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Ever since the housing bubble inflated and deflated at the beginning of the 21st century, housing price data has been important.

As it should be. That data can be used to tell you quite a lot about the relative health of the U.S. homebuilding and real estate industries. It can also tell you quite a lot about how affordable homes are for a typical American household.

But not all housing price data is that useful. Some housing price data might even be considered to be less than useful.

When economist Mark Zandi was asked in 2010 about what he thought were the most overrated economic indicators, here's how he answered:

I cherish all economic indicators, although it is fair to say some indicators are more useful than others. It is also important to point out the indicators I value most vary according to where we are in the business cycle and what is driving the cycle. Having said this, the indicators that I generally pay less attention to include: FHFA house prices; weekly chain store sales; Challenger layoff announcements; and the producer price index. These indicators are either often misleading or the signal to noise ratio is very high and thus hard to interpret.

If the criteria of the signal-to-noise ratio is the issue, with noise exceeding signal being a bad thing, it's not surprising the FHFA House Price Index would be the first official metric that might come to Zandi's mind when asked.

This quarterly index aims to measure the average changes in housing prices based on the sales or refinancing of single family homes whose mortgages were either purchased or securitized by Fannie Mae or Freddie Mac. The Federal Home Financing Agency's description of what it does to update its data series goes a long way toward explaining why its House Price Index (HPI) is particularly noisy:

Each month, Fannie Mae and Freddie Mac provide FHFA with information on their most recent mortgage transactions. These data are combined with the data from previous periods to establish price differentials on properties where more than one mortgage transaction has occurred. The data are merged, creating an updated historical database that is then used to estimate the HPI.

The FHFA continues to explain how and why it revises its data series each quarter:

Historical estimates of the FHFA HPI revise for three primary reasons:

(a) The FHFA HPI is based on repeat transactions. That is, the estimates of appreciation are based on repeated valuations of the same property over time. Therefore, each time a property "repeats" in the form of a sale or refinance, average appreciation since the prior sale/refinance period is influenced.

(b) Fannie Mae and Freddie Mac (the Enterprises) purchase seasoned loans, providing new information about prior quarters.

(c) Due to a 30- to 45-day lag from loan origination to Enterprise funding, FHFA receives data on new fundings for one additional month following the last month of the quarter. These fundings contain many loans originating in that most recent quarter, and especially the last month of the quarter. This will reduce with subsequent revisions, however data on loans purchased with a longer lag, including seasoned loans, will continue to generate revisions, especially for the most recent quarters.

The result is the FHFA's House Price Index can bounce around a lot before settling, months after its data would no longer provide anything like a near real-time picture of what's happening with housing prices.

Zandi highlighted another weakness that contributes to the FHFA's weakness as an economic data series in a 29 November 2022 tweet:

Did you notice that the Case Shiller house price index fell 1% in Sept, while the FHFA index increased 0.1%? CS measures the entire market, while FHFA measures about 1/2 the market, mostly for mid and lower-priced homes. This suggests prices for higher-priced homes were crushed.

— Mark Zandi (@Markzandi) November 29, 2022

In a reply to that tweet, Zandi noted the FHFA doesn't adequately cover data from California, which means the HPI is missing changes happening in high-priced markets like California. Others responding to Zandi's tweet noted the FHFA data is also subject to larger residual seasonality, which adds to its fickleness.

From our perspective, the FHFA's House Price Index became all but obsolete as a national index with the introduction of the S&P CoreLogic Case-Shiller U.S. National Home Price Index in 2006. But it seems the FHFA House Price Index may be occasionally useful in a way other than it is intended to be. We find it interesting that economists like Zandi are using its known limitations to extract information about the state of the housing market that might otherwise remain hidden.

That puts it into a class similar to the consumer confidence data that we likewise discovered might be occasionally useful. It just takes the information from a better house price index combined with an uncommon situation to make it so.

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