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Real Estate Research provided analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog included the Atlanta Fed's Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.

In December 2020, content from Real Estate Research became part of Policy Hub. Future articles will be released in Policy Hub: Macroblog.

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October 23, 2013

When Will the Housing Market Recovery Boost Retail Consumption?

Some good news for the economy: August home prices were 12.4 percent higher than they were last year. Real estate constitutes about a third of Americans' net wealth, according to the Federal Reserve's Financial Accounts of the United States (previously Flow of Funds Accounts of the United States). So just using common sense, you'd think that strong growth in home prices might translate into stronger retail sales and a stronger economy. But real personal consumption expenditures remain sluggish, according to the Bureau of Economic Analysis, growing by just 0.2 percent in August and 0.1 percent in July.

Should we expect home price gains to translate into stronger retail sales? In a former post, Kris Gerardi outlined the debate between economists about how strong this "wealth effect" is. Most researchers estimate that the marginal propensity to consume (MPC) from home price gains is about 3–5 percent. That is, for every $100 increase in home price value, retail consumption rises by $3–$5. This may sound small, but in practice, this effect can be quite large: the 30 percent decline in home values from 2005–09 translates to a yearly $350 billion decline in retail sales. There hasn't been much research using household-level data, which we'd prefer, in order to control for omitted variable bias. One 2013 study (by Sumit Agarwal and Wenlan Qian), which looked at the wealth effect in Hong Kong from 2000 to 2002 using individual-level data, found a smaller 2 percent MPC.

In a recent paper, Karl E. Case, John M. Quigley, and Robert J. Shiller (hereafter CQS) find effects in the 2–6 percent MPC range. Their paper looks at quarterly U.S. home prices and retail sales, by state, from 1975 through second-quarter 2012. CQS estimate that, on average, when housing wealth increases, consumption rises by about 2 percent, and when housing wealth falls, consumption falls by as much as 6 percent. These findings suggest that it may not be easy for the wealth effect from a housing market recovery to be the primary spark to strong economic growth—home prices would have to rise much higher than 2005 levels to compensate for lost ground. Going back to our August 2013 data, the CQS estimates suggest that a 12.4 percent increase in housing wealth should correspond to a 0.4 percent increase in retail sales, which is not far off from what we are seeing: consumption growth that nonetheless falls short of a full recovery.

CQS also investigate whether the consumption elasticity of home price change is larger or smaller than that of the consumption elasticity of stock market change. They find that home prices have a bigger effect. You'd expect this to be true: household wealth forms a much larger fraction of household portfolios than stocks (29 percent versus 22 percent in 2012). And household wealth extends further into the middle and lower-middle classes, whom we might assume have a larger relative MPC than the wealthy.

In a recent working paper, Richard Ashley and Guo Li find that the impact of the wealth effect depends strongly on the persistence of the wealth change. Fluctuations with five quarters' to four years' persistence have the largest impact for housing wealth, while the stock market has the greatest effect when fluctuations are either a year or shorter, or longer than four years.

CQS identify two potential channels between home prices and consumption. The first is behavioral: when home prices decline, households may feel poorer, or predict they have lost a future income source when they sell their home. In response, they spend less. The second effect is more tangible and involves the use of homes as collateral. The Tax Reform Act of 1986, which gave tax incentives to mortgage debt, was followed by the fertile lending environment provided by the Great Moderation. During this era, American homeowners used their homes to pad their incomes, either by refinancing their mortgages and lowering their monthly payments or by taking cash-out refinances, home equity lines of credit (HELOCs), or other loans against their homes. According to a 2007 paper by Alan Greenspan and James Kennedy, from 2001 to 2005, American homeowners extracted $700 billion of equity each year through loans, cash-out refinances, and second mortgages. (That's $6,400 per household per year!)

So how do these two channels look in the current recovery? Some rough calculations suggest that the expectations channel is unblocked, but the credit channel is still constrained. The chart below shows in blue the market value of U.S. owner-occupied real estate per household. The chart demonstrates that housing wealth—as far as prices are concerned—has recovered on a per-household basis to 2005 levels, though not to where they would have been had they followed the upward trend from 1997 onwards. If consumption is driven by people's sense of home value, this channel could be considered "unblocked."

Market Value of Owner-Occupied Real Estate, Per Household

In green, the chart also shows homeowners' equity as a percentage of real estate. This gives a sense of how homeowners' assets have recovered relative to liabilities—or perhaps another way of thinking about it is as a measure of total loan-to-value. We can see there has been strong recovery in homeowner equity, due to the combined effects of deleveraging, write-downs, and price recovery (given the timing of the strong uptick in late 2011, the last is likely the most important). But on aggregate, we're still 10 percentage points lower than the precrisis average of 60 percent. To the extent that the wealth effect of housing is driven by cash flows and the use of houses as collateral for loans, this channel could be considered "blocked." If so, perhaps all that is needed is for home prices to recover a bit more.

By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department, and doctoral student, School of City and Regional Planning at Georgia Institute of Technology, and

Photo of Carl HudsonCarl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department

June 12, 2013

Atlanta Fed Blog Gets Renewed Focus

By many accounts, the housing market is considered to be integral to the U.S. economy. It serves as an excellent bellwether for the health of the national economy, be it through the lens of payroll employment; its contribution to GDP growth; exposures of financial institutions to acquisition, development, and construction lending and residential mortgages; or changes in the level of personal consumption due to wealth effects. Given that real estate is more important to the Southeast's economic activity compared to that of the rest of the country, we at the Atlanta Fed like to stay on top of emerging trends in real estate in general, and in housing in particular.

The Atlanta Fed's other blogs have covered real estate and housing, along with a wide variety of other topics relating to monetary policy, macroeconomic developments, and regional economic matters (see the links below). With this relaunch, we hope to foster dialogue on topical research and current issues in the fields of housing and real estate economics. We intend to publish weekly posts covering takeaway points from our conferences and speakers, analysis of recent academic research, commentary and observations on recent data releases and survey results, and highlights of working papers we find to be especially interesting. We encourage your active participation in this blog and look forward to collaborating with you.

Opinions expressed in this blog do not necessarily reflect the views of the Federal Reserve Bank of Atlanta or of the Board of Governors of the Federal Reserve System.

Photo of Melinda PittsBy Carl Hudson, director of the Atlanta Fed's Center for Real Estate Analytics

Recent Atlanta Fed Real-Estate-Related Posts



February 11, 2011

Figure 3

Figure 3
States with Judicial Foreclosure Requirement

States shaded in dark gray require judicial foreclosure. The data come from RealtyTrac.com and are available at http://www.realtytrac.com/foreclosure-laws/foreclosure-laws-comparison.asp.

February 11, 2011

Other conceptual and data issues

1. The border-discontinuity approach is not a true regression-discontinuity exercise
A regression-discontinuity design in this context would correspond to a scenario in which there is a discontinuous treatment effect of foreclosures at the state border. However, this does not necessarily appear to be the case. There is no reason to expect that foreclosures on one side of the border will not influence prices (or other outcomes) on the other side of the border. If the story is about foreclosure contagion effects, this will certainly not be the case. A cluster of foreclosures in Florida that is right next to the Florida/Georgia border should be expected to exert the same effect on property values 1/10 of a mile from the border on the Georgia side as it will 1/10 of a mile from the border on the Florida side.

If the story is instead about a supply effect from foreclosures (i.e. that a cluster of foreclosures increases the supply of houses in a market, which puts downward pressure on prices), then one would have to argue that the housing market immediately across the Georgia side of the border is separate from the housing market immediately across the Florida side of the border. But if homes on either side of a state border really are part of the same housing market, then foreclosures on one side of the border will influence prices on the other side of the border. To be clear, this logic does not invalidate the authors' border regressions; it just points out that the regression discontinuity label is misguided.

Furthermore, as Van der Klauw puts it, in a regression discontinuity context, "under certain comparability conditions, the assignment near the cut-off can be seen as behaving almost as if random." In other words, it must be the case that the properties and neighborhoods on one side of the border have the same characteristics as those on the other side of the border, so that the only difference between the two sides comes from the different foreclosure laws. Arguably, this might be the case if one were focused on a very short radius from the border, say 1/10 of a mile. But the authors' shortest radius is 5 miles. This means that part of the comparison includes homes that are close to 10 miles apart from each other! In many cities, travelling only 1 or 2 miles is sufficient to move from a wealthy, up-scale neighborhood, to a poor, downtrodden area, so a 5 mile radius is much too long to impart confidence that the border sample is uncontaminated by unobserved heterogeneity. Thus, it is likely the case that the authors' results are simply picking up the fact that they are comparing completely different housing markets with different foreclosure rates (and average durations) stemming from something that is not related to differences in foreclosure laws.

The authors could try to shorten the radius, but in doing so they would run into a couple of issues. First, they would need very detailed and disaggregated data (likely loan-level data on foreclosures and values), and they would need a large enough sample of foreclosures very close to the borders between judicial and nonjudicial foreclosure states to be able to estimate the regressions. Second, based on our logic above, it isn't clear that we should expect foreclosures close to one side of the border to exert a discontinuous treatment effect on housing values on that side of the border relative to the other side of the border.

2. Are differences in laws expected to generate differences in foreclosure numbers or timelines?
Assume that a borrower simply stops paying his mortgage in a judicial state. It is not likely that judicial requirements will prevent a foreclosure from ever taking place. The borrower will lose his house eventually, though the judicial requirements may stretch out the "foreclosure timeline" longer than it would be in a nonjudicial state. If so, the authors might find in some given year that there are fewer foreclosures, relative to delinquencies, in judicial states. But these foreclosures are coming eventually, and if they are, it's hard to see how forward-looking agents would ignore them when setting housing prices, as the authors implicitly assume.

3. State laws do not necessarily make a foreclosure out of a delinquency
Another issue concerns the link between state laws and the probability that a delinquency turns into a foreclosure. The authors implicitly assume that they can isolate variation in the probability of transition from delinquency using state laws. But these transitions (called "roll rates" in the industry) often change over time within states, even when the relevant state laws stay the same. What's worse, there is both theoretical and empirical support for the idea that these changes are driven by changes in housing prices. The standard "double-trigger" theory of default predicts that borrowers become delinquent when they have financial problems (like job loss), but that delinquencies don't turn into foreclosures unless house prices have fallen and owners have negative equity. As we have found, the predictions of double-trigger theory are borne out strikingly in Massachusetts data. Bay State delinquency levels were similarly high in the recessionary years of 1991 and 2001, but there were far more foreclosures in the first recession than in the second. Why? Prices were in free fall in 1991 but rising ten years later. The causality clearly went from the prices to the foreclosures, not the other way around. In short, the claim that foreclosure-delinquency ratios stem from state-specific and constant "propensities to foreclose," which are themselves driven by state laws, is simply wrong.

4. The use of problematic datasets further impedes ability to identify causality
The conceptual issues we just outlined raise serious doubts about whether it is even possible to identify the direct effect of foreclosures on prices with the authors' approach. But even without these issues, confidence in the authors' results is further reduced by some serious issues with their datasets, most notably the foreclosure data from RealtyTrac.com. As we have found in our research, measuring foreclosures is tricky even within a state. It is no doubt harder to come up with consistent measures of foreclosures across state lines. Lenders often file multiple foreclosure deeds for the same property (a very common practice with Fannie Mae), and courts occasionally compel lenders to refile. (One such instance has achieved a great deal of attention in the now-famous U.S. Bank v. Ibañez case, litigated in Massachusetts. Details of this case appear in "Is Massachusetts a judicial state?") Figuring out whether a property has actually been transferred from the delinquent borrower to the lender (and become real-estate owned, or REO) is also a nontrivial effort, because a foreclosure deed does not distinguish whether the buyer at the auction was the lender or some third-party buyer. Even subsequent sales may reflect transfers within different legal incarnations of the bank and not true transfers of property.

The authors claim that they dealt with the problem of multiple deeds by looking at the last filing in a given year:

To avoid double-counting filings for the same property, RealtyTrac.com provided us totals for the last filing in the process for a given property in a given year. For example, if a borrower received a notice of default and a notice of trustee sale in the same year, RealtyTrac.com records one notice of trustee sale for the property. (p. 7)

This procedure only addresses multiple filings within a given calendar year, which is hardly the only problematic scenario. The Ibañez case in Massachusetts (see the discussion below) clearly illustrates one of many other potential issues. U.S. Bank foreclosed on the Ibañez property in July 2007. Because the Massachusetts Land Court invalidated that foreclosure, the bank will at some point have to foreclose on the property again. Only a very careful search of the documents reveals that the new foreclosure on the property is in fact a do-over and that one should subtract a foreclosure from 2007 and add one to 2011. The authors here would simply count the two deeds as two different foreclosures. And this is true for any set of repeat foreclosure deeds that occur over two or more years.

To make matters worse, the authors do not simply look at foreclosure deeds:

Our measure of total foreclosures in a zip code is the total number of notices of trustee sale, foreclosure sales, or real estate owned. (p. 7)

Given the length of the foreclosure process, it is likely that two of these events for a single property will take place in different years, and the authors will consequently count them twice. If foreclosure procedures were the same across state and time, this double-counting wouldn't be a big deal; the parameters may not be estimated as well. However, it seems quite likely that the legal variation in foreclosure process could vary the number of duplicative filings. For example, nonjudicial states may be more amenable to automated foreclosure procedures that reduce the cost of multiple filings but also the error rate and thus the need for amended filings, to take just one example.

And none of this considers the overwhelming problem of comparing records across states, each with different laws and different recording requirements.