Short-Term Rental & Hospitality: AI for STR Operatorsexpert · ~240 min

Before You Buy the Next One — an STR Underwriting Model That Refuses to Flatter You

Have you ever wondered what occupancy a property you are about to buy would actually *need* to cover its own mortgage — not what a listing agent's projection says, and not what a market-data screenshot implies?

A short-term rental purchase is a six-figure decision resting on about eight assumptions, and in most bad deals every one of those assumptions was individually defensible and collectively fantasy. This project turns the decision into a model that carries a source for every number, refuses to print a verdict when a required input is still a guess, shows you the break-even occupancy next to what comparable listings actually achieve, and tells you the price at which the deal would work — so the honest answer arrives in an afternoon instead of in your second year of ownership.

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What you'll do

A local, testable underwriting model for short-term-rental acquisitions that you own: an assumption ledger where every number carries its source, the date you obtained it, and whether it is a quote, a document, an observation, or an admitted guess; a seasonal revenue model built from comparable listings and your own realized ADR rather than a flat annual occupancy; an expense schedule that includes the maintenance and capital reserves most projections omit; a tested math core with a hand-computed golden case so you can trust the arithmetic; break-even occupancy, DSCR, and cash-on-cash for the base case; a sensitivity grid across ADR and occupancy plus three stress scenarios including a regulatory ban and a long-term-rental floor; a fail-closed memo generator that refuses to produce a verdict while any required assumption is unsourced; and a solve-for-price mode that names the highest purchase price at which the deal clears the coverage you decided on before you ever saw the answer.