Forecasting with Report Builder

Forecasting with Report Builder

You can now use Report Builder to forecast future rental revenue and occupancy for your listings. This helps you make smarter business decisions by giving you a better idea of what to expect in the months ahead.

Key Features

  1. New Forecasting Template: There’s a new “Forecasting” template available in Report Builder.
  2. Key Forecasts: You can now see predictions for:
    1. Rental Revenue
    2. Occupancy
  3. Which Listings Are Supported?
    1. Only single-unit listings (multi-units coming soon)
    2. Listings that are actively syncing
  4. Forecast Timeframe: Predictions cover the next 12 months.
  5. Update Frequency: Forecasts are updated on the 1st of every month.
  6. View Levels: You can view forecast for one or more listings
    1. By individual listing
    2. By group or sub-group
    3. At the full account level

How to Access Forecasting Functionality

  1. Head to Portfolio Analytics → Report Builder
  2. Under Report Templates, ensure that you are on All Templates or PriceLabs Templates
  3. Click Forecast Rental Revenue & Occupancy

Customizing Your Report

Selecting the Date Range

You can set the time frame or a specific date range for your report by setting it on the Stay Date Range section.

Filtering Listings to Add to the Forecast

To select which listings or set of listings to include in your report, you can apply listings filters like elsewhere in PriceLabs. 

How the Forecast is Generated

The forecasting process uses a machine learning model to predict future performance by combining various data sources.
  1. Data Inputs: The report uses pricing logs, historical booking data, and market occupancy information.
  2. Predictive Model: A machine learning model is trained to identify the relationship between factors like minimum stay, weekends, holidays, market demand, and past booking patterns.
  3. Daily Prediction: At the time of forecasting, the model predicts the likelihood that a listing will get booked on each day.
  4. Adjustments:
    1. Predictions are adjusted based on a listing's and the market's pacing to ensure realistic expectations at a monthly level. When a listing has insufficient data, the model uses market-level data to make these adjustments.
    2. Uncustomized prices are adjusted based on past and current Average Daily Rates (ADRs) at a monthly level.
  5. Final Calculation:
    1. The monthly revenue forecasts are calculated by combining the predicted demand with the adjusted uncustomized prices.
    2. Please note that the forecasted occupancy calculated is equivalent adjusted occupancy, where we remove blocks.

Accuracy of the Forecast

  • Timeframe: The forecast is more accurate for upcoming months compared to months further out.
  • Historical Data: While not required, the forecast is more accurate if last year's reservation data is available, as it provides the model with a more robust baseline for its predictions. If your last year's bookings are not showing in PriceLabs, you can upload them following Upload Reservations Using CSV in Portfolio Analytics.

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