Free Rental Arbitrage Deal Analyzer Spreadsheet (2026 Template)

We built the exact spreadsheet our students use to analyze 50+ deals before signing a single lease. After watching over 3,000 aspiring rental arbitrage operators make their first deal decision, one pattern became painfully clear: the people who ran the numbers won. The people who “felt good about it” lost thousands.

This isn’t a generic real estate investing calculator. Those are everywhere—and they’re designed for people buying property with mortgages and down payments. Rental arbitrage is a fundamentally different business model. You’re leasing, not buying. Your risk profile, expense structure, and profit timeline look nothing like traditional investing. So why would you use the same spreadsheet?

You wouldn’t. That’s why we built a rental arbitrage deal analyzer specifically for operators who want to evaluate properties in under 10 minutes and know—with real data—whether a deal is worth pursuing.

Why You Need a Rental Arbitrage Deal Analyzer

Here’s the uncomfortable truth about rental arbitrage: the margin between a deal that prints money and a deal that drains your bank account is shockingly thin. We’re talking $200-400/month in misjudged expenses. That’s it. One wrong assumption about cleaning costs or occupancy rates, and a “great deal” turns into a monthly liability.

Gut Feeling vs. Data-Driven Decisions

Every single student who’s lost money on a deal had the same story: “I thought the numbers would work out.” Thought. Not calculated. Not stress-tested. Not compared against three other properties. They walked a nice apartment, imagined it as a cute Airbnb listing, and signed the lease.

Meanwhile, our most successful students—the ones running 5, 10, 20+ units—won’t even tour a property until the spreadsheet says yes. They analyze 30-50 deals for every one they sign. That ratio matters. It’s the difference between building a business and gambling.

A proper Airbnb deal analyzer removes emotion from the equation. It takes your best-case scenario and stress-tests it with conservative numbers. It forces you to account for expenses you’d otherwise forget. And it gives you a clear, binary answer: this deal works, or it doesn’t.

The Real Cost of a Bad Deal

Let’s put actual dollars on this. Say you sign a 12-month lease on a property with $1,900/month rent. You spend $5,000 furnishing it. Your projected monthly revenue was $4,200, but reality comes in at $2,800 because you overestimated occupancy by 15%.

Now you’re losing $300/month after all expenses. Over 12 months, that’s $3,600 in operating losses plus the $5,000 furniture investment. You’re $8,600 in the hole—and that doesn’t count the opportunity cost of the deal you could have taken instead.

We’ve seen it happen hundreds of times. And it’s almost always preventable with 10 minutes of spreadsheet analysis.

What Most Spreadsheets Get Wrong

Search “Airbnb spreadsheet” and you’ll find plenty of options. The problem? Almost every one is built for real estate investors—people buying properties with mortgages, calculating cap rates, and thinking in terms of appreciation and equity.

Rental arbitrage operators don’t care about cap rates. You don’t have a mortgage. You don’t build equity. Your entire model is built on the spread between lease cost and short-term rental revenue. That requires completely different inputs, different metrics, and different risk thresholds.

Here’s what investment-focused spreadsheets miss for arbitrage:

• No lease term analysis—they assume you own the property forever
• No furniture depreciation—because investors use existing fixtures
• No break-even timeline—investors think in years, you need to think in months
• No seasonal cash flow modeling—they average everything annually
• No side-by-side comparison—arbitrage operators evaluate multiple properties simultaneously

Our rental arbitrage spreadsheet fixes all of this. It’s purpose-built for the arbitrage model, because that’s the only model we teach.

What’s Inside the Free Spreadsheet

The deal analyzer has four tabs, each designed to answer a specific question about your potential deal. No fluff. No 47-tab monster spreadsheet that takes an MBA to understand. Four tabs. Clear inputs. Instant outputs.

Tab 1: Deal Analyzer (Main Calculator)

This is the core of the spreadsheet. It’s split into two clean sections:

Input Section (left side):

• Monthly rent—your lease payment
• Average nightly rate—what comparable listings charge
• Expected occupancy rate—percentage of nights booked per month
• Average cleaning fee per turnover—what you pay your cleaner
• Guest cleaning fee charged—what you charge guests (often these differ)
• Monthly utilities estimate—electric, gas, water, internet, trash
• Monthly supplies budget—toiletries, coffee, paper products, laundry
• Monthly insurance—short-term rental policy premium
• Furniture/setup cost—one-time investment to furnish the unit
• Platform service fee percentage—Airbnb takes 3%, VRBO varies
• Property management fee—if you’re not self-managing (typically 15-25%)

Output Section (right side, auto-calculated):

• Gross monthly revenue—nightly rate × nights booked
• Net cleaning income—guest fees minus actual cleaning costs
• Total monthly expenses—rent + utilities + supplies + insurance + platform fees
• Monthly net profit—what actually hits your bank account
• Annual projected profit—monthly profit × 12 (with seasonal adjustments)
• Cash-on-cash return—annual profit divided by furniture investment
• Break-even point—how many months until furniture costs are recovered
• Rent-to-revenue ratio—gross revenue divided by monthly rent (target: 2.5x+)

Every output cell is color-coded. Green means the metric passes our thresholds. Yellow means caution. Red means walk away. You don’t need to interpret anything—the spreadsheet tells you.

Tab 2: Comparison Tab (Side-by-Side Analysis)

This is where the magic happens for serious operators. You can evaluate up to three properties side-by-side with identical inputs across the top row and all calculated metrics stacked below. At a glance, you see which property has the highest profit margin, fastest break-even, and best rent-to-revenue ratio.

It also calculates a Deal Score from 0-100 based on weighted factors: profit (40%), break-even timeline (25%), rent-to-revenue ratio (20%), and risk buffer (15%). The highest-scoring deal isn’t always the most profitable—sometimes it’s the most reliable.

Tab 3: Seasonal Adjustment Tab

Annual averages lie. A property in a seasonal market might do $6,000/month in summer and $1,800/month in winter. If you based your analysis on the average ($3,900), you’d be fine—but you’d also have four months of negative cash flow that could break you if you’re not prepared.

The seasonal tab lets you input month-by-month occupancy and rate adjustments. It calculates your worst month, best month, and—critically—how many consecutive months you’ll be below break-even. That number matters more than annual profit for cash flow planning.

We recommend every student use this tab, even in “year-round” markets. There’s no such thing as a market without seasonality. Some just have less.

Tab 4: Expense Deep Dive

The fourth tab is a detailed expense tracker that breaks down every cost category into line items. Most new operators underestimate expenses by 20-30% because they forget the small stuff. This tab forces you to think through:

• Furniture and decor (bed frames, mattresses, linens, towels, kitchen supplies, decor)
• Technology (smart lock, noise monitor, Wi-Fi router, streaming subscriptions)
• Recurring consumables (coffee, shampoo, dish soap, trash bags, laundry pods)
• Maintenance reserve (3-5% of revenue for repairs and replacements)
• Platform costs (Airbnb fees, dynamic pricing tool subscription, channel manager)
• Administrative (insurance, accounting software, business entity filing)

The totals from this tab feed back into Tab 1 automatically. Fill this out once per property type (1BR, 2BR, etc.), and you’ll have a realistic expense baseline for every future deal.

How to Use the Deal Analyzer (Step-by-Step)

Having the spreadsheet is step one. Knowing how to feed it accurate data is what separates profitable operators from hopeful ones. Here’s the exact process our students follow for every deal they evaluate.

Step 1: Research Your Market

Before you analyze a single property, you need to understand the market. What’s the average nightly rate for a 2-bedroom in your target area? What’s typical occupancy? Are there seasonal swings?

Start with our guide to the best Airbnb markets in 2026 to identify high-opportunity areas. Then pull market-level data from AirDNA or Mashvisor to get average nightly rates, occupancy percentages, and revenue benchmarks.

Write these numbers down. They’re your baseline—the “market average” you’ll compare every deal against.

Step 2: Find Comparable Listings

Market averages are useful, but comparable listings are better. Go to Airbnb and search for properties similar to the one you’re evaluating. Same bedroom count. Same neighborhood. Similar quality.

Look at 8-12 listings and note:

• Nightly rate (base price, not the lowest or highest)
• Number of reviews and review frequency (a listing with 4 reviews/month is booking ~8-10 nights/month minimum)
• Cleaning fee amount
• Calendar availability (block out dates to estimate their occupancy)

This manual comp analysis takes 20-30 minutes but gives you far more accurate inputs than any automated tool alone. The combination of tool data and manual research is what our top students use.

Step 3: Input Your Numbers

Now open Tab 1 and fill in every field. Don’t skip any. If you’re not sure about a number, use your comp research as the baseline and then adjust it down by 10-15%. Why? Because new listings almost always underperform established ones for the first 2-3 months while they build reviews.

For rent, use the actual asking price from Zillow or Apartments.com. Don’t estimate—pull the actual number. If the listing has been up for 30+ days, you may have room to negotiate 5-10% off asking.

For startup costs, use Tab 4 to build a detailed estimate, then let it feed into Tab 1 automatically.

Step 4: Stress-Test with Conservative Estimates

This is the step most people skip. And it’s the most important one.

After your initial analysis, create a second scenario with these adjustments:

• Reduce occupancy by 10 percentage points (if you estimated 70%, test with 60%)
• Reduce nightly rate by 15% (accounts for slow seasons and price wars)
• Increase expenses by 10% (things always cost more than you think)

If the deal still shows profit under these conditions, it’s a strong deal. If it goes negative with conservative numbers, you’re depending on best-case scenarios to make money. That’s not a business—that’s a bet.

Our rule: if a deal doesn’t profit under stress-test conditions, we don’t take it. Period. There are always more properties.

Step 5: Compare Multiple Properties

Never evaluate a deal in isolation. Open Tab 2 and run your top 3 candidates side-by-side. Even if one deal looks great on its own, comparing it against alternatives often reveals a better option.

We’ve had students who were “100% sure” about a property until they compared it against two others. The “obvious” choice had a 4.2-month break-even. The alternative they almost ignored? 2.1 months. Same market. Same effort. Twice the return speed.

Always compare. The pricing strategy that works for one property might not work for another in the same zip code.

Step 6: Make Your Decision

After running the numbers, stress-testing, and comparing options, you’ll have a clear picture. The spreadsheet’s Deal Score on Tab 2 gives you a final ranking, but here’s our decision framework:

• Score 80-100: Move fast. Tour the property today. Apply tomorrow
• Score 65-79: Good deal with some risk. Negotiate rent down or revisit assumptions
• Score 50-64: Marginal. Only pursue if you have experience and can optimize revenue above market average
• Score below 50: Walk away. Don’t try to “make it work”

Key Metrics Every Arbitrage Deal Must Pass

Forget vanity metrics. These are the four numbers that actually predict whether a rental arbitrage deal will make or lose money. We didn’t invent these thresholds—we extracted them from analyzing over 10,000 deals our students have run through the spreadsheet.

Rent-to-Revenue Ratio: 2.5x or Higher

This is the single most important metric in rental arbitrage. Take your projected gross monthly revenue and divide it by monthly rent. If that number is below 2.5, the deal is almost certainly too tight.

Why 2.5x? Because after you subtract Airbnb’s service fee (3%), cleaning costs, utilities, supplies, insurance, and a maintenance reserve, you’re left with roughly 40-50% of gross revenue as profit. At 2.5x, that gives you a meaningful margin. Below 2.5x, one bad month wipes out your buffer.

Ratio	Assessment	Example ($1,500 rent)
3.0x+	Excellent—strong profit margin	$4,500+ gross revenue
2.5x–3.0x	Good—healthy deal	$3,750–$4,500 gross revenue
2.0x–2.5x	Risky—thin margins	$3,000–$3,750 gross revenue
Below 2.0x	Danger—likely unprofitable	Under $3,000 gross revenue

Minimum Monthly Profit: $1,000+

This might seem arbitrary, but it’s rooted in operational reality. Below $1,000/month net profit, a single unexpected expense—a broken appliance, a guest damage claim, a slow booking week—can push you into the red for that month.

More importantly, deals under $1,000/month profit don’t justify the time investment. Between guest communication, cleaning coordination, restocking, and maintenance, each property requires 5-10 hours/month of management. At $500/month profit, you’re earning $50-100/hour. At $1,500/month, you’re at $150-300/hour. The effort is nearly identical.

Target $1,000 minimum. Aim for $1,500+.

Break-Even Timeline: 3 Months or Less

Break-even means the point where your cumulative profit equals your furniture and setup investment. If you spent $5,000 furnishing a unit and profit $1,800/month, you break even in 2.8 months.

Why 3 months? Because most rental arbitrage leases are 12 months. If it takes 3 months to recoup your setup cost, you have 9 months of pure profit. If it takes 6 months, you only have 6 profitable months—and any hiccup could push that further out.

We’ve seen students sign deals with 5-6 month break-even timelines and then panic when a slow season hits in month 4. They end the lease barely ahead—or behind—after 12 months of work.

Occupancy Floor: 55%+

Your conservative occupancy estimate should be at least 55%. That means roughly 17 nights booked per month as your baseline expectation.

Note: this isn’t your target. Your target should be 65-75%. But your deal analysis should still be profitable at 55%. That’s your downside scenario—the floor below which the deal stops working.

Check AirDNA’s Rentalizer for market-level occupancy data, then cross-reference with manual comp research. Markets with average occupancy below 55% are risky for new operators unless you have a specific edge (unique property, underserved niche, superior listing optimization).

Real Deal Analysis Examples

Theory is useful. Walking through actual numbers is better. Here are three real-world scenarios our students have analyzed using the spreadsheet. Names and exact addresses are changed, but the numbers reflect actual deals from late 2025 and early 2026.

Example 1: 2-Bedroom in Nashville, TN

Input	Value
Monthly rent	$1,800
Average nightly rate	$150
Occupancy rate	72%
Nights booked/month	21.6
Gross monthly revenue	$3,240
Cleaning fee (per turn, 8 turns/mo)	$85 × 8 = $680
Guest cleaning fee income	$100 × 8 = $800
Net cleaning income	+$120
Airbnb service fee (3%)	-$97
Utilities	-$180
Supplies	-$65
Insurance	-$95
Maintenance reserve (3%)	-$97
Total monthly expenses	$2,214
Monthly net profit	$1,146
Furniture investment	$6,200
Break-even	5.4 months
Rent-to-revenue ratio	1.8x

Wait a minute. The monthly profit looks decent at $1,146, but the rent-to-revenue ratio is only 1.8x. That’s below our 2.5x threshold. And the break-even is 5.4 months—well over our 3-month target.

Nashville is a competitive market with high rents relative to nightly rates. This deal works if everything goes perfectly, but it has almost zero margin for error. One slow month and you’re underwater. Under stress-test conditions (60% occupancy, $130/night), this deal loses $287/month.

Verdict: Pass. The numbers look OK on paper but fail our risk thresholds. This is exactly the kind of deal the spreadsheet saves you from.

Example 2: 1-Bedroom in Scottsdale, AZ

Input	Value
Monthly rent	$1,200
Average nightly rate	$125
Occupancy rate	68%
Nights booked/month	20.4
Gross monthly revenue	$2,550
Cleaning fee (per turn, 7 turns/mo)	$65 × 7 = $455
Guest cleaning fee income	$85 × 7 = $595
Net cleaning income	+$140
Airbnb service fee (3%)	-$77
Utilities	-$150
Supplies	-$45
Insurance	-$80
Maintenance reserve (3%)	-$77
Total monthly expenses	$1,629
Monthly net profit	$1,061
Furniture investment	$3,800
Break-even	3.6 months
Rent-to-revenue ratio	2.1x

Better, but still not where we want it. The rent-to-revenue ratio is 2.1x (below 2.5x) and break-even is 3.6 months (slightly over 3 months). This is a marginal deal.

However, Scottsdale has a massive high season from January to April (Super Bowl, spring training, golf season) where nightly rates jump 40-60%. If we model that seasonality in Tab 3, peak months bring in $1,800+ profit while summer dips to $400-500. Annual profit comes out to roughly $14,000.

Verdict: Conditional yes. Only take this if you have cash reserves to cover summer slow months and can push rates aggressively during peak. Not ideal for a first deal.

Example 3: 3-Bedroom in Atlanta, GA

Input	Value
Monthly rent	$1,600
Average nightly rate	$165
Occupancy rate	70%
Nights booked/month	21
Gross monthly revenue	$3,465
Cleaning fee (per turn, 6 turns/mo)	$110 × 6 = $660
Guest cleaning fee income	$135 × 6 = $810
Net cleaning income	+$150
Airbnb service fee (3%)	-$104
Utilities	-$210
Supplies	-$75
Insurance	-$110
Maintenance reserve (3%)	-$104
Total monthly expenses	$2,203
Monthly net profit	$1,412
Furniture investment	$7,500
Break-even	5.3 months
Rent-to-revenue ratio	2.2x

Now here’s something interesting. The monthly profit is strong at $1,412—well above our $1,000 minimum. But the rent-to-revenue ratio (2.2x) and break-even (5.3 months) both miss our thresholds.

What makes Atlanta different? The 3-bedroom format. Larger properties have higher absolute revenue but also higher furniture costs. The key insight: if this operator can negotiate rent down to $1,400 (which is realistic in Atlanta’s competitive rental market), watch what happens:

• Monthly profit jumps to $1,612
• Rent-to-revenue ratio improves to 2.5x (hits our threshold)
• Break-even drops to 4.7 months

A $200/month rent negotiation transformed a marginal deal into a solid one. This is why we teach negotiation as a core skill in our Airbnb business program. The spreadsheet shows you exactly where to apply leverage.

Verdict: Yes, with negotiated rent. At asking price, it’s marginal. At $1,400/month, it’s a strong deal with $1,600+ monthly profit potential.

Common Spreadsheet Mistakes That Kill Deals

Even with the best spreadsheet in the world, garbage inputs produce garbage outputs. We’ve reviewed thousands of student deal analyses and the same mistakes show up over and over. Here’s what to watch for.

Forgetting the “Invisible” Expenses

Everyone remembers rent, cleaning, and utilities. The expenses that kill margins are the ones you don’t think about until they hit:

• Furniture depreciation—sofas, mattresses, and linens don’t last forever. Budget 5-8% of furniture cost per year for replacements
• Consumable supplies—coffee, shampoo, dish soap, paper towels, trash bags. Seems small until you’re spending $60-80/month per unit
• Dynamic pricing tool—PriceLabs or Beyond Pricing costs $20-40/month per listing. Worth it, but account for it
• Platform payment processing—Airbnb takes 3% host fee on every booking. On $3,000/month revenue, that’s $90 gone
• Damage and incident reserve—not every guest claim gets covered by Airbnb’s Host Guarantee. Keep 2% of revenue aside

Using Peak-Season Numbers Year-Round

This is the #1 mistake among new operators in seasonal markets. You research comps in July, see $200/night rates and 80% occupancy, and plug those numbers into your analysis. Then December hits and you’re at $95/night and 45% occupancy.

Always use the annual average for your primary analysis, then model seasonality in Tab 3. If your deal only works at peak-season rates, it doesn’t work.

Ignoring Vacancy Between Guests

70% occupancy doesn’t mean 21 nights with one guest and 9 empty. It means multiple shorter stays with gaps between them. Each turnover takes a minimum of 4-6 hours (checkout, cleaning, inspection, next check-in). Some days are unbookable because the checkout/check-in gap is too tight.

A realistic model accounts for 1-2 “dead nights” per month that don’t show up in occupancy rates but reduce your actual bookable inventory. In the spreadsheet, reduce your occupancy by 3-5% from what AirDNA reports to account for this.

Not Accounting for Airbnb’s Fee Structure

Airbnb’s pricing display confuses new operators. The nightly rate guests see isn’t what you receive. Airbnb charges hosts a 3% service fee on every booking. They also charge guests a service fee (typically 14-16%), which doesn’t affect your payout but does affect booking behavior—higher total prices mean fewer bookings.

In your spreadsheet, always use the net-to-host nightly rate (what you actually receive after Airbnb’s cut), not the listed rate. A $150/night listing nets you about $145.50 after Airbnb’s 3% host fee.

Overestimating Your First 90 Days

Brand-new listings don’t perform at market average from day one. Airbnb’s algorithm gives new listings a visibility boost for the first 2-3 weeks, but you typically need 5-10 five-star reviews before booking velocity normalizes. Plan for 40-50% occupancy in month one, 55-65% in month two, and market rate by month three.

The spreadsheet models steady-state performance. Mentally subtract $500-1,000 from your first two months and factor that into your break-even calculation.

Free Tools to Get Your Input Numbers

Your spreadsheet is only as good as the data feeding it. Here’s where to get accurate inputs without paying for expensive subscriptions.

Revenue and Occupancy Data

AirDNA is the industry standard for short-term rental market data. Their free tier gives you limited market overviews, and their Rentalizer tool estimates revenue for specific addresses. Paid plans start at $250/year. Worth it if you’re analyzing multiple markets.

Mashvisor provides similar market intelligence with a focus on investment analysis. Their free trial gives you enough data for initial research.

Manual Airbnb Comp Analysis is free and often more accurate than any tool for hyperlocal data. Search Airbnb for your target neighborhood, filter by bedroom count and property type, and record rates and review counts for 10-15 comparable listings. We walk through this process in detail in our Airbnb business startup guide.

Rental Rate Data

Zillow and Apartments.com are the best sources for current rental listings. Filter by bedroom count, neighborhood, and price range. Look at 15-20 listings to understand the market range.

Pro tip: listings that have been up for 30+ days are negotiable. Landlords with vacant units are losing money every day. This is where your negotiation skills directly impact your spreadsheet numbers.

Dynamic Pricing Intelligence

PriceLabs offers a market dashboard that shows rate recommendations and demand patterns. Even on the free tier, you can get a sense of seasonal pricing trends. This data feeds directly into your Tab 3 seasonal adjustments.

Google Sheets is the platform the spreadsheet runs on. It’s free, cloud-based, and accessible from any device. No Excel required.

Scaling Beyond Your First Deal

Once you’ve proven the model with your first profitable property, the spreadsheet becomes even more powerful. Here’s how experienced operators use it differently.

Portfolio-level analysis. Instead of evaluating deals individually, experienced operators compare new deals against their existing portfolio’s average metrics. If your current three units average $1,300/month profit with a 2.8x ratio, you only take new deals that match or beat those numbers. Your standard goes up with every deal.

Rapid market screening. When expanding to new markets, run 10-15 deals through the spreadsheet within a single afternoon. You’ll know within two hours whether a market has enough spread between rent and revenue to support arbitrage.

Renegotiation leverage. When leases come up for renewal, the spreadsheet gives you ammunition. “My profit margin at current rent is X. At renewal price, it drops to Y. I can’t renew above $Z.” Landlords respect tenants who speak in numbers, not emotions.

Exit planning. The spreadsheet also helps with exit strategy decisions. When a property’s profit dips below your portfolio average for 3+ consecutive months, the data tells you it’s time to let the lease expire and reallocate that furniture to a better deal.

Thinking about mid-term rental arbitrage? The spreadsheet adapts to 30+ day stays too. Just adjust the nightly rate to a monthly rate, drop cleaning frequency to once per turnover, and recalculate. The same framework applies.

Frequently Asked Questions

Is this spreadsheet really free?

Yes. The deal analyzer spreadsheet template is 100% free when you join the 10XBNB community. We don’t gate it behind a paywall because we want every aspiring arbitrage operator to make data-driven decisions. Bad deals hurt the industry, and we’d rather have more smart operators in the market.

Do I need Google Sheets to use it?

The spreadsheet is built in Google Sheets, so you’ll need a free Google account. You can also download it as an Excel file if you prefer working in Microsoft Excel, though some formatting may shift. We recommend keeping it in Google Sheets for automatic formula updates.

How accurate are the profit projections?

The spreadsheet is only as accurate as the data you input. With solid comparable research (step 2 in our guide above), students typically report actual results within 10-15% of their projections. The stress-test feature exists specifically to account for that variance. If you use conservative inputs and still see profit, you’re in good shape.

Can I use this for markets outside the US?

The math works globally—revenue minus expenses equals profit regardless of currency. However, the specific thresholds we recommend (2.5x ratio, $1,000 minimum profit) are calibrated for US markets. International operators should adjust thresholds based on local cost of living and market dynamics.

What if I’m managing the property myself vs. hiring a manager?

The spreadsheet includes a property management fee input. If you’re self-managing, set this to 0%. If you’re hiring a co-host or property manager, input their rate (typically 15-25% of revenue). Self-managing dramatically improves your margins, which is why most successful arbitrage operators start by managing their first 3-5 units themselves before hiring help.

How often should I update my analysis?

Re-run your numbers every quarter for existing properties. Market conditions shift—new supply enters, demand patterns change, competitor pricing adjusts. A deal that scored 85 six months ago might score 70 today. We also recommend re-running the analysis 60 days before lease renewal to make a data-driven keep-or-exit decision.

Can this spreadsheet replace AirDNA or PriceLabs?

No—and it’s not trying to. AirDNA and PriceLabs provide input data (market rates, occupancy, demand patterns). Our spreadsheet is the decision engine that takes their data and tells you whether a specific deal makes financial sense. Use them together. The spreadsheet without good data is useless. Good data without the spreadsheet is just numbers without a decision.