The rate at which reservations are being made for future pickup dates, measured week by week. Booking pace is the primary signal for whether demand is running ahead or behind the prior year at the same point in time. In RateCast, the Booking Pace report shows cumulative and weekly booking counts with full year-over-year comparison across all locations.

A comparison of a metric in the current period against the same period in the prior year. YoY comparisons remove seasonal effects — comparing February to February rather than February to January — making them the standard benchmark in car rental analytics. RateCast provides YoY overlays on every major chart and KPI callout.

The number of days between when a reservation is made (date booked) and when the customer picks up the vehicle (pickup date). A longer booking window indicates more advance planning; a shorter window indicates last-minute bookings. Booking window trends directly inform rate strategy — operators with long windows have more time to adjust pricing before peak periods. In RateCast, the Booking Window report shows the distribution of booking windows with YoY comparison.

The running total of reservations made from the start of the year through any given week. Comparing cumulative bookings between years shows whether the overall volume is tracking ahead or behind at any point in the booking cycle.

How quickly reservations are accumulating for a specific upcoming pickup week, compared to how quickly they were accumulating for the same pickup week in the prior year. Velocity is a leading indicator — it tells you what demand will look like before the actual pickup date arrives.

The distribution of reservations across vehicle categories (Economy, Compact, Intermediate, Full-size, SUV, etc.). Rate class mix directly impacts average daily rate — a shift toward economy vehicles drives T&M down even if volume stays flat. The Bookings by Class report in RateCast tracks this distribution with YoY comparison and location filtering.

The date a reservation was created in the system. This is distinct from the pickup date (when the customer actually collects the vehicle). In RateCast, the Bookings — Date Booked report shows booking volume, revenue, and average rate grouped by the date the reservation was made, with MTD and YTD KPIs.

The total number of new reservations created on a specific date, grouped by their pickup dates. DNB answers "how many bookings did we take yesterday (or any selected day), and when are those customers picking up?" This is pulled directly from the bookings master data (rawData) — not the forward reservations file — so it includes same-day pickups that would otherwise be missed. The DNB report shows a bar chart of booking counts by pickup date, a rate chart, and a detailed table.

Breakdown of reservation pickups by day of the week. In airport car rental, Thursday and Friday typically show the highest volume (business travelers departing for weekend), while Saturday is often the lowest. Understanding daily patterns enables targeted rate adjustments and staffing optimization.

The percentage of reservations that are cancelled before pickup. A rising cancellation rate can signal price sensitivity (customers finding better rates elsewhere), poor customer experience, or external demand shocks. Tracking cancellations separately from net bookings gives a cleaner picture of true committed demand.

A snapshot of all open (uncancelled, not yet picked up) reservations for future dates, grouped by pickup week. Forward pace shows the current demand pipeline — how many customers are booked for each upcoming week. It uses the Forward Reservations (Fwd Res) file, which is a point-in-time snapshot of all open bookings. Compare forward pace to the same point last year to gauge whether upcoming weeks are tracking stronger or weaker.

Reservation volume and rate measured by pickup date (when the customer actually collects the vehicle), with optional actuals overlay. Rental Pace bridges the gap between booking data (what's been reserved) and actuals (what actually happened). It shows weekly pickup counts, utilization, average rate, and fleet size with full YoY comparison.

The booking source or channel through which a reservation was made — such as direct website, Priceline, Expedia, AAA, or corporate accounts. In TSD data, referral agencies are identified by IATA-style codes that RateCast resolves to readable names using a lookup table. Filtering by referral agency reveals which channels drive the most volume and at what rates.

A code that identifies the pricing tier or promotional rate applied to a reservation. Rate codes distinguish between standard rack rates, corporate negotiated rates, insurance replacement rates, promotional discounts, and OTA rates. Filtering by rate code reveals pricing strategy effectiveness across different customer segments.

The current state of a reservation in the system. Common statuses include: Opened (active rental in progress), Pending (future reservation not yet picked up), Cancelled (customer cancelled before pickup), Voided (removed from system), and Closed (rental completed and returned). Most RateCast reports default to filtering by "Opened" and "Pending" statuses to focus on active and committed demand.

The average revenue earned per rental day. T&M is the primary rate health metric in car rental — it normalizes revenue against rental length so operators can compare pricing performance regardless of whether customers are renting for one day or two weeks. In RateCast, T&M appears as a KPI across booking, rental pace, and actuals reports.

The base rental charge on a rental agreement — the rate-per-day multiplied by the number of days. T&M excludes ancillary charges like fuel, insurance waivers, and surcharges. It is the core revenue metric used to calculate T&M and is the primary driver of rate performance analysis. In TSD daily activity reports, T&M is reported both daily and MTD.

The practice of adjusting prices in real time to maximize revenue based on demand forecasts, booking pace, competitor pricing, and remaining fleet availability. Borrowed from airline revenue management, yield management is the strategic discipline behind every rate adjustment decision in car rental.

Total revenue divided by the total number of available vehicle days. RevPAU accounts for both pricing AND utilization — a vehicle earning $80/day that sits idle half the time produces a lower RevPAU than a vehicle earning $60/day with 90% utilization.

Your average rate expressed as a ratio to the market average rate. An RPI above 1.0 means you are priced above market; below 1.0 means below market. RPI is the key input for price elasticity modeling.

How much booking volume changes in response to a price change. An elasticity of -1.5 means a 10% price increase above market reduces bookings by 15%. Airport car rental typically shows elasticity between -1.2 and -1.8.

The average number of days each rental agreement covers, calculated from the pickup date to the return date. LOR is a key driver of total revenue per transaction — a longer LOR at the same daily rate generates more total revenue. Airport rentals typically range from 3-5 days, while insurance replacement rentals average 10-14 days. LOR is shown in DNB and rental pace reports.

A per-transaction fee charged by the airport authority to rental car companies for use of the consolidated rental car facility (CONRAC). CFCs are passed through to customers as a line item on rental agreements.

Total revenue collected by a rental company at a specific airport location during a reporting period, before any deductions. Airport authorities use gross receipts to calculate concession fees and track market share.

The percentage of your useable fleet that is rented on any given day. Utilization is the primary fleet efficiency metric. Too low means you're carrying excess fleet cost; too high means you're turning away customers and risking dissatisfaction. Airport car rental targets typically range from 75% to 90% depending on the season. RateCast tracks both useable fleet utilization and total fleet utilization in the Actuals Dashboard.

Useable fleet is the number of vehicles available and ready to rent — it excludes vehicles in maintenance, awaiting repair, or in transit. Total fleet includes every vehicle the operator owns or leases at that location, regardless of status. The gap between useable and total fleet reveals how much capacity is lost to maintenance and non-revenue status.

The total number of rental days on completed (closed) rental agreements. Revenue days is the volume denominator for T&M calculation on actuals data. A customer who rented for 5 days and returned the vehicle contributes 5 revenue days. This differs from "rental days" on open bookings, which are estimated from the reservation dates.

The number of vehicles actively rented to customers on a given day. This is a daily snapshot metric — it fluctuates with checkouts and returns throughout the day. The TSD daily activity report captures it as an end-of-day count.

Vehicles in the useable fleet that are available and ready to rent but are not currently on rent. High "units sitting" on peak demand days signals a pricing opportunity — rates may be too high or marketing reach is insufficient.

Vehicles that are temporarily unavailable for rental due to scheduled maintenance, damage repair, or recall campaigns. Monitoring the maintenance percentage of total fleet helps identify operational inefficiencies — a high maintenance ratio during peak demand periods directly suppresses achievable utilization.

Rental Agreements Opened — the number of vehicles that actually left the lot with a customer on a given day. R/A Opened is the "actuals" metric in the daily activity report and the ground truth for demand. It differs from reservations (which are advance bookings) — some reservations become no-shows or cancellations and never convert to opened R/As.

Rental Agreements Closed — the number of vehicles returned to the lot on a given day. The difference between R/A Opened and R/A Closed on any day represents the net change in on-rent fleet.

A reservation where the customer did not arrive to pick up the vehicle and did not cancel in advance. No-shows represent lost revenue and idle fleet time. The No Show / Cancellations report in RateCast tracks no-show rates by location and over time.

The RateCast report that displays historical operational metrics from TSD Daily Activity reports. It shows fleet utilization, units on rent, R/A opened and closed, T&M revenue, and rate performance over time with year-over-year comparison. Data is filterable by location, year, and month. The Actuals Dashboard is powered by the daily activity CSV files that the SFTP sync pulls from TSD each day.

A daily CSV report generated by TSD (the reservation system provider) for each location. It contains three tables: fleet metrics (units on rent, fleet size, utilization), reservation activity (R/A opened, closed, cancelled, no-shows), and revenue details (T&M, rate charges, ancillary revenue). Each report covers the full historical period from January 1, 2024 through the current date, and is identified by location code (e.g., MSP, DFW, ATL, CHI, ORD).

Historical rental agreement data grouped by the actual pickup date. Unlike forward reservations (which are a point-in-time snapshot of future bookings), pickup actuals show what actually happened — how many vehicles were rented, at what rate, and for how long. Pickup actuals data is used in the Rental Pace report's prior-year overlay to compare current booking pace against what actually materialized last year.

The cumulative total from the first day of the current month through the reporting date. TSD daily activity reports include MTD columns alongside daily values for most metrics (MTD Units On Rent, MTD Useable Fleet, MTD T&M, etc.), providing running monthly context without requiring separate aggregation.

Your gross receipts as a percentage of total on-airport rental revenue at a given location. Market share is reported monthly by airport authorities and is the primary competitive benchmark. The Market Share report in RateCast (under Market Intelligence) opens as a separate page and tracks share trends across airports. Access is restricted to manager and admin roles.

The RateCast navigation section that houses competitive and market analysis tools, including Market Share. This section is role-gated — only visible to managers, company admins, and super admins — because it may contain sensitive competitive data and airport authority reports.

The ownership groupings of rental car brands. Many brands that appear as separate competitors are owned by the same parent company and share fleet, technology, and pricing systems. The three main families are: Hertz Global (Hertz, Dollar, Thrifty), Avis Budget Group (Avis, Budget, Payless, Zipcar), and Enterprise Holdings (Enterprise, National, Alamo).

On-airport companies operate from the airport's consolidated rental car facility (CONRAC) and are accessible via the shuttle bus. Off-airport companies operate from nearby locations and typically offer lower rates. Airport market share reports track both segments separately.

Rental platforms like Turo where private vehicle owners rent their personal cars to travelers. P2P volume is tracked separately in airport reports and represents an emerging competitive category.

The number of passengers arriving at an airport (as opposed to connecting passengers who don't leave the terminal). Destination passengers are the addressable market for airport car rental — a rising destination passenger count typically correlates with rising rental demand.

The cumulative total from the start of the fiscal year through the most recent reporting month. Many airports use an October–September fiscal year, so February FYTD covers October through February (5 months).

The RateCast module that tracks known high-demand events (concerts, sporting events, conventions, holidays) by market/airport. Events are synced automatically and used both as visual context on demand charts and as lift factor inputs for the demand forecast model. The Event Calendar is accessible from the Reservations — Pickup Date navigation section.

A model-generated prediction of future rental demand, expressed as expected daily or weekly reservation volume. RateCast uses a multiplicative seasonal model combining a historical baseline, day-of-week patterns, monthly seasonality, and event lift factors. Forecasts guide fleet positioning and rate setting decisions.

A forward-looking projection of rental revenue based on demand forecasts and rate assumptions. The Revenue Forecast report in RateCast combines predicted booking volume with historical rate patterns and current booking pace to estimate future T&M revenue by period.

A forward projection of fleet utilization based on demand forecasts and planned fleet levels. This report helps operators anticipate periods of excess capacity (opportunity to defleet or lower rates) and constrained supply (opportunity to raise rates or add fleet).

A multiplier that adjusts a baseline demand level up or down for a specific time period. A July monthly index of 1.20 means July demand runs 20% above the annual average; a January index of 0.57 means January runs 43% below average.

A multiplier applied to the baseline forecast during known high-demand events. Lift factors are measured empirically by comparing actual demand during past events against what the seasonal model predicted. The Balloon Fiesta in Albuquerque, for example, has a measured lift of +67%.

The standard measure of forecast accuracy. MAPE expresses the average error as a percentage of actual demand. Well-tuned car rental demand models typically achieve 8-15% MAPE for 30-day-out forecasts and 5-9% for 7-day-out forecasts.

An advanced machine learning approach that builds an ensemble of decision trees, where each tree corrects the errors of the previous one. LightGBM and XGBoost are the leading implementations. Gradient boosting is the standard forecasting method used by major rental companies and OTAs because it handles the complex interactions between seasonality, events, pricing, and booking pace that simpler models miss.

Historical values from prior time periods used as inputs to a forecasting model. In car rental demand forecasting, the most powerful lag features are recent booking pace metrics — how many reservations have been made in the last 7, 14, and 28 days for a specific future pickup week.

The range within which actual demand is expected to fall with a specified probability. RateCast shows an 80% confidence interval. The band widens for longer forecast horizons and narrows as the pickup date approaches and booking pace provides more signal.

Assessment of the current market value of vehicles in the fleet, factoring in age, mileage, condition, and wholesale market trends. The Fleet Valuation report in RateCast helps operators understand their fleet's asset value and make informed decisions about hold vs. dispose timing to maximize residual value recovery.

AI-powered fleet optimization that combines disposition priority scoring, disposal timing recommendations, auction market analysis, and fleet rebalancing across locations. Fleet Intelligence answers questions like "which vehicles should we sell first," "when is the optimal time to dispose," and "which auction markets yield the best returns for each vehicle class."

Forward-looking fleet capacity planning that uses demand forecasts and utilization targets to recommend optimal fleet size by location and time period. Fleet planning helps operators decide when to acquire or dispose of vehicles to maintain target utilization without turning away customers during peak demand.

The wholesale market where rental companies sell used vehicles, typically through physical or digital auto auctions (Manheim, ADESA, etc.). Auction market values fluctuate seasonally and by vehicle type. The Fleet Intelligence market optimizer tracks estimated wholesale value indices by auction market to help operators route disposals to the highest-returning channels.

The Rate Evolution report in RateCast tracks how your average daily rate has changed over time — daily, weekly, or monthly — with YoY comparison. It reveals rate trends, seasonal pricing patterns, and the impact of rate strategy changes. Rate evolution is the key diagnostic tool when T&M moves unexpectedly — it shows exactly when and how fast the rate shifted.

The monthly performance dashboard published by airport authorities showing aggregated rental car market data. Covers gross receipts, transaction days, T&M, CFC, and bus ridership — broken down by company with prior-year and budget comparisons. These reports are typically published 4-6 weeks after month-end.

The shared rental car complex at major airports where all on-airport companies operate under one roof. Customers take a shuttle bus from the terminal to the CONRAC. Bus ridership data in airport reports tracks the volume of customers using CONRAC shuttles.

Comparison of current period results against the pre-approved financial plan. Airport reports typically show three comparisons: current year vs prior year, current year vs budget, and FYTD vs FYTD prior year.

The 12-month accounting period used for financial reporting. Many airports use a fiscal year running October through September — so FY26 covers October 2025 through September 2026. This differs from the calendar year (January–December) used by most rental companies internally. Always confirm which convention applies when comparing airport reports to internal data.

Airport or branch codes that identify each rental location. RateCast supports multi-location operations — all reports can be filtered by location using the location dropdown. Common codes include MSP (Minneapolis), DFW (Dallas-Fort Worth), ATL (Atlanta), ORD (Chicago O'Hare), CHI (Chicago city), and ABQT01 (Albuquerque). Location codes originate from the TSD reservation system and are consistent across all data files.

The reservation system provider that generates the daily data files consumed by RateCast. TSD delivers files via SFTP in a flat CSV format (no header row — headers are injected by the sync script). TSD produces several file types: bookings (all reservations), forward reservations (open bookings for future dates), daily activity (fleet and revenue metrics per location), and fleet snapshots.

The automated data pipeline that runs daily on the RateCast server. It connects to TSD's SFTP server, downloads new daily files, appends them to master CSV files (accumulating historical data), compresses the masters with gzip, uploads them to S3 via multipart upload, and records the upload in Supabase. The sync runs via Windows Task Scheduler each morning after TSD delivers files.

The primary booking dataset in RateCast, containing every reservation ever made — regardless of status, pickup date, or booking channel. Each record includes the date booked (_dateStr), pickup date (_puStr), location, status, rate, rental days, revenue, vehicle class, rate code, and referral agency. rawData powers the booking analytics reports: Booking Pace, Booking Window, Bookings by Class, Date Booked, and Daily New Bookings. Currently 659K+ records for the Walser portfolio.

A point-in-time snapshot of all open (uncancelled, not yet picked up) reservations for future dates. Generated daily by TSD as the "Fwd Res" file. Because same-day pickups check out before the file is generated, they are not included — this is by design, since forward reservations represent the demand pipeline for future dates. pickupFileData powers the Forward Pace report and provides the "Total Open Reservations" context in DNB.

RateCast supports operators with multiple airport locations. Every report includes a location filter dropdown that allows viewing data for a single location or all locations combined. Filters for status, rate code, vehicle class, referral agency, and month are also available globally and persist across report views.

RateCast uses role-based access control to restrict sensitive features. Roles include: viewer (standard dashboard access), manager (adds Market Intelligence and administrative views), company_admin (adds Admin Panel, company settings), and super_admin (adds cross-company switching and full system access). Roles are assigned per user in the Admin Panel.

RateCast stores all data files (bookings, actuals, forward reservations, fleet, daily activity) in Amazon S3. Large files are gzip-compressed before upload (reducing 94MB bookings files to ~16MB) and uploaded using multipart chunking for reliability. The browser automatically decompresses gzipped files on download — no dashboard code changes needed.

The backend database and authentication service for RateCast. Supabase stores user accounts, company configurations, file metadata (which files are available for each company), and role assignments. When the dashboard loads, it queries Supabase for the user's company, retrieves file URLs from the company_files table, and downloads the data from S3.