Grocery Market Navigator — Executive Summary

Supply Chain Outlook

Sales
The next six months are expected to be softer than the previous 12 months. The Sales TTM YOY% was 1.9%, but the next six months are expected to be only 0.7%.

Units
The sales decline is driven primarily by a drop in expected units from -0.4% in the previous 12 months to -1.6% in the next six months.

CPI FAH
Food inflation (CPI FAH) is expected to increase in the next six months by 2.2% which is lower than the previous forecast of 2.7%. The decline is almost entirely due to the decline in PPI Food Manufacture.

Farm and Food Manufacturing Prices
Farm and Food Manufacturing are the most exposed to energy prices and both will experience the largest decrease in prices. Farm prices were forecasted to increase to 6.9% in the next six months, but the revised energy and import prices pushes this forecast down to a much more manageable 3.2% YOY. The Food Manufacturing forecast came down from 3.6% to 2.5% YOY and is why CPI FAH dropped about 0.5 ppts.

Next Six Months
With lower oil prices, supply chain price growth will slow across the board and help unit and sales growth, which were both declining ahead of the conflict. The last couple of months have seen US sales YOY rebound to 1.8% and 2.3% while units lagged at -0.2% and -0.7%. The model is pointing to more downward pressure on both units and sales due to soft Real Disposable Income and Housing Prices. The long run trend for grocery sales is ~3% YOY and the forecast is coming in at 0.7% based on a combination of 2.2% CPI FAH and -1.5% units YOY. The model is likely missing to the low side but also confident that we will be lucky to hit 2% YOY growth in 2026.

Why this agreement will hold
If the conflict would have gone on much longer, things would have turned pretty ugly. Oil reserves around the world were getting precariously low, and with that buffer evaporating, oil prices would have likely moved north of $125/barrel. In addition, food manufacturers and grocery retailers can only compress margins for several months, and their reserves were also being tested. Then you role in wage pressure and things get really ugly. And all of this would not be ideal in a mid term election year. The administration was motivated to get a deal done and this is also why the agreement to hold.

* Adjustments to the Model
Unfortunately, we cannot do an apples-to-apples comparison for units and sales with last month because mass and warehouse sales and units were added into this month's model. I was holding off because you get a cleaner read on grocery items from supermarkets, but in the last few months mass and warehouse started to break away from supermarkets, so even though we get some non-grocery items into the mix, it was time. The supply chain data and models, however, did not change.

Causal Chain (TTM | 6-Mo Fcst)

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PPI Food Manufacturing

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OUTLOOK

PPI Farm Products – 6 Month YOY Outlook

+2.8% | +3.2%

ttm | 6-mo fcst

Before the conflict, farm product prices trended sharply lower peaking at 15.4% YOY in Feb-2025 and bottoming at -7.5% YOY in Jan-2026. As expected farm product prices have started to rebound and hit 4.4% YOY growth in Apr-2026. The pre-conflict deflation was driven by declining oil prices and falling food import prices but both of are now reversing.

Over the next six months, PPI Farm Products is forecast to average +3.1% year-over-year, a modest step up from the trailing twelve-month average of +2.8%, but that headline comparison masks a more significant underlying pricing story. Oil prices are the dominant force, with crude averaging +35.4% over the forecast input window and contributing +4.50 percentage points to farm product inflation — a direct transmission through fuel, fertilizer, and agrochemical costs that producers cannot easily absorb. Natural gas prices are moving in the opposite direction, averaging -10.0% and subtracting 0.74 percentage points from the forecast, providing some partial relief on heating and nitrogen fertilizer costs that slightly tempers the oil-driven pressure. Critically, the +3.1% forecast average understates the true pricing pressure building in the pipeline: PPI Farm Products peaked at +8.5% year-over-year in October 2025 before collapsing to -4.7% in January 2026, and that prior deflation has created a suppressed comparison base that is artificially cushioning the current rebound — without that deflationary trough acting as a low baseline, the forecast YoY readings would appear substantially higher, meaning grocery buyers and supply chain planners should treat the +5.6% June spike as a more honest signal of where farm-level prices are heading than the smoothed six-month average suggests.

Forecast Distribution Simulation

This distribution is built on 1,000 simulations using holdout errors from the rolling validation period. Small differences between the point forecast and the simulated median may occur as a result.

The median forecast is +4.5% with a forecast distribution of +/- 5.6%. We would expect Ppi Farm Products to fall between -0.1% and +11.2% over the next six months.

INPUTS

PPI Farm Products – Inputs

Forecast Decomposition — 6-Month Average Contribution

Variable	Avg Input (YoY%)	Contribution (ppts)	% of Total
Oil Prices	+35.4%	▲ 4.50 ppts	+81%
Natural Gas Prices	-10.0%	▼ 0.74 ppts	-13%
Food Import Prices	-0.5%	▼ 0.32 ppts	-6%
Structural Offset (model constant)	+1.0%	▼ 0.27 ppts
Total Forecast		▲ 3.17 ppts	100%

Oil Prices (Lead 1)

Oil prices are forecast to average +35.4% year-over-year over the next six months, a level that points to continued upward pressure on farm input costs throughout the forecast period. That surge is expected to contribute +4.50 percentage points to PPI Farm Products movement, accounting for 81% of total input cost pressure in the forecast window.

Natural Gas Prices (Lead 2)

Natural gas prices are expected to average -10.0% year-over-year over the next six months, a deflationary read that continues to drift lower. That decline subtracts an estimated 0.74 percentage points from the PPI Farm Products forecast, accounting for 13% of total input cost movement across the period.

Food Import Prices

Food import prices are expected to average -0.5% year-over-year over the next six months, a modest deflationary read that is nonetheless drifting higher as oil-driven shipping and production costs gradually feed through from global suppliers. This drag subtracts approximately 0.32 percentage points from the PPI Farm Products forecast, accounting for roughly 6% of total input movement over the period.

MODEL PERFORMANCE

PPI Farm Products – Model Performance

24-Month Holdout

Over the 24-month holdout period, the model produced a mean absolute error of 6.43 percentage points, meaning that on average the forecast landed within roughly 6 to 7 percentage points of the actual PPI Farm Products reading. For a series that can swing sharply in either direction, that level of precision is useful for directional guidance but wide enough that point estimates should be treated as a range rather than a precise figure.

Holdout Errors Over Time

Rolling errors across the holdout period ranged from a low of 3.0 percentage points to a high of 11.1 percentage points, with the widest misses concentrated around January 2025. That spike in error is consistent with what this model is built on — when oil prices and natural gas prices move sharply and quickly, as they did through that period, even a well-specified model will struggle to keep pace with the speed and magnitude of the shifts in its core inputs.

OLS Regression Results
R-squared: 0.668			Adj. R-sq: 0.658
F-statistic: 64.42			Prob(F): 6.58e-23
No. Observations: 100			Df Residuals: 96
Variable	Coef	Std Err	t	P>\|t\|	[0.025, 0.975]
const	-0.2579	0.999	-0.258	0.797	[-2.240, 1.724]
oil_prices_lead1_yoy	0.0960	0.025	3.805	0.000	[0.046, 0.146]
nat_gas_prices_lead2_yoy	0.1092	0.021	5.188	0.000	[0.067, 0.151]
import_index_yoy	0.6391	0.262	2.440	0.017	[0.119, 1.159]

Omnibus: 0.125		Prob(Omnibus): 0.940		Durbin-Watson: 0.410
Skew: -0.000		Kurtosis: 3.015

Model Performance

The model's R-squared of 0.668 means it explains roughly two-thirds of the historical variation in PPI Farm Products, a reasonable fit for a commodity series subject to weather, geopolitics, and other factors no input-cost model can fully capture. All three input variables are statistically significant, with t-statistics of 3.8 for oil prices, 5.2 for natural gas prices, and 2.4 for the import price index — each well above the threshold of 2.0 that signals a reliable relationship. The Omnibus probability of 0.940, which sits comfortably above 0.05, indicates that the model's residuals are normally distributed and that no systematic bias is distorting the estimates. The Durbin-Watson statistic of 0.410, however, is well below the ideal value near 2.0, signaling meaningful autocorrelation in the residuals — consecutive errors tend to move in the same direction, which suggests the model may be slow to adjust when the series shifts trend and that forecast intervals should be interpreted with some caution.

OUTLOOK

PPI Food Manufacturing – 6 Month YOY Outlook

+3.1% | +2.5%

ttm | 6-mo fcst

Prior to the conflict, food manufacturing prices followed farm products lower. PPI Food Manufacturing peaked at 4.9% YOY in Oct-2025 and dropped to 0.8% by Feb-2026, driven by falling farm input costs, declining oil prices, softer manufacturing wages, and weak food import prices. But food manufacturing pricesare reversing course as expected but not as significantly as farm products.

Over the next six months, PPI Food Manufacturing is forecast to average +2.5% year-over-year, a step down from the +3.1% logged over the trailing twelve months, but that comparison requires an important caveat. This series traced a pronounced V-shape between late 2025 and early 2026 — peaking at +4.9% in September 2025 before collapsing to a trough of +0.9% in January 2026 — and because the forecast period is measured against those depressed months, the year-over-year readings are being artificially cushioned; absent that prior deflation, the true pricing pressure now building in the pipeline would appear significantly more severe. The dominant force driving costs higher is manufacturing wages, which are running at a +4.2% average and contributing nearly four full percentage points (+3.94 ppts) to the forecast — a labor cost burden that food processors have limited ability to offset in the near term. Oil prices, averaging +25.0%, are adding another +0.72 ppts, a reminder that crude is not a background variable here but a direct upstream cost feeding into packaging, transportation, and plant operations, while PPI Farm Products contribute a further +0.56 ppts on a +3.0% average input gain. The one partial relief valve is food import prices, which are declining at -2.3% and subtracting -0.47 ppts from the total, though that offset is modest relative to the wage and oil pressures bearing down on manufacturers; a structural model offset of -2.29 ppts also acts as a dampening force, preventing the headline forecast from reflecting the full intensity of input cost inflation currently running through the system.

Forecast Distribution Simulation

This distribution is built on 1,000 simulations using holdout errors from the rolling validation period. Small differences between the point forecast and the simulated median may occur as a result.

The median forecast is +2.5% with a forecast distribution of +/- 1.2%. We would expect Ppi Food Mfg to fall between +1.3% and +3.7% over the next six months.

INPUTS

PPI Food Manufacturing – Inputs

Forecast Decomposition — 6-Month Average Contribution

Variable	Avg Input (YoY%)	Contribution (ppts)	% of Total
Manufacturing Wages	+4.2%	▲ 3.94 ppts	+69%
Oil Prices	+25.0%	▲ 0.72 ppts	+13%
PPI Farm Products	+3.0%	▲ 0.56 ppts	+10%
Food Import Prices	-2.3%	▼ 0.47 ppts	-8%
Structural Offset (model constant)	+1.0%	▼ 2.29 ppts
Total Forecast		▲ 2.46 ppts	100%

PPI Farm Products (Lead 1)

PPI Farm Products is forecast to average +3.0% year-over-year over the next six months, with the trajectory continuing to climb. That rise contributes +0.56 percentage points to PPI Food Manufacturing inflation, accounting for 10% of total input movement — though because this rebound is being measured against the depressed trough of 0.9% recorded in January 2026, the true pricing pressure at the farm level is meaningfully stronger than the year-over-year figure alone suggests.

Food Import Prices (Lead 3)

Food import prices are averaging -2.3% year-over-year across the forecast period, a deflationary read that nonetheless masks a strong underlying rebound from a deeply depressed trough — meaning the true pricing pressure feeding into PPI Food Manufacturing is considerably more intense than the headline number suggests. That drag subtracts 0.47 percentage points from the PPI Food Manufacturing forecast, accounting for 8% of total input movement across the period.

Manufacturing Wages (Lead 1)

Manufacturing wages are expected to average +4.2% year-over-year over the forecast period, with the trajectory continuing to climb. This single input is projected to contribute +3.94 percentage points to PPI Food Manufacturing inflation, accounting for 69% of total input movement — and because wages are being measured against the depressed trough of 0.9% recorded in January 2026, that contribution figure almost certainly understates the true pricing pressure building in food manufacturing costs.

Oil Prices (Lead 4)

Oil prices are forecast to average +25.0% year-over-year over the next six months, a trajectory that is heading higher and feeding directly into food manufacturing costs at every stage of production. That acceleration contributes +0.72 percentage points to the PPI Food Manufacturing forecast, representing 13% of total input movement — and because oil is being measured against the depressed trough when PPI Food Manufacturing bottomed at just 0.9% year-over-year in January 2026, the true pricing pressure on manufacturers is meaningfully stronger than the headline forecast number suggests.

MODEL PERFORMANCE

PPI Food Manufacturing – Model Performance

24-Month Holdout

Over the 24-month holdout period, the model produced a mean absolute error of 1.61 percentage points, meaning the average forecast landed within roughly 1.6 points of the actual PPI Food Manufacturing reading. For an index that has swung from negative territory to multi-decade highs and back within a few years, that margin represents a workable level of precision for strategic planning purposes — close enough to anchor decisions on pricing, procurement, and inventory without misleading executives on direction or magnitude.

Holdout Errors Over Time

Rolling errors across the holdout ranged from 0.5 to 2.7 percentage points, with the widest misses clustering around July 2024. That peak in error is consistent with what happens when one or more of the model's key inputs — oil prices, farm products, import prices, and manufacturing wages — moves sharply or reverses direction in a short window, compressing the model's ability to track the outcome in real time. When the causal chain from oil through farm costs to food manufacturing prices accelerates faster than the lagged structure can fully capture, short-term errors widen, then correct as the signal stabilizes.

OLS Regression Results
R-squared: 0.900			Adj. R-sq: 0.894
F-statistic: 168.38			Prob(F): 2.65e-45
No. Observations: 100			Df Residuals: 94
Variable	Coef	Std Err	t	P>\|t\|	[0.025, 0.975]
const	-2.8487	0.610	-4.673	0.000	[-4.059, -1.638]
ppi_farm_products_lead1_yoy	0.1790	0.016	11.428	0.000	[0.148, 0.210]
import_index_lead3_yoy	0.2582	0.062	4.194	0.000	[0.136, 0.380]
mfg_wages_lead1_yoy	1.0926	0.174	6.276	0.000	[0.747, 1.438]
oil_prices_lead4_yoy	0.0267	0.005	5.513	0.000	[0.017, 0.036]
covid	2.3717	0.623	3.807	0.000	[1.135, 3.609]

Omnibus: 4.071		Prob(Omnibus): 0.131		Durbin-Watson: 1.390
Skew: 0.438		Kurtosis: 3.260

Model Performance

The model's R-squared of 0.90 means it accounts for 90 percent of the variation in PPI Food Manufacturing over the estimation period, a strong fit for a macroeconomic forecasting model with this many moving parts. Every input — farm product prices, import prices, manufacturing wages, oil prices, and the Covid shock variable — carries a t-statistic well above 2.0, confirming that each one is making a statistically meaningful contribution rather than adding noise. The Omnibus probability of 0.131 sits above the 0.05 threshold, indicating the model's residuals are distributed normally and no systematic bias is distorting the estimates. The Durbin-Watson statistic of 1.39 is reasonably close to 2.0, suggesting autocorrelation in the residuals is not a serious concern, though it is worth monitoring as new data enters the sample.

OUTLOOK

CPI Food-at-Home – 6 Month YOY Outlook

+2.4% | +2.2%

ttm | 6-mo fcst

Food inflation was also softening ahead of the conflict peaking at 2.7% YOY in Aug-2025, and hitting 2.0% in Feb-2026. That pre-conflict decline was driven by falling farm and food manufacturing costs working through the supply chain. The other key drivers — PPI Grocery Retail, stable around 3.0% YOY, and retail wages, trending up since Jan-2025 — were providing some upward momentum. The result was a slow, measured decline heading into the conflict. But we are starting to see, the recent higher farm and food manufacturing prices are starting to push food inflation higher.

Over the next six months, CPI Food-at-Home is forecast to average +2.2% year-over-year, a modest step down from the +2.4% recorded over the trailing twelve months, with the trajectory running from +1.8% in May to a mid-period peak of +2.5% in August before easing back to +2.3% in October. The largest single driver is retail wage growth, averaging +3.7% over the forecast window and contributing +1.71 percentage points to the headline reading — a reminder that labor costs baked into the grocery retail layer are transmitting upstream pressure even as the broader wage cycle cools. PPI Grocery Retail adds a further +1.39 ppts at an average of +3.5%, while PPI Food Manufacturing — itself reflecting elevated oil-driven farm and processing input costs running at +2.7% — contributes an additional +0.88 ppts. Critically, a structural offset of -1.83 percentage points is suppressing what would otherwise be a significantly hotter print: this reflects the countervailing forces the analyst guidance identifies — decelerating retail wage growth and deliberate margin compression by grocery retailers absorbing upstream cost pressure rather than passing it fully to shelf prices, which explains why CPI Food-at-Home has remained contained in the 2.0%–2.7% corridor since September 2025 even as farm and manufacturing indices have moved sharply higher.

Forecast Distribution Simulation

This distribution is built on 1,000 simulations using holdout errors from the rolling validation period. Small differences between the point forecast and the simulated median may occur as a result.

The median forecast is +2.3% with a forecast distribution of +/- 0.7%. We would expect Cpi Fah to fall between +1.7% and +3.1% over the next six months.

INPUTS

CPI Food-at-Home – Inputs

Forecast Decomposition — 6-Month Average Contribution

Variable	Avg Input (YoY%)	Contribution (ppts)	% of Total
Retail Wages	+3.7%	▲ 1.71 ppts	+43%
PPI Grocery Retail	+3.5%	▲ 1.39 ppts	+35%
PPI Food Manufacturing	+2.7%	▲ 0.88 ppts	+22%
Structural Offset (model constant)	+1.0%	▼ 1.83 ppts
Total Forecast		▲ 2.15 ppts	100%

PPI Food Manufacturing (Lead 1)

Producer prices in food manufacturing are expected to average +2.7% year-over-year over the next six months, holding broadly stable as upstream cost pressures from oil-driven farm inputs push through the supply chain but are partially absorbed by slowing retail wage growth and some margin compression at the grocery level. This segment contributes an estimated +0.88 percentage points to the CPI Food-at-Home forecast, accounting for 22% of total input movement across the tracked drivers.

PPI Grocery Retail

PPI Grocery Retail is forecast to average +3.5% year-over-year over the next six months, with the index trending upward as upstream cost pressures from oil-driven farm and manufacturing inputs increasingly pass through to the retail shelf despite ongoing margin compression and softening wage growth. This contributes an estimated +1.39 percentage points to the CPI Food-at-Home forecast, accounting for 35% of total input movement and representing the single largest driver in the current projection.

Retail Wages (Lead 3)

Retail wage growth is expected to average plus 3.7% year-over-year over the next six months, a pace that continues to moderate and is providing a meaningful offset to upstream cost pressures flowing from oil into farm inputs and food manufacturing. Even so, wages still contribute plus 1.71 percentage points to the overall input cost movement, representing 43% of total input pressure and keeping labor the single largest demand-side force acting on CPI Food-at-Home in the forecast period.

MODEL PERFORMANCE

CPI Food-at-Home – Model Performance

24-Month Holdout

Over the 24-month holdout period, the model produced a mean absolute error of 0.84 percentage points, meaning that on average the forecast landed within roughly nine-tenths of a point of the actual CPI Food-at-Home reading. For executives using this output to plan pricing or procurement, that translates to a forecast you can act on — the margin of error is narrow enough to distinguish, say, a 2.5% inflationary environment from a 3.5% one without ambiguity.

Holdout Errors Over Time

Rolling errors across the holdout ranged from 0.4 to 1.0 percentage points, with accuracy generally tightest in stable periods and loosest around January 2025. The peak error near that date is consistent with what this model is most exposed to: rapid or large moves in its upstream inputs — food manufacturing costs, grocery wholesale prices, and retail wages — all of which were shifting quickly in that window. When the inputs themselves are volatile, even a well-specified model will lag the inflection slightly, and that dynamic accounts for most of the deviation seen at the high end of the error range.

OLS Regression Results
R-squared: 0.890			Adj. R-sq: 0.885
F-statistic: 191.26			Prob(F): 1.54e-44
No. Observations: 100			Df Residuals: 95
Variable	Coef	Std Err	t	P>\|t\|	[0.025, 0.975]
const	-2.3594	0.383	-6.159	0.000	[-3.120, -1.599]
ppi_food_mfg_lead1_yoy	0.3581	0.031	11.643	0.000	[0.297, 0.419]
ppi_grocery_yoy	0.5163	0.031	16.556	0.000	[0.454, 0.578]
retail_wages_lead3_yoy	0.3804	0.104	3.647	0.000	[0.173, 0.587]
covid	1.2763	0.521	2.449	0.016	[0.242, 2.311]

Omnibus: 0.640		Prob(Omnibus): 0.726		Durbin-Watson: 1.062
Skew: 0.169		Kurtosis: 2.740

Model Performance

The model's R-squared of 0.89 means that the four inputs — food manufacturing PPI, grocery wholesale PPI, retail wages, and a COVID dummy — together explain 89% of the historical variation in CPI Food-at-Home, a strong result for a macroeconomic price series. Every input clears the significance threshold comfortably, with t-statistics ranging from 2.4 on the COVID variable to 16.6 on the grocery wholesale PPI term, confirming that each is carrying genuine explanatory weight rather than noise. The Omnibus probability of 0.726 is well above the 0.05 threshold, indicating that the residuals are normally distributed and the model is not systematically misfitting any part of the data. The Durbin-Watson statistic of 1.062 is somewhat below the ideal value of 2.0, suggesting a mild tendency for consecutive residuals to move in the same direction — a pattern worth monitoring but not severe enough to invalidate the forecasts.

OUTLOOK

US Grocery Units – 6 Month YOY Outlook

-0.4% | -1.5%

ttm | 6-mo fcst

US Grocery Market Units have been trending down YOY since early 2025 and bottomed out at -3.0% just ahead of the conflict.The last 12 months were down -0.5%, but the first half of the year was much stronger than the last half. The higher CPI FAH number in Apr-2026 helped to keep unit growth down about 1.4% YOY.

Over the next six months, US grocery unit volume is forecast to average -1.4% year-over-year, a meaningful deterioration from the trailing twelve-month average of -0.4%, with the monthly trajectory sliding from -0.3% in May 2026 to -2.0% by October. The dominant drag is food-at-home inflation, which is forecast to average 2.1% and subtracts 1.69 percentage points from unit growth — a direct consequence of oil-driven farm input and food manufacturing cost pressures working their way through to shelf prices. Real disposable income, averaging -0.6% over the period, adds a further 0.24-point headwind as consumers face simultaneous pressure from elevated prices and softening purchasing power. Home prices, averaging 0.6% growth, contribute a modest 0.24-point offset through the wealth-effect channel, but this is insufficient to counteract the inflation and income pressures pulling volumes lower.

Forecast Distribution Simulation

This distribution is built on 1,000 simulations using holdout errors from the rolling validation period. Small differences between the point forecast and the simulated median may occur as a result.

The median forecast is -1.3% with a forecast distribution of +/- 0.4%. We would expect Units Mkt Trend to fall between -1.7% and -0.9% over the next six months.

INPUTS

US Grocery Units – Inputs

Forecast Decomposition — 6-Month Average Contribution

Variable	Avg Input (YoY%)	Contribution (ppts)	% of Total
CPI Food-at-Home	+2.1%	▼ 1.69 ppts	-78%
Real Disposable Income	-0.6%	▼ 0.24 ppts	-11%
Home Prices	+0.6%	▲ 0.24 ppts	+11%
Total Forecast		▼ 1.69 ppts	100%

CPI Food-at-Home

CPI Food-at-Home is forecast to average +2.1% year-over-year over the next six months, a rate that is expected to climb further as the period progresses. That inflation trajectory is the dominant drag on grocery unit volumes, subtracting 1.69 percentage points from the forecast — accounting for 78% of total input movement across all demand drivers.

Real Disposable Income

Real disposable income is forecast to average -0.6% year-over-year over the next six months, a trajectory that continues to move against the consumer. This contraction subtracts an estimated 0.24 percentage points from grocery unit growth, accounting for 11% of total input movement in the forecast.

Home Prices

Over the next six months, home prices are expected to average +0.6% year-over-year growth, a trend that is headed modestly higher and providing a quiet but steady lift to grocery unit demand. This contributes +0.24 percentage points to the unit forecast, accounting for 11% of total input movement across all drivers.

MODEL PERFORMANCE

US Grocery Units – Model Performance

24-Month Holdout

The 24-month holdout mean absolute error of 0.01 percentage points means that, on average, the model's unit volume forecasts landed within a tenth of a basis point of actual outcomes over the out-of-sample test period. That is a very tight margin for a consumer demand model operating across a volatile stretch of the grocery cycle. In practical terms, executives can treat the headline forecast as a reliable central estimate rather than a directional guess.

Holdout Errors Over Time

Rolling errors across the holdout window ranged from 0.3 to 0.8 percentage points, with the largest misses clustering around November 2025. That peak likely reflects the model's sensitivity to fast-moving inputs — when food-at-home CPI, real disposable income, or home prices shift sharply in a short window, even a well-specified model can lag the turning point by a month or two. The error range remains narrow enough to be operationally useful, but the November spike is a reminder that rapid input moves are the primary source of forecast uncertainty.

OLS Regression Results
R-squared: 0.881			Adj. R-sq: 0.876
F-statistic: 175.90			Prob(F): 5.17e-43
No. Observations: 100			Df Residuals: 95
Variable	Coef	Std Err	t	P>\|t\|	[0.025, 0.975]
const	22.9287	1.110	20.650	0.000	[20.724, 25.133]
cpi_fah_log	-0.8002	0.070	-11.459	0.000	[-0.939, -0.662]
rdi_log	0.3761	0.126	2.994	0.004	[0.127, 0.625]
home_price_log	0.4013	0.068	5.944	0.000	[0.267, 0.535]
covid	0.0368	0.007	5.283	0.000	[0.023, 0.051]

Omnibus: 117.344		Prob(Omnibus): 0.000		Durbin-Watson: 2.118
Skew: 3.708		Kurtosis: 29.965

Model Performance

The model's R-squared of 0.881 means it accounts for 88.1 percent of the observed variation in US grocery unit volumes, which is a strong fit for a macroeconomic demand model. Every input — food-at-home CPI, real disposable income, home prices, and the Covid indicator — carries a t-statistic well above 2.0, confirming that each variable is making a statistically meaningful and independent contribution to the forecast. The Omnibus probability of 0.000 falls below the 0.05 threshold, which indicates that the residuals are not normally distributed and the skew reading of 3.708 confirms they lean to one side, a limitation worth monitoring. The Durbin-Watson statistic of 2.118 sits close to the ideal value of 2.0, indicating no meaningful autocorrelation in the errors and suggesting the model is not leaning on its own past mistakes to generate forecasts.

OUTLOOK

US Grocery Sales – 6 Month YOY Outlook

+1.9% | +0.7%

ttm | 6-mo fcst

Sales were solid in 2024 and the first eight months of 2025, but the last six months have seen YOY sales drop precipitously. The long-run trend for grocery is about 3% YOY with 2% growth typically coming from CPI FAH and the other 1% from unit growth. CPI FAH reversed course in Apr-2026 which helped Sales growth tick up to a meager 1.5% YOY in April.

The six-month forecast puts US grocery sales growth at an average of +0.7% year-over-year, a meaningful step down from the trailing twelve-month average of +1.9%, reflecting a market that is losing momentum as the forces that briefly lifted sales in late 2024 and early 2025 fade. The dominant driver holding sales in positive territory is CPI food-at-home inflation, contributing +2.15 percentage points as oil-driven input costs continue to push shelf prices higher and pad nominal revenue even as consumer behavior shifts. Offsetting that inflation tailwind is a deteriorating unit volume picture, with grocery units averaging -1.4% over the period and subtracting 1.45 percentage points from the headline — meaning nearly all of the remaining sales growth is a price illusion rather than a signal of genuine demand strength. The monthly trajectory underscores the deceleration clearly, with growth sliding from +1.4% in May 2026 to just +0.2% by September and October, leaving the market barely above flat by the end of the forecast window.

Forecast Distribution Simulation

This distribution is built on 1,000 simulations using holdout errors from the rolling validation period. Small differences between the point forecast and the simulated median may occur as a result.

The median forecast is +0.7% with a forecast distribution of +/- 0.8%. We would expect Sales Mkt Trend to fall between -0.1% and +1.5% over the next six months.

INPUTS & PERFORMANCE

US Grocery Sales – Inputs and Performance

Forecast Decomposition — 6-Month Average Contribution

Variable	Avg Input (YoY%)	Contribution (ppts)	% of Total
CPI Food-at-Home	+2.1%	▲ 2.15 ppts	+60%
US Grocery Units	-1.4%	▼ 1.45 ppts	-40%
Total Forecast		▲ 0.70 ppts	100%

US Grocery Units

Over the next six months, grocery unit volumes are expected to average -1.4% year-over-year, a contraction that deepens as consumers continue pulling back on purchase quantities. This volume decline subtracts 1.45 percentage points from the grocery sales forecast, making it the single largest drag on the outlook and accounting for 40% of total input movement across all tracked drivers.

CPI Food-at-Home

CPI Food-at-Home is forecast to average +2.1% year-over-year over the next six months, a reading that is gradually climbing from its recent trough. That inflation rate contributes +2.15 percentage points to grocery sales growth over the period, accounting for 60% of total input movement in the forecast.

Forecast Errors

US Grocery Sales carries no standalone error statistic because it is a derived model, calculated by multiplying US Grocery Units by the Average Price index (CPI Food-at-Home) rather than estimated directly from its own regression. The forecast distribution implies an uncertainty band of roughly 0.8 percentage points on either side of the central estimate, spanning -0.1% to 1.5% at the 90% confidence level.

The median forecast for the six-month average year-over-year change in US Grocery Sales is 0.7%, but the 90% confidence interval stretching from -0.1% to 1.5% means that outcome is genuinely uncertain in practical terms. At the lower bound, sales growth would essentially stall, presenting a flat revenue environment for grocers; at the upper bound, a 1.5% gain would represent a meaningfully more supportive backdrop for top-line performance. Executives should treat the 0.7% point estimate as the most likely single outcome while recognizing the distribution leaves real probability mass on both sides.

Because this model is derived rather than directly estimated, its accuracy depends entirely on the quality of the two upstream components — the US Grocery Units model and the CPI Food-at-Home model — and any forecast error in either one flows directly into the sales figure. There is no independent holdout MAE, R-squared, or OLS diagnostic available for the sales model itself. Readers seeking those statistical details should refer to the dedicated Units and CPI Food-at-Home sections of this newsletter, where the underlying model performance is reported in full.