Short-Term Rental Investing in 2026: What the Real Numbers Look Like After Year One

The number that changed Marcus’s whole plan came in on a Tuesday morning in October. He had closed on a three-bedroom cabin outside Gatlinburg eight months earlier, projecting $72,000 in annual gross revenue based on comparables he had pulled from a free platform. By that Tuesday, with October half over and the November shoulder season approaching, his trailing-twelve-month gross was tracking toward $58,000. The math still worked. But it worked differently than he expected, and the difference lived in a line item he had barely glanced at: cleaning.

“I budgeted $90 per turnover,” he told me. “My actual rate with the local crew I use is $165 for a three-bedroom, and I had 87 turnovers in the first year. That is $13,455 I did not plan for.” He paused. “The property is still profitable. But I could have been more prepared.”

Marcus is not unusual. First-year STR owners across the country are discovering exactly the same thing right now: short-term rental investing in 2026 works, but it works differently than the spreadsheets suggest. The gap between projected gross revenue and actual net operating income is real, it is predictable, and it is survivable when you know where to look.

This article is not a passive income pitch. It is a financial autopsy of what year one actually looks like across three different market types, built on real numbers from the StaySTRA database and the investors who have lived through them. Think of it as your due diligencia (due diligence) before you commit.

The Math Framework: Four Numbers That Define an STR Investment

Before getting into the case studies, let’s build a shared vocabulary. Four numbers define the picture for any STR investment.

Gross Revenue

This is total rental income before any expenses come out. For a 3-bedroom property in a mid-tier leisure market, StaySTRA data shows a realistic range of $55,000 to $90,000 annually, depending on market, location within the market, amenity set, and management quality. Top-quartile operators in strong markets push past that range. Poorly positioned or poorly managed properties underperform it badly.

Do not anchor on platform estimates. They show gross potential, not realistic performance for a specific property managed by a first-year owner still learning the market’s seasonal rhythms.

Operating Expenses (What Actually Comes Out)

Here is where first-year investors get surprised most consistently. The expense stack on a typical self-managed STR looks like this:

• Platform fees (Airbnb/VRBO): 3 to 5 percent of gross, typically withheld before payout
• Cleaning costs: 8 to 12 percent of gross revenue, almost always underestimated
• Property management (if used): 15 to 20 percent of gross, on top of all other costs
• Supplies and maintenance: 5 to 8 percent of gross, higher in year one as the property gets broken in
• Insurance: 2 to 3 percent of gross for most markets, more on coastal properties or in high-claim zones
• Utilities: 4 to 6 percent of gross for most three-bedroom properties
• Taxes and licensing fees: Variable by market, often 1 to 3 percent of gross

Add those up and a self-managed property in a stable market burns 30 to 40 percent of gross in operating expenses before debt service. A PM-managed property runs 45 to 55 percent or more. What remains after operating expenses is your net operating income.

Net Operating Income (NOI)

NOI is gross revenue minus all operating expenses, before debt service. On a well-run self-managed property generating $65,000 gross, NOI typically lands between $38,000 and $45,000 in year one. The question is whether that number exceeds your debt service by enough to satisfy your lender and leave you with positive cash flow.

Cash-on-Cash Return and DSCR

Cash-on-cash measures what you earn on the capital you actually deployed. If you put $100,000 down on a $400,000 property and net $12,000 after debt service, your cash-on-cash is 12 percent. That is a strong return on equity in the current environment.

DSCR (Debt Service Coverage Ratio) divides your NOI by your annual debt service. Lenders want to see 1.2x to 1.4x for STR DSCR loans. A property generating $38,000 NOI with $28,000 in annual debt service hits a 1.36x DSCR. That is a financeable deal. Current DSCR rates for well-positioned STR properties run near 7.5 percent for 30-year terms at 75 percent LTV.

The properties that pencil are not the flashiest ones. They are the ones where the math holds at conservative occupancy assumptions.

Three Investor Case Studies: Mountain, Coastal, and Drive-To

The following composites are drawn from real investor scenarios in three distinct market types. The names and identifying details are changed, but the financial structures are grounded in current StaySTRA market data and actual expense patterns from operators in each market.

Case Study A: Mountain Leisure Market (Gatlinburg, Tennessee Area)

Let’s call him Daniel. He bought a three-bedroom cabin in a mountain community outside Gatlinburg in early 2025 for $410,000, putting 25 percent down. He self-manages using a local cleaning crew and a virtual assistant for guest communications.

Property: 3BR/2BA cabin, hot tub, mountain views, sleeps 8
Purchase price: $410,000
Down payment: $102,500 (25%)
Loan: $307,500 at 7.625%, 30-year DSCR term, monthly payment $2,176 ($26,112 annual)

StaySTRA data for the Gatlinburg market shows average annual revenue of $63,531 across active listings, with average occupancy of 48.7 percent and an ADR of $353. Daniel’s property outperformed market average slightly due to the hot tub and view-facing deck, finishing year one at $68,400 gross.

DSCR: $39,151 / $26,016 = 1.50x
Cash-on-cash return: $13,039 / $102,500 = 12.7%

Daniel’s biggest surprise was not the cleaning cost, though that stung. It was January and February. “I knew mountain markets slow down after the holidays,” he said. “I did not know I would book exactly four nights in January and four in February. February was $840 for the whole month. My mortgage alone was $2,168.”

The seasonality gap in mountain markets is real. It is survivable when you model it before closing. Daniel had set aside three months of mortgage payments as a reserve, which covered the valley without panic. By April, the property was generating $7,200 a month. By summer, the reserve was fully replenished.

The annual number landed strong. But the cash flow shape across twelve months looked nothing like his initial spreadsheet.

Case Study B: Coastal Market (Gulf Shores, Alabama)

Let’s call her Sofia. She and her partner bought a three-bedroom gulf-front condo in Gulf Shores in spring 2025 for $485,000. They hired a local property manager at 18 percent of gross, reasoning correctly that coastal management requires on-the-ground presence they could not provide from Nashville.

Property: 3BR/2BA gulf-front condo, shared pool, beach access, sleeps 8
Purchase price: $485,000
Down payment: $121,250 (25%)
Loan: $363,750 at 7.75%, 30-year DSCR term, monthly payment $2,606 ($31,272 annual)

StaySTRA data for Gulf Shores shows average annual revenue of $60,222 at 41.3 percent occupancy and $380 ADR. Sofia’s gulf-front unit commanded premium ADR and finished year one at $72,800 gross, above market average.

DSCR: $37,296 / $31,272 = 1.19x
Cash-on-cash return: $6,024 / $121,250 = 5.0%

Sofia’s property pencils, but the return is modest. The variable that blindsided her was the HOA special assessment. “Nobody told me condos can do that,” she said. “The PM was excellent. The building itself surprised us.”

Coastal insurance was the other number that stung. She had budgeted $2,400 based on an online quote. The actual policy, which included flood coverage for a ground-floor unit and the STR commercial rider her carrier required, came in at $4,200. That $1,800 gap changed her year-one cash-on-cash by nearly two full percentage points.

Year two looks stronger. With a full cycle of seasonal pricing data and no special assessment expected, the NOI should climb toward $42,000. The property will perform the way the model suggested once the one-time year-one costs clear the picture.

Case Study C: Drive-To Entertainment Market (Branson, Missouri Area)

Let’s call him Kevin. He bought a four-bedroom house near Branson in summer 2024 because the acquisition cost was compelling: $278,000 for a property with a private pool and game room, eight minutes from the Branson entertainment strip. He manages remotely using a local co-host who earns 12 percent of gross.

Property: 4BR/3BA house, private pool, game room, sleeps 10
Purchase price: $278,000
Down payment: $69,500 (25%)
Loan: $208,500 at 7.5%, 30-year DSCR term, monthly payment $1,458 ($17,496 annual)

StaySTRA data for the Branson market shows average annual revenue of $37,537 at 40.2 percent occupancy and $239 ADR. Kevin’s four-bedroom with pool and game room outperformed that average notably, generating $54,600 gross in year one.

DSCR: $22,010 / $17,496 = 1.26x
Cash-on-cash return: $4,514 / $69,500 = 6.5%

Kevin’s property generates positive cash flow and a DSCR that clears lender minimums. The pool was simultaneously his biggest selling point and his biggest cost surprise. He had budgeted $1,800 for pool service. The actual cost, including one pump replacement in September, came to $4,100.

“The pool is why I get $289 ADR when the market average is $239,” he said. “It earns its keep. But you have to maintain it or you lose your reviews fast, and in this market, your reviews are your marketing.”

The drive-to entertainment market has a different demand profile than mountain or coastal. Kevin’s best months are June through August and the holiday weekends, with a secondary peak around October for Branson’s fall theater season. January through March is genuinely slow, but his lower debt service means those months do not hurt the same way they would on a higher-priced coastal property.

Where Year-One Investors Go Wrong Most Often

The three case studies above all ended the year cash-flow positive. That is not accidental. Each investor bought in a market with documented demand history, structured their financing for a realistic rent roll, and kept reserves. Here is where investors who struggle make different decisions.

Modeling at Peak Occupancy

The most common underwriting mistake is projecting at 60 or 65 percent occupancy because that is what a comparable property earned in its best quarter. Annual occupancy across most mid-tier leisure markets runs 40 to 50 percent. Model at the market median, not the peak month, and you will never be caught off guard by shoulder season.

Treating Cleaning as Revenue-Neutral

Cleaning fees are often shown as a guest-paid line item on platforms. That creates the illusion that cleaning is a wash. It is not. You pay your cleaner for every turnover, and the guest-facing cleaning fee covers only part of that cost at most. At 70 to 90 turnovers per year on a busy three-bedroom property, cleaning runs $10,000 to $15,000. Budget it as an operating expense. Track it monthly.

Skipping the Reserve

Year one always produces unexpected expenses. A water heater. A roof leak. A guest who damages the hot tub. Experienced operators hold three to six months of mortgage payments in liquid reserves before their first booking goes live. Operators who do not often panic-cut rates during shoulder months to fill the calendar, which tanks their reviews and compounds the problem into year two.

Buying Into Markets With No Data History

New STR markets that developed post-2022 do not have stable demand patterns yet. Some will develop strong fundamentals. Others will not. Established markets with three to five years of documented data provide a much cleaner basis for year-one projections. The uncertainty in your model drops significantly when you are working with a real occupancy history rather than extrapolating from a single boom year.

Underestimating the Learning Curve Penalty

Self-managed properties typically run 8 to 12 percent lower gross revenue in year one compared to year two, as the operator learns the market’s seasonal patterns, refines their pricing strategy, and improves the listing quality. Factor in a learning curve adjustment when modeling year-one projections. Year two is usually materially better with no change to the market or the property itself.

How to Model Your Own Deal Before You Make an Offer

The three case studies above give you a framework. But your deal has a specific address, a specific acquisition price, and a specific financing structure. Generic market averages will only get you so far.

Walking through the StaySTRA analyzer before you commit to an offer is how you convert “I wonder if this pencils” into “I know exactly what to model.”

The workflow is straightforward. Enter the address and get market data for the specific submarket around the property, not a generic city average. A cabin two miles from the Gatlinburg Parkway performs differently than one twelve miles out. Set your bedroom count and amenities, because a hot tub or pool affects ADR within the comps model. Review the occupancy and ADR projections, apply the conservatism adjustment if the numbers look optimistic, and plug in your actual acquisition cost and DSCR financing terms. Then layer on the expense categories from the framework above. The analyzer gives you the gross revenue projection. You build the full P&L.

If the DSCR hits 1.25x or better at conservative occupancy assumptions, you have a deal worth pursuing. If the math only works at optimistic projections, that is a signal to negotiate on price, increase your down payment, or keep looking.

To understand how STR financing actually works before you start shopping, the STR Financing Guide covers the full DSCR loan landscape: how gross revenue translates to qualifying income, what lenders are looking for, and which markets have the most favorable underwriting environments in 2026.

For market selection, Best Airbnb Markets 2026 gives you a data-backed starting point ranked by return potential and regulatory stability. And if you are still in the research phase, How to Buy an Airbnb Property in 2026 covers the full acquisition process from market selection through closing.

Hay una frase (there is a phrase) I hear from almost every investor who makes it through year one and comes out the other side: “I wish I had modeled it more conservatively.” Not because the property failed. Because the conservative model would have removed the anxiety and let them focus on what actually moves the business: guest experience, listing quality, pricing strategy, the work of getting better at something worth doing.

The numbers in this article are not meant to discourage you from buying. They are meant to help you arrive at year two with cash in the bank, a clear picture of what your property actually earns, and the confidence that comes from having made a data-informed decision. That is what separates the investors who build real wealth through STRs from the ones who have a cautionary story to share at dinner.

Run the analyzer. Build the honest model. And then go buy the right property.

We do our best to keep our content accurate and up to date, but things change and we are only human. Always verify details directly with local sources before making decisions.