Dune Lakes revenue analytics

The problem

Dune Lakes runs on OwnerRez, whose raw booking export is messy: linked $0 “block” rows for multi-cottage bundles, mixed channels with very different fee structures, and cancellations that may or may not have touched real money. The business needed a clean answer to basic questions: how much revenue is actually on the books, which units and channels earn it, and what margin is left after variable costs.

What I did

I built a one-command Python pipeline (pandas) that ingests the OwnerRez Booking Detail export and produces a formatted Excel workbook and charts:

• Data cleaning: filtered out the $0 linked block rows so only revenue-carrying bookings count, and validated fees against the actual Host Fees column instead of assuming rates.
• Revenue on the books: confirmed gross and net revenue, nights, and ADR by arrival month, split into realized and upcoming.
• Channel economics: revenue and real fee load by channel. Airbnb carries a 15.5% host fee versus 2.8% direct and 2.2% on Vrbo, so every booking shifted from Airbnb to direct is worth about 13 points of margin.
• Unit performance: the whole-compound “Cottage Trio” listing alone is 52% of revenue at a $2,790 ADR. The large-group listings carry the business.
• Cancellations and margin: separated real refunds ($7.5K across 2 paid cancellations) from pre-payment noise, and built a contribution margin and sensitivity view (roughly 86% before fixed costs).

Honest caveats, on purpose

The analysis only claims what the data supports. True occupancy is deliberately not reported because the export lacks availability calendars, and I’d rather omit a metric than fake it. Operating cost estimates are clearly labeled as estimates.

Skills demonstrated

Financial analysis, revenue analytics, unit economics, channel and margin analysis, scenario and sensitivity analysis, data cleaning, KPI reporting, Python (pandas), Excel model build.