Board of Governors of the Federal Reserve System

September 28, 2018

A New Measure of Housing Wealth in the Financial Accounts of the United States

Hannah Hall, Eric Nielsen, and Kamila Sommer¹

Summary

This note introduces a new method for measuring the aggregate value of own-use residential real estate in the United States from 2001 to present. Beginning with the 2018:Q2 publication, this alternative series will be included in the Enhanced Financial Accounts as a supplement to estimates in the Federal Reserve's Financial Accounts of the United States (FAUS). The method and estimates discussed in this note represent an alternative to the current estimates of own-use residential real estate presented in Table B.101 (line 4), the Balance Sheet of Households and Nonprofit Organizations. This note describes the new method, which is based on property value estimates from the real-estate analytics company Zillow. We evaluate the method's strengths and weaknesses and argue that it has the potential to overcome some of the shortcomings of the current method used in the Financial Accounts. We compare the estimates from the new method to the series currently published in the Accounts, and show that the new measure results in a somewhat different path of aggregate housing wealth over the Great Recession and recovery. The method and findings in this note are based on an analysis in Gallin, Molloy, Nielsen, Smith, and Sommer (2018) (PDF).

Background

Housing wealth is a major component of total household wealth, and as such it has important macroeconomic implications. The Financial Accounts estimates aggregate own-use housing wealth at about $25 trillion as of the first quarter of 2018, almost exactly a quarter of total household net worth.² Despite its importance, the aggregate value of the housing stock is quite difficult to measure accurately because homes sell infrequently and differ widely in characteristics such as size, location, and condition. Understanding how values are changing for all homes based on the few homes that have sold recently is inherently challenging.

The measurement of housing wealth in the Financial Accounts and academic research has traditionally relied on two sources: homeowners' self-reported values in surveys and extrapolations based on repeat-sales house price indexes. Both of these approaches have strengths and weaknesses. Household surveys are typically nationally representative and can capture quality changes known by the homeowner; however, research has found that homeowners' self-reported values are often biased upward. While the bias is relatively modest during normal times, it can widen considerably during market turning points, as owner-reports seem to lag behind market indicators.³ Repeat-sales indexes, which are based on price changes of the same houses across multiple transactions, are not subject to these behavioral biases. However, since the repeat-sales methodology attempts to hold quality constant between sales, such price indexes generally do not reflect the value of home improvements and the higher quality of new houses. Moreover, price indexes may be a misleading measure of changes in the value of the housing stock if the homes that sell in a given period are not representative of the housing stock as a whole. For instance, if the homes that sold during the Great Recession were disproportionately experiencing above average price declines, perhaps because of their geographic location or the financial distress of their owners, then a repeated sales index would overstate the decline in the value of the overall housing stock.⁴

The current method for estimating housing wealth in the Financial Accounts makes use of both owner reports from surveys and price changes from repeat-sales price indexes. From the mid-1980s through 2005, the FAUS series was benchmarked every two years to homeowners' reported values in the American Housing Survey (AHS), a biennial Census survey that is representative of the national housing stock. As part of the benchmarking procedure, the owner-reported values in the AHS were lowered by 5.5 percent to reflect the average upward bias in owner reports mentioned above. Even with this adjustment, however, the potential for additional bias became evident in 2007, when homeowner-reported valuations continued to rise in the AHS, even as market transaction data indicated that prices had started to come down. In response to the divergence between survey data and transactions data, the benchmarking of the FAUS to the AHS was discontinued in 2005. Since then, the FAUS housing wealth series has been extrapolated using repeat-sales price index, combined with net fixed investment (i.e., net additions to the housing stock) from the Bureau of Economic Analysis. This solution effectively prioritized transaction data over survey data. As described below, the new method offers the ability to combine some of the best features of both transaction and survey data.

A New Measure of Housing Wealth

As detailed in Gallin et al. (2018), the new method for valuing aggregate residential real estate wealth is based on estimates from Zillow's automated valuation model (AVM).⁵ Zillow's AVM (and AVMs in general), can loosely be thought of as a set of algorithms that combine large amounts of information on a home's characteristics, neighborhood amenities, nearby sales, and other data to produce an estimate for the current market value of the home. Zillow has made available to us the average and total value of homes in each state and county as estimated by their AVM.

Zillow's AVM has several notable advantages and some disadvantages from the point of view of aggregate wealth estimation. A major advantage is that, like a repeat-sales index, the AVM estimates are disciplined by recent market prices and can be updated very frequently and with minimal lag. A further important advantage is that, unlike a repeat-sales index, the AVM estimates values for the full housing stock, not just transacting homes. Especially if the observable characteristics of traded units are not representative of the non-traded stock, the AVM should provide a more accurate pricing signal for the non-traded units than a house price index.⁶ The Zillow estimation universe is also larger than that of a repeat-sales index, making it easier to pick up pricing signals in sparsely traded markets.⁷ In turn, Zillow is able to produce estimates by structure type (e.g., single-family vs. condo/coop), whereas house price indexes are typically constructed from sales of single-family structures only. Finally, the AVM can account for an unusually rich set of observable property characteristics, and thus can in principle account for changes in the quality of the housing stock over time.⁸

One disadvantage of any measure of national housing wealth based on public sales records, including both AVMs and repeat-sales indexes, is that sales records are not nationally representative, due to gaps in coverage in less densely populated housing markets. For this reason, the AVM does not provide estimates for every home, and we cannot simply add up the Zillow AVM estimates into a representative national measure for the Financial Accounts. To address this coverage issue, our method multiplies the county-level average AVM value by the county-level number of owner-occupied properties from the American Community Survey (ACS), a nationally representative survey produced by the Census Bureau.⁹ Our aggregate estimates therefore rely on Zillow for prices and the ACS for quantities.¹⁰ This method should produce accurate estimates of housing wealth under several assumptions, discussed in detail in Gallin et al. (2018).

A second disadvantage is that Zillow's AVM does not always provide an unbiased estimate of home values for a particular area. However, Zillow has provided high-frequency measures of that bias (on a confidential basis). In particular, Zillow compares AVM-generated estimates of a property's value with the arm's-length transaction price for that property when it sells. While we cannot observe the error for properties that did not trade in a given period, the error data for traded units allows us to construct adjustment factors to correct the AVM estimates for the small positive bias observable in the error data.¹¹

A third disadvantage for the purpose of constructing a national housing wealth measure is that the Zillow data include valuations on some rental units, which by definition are excluded from our concept of housing wealth.¹² This will bias down our estimates because rental units likely have lower values, on average, than own-use properties. Gallin et al. (2018)--using confidential data from the Census Bureau that includes data on both rentals and own-use properties merged with an alternative, but similar, AVM for 2014--find evidence that rental homes are on average 20-30 percent less valuable than own-use homes valued by the AVM in that year. If we applied the same price difference to Zillow's AVM, it would imply a 6 percent downward "rental-inclusion bias" in the aggregate Zillow-based estimate. Because this evidence is only suggestive--as it is not based on the Zillow AVM--and because we do not know the size of the bias in other years, we do not adjust for this bias in the figures shown here.

Comparing Measures of Housing Wealth

Figure 1 shows our new, AVM-derived measure of aggregate owner-occupied housing wealth for the period 2001-2017 (in red). For comparison, we also plot the measure from the 2018:Q1 release of the Financial Accounts (in black) and a measure based on owner-reported values from the ACS (in blue).¹³ While the three measures are largely in line with one another from 2001 to 2006, they diverge notably from 2006 to 2016, a time period that included an enormous housing bust and gradual recovery. In particular, the Financial Accounts measure shows a very pronounced downturn and recovery, while owner-reports show a significantly milder cycle. The AVM-based measure lies in between the two. The differences between these three measures over the Great Recession are substantial--the ACS series is about $3 trillion above the Zillow series at the widest point in 2008, while the Zillow series in turn peaks at over $2.7 trillion above the Financial Accounts measure in 2011. We also find that the timing of the cycle differs among the measures. Whereas the Zillow AVM and Financial Accounts measures date the peak of the cycle in 2006, the owner-reported measure does not peak until 2008.

Figure 1

The comparison between these three series highlights some of the pitfalls of using survey data and house price indexes to measure housing wealth. In particular, the comparison between Zillow and the ACS suggests that survey respondents who did not sell their homes were either unaware of market fluctuations in real time, or they believed that their home values were different than those in the surrounding market. To the extent that owners did acknowledge changes in the market, it appears that they were late to do so. Similarly, the much more pronounced swings in wealth in the Financial Accounts likely result from applying to all homes the house-price dynamics of homes that transacted during the housing bust and recovery, suggesting that transacting homes did not fully represent the whole housing stock.

Concluding Thoughts

The new method for estimating aggregate own-use housing wealth described in this note, though imperfect, represents a promising addition to existed methods, as it has the potential to combine some of the best features of both survey-based and price-index-based methods. In particular, our new method is market-based, representative, and makes use of very detailed property characteristics. This new method may be particularly appropriate during market turning points and could be used in a variety of research and policy contexts including time series studies and quantitative macro models of consumption and other economic phenomena. As we develop this new measure further, we may begin to incorporate some version of it in the Financial Accounts in future publications.

References

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