housing master plan); the physical and title file (property-condition assessment, ALTA survey, Phase I environmental, capital plan); the municipal file (rental license, certificate of occupancy, and any 'unrelated adults' occupancy-limit ordinance that caps legal bed count); and, if the asset sits on a university ground lease, the ground lease, the affiliation agreement, and the estoppel and consent documents. Lead with the velocity package and the distance story, keep the guaranty file gated and view-only, and the room reads exactly the way an institutional underwriter thinks.

How do I present prelease velocity so buyers trust the number?

You present it as a verifiable curve, not a headline — the same-date-last-year lease-up plotted straight from the property-management system, with the signed leases and guaranties countable in the room behind it. A buyer will not take '94% preleased' on faith, and they should not have to. Trust comes from three layers. First, the curve: show signed leases as a percentage of beds at today's date and at the identical calendar date in each of the prior two years, so the buyer sees whether the machine is running ahead of or behind its own history — this same-date framing matters more in a normalizing market, where the sector's May preleasing (78% across the Yardi 200) is healthy but below the 2022-2024 pace. Second, the source: export the lease-up and rent-roll data directly from Entrata, StarRez, or RealPage rather than retyping it into a slide, because a buyer's analyst wants to reconcile the headline number to the system of record. Third, the audit trail: make the signed leases and the parental guaranties individually countable in the room (redacted for PII), so a diligence team can tie the 94% to a specific set of executed documents rather than a summary cell — the number becomes something they verify, not something they trust. Add the turn-cost history and the renewal-versus-new-lease split so the velocity is legible as a repeatable operation rather than a one-year spike. Presented this way, the prelease number stops being a claim you assert and becomes a fact the buyer confirms for themselves — which is the only version that survives diligence.

How do I keep the university from learning I'm selling the ground-leased asset?

You control the timing and the access, because on a ground-leased asset the university is a counterparty to your diligence — its consent or estoppel is a document buyers will demand — and a leak before you are ready can complicate the process. The tension is real: the buyer needs to see the ground lease, the affiliation agreement, and ideally an estoppel confirming the lease is in good standing, but the act of requesting a formal estoppel or a consent-to-assignment is precisely what tells the university a transfer is underway. Manage it in stages inside the room. Early, stage the executed ground lease and affiliation agreement (which you already hold) so buyers can underwrite the leasehold economics and the reversion terms without anyone contacting the university. Keep the formal estoppel and consent-to-assignment as a later-stage, winner-or-short-list-only workstream, triggered once you have a buyer serious enough to justify the outreach — so you approach the university once, at the right hour, rather than tipping your hand to a dozen tire-kickers. Use folder-level permissions and an NDA gate so the ground-lease materials sit behind confidentiality, per-viewer watermarks so any leaked page is traceable, and page-level analytics so you know who has been in the leasehold folder. The room cannot make the university's consent unnecessary, but it lets you sequence the sensitive outreach deliberately and keep the process quiet until the moment the university actually needs to be brought in. Coordinate the exact timing and language of that outreach with your counsel and broker.

How much does a university ground lease discount my price?

A ground lease almost always trades at a discount to the equivalent fee-simple asset, but the magnitude is deal-specific and depends far more on the lease terms than on any rule of thumb — so treat any single percentage you hear with suspicion. You are selling a leasehold interest, not the land, which means a buyer is underwriting a wasting asset with a defined term, ground rent that services differently than a mortgage, reversion of the improvements to the university at term end, and consent and use restrictions baked into the lease. The factors that actually move the discount are the remaining term (a fresh 55-to-99-year ground lease, common in university public-private partnerships, behaves very differently from one with 20 years left), the ground-rent structure and escalators, the reversion and any purchase or extension options, and how restrictive the university's consent and operating covenants are. Financing is part of it too: leasehold debt is available but typically on tighter terms, which compresses the price a leveraged buyer will pay. Because all of that is specific to your lease, the honest answer is that the discount can be modest for a long, clean, lender-friendly ground lease and material for a short or restrictive one — and the way you protect your price is to put the full ground lease, the affiliation agreement, the rent schedule, and (at the right stage) an estoppel in the room so buyers price the actual terms rather than pricing in worst-case uncertainty. A well-documented leasehold is discounted less than an ambiguous one. Have a broker with student-housing leasehold experience value the specific lease.

What do brokers charge on a $68M student-housing sale?

On an institutional-quality asset in the roughly $68 million range, a full-service investment-sales brokerage commission typically lands somewhere in the neighborhood of 1% of the sale price — often a little under — with the exact number depending on the brokerage, the scope, and how competitive the assignment is. Student housing is a specialized asset class, so sellers usually engage a brokerage with a dedicated student-housing capital-markets team rather than a generalist, and the fee reflects a full marketing process: an offering memorandum, a targeted campaign to the student-housing funds and REITs that actually buy beds, tour coordination, and management of the bidder field through closing. Smaller deals carry higher percentages (a sub-$10 million asset can run 2-4%), while large institutional trades compress toward and below 1% because the dollar fee is still substantial. On a single asset near this size, do not be surprised by a tiered or flat structure, a marketing-cost budget on top of the commission, or an incentive tier that rewards the broker for clearing above a target price. Verify the current market rate and negotiate the structure with the brokerages you interview — fees are negotiable, especially when you bring a clean, well-documented room that shortens the broker's work. The data room does not replace the broker, but a room that already proves the prelease machine and walls off the guarantor file makes the assignment easier to run and gives you leverage on the fee.

What does a data room cost for a deal this size?

Far less than most sellers expect — and, on a modern flat-rate platform, a rounding error against a $68 million sale. The data room is one of the few line items in a disposition that should not scale with the size of the deal or the number of buyers. On Peony, pricing is flat per admin per month: Free at $0 for a tiny room, Business at $30 per admin per month, the Data Room plan at $52 per admin per month (the most popular tier, with dynamic watermarking, screenshot protection, NDA gating, and page-level analytics), Deal Team at $64 per admin per month (minimum four admins), and Enterprise for custom needs — with unlimited viewers, unlimited rooms, and unlimited storage, and no per-page fees. That flat, unlimited-viewer model is the point for a student-housing sale specifically: when you bring 12 buyer groups and each group adds its own analysts, lenders, and counsel, you can invite all of them for free, because you pay per admin (your side of the table), not per viewer. A three-month sale process on the Data Room tier runs a couple hundred dollars total. Compare that to legacy vendors: Datasite averages around $68,000 a year on buyer-reported data and prices per page, and iDeals lists in the roughly $500-1,000 per month range — the kind of per-page or per-project meter that turns a document-heavy, many-bidder student-housing room into a variable cost. For a disposition where the whole thesis is 'prove the lease-up machine to as many qualified buyers as possible while protecting the guarantor file,' a flat room with unlimited free viewers is the structural fit. See Peony pricing for the current plans.

Sources

• Yardi Matrix — National Student Housing Report (June 2026) — fall-2026 preleasing 78% in May (up 140 bps YoY), below the 79.8% May average of 2022-2024; average rent $933/bed (+1.7%); leasing-season rent growth 5.9% → 2.6% → 0.9% across three seasons; market divergence (50 highest-preleased markets 92.1% vs 50 lowest 54%), measured across the "Yardi 200."
• RealPage Analytics — student housing rents by distance (July 2024) — pedestrian properties (within half a mile) 9% premium ($993/bed); over one mile 19% discount ($741/bed); overall average ~$913/bed; 175 universities tracked.
• WICHE — Knocking at the College Door, 11th edition (December 2024) — U.S. high-school graduates peak in 2025 (~3.9M), then decline ~13% through 2041 (~3.4M). A projection of high-school-graduate supply, not college enrollment.
• National Center for Education Statistics — Projections of Education Statistics — total undergraduate enrollment projected up ~9% over a comparable horizon; the national-enrollment picture is less dire than the cliff headline.
• Urban Institute — Trends in Enrollment Growth at Public Flagship Universities — flagship-vs-regional enrollment divergence; flagships absorbing share as the overall pool tightens.
• CNBC / Financier Worldwide — Blackstone acquires American Campus Communities (2022) — ~$12.8 billion including debt, $65.47 per fully diluted share, all cash, closed August 9, 2022; 166 properties (Blackstone's own release rounds to ~$13 billion — same deal).
• Multifamily Dive — KKR buys student-housing portfolio from BREIT (2024) — 19 properties, more than 10,000 beds, ~$1.64 billion, leading four-year public universities.
• Multi-Housing News — Scion acquires portfolio from Harrison Street (2024) — 8,724 beds, 14 communities, $893 million.
• Virginia Tech Student Legal Services — leasing terms — by-the-bed vs joint-and-several liability; parental guaranty structure.
• FindLaw (student housing laws); Village of Belle Terre v. Boraas, 416 U.S. 1 (1974) — landlord-tenant vs FERPA line; constitutional basis for "unrelated adults" municipal occupancy limits.
• Property-management systems — Entrata, StarRez, RealPage (and Yardi, AppFolio) as the systems of record whose exports let buyers verify the prelease number.
• 2025 U.S. student-housing transaction volume — industry estimates put it in the neighborhood of $12-15 billion (secondary market commentary; treat as an estimate, not a primary figure).

This article is general information for deal teams, not legal, tax, or investment advice. Privacy-law obligations (state PII and breach-notification statutes, FCRA, GLBA-adjacent duties), preleasing and rent figures, distance-premium data, enrollment projections, university residency and ground-lease terms, municipal occupancy limits, broker fees, and the specific deals and numbers referenced vary by jurisdiction, university, and asset, and change over time — verify current requirements and the specifics of your property with qualified privacy counsel, real-estate counsel, and a licensed broker or appraiser before relying on them. Peony is a data room provider, not a privacy-law firm, broker, or lender; the redaction and access protocol described here is a workflow, not legal advice.