US metro price-level intelligence (BEA RPP, annual)

Commercial use OKFresh

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

US metro price-level intelligence from the BEA Regional Price Parities (RPP, 2008–2024 vintage, keyless FRED redistribution, U.S. public domain): ~384 metropolitan statistical areas (OMB 2023 delineations), annual, US = 100. Method: the closed RPPALL<CBSA> universe is pulled via fredgraph.csv; every series must span 2008 to the latest vintage with zero gaps and plausible values or the ingest fails loudly. Derived signals per metro-year: cost_premium_pct (RPP − 100), a documented 5-band price_tier (very_low <90 … very_high ≥110), within-year national_rank (1 = most expensive), 1-year and 5-year index-point drift (rpp_yoy_pp / rpp_5y_pp), and local_purchasing_power_100 (10000/RPP: the national-dollar value of $100 spent locally). Use as prediction features: join to customers/orders/leads by cbsa_code + year for territory-level demand models, site selection, regional pricing, and real (price-level-adjusted) market sizing — the state panel's coarser sibling for metro granularity. Caveats: RPPs compare price levels across places, not inflation over time; the latest 1–2 vintages are routinely revised.

Source: U.S. Bureau of Economic Analysis
Rows: 6,271
Columns: 16
Source cadence: Yearly
Last refreshed: Oct 2, 2026
Theme: economy

Column	Type	Description
year	integer	Reference year of the RPP vintage.
country	string	Country name (United States).
country_code	string	ISO alpha-3 country code (USA).
cbsa_code	string	5-digit CBSA code (OMB 2023 delineations).
metro_name	string	Metropolitan statistical area title.
metro_states	string	State codes spanned by the metro, hyphen-joined.
price_level_index	float	BEA Regional Price Parity: price level as a percentage of the overall national price level (US = 100).
cost_premium_pct	float	Signed premium (+) or discount (−) vs the national average: price_level_index − 100.
price_tier	string	Documented 5-band cut: very_low (<90), low (90–95), average (95–105), high (105–110), very_high (>=110).
national_rank	integer	Within-year rank of price_level_index across the universe (1 = most expensive; ties share the rank).
rpp_yoy_pp	float	1-year change of the index in percentage points (relative price drift).
rpp_5y_pp	float	5-year change of the index in percentage points (structural drift).
local_purchasing_power_100	float	10000 / price_level_index: the national-dollar value of $100 spent locally — deflate nominal amounts by this / 100.
row_hash	string	Content hash (sha256, 16 hex) over geography, year, and index.
provenance_url	string	Exact fredgraph.csv URL behind this row's series.
source_url	string	Canonical BEA Regional Price Parities page.

First 10 sample rows — a preview, not the complete dataset.

year	country	country_code	cbsa_code	metro_name	metro_states	price_level_index	cost_premium_pct	price_tier	national_rank	rpp_yoy_pp	rpp_5y_pp	local_purchasing_power_100	row_hash	provenance_url	source_url
2,008	United States	USA	10180	Abilene, TX	TX	92.197	-7.803	low	270	—	—	108.46	d23189953e3831f6	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10180	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10420	Akron, OH	OH	92.502	-7.498	low	256	—	—	108.11	7912d1460f953499	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10420	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10500	Albany, GA	GA	86.48	-13.52	very_low	351	—	—	115.63	a7c854cf3ef164a6	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10500	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10540	Albany, OR	OR	99.323	-0.677	average	82	—	—	100.68	913d6c644e3d5d58	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10540	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10580	Albany-Schenectady-Troy, NY	NY	102.446	2.446	average	51	—	—	97.61	62a9f796003f9c71	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10580	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10740	Albuquerque, NM	NM	96.974	-3.026	average	133	—	—	103.12	baea54f3cf0d1632	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10740	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10780	Alexandria, LA	LA	89.683	-10.317	very_low	319	—	—	111.5	8a64d0f7acb23079	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10780	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	10900	Allentown-Bethlehem-Easton, PA-NJ	PA-NJ	101.062	1.062	average	60	—	—	98.95	645dd868d4e3437e	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL10900	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	11020	Altoona, PA	PA	93.889	-6.111	low	221	—	—	106.51	0935593cd5894b2e	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL11020	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area
2,008	United States	USA	11100	Amarillo, TX	TX	95.81	-4.19	average	162	—	—	104.37	42156dcb3150da3d	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL11100	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area

Profiled Oct 2, 2026 from snapshot 20261002T024141Z-144011bec841

Measured

Completeness: 97.8%
Rows: 6,271
Columns: 16
Columns with gaps: 2

Rows per yearyear · 2008–2024 · 17 years

20082024

Column	Missing	Distinct	Range	Distribution
yearbigint	0%	20	2,008 → 2,024median 2,016
countryvarchar	0%	1	—	United States6,271
country_codevarchar	0%	1	—	USA6,271
cbsa_codevarchar	0%	351	—	1018017 1042017 1050017
metro_namevarchar	0%	412	—	Abilene, TX17 Akron, OH17 Albany, GA17
metro_statesvarchar	0%	83	—	CA425 TX408 FL357
price_level_indexdouble	0%	5,048	78.28 → 122.87median 94.32	126 outside 1st–99th percentile
cost_premium_pctdouble	0%	4,795	-21.72 → 22.87median -5.68	126 outside 1st–99th percentile
price_tiervarchar	0%	5	—	average2,323 low2,312 very_low1,144
national_rankbigint	0%	403	1 → 369median 185	100 outside 1st–99th percentile
rpp_yoy_ppdouble	5.9%	3,791	-9.56 → 8.47median -0.0875	120 outside 1st–99th percentile
rpp_5y_ppdouble	29.4%	2,842	-10.45 → 12.03median -0.6285	90 outside 1st–99th percentile
local_purchasing_power_100double	0%	2,281	81.39 → 127.75median 106.03	126 outside 1st–99th percentile
row_hashvarchar	0%	5,642	—	0051cfc914b488991 005b6ca813df17701 00645207c685523a1
provenance_urlvarchar	0%	400	—	https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL1018017 https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL1042017 https://fred.stlouisfed.org/graph/fredgraph.csv?id=RPPALL1050017
source_urlvarchar	0%	1	—	https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area6,271

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/bea_rpp_intel/bea_metro_rpp_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/bea_rpp_intel/bea_metro_rpp_annual").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/bea_rpp_intel/bea_metro_rpp_annual

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261002T024141Z-144011bec841 and its content hash, so readers get exactly the data you used.

APA

U.S. Bureau of Economic Analysis. (2026). US metro price-level intelligence (BEA RPP, annual) [Data set, snapshot 20261002T024141Z-144011bec841, sha256 144011bec841]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/bea_rpp_intel/bea_metro_rpp_annual?snapshot=20261002T024141Z-144011bec841

BibTeX

@misc{dz_bea_rpp_intel_bea_metro_rpp_annual_144011be,
  title        = {{US metro price-level intelligence (BEA RPP, annual)}},
  author       = {{U.S. Bureau of Economic Analysis}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/bea_rpp_intel/bea_metro_rpp_annual?snapshot=20261002T024141Z-144011bec841}},
  note         = {Snapshot 20261002T024141Z-144011bec841, sha256 144011bec841a7e3a24fa47f1628d3782b7b4979464383eb22e2d35fd7c8ea41; accessed 2026-10-02}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=bea_rpp_intel%2Fbea_metro_rpp_annual&lang=en&theme=auto&snapshot=20261002T024141Z-144011bec841&x=year&y=year&agg=avg" title="US metro price-level intelligence (BEA RPP, annual)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

US metro price-level intelligence (BEA RPP, annual)

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key