How to analyze a mid-term rental market: a 2026 playbook

1. What MTR market analysis actually means

A mid-term rental market analysis answers a specific question: “If I buy a furnished property in this ZIP code and rent it monthly (30+ nights at a time), what is it actually worth per month, who will rent it, and does the deal underwrite?”

That question is different from short-term rental analysis (which optimizes nightly rate and occupancy by weekend) and from long-term rental analysis (which assumes a 12-month unfurnished lease at fair-market rent). Furnished monthly tenants are travel nurses on 13-week contracts, business travelers on multi-month consulting engagements, insurance-displacement tenants whose homes are being rebuilt, corporate relocations between permanent housing, and digital nomads staying somewhere for a season. They book by the month, sign month-to-month leases or fixed-term contracts, and are usually paid for by an employer, hospital, insurer, or staffing agency.

Three implications for your analysis:

• Your pricing unit is the Average Monthly Rate (AMR), not the nightly ADR. AMR is what 30+ night listings actually achieve over a trailing month, expressed in dollars per month.
• Your demand drivers are institutions, not tourist attractions. Hospitals, universities, military bases, large employers, and major construction projects determine whether a market has reliable monthly tenant flow.
• Your regulatory exposure is much lower than short-term rentals, because most cities treat 30+ night stays as regular tenant leases rather than short-term rentals.

2. The four questions every analysis answers

No matter how complicated a market looks, every honest mid-term rental market analysis comes down to four questions:

1. Is this market right for an MTR at all? Some markets are tourism-driven, where 30+ night stays don't exist as a meaningful share of the rental supply. You need to see real 30+ night marketplace listings before going further.
2. What rent and occupancy should I expect? An honest answer requires real per-bedroom AMR bands, not national averages. Bedrooms and bathrooms matter more than they do in long-term rentals because mid-term tenants frequently come as couples or small families.
3. Who needs housing here and how do I reach them? Demand drivers tell you whether your tenants will be travel nurses, corporate relocations, or something else - and the answer determines how you market the listing, which amenities matter, and what stay lengths to target.
4. Does the deal underwrite? If the property is for sale, you need the cap rate, cash-on-cash return, DSCR, and the MTR vs long-term rental premium to know whether the deal pencils relative to other options.

The product wraps these four questions into one workflow: MTR Analytics takes a city or ZIP code and returns a market takeaway, real comparables, demand-driver trends, and a deal underwrite in under a minute. The rest of this guide walks through how to read what comes back.

3. Picking your market and your radius

Mid-term rental analysis starts at the ZIP code level, not the city level. Travel nurses pick housing based on a 20-to-30-minute drive time to their assigned hospital, not the city the hospital sits in. Insurance-displacement tenants pick housing within the same neighborhood as their damaged property. Corporate relocations are usually willing to stretch further but still prefer to be near specific office locations.

When you sit down to evaluate a market, get specific:

• Identify the single most important demand-driver address (usually a hospital or major employer) and look at ZIP codes inside a 20-minute drive time of it.
• Pull the AMR for each candidate ZIP, then compare like-for-like bedroom counts. Neighboring ZIP codes can differ by more than $1,000 per month: in July 2026 the average furnished monthly rate in downtown Austin's 78701 was $3,589, while neighboring 78705 averaged $2,326. Pick the ZIP based on where the AMR is highest and where supply is thinnest.
• Verify with at least one local source - a hospital housing coordinator, a staffing agency office, or a local host with a long review history - that the demand you're seeing in the data is actually filling units month after month.

If you don't have a market in mind yet, work the other direction. The Market Finder screens every U.S. market at once and lets you stack the criteria that matter - minimum population, AMR above a threshold, occupancy floor, healthcare-employment size - so you start from a shortlist of markets that already clear your bar instead of guessing where to look.

4. Pulling real 30+ night comparables

Comparables are the most-skipped step in mid-term rental analysis and the one that ruins more pro formas than any other. The instinct is to look at a couple of marketplace listings, calculate an average, and call it your AMR. That number is almost always wrong - usually because it includes cherry-picked listings that are aspirationally priced and never actually book at the displayed rent.

A real mid-term rental comp set has three properties:

1. It spans multiple marketplaces. Different mid-term channels attract different tenant pools - travel-nurse and corporate-housing search on one side, longer-stay leisure and remote-work bookings on the other. A comp set drawn from only one channel misses half the market.
2. It includes actual booked revenue, not asking rent. Asking rent on a listing detail page is what the host wants. What you need is trailing-twelve-month revenue and last-90-day occupancy, which is what the unit actually achieved. The gap between the two is often 15-25%.
3. It is filtered to match your property. Bedrooms, baths, max guests, square footage range, and key amenities (in-unit laundry, parking, pet policy). A three-bedroom comp tells you very little about a one-bedroom deal.

The Comparables pulls live 30+ night listings from multiple marketplaces with estimated trailing revenue and recent occupancy attached, plus filters for bedrooms, baths, max guests, and amenities. You select the comps that match your property and the per-bedroom AMR band updates automatically.

5. Reading per-bedroom AMR bands

The headline number of any MTR market analysis is the per-bedroom AMR band: the monthly rent range that real 30+ night listings actually achieve, broken down by 0BR (studio), 1BR, 2BR, 3BR, and 4BR+. A useful band looks like this:

• Low (25th percentile) - conservative planning number for cash flow modeling.
• Median (50th percentile) - the rate a well-priced, well-marketed listing should target.
• High (75th percentile) - achievable if the property has standout amenities, the platform listing is heavily optimized, and the market has a sustained demand spike (new hospital wing opening, a major project starting).

Underwrite at the median or below. The biggest source of bad mid-term rental investments is operators who anchored on the 75th-percentile AMR and assumed they would consistently achieve it. Real-world average occupancy and seasonality mean that annual revenue almost never comes in at the high end of the band.

6. Estimating occupancy honestly

Mid-term rental marketing often quotes 90% occupancy. For planning, a typical operator should assume 70-80%. The difference between those two numbers is the difference between a deal that cash-flows and a deal that doesn't.

Honest occupancy planning takes three factors:

• Turnover gaps. A 13-week travel nurse contract is often followed by a one-to-two-week gap before the next placement. Across a year of back-to-back contracts, those gaps add up to roughly 7-13% of the calendar.
• Seasonality. Most MTR markets have a quieter stretch in mid-winter or mid-summer when travel-nurse contracts ebb. Plan for two slower months per year.
• Platform behavior. Listings priced at the top of the band sit on the market longer. Listings priced conservatively rent faster but leave money on the table. The AMR you choose interacts directly with your achievable occupancy.

The Market Intelligence view inside MTR Analytics shows recent occupancy, where enough data exists, alongside asking and achieved rents for each comparable, so you can see how the platform-behavior pattern is playing out in your ZIP rather than guessing.

7. Validating demand drivers with BLS and Census

Real demand drivers are institutional. They're hospitals, universities, large employers, military bases, and major construction projects that create reliable monthly tenant flow independent of tourism or the local economy. Two government data sources help you verify the strength of those drivers:

• U.S. Bureau of Labor Statistics publishes county-level employment counts and wage data by industry. Healthcare, education, and government employment counts are proxies for the size of the travel-nurse, university-affiliate, and government-contractor housing demand in a market. Pull the Quarterly Census of Employment and Wages for the county and look for healthcare employment in the 10,000+ range as a strong signal.
• U.S. Census Bureau ACS5 data gives you median household income, age distribution, and renter-to-owner ratios. The American Community Survey lets you check whether the rent you're planning to charge is realistic relative to local income and whether the area already has a healthy renter base.

Both data sources are baked into MTR Analytics. The Market Intelligence view shows trend lines for the relevant employment and income series automatically so you don't have to download CSV files from federal websites every time you analyze a market.

8. Underwriting the MTR vs LTR premium

The economic case for a mid-term rental over a traditional long-term rental is the revenue premium. In the median U.S. ZIP code, the furnished monthly rate runs 77% above the local Census median gross rent, and the middle half of ZIPs run 47% to 110% above it (national numbers). Census rents trail current asking rents, so the premium over a lease you could sign today is smaller. That premium has to cover:

• Higher operating costs (utilities, internet, cleaning, furnishing depreciation).
• Higher property management cost or your own labor for turnovers.
• Vacancy at the gaps between tenants (covered in step 6).

Once those costs come out, the net advantage is far smaller than the gross premium. When it disappears, the MTR strategy is worse than just leasing the property long-term. The Property Analyzer inside the product runs this calculation explicitly: cap rate, cash-on-cash return, DSCR, NOI, and the side-by-side MTR vs LTR underwrite, using real local comparables rather than national averages.

9. The five mistakes operators keep making

1. Anchoring on a single marketplace listing's asking rent instead of pulling real trailing revenue across a full comp set.
2. Modeling 95%+ occupancy as a baseline. Once placement gaps, seasonality, and launch time are counted, plan on 70-80% for a typical MTR; a plan above 85% leaves no room for a slow quarter.
3. Ignoring per-bedroom dynamics. A one-bedroom AMR in the same ZIP can be 60% of the two-bedroom AMR, and the demand profile is completely different. Bedroom-blind analysis kills small-unit deals.
4. Skipping the demand-driver step entirely. Without a real institutional driver, AMR is just a number on a screen with no force keeping it there.
5. Assuming local regulations don't apply because the stay is 30+ nights. Most cities exempt true MTR stays from short-term rental rules, but HOAs, lease covenants, and a handful of stricter jurisdictions still apply. Always pull a regulation summary before closing.

10. Worked example: Austin 78704

To make the playbook concrete, here's what a real Austin 78704 analysis looks like end-to-end, using MTR Analytics data for July 2026. The same workflow runs automatically inside MTR Analytics for any U.S. ZIP code, and the current numbers are on the 78704 market data page.

• Market verdict: Strong. 78704 is south-central Austin, with consistent 30+ night demand from travel nurses, business travelers, and corporate relocations tied to the South Congress employer base.
• Supply and AMR: 272 active furnished monthly listings with an average monthly rate of $3,124 across all unit types.
• Bedroom bands: underwrite your unit against its own bedroom count, never the all-unit average. Per-bedroom AMR for 78704 is in Market Intelligence.
• Occupancy: plan on the 70-80% range from step 6 until the comps you pull for your exact unit say otherwise.
• Demand drivers: St. David's South Austin Medical Center sits inside the ZIP, Dell Seton Medical Center at The University of Texas is a short drive north (travel nurses), the University of Texas at Austin brings visiting faculty and research staff, and South Congress employers drive corporate relocations.
• Local economics: 50,479 residents, 67% of homes renter-occupied, and a median household income of $97,160 (U.S. Census ACS 5-year estimates).
• Regulation: Austin Type-2 short-term rental rules do not apply to 30+ night stays. HOA and condo bylaws may still restrict furnished rentals; verify per-property.
• MTR vs LTR premium: the $3,124 AMR sits 78% above the ZIP's Census median gross rent of $1,755. Census rents trail current asking rents, so treat that as an upper bound and rerun the comparison against today's long-term rent for your exact unit. The Property Analyzer runs the full cap rate, cash-on-cash, and DSCR comparison against that long-term baseline.

That's the playbook. The rest is repetition: pick a ZIP, pull the data, validate the demand driver, underwrite the premium, and decide.