Understanding Regional Price Parities
Last updated: March 15, 2026
What Are Regional Price Parities?
Regional Price Parities (RPPs) are the government's official measure of how prices differ across US states and metropolitan areas. Published annually by the Bureau of Economic Analysis (BEA), they compare the price levels of goods, services, and rents in each region to the national average, which is set at 100.
When we say "the cost of living in Seattle is 125," we mean that the BEA's RPP data shows prices in the Seattle metro area are roughly 25% above the national average across a weighted basket of consumer goods and services.
How They Are Calculated
The BEA uses price data from the Bureau of Labor Statistics' Consumer Price Index (CPI) program. CPI field agents physically collect prices on thousands of specific items (a gallon of milk, a men's dress shirt, a dental filling, a month's rent) in hundreds of locations across the country. These prices are weighted by consumer spending patterns from the Consumer Expenditure Survey to create a composite index.
The RPPs break down into three components: goods (about 35% of the index), services (about 25%), and rents (about 40%). This weighting means that housing costs have the single largest influence on the overall RPP, which is why cities with expensive housing consistently rank above the national average even if their grocery and transportation costs are moderate.
What the Categories Mean
Goods RPP captures tradeable physical products: groceries, clothing, electronics, household goods. These tend to vary less across regions because goods can be shipped. Hawaii is the notable exception, where the cost of shipping everything across the Pacific pushes goods prices 25 to 40% above the mainland average.
Services RPP captures non-tradeable services: haircuts, medical care, auto repair, childcare. Services vary more than goods because they depend on local labor costs. A plumber in San Francisco charges more than a plumber in Memphis because the San Francisco plumber has to live in San Francisco.
Rents RPP captures housing costs and varies the most dramatically. The rent RPP in San Francisco is above 200 (more than double the national average) while Mississippi's is below 65 (more than a third cheaper). This single category drives most of the variation in overall cost of living between expensive and affordable locations.
What RPPs Do Not Capture
RPPs have important blind spots. Taxes are not included. State income tax, local income tax, sales tax, and property tax rates vary enormously and can add thousands of dollars per year to the true cost of living in a location. A state with no income tax but high property taxes (Texas) and a state with high income tax but low property taxes (Oregon) may have similar RPPs but very different financial realities for a homeowner.
Insurance costs are not directly measured, and they matter. Homeowner's insurance in Florida can cost $5,000 to $10,000 per year, several times the national average. Auto insurance in Michigan and Louisiana is far above average. These costs are real monthly expenses that standard indices understate.
Quality is invisible to the data. A healthcare RPP of 85 in Mississippi tells you care costs 15% less than average, but it says nothing about whether the available care meets your needs.
How to Use This Data
Use RPPs as a starting point, not a conclusion. The overall index gives you a quick sense of relative cost. The category breakdown (goods, services, rents) tells you where the differences are. Then layer on the factors RPPs miss: taxes, insurance, and the qualitative factors that affect your specific situation.
Our salary calculator uses RPP-based data enhanced with tax calculations to give you a more complete picture than the raw index alone. The category-by-category breakdown helps you see whether a city's higher cost comes from housing (which you might manage by choosing a different neighborhood) or from across-the-board price levels (which are harder to avoid).
The BEA publishes updated RPP data annually, typically in December. The most recent data available may be from one to two years prior to the current date. Cost of living conditions change over time, and the most recent data should be treated as the best available approximation rather than a real-time measurement.