{"id":18896,"date":"2026-04-29T08:00:00","date_gmt":"2026-04-29T01:00:00","guid":{"rendered":"https:\/\/beup.space\/?p=18896"},"modified":"2026-05-11T21:50:30","modified_gmt":"2026-05-11T14:50:30","slug":"managing-multiple-airbnb-properties-with-excel","status":"publish","type":"post","link":"https:\/\/beup.space\/en\/managing-multiple-airbnb-properties-with-excel\/","title":{"rendered":"Managing multiple Airbnb properties with Excel \u2014 from occupancy to actual nightly profit"},"content":{"rendered":"

\u23f1 7 minutes read<\/p>\n

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Short answer:<\/strong> Effectively managing multiple Airbnb properties with Excel requires 5 modules: a booking tracker by platform, calculating net revenue after OTA fees (Airbnb ~17%, Booking.com 15\u201318%, Agoda\/Traveloka variable), monitoring operating costs for each property, dynamic pricing logs, and KPIs for net profit per night per property. A multi-property dashboard allows hosts with 2\u201310 properties to see their entire portfolio in one file \u2014 instead of being scattered across non-interconnected OTA apps.<\/p>\n<\/div>\n

Many Airbnb hosts managing 3\u20135 properties know their monthly gross revenue. However, when asked which properties are actually profitable after deducting OTA fees, cleaning costs, maintenance, and utilities \u2014 most don't have an immediate answer. This isn't because they're not interested, but because the information is scattered across different platforms: the Airbnb app, the Booking.com app, and their own memory. Without a holistic view, decisions are made based on intuition.<\/p>\n

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Article contents<\/p>\n

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  1. High occupancy doesn't mean profitability<\/a><\/li>\n
  2. The problem of the portfolio \u2014 not the problem of individual units<\/a><\/li>\n
  3. 5 necessary modules in a multi-unit management overview<\/a><\/li>\n
  4. 3 decisions that data completely changes<\/a><\/li>\n
  5. Frequently asked questions<\/a><\/li>\n<\/ol>\n<\/div>\n
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    High occupancy doesn't mean profitability<\/h2>\n

    A host with 3 units, each with 75% occupancy \u2014 sounds good. 75% of 30 nights is 22\u201323 booked nights, multiplied by the listing price, it's a nice number. But the listing price isn't the actual revenue. Airbnb takes a 3% host fee directly, plus a ~14% guest service fee \u2014 the total impact on the price the host actually receives is much higher than the 3% shown in the overview. Booking.com takes a 15\u201318% commission straight from the host, not from the listing price. After OTA fees: each check-out incurs a cleaning cost, periodic maintenance, repairs, utilities, insurance per unit.<\/p>\n

    When you subtract all costs, the remaining number is the real profit \u2014 and that number is often 40\u201350% lower than the gross revenue. Hosts who look at occupancy rates to evaluate efficiency are using the wrong metric. Occupancy tells you if the unit is occupied or not. It doesn't tell you how much each occupied night contributes to the host's pocket after all costs are accounted for.<\/p>\n

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    Net profit per night<\/strong> is the actual revenue received from one night's booking after subtracting all possible costs: OTA commissions, cleaning costs per check-out, maintenance and utility allocations per night used. This is the KPI that truly evaluates the efficiency of each unit in the portfolio.<\/p>\n<\/div>\n

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    The problem of the portfolio \u2014 not the problem of individual units<\/h2>\n

    When you have one unit, the problem is simple: one revenue stream, one expense stream, easy to track with any tool. But with 2\u20133 units or more, the nature of the problem changes \u2014 this is portfolio management, not single-unit management. Each unit has a completely different profile: location, type, price level, operating expenses, and profit margin. Unit A has 80% occupancy but high maintenance and low nightly prices, so its actual net profit per night isn't high. Unit B has only 55% occupancy but higher nightly prices and less wear and tear \u2014 its net profit per night surpasses Unit A. If you only look at occupancy, you'll prioritize investments in Unit A, while the real ROI is in Unit B.<\/p>\n

    Most hosts with multiple units can't see this picture because information about each unit isn't aggregated in the same structure in one place. As a result, major decisions \u2014 whether to add another unit, which unit to increase prices for during peak season, which unit to invest in interior upgrades to push ADR \u2014 are made based on intuition. Not because hosts lack analytical skills, but because they lack properly organized data.<\/p>\n

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    5 necessary modules in a multi-unit management overview<\/h2>\n

    An effective short-term rental portfolio tracking system doesn't need to be complex \u2014 but it does need these five interconnected layers of data.<\/p>\n

    Multi-platform booking tracker<\/h3>\n

    It's not just a list of bookings. An effective multi-unit booking tracker links each booking to a specific property, booking platform (Airbnb, Booking.com, Agoda, Traveloka, or direct), check-in\/out dates, and the actual revenue received after OTA fees. Additionally, it includes automatic alerts when input prices deviate from the pre-set dynamic prices for that period. This is the source data \u2014 all subsequent modules run from this.<\/p>\n

    Net revenue by platform and by unit<\/h3>\n

    Airbnb, Booking.com, Agoda, Traveloka, and direct bookings have different commission structures. The dashboard needs to separate revenue by platform so hosts know not just the total revenue but which channel provides the best net ROI. Some hosts, after measuring for the first time, discover that direct bookings \u2014 although a small percentage \u2014 yield the highest net profit because they don't incur commissions. This data informs the decision to push the direct channel.<\/p>\n

    Operating expenses per unit<\/h3>\n

    Cleaning, maintenance, repairs, utilities \u2014 each expense must be attached to a specific unit, not aggregated across the entire portfolio. Only with unit-level data can hosts discover which units have unusual operating expenses \u2014 often a sign that proactive maintenance or adjustments to the cleaning fee are needed. Untracked expenses aren't non-existent expenses \u2014 they're just hidden profits.<\/p>\n

    Dynamic price log<\/h3>\n

    Recording prices by stage: holidays, peak seasons, local events, promotional fill-in rates. After 6\u201312 months of operations, hosts can look back and see where demand was truly high and how much they capitalized on it \u2014 and where they missed opportunities to increase prices. Dynamic pricing doesn't require complex software in the early stages; it needs a good enough log to learn from historical data.<\/p>\n

    Net profit per night per property<\/h3>\n

    The ultimate composite KPI. For each night booked in each unit, after subtracting all possible costs, what's left. This is the number to compare which unit is performing most efficiently in the portfolio \u2014 and the basis for deciding which direction to expand.<\/p>\n

    Summary:<\/strong> These five modules are not independent \u2014 booking data feeds revenue calculation, revenue calculation combines with expense tracking to yield net profit, and price logs enrich seasonal analysis. An effective dashboard is when all five layers are automatically linked: enter data once, and the overview table updates automatically.<\/p>\n

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    3 decisions that data completely changes<\/h2>\n

    When there's enough data from 4\u20136 months of organized operations, these three types of decisions become much clearer than when relying on intuition.<\/p>\n

    Deciding on upgrade investments: the unit that needs investment in interior upgrades to increase ADR isn't necessarily the one with the highest occupancy, but rather the one with pricing headroom \u2014 meaning similar units in the area have significantly higher listing prices, while maintaining stable occupancy. Without net profit per night data, hosts often invest in the wrong direction.<\/p>\n

    Deciding on channel allocation: the platform with the best ROI isn't necessarily the one with the most bookings. The 18% commission from Booking.com vs. the 3% host fee from Airbnb creates a significant difference on the same listing price. After measuring by platform, many hosts adjust their allocation strategy: some platforms are better for filling occupancy during low seasons, while others are better for maximizing revenue during peak seasons.<\/p>\n

    Deciding on seasonal pricing: which months to push for higher prices and which to prioritize filling occupancy with lower prices to ensure cash flow. The answer varies by unit and market \u2014 there's no one-size-fits-all formula. But a dynamic price log after one year provides enough data for hosts to build an accurate pricing plan for the next year.<\/p>\n

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    \u270d Key Takeaways<\/p>\n