ACA Marketplace Analytics Dataset

About the Dataset

The ACA Marketplace Analytics Dataset (Affordable Care Act / Obamacare) provides comprehensive, analytics-ready access to the CMS Health Insurance Exchange Public Use Files (Exchange PUFs) from the Centers for Medicare & Medicaid Services, spanning Plan Years 2014 through 2026. Seven government data files from HealthCare.gov and the Federally Facilitated Exchange (FFE) are combined into a single, query-ready Snowflake database: the Rate PUF (premium rates by age and geography), Plan Attributes PUF (deductibles, out-of-pocket maximums, metal levels), Benefits and Cost Sharing PUF (copays, coinsurance, EHB status), Plan ID Crosswalk PUF (year-over-year plan mappings), Service Area PUF (issuer geographic coverage), Business Rules PUF (rate application rules), and Network PUF (provider network references). Built for health insurance analysts, actuaries, health policy researchers, benefits consultants, and anyone working with Qualified Health Plan (QHP) data from the ACA individual marketplace.

🔗 Find the ACA Marketplace Analytics Dataset on the Snowflake Marketplace.

Quick Access

Tables: RATES, PLAN_ATTRIBUTES, BENEFITS_COST_SHARING, PLAN_CROSSWALK, SERVICE_AREAS, BUSINESS_RULES, NETWORK Sources: 7 CMS Exchange Public Use Files from cms.gov Update Frequency: Annual initial release (~October), with 2-3 mid-year corrections Coverage: Federally Facilitated Exchange (FFE) states (~30 states for PY2026)

Overview

The ACA Marketplace Analytics Dataset provides comprehensive access to CMS Exchange PUF data including:

• RATES (Rate PUF) - Premium rates by plan, age band, tobacco use, and geographic rating area (~2.2M rows per plan year)

• PLAN_ATTRIBUTES (Plan Attributes PUF) - Plan-level details: metal level, plan type, deductibles, MOOP, HSA eligibility, SBC scenario costs (~22K plan variants per year)

• BENEFITS_COST_SHARING (Benefits & Cost Sharing PUF) - Benefit-level copays, coinsurance, and coverage details per plan variant (~1.5M rows per year)

• PLAN_CROSSWALK (Plan ID Crosswalk PUF) - Year-over-year plan mappings for multi-year trend analysis (~159K rows per year)

• SERVICE_AREAS (Service Area PUF) - Geographic coverage by issuer, county, and ZIP code (~8.8K rows per year)

• BUSINESS_RULES (Business Rules PUF) - Rate application rules: age determination, tobacco use, dependent rules (~5.1K rows per year)

• NETWORKS (Network PUF) - Provider network names and URLs per issuer (~550 rows per year)

All tables include 13 plan years (PY2014-PY2026) of historical data, enabling longitudinal trend analysis across the full history of the ACA marketplaces.

Metadata Tables

Every Dataplex data product includes these standard metadata tables:

Table	Purpose
FEEDS	Dataset catalog — available tables, descriptions, update dates
FEEDS_FILES	Batch load history with is_latest flag for data freshness
CHANGELOG	Change log — data loads, schema changes, corrections
DATA_DICTIONARY	Column descriptions for all tables

Entity Relationship Diagram

ACA Marketplace Entity Relationship Diagram

The core join key across RATES, PLAN_ATTRIBUTES, and BENEFITS_COST_SHARING is plan_id. SERVICE_AREAS links via issuer_id + state_code. PLAN_CROSSWALK maps plans across years via plan_id_prior and plan_id_current. Every table includes plan_year for period-partitioned queries and links to FEEDS/FEEDS_FILES via feed_id and feeds_files_id for data lineage tracking.

Data Tables

RATES (Rate PUF)

Premium rate data by plan, subscriber age, tobacco use, and geographic rating area. Each row represents one plan in one rating area for one age/tobacco combination.

Key Features:

• ~2.2M rows per plan year (PY2026), ~29M rows across all 13 years

• Individual and family tier premiums (couple, subscriber+dependents, etc.)

• 30 FFE states, 359 issuers, 5,144 unique plans (PY2026)

• Annual data with mid-year issuer corrections

Column Reference

Column	Type	Description
plan_year	NUMBER	Plan year (2014-2026) — use for period filtering
state_code	VARCHAR	Two-letter state abbreviation (FFE states only)
issuer_id	NUMBER	CMS-assigned issuer identifier
plan_id	VARCHAR	14-character HIOS plan identifier
rating_area_id	VARCHAR	Geographic rating area within state
tobacco	VARCHAR	Tobacco use indicator
age	VARCHAR	Subscriber age band ("0-14", "15", ..., "64", "65 and over", "Family Option")
individual_rate	DECIMAL	Monthly premium for individual subscriber
individual_tobacco_rate	DECIMAL	Monthly premium with tobacco surcharge
couple	DECIMAL	Monthly premium for couple tier
primary_subscriber_and_one_dependent	DECIMAL	Premium for subscriber + 1 dependent
primary_subscriber_and_two_dependents	DECIMAL	Premium for subscriber + 2 dependents
primary_subscriber_and_three_or_more_dependents	DECIMAL	Premium for subscriber + 3+ dependents
couple_and_one_dependent	DECIMAL	Premium for couple + 1 dependent
couple_and_two_dependents	DECIMAL	Premium for couple + 2 dependents
couple_and_three_or_more_dependents	DECIMAL	Premium for couple + 3+ dependents
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

PLAN_ATTRIBUTES (Plan Attributes PUF)

Plan variant-level data including marketing name, metal level, plan type, HSA eligibility, deductibles, maximum out-of-pocket amounts, and SBC scenario costs.

Key Features:

• ~22,000 plan variants per year across all FFE states

• 151 columns including cost sharing at multiple tier/network levels

• SBC standard scenarios (having a baby, managing diabetes, simple fracture)

• Metal level, plan type (HMO/PPO/POS/EPO), and CSR variant classification

Column Reference

Column	Type	Description
plan_year	NUMBER	Plan year (2014-2026)
state_code	VARCHAR	Two-letter state abbreviation
issuer_id	NUMBER	CMS-assigned issuer identifier
standard_component_id	VARCHAR	14-character standard plan component ID
plan_id	VARCHAR	Full HIOS plan ID including variant suffix
plan_marketing_name	VARCHAR	Consumer-facing plan name
plan_type	VARCHAR	Plan network type: HMO, PPO, POS, or EPO
metal_level	VARCHAR	Metal tier: Bronze, Silver, Gold, Platinum, or Catastrophic
is_new_plan	VARCHAR	Whether this plan is new for the current year
is_hsa_eligible	VARCHAR	Whether the plan qualifies for Health Savings Account
mehb_inn_tier1_individual_moop	DECIMAL	Maximum out-of-pocket for in-network individual (MEHB)
mehb_ded_inn_tier1_individual	DECIMAL	Annual deductible for in-network individual (MEHB)
sbc_having_a_baby_deductible	DECIMAL	SBC scenario: total deductible for having a baby
sbc_having_diabetes_deductible	DECIMAL	SBC scenario: total deductible for managing diabetes
csr_variation_type	VARCHAR	Cost-sharing reduction variant type
issuer_actuarial_value	DECIMAL	Actuarial value of the plan
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

BENEFITS_COST_SHARING (Benefits & Cost Sharing PUF)

Benefit-level cost sharing details: copays, coinsurance rates, quantity limits, and EHB status per plan variant and benefit category.

Key Features:

• ~1.46M rows per plan year (one row per plan variant per benefit)

• In-network Tier 1/2 and out-of-network copay and coinsurance

• Essential Health Benefit (EHB) classification

• Coverage and exclusion indicators

Column Reference

Column	Type	Description
plan_year	NUMBER	Plan year (2014-2026)
state_code	VARCHAR	Two-letter state abbreviation
issuer_id	NUMBER	CMS-assigned issuer identifier
plan_id	VARCHAR	HIOS plan ID with variant — join to PLAN_ATTRIBUTES
benefit_name	VARCHAR	EHB benefit category (e.g., "Primary Care Visit to Treat an Injury or Illness")
copay_inn_tier1	VARCHAR	In-network Tier 1 copay amount
copay_inn_tier2	VARCHAR	In-network Tier 2 copay amount
copay_out_of_net	VARCHAR	Out-of-network copay amount
coins_inn_tier1	VARCHAR	In-network Tier 1 coinsurance rate
coins_inn_tier2	VARCHAR	In-network Tier 2 coinsurance rate
coins_out_of_net	VARCHAR	Out-of-network coinsurance rate
is_ehb	VARCHAR	Essential Health Benefit flag
is_covered	VARCHAR	Whether the benefit is covered by the plan
quant_limit_on_svc	VARCHAR	Whether a quantity limit applies
limit_qty	VARCHAR	Quantity limit value
limit_unit	VARCHAR	Quantity limit unit (e.g., visits, days)
exclusions	VARCHAR	Benefit exclusion notes
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

PLAN_CROSSWALK (Plan ID Crosswalk PUF)

Year-to-year plan mappings at the county/ZIP level. Maps QHPs from the prior plan year to the current year, enabling multi-year trend analysis and plan continuity tracking.

Key Features:

• ~159K rows per year (PY2015-PY2026; PY2014 has no prior year)

• County and ZIP-level geographic granularity

• Crosswalk reason codes (renewal, discontinuation, new issuer)

• Metal level change tracking and age-off plan mappings

Column Reference

Column	Type	Description
state	VARCHAR	Two-letter state abbreviation
plan_id_prior	VARCHAR	Plan ID from the prior year
plan_id_current	VARCHAR	Mapped plan ID for the current year (NULL if discontinued)
issuer_id_prior	NUMBER	Issuer ID from prior year
issuer_id_current	NUMBER	Issuer ID in current year
metal_level_prior	VARCHAR	Metal tier in the prior year
metal_level_current	VARCHAR	Metal tier in the current year
crosswalk_level	VARCHAR	Match confidence level (plan-to-plan, issuer-level)
reason_for_crosswalk	VARCHAR	Why this mapping exists (renewal, discontinuation, etc.)
fips_code	VARCHAR	5-digit FIPS county code
zip_code	VARCHAR	ZIP code
dental_plan	VARCHAR	Whether this is a dental plan
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

SERVICE_AREAS (Service Area PUF)

Geographic service area coverage by issuer, mapping issuers to the counties and ZIP codes where they offer plans.

Key Features:

• ~8,800 rows per year

• Issuer-to-geography mapping at county and ZIP level

• Essential for geographic coverage gap analysis

Column Reference

Column	Type	Description
plan_year	NUMBER	Plan year
state_code	VARCHAR	Two-letter state abbreviation
issuer_id	NUMBER	CMS-assigned issuer identifier
service_area_id	VARCHAR	Issuer's service area code
service_area_name	VARCHAR	Service area display name
county_name	VARCHAR	County name
zip_code	VARCHAR	ZIP code
partial_county	VARCHAR	Whether partial county coverage applies
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

BUSINESS_RULES (Business Rules PUF)

Rate application rules: age determination, tobacco policy, dependent coverage limits, and market coverage scope per standard component.

Key Features:

• ~5,100 rows per year

• Rules define how premiums are calculated and applied

• Covers age determination, tobacco-free periods, dependent age limits

Column Reference

Column	Type	Description
plan_year	NUMBER	Plan year
state_code	VARCHAR	Two-letter state abbreviation
issuer_id	NUMBER	CMS-assigned issuer identifier
product_id	VARCHAR	HIOS product identifier
standard_component_id	VARCHAR	Standard component identifier
medical_dental_both	VARCHAR	Whether the rule applies to medical, dental, or both
medical_dental_indicator	VARCHAR	Medical or dental indicator
maximum_underage_dependent	VARCHAR	Maximum age for dependent coverage
dependent_maximum_age_rule	VARCHAR	Rule for maximum dependent age
age_determination_rule	VARCHAR	How age is determined for rating purposes
minimum_tobacco_free_months_rule	VARCHAR	Minimum months tobacco-free to qualify as non-tobacco user
cohabitation_rule	VARCHAR	Cohabitation rule for domestic partners
market_coverage	VARCHAR	Market coverage type (Individual, SHOP, or Both)
dental_only_plan	VARCHAR	Whether this is a dental-only plan
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

NETWORK (Network PUF)

Provider network names and directory URLs for each issuer, enabling consumers to check which network a plan uses and find providers.

Key Features:

• ~550 rows per year

• Maps issuer to network name and provider directory URL

• Essential for provider search and network adequacy analysis

Column Reference

Column	Type	Description
plan_year	NUMBER	Plan year
state_code	VARCHAR	Two-letter state abbreviation
issuer_id	NUMBER	CMS-assigned issuer identifier
network_id	VARCHAR	Network identifier
network_name	VARCHAR	Consumer-facing network name
network_url	VARCHAR	URL to the provider directory
feed_id	VARCHAR	FK to FEEDS — identifies which dataset this row belongs to
feeds_files_id	VARCHAR	FK to FEEDS_FILES — identifies which batch loaded this data
created_at	TIMESTAMP	When the source data was loaded into the warehouse
updated_at	TIMESTAMP	When the table was last rebuilt

Data Quality

Standardization

• Column normalization: CMS PUF column names are standardized to snake_case across all plan years

• Type casting: Premium amounts cast to DECIMAL; IDs maintained as VARCHAR for leading-zero preservation

• Schema alignment: PY2014-PY2022 schema differences (TIN/HPID removal, DesignType addition) are normalized to the current schema

• UTF-8 BOM handling: Some plan years include BOM markers that are stripped during extraction

• Quoting consistency: CSV quoting changed between PY2020 (quoted) and PY2022+ (unquoted) — handled transparently

Coverage Scope

Exchange PUFs cover states on the Federally Facilitated Exchange (FFE) and State-Based Exchanges on the Federal Platform (SBE-FP) — approximately 30 states for PY2026. The following 21 states run fully independent State-Based Exchanges (SBEs) and are not included: CA, CO, CT, DC, ID, KY, ME, MD, MA, MN, NV, NJ, NM, NY, OR, PA, RI, VT, VA, WA.

Data Freshness

Check when data was last updated:

SELECT source_name, data_period, row_count, created_at, is_latest
FROM DWV.FEEDS_FILES
WHERE is_latest = TRUE;

Getting Started

Platform Schema Reference

This dataset is available on both Snowflake and Databricks. Queries use schema-only references — the database is already set by the share or catalog context:

Platform	Schema	Example
Snowflake	DWV	DWV.RATES
Databricks	aca_marketplace_dwv	aca_marketplace_dwv.rates

Discover Available Data

Start with the FEEDS table to see what's available, and FEEDS_FILES to understand data freshness and load history.

Snowflake

-- Browse the data catalog
SELECT table_name, description, latest_data_period,
       latest_row_count, last_loaded_at, total_batches
FROM DWV.FEEDS
ORDER BY table_name;

Databricks

Snowflake

-- Check data freshness per source
-- is_latest = TRUE marks the current batch for each source
SELECT source_name, data_period, row_count, row_count_delta,
       created_at AS loaded_at, is_latest
FROM DWV.FEEDS_FILES
ORDER BY source_name, created_at DESC;

Databricks

Working with Data Lineage

Every data row links to FEEDS_FILES via feeds_files_id, which tells you exactly which batch loaded that data. Use this to filter to the current data version or trace any row back to its source load.

Snowflake

-- Get current rate data with batch lineage
SELECT
    r.state_code,
    r.plan_id,
    r.rating_area_id,
    r.age,
    r.individual_rate,
    r.plan_year,
    ff.data_period,
    ff.created_at AS data_loaded_at
FROM DWV.RATES r
JOIN DWV.FEEDS_FILES ff ON r.feeds_files_id = ff.id
WHERE ff.is_latest = TRUE
  AND r.plan_year = 2026
  AND r.age = '40'
  AND r.tobacco = 'No Preference'
ORDER BY r.individual_rate DESC
LIMIT 20;

Databricks

Premium Rate Benchmarking

Snowflake

-- Average premiums by metal level and state for a 40-year-old
-- Join RATES to base plan variant (%-00) via standard_component_id
SELECT
    r.state_code,
    pa.metal_level,
    COUNT(DISTINCT r.plan_id) AS plan_count,
    ROUND(AVG(r.individual_rate), 2) AS avg_premium,
    ROUND(MIN(r.individual_rate), 2) AS min_premium,
    ROUND(MAX(r.individual_rate), 2) AS max_premium
FROM DWV.RATES r
JOIN DWV.FEEDS_FILES ff_r ON r.feeds_files_id = ff_r.id AND ff_r.is_latest = TRUE
JOIN DWV.PLAN_ATTRIBUTES pa
  ON r.plan_id = pa.standard_component_id
  AND r.plan_year = pa.plan_year
  AND pa.plan_id LIKE '%-00'
JOIN DWV.FEEDS_FILES ff_pa ON pa.feeds_files_id = ff_pa.id AND ff_pa.is_latest = TRUE
WHERE r.plan_year = 2026
  AND r.age = '40'
  AND r.tobacco = 'No Preference'
  AND pa.metal_level IN ('Bronze', 'Silver', 'Gold', 'Platinum')
GROUP BY r.state_code, pa.metal_level
ORDER BY r.state_code, pa.metal_level;

Databricks

Premium Trend Analysis (Multi-Year)

Snowflake

-- Track average Silver plan premiums over time for a 40-year-old
-- Uses all 13 years of historical data (PY2014-PY2026)
SELECT
    r.plan_year,
    COUNT(DISTINCT r.state_code) AS states,
    COUNT(DISTINCT r.plan_id) AS silver_plans,
    ROUND(AVG(r.individual_rate), 2) AS avg_silver_premium,
    ROUND(MEDIAN(r.individual_rate), 2) AS median_silver_premium
FROM DWV.RATES r
JOIN DWV.PLAN_ATTRIBUTES pa
  ON r.plan_id = pa.standard_component_id
  AND r.plan_year = pa.plan_year
  AND pa.plan_id LIKE '%-00'
WHERE r.age = '40'
  AND r.tobacco = 'No Preference'
  AND pa.metal_level = 'Silver'
GROUP BY r.plan_year
ORDER BY r.plan_year;

Databricks

Market Concentration Analysis

Snowflake

-- Issuer competition by state: count issuers and plans per metal level
-- Filter to base variant (%-00) to count distinct plans, not CSR variants
SELECT
    pa.state_code,
    pa.metal_level,
    COUNT(DISTINCT pa.issuer_id) AS issuer_count,
    COUNT(DISTINCT pa.standard_component_id) AS plan_count,
    CASE
        WHEN COUNT(DISTINCT pa.issuer_id) = 1 THEN 'Monopoly'
        WHEN COUNT(DISTINCT pa.issuer_id) <= 3 THEN 'Limited'
        ELSE 'Competitive'
    END AS market_type
FROM DWV.PLAN_ATTRIBUTES pa
JOIN DWV.FEEDS_FILES ff ON pa.feeds_files_id = ff.id AND ff.is_latest = TRUE
WHERE pa.plan_year = 2026
  AND pa.metal_level IN ('Bronze', 'Silver', 'Gold', 'Platinum')
  AND pa.plan_id LIKE '%-00'
GROUP BY pa.state_code, pa.metal_level
ORDER BY issuer_count ASC, pa.state_code;

Databricks

Plan Continuity Tracking

Snowflake

-- Plan continuity analysis: renewals vs discontinuations by state
-- PLAN_CROSSWALK maps prior-year plan IDs to current-year IDs
SELECT
    cw.state,
    COUNT(*) AS total_mappings,
    COUNT(DISTINCT cw.plan_id_prior) AS prior_plans,
    COUNT(DISTINCT cw.plan_id_current) AS current_plans,
    COUNT(CASE WHEN cw.metal_level_prior <> cw.metal_level_current THEN 1 END) AS metal_level_changes
FROM DWV.PLAN_CROSSWALK cw
JOIN DWV.FEEDS_FILES ff ON cw.feeds_files_id = ff.id AND ff.is_latest = TRUE
GROUP BY cw.state
ORDER BY cw.state;

Databricks

Benefit Cost Comparison

Snowflake

-- Compare primary care visit copays across Silver plans in a state
SELECT
    pa.plan_marketing_name,
    pa.plan_type,
    pa.issuer_id,
    bcs.copay_inn_tier1 AS primary_care_copay,
    bcs.coins_inn_tier1 AS primary_care_coinsurance,
    bcs.is_ehb
FROM DWV.BENEFITS_COST_SHARING bcs
JOIN DWV.FEEDS_FILES ff_bcs ON bcs.feeds_files_id = ff_bcs.id AND ff_bcs.is_latest = TRUE
JOIN DWV.PLAN_ATTRIBUTES pa ON bcs.plan_id = pa.plan_id AND bcs.plan_year = pa.plan_year
JOIN DWV.FEEDS_FILES ff_pa ON pa.feeds_files_id = ff_pa.id AND ff_pa.is_latest = TRUE
WHERE bcs.plan_year = 2026
  AND bcs.state_code = 'TX'
  AND pa.metal_level = 'Silver'
  AND bcs.benefit_name = 'Primary Care Visit to Treat an Injury or Illness'
  AND bcs.is_covered = 'Covered'
ORDER BY bcs.copay_inn_tier1;

Databricks

Tracking Data Changes Over Time

FEEDS_FILES records every batch load with row_count_delta showing what changed. Use this to monitor source data updates and understand how the dataset evolves.

Snowflake

-- Track how rates data has evolved across batches
-- row_count_delta shows net row changes per load
SELECT
    source_name,
    data_period,
    row_count,
    row_count_delta,
    created_at AS loaded_at,
    is_latest
FROM DWV.FEEDS_FILES
WHERE source_name = 'rates'
ORDER BY created_at;

Databricks

Ready to access ACA Marketplace data?

Platform	Action
Snowflake	Coming soon to Snowflake Marketplace
Databricks	Free Trial

Questions? Contact our team for a walkthrough.

Data Sources

• CMS Health Insurance Exchange Public Use Files

• CMS State-Based Exchange PUFs

• Healthcare.gov QHP Landscape Files

• CMS Marketplace API Documentation