How BEA Regional Price Parities Are Calculated

What Regional Price Parities measure

RPP are indexes that compare the price level of goods and services in a region to the national average. The national RPP is always 100. A state RPP of 115 means the overall price level in that state is 15% higher than the national average. RPP 85 means 15% lower.

BEA publishes RPP for states, metropolitan areas, and metropolitan divisions. State RPP covers every city in the state; metro RPP is more granular but only covers designated metro areas. We use state RPP on this site because it covers all 1,000 cities in our database, including smaller towns that metro RPP excludes.

RPP is an annual publication, typically released about 10 months after the reference year. The latest vintage in our database is BEA RPP 2024. The lag means RPP reflects the price level over the reference year, not real-time prices.

What's in the RPP basket

RPP covers the full range of goods and services that households purchase, organized into expenditure categories. The basket is weighted by what typical households actually spend money on, using Consumer Expenditure Survey data:

• Housing (rent, owners' equivalent rent, utilities) — the heaviest weight, typically 30–40% of the basket. This is why high-rent states like California and Hawaii have high RPP.
• Food (groceries and dining out) — about 12–15% of the basket.
• Transportation (vehicles, fuel, public transit) — about 10–12%.
• Education and health care — about 15–20% combined.
• Recreation, clothing, personal care, and other goods and services — about 15–20%.

How BEA collects the price data

BEA doesn't survey prices directly. It draws on multiple existing data sources:

• Census Bureau ACS — for rent data, the largest housing component. The ACS 5-year estimates provide median rents for over 29,000 places.
• Bureau of Labor Statistics (BLS) — Consumer Price Index (CPI) data for goods and services, and the American Time Use Survey for weights.
• Private data providers — for items not covered by government surveys, BEA uses commercial price data (e.g., for certain retail goods).
• BEA's own Personal Consumption Expenditures (PCE) data — for national-level expenditure weights that determine how much each category matters.

State RPP vs. metro RPP: the tradeoff

BEA publishes both state and metro RPP. State RPP gives one number per state — every city in Texas gets the same Texas RPP. Metro RPP gives one per metro area — Dallas and Houston get different numbers, but a small town in rural Texas gets nothing (it's not in a tracked metro).

We use state RPP on this site for a simple reason: coverage. Our database has 1,000 cities, many in small metros or outside tracked metros entirely. Metro RPP would leave hundreds of cities with no price index. State RPP is coarser — it can't distinguish Manhattan from upstate New York — but it ensures every city gets a comparable index.

The tradeoff is precision. State RPP may underestimate the cost of living in a state's expensive cities (Manhattan is far pricier than the NY state average) and overestimate it in cheaper areas. Metro RPP is more accurate for large cities but creates gaps for smaller ones. For our purposes — comparing cities across the country with a consistent methodology — state RPP is the right choice.

Why RPP differs from cost-of-living calculators

Many cost-of-living calculators use proprietary or crowdsourced data. Numbeo, for example, uses user-submitted prices. These can be more current than RPP but less transparent — you can't trace a Numbeo number to a published government source.

RPP has a clear methodology published by BEA. You can read exactly what's in the basket, how it's weighted, and how the index is constructed. This is why we cite RPP as our source — it's traceable, and traceability is our core value proposition. Every number on the site links to a published government source with a known asOf date.

The downside: RPP is slow. If a city's rent spiked in the last six months, RPP won't show it yet. Our city pages display the asOf date so you know exactly how old the data is. Use our numbers as a baseline, and verify with current local data if you're making a real decision.