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. All Single-Unit listings 
    2. All Multi-Unit properties.
    3. Listings that are actively syncing
  4. Forecast Timeframe: Predictions cover future dates for which we generate prices (based on your PMS).
  5. Update Frequency: Forecasts are updated weekly, every Monday.
  6. View Levels: You can view the 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 in 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 relationships among factors such as minimum stay, weekends, holidays, market demand, and historical booking patterns.
  3. Daily Prediction: The model forecasts the likelihood that a listing will be booked on each day.
  4. Adjustments:
    1. Predictions are adjusted based on a listing's and the market's pace to ensure realistic monthly expectations. 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 predicted demand with adjusted, un-customized prices.
    2. Please note that the calculated forecasted occupancy is equivalent to the adjusted occupancy, after removing blocks.

Accuracy of the Forecast

  • Timeframe: The forecast is more accurate for the next few months than for those further out.
  • Historical Data: While not required, having last year's reservation data available makes the forecast more accurate, 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 by following Upload Reservations Using CSV in Portfolio Analytics.

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