Idaho STR Revenue Discovery

Revenue-side geographic discovery · not a buy box

Where do Idaho's strongest short-term rentals concentrate?

Built from AirDNA/USPPD data (5,736 Idaho listings), this tool visualizes where the state's strongest short-term rentals concentrate — it does not pick a market or build a buy box. See Methodology for filters and sourcing.

Section 1

Statewide Market Context

Actual (LTM) is what a listing earned over the trailing 12 months; Potential (LTM) is AirDNA's modeled ceiling under an optimized calendar — both are switchable throughout this page.

Revenue Distribution, Statewide

Section 2 · the centerpiece of this page

The Map

Every dot is one listing, plotted at its real coordinates. Green marks listings at or above the revenue threshold; size within that green group scales with how far above the threshold a listing sits, so an exceptional outlier reads as bigger, not just "qualifying." Everything else recedes to a small, muted gray dot — still visible, because the surrounding market density is part of the story, but out of the way. Use the threshold slider, the Actual/Potential toggle, and the filters to test what's producing the pattern you see; use Inspect area (top-left of the map) to drop a radius of up to 50 km anywhere — a ~5 mile (8 km) first-pass scale is the default — and get a full local market summary: hit rate vs. the statewide baseline, revenue/ADR/occupancy medians, property mix, amenities, and the strongest listings nearby, rather than guessing from dot density alone. Click any listing for its full detail card — the Airbnb link is a button inside it, never an automatic redirect.

Inspect area results

Section 3 · discovered, not assumed

Natural Revenue Regions

These 10 regions were not drawn by hand and don't follow city limits, ZIP codes, or AirDNA's own market boundaries. Each one is a data-driven cluster of listings at or above $90,000 actual revenue that sit close together geographically (density-based clustering, adjacent pockets merged, then grown by 15 km to capture the full surrounding market) — see Methodology for the exact method. A region's size reflects how many strong listings cluster there, not a target headcount; some are tight three-figure pockets, and that's the point. Open any region for its own map, findings, and STR regulatory research.

Section 4

Findings: What's Behind the Green Dots

These are supporting observations to help interpret the map — not a ranking, and not a recommendation. Every statistic below is computed directly from the same working dataset, at a fixed reference threshold.

Rate, Not Occupancy, Separates the Top of the Market

Listings at or above the threshold aren't filling more nights than everyone else — they're charging far more per night, and they tend to be considerably larger properties. The chart below indexes each metric's median for the $90k+ group against the median for everyone below it (1.0× = no difference).

Property Type & Setting

Houses and cabins make up the large majority of $90k+ listings, and cabins convert to $90k+ at a noticeably higher rate than houses do. Geographically, "Destination/Resort" listings (AirDNA's mountain/lake location type) hold a similarly outsized share — about four times the hit rate of mid-size-city or small-city/rural listings, even though they're not the largest slice of total inventory.

Property Type

Location Type

Each pair compares a category's share of all listings against its share of $90k+ listings. A category that overshoots its "all listings" bar is over-represented among strong performers.

The Hot Tub Signal

Listings with a hot tub reach the $90k+ threshold at a far higher rate than those without — and the gap holds up even after splitting by property size, so it isn't purely a proxy for "bigger home." That said, correlation isn't causation: hot tubs are also far more common in destination/resort settings and on larger, higher-end properties, so some of this signal is likely shared with those factors rather than the amenity alone. Read it as a strong, investigable pattern — not a standalone rule.

Section 5

Methodology & Scope

  • Source: AirDNA/USPPD Idaho property-level export, pulled via Snowflake (Iqbal_Idaho_2026-09-16-1857.csv).
  • Working population: Entire home/apt listings in Idaho with 20+ reviews and 230+ active listing nights in the trailing twelve months, minus 23 listings flagged by QC as boutique-hotel/event-venue properties rather than genuine single-unit STR comps — 5,736 listings.
  • Actual (LTM) revenue = REVENUE_LTM: real trailing-twelve-month gross revenue. Potential (LTM) revenue = REVENUE_POTENTIAL_LTM: AirDNA's modeled revenue under an optimized booking calendar.
  • $90,000 reference threshold: a starting point for visual emphasis and the Findings-section comparisons, adjustable live on the map — not a market cutoff or investment rule.
  • Region discovery: DBSCAN density clustering (haversine distance) over listings ≥ $90k actual revenue — eps=12km, min 4 listings per cluster — with clusters whose centroids sit within 30km merged into one region. Each region's boundary is the convex hull of its qualifying listings, buffered 15km outward to capture the surrounding market at every revenue level. Fully reproducible: scripts/generate_map_data.py. This finds concentrations of strong listings, not just dense listing counts — it will not simply rediscover Boise.
  • STR regulatory research (regional pages): sourced from primary government/statute text where available, dated at verification, with uncertainty explicitly flagged where a primary source could not be confirmed. Not legal advice — verify directly before relying on it for an acquisition or operating decision.
  • Scope: this is a revenue-side geographic discovery and research tool, the first trial of a workflow meant to generalize to other states. It does not select a market, define a buy box, or make an acquisition recommendation — those calls stay with the analyst.