What is the most unaffordable state to live in?

How affordability is measured: the common components analysts use

Price-level indexes and why they matter

State affordability rankings are not a single number but a composite of several measurable components that together show differences in purchasing power across places. One widely used measure is the BEA Regional Price Parities table, which captures broad consumer price differences across states and is often used as the price-level anchor in comparative analyses, and this article follows that convention BEA Regional Price Parities dataset.

When analysts combine price-level indexes with housing, income, taxes and wages they aim to reflect how far a given income goes in each state. Below we explain each component and why it matters for a transparent state comparison.

Housing metrics, income, taxes, and wages

Housing measures typically include state-level home-value indices and rent indices such as Zillow ZHVI and rental indexes, plus median existing-home prices from national trade data; these housing metrics are crucial because they often constitute the largest share of household budgets and therefore the largest single contributor to state-to-state affordability gaps Zillow Research datasets.

Analysts then add median household income, state and local tax burden, and state wage levels to moderate or amplify those price differences; each component is distinct and is described in detail in the sections that follow.

Why housing dominates unaffordability in high-cost states

Housing cost levels, both for owners and renters, are the most variable component across states and drive the biggest differences in typical household expenditures. Large home-price gaps or high rent indices can push a state to the top of unaffordability rankings even when other costs are only moderately higher.

Evidence from state housing datasets shows that very high home values and rents are concentrated in a small set of states, and those housing gaps explain most of the difference in overall cost measures between the most expensive and median states NAR state housing statistics.

To make this concrete, analysts often compute a home-price-to-median-income ratio for each state and compare that ratio across places; where that ratio is much larger than the national median, housing affordability pressures are likely to dominate other cost differences.

Income and wages: how earnings change the picture

Median household income varies substantially across states and can shift an affordability ranking when compared to local housing prices; readers should check Census median household income tables to see these state differences directly U.S. Census income and poverty release.

State wage levels and occupational pay structure also matter, because higher average wages in a state can partly offset higher prices; the BLS state occupational wages provide a public benchmark analysts use to compare pay levels across states BLS state wage summaries.

Even when wages are higher, however, wage growth often lags the fastest-moving housing-cost increases in the most expensive states, which is why income alone rarely neutralizes housing-driven unaffordability.

State and local taxes and other non-housing costs

State and local tax burden is a measurable part of the affordability equation; Tax Foundation comparisons show differences in overall tax load that add to household cost but are typically smaller than the largest housing-driven gaps Tax Foundation purchasing-power data.

Other recurring non-housing costs that analysts sometimes include are transportation, utilities, child care, and health insurance premiums. These items vary by household and place, and they can be important locally, but they usually do not eclipse large differences in housing prices when ranking whole states.

A reproducible combined methodology you can follow

To reproduce a state affordability ranking you can use a small set of public datasets: BEA RPPs, Zillow ZHVI and rent indices, NAR state median existing-home prices, Census median household income tables, Tax Foundation state and local tax-burden measures, and BLS state wage summaries BEA Regional Price Parities dataset. Collecting those same-year tables gives the most defensible comparison. For recent commentary and links to source files see the site news index news.

Step-by-step, a simple spreadsheet approach is: normalize each metric so higher means less affordable, choose weights that reflect your priorities, compute a weighted sum for each state, and rank the combined scores. A common default weighting is 40 percent BEA RPP (price level), 40 percent housing price-to-median-income ratio, 10 percent median income inverse, and 10 percent tax burden, with an optional wage adjustment; try sensitivity checks by varying the housing weight up and down to see how rankings move.

When you normalize metrics, use consistent direction: for example, convert median household income and wages to a affordability-reducing scale so a higher value lowers the score, or use reciprocal transforms where appropriate. Document the exact table names and years in your sheet so others can reproduce the steps.

Which state comes out as most unaffordable in common 2023-2024 datasets

Using a combined approach anchored on BEA RPP and housing price-to-income ratios commonly identifies California as the single most unaffordable state in the 2023 and 2024 public datasets, with Hawaii often close behind depending on weight choices BEA Regional Price Parities dataset.

This common-result pattern stems from the BEA price-level finding for California and the concentration of very high home values and rents documented by housing datasets; depending on whether the analysis gives heavier weight to overall price levels or to housing price-to-income ratios, the top-ranked unaffordable state can vary between California and Hawaii Zillow Research datasets.

Readers who want a simple check can run the two sensitivity runs described above: an RPP-focused run and a housing-focused run, then compare how often each state appears at the top across weighting choices. For background on RPP time series for California see the FRED series CARPPALL.

How different weighting choices and assumptions change the answer

A weighting scheme that emphasizes BEA RPP will favor states with broadly high consumer price levels, which often puts California at the top because the RPP captures many non-housing prices along with housing-related rents BEA Regional Price Parities dataset. Alternative RPP resources and supplementary tables are available from sources such as ProximityOne.

By contrast, a housing-price-to-income-focused scheme increases the importance of raw housing cost gaps and can move Hawaii or selected high-rent states to the top when their home-price-to-income ratios exceed California’s on the chosen year; analysts should run both scenarios and report both rankings rather than relying on a single number NAR state housing statistics.

Simple sensitivity checks include changing the housing weight by plus or minus 20 percentage points, replacing median household income with a wage index from BLS state summaries, and testing whether metro-level versus whole-state tables change the ranking for borderline cases.

Short state profiles: California, Hawaii, and close contenders

California shows the highest overall price level in the BEA RPP tables for recent years, and California’s very high home values and rents are the proximate reason it commonly ranks as most unaffordable in combined metrics BEA Regional Price Parities dataset. California RPPs are also available in public time-series repositories such as FRED CARPPALL.

Hawaii’s housing market exhibits some of the highest rent and home-price readings in the nation, which is why housing-focused measures often place it near the top of unaffordability lists; when analyses privilege housing price-to-income ratios, Hawaii can appear as the highest-cost state Zillow Research datasets.

Other states sometimes move up in particular runs: high-home-price states with relatively high median incomes or lower tax burdens will rank lower than their housing costs alone suggest, while states with more modest incomes but rising home prices can climb the list; Census income tables and Tax Foundation measures are useful for these adjustments U.S. Census income and poverty release.

Typical mistakes and how to avoid them when reading rankings

A common mistake is treating a high state price level as universally unaffordable for every resident; price-level measures describe average costs and purchasing power differences but do not capture variation within income groups or metros.

Another pitfall is relying on a single metric, such as home-price rankings alone, without checking income and tax context; a headline that lists a state as most expensive to buy a home is not the same as declaring it the most unaffordable for typical households NAR state housing statistics.

Practical steps for readers: assessing affordability where you live

Start with the local BEA RPP value to understand how general consumer prices compare to the national baseline, then check local Zillow rent or ZHVI trends to see whether housing is moving faster than broad prices BEA Regional Price Parities dataset.

A simple homeowner affordability check is to divide local median home price by the state median household income and compare that ratio to the national median; for renters, compare typical local rent to monthly median household income to see relative pressure. Also examine your occupation’s typical wage in BLS tables to see whether local pay offsets higher living costs BLS state wage summaries.

When in doubt, run the reproducible methodology in the appendix and note how results change if you use metro-level tables rather than whole-state averages, or if you substitute a wage index for median household income.

Open questions and short-term uncertainties into 2026

Short-term housing market shifts after 2024 could move some states up or down a few ranks, so updated Zillow and NAR tables matter for anyone re-running these comparisons in 2025 or 2026 Zillow Research datasets.

Similarly, post-2024 wage data releases or state tax changes can alter affordability calculations in ways that are small relative to large housing gaps but material for states near the top of the list; analysts should re-run their weights and document changes when new public tables are released U.S. Census income and poverty release.

Appendix for replicating the ranking: data links and checklist

Datasets to download and the exact table names: BEA Regional Price Parities by state, Zillow Research state-level ZHVI and rent index exports, NAR state-level median existing-home price tables, Census median household income tables, Tax Foundation state and local tax-burden report, and BLS state occupational wage summaries BEA Regional Price Parities dataset.

Minimal spreadsheet steps: normalize each metric to a common scale, choose and document weights, compute a weighted score for each state, and rank the scores. Keep a separate sensitivity sheet that re-runs the ranking under at least two contrasting weighting schemes.

The public data most analysts use show that California records the highest overall state price level in BEA RPPs and that housing metrics place California and Hawaii at the top of housing-driven unaffordability measures; these patterns explain why combined methods commonly identify California as the most unaffordable state in 2023-2024 source data BEA Regional Price Parities dataset.

A final caveat is that reasonable, transparent weighting choices can change which state appears at the very top, so readers should treat any single-label headline as a summary of an assumption set and consult the reproduced tables to confirm which choices produced the ranking.

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