The November 2025 CPI release exposed a critical vulnerability in how financial markets track inflation. For the first time in recent memory, the Bureau of Labor Statistics published annual inflation numbers without measuring most price categories for an entire month.

What Actually Happened

On December 18, 2025, the BLS released November CPI data showing headline inflation at +2.7% year-over-year and core inflation at +2.6% YoY. Wall Street consensus had predicted +3.1% headline and +3.0% core. XTech's forecast: 3.0% for both metrics.

Not a single major bank came within 0.1% of the official number. Bank of America, Goldman Sachs, and Wells Fargo all missed. JP Morgan, Morgan Stanley, and Deutsche Bank missed. TD Securities, the highest forecast at 3.2% headline, was off by 50 basis points.

But here's the problem: the BLS didn't actually measure core inflation in October 2025.

The Data Gap

Due to the government shutdown from October 1 to November 12, 2025, BLS field representatives couldn't collect their standard survey data. No personal visits to retail establishments. No telephone price checks. No web or app collection across the 22,000+ retail locations they normally track.

For October, the BLS simply carried forward September's prices for most categories. They had nonsurvey data for only a handful of indexes—gasoline, used cars, and a few others. The result? They couldn't calculate month-over-month changes, marked with dashes in their official tables.

Yet they still published year-over-year numbers based on incomplete data.

Think about what this means: the annual inflation rate that moved markets, influenced Fed policy expectations, and shaped billions of dollars in trading decisions was calculated by assuming prices didn't change for an entire month across most of the consumption basket. And traders had no month-over-month data to validate whether the year-over-year numbers made sense.

Deeper Problems: November Data Collection Was Also Compromised

The October gap is just the beginning of the data quality issues. According to the BLS, regular data collection didn't resume until November 14, 2025—meaning they attempted to retroactively capture an entire month of price changes in just two weeks. Some November data is inevitably missing.

The geographic and category-level data reveals troubling anomalies:

Regional volatility suggests incomplete sampling:

• Midwest Size Class A: -0.7% YoY (data appears missing entirely in some metro areas)

• Mountain Region: -0.5% YoY

• West South Central: -0.5% YoY

• New England: -0.4% YoY

These aren't normal regional divergences—they're statistical artifacts of disrupted data collection showing up as implausibly large deflation in multiple regions simultaneously.

The Food at Home anomaly:

CPI headline dropped sharply from 3.0% YoY in September to 2.7% in November. The primary driver? Food at home inflation collapsing from 2.7% YoY to just 1.9% YoY in two months.

Some deceleration is reasonable—we expected it. But a 0.8 percentage point swing in food prices over two months, when one of those months had no measurement at all, strains credibility. This category alone accounts for roughly 8% of the CPI basket, making it large enough to materially distort the headline number when measurement breaks down.

The Services mystery:

Core CPI dropped from 3.0% in September to 2.6% in November, with services inflation mysteriously decelerating from 3.5% to 3.0% YoY. This is particularly puzzling because:

1. Wage growth remains stable across service sectors

2. Labor costs are the dominant variable in service pricing

3. Our real-time data shows no such deceleration in actual service pricing

The biggest contributor to this services slowdown? Hotel lodging, which the BLS reports dropped -4.1% YoY in November. This single category became the primary driver pulling shelter inflation down from 3.6% to 3.0%.

Here's the problem: our transaction data, industry surveys, and alternative pricing sources show nothing resembling a 4.1% annual decline in hotel rates. In fact, lodging demand and pricing remained robust through November across major metros.

When wages are stable, service demand is solid, and alternative data contradicts official figures by this magnitude, the most likely explanation isn't an economic shock—it's measurement error compounded by disrupted data collection.

Why Everyone's Forecast Was "Wrong"

The forecasting community wasn't wrong—the measurement framework broke down. When you look at the Wall Street consensus clustering around 3.0-3.1%, these estimates reflected what CPI would have shown under normal measurement conditions.

XTech's YoY 3.0% and MoM +0.33% forecasts were built on continuous data streams that didn't pause for government appropriations battles. Our models use historical data, consumer survey data, and alternative datasets that operate independently of BLS field operations.

See How This Works – Book Demo

The gap between consensus expectations (~3.0-3.1%) and the official number (2.7%) isn't forecast error—it's measurement methodology divergence. The BLS was forced to use carry-forward imputation across broad swaths of the consumption basket, mechanically suppressing measured inflation relative to what was actually happening in the economy.

More critically, when November data collection resumed on the 14th—halfway through the measurement period—the compressed sampling window likely introduced additional noise exactly where the data was already most fragile.

The Reliability Problem

This isn't just an academic concern. Consider the market implications:

• Bond traders positioned for 3%+ inflation faced a 40 basis point surprise driven by questionable food and hotel data

• Currency markets moved on inflation data with obvious regional and categorical anomalies

• Fed policy expectations shifted based on services deceleration that contradicts wage and demand fundamentals

• Inflation-linked securities repriced on shelter numbers driven entirely by implausible hotel lodging deflation

• Momentum traders had no month-over-month data to assess whether price trends were accelerating or decelerating

The BLS did nothing wrong—they operated within constraints imposed by external factors and were transparent about data limitations. But their transparency about the gaps (clearly marked with dashes and footnotes) actually highlights the problem: markets need continuous, reliable inflation measurement that doesn't depend on government funding cycles or compressed sampling windows.

And we'll need to wait for subsequent releases to see how the BLS revises this data as they backfill missing information and correct for sampling distortions. History suggests meaningful revisions are likely when data collection is this disrupted.

What Independent Measurement Delivers

XTech's approach to inflation forecasting operates outside the vulnerabilities that affected the November release:

Dual-forecast early warning system: XTech produces two independent CPI forecasts for maximum intelligence value:

• First Forecast: Released on average 20 calendar days before the official BLS release—before Wall Street consensus even forms

• Second Forecast: Released on average 5 calendar days before the official release, incorporating additional data as it becomes available

Both forecasts provide complete MoM and YoY readings across headline, core, and individual CPI categories. While the BLS couldn't publish month-over-month changes for November, XTech clients had continuous access to both metrics throughout the period.

Advanced methodology: Our forecasting leverages a "Teacher Forcing" or one-step-ahead modeling technique. This means the model constantly improves by learning from the most recent actual data rather than relying on previous forecasts that may not accurately reflect reality. This approach delivers:

• First Forecast: 87% correlation, 92% sign accuracy, 0.001 MAE on MoM changes

• Second Forecast: 88% correlation, 95% sign accuracy, 0.0009 MAE on MoM changes

Category-level granularity: We forecast individual CPI components (Gasoline, Food, Shelter, Transportation Services, Used Cars and Trucks, Medical, Education, Recreation, Electricity, Apparel, Utility) on the same dual-forecast timeline, providing unprecedented detail weeks before official data.

Uninterrupted data collection: Our models ingest real-time transaction data, pricing feeds, and bespoke alternative datasets that don't pause for appropriations lapses. We track actual transaction prices daily across thousands of products and services using advanced machine learning techniques.

Real-time validation: We saw no 4.1% hotel deflation in our lodging data. We saw no 0.8pp food deceleration in functional time. We provide this data—including both month-over-month and year-over-year readings—largely for free, precisely so policymakers and market participants can validate official statistics against ground truth.

Proven track record: Since launching our Global Macro Forecasts product, XTech has consistently outperformed consensus on both accuracy and timing. Our initial forecasts beat individual economists by 14 percentage points on hit rate (44.2% vs. 31.6%) while being released 18 days earlier. Our final forecasts achieve 48.4% hit rates just 5 days before release—still ahead of the 33.7% consensus final hit rate achieved just 2 days before release.

See How This Works – Book Demo

The Path Forward

This episode demonstrates why professional investors increasingly supplement official statistics with independent analytical capabilities:

1. Official data can be disrupted by factors beyond statistical agencies' control

2. Missing MoM data eliminates critical momentum indicators exactly when markets need them most

3. Compressed sampling windows introduce noise exactly when data reliability matters most

4. Consensus forecasts reflect economic reality that official numbers may miss due to methodology constraints

5. Category-level anomalies (Food at Home, Lodging away from Home) signal deeper measurement problems

6. Alternative measurement frameworks provide critical redundancy and validation when traditional sources face limitations

The question isn't whether BLS data is valuable—it absolutely is. The question is whether markets can afford to rely exclusively on any single data source, especially when that source is vulnerable to operational disruptions and shows obvious anomalies at the category level.

XTech's Track Record

Our November forecast of 3.0% (both headline and core) aligned closely with Wall Street consensus and what most economists expected under normal measurement conditions. More importantly, we delivered that forecast weeks in advance, giving clients time to position accordingly.

And throughout this period, we continued to publish both our First and Second Forecasts with complete MoM and YoY readings, providing the momentum indicators that official statistics couldn't deliver. When the BLS marked October data with dashes, XTech clients had continuous access to granular inflation metrics at both the headline and category level.

This isn't about claiming victory when methodology breaks down. It's about demonstrating that continuous, independent measurement using teacher-forcing machine learning techniques and alternative datasets provides critical intelligence—especially when traditional data sources face unexpected constraints.

We encourage everyone—especially policymakers—to use real-time and real-price data such as we provide on a daily basis, including both MoM and YoY inflation metrics across all major CPI categories. Accurate inflation measurement is too important to depend on any single methodology or operational framework.

The November 2025 CPI release was unprecedented, but it won't be the last time markets face data uncertainty. The institutions that thrive will be those that diversified their intelligence sources before reliability became a question.

Want to see how XTech's dual-forecast system with continuous MoM and YoY CPI data can improve your macro positioning?

See How This Works – Book Demo

About XTech Global Macro Forecasts

Exponential Technology provides institutional-grade macroeconomic forecasts powered by alternative data and machine learning. Our CPI forecasting track record:

• Two forecasts per release: First forecast ~20 days before (87% correlation), second forecast ~5 days before (88% correlation, 95% sign accuracy)

• 35.1% hit rate (2017-2025) vs. 30.9% for Reuters Poll consensus

• 24 days average lead time vs. 2 days for consensus final polls

• Operated continuously through 2025 government shutdown when official data collection stopped

When the BLS can't collect data, XTech can.