FHFA House Price Index (HPI)

The FHFA House Price Index tracks changes in U.S. single-family home values using repeat transactions, with several datasets for different analytical needs.

The FHFA House Price Index (HPI) is a family of U.S. indexes that measures average changes in single-family home values by comparing repeat transactions on the same properties. The Federal Housing Finance Agency produces the indexes primarily from mortgages purchased or securitized by Fannie Mae and Freddie Mac.

The name does not identify one universal series. FHFA publishes purchase-only, all-transactions, expanded-data, distress-free, annual, and developmental indexes for different periods and geographies. When a news release refers simply to the FHFA HPI, it usually means the seasonally adjusted purchase-only index, FHFA’s flagship measure.

Key Takeaways

  • The FHFA HPI measures price change, not the median sale price or the dollar value of a particular home.
  • Its repeat-sales method compares the same property across transactions, reducing distortions caused by changes in the mix of homes sold.
  • The flagship purchase-only sample uses eligible conventional, conforming mortgages acquired by Fannie Mae or Freddie Mac; it is not a census of every U.S. home sale.
  • Other FHFA series add refinance appraisals, FHA-backed purchases, county-record data, or smaller geographies. Those variants are not interchangeable.
  • Published FHFA HPI figures are nominal. A 5% index increase is not necessarily a 5% increase after inflation.
  • Recent values and historical observations can be revised as FHFA receives additional mortgage records and identifies new repeat transactions.
  • The index can inform housing, mortgage, and credit analysis, but it cannot replace a current property appraisal or predict future home prices.

What the FHFA HPI Measures

The index estimates the average rate at which values changed for eligible single-family properties in a defined geography and period. It is a constant-quality measure in a statistical sense: matching a home with its own earlier transaction reduces the effect of one month’s sales containing larger, newer, or more expensive homes than another month’s sales.

This differs from a median-price series. A median can rise because the market sold a larger share of expensive homes, even if no individual home appreciated. A repeat-sales index is designed to isolate price movement more effectively, although it cannot perfectly observe renovations, deterioration, or other changes to a property between transactions.

The index can be reported for the United States, Census divisions, states, metropolitan areas, and smaller geographies, depending on the chosen dataset. Availability varies because a reliable repeat-sales estimate requires enough matched transactions.

How the Repeat-Sales Method Works

FHFA uses a modified geometric weighted Repeat-Sales Methodology. In practical terms, the process is:

  1. Identify an eligible mortgage transaction for a single-family property.
  2. Match it with an earlier eligible transaction on the same property.
  3. Calculate the change in the property’s observed sale price or valuation between the two periods.
  4. Combine many matched pairs in a statistical model to estimate market-level price changes.
  5. Join local estimates into larger geographic indexes using housing-stock weights where applicable.
  6. Re-estimate the history as new mortgage records and repeat transactions arrive.

The flagship index uses the mortgage origination date as the relevant transaction date, not the later date when an Enterprise acquires or securitizes the loan.

Why Weight Repeat Transactions?

A sales pair separated by many years may be less comparable than a pair separated by a shorter period. The longer interval creates more opportunity for remodeling, deferred maintenance, additions, or unobserved changes. Weighted repeat-sales methods account statistically for the fact that some pairs provide noisier evidence than others.

Weighting reduces noise; it does not prove that a house remained unchanged. A major renovation can still make part of the measured gain property-specific rather than market-wide.

Choose the Correct FHFA Index

The index variant determines which observations enter the sample. Before quoting an FHFA figure, identify the variant, frequency, geography, seasonal treatment, and data vintage.

FHFA indexMain observationsTypical analytical use
Purchase-Only HPIEligible sales prices tied to conventional, conforming mortgages purchased or securitized by Fannie Mae or Freddie MacStandard national, regional, state, and major-metro price-trend analysis
All-Transactions HPIPurchase-only observations plus appraisal values from Enterprise refinance mortgagesLonger or more geographically detailed analysis where added valuation observations help sample size
Expanded-Data HPIEnterprise purchase data plus FHA-backed purchases and licensed county-recorder sales below the annual loan-limit ceilingBroader view of the conforming single-family purchase market; FHFA also uses this series when adjusting conforming loan limits
Distress-Free HPIA purchase-only variant that removes short sales and sales of bank-owned propertiesStudying price movement without those distressed-sale observations
Annual HPIAll-transactions data grouped annuallySmaller areas such as counties, ZIP codes, and census tracts where quarterly samples may be insufficient
Manufactured Housing HPIEligible Enterprise mortgages on real-property manufactured homes; personal-property loans are excludedDevelopmental analysis of manufactured-home price change

The variants often show similar long-run direction, but short-run growth rates can differ. That is expected when samples, observations, frequencies, or seasonal adjustments differ.

Flagship Purchase-Only Coverage

The standard purchase-only sample is broad, but it has clear boundaries. It is based on mortgages that are:

  • associated with single-family properties
  • conventional rather than insured or guaranteed by a federal program
  • conforming to Enterprise underwriting and loan-limit requirements
  • purchased or securitized by Fannie Mae or Freddie Mac

The flagship sample therefore does not directly represent cash purchases, jumbo loans, FHA- or VA-backed mortgages, or loans that never enter the Enterprises’ data. FHFA also restricts condominiums, cooperatives, multi-unit properties, and planned unit developments from that flagship sample and applies filters for incomplete, implausible, or potentially erroneous records.

These boundaries do not make the index unusable. They define the population to which the estimate most directly applies. The expanded-data index exists partly to broaden purchase coverage, but even that series should not be described as a complete record of every U.S. home sale.

How to Read an HPI Value

An HPI level is a relative number tied to a base period. It is not a price in dollars. Normalization varies by dataset: for example, FHFA normalizes purchase-only indexes to 100 in the first quarter of 1991, while state and division all-transactions series use a different base.

The base affects the displayed level but not the percentage change between two observations. Calculate the change as:

1Percentage change = (Later index / Earlier index - 1) x 100

If an index rises from 280 to 294:

1(294 / 280 - 1) x 100 = 5.0%

The selected market index increased 5% over the stated period. It does not mean every home appreciated 5%, the median home price rose 5%, or a future period will produce the same result.

A Property-Value Illustration

Suppose a mortgage analyst starts with a historical property value of $350,000. If the relevant local HPI moves from 280 to 294, a simple index-based update would be:

1Estimated updated value = $350,000 x (294 / 280) = $367,500

This is a portfolio estimate, not an appraisal. The actual property may have changed differently because of its neighborhood, condition, renovations, property type, or transaction circumstances. A lender or appraiser must use the evidence and valuation process required for the specific decision.

Period, Seasonality, and Inflation

An HPI growth rate is incomplete unless the period and adjustment basis are stated.

LabelWhat it comparesMain caution
Month-over-monthOne month with the immediately preceding monthCan be volatile and sensitive to recurring seasonal patterns
Quarter-over-quarterOne quarter with the preceding quarterSmoother than monthly data but still a short-run measure
Year-over-yearA month or quarter with the same period one year earlierEasier seasonal comparison, but slower to show turning points
Cumulative changeTwo selected index levels, often across several yearsDepends on the exact start and end dates

FHFA publishes many indexes in both seasonally adjusted (SA) and not seasonally adjusted (NSA) form. Seasonal adjustment estimates and removes recurring calendar patterns so adjacent periods are more comparable. It can also be revised as new data change the estimated seasonal pattern.

Do not divide an SA value by an NSA value. Use observations from the same series, and label the result clearly.

Published FHFA HPI values are nominal, meaning they are not adjusted for inflation. If the HPI rises 4% while the relevant general price measure rises 3%, the implied inflation-adjusted change is much smaller than 4%. The exact real-growth calculation depends on the selected inflation measure and matching dates.

Publication Lag and Revisions

FHFA receives recent origination data after the loans move through Enterprise funding and data processing. The agency states that this creates roughly a two-month delay for new observations.

Historical values can change for three related reasons:

  • a later sale or refinance creates a new repeat pair and adds information about the earlier period
  • Fannie Mae or Freddie Mac acquires a seasoned loan that contains older transaction information
  • delayed records arrive after an initial index estimate is published

Revisions are generally most relevant near the end of the series, but adding a repeat transaction can affect measured appreciation since the property’s previous observation. For reproducible analysis, retain the release date or data vintage rather than assuming today’s historical file exactly matches what was available at an earlier decision date.

FHFA HPI vs. Case-Shiller

The FHFA HPI and Case-Shiller Home Price Index both use repeat transactions, but they are not substitutes in every analysis.

FeatureFHFA HPICase-Shiller
Primary data sourceMortgage records associated mainly with Fannie Mae and Freddie Mac; some variants add other sourcesPublic deed, recorder, and assessor sale records
Common headline seriesSeasonally adjusted purchase-only HPIU.S. National, 10-City Composite, and 20-City Composite indexes
Financing boundaryFlagship series centers on conventional, conforming Enterprise mortgagesEligible transactions are not limited to Enterprise-financed purchases in the same way
Additional variantsIncludes all-transactions, expanded-data, distress-free, annual, and developmental seriesIncludes national, composite, metro, and price-tier series
GeographyNational through state and metro levels, with annual or developmental products for some smaller areasNational and selected major-metro products
Aggregate weightingLarger areas are built from component geography growth rates using one-unit detached housing-stock sharesNational and composite indexes use housing-market value weights
Revision sourceNew Enterprise deliveries, seasoned loans, and newly identified repeatsLater-arriving public transaction records and model updates

Differences between the two indexes do not necessarily indicate an error. A divergence may reflect sample composition, geography, weighting, index variant, seasonal treatment, or revision timing. A careful comparison uses the same period and clearly labels both series.

Why the FHFA HPI Matters in Finance

Housing values affect mortgage collateral, household balance sheets, credit losses, prepayments, and financial-system exposure. Analysts may use an FHFA series to support:

  • Mortgage portfolio monitoring: estimating broad changes in collateral values and possible Loan-to-Value Ratio pressure
  • Credit-risk analysis: testing how regional price declines could affect default frequency and loss severity
  • Prepayment analysis: adding housing-price and equity conditions to models of refinancing or home-sale behavior
  • Securities analysis: updating assumptions for mortgage pools while recognizing that an index-based value is an estimate
  • Economic analysis: comparing housing-price momentum across states, divisions, and metropolitan areas
  • Policy analysis: observing the expanded-data index used in the process for adjusting conforming loan limits
  • Household-finance analysis: studying broad changes in Home Equity and housing affordability

The HPI is usually more informative when paired with mortgage rates, household income, inventory, sales volume, rents, delinquency, construction, and local labor-market data. Price appreciation alone does not establish affordability, liquidity, credit quality, or investment value.

What the Index Does Not Measure

The FHFA HPI is not:

  • the average or median dollar price of homes sold
  • a real-time measure of today’s housing market
  • a complete sample of every property type, financing channel, or cash transaction
  • a rent, construction-cost, or home-maintenance index
  • an appraisal or automated valuation of one property
  • a measure of a homeowner’s total return after mortgage interest, taxes, insurance, maintenance, and transaction costs
  • automatically adjusted for inflation
  • a forecast of future home prices, defaults, or mortgage returns

Common Mistakes

  • Quoting “the FHFA HPI” without naming the variant: Purchase-only and all-transactions indexes use different observations.
  • Reading an index point as dollars: An index level of 300 does not mean a $300,000 home price.
  • Treating the flagship sample as all sales: Cash, jumbo, government-backed, and other non-Enterprise transactions are not represented in the same way.
  • Using a national index for local collateral: National appreciation can hide substantial state, metro, neighborhood, and property-level differences.
  • Mixing frequencies or adjustments: Monthly SA, quarterly NSA, and annual data answer different questions.
  • Ignoring revisions: A historical download may include information that was unavailable when the original decision was made.
  • Calling nominal growth a real gain: Inflation can materially change the economic interpretation.
  • Using an index update as an appraisal: Broad appreciation does not establish a property’s market value or condition.
  • Inferring causation: The HPI shows measured price movement; it does not by itself explain why prices changed.

Analyst Checklist

Before using an FHFA HPI figure, verify:

  1. the purchase-only, all-transactions, expanded-data, distress-free, annual, or developmental variant
  2. the national, division, state, metro, county, ZIP-code, or census-tract geography
  3. the monthly, quarterly, or annual frequency
  4. whether the series is seasonally adjusted or not seasonally adjusted
  5. the observation period and the release date
  6. whether the calculation is month-over-month, quarter-over-quarter, year-over-year, or cumulative
  7. whether the analysis requires an inflation adjustment
  8. whether current historical data contain revisions unavailable at the original decision date
  9. whether the sample matches the properties, financing channels, and geography being analyzed
  10. whether property-level evidence or another housing indicator is also required

Authoritative Sources

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FAQs

Is the FHFA HPI the average U.S. home price?

No. It is an index of average price change for eligible repeat transactions. Its level is relative to a base period and is not a mean or median price in dollars.

Does the FHFA HPI include every home sale?

No. The flagship purchase-only index is based on eligible conventional, conforming mortgages purchased or securitized by Fannie Mae or Freddie Mac. Other FHFA variants broaden or change the sample, but no single series should be assumed to represent every property and financing channel.

How often is the FHFA HPI released?

FHFA publishes reports monthly and quarterly. Different datasets are available at monthly, quarterly, or annual frequencies, and not every index variant is available for every geography.

Is the FHFA HPI adjusted for inflation?

No. FHFA describes the HPI as nominal. Analysts evaluating purchasing power must apply a suitable inflation measure for matching periods and state that adjustment separately.

Can the FHFA HPI estimate a specific home's value?

It can support a rough index-based update when the starting value, geography, dates, and series are appropriate. It cannot account fully for the home’s exact location, condition, renovations, features, or current comparable sales, so it is not a substitute for a property-specific valuation.

Can the FHFA HPI predict future home prices?

No. The index reports estimated historical price movement. It may be an input to a forecast, but future prices also depend on interest rates, credit conditions, income, supply, migration, local employment, and other factors.

This article is educational and does not provide an appraisal, lending decision, investment recommendation, or individualized financial advice.

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