Glossary

The short-term rental glossary

Plain-English definitions of the revenue, pricing, and STR terms that actually move rate.

7-day pace: What “pace” means at Quibble, and only this: this year’s 7-day pickup minus last year’s 7-day pickup for the same stay dates at the same number of days before arrival. It is described as positive or negative.
ADR: Average Daily Rate — total room revenue divided by the number of booked nights. Says nothing about occupancy on its own.
annual rent target: The annual rent a listing is expected to earn. The model prices toward it, and changing it triggers a recomp toward comps that produce similar rent. The better it is set, the fewer controls a listing needs.
base price: A fixed starting nightly rate that rule-based tools adjust up or down. Quibble replaces it with a model that solves for the optimal rate directly.
blocked days: Nights that owners or maintenance hold out of sale. They are removed from available nights, so occupancy = booked ÷ (total − blocked).
Bookable Search™: Searches that ended in a purchase: your listing appeared in the search and the shopper booked something very similar. Predicted bookings across the comp set set seasonality; revenue managers do not hand-pick seasons. How the model works →
booking probability: The chance that a night books at a given price. Quibble estimates it with a discrete choice model; expected revenue is the price times this probability. How the model works →
booking window: The period between when a booking is made and the stay itself. Pricing strategy changes as the window narrows.
comp set: The listings a property is compared with. Quibble attaches about 30 comps to each listing, matched the way a guest would compare them (radius starting at 10 km, same-bedroom listings first with backfill, a 75–200% rate band, scored on bedrooms, rate and distance); for each night, the 10 best matches that are still available go into the model. Read the deep-dive →
computer vision: AI that scores listing photos the way a guest perceives them — light, composition, and clutter that correlate with bookings. Read the deep-dive →
consideration set: The small group of listings a shopper actually weighs before booking, about 10. Quibble attaches about 30 comps to each listing and puts the 10 best matches still available each night into the model. How the model works →
demand forecasting: Predicting future booking demand for a listing or market so pricing can react before the booking window closes. Read the deep-dive →
discrete choice: A family of models for predicting which one option a person picks from a set; here, which listing a shopper books. Quibble uses the multinomial logit form, developed largely by Daniel McFadden, who shared the 2000 Nobel Memorial Prize in Economic Sciences for theory and methods for analyzing discrete choice. How the model works →
dynamic pricing: Pricing that changes frequently in response to demand. It describes how often the price moves — not how the price is decided. Read the deep-dive →
expected revenue: Price × the probability that the night books at that price. The Pricing Engine sets each night’s price where it is highest. How the model works →
floor: The lowest price a listing can be set to. Floors exist for owner minimums, protection under large negative adjustments, and holiday or peak-season protection. The model rarely prices below the lowest comp on its own.
gap night: An unbookable single night left between two reservations because of minimum-stay rules — lost revenue that smart min-stay settings reduce.
lead time: How far in advance a guest books. A single day of lead time can be the difference between capturing a demand spike and missing it.
length of stay: The number of nights in a reservation. Pricing and discounts can be tuned to encourage more profitable stay lengths. Read the deep-dive →
market pace: Your 7-day build this year compared with your comp set’s 7-day build this year (the comp side aggregates all the comps’ data for the listing, group or portfolio selected). It is not a year-over-year measure. Shown on Insights, on the Occupancy KPI card.
market share: The probability that the next qualified shopper, someone who will book something in the comp set, picks your listing. Price, amenities, ratings, reviews, and photos all move it. How the model works →
minimum stay: The shortest reservation a listing will accept for a given date. A lever for protecting high-demand dates and avoiding orphan gaps. Read the deep-dive →
multiplier: A percentage adjustment applied on top of the optimized price at a scope: portfolio, market, group, property, or a date range. Multipliers add as percentage points, and they are the first control to use: price = clamp(optimized × (1 + Σ multipliers ÷ 100), floor, max), unless an override is set. How the model works →
occupancy: The share of available nights that are booked. A demand gauge, not a goal — high occupancy at low rates can leave revenue on the table. Read the deep-dive →
on the books vs. last year: The year-over-year gap for a future period: what is on the books today compared with what was on the books at the same point a year ago. It is not pace.
optimization model: A mathematical model that searches for the rate that maximizes expected revenue for a given night and listing. Read the deep-dive →
OTA: Online Travel Agency — a channel like Airbnb, Vrbo, or Booking.com where listings are distributed and booked.
override: A fixed price or minimum stay pinned to specific dates, above all other pricing logic. It is the last resort, for when a client explicitly wants a fixed rate; use multipliers first.
pickup: Bookings added over a period, e.g. the last 7 days.
price optimization: Using a model to solve for the revenue-maximizing nightly rate, as opposed to applying a base price plus manual rules.
revenue management: The practice of varying price and availability over time to maximize total revenue, rather than chasing occupancy or a single nightly rate. Read the deep-dive →
reviews: Guest feedback that both reflects and shapes achievable rate — strong sentiment supports higher pricing, weak sentiment suppresses it. Read the deep-dive →
RevPAR: Revenue Per Available Rental night — revenue divided by available nights. The single metric that captures occupancy and rate together. Read the deep-dive →
seasonality: Recurring high, shoulder, and low demand periods across the year that pricing must anticipate rather than react to. Read the deep-dive →
sentiment analysis: Distilling thousands of reviews into the themes that actually move a guest’s willingness to pay.