GroceryPulse Canadian Grocery Price Index (CGPI)

A real-time alternative food-price dataset for macro, rates, and consumer-sector analysts

Version 1.2 — 2026-06-12 Sung Ha Hwang · Co-founder and Research Director GroceryPulse Research · hello@grocerypulse.ca · grocerypulse.ca

Abstract

Statistics Canada publishes the Consumer Price Index (CPI) monthly with a three-week reporting lag. The Food purchased from stores sub-index is the single largest contributor to recent headline-CPI surprises and is disproportionately watched by the Bank of Canada, fixed-income desks, and consumer-sector equity analysts. GroceryPulse closes that information gap through weekly collection of a standardized 50-item basket of essential grocery products from 22 banners (six retailer families, roughly 160 active stores) across 13 Canadian cities, publishing a chained Jevons price index (methodology v2) with same-week availability.

This paper describes the data, the index construction, the quality-control protocol, and the commercial API product. It is intended as reference documentation for subscribers evaluating GroceryPulse as an alternative-data input to inflation nowcasts, rate forecasts, and consumer-sector research.

1. Why another price index?

1.1 The reporting-lag problem

StatCan's monthly CPI release is authoritative but slow. An April CPI print lands in mid-May and describes a basket priced in March and early April. For rate-setters and macro traders, this is a 4–7 week lag on the most politically salient component of inflation.

1.2 The transparency problem

StatCan publishes basket weights and methodology but not the underlying item-level prices or retailer-store pairings. Users cannot audit, decompose, or reproduce the food sub-index. Private replication by bank economics teams and hedge funds is common but expensive and fragmented.

1.3 The granularity problem

StatCan reports a single national food-CPI plus ten provincial indices. It does not publish city-level, retailer-level, or banner-level breakdowns — all of which matter for regional consumer-spending models, retail competitive analysis, and store-closure forecasting.

GroceryPulse is designed to address all three gaps simultaneously: weekly, transparent, granular.

2. Data coverage

2.1 Cities (13)

Vancouver, Calgary, Edmonton, Saskatoon, Winnipeg, Toronto, Ottawa, Montreal, Quebec City, Moncton, Halifax, Charlottetown, St. John's.

Together these cities represent approximately 55% of Canadian grocery spend and all ten provinces.

2.2 Retailer banners (22 active, six retailer families)

Loblaw Companies — Loblaws, No Frills, Real Canadian Superstore, Atlantic Superstore, Dominion, Maxi, Provigo, Fortinos, Wholesale Club, Your Independent Grocer.

Empire Company — Sobeys, FreshCo, IGA, Safeway, Foodland, Thrifty Foods.

Voilà (Empire online) — collected as its own banner; the Voilà storefront also carries Farm Boy products.

Metro Inc. — Metro, Food Basics, Super C.

Independents / regionals — Save-On-Foods (Pattison), Giant Tiger.

Coverage spans the three largest Canadian grocery parents (Loblaw, Empire, Metro), Empire's Voilà online storefront, and two regional players (Pattison's Save-On-Foods, Giant Tiger), totalling roughly 160 active stores. Giant Tiger is a general merchandiser and stocks only part of the basket — see §7.5. Walmart Canada and Costco are not currently covered — see §7.2.

2.3 Basket (50 items, 9 categories)

Weights are calibrated to the StatCan Food purchased from stores sub-basket using the most recent published weight-share release, renormalized to 100%.

2.4 Collection cadence and observation volume

Prices are collected weekly, on Thursdays, with an automatic Friday catch-up run for any city below 90% store coverage. The published series begins June 2026 — the v2 quality baseline; an earlier March–June 2026 pilot is retained for continuity under series=v1 (see §3.1). Reporting weeks are bucketed by Canadian local time (America/Toronto) and end on Sundays.

Data is stored append-only in PostgreSQL 15 (Supabase) with partitioning readiness on observed_at.

3. Index construction

3.1 Primary index: chained matched-model weighted Jevons (methodology v2)

For each reporting week t, each city c, each banner b, and each category, the published series is built from week-over-week links:

link(c, b, t) = exp( Σ over i ∈ S(t) of w̃ᵢ · ln(Pᵢ,t / Pᵢ,t−1) )
I(c, b, t)    = I(c, b, t−1) × link(c, b, t)

where S(t) is the set of basket items priced in both week t and week t−1, and w̃ᵢ are the basket weights renormalized over S(t) for each link. Links accumulate into the published level. Because each link is computed over the same matched set in both weeks, week-over-week changes are same-composition by construction. Each series equals 100 in its own entry week — a level reads as "change since that series entered the panel", not "change since a common base date".

A cell publishes only when at least 60% of its basket slice (and at least 2 items) is priced in both linked weeks.

We use the Jevons (geometric-mean) form rather than Laspeyres (arithmetic-mean) for two reasons:

1. Substitution bias. The geometric mean implicitly assumes unit-elastic substitution, which more closely reflects observed consumer behaviour than Laspeyres' zero-elasticity assumption.
2. Symmetry. A +50% and –50% price change offset exactly under Jevons; Laspeyres is biased upward.

This is the form recommended by the ILO/IMF Consumer Price Index Manual (2020) for elementary aggregates and is the form used by Eurostat for HICP sub-indices.

Methodology change log

• v1 (2026-03-30) — fixed-base matched-model weighted Jevons against the first complete collection week (base week 2026-03-30). Known limitations: week-over-week changes could reflect composition drift, and banners or cities onboarded after the base week never entered the series. The v1 series is frozen and remains available through the API with series=v1 for continuity.
• v2 (2026-06-12) — chained matched-model weighted Jevons with read-time normalization: every stored observation is re-normalized onto the canonical basis at compute time by a single versioned code path. v2 is the published series; its continuous published history is quality-gated to the June 2026 baseline (2026-06-01). The earlier March–June 2026 observations are retained under series=v1 as a pilot period and are not part of the headline series.

3.2 Effective-price selection

Within each retailer banner, each matched canonical basket item contributes its most recent observed effective price for the week — the sale price when the item is on sale, otherwise the regular price.

3.3 City and national aggregates

City and national aggregates chain the basket-cost-weighted mean of the constituent links (not levels). Aggregating links rather than levels means newly onboarded banners and cities enter the panel cleanly and never distort the aggregate history.

3.4 Consumer-friendly secondary: basket cost

Alongside the index, we publish a per-banner basket cost in CAD: the sum, over matched items, of each item's most recent observed effective price (sale price when on sale, otherwise regular) within that banner that week. Because missing items deflate a basket total, cheapest-banner rankings are restricted to near-complete baskets (≥48/50 matched items). This figure has no index-number properties but is highly legible for general audiences and media partners.

4. Data quality protocol

4.1 Collection

Prices are collected from publicly accessible retailer search and product pages. For Loblaw banners (Loblaws, No Frills, Superstore, Wholesale Club) we parse the __NEXT_DATA__ payload embedded in the server-rendered HTML of store-locator-scoped search results. Analogous approaches are used for the other banners, with a Playwright fallback where sites require client-side rendering.

4.2 Three-tier matching

Each retailer SKU is matched to a canonical basket item via:

1. UPC barcode match (where available from the retailer payload).
2. Canonical specification match — product type, variant attributes (e.g. salted butter, whole-wheat bread), and package size within a ±10% tolerance band.
3. TF-IDF fuzzy fallback over product title, used only when UPC and spec matching both fail. Fallback matches are logged for manual review.

Each matched item then contributes its most recent effective price (see §3.2). Verified wrong-product mappings are excluded via a maintained quarantine list (§4.3).

4.3 Normalization and filters (versioned, applied at compute time)

The published index is recomputed from the full observation history under the current, versioned normalization code. A single code path re-normalizes every stored observation onto the canonical basis at compute time:

• Basis normalization — weight-priced observations are restated per kg onto the item's canonical basis; per-each prices that cannot be restated are rejected; per-item plausibility bands reject implausible values.
• Mapping quarantine — verified wrong-product mappings are excluded via a maintained quarantine list.
• Statistical outlier filter — any retailer-product whose weekly median sits outside [1/3, 3]× the cross-banner product median is excluded; the filter is evaluated only where a product-week has ≥8 observations across ≥3 banners.
• Publication floor — a city × banner (× category) cell publishes only when ≥60% of its basket slice (and ≥2 items) is priced in both linked weeks.

A data-quality remediation shipped on 2026-06-12: per-each/per-kg basis normalization, French-locale price parsing, the wrong-product mapping quarantine, and sale-price capture on Empire banners. Because observations are append-only and the index is recomputed at read time, the corrected series incorporates the full history; the superseded v1 series remains available (§4.5).

4.4 Shrinkflation tracking

Package sizes are captured at each observation, and size changes are published as shrinkflation events only after persistence verification: the new size must hold for at least 2 subsequent weeks and the old size must never reappear on that product. Weigh-by-unit items (per-kg, per-head) are excluded. Not every detected size change is shrinkflation — transient listing errors and packaging variants are filtered out by the persistence gate. 125 events have been verified to date. Verified events let users decompose nominal price changes into (a) true price movement and (b) shrinkflation.

4.5 Revision and integrity policy

Price observations are append-only: once collected, an observation is never edited or deleted. The published index is recomputed from the full observation history under the current, versioned normalization code. When methodology or normalization changes, the change is dated and documented (see the §3.1 methodology change log and Appendix B), and the superseded series remains available — the v1 fixed-base series is frozen and served through the API with series=v1. Point-in-time vintages are part of the commercial feed, so subscribers can backtest against exactly the values that were published on any given date.

4.6 Accuracy verification (two-tier)

Accuracy is verified at two tiers:

1. Automated re-scrape checks (weekly). A stratified sample is re-queried against the live retailer sites after each run. These confirm reproducibility and mapping liveness, but they re-run the same pipeline — they are not independent evidence of accuracy.
2. Independent browser-based spot audits. A browser agent reads prices off the retailer page the way a human shopper would and compares them with stored values. This sample is still small (70 audits to date) and predates the 2026-06-12 remediation; results will be published on the methodology page as the stratified monthly program (target N ≥ 100) accumulates.

We deliberately do not publish a headline accuracy percentage until the independent audit sample is large enough to support one.

5. Comparison with StatCan food CPI

GroceryPulse is designed to complement StatCan CPI, not replace it. Subscribers typically use CGPI as a high-frequency nowcasting input alongside StatCan as the ground-truth benchmark.

6. Use cases

6.1 CPI nowcasting (macro / rates)

Bank of Canada rate decisions are materially influenced by the Food sub-index. A weekly grocery price signal materially shortens the information lag between real price movements and CPI prints. Typical workflow:

1. Subscribe to the weekly CGPI feed.
2. Regress historical StatCan monthly food-CPI changes on concurrent CGPI changes to estimate a nowcast coefficient.
3. Roll the CGPI signal into a full CPI nowcast alongside other high-frequency components (energy, core-goods scanner data).

6.2 Consumer-sector equity research

Analysts covering Loblaw Companies (L.TO), Empire Company (EMP.A.TO), Metro Inc. (MRU.TO), and George Weston (WN.TO) can track banner-level price competitiveness week by week. Specific signals:

• Banner basket-cost divergence → pricing-strategy shifts.
• Category-level markup expansion or compression.

6.3 Retail competitive intelligence

Independent and regional grocers without Nielsen/NielsenIQ subscriptions can benchmark their category pricing against the Big 3 in the cities where they operate. Typical deliverable: weekly category-level competitive dashboard.

6.4 Academic and policy research

Universities, policy think tanks, and government economic research units license the full historical panel for inflation-expectations research, pass-through studies, and regional-disparity analysis.

7. Known limitations

7.1 Online-list price

All prices are collected from retailer websites. In-store shelf prices can differ, particularly for weight-priced produce and meat where online prices are often indicative. We do not yet publish a quantified online/in-store gap; the independent browser audits (§4.6) measure our fidelity to the retailer page, not the page's fidelity to the shelf.

7.2 Walmart and Costco

Walmart Canada and Costco are not currently covered. Both employ aggressive anti-bot protection (PerimeterX and CAPTCHA-gated member-only listings respectively) that make automated collection unreliable. Walmart coverage is on the Q3 2026 roadmap pending a residential-proxy or data-licensing arrangement; Costco is unlikely to be feasible without a commercial partnership.

7.3 Basket size

A 50-item basket does not capture every movement in StatCan's much larger food basket (several hundred representative products, revised monthly). The preliminary 2026 overlap shows directional agreement with StatCan monthly food-CPI changes; the overlap is still far too short for formal correlation statistics (see §8).

7.4 No quality adjustment

When a product is reformulated or a brand replaces a variant, we observe the price change but do not adjust for quality differences. Shrinkflation (pack-size reduction) is tracked explicitly and persistence-verified (§4.4); other reformulations are not.

7.5 Partial banner coverage

Giant Tiger is a general merchandiser and stocks only part of the 50-item basket. Its cells publish only where they clear the §4.3 publication floor, and it is excluded from cheapest-banner rankings whenever it matches fewer than 48 of 50 items (§3.4).

7.6 Young independent-audit sample

The independent browser-audit sample (70 audits to date) is too small to support a headline accuracy figure and predates the 2026-06-12 remediation. See §4.6 for the publication plan.

8. Historical validation (preliminary)

A formal backtest of CGPI vs StatCan Food purchased from stores will be published in the Q3 2026 version of this whitepaper, once the overlap window is long enough to support meaningful statistics. The weekly series begins 2026-03-30 and deepens every week; with only a handful of overlapping monthly prints to date, we deliberately do not publish correlation or magnitude figures yet. The 2026-06-12 methodology v2 cutover does not interrupt the series: v2 is recomputed from the full observation history back to 2026-03-30, and the superseded v1 series remains available for comparison (series=v1).

What the preliminary overlap shows so far: weekly CGPI movements have agreed in direction with the StatCan month-over-month food-CPI change, and they are observable weeks before each official print. Treat this as a design property of the series (same-week shelf prices vs a lagged monthly survey), not yet as validated forecasting performance.

Trial and subscription users receive the weekly index series and can replicate the comparison directly against published StatCan figures.

9. Commercial access

9.1 Tiers

Subscriptions are billed in USD; grocery values are reported in CAD.

9.2 Delivery

• REST API (grocerypulse.ca/api/v1/…) — JSON, OpenAPI 3.1 specification. The index endpoint accepts series=v2 (default; chained methodology) or series=v1 (frozen fixed-base series, retained for continuity); responses carry meta.series and meta.methodology.
• Free data pack — monthly CSV bundle (national, per-city, per-banner, per-category), published on a 14-day delay, attribution required.
• Point-in-time panel — Parquet dumps, monthly cut (commercial tier).

9.3 Licensing

Data is licensed for internal research use. Redistribution and sub-licensing require written consent. Attribution required in published research: "Source: GroceryPulse Canadian Grocery Price Index. Research led by Sung Ha Hwang, Co-founder and Research Director."

9.4 Trial

A 14-day evaluation license is available on request. Contact hello@grocerypulse.ca.

Appendix A — Full basket

See /methodology on grocerypulse.ca for the full live basket with weights and match criteria. The basket is held constant within each reporting year; annual re-weighting occurs each January.

Appendix B — Change log

• 1.2 (2026-06-12) — Methodology v2 cutover: the published series is now a chained matched-model weighted Jevons with read-time normalization; the v1 fixed-base series (base week 2026-03-30) is frozen and remains available via series=v1. Coverage corrected to 22 active banners (six retailer families, roughly 160 active stores). Revision policy restated as append-only observations with dated, versioned recomputation — an earlier "never revised" claim was inaccurate and has been removed. Accuracy claims replaced by the two-tier verification program (§4.6). Data-quality remediation disclosed: per-each/per-kg basis normalization, French-locale parsing, wrong-product mapping quarantine, Empire sale-price capture. Shrinkflation feed reinstated as a persistence-verified event feed (125 verified events to date).
• 1.1 (2026-06-11) — Cadence corrected to weekly. Commercial pricing published (US$2,500 / month or US$30,000 / year, 14-day trial). Validation section rewritten to defer correlation statistics until the StatCan overlap supports them. Shrinkflation event feed temporarily removed from commercial deliverables while the detector matured.
• 1.0 (2026-04-20) — First public release. Commercial API announced.

GroceryPulse is an independent research publisher. The CGPI is not endorsed by, affiliated with, or sponsored by Statistics Canada, the Bank of Canada, or any retailer listed in this document.