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.
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July 10, 2013
Why Housing Rebound May Continue at a Slower Rate Than Hoped For
Perhaps it's because I've worked with bank examiners for many years, but I often question financial news that seems too optimistic. On July 1, 2013, the U.S. Census Bureau reported that overall construction spending increased in May. Private residential construction, which generally leads the economy, grew 24.4 percent from May 2012 to May 2013. Beyond being cautious with one data point, I think that there are several reasons why housing's rebound may be slower than hoped.
To be clear, residential real estate conditions have been improving, albeit from record low levels of activity. Sales of both new and existing houses have been trending up recently, but remain near historically low levels. Additionally, the quantity of new and existing homes readily available for sale is low. Homebuilders in the Sixth Federal Reserve District (which includes Alabama, Florida, and Georgia and parts of Louisiana, Mississippi, and Tennessee) recently reported that new home sales and construction have been ahead of year-earlier levels and that buyer traffic remains strong (see this SouthPoint post). Builders noted, however, that access to financing and a shortage of developed lots continued to constrain construction activity. In conjunction with the recent construction spending data release, it is this last point that I aim to dig into a bit deeper in this blog post.
Since the housing bust, construction and development (C&D) lending has been in sharp decline in terms of aggregate dollars and as a percent of total bank assets. Using year-end data, we find that C&D loans peaked in 2007 at $629.4 billion. As of 2012, they stood at $203.8 billion. As of March 2013, C&D loans accounted for 1.4 percent of bank assets, unchanged from December 2012 and the lowest level since at least 1991. The decline in C&D lending is broad based given that similar trends are seen for banks under and over $1 billion in total assets. With the recent reports of growing construction spending, will bank lending practices dampen construction growth going forward?
Banks represent a significant funding source for homebuilders, especially nonpublic homebuilders. Using data from 1991 to 2012, there appears to be a strong, positive relationship between bank construction lending and private residential construction put in place—see the chart. Of course, it's impossible to tell from this chart whether construction activity is responding to changes in credit supply or credit supply is responding to changes in construction demand. However, banks have been extremely tight with credit in the aftermath of the financial crisis, and there aren't many signs that banks plan to change course any time soon. So it may be reasonable to assume that a continued reduction in bank C&D lending is likely to limit future gains in construction activity.
A case for optimism
In conversations with banks of various sizes, two things are often repeated. First, bankers indicate there is little appetite for growth in C&D lending and second, banks of various sizes want to increase commercial and industrial lending (C&I). For many banks, a move from C&D lending to C&I lending is easier said than done—the skillsets needed for C&I lending differ from those associated with C&D. Acquiring C&I expertise is a challenge particularly for smaller banks. So what's a community bank going to do?
An old adage is to do what you know best. For many community banks that would be C&D lending. Given the reports of lot shortages and house inventory being low, it would seem that profitable opportunities for C&D lending exist. There is nothing wrong with C&D loans appropriately underwritten and subject to reasonable risk management. A key question is when banks start moving back to C&D lending, will they be able to resist the shortcuts of the last cycle? Let's hope that banks can successfully navigate a return to C&D lending so that the housing market can continue to recover.
By Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
May 5, 2010
Can we identify foreclosure contagion effects?
Negative externalities of foreclosures are the primary reason that policymakers focus on implementing policies to avert foreclosures and keep families in their homes. If the costs of foreclosures were completely internalized by the households experiencing them, then the focus would likely be on a different set of policies—for example, providing rental housing assistance or counseling on how to rebuild credit histories. Despite their importance, the empirical evidence of negative externalities is extremely tenuous, because they are so difficult to measure accurately. The papers that have tried have, for the most part, found huge effects. One of the most-cited papers in the literature was written by Dan Immergluck and Geoff Smith. They looked at the Chicago housing market in the late 1990s and found significant negative effects of foreclosures on nearby property values: *
Cumulatively, this means that, for the entire city of Chicago, the 3,750 foreclosures that occurred in 1997 and 1998 are estimated to have reduced nearby property values by more than $598 million, for an average of $159,000 per foreclosure. This does not include effects on the value of condominiums, multifamily rental properties, and commercial buildings.
This is an enormous effect, and right away it should make us slightly skeptical because, like most papers that have attempted to estimate these externalities, it is based on a hedonic regression model. For readers who are not close to the academic housing literature, a hedonic regression model is simply a cross-sectional regression of housing transaction prices on characteristics of the house and neighborhood. The methodology can be very useful for measuring the price of certain housing characteristics (for example, how much an extra bathroom or bedroom is worth), but it is not so useful in measuring the contagion effect of foreclosures because it does not solve two severe econometric issues: a reverse causality problem stemming from the fact that declines in housing prices result in higher foreclosure rates (the recent crisis for example!), and the problem that there are likely many unobserved neighborhood characteristics that are correlated with both housing prices and the number of foreclosures in a given neighborhood.
In a recent paper published in the Journal of Urban Economics, John Harding, Eric Rosenblatt, and Vincent Yao try to overcome these econometric issues by employing the repeat-sales methodology that is usually used to estimate house price indices. This model uses the difference in sale prices for repeat transactions of the same properties to estimate the average trend of house prices in a given area. By taking differences, all of the characteristics of a property and neighborhood that do not change between the sales drop out, and so we do not need to account for them. The only characteristics that we need to worry about are those that vary over time (between sales).
Harding et al. make a slight modification to the repeat-sales methodology by including as an additional covariate the number of foreclosures surrounding a property in the regression. In this respect, the model becomes a hybrid between a repeat-sales regression and a hedonic regression. Most importantly, this methodology can control for the average trend in prices in an area to at least partially address the reverse causality issue—price declines, through their effect on equity positions, are causing increased foreclosures. In addition, because time-invariant property and neighborhood characteristics fall out of the regression, omitted variable bias (the possibility that there are unobserved variables correlated with foreclosures and house prices) is less of an issue, although it could still be a problem if there are time-varying unobserved variables that are correlated with both foreclosures and house values.
Findings support significant but reduced negative externalities
Using data from seven markets—Los Angeles, Atlanta, St. Louis, Charlotte, Las Vegas, Columbus, and Memphis—the authors find significant negative effects of foreclosures on property values, but the effects are smaller than those previous studies have found. According to the authors' estimates, the peak discount of a property's value due to a nearby foreclosure is about 1 percent, and this effect diminishes quickly as the distance to the foreclosure increases. The authors interpret these results to be contagion effects that largely come from poor aesthetics resulting from the deferred maintenance and neglect of properties in the state of foreclosure. In the conclusion, they state (p. 178): "We interpret these different patterns as suggesting that the negative externality from immediate neighbors is attributable to property neglect and uncertainty about the future owner."
In our opinion, this paper is a significant improvement over the previous literature, as it includes a number of methodological improvements over and above its use of the repeat-sales method. With their data, the authors are able to pinpoint two aspects of foreclosed properties that the previous literature has not been able to identify. First, the authors can identify the particular legal phase of any foreclosure proceeding. That is, they know when a lender has filed the initial foreclosure documents, when it has taken legal possession of a house, and when it has sold a house to a new owner. Second and perhaps most important, the authors are able to geocode the location of each foreclosure relative to any house that contributes observations to the repeat-sales dataset. The authors then draw four concentric rings with different radii (0–300 feet, 300–500 feet, 500–1,000 feet, and 1,000–2,000 feet) around each repeat-sales transaction and count the number of foreclosures in each ring. Consequently, in their empirical regressions, the authors can control for the distance between a repeat-sales transaction and any surrounding foreclosures, as well as account for the particular phase of the foreclosure process for each house in each ring. Consistent with the intuitive concept of contagion, the authors find that the negative effect of a foreclosure on the prices of other homes diminishes with distance. Moreover, the negative effect is strongest around the time of the foreclosure auction/sale and the real-estate owned (REO) sale, as opposed to the time period before the auction/sale. This finding also makes sense because the time after the formal foreclosure and before the REO sale is when the property is most likely to be in a state of deferred maintenance.
True contagion effects may be even smaller
The paper's empirical findings that both distance and the phase of the foreclosure process matter are not only very intuitive, but they also provide quite a bit of evidence in support of the contagion hypothesis. But for reasons we describe below, we believe the true effects of contagion may be even smaller than the reduced effects that the authors find.
First, there could be significant measurement error causing an upward bias in their estimates of the contagion effect. The authors estimate separate regressions for each of the seven Metropolitan Statistical Areas (MSAs) in their sample and are thus able to control for average price appreciation at the level of the MSA. However, an MSA is a relatively large geographical area that includes many heterogeneous areas. For example, in the Boston/Cambridge metro-area there are wealthy areas like Brookline and very poor areas like Dorchester. House price trends were very different in these areas, and foreclosure levels were also extremely different. Not controlling for these different trends could bias the estimates of the contagion effect. For example, since Dorchester experienced significantly more house price depreciation than the average area in Boston, the residuals corresponding to properties in Dorchester in the regression will be mostly large and negative. In addition, Dorchester experienced significantly more foreclosures. If the larger price declines caused the increased foreclosures in Dorchester (and likewise the smaller price declines caused the lower foreclosure numbers in Brookline), then the residuals will be correlated with the number of foreclosures, and the contagion estimate will be biased upward. One way to try to address this problem would be to estimate the repeat-sale regressions at a more disaggregated level, such as the town/city level or even at the ZIP code level.
Another potential problem comes in the way the authors treat REO sales. REO sales are not used in the construction of the repeat-sales pairs and thus are not reflected in the independent variable in the regressions. This is a normal assumption to make when constructing repeat-sales price indices, with the rationale being that distressed sales may not reflect true market prices. This approach implies that the estimates of the average MSA price trends in the regressions do not reflect foreclosure sales. But, if foreclosure sales do lower sale prices of non-distressed properties through channels independent of contagion, such as by increasing the supply of houses on the market, and the price declines result in more foreclosures (through the channel discussed above), then the estimated contagion effect will be biased upward. Basically, this would introduce measurement error into the price trend, which would in turn be correlated with the foreclosure contagion variables in the regression. However, the authors could easily check for error by simply including REO sales in the repeat-sales sample to see how the contagion estimates are affected.
Finally, as the authors acknowledge, there could be some omitted time-varying property or neighborhood characteristic that is correlated with both the residuals and the number of foreclosures surrounding a property. The authors try to deal with this issue by placing restrictions on their sample of repeat-sale pairs to eliminate properties that have likely changed significantly over time, and find the results to be robust to such changes. This finding certainly takes care of property characteristics that may be changing significantly over time and adding (or subtracting) value from the property, but it does not control for neighborhood characteristics.
The authors also use an instrumental-variables (IV) strategy whereby they try to find variables that explain the number of foreclosures but that aren't correlated with unobserved variables explaining house values in a given area. For instruments, they use FICO scores (90th percentile of the distribution), loan-to-value (LTV) ratios (90th percentile of the distribution), homeowner income (median), property size, and the stock of housing. They estimate the IV regression for one MSA (Los Angeles) and find that their results do not substantially change. Based on the results of the IV estimation, the authors conclude that omitted variables are not a problem. However, this particular exercise isn't completely convincing, because if the first critique above is a problem (not having a disaggregated measure of average house price appreciation), then the instruments will likely be correlated with the regression residuals. To see this, think about our Dorchester/Brookline example from above. Properties in Dorchester will have large negative residuals in the regression. In addition, since Dorchester is a lower-income area, the credit score distribution of its homeowners is likely lower than the average area in the Boston metro-area, the LTV distribution likely higher, and median income likely lower. In contrast, Brookline is probably the opposite in terms of the credit score, LTV, and income distributions of its homeowners. Thus, the regressions residuals will be correlated with the instruments, and the IV estimation will not solve the underlying econometric issues.
Paper is a nice starting point
Despite these econometric issues, the pattern of the findings seems to imply a contagion effect, even if the quantitative magnitude might not be measured accurately. As we discussed above, the authors go to great lengths to control for the distance from foreclosed properties as well as for the different phases of the foreclosure process, and estimate a very flexible specification for these variables. For example, they find very little effect from properties that are a year away from foreclosure and a much larger effect between the time of foreclosure sale/auction and the eventual REO sale. In addition, they find that the effects from foreclosures near the property (within 300 feet) are much stronger than the effects from foreclosures farther away (beyond 500 feet).
As a whole, we think this paper is an important contribution to the literature, as its econometric specification is much more robust and flexible than prior externality studies. There are still important econometric issues that future research must address in order to really pin down the quantitative magnitude of the effect of nearby foreclosures on the value of a non-distressed property, but this paper provides a nice starting point.
*In addition to effects on surrounding property values, foreclosures have been found to have negative impacts on other neighborhood characteristics such as vacancy rates and crime rates.
March 31, 2010
Anti-foreclosure policy and aggregate house price indexes
A new paper by researchers at the New York Fed and New York University argues that the Federal Housing Authority (FHA), the government's insurer of relatively high-risk loans, is seriously understating the amount of risk in its portfolio. The paper makes a number of different points, but we want to comment on one claim in particular that has policy relevance beyond the issue of FHA risk. In fact, if this claim is correct, then any policy designed to reduce foreclosures by eliminating negative home equity could face significant problems when put into effect.
Repeat sales indexes a poor predictor of individual home price
The specific issue we want to address is how well an aggregate house price index can predict the price of an individual home. A number of aggregate indexes measure average house prices for a particular area, from the national level to the ZIP-code level. Often, these indexes are based on repeat sales, meaning that they combine the price changes of individual homes over time. If a house sold for $200,000 in 1997 and $220,000 in 2001, this repeat sale provides a data point indicating that house prices rose by 10 percent from 1997 to 2001. It is true that the 2001 buyer might have gotten a great deal in that the house really should have sold for more than $220,000 at the second sale. However, the assumption is that the influence of good and bad deals washes out when data from many repeat sales are aggregated together. If they do, then researchers can infer the average, overall path of house prices.
The problem occurs, the authors of the paper say, when one uses the resulting aggregate index to predict the price of an individual home. Consider someone who purchased a home for $200,000 in 2007. Now assume that over the next two years the aggregate house price index for that particular area declined by 10 percent. The authors point out that the decline in the index does not necessarily mean that this particular homeowner would have sold the house for $180,000 in 2009. The owner may have taken extremely poor care of the house, or a beautiful park that was across the street from the house at the time of purchase may have become a strip mall. In either case, the homeowner was likely to have sold for less than $180,000. On the other hand, the homeowner may have made some improvements to the home that would have resulted in a sale of more than $180,000.
Research paper provides careful analysis of valuation errors in aggregate indexes
Potential problems with repeat-sales indexes were well known before the FHA paper was written. What the new paper contributes is a careful analysis of how large these so-called valuation errors can be and how they might relate to the probability of having negative equity. Using residential sales from Los Angeles County, the authors compare the actual sales prices of houses with predictions generated by different aggregate price indexes. The authors make two important findings.
First, the repeat-sales indexes are often biased, in the sense that the mean of the predictions does not match the mean of the recorded prices. For 2008 and 2009, repeat-sales indexes tended to overpredict house prices by 7 to 18 percent. In 2007, the indexes underpredicted house prices by about 4 percent. Second, dispersion in individual valuation errors is large—the standard deviation of valuation errors is about 20 to 25 percent, depending on the aggregate index used. Putting these two facts together gives a clear message: Using standard methods, it is difficult to predict what any individual house will sell for at any particular time.
Valuation errors undermine mortgage balance reduction policies
On a general level, this observation is not an indictment of the FHA, since a lot of other people also use aggregate indexes to infer prices of individual homes—including us. Moreover, without knowing the ins and outs of the FHA's default-prediction model, it is hard to know the quantitative importance of valuation errors in the calculation of FHA risk. But moving beyond this issue, it is not hard to see how large valuation errors could undermine the effectiveness of a policy that attempted to ease foreclosures by reducing mortgage balances for individual negative-equity homeowners. As we have blogged recently, some observers have claimed that many, if not most, foreclosures occur because owners with large amounts of negative equity simply walk away from their homes. The ostensible policy implication is to reduce these homeowners' mortgage balances to give them more of an incentive to stay.
If valuation errors are large, however, it is very difficult to know who has severe negative equity and who doesn't. This problem undermines the effectiveness of balance-reduction policies. Effective policymakers must know how to price individual homes to assess the depth of negative equity for those homes. Consider two homes that, according to an aggregate price index, have 30 percent negative equity. That amount may or may not be severe enough to get an owner thinking about walking away. If it is, then an appropriate policy might reduce both homes' mortgage balances by 20 percent, thereby reducing the negative equity to about 10 percent. (Leaving a little bit of negative equity is probably a good idea in practice because it may prevent homeowners from selling the moment that a balance reduction is made.)
Effective foreclosure prevention would consider both job loss and negative equity
If in reality one of these homes actually has 50 percent negative equity and the other has 10 percent negative equity, then the balance-reduction policy is likely to prevent no foreclosures. The owner with 50 percent negative equity remains underwater, to the tune of 30 percent, so is probably still thinking about walking away—according to the theory of default that motivated the balance-reduction policy in the first place. On the other hand, the owner with 10 percent negative equity was not going to walk, unless perhaps a job loss went along with the negative equity. But if the combination of job loss and negative equity is the real problem in the housing market rather than severe amounts of negative equity alone then we can devise much more cost-effective policies to reduce foreclosures than large-scale balance reductions.
The authors of the paper do not discuss balance reductions. However, in other papers, they argue that anti-foreclosure policy should consider balance reductions. We believe that the valuation-error results uncovered in the FHA paper indicate that balance-reduction policies face substantial hurdles in actual practice.
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