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Fannie Mae Home Price Index (FNM-HPI)

Summary

Fannie Mae’s home price index model produces a national 

historical home price index. Fannie Mae’s home price index 

(FNM-HPI) measures the average historical price changes for 

single-family residential properties in the United States 

housing market for each quarter from 1975 Q1 through the 

most recent quarter. The index is derived from arms-length 

property transactions in the single-family housing market.

Our data sources include transactions with and without 

conforming mortgages, ensuring our index captures price 

changes throughout the broad single-family housing 

market. Condominium data are excluded from our FNMHPI. Our separate seasonally adjusted and non-seasonally 

adjusted FNM-HPI series reveal average price appreciation 

(or depreciation) over time, with or without consideration of 

seasonal effects1, respectively.

The FNM-HPI is estimated with arm’s length home purchase 

transaction data from various data sources, supplemented 

by appraisal data of refinance loans when such data are 

needed due to thin purchase data2. The national HPI is 

produced by aggregating county-level HPIs to create a 

national index that is representative of the entire country 3. 

The weights for each county are based on the number of 

single-family housing stock units in each county, so the U.S. 

HPI represents the general national housing market trend4. 

This approach is consistent with the way many other 

economic variables are computed, that is, not limited to the 

business exposure of a particular company or group of 

companies. The FNM-HPI serves as an indicator of general 

market home price trends and provides an analytic tool for 

economic analysis.

This document describes important modeling concepts of 

the FNM-HPI. The next section includes index methodology. 

The following two sections provide details on data sources 

used in FNM-HPI and the approach to geographic 

aggregation. The final section discusses FNM-HPI 

availability.

1 The seasonal effects are computed based on X-11 methodology and the 

quarterly data series of non-seasonally adjusted U.S. national HPI.

2 The threshold for determination of sufficient data to estimate local level 

HPI based on purchase transactions alone or a combination of purchase 

transaction and appraisal data is based on a proprietary formula. 

3

If a county does not have sufficient data for estimation of its own HPI, then 

a state level HPI is used to infill that county’s HPI. 

Index Methodology 

We employ a variation of a Repeat Transaction Index (RTI) 

model to estimate the FNM-HPI. The original Repeat 

Transactions Index (RTI) model was proposed by Bailey et al 

(1963)5. Since then, researchers in industry and academia 

have proposed various enhancements to the original RTI 

model. Today, variations of the RTI modeling approach are 

still used widely in the housing market industry for 

producing home price indices.

The RTI model approach is an industry standard because it 

controls for the varying characteristics of homes in the 

housing market and has minimum data requirements. 

Because of the heterogeneity of homes in the housing 

market, a useful measure of home price trend should 

control for the varying underlying mix of home 

characteristics reflected in the individual home transaction 

prices. Without such control, the derived home price trend 

will be of limited usefulness for economic analysis. The RTI 

model controls for home characteristics by estimating price 

trends based on pairs of transactions of the same home over 

time. Another desirable feature of the RTI model is its 

minimal data requirements. The model only needs a unique 

home identifier (such as address), geographic variables for 

aggregation purposes, the dates of home transaction, and 

the transaction prices.

The original RTI model advocates using purchase 

transaction data to derive the HPI for the market. This 

approach works well for large housing markets such as 

major metropolitan statistical areas (MSAs), states, and the 

U.S. national. In practice, however, because of concerns 

about thin purchase transaction data at the local market 

level, HPI practitioners have frequently used the appraised 

values of refinance transactions to supplement thin 

purchase data.

Subsequent research has shown that the appraised values 

used in refinance loans are often biased when compared to 

4 Annual estimates of housing stock from the American Community Survey 

and Moody’s Analytics are used for HPI aggregation.

5 Bailey, M., Muth, R., and Nourse, H. (1963), “A Regression Method for Real 

Estate Price Index Construction,” Journal of American Statistical 

Association, 58, 933-942.
© 2022 Fannie Mae 4.14.22 Page 2 of 2

the market values indicated by purchase transactions6. 

Indices that use refinance transaction data thus can 

potentially exhibit a market trend that is very different from 

that of the purchase market if any refinance appraisal bias 

effect is not controlled for. Sometimes the differences 

between HPIs with and without refinance data can be 

significant, especially during refinance boom periods.

To avoid any issue with biased refi appraised data values 

while capturing the benefits of a larger transaction dataset, 

the FNM-HPI model uses both purchase and refinance 

transactions data, with a valuation correction performed on 

the refinance data to adjust the potential systematic 

valuation bias in appraisal values in refinance data. It makes 

use of all available transactions data including purchase 

transactions and refinance transactions data, identifies and 

corrects the valuation bias in refinance transactions, and 

ultimately produces a HPI that has lower levels of 

uncertainty than a HPI estimated by purchase-only 

transaction data.

Data Sources

Fannie Mae’s home price index is calculated using singlefamily property transaction data on loans purchased by 

Fannie Mae and Freddie Mac as well as additional home 

sales transactions based on third-party data providers. Due 

to the inclusion of third-party home sales data, our HPI 

incorporates transactions with and without conventional, 

conforming loans, thus capturing price changes broadly 

across the single-family housing market. Condominium data 

are excluded from our FNM-HPI. 

We also filter our home transaction data to include armslength transactions only. Non-standard transactions such as 

estate sales, sales between family members, and 

foreclosure sales are excluded. We exclude these 

transactions because they either do not represent fair 

market transactions values or they are frequently associated 

with properties that have undergone significant home 

condition change (and thus may not have the constant 

quality across transactions necessary for the reliability of 

the RTI approach). Our outlier removal process also 

identifies and removes other transactions of homes that 

have experienced significant changes in condition or 

characteristics, such as teardowns and rebuilds.

Geographic Aggregation

6 Chinloy, P., Cho, M. & Megbolugbe, I.F. (1997), “Appraisals, Transaction 

Incentives, and Smoothing", The Journal of Real Estate Finance and 

Economics 14, 89–111.

Our county-level HPI is the basic building block of the U.S. 

national HPI. The county-level HPI is estimated based on 

purchase-only transaction data if sufficient transaction 

volume exists, otherwise on a combination of purchase and 

refi transaction data to mitigate the issue of thin data, 

where the values of the refi transactions are adjusted to 

minimize the impact of valuation bias (as described above). 

The national HPI is aggregated from county-level HPIs using 

single-family housing stock weights.

County was chosen as the basic unit of aggregation for 

several reasons. For example, it is the most local level for 

which housing stock data are readily available. Additionally, 

we use the county-level housing stock weighted aggregation 

approach to mitigate the potential problem of overrepresentation of localities with frequent home transactions 

in the construction of the national HPI. This approach aligns 

with our intention for the FNM-HPI to represent the average 

home price trend of the general housing market.

Availability

The FNM-HPI at the U.S. national level is publicly available 

as a quarterly series starting from Q1 1975 and extending to 

the most recent quarter. It is available in both nonseasonally adjusted and seasonally adjusted series. The 

FNM-HPI will be published in the first month of each quarter

beginning with Q2 2022, with the final period of each FNMHPI series covering the prior quarter. Although the FNM-HPI 

is published in the first month of the quarter, the FNM-HPI is 

a quarterly series with all transactions in each quarter 

treated the same.

Fannie Mae’s home price estimates are based on 

preliminary data available as of the date of index estimation 

and are subject to change as additional data become 

available in the future. The FNM-HPI is updated on a 

quarterly basis as new input data arrive and the historical 

FNM-HPI is recalculated using the incrementally expanded 

dataset. Due to the repeat transactions methodology used 

to derive the FNM-HPI, the quarterly re-estimation process

may cause revision to the FNM-HPI levels in periods as early

as 1975 Q1. Revisions to the FNM-HPI levels tend to be 

highest in the most recent two quarters due to relatively 

thin data in recent periods. Revision levels are significantly 

reduced in periods more than two quarters ago. Revision in 

periods far in the past are typically very small.