How One Investor Used STRProfitMap to Walk Away From a $250K Mistake

Sometimes the smartest investment decision is the one you don’t make. In a maturing short‑term rental market, where supply growth is outpacing and demand, investors can’t afford to chase inflated averages. This case study shows how one investor leveraged STRProfitMap’s curated data and Excel workflow to avoid a six‑figure misstep.

The Setup: A Too‑Good‑to‑Be‑True Opportunity

A Canadian investor with limited capital found an “amazing” studio deal in a mid‑tier U.S. city. Popular data aggregators showed high average revenue for the ZIP code, and AirDNA’s dashboard suggested a healthy occupancy rate. But the investor sensed something was off. Social media threads were warning about oversaturation. In Phoenix, for example, the number of active Airbnb listings grew from 5,000 in 2017 to over 21,000 by 2025, leading to softer occupancy and falling nightly rates. Could the numbers he was seeing be inflated by properties that weren’t actually comparable?

The Problem: Noisy Data and Misleading Averages

Many popular tools include every listing they can scrape, regardless of how long it has been active or whether it has verifiable guest stays. When oversupply floods a market, those unfiltered datasets inflate averages and hide volatility. Airbnb itself removed 100,000 low‑quality listings in 2025, underscoring how much noise exists in public data. Without proper filtering, an investor could mistake a one‑time festival spike or a luxury mansion for a stable comp. The investor realized he needed a more disciplined approach.

The Process: Filtering With STRProfitMap and Excel

He opened STRProfitMap and navigated to the ZIP in question. By default the platform shows only reliable listings, defined as entire‑home properties with at least nine months of booking history and verified guest stays. On the profit map, he filtered by studio bedroom count and aligned the ADR with his budget. The map’s color‑coded ROI made it obvious which blocks were over‑saturated. He added the saturation and regulation overlays to avoid areas with permit caps and high competition. STRProfitMap then allowed him to export the list of matching properties, including monthly revenue and occupancy.

In Excel, the investor sorted the comps by revenue and checked the consistency of bookings. He plugged the numbers into his underwriting model, which incorporated property fundamentals, seasonality, operating expenses, financing costs and exit assumptions. Unlike the generic averages he’d seen before, these comps reflected realistic nightly rates and occupancy for a studio unit in that ZIP.

The Outcome: Data Changed the Decision

The verdict was clear: the projected revenue ceiling was far lower than what the initial aggregates suggested. After accounting for rent, platform fees and variable expenses, the deal produced a negative cash‑on‑cash return. With a $250,000 capital requirement, it simply didn’t pencil out. Instead of chasing a mirage, the investor walked away and redirected his focus to a different ZIP where STRProfitMap showed lower saturation and steadier occupancy. That decision preserved his capital and freed him to pursue a deal that actually fit his criteria.

Why It Matters

The short‑term rental industry is evolving fast. Demand growth is expected to slow to around 4.7 % while supply continues to grow. Competition and regulation are increasing, making it more important than ever to vet assumptions. Tools that lump all listings together can obscure risks. STRProfitMap’s focus on verified comps and granular filters empowers investors to make decisions based on reality rather than hype. As this case study shows, sometimes the biggest win is the mistake you didn’t make.

Next Steps

Ready to run your own numbers? Use STRProfitMap’s ZIP export to test your next deal. Download the toolkit and see how clean data can save you from expensive regrets.