New orders for US-manufactured durable goods jumped 5.3% month over month in November 2025, rebounding from a revised 2.1% decline in October and beating market expectations for a 3.7% increase. The gain was driven by a sharp rebound in transportation equipment orders, which surged 14.7% after a 6.3% fall in October, led by a 97.6% spike in civilian aircraft bookings. Elsewhere, orders also rose for electrical equipment, appliances and components (1.7% vs. -0.5%), fabricated metal products (1.0% vs. 0.8%), machinery (0.5% vs. 0.6%), and computers and electronic products (0.2%, unchanged). Excluding transportation, new orders increased 0.5% after a 0.1% gain in October, while orders excluding defense surged 6.6%, reversing a 1.3% decline in the previous month. Meanwhile, orders for non-defense capital goods excluding aircraft, a closely watched proxy for business spending plans, rose by 0.7%, after a 0.3% gain the previous month. source: U.S. Census Bureau
Durable Goods Orders in the United States increased 5.30 percent in November of 2025 over the previous month. Durable Goods Orders in the United States averaged 0.35 percent from 1992 until 2025, reaching an all time high of 26.40 percent in July of 2014 and a record low of -21.20 percent in August of 2014. This page provides the latest reported value for - United States Durable Goods Orders - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news. United States Durable Goods Orders - data, historical chart, forecasts and calendar of releases - was last updated on February of 2026.
Durable Goods Orders in the United States increased 5.30 percent in November of 2025 over the previous month. Durable Goods Orders in the United States is expected to be 0.90 percent by the end of this quarter, according to Trading Economics global macro models and analysts expectations. In the long-term, the United States Durable Goods Orders is projected to trend around 0.30 percent in 2027, according to our econometric models.