Mid-term rental statisticsThe MTR gap in short-term rental portfolios
Hostfully analyzed more than 500,000 US reservations across approximately 16,500 professionally managed listings to measure how stays of 30 nights or longer appear inside short-term rental portfolios.
Mid-term rentals represented 1.7% of reservations but appeared in 39% of operator accounts. Using this study's conservative method for estimating stay length, they accounted for approximately 20% of booked nights. Mid-term nightly rates averaged 47% below short-term rates nationally, with substantial variation between states.
The report examines booking frequency, length of stay, nightly rates, portfolio size, market variation, and the calendar conditions under which a longer stay may compete with short-term revenue.
Mid-term rental is common as an occasional booking and rare as an operating model
Hostfully analyzed more than 500,000 US reservations by length of stay to measure how much mid-term rental (MTR) business runs through short-term rental (STR) portfolios. Stays of 30 nights or longer accounted for fewer than 2% of reservations but appeared in nearly 39% of accounts. Mid-term rates averaged 47% below short-term rates nationally, though that relationship swung from 28% to 95% of short-term rates between states. The findings suggest operators should calculate their own crossover point rather than work from national averages.
Sections 01 through 08 report what the dataset measured. Sections 09 onward describe what operators can test in response, which is inference rather than measurement. Commercial suggestions, including the use of dedicated mid-term distribution, were not tested by this analysis.
What a short-term portfolio actually sells today
We looked at what short-term rental businesses currently capture in mid-term demand. It is worth noting that the majority of Hostfully customers are not furnished housing companies or corporate housing providers, but short-term rental operators. This is reflected in their booking mix:
| Stay length | Reservations | Share | |
|---|---|---|---|
| 1 to 2 nights | 199,073 | 38.6% | |
| 3 to 6 nights | 249,664 | 48.4% | |
| 7 to 13 nights | 47,053 | 9.1% | |
| 14 to 29 nights | 10,999 | 2.1% | |
| 30 to 59 nights | 6,804 | 1.3% | |
| 60 to 89 nights | 1,126 | 0.2% | |
| 90+ nights | 965 | 0.2% |
87.0% of reservations lasted six nights or less. The median operator ran an average stay of 3.7 nights. Only 2.2% of accounts recorded an average stay of 30 nights or more.
That average of 3.7 nights sets the terms for the analysis that follows. It defines the prevailing short-term rate environment, the turnover frequency embedded in the operating model, and the context in which a 45 night reservation appears unusual. Mid-term is not a separate industry in this analysis. It is the long edge of the calendar already being managed by short-term rental operators.
Four in ten operators have already taken a mid-term booking. Almost none run on it.
A small number of accounts in this dataset do run mid-term as their business model, and section 08 examines them. They are not where the volume comes from. The great majority of mid-term reservations were recorded by accounts for which long stays are a small fraction of activity, which means the distribution is thin and wide rather than concentrated.
The median mid-term active operator booked four long stays across the whole year and made 1.7% of their reservations from them. At the 75th percentile it was eleven bookings. At the 90th percentile, twenty-eight. Beyond that the numbers thin out fast.
| Mid-term share of an operator's bookings | Share of all accounts | |
|---|---|---|
| More than 5% | 9.0% | |
| More than 10% | 5.7% | |
| More than 20% | 3.4% | |
| More than 50% | 1.8% |
Most operators are not running a mid-term rental business. They are accepting mid-term reservations inside a short-term rental business.
The distinction is measurable. An operator running mid-term as a line of business shows four signatures in the data: rates set at a defined relationship to short-term rates, long stays concentrated in identifiable calendar windows, a wider channel footprint, and a stay length distribution that clusters rather than scatters. An operator absorbing mid-term as an exception shows none of them.
Only one of those four signatures is directly measurable at account level in this dataset, and it points the same way: 3.4% of accounts made mid-term more than a fifth of their bookings. The sections that follow examine each signature in turn, taking rate relationship first, then calendar allocation, location, distribution and operational load.
The bigger the portfolio, the harder mid-term exposure is to ignore
Exposure climbs steeply with property count. Some of that is arithmetic: more properties produce more bookings, and more bookings produce more chances to catch a long one. But the practical consequence still holds.
| Portfolio size | Exposure | |
|---|---|---|
| 1 property | 7.3% | |
| 2 to 5 | 25.3% | |
| 6 to 20 | 68.3% | |
| 21 to 50 | 81.3% | |
| 51 to 100 | 95.5% | |
| 101+ | 100% |
By the 6 to 20 property band, mid-term exposure becomes common rather than exceptional. Above twenty properties, four operators in five recorded at least one.
This table measures exposure, not preference. A 60 property operator processes far more reservations than a single property host, which makes the odds of catching at least one long stay much higher regardless of strategy. The table above should not be interpreted as strategy or distribution preference.
Three quarters of mid-term stays run 30 to 59 nights
Among all 8,895 reservations of 30 nights or more, the distribution is concentrated at the shorter end:
Three quarters of mid-term bookings fall in the one to two month range. The dataset does not record why a guest booked, so the purpose behind these stays, whether corporate or an extended vacation, cannot be established here.
Stay length thresholds change the legal picture in many markets. Some jurisdictions treat 30 nights as the point where tenancy protections begin, and some short-term rental permits explicitly exclude longer stays. Local rules, lease terms and management agreements should therefore be reviewed before a property is listed for mid-term occupancy, because this is one area the dataset cannot resolve.
Rate against nights occupied
Nightly rates diverge sharply by stay length. Weighted by reservation count across the dataset:
The 47% gap is the largest single differential in the dataset and it works against mid-term on any night that would otherwise sell at the short-term rate. Its significance therefore depends on the counterfactual: what a given night would have earned had it not been sold to a long stay. That reframes the question from rate comparison to occupancy.
What the national rates imply
Applying both rates across a full year of available nights gives the following:
| Strategy | Rate | Occupancy | Annual revenue |
|---|---|---|---|
| Short-term | $307 | 70% | $78,400 |
| Short-term | $307 | 60% | $67,200 |
| Short-term | $307 | 53% | $59,400 |
| Short-term | $307 | 40% | $44,800 |
| Mid-term | $164 | 90% | $53,900 |
| Mid-term | $164 | 100% | $59,900 |
A perfectly full mid-term calendar earns what a short-term calendar earns at 53% occupancy. A 90% full mid-term calendar matches short-term at 48%.
At national aggregate rates, a wholesale switch produces less gross accommodation revenue than short-term at any occupancy materially above 53%. An operator achieving normal short-term occupancy would need a mid-term calendar close to permanently full to match it. On revenue alone, mid-term does not function as a replacement for short-term inventory, though this comparison excludes operating costs, which section 07 discusses and this dataset cannot measure.
What the same numbers do support is mid-term as a supplement. The comparison only favours short-term on nights that actually sell at the short-term rate. Applied to a soft season, a persistent gap, or a property running well below portfolio average occupancy, the mid-term rate is being compared against an empty night rather than a $307 one. That is the case the aggregate data makes: mid-term firms up the soft parts of the calendar rather than replacing the strong ones.
One qualification before that 53% is used anywhere. It is a national figure, and the next section shows it is the most location dependent number in this analysis, ranging from 28% to 95% between states.
The occupancy scenarios above use aggregate rates across roughly 16,500 properties in many different markets. They illustrate the shape of the tradeoff. They are not forecasts for individual properties, and property-level rates and occupancy can move the crossover point materially in either direction.
The revenue estimates exclude cleaning fees, taxes, cancellations, owner stays, blocked nights, management fees, and every operating cost. Mid-term stays carry fewer turnovers and typically fewer fee lines, so the fee side of the comparison tends to work against mid-term while the cost side tends to work for it. We cannot quantify either from this data.
The 53% crossover shifts materially by market
Everything in the previous section used national averages, and national averages hide the most useful thing in this dataset. When we resolve listings to their ZIP code and roll them up by state, the mid-term discount turns out to vary more than any other figure we measured.
Nationally, mid-term nightly rates run at 53% of short-term rates. Across individual states with enough mid-term volume to measure, that ratio runs from 28% to 95%, with a median of 56%. The same strategy that destroys value in one market is close to free in another.
| State | STR rate | MTR rate | Break-even | |
|---|---|---|---|---|
| Connecticut | $186 | $51 | 28% | |
| Pennsylvania | $285 | $99 | 35% | |
| Massachusetts | $309 | $124 | 40% | |
| Michigan | $392 | $163 | 42% | |
| Wisconsin | $317 | $144 | 46% | |
| Georgia | $276 | $132 | 48% | |
| New York | $316 | $162 | 51% | |
| Alabama | $210 | $111 | 53% | |
| North Carolina | $271 | $147 | 54% | |
| Ohio | $300 | $172 | 58% | |
| Indiana | $242 | $141 | 58% | |
| Arizona | $304 | $177 | 58% | |
| Missouri | $197 | $123 | 62% | |
| Tennessee | $250 | $162 | 65% | |
| Texas | $245 | $169 | 69% | |
| Florida | $228 | $162 | 71% | |
| Colorado | $339 | $265 | 78% | |
| California | $397 | $376 | 95% |
California is the most striking line. Mid-term rates there averaged $376 against $397 for short-term, or roughly 95%. Colorado came in at 78%. At the other end, Connecticut and Pennsylvania recorded 28% and 35%.
These are portfolio level comparisons, not like-for-like ones. The mid-term and short-term rates in each state are drawn from different mixes of property, location and season, so a state where mid-term rates run close to short-term rates may reflect a different inventory mix rather than a smaller discount on any individual property. What the table establishes is that the relationship between the two rates varies enormously by market. It does not establish what an individual operator in that market would give up by moving a night from one to the other.
There is no single mid-term economic case. The relevant break-even point depends on the market, property, season and achievable rate. Portfolio-level planning should therefore use property-specific economics rather than the national 53% benchmark.
State boundaries are a blunt instrument for a market that is genuinely local, and rate mix within a single state varies substantially between a resort county and a city centre. These figures narrow the question rather than answer it for an individual property.
ZIP level analysis covers the 87% of listings whose postal codes resolved to a valid US ZIP. The remainder carried codes we could not confirm and are excluded from this section only.
What the data cannot measure
Platform data records bookings, nights and nightly rates, but nothing about cleaning costs, linen replacement, staff hours or how quickly a property wears. Those factors shape most operators' actual experience of mid-term, so they belong in the discussion even though this analysis cannot quantify them.
One quantity does carry across from the data: how many separate reservations it takes to fill a stretch of calendar. At the median average stay of 3.7 nights, 45 nights of short-term occupancy requires roughly 12 reservations. The same window as a single mid-term booking requires one.
| Operational event | Short-term | Mid-term |
|---|---|---|
| Reservations | ~12 | 1 |
| Turnovers | ~12 | 1 |
| Cleans, including mid-stay | ~12 | ~5 |
| Check-ins and check-outs | ~24 | 2 |
| Booking-to-checkout message cycles | ~12 | 1 |
| Potential gaps between stays | ~11 | 0 |
The intangibles
The rest of the operational picture is not visible in PMS usage data at all. The following factors come up consistently in operator discussion of mid-term and are worth weighing, but nothing in this dataset confirms or quantifies them:
- Fewer failure points. Most operational mistakes happen at the seams: a missed clean, a late arrival, a lockbox code that did not send. Twelve seams instead of one changes the odds.
- Wear and tear. Operators frequently report that guest type matters more than night count: a short-term guest is on holiday and treats the property as the centre of the trip, while a mid-term guest is more often working, out of the property during the day, and living in it rather than celebrating in it. This dataset contains nothing on guest behaviour or property condition.
- Predictability for owners. A booked eight week block is easier to report to a nervous owner than a hopeful forecast, particularly in a soft season.
- Team load. Mid-term concentrates the work and makes it easier to predict, which can suit a small team or an operator keeping overhead low.
Assessing the top MTR performers
Fifteen accounts in the dataset cleared both of our thresholds for meaningful mid-term activity: at least 30 long stays in the year, and at least a fifth of all reservations lasting 30 nights or more. They manage between 8 and 91 properties, with a median of 26. Across that group, roughly a third of bookings accounted for 85% of booked nights.
They also carry a markedly wider distribution footprint. The top performers run an average of 7.8 active channels, against 4.8 for accounts that never take a mid-term booking. Because larger portfolios tend to run more channels regardless of strategy, we re-ran the comparison against operators of similar size. Among accounts in the same 8 to 91 property range, those with no mid-term activity average 5.1 channels, so the top performers still run 53% more. The pattern holds inside each size band, at 7.2 against 5.3 for 6 to 20 property operators and 6.2 against 5.0 at 21 to 50.
This does not establish that distribution causes mid-term success. What it shows is that the operators capturing the most mid-term business are not doing it on a narrow channel mix.
What the top five have in common
Everything above this point was produced from anonymized data. To understand what drives those numbers, the five accounts with the highest mid-term share were re-identified internally by authorized Hostfully staff, and only so that their publicly available websites and listings could be reviewed. The pattern across those five was consistent enough to report in full.
- They are furnished rental businesses rather than STR operators who added mid-term, with four of the five self-identifying as furnished or corporate rental providers and the fifth being a long-term rental business that captures mid-term and short-term demand on selected properties.
- All five run their mid-term listings in urban areas.
- Listings cluster around a downtown core, a hospital, or an industrial area.
- Amenities are marketed to working guests rather than holidaying ones, with fast wifi, a workspace, charging stations and storage set out either on a dedicated amenities page, in three cases, or on the listings themselves.
- All five itemize a complete kitchen down to minor appliances such as a blender and a toaster, with washer and dryer on site and included rather than shared or coin operated.
- Parking is included in every case, at a minimum of one vehicle.
- Four of the five specify the type of coffee maker, which suggests listings written for someone setting up a daily routine rather than booking a stay.
Soft periods are where mid-term is most likely to compete
The research suggests a distinction between passively receiving occasional long stays and deliberately competing for mid-term demand. Furnished Finder provides a dedicated source of monthly-stay demand, and Hostfully is the only PMS that brings it into the same calendar and operating workflow as the short-term business.
Book a demoTurning the findings into a working method
The findings suggest that mid-term can complement short-term rental portfolios in markets, properties and seasons where occupancy underperforms. Acting on that requires identifying the soft periods, calculating a property-level break-even, protecting peak dates, adding mid-term distribution, and building the payment and agreement processes a longer stay needs.
The mid-term rental financial calculator compares MTR and STR revenue across the same dates using your own occupancy, rates and operating costs, and returns your break-even MTR rate. Free, no sign-up.
Open the mid-term rental financial calculatorWhat this data does not show
While this research draws on more than 500,000 reservations, it comes with limitations. A few are worth keeping in mind:
- It does not show that mid-term properties earn more per year than short-term properties. We compared strong mid-term operators against geographically similar short-term inventory and the results were mixed. Too many variables fall outside the dataset.
- It does not establish cause. Operators may run mid-term because of seasonality, regulation, owner preference, market softness, or deliberate strategy. The data cannot tell these apart.
- It does not measure profit, cost, or wear. Every figure here is gross accommodation revenue before cleaning, management fees, utilities, taxes, and platform commission. The operational section of this report is arithmetic and operator experience, not measurement, and we have labelled it that way.
- It does not include exact stay lengths. Reservations arrive grouped in length ranges, so every revenue and night estimate uses the shortest stay in each range. The real numbers are higher than the ones published here.
- State level rates are still averages, and they are not like-for-like. Mid-term and short-term rates within a state come from different mixes of property, location and season, so the ratio between them describes the market rather than the choice facing any single property.
- It does not record why guests book. Segment descriptions in this report reflect industry context and operator observation, not data captured by the platform.
- It does not represent the whole market. This is one property management platform's US customer base, weighted toward professional STR operators.
Mid-term rental, answered plainly
How we ran the analysis
- Source
- Hostfully analyzed more than 500,000 recent US bookings transacted through its customers' property management software, covering a trailing twelve month period across roughly 16,500 listings. All booking data and customer names were anonymized before export and analysis.
- Scope
- Scope was limited to US properties so that location could be examined at ZIP code level, since adding international markets would have made that comparison unreliable. One large churned account was excluded from every calculation to prevent a single former customer from distorting the results.
- Mid-term definition
- Any reservation of 30 nights or longer, a common industry threshold that aligns with the length of stay ranges available in the source data.
- Length of stay and rates
- Reservations are grouped into ranges of 1 to 2, 3 to 6, 7 to 13, 14 to 29, 30 to 59, 60 to 89, and 90 or more nights, each carrying its own average nightly rate. Reported rates are weighted by reservation count across all properties rather than averaged across accounts.
- Night and revenue estimates
- Because exact stay lengths are unavailable, every reservation is assigned the shortest stay in its range. A 59 night booking counts as 30 nights and a 200 night booking counts as 90. Absolute night and dollar figures are therefore minimums under this method. Percentage shares are not: both the mid-term and short-term sides are understated, so a share such as 20% of nights is an estimate that could move in either direction once exact stay lengths are known.
- Location analysis
- Listings were resolved to state via ZIP code, covering 87% of listings. States were included in the rate comparison only where they carried at least 100 mid-term bookings, which leaves 18 states.
- Top performer cohort and review
- Accounts with at least 30 mid-term reservations and at least 20% of reservations lasting 30 nights or more. Both conditions are required, which prevents very large short-term operators from qualifying on volume alone. The thresholds are an analytical choice rather than an industry standard. The five accounts with the highest mid-term share were re-identified internally by authorized staff solely to permit review of their public websites and listings, and that review is qualitative.
- Occupancy scenarios
- Annualized figures multiply the aggregate weighted nightly rate by 365 nights and the stated occupancy. They illustrate the shape of the tradeoff at dataset level and are not a forecast for any individual property or market.