AI Analysis: Raw materials price index, monthly

Executive Summary

Statistics Canada's Raw Materials Price Index (Table 18100268) tracks 84 commodity categories monthly from January 1981 to March 2026, revealing a dramatic long-term rise in Canadian raw material costs — from a historic low of 38.5 in July 1986 to an all-time high of 188.6 in January 2026, nearly five times the 1986 trough. The most recent reading of 188.0 in March 2026 confirms prices remain near record levels, almost double the 2020 baseline of 100. Across 28,635 records, price volatility varies sharply by commodity, with gold ores being the most volatile category and fresh fruit and nuts the most stable.

Key Findings

• The Total RMPI reached an all-time high of 188.6 in January 2026 — nearly 5 times the historic low of 38.5 recorded in July 1986 and almost double the 2020 baseline of 100.
• The most recent index reading (March 2026) stands at 188.0, confirming that Canadian raw material prices remain near historic highs with no significant retreat from the 2026 peak.
• Across 28,635 records, the average price index is 90.86 with a standard deviation of 41.92, reflecting substantial variability across the 84 NAPCS commodity categories and 45-year time span.
• 55 months were flagged as statistical outliers (Z-score > 2.0), with the most extreme values concentrated in 2025–2026, underscoring an accelerating price surge in recent years.
• Gold ores, concentrates and mill bullion is the most volatile raw material category (Coefficient of Variation = 0.87), while fresh fruit and nuts is the least volatile (CV = 0.07), highlighting stark differences in price stability across commodities.
• The middle 50% of all index values fall between 62.90 and 109.05 (IQR = 46.15), but the full range of 553.60 (16.3 to 569.9) reveals extreme price swings in select high-volatility categories.
• Most raw material categories show positive correlation with one another over time, reflecting broad commodity market co-movement, though energy, metals, and agricultural groups each display distinct clustering patterns.