Census API Monthly Retail Trade Survey (MRTS) Data
Datadory delivers Census API Monthly Retail Trade Survey (MRTS) data covering monthly sales, inventories and inventory-to-sales ratios by kind of business across the U.S. retail sector, including furniture stores, home furnishings stores, and building material and supplies dealers. The series is delivered as a clean time series ready for joining into category-level demand, working-capital, and category-mix models, and the same payload is offered as a single industry cut or the full retail panel.
What this dataset is
Datadory delivers Census API Monthly Retail Trade Survey (MRTS) data, the U.S. Census Bureau's long-running monthly time series for retail sales, inventories and inventory-to-sales ratios by kind of business. Where the advance MARTS release offers an early read on a small set of headlines, MRTS is the full monthly series with revised estimates and the full inventory block, which makes it the right backbone for category-level demand models, working-capital analysis, and category-mix planning. The home furnishings view centers on furniture stores, home furnishings stores, and building material and supplies dealers, with the rest of retail available on the same backbone.
Each row pins one cell value to a specific kind of business, measure, and month, split by seasonal adjustment status. The series is built for joining: a stable category_code plus a clean time dimension means it sits cleanly next to weekly scanner data, store-level POS extracts, and macro indicators.
Sample rows
Rows below show the shape of MRTS as Datadory delivers it. The full panel spans decades of monthly history for every kind of business, with the home furnishings and home improvement lines available as a focused cut.
Sample data (mono)
period | category_code | category_desc | data_type | seasonally_adj | value
Jan-1992 | 442 | 442: Furniture and home furnishings stores | SM (Sales - Monthly) | true | 4712
Jul-2026 | 442 | 442: Furniture and home furnishings stores | SM (Sales - Monthly) | true | 12380
Jun-2026 | 442 | 442: Furniture and home furnishings stores | IM (Inventories - Monthly) | true | 27840
Jun-2026 | 442 | 442: Furniture and home furnishings stores | IR (Inventories/Sales Ratio) | true | 2.21
Jan-1992 | 444 | 444: Building material and garden equip dealers | SM (Sales - Monthly) | true | 7210
Jun-2026 | 444 | 444: Building material and garden equip dealers | SM (Sales - Monthly) | true | 41205
Jun-2026 | 444 | 444: Building material and garden equip dealers | IM (Inventories - Monthly) | true | 71880
Jun-2026 | 444 | 444: Building material and garden equip dealers | IR (Inventories/Sales Ratio) | true | 1.74
Jan-1992 | 4421 | 4421: Furniture stores | SM (Sales - Monthly) | true | 2980
Jul-2026 | 4422 | 4422: Home furnishings stores | SM (Sales - Monthly) | true | 5880
Jun-2026 | 4422 | 4422: Home furnishings stores | IM (Inventories - Monthly) | true | 14620
Jun-2026 | 4422 | 4422: Home furnishings stores | IR (Inventories/Sales Ratio) | true | 2.49Values are reported in millions of dollars (sales, inventories) or as a ratio. The data_type_code column is the seam that lets you pivot between the sales, inventory, and inventory-to-sales measures for any kind of business.
Field dictionary
Every column Datadory ships with the MRTS dataset, with type, definition, and a worked example from the home furnishings view.
Fields
| field | type | definition | example |
|---|---|---|---|
| period | string | Reference month for the observation, expressed as ISO month label. | 2026-06 |
| time | string | ISO-8601 date/time value supporting year and month predicates. | 2026-06 |
| category_code | string | NAICS-based kind of business code from the retail industry list. | 442 |
| category_desc | string | Human-readable description of the kind of business. | 442: Furniture and home furnishings stores |
| data_type_code | string | Item type distinguishing sales, inventories and inventory-to-sales ratios. | SM (Sales - Monthly) |
| cell_value | number | Reported estimate value for the selected item type, category and period. | 12380 |
| seasonally_adj | enum | Yes or no indicator of seasonal adjustment. | true |
| error_data | boolean | Flag indicating whether the row carries sampling error data. | false |
| time_slot_id | string | Time slot grouping identifier used by the source. | t-2026-06 |
| geo_level_code | string | Geographic level code for the observation. | 010 (US national) |
| program_code | string | Component name identifying the survey program. | MRTS |
Additional fields available on request
Datadory can include any of the following fields without a contract change. Ask for them in the sample request form and they will be added to the standard delivery.
- cell_path / is_total
- seasonally_adj_code (raw S, NSA, SA codes)
- footnote_codes
- series_id (per kind-of-business × measure key)
- error components and relative standard error
- parent_NAICS / aggregation_level
- release_date and revision flags
Coverage chips
Geography: United States, national level
Granularity: Monthly observations per kind of business and measure, split by seasonal adjustment status
Time span: Long monthly history per kind of business, extending back decades for the major retail categories
Categories in focus: Furniture and home furnishings stores (NAICS 442), home furnishings stores (NAICS 4422), building material and garden equipment dealers (NAICS 444), with the full retail panel available on the same backbone.
Delivery
API, files, or your warehouse. Daily, weekly, or hourly.
Datadory delivers the MRTS data in the shape your team actually works in. Pull it through a managed API, land it as scheduled flat files, or stream it directly into the warehouse you already query. The cadence is your call - daily, weekly, or hourly - so the latest monthly print shows up on the schedule that fits your forecast cycle.
Use cases
Category demand modeling. Pin sales by NAICS-level kind of business to your own weekly scanner or store data to triangulate category demand for furniture, home furnishings, and building materials.
Inventory and working-capital tracking. The IM (Inventories - Monthly) and IR (Inventories/Sales Ratio) item types give an official benchmark for sell-through and stock-to-sales behavior across cycles, useful for working-capital and procurement planning.
Category-mix and channel-mix analysis. Compare furniture, home furnishings, and building material dealers against the rest of retail to size how each category is contributing, and how channel mix is shifting between categories.
Forecast grounding. A long, official monthly history is the right input to backtest demand forecasts, especially for categories that look back decades before scanner data existed.
Macro and sector briefings. MRTS is one of the cleanest public series for sizing U.S. discretionary retail on a monthly cadence, with the right lineage for inclusion in macro decks and sellside reports.
Personas who pull this
Buyside equity analysts building category-level retail comp sheets and stress-testing home improvement and home furnishings names against official monthly prints.
Sellside retail analysts writing sector notes and using MRTS as the canonical monthly history for sizing the furniture, home furnishings, and building materials lines.
Inventory and supply chain planners benchmarking sell-through and inventory-to-sales ratios across the year, with the full retail panel as a comparison set.
Strategy and corporate development teams at home improvement and home furnishings retailers and brands, sizing category mix, working capital, and growth opportunities.
Frequently asked questions
See the FAQ block above for the full set of answered questions.
Questions buyers ask
What is the Census MRTS dataset?
Datadory's MRTS dataset packages the U.S. Census Bureau's Monthly Retail Trade and Food Services time series, with sales, inventories and inventory-to-sales ratios broken out by kind of business at the national level. The home furnishings cut isolates furniture stores, home furnishings stores, and building material and supplies dealers.
What kinds of business does the home furnishings MRTS view include?
The home furnishings view centers on NAICS 442 furniture stores, 4422 home furnishings stores, and 444 building material and supplies dealers, with parent and child categories available on request. The same data backbone can be pivoted to apparel, electronics, motor vehicles or any other retail kind of business in the source.
What time period does the MRTS series cover?
MRTS is a long monthly history, with each kind of business and measure carried as its own time series. Series start dates vary by category, so the working history for any given cut is confirmed in the sample Datadory prepares for you.
How do teams use MRTS data for home furnishings and home improvement retail?
Buyside and sellside analysts use the series to size category demand, track inventory build versus sell-through, and benchmark working capital against the rest of retail. Operators use it for category-level planning and to ground forecasts in a long, official monthly history.
Can I get MRTS as a one-industry cut rather than the full retail panel?
Yes. Datadory can deliver MRTS as a focused home furnishings / home improvement cut, as a custom subset of kinds of business, or as the full retail panel. The same field dictionary and delivery cadence apply regardless of the cut.
How fresh is the MRTS data on Datadory?
Datadory refreshes MRTS on the cadence you choose - daily, weekly, or hourly - so your warehouse sees the latest monthly print on the schedule that fits your workflow. The exact refresh window is set in the delivery contract.
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