In an interest rate-driven cycle, the housing market feels the impact first. Historically, an 8% drawdown in residential construction payrolls precedes a broader recession. The absence of this drawdown, due to labor hoarding by builders, is a key reason the US economy has remained resilient.

The ratio of leading-to-coincident economic indicators is at historic lows seen only in deep recessions (1982, 2009). However, this may be skewed by the leading indicators' reliance on extremely negative consumer sentiment surveys. This divergence suggests we might be at the bottom of a cycle, not the beginning of a downturn.

The sharp drop in the fiscal impulse represents a direct, dollar-for-dollar hit to nominal GDP that has already occurred. This indicates a recession is underway, not forthcoming. The National Bureau of Economic Analysis (NBER) will likely backdate the start of this recession to the third quarter of 2025.

The podcast's economists assess the probability of a recession in the next year at 40-45%, significantly higher than the consensus view of 25-30%. This heightened risk is based on deteriorating labor market trends and is corroborated by Moody's own machine learning models.

In a machine learning algorithm designed by Moody's to predict recessions, aggregate building permits (single-family and multifamily) emerged as the single most important variable. A decline in permits is a powerful signal of elevated recession risk for the entire economy.

The Sahm Rule provides a clear signal that a recession has begun: when the three-month moving average unemployment rate rises by more than 0.5 percentage points above its low from the previous year. This metric is useful for cutting through noise and identifying when a slowly weakening job market has definitively tipped into a downturn.

A Moody's machine learning model, which analyzes leading economic indicators, had already calculated a 48.6% probability of recession *before* the Iran conflict began. The primary driver for this high reading was a deteriorating labor market, indicating underlying economic weakness.

While any individual economic indicator can be misleading or explained away by unique factors, a collective alignment of multiple, diverse signals (like commodities, specific equities, and bond yields) creates a powerful, trustworthy forecast for stronger global growth.

To navigate conflicting economic signals, Moody's built a model that uses a machine learning technique called a random forest. It aggregates 'votes' from numerous decision trees based on economic data, with labor markets carrying the most weight, to produce a single 12-month recession probability.