How Price Benchmarking Works
Price benchmarking is the core engine behind Market Intelligence. It matches your group’s profile to USDA auction data and produces a price estimate. This article explains the mechanics of how benchmarking works.
The Matching Process
Benchmarking follows these steps:
- Determine the market segment. The system identifies the dominant animal type and sex in your group (e.g., Feeder Steers).
- Calculate average weight. The group’s average weight is computed from the most recent weight records, or estimated if no weights are recorded.
- Find matching auction data. USDA auction results for the matching commodity and weight bracket (within a 75-point tolerance) are retrieved.
- Rank sale barns. Auctions are ranked by head count to identify the most active (“hot”) barns.
- Compute the benchmark price. The weighted average price per cwt across matching auctions is calculated.
75-Point Weight Tolerance
The system uses a 75-point tolerance when matching weights. For example, if your group averages 625 lbs, the system will consider auction data from the 550–700 lb range. This ensures enough data points for a reliable average, even if your exact weight is between standard brackets.
Fallback to Statewide Data
If a specific sale barn does not have enough recent data for your weight bracket and commodity, the system automatically falls back to statewide benchmark data. This means:
- You always get a price reference, even for niche markets or infrequently reported barns.
- The recommendation will note when statewide data is being used instead of barn-specific data.
Lookback Windows
| Data Source | Lookback Period |
|---|---|
| USDA auction data | 45 days |
| Your sale history | 365 days |
The 45-day window for auction data ensures you see current market conditions. The 365-day window for your own sales provides historical context.
Tips
- Keep weights current. The more recent and accurate your weight data, the better the benchmark match.
- Use homogeneous groups. Groups with a consistent type and sex produce cleaner market matches than mixed groups.
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