JCR Operations & Strategy

Short-term rentals run closer to the hospitality industry rather than the company’s traditional real estate business. I believe this was ultimately the friction JCR faced. In the hospitality industry, there are constant ground operations, such as coordinating cleaning and repair staff. There is an emphasis on customer satisfaction, and there are intricate strategies regarding dynamic pricing structures.

• Double Bookings & Errors: Issues included turnover events being double-booked and a last-minute booking resulting in a missed turnover, negatively impacting reviews.
• Low GOPPAR: Usage of suboptimal native platform pricing resulted in low Gross Operating Profit per Available Room.
• Strategic Blindspots: Leadership was flying blind, unable to speak on hospitality metrics or identify KPIs like Lead Time, LOS, or inventory decisions.

With over eight years in technical business operational roles, I utilized a standard operational process to understand the landscape, gather requirements, and create a project plan. I mapped the needs of two distinct user groups: the stakeholders and the ground team. The executive stakeholders needed consolidated financial reporting to view metrics like net profit and ADR without logging into multiple platforms, while the ground staff needed simple, automated schedules without the friction of learning a new software tool.

More than half (56%) of our bookings occurred within a week of check-in, with more than a third (34%) booked just three days out. While we expect last-minute stays in this location, our pricing curve was too steep in the middle, creating uncompetitive pricing eight or more days out. I optimized the pricing to target the 8-15 day booking window by flattening the pricing curve in the middle and reserving the price drop-off for only the last couple of days. This created a more competitive structure for the 8-15 day window while preserving our edge for last-minute bookers.

Increasing the average LOS helps book up low-demand days and lessens orphan days, which we previously had to heavily discount. While our baseline rule required a two to three-night minimum stay to protect the calendar, I built dynamic rules to bypass this specifically for last-minute booking windows. This allowed us to capture high-intent short stays without sacrificing far-out availability. By actively managing these calendar gaps, we increased one-day fill-in stays by about 10%.

To drive longer, more profitable bookings, I completely restructured our fee architecture. Instead of baking cleaning costs into the nightly rate, I decoupled them as a separate, fixed line item. This created a built-in mathematical incentive for guests: because the fixed cleaning cost amortizes over the duration of the trip, the total "per-night" cost becomes significantly cheaper for guests booking four or more days. This fee restructuring successfully incentivized extended stays while lowering our overall turnover costs.

70% of all bookings made more than two weeks out resulted in cancellations. Our medium to far-out pricing was competitive at the time of booking, but as the date approached, guests would jump to cheaper listings. After modeling different scenarios, I made the strategic choice to lower our far-out prices to secure committed bookings. This significantly lowered our cancellation rate by more than 60%.

Because each listing had different target demographics and bedroom counts, I made individual adjustments. For example, our one-bedroom listing had a higher turnover to profit cost, so I optimized for longer stays. Our three-bedroom listing was in a prime location with high occupancy, so I optimized for premium rates on high-demand days.