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Why Data Beats Depreciation Every Time

by FeeOnlyNews.com
10 hours ago
in Markets
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Why Data Beats Depreciation Every Time
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If you hang out with real estate investors, you’ve probably noticed there’s a buzz in the air again about Airbnbs. The short-term rental (STR) tax loophole, the strategy that allows W-2 earners to classify rental income as “non-passive” and use paper losses to offset their active income, has sprung back to life. 

The reason is that 100% bonus depreciation is returning. Under the new legislation, qualifying property placed in service on or after Jan. 20, 2025, can once again be depreciated 100% in the first year. For high-income professionals, the savings can be substantial—sometimes six figures in the first year.

Why the Buzz Alone Isn’t Enough

There’s a dark side to the frenzy: You can’t deduct your way out of a bad deal. Bonus depreciation doesn’t matter if your property bleeds cash. Too many investors hear “six-figure write-off” and rush to buy anything that qualifies. The reality is that selecting the wrong market, house, or having unrealistic revenue projections can wipe out your tax benefit.

John Bianchi (widely known in the STR world as The Airbnb Data Guy) has spent the last five years helping investors avoid exactly this trap. He has watched people buy in oversupplied markets, fall for glossy photos, or assume that the previous year’s numbers will magically appear for them. In a mature, competitive STR landscape, those mistakes are costly.

The data backs him up. The short-term rental market isn’t the Wild West of 2018 anymore. An iGMS analysis of Airbnb demand notes that the market has matured, with numerous players entering the game and supply saturation in recent years. Regulation is tightening in cities, and natural disaster risks are increasing. Without careful analysis, investors face lower occupancy and thinner margins: not the windfall they expected.

One of Bianchi’s favorite examples compares two nearly identical homes in the same neighborhood. Both were four-bedroom, three-bath properties with similar amenities. One was purchased for $842,000 in 2023, the other for $2 million in 2024. 

On paper, they should have performed similarly, but in reality, the $842,000 property generated $279,000 in revenue and over $100,000 in free cash flow. Meanwhile, the $2 million property, despite being down the street, only produced $194,000 in revenue and -$24,000 in cash flow. The difference was data-driven selection versus impulse buying.

So how do you ride the tax wave without drowning in a bad investment? By treating short-term rentals like a business from day one. John Bianchi’s process, honed over years of analyzing thousands of properties, is built to do just that.

The Data-First Process for STR Success

1. Choose the right market

Great STR deals start at the macro level. Bianchi’s first step is to identify markets where demand outpaces supply and local regulations support short-term rentals. You need to study why travelers go there, seasonal booking patterns, and guest demographics. This, in turn, leads to checking local occupancy rates to see whether the market is saturated or still growing.

Is the destination a year-round draw, or does it rely on a single season? Are local laws friendly to STRs? Are natural-disaster risks or insurance costs likely to spike? These questions matter more now than ever.

Numbers bring this home. One of Bianchi’s clients picked up a property for $625,000. By carefully selecting the right market and buy box, that home went on to generate $183,000 in its first year. Another client landed a $550,000 home that produced a jaw-dropping $269,000 in year one; an extreme outlier, but proof of how a powerful market and property fit can be.

2. Build your buy box

Once you know the market, Bianchi narrows his focus to a specific type of property that is proven to perform there. This “buy box” is not a guess. It’s built from data about what size, layout, and amenities drive bookings in that area. 

Does your market cater to families who need four bedrooms and a game room? Or to couples seeking a one-bedroom cottage with a hot tub? By defining a buy box upfront, you avoid chasing shiny objects and focus on listings that actually align with your income goals.

The $842,000 versus $2 million case study demonstrates precisely how crucial this step is. Both homes looked attractive, but only one lined up with proven guest demand in that market. The buy box acted as a filter to separate a profitable deal from a money pit.

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3. Hunt for properties, but stay disciplined

Armed with a clear buy box, the search becomes intentional. Bianchi keeps a list of candidate properties that meet his criteria, and ignores those that don’t. These deals are found through repetition and patience, not impulse buys. 

If a property is missing essential features, such as a view or the number of bedrooms your guests demand, keep walking. In a competitive market, discipline is a superpower.

For example, Allison, another client, purchased a home for under $400,000 at a painful 9% interest rate. Most investors would have walked away. But because the property matched the buy box perfectly, it generated $120,000 in year one, producing around $2,500 per month in actual cash flow even with that steep interest rate.

4. Forecast revenue based on real data

This is where most amateur investors fail. They look at the current owner’s Airbnb revenue and assume they’ll do the same. 

However, revenue is a function of pricing strategy, seasonality, and amenities, rather than just location. Using data tools to model nightly rates, occupancy, and seasonality using comparable listings can set you up for massive success.

Bianchi also ensures that he factors in how he will improve the property (e.g., better photos, dynamic pricing, adding a hot tub) and builds a forecast accordingly. Without that modelling, you’re guessing.

The Allison case study highlights this perfectly: What looked like a risky bet turned into a six-figure revenue property simply because her underwriting accounted for market demand, comps, and realistic pricing adjustments.

5. Underwrite the deal like a business

After forecasting revenue, smart underwriters will delve into expenses, including:

Mortgage payments

Insurance

Property taxes

Cleaning

Utilities

Management fees

Maintenance

Furnishings

Reserves 

Most amateurs overlook capital expenditures, so be sure to account for them. Yes, the roof will eventually need replacing. No, it is not a lifetime roof (they don’t exist, unfortunately).

When you compare the $842,000/$279,000 property to the $2 million/$194,000 one, the underwriting gap becomes crystal clear. On paper, both could look “fine” if you only looked at top-line revenue. But once you account for debt service, insurance, and ongoing expenses, one delivers over $100,000 in free cash flow, while the other goes into the negative.

6. Repeat until the outlier appears

Here’s the secret sauce: The more deals you run through this process, the easier it is to spot the exceptional ones. Bianchi encourages investors to analyze dozens of properties. Most will be “close but not quite.” 

Then, once in a while, an outlier appears: a property that clearly beats the rest on projected cash flow and appreciation. That’s when you move quickly and confidently.

Ready to Dive Deeper? Join the FREE 7-Day Airbnb Data Challenge

To help investors apply this framework, John Bianchi created the 7-Day Airbnb Data Challenge. It’s a free course that walks you through market selection, buy-box building, revenue forecasting, and underwriting: the exact steps he uses with paying clients. 

Bianchi developed the challenge after helping acquire more than 120 properties over 18 months, each of which was profitable. The challenge offers daily assignments, videos, and worksheets to help you master the numbers, enabling you to invest with confidence.

The STR tax loophole can be life-changing, but it’s not a shortcut to wealth. Data is the difference between a write-off and a wipeout. Use the coming tax opportunities as your tailwind, but let the numbers steer the ship.



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