{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/71e84cd027f34940b1d10a195ca68a1d\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/71e84cd027f34940b1d10a195ca68a1d-8978733643949e20.gif","duration":404.832,"title":"Bookby.date, AI Pricing for Rentals","description":"This Loom explains Victor Kane’s bookbuy.date system for automatically optimizing short-term rental pricing instead of relying on Wheelhouse alone. He describes managing via panic discounts such as 15% at 6 weeks out and 25% at 2 weeks out, after noticing vacancies when prices were misaligned. The app targets an ideal “book by” day pattern using Wheelhouse data plus guest booking and historical comps, with an AI partner Greg running repeated checks and recalculating prices after every booking or cancellation to keep the calendar stable. It also accounts for stay protection by geometry and a constraint that the cleaner will not work on Sundays, and it is actively running pricing now."}