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June 2013 (May 10, 2013-June 11, 2013) In This Issue: Banking and Financial Markets Banks Increase their Holdings of Safe Assets Households and Consumers The Ever-Updated Personal Saving Rate Inflation and Prices Recent Trends in Various CPI-Based Inflation Measures Monetary Policy Yield Curve and Predicted GDP Growth, May 2013 Regional Economics Housing Recovery? Banking and Financial Markets Banks Increase their Holdings of Safe Assets 06.03.13 by William Bednar and Mahmoud Elamin Safe Assets of Large Domestic Banks Percentage of total assets 20.0 18.0 16.0 14.0 Treasury or agency securities 12.0 10.0 8.0 6.0 Cash 4.0 2.0 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Note: Shaded bar indicates a recession. Source: H.8 Statistical Release, Board of Governors of the Federal Reserve System. The banking sector seems to have transitioned to a new state in which a higher percentage of bank assets is held in safe forms. During the last recession, holdings of safe assets, such as cash, Treasury securities, and federal agency securities, rose steeply. But almost five years later, banks both large and small are still holding on to elevated cash levels and higher amounts of safe securities than before the recession. Many factors may have kept the trend going: the need to mitigate liquidity risks, a desire to prepare for expected regulatory changes, or an attempt to make banks worth more in a possible bankruptcy, to name a few. Safe Assets of Small Domestic Banks Percentage of total assets 20.0 Treasury or agency securities 18.0 16.0 14.0 12.0 Cash 10.0 8.0 6.0 4.0 2.0 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Note: Shaded bar indicates a recession. Source: H.8 Statistical Release, Board of Governors of the Federal Reserve System. Loans and Leases of Large Domestic Banks Percentage of total assets 70.0 68.0 66.0 64.0 62.0 60.0 58.0 56.0 54.0 52.0 50.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Note: Shaded bar indicates a recession. Source: H.8 Statistical Release, Board of Governors of the Federal Reserve System. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 A look at the data helps us distinguish between the probable causes of the trend. Cash may have been boosted in such a steep fashion during the recession because of the Fed’s increase in bank reserves. But other safe assets experienced a similar trend, without the sudden rise during the recession, which suggests deeper factors than the Fed’s actions are at play. New Basel III regulations on liquidity ratios could have motivated the trend, but these surfaced around December 2010, long after the trend was well established. Expected regulatory changes related to “too-big-to-fail” do not seem to be a dominant factor either, because banks both small and large have shared in the trend. The story most supported by the data is that the rise in safe assets may substitute for a lack of sufficient interbank liquidity, or as a signal by banks that they are safer, raising the liquidation value of their bank in default. Cash levels at large domestic banks experienced a big spike during the recession, rising from about 3 percent of total assets to about 9 percent near the end of 2009. The trend seems to have settled, with the level now hovering around 8 percent. Treasury and agency securities levels had been declining since 2005, but the recession reversed the trend. Combined, holdings of these assets rose from around 10 percent at the beginning of the recession to about 15 percent in the first quarter of 2013. 2 Small banks exhibited nearly identical trends. Cash levels at small domestic banks spiked during the recession, rising from about 3 percent of total assets to about 8 percent near the beginning of 2010. The level now hovers around 8 percent. Holdings of Treasury and agency securities had also been declining at small banks before the recession but they reversed course during it. Holdings rose from around 11 percent in the recession to about 16 percent in the last quarter. Other Assets of Large Domestic Banks Percentage of total assets 30.0 25.0 Other assets 20.0 15.0 10.0 Other securities 5.0 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Note: Shaded bar indicates a recession. Source: H.8 Statistical Release, Board of Governors of the Federal Reserve System. Loans and Leases of Small Domestic Banks Percentage of total assets 80.0 We see a similar trend at small domestic banks. Loans and leases at these banks experienced a rise from 65 percent of total assets in 2005 to a bit over 70 percent in the recession, and then fell off a cliff to about 60 percent in the first quarter of 2013. The steep jump seen in March 2010 is a result of an accounting change, where banks took off-balance sheet items onto their balance sheet. 75.0 70.0 65.0 60.0 55.0 50.0 2005 For this rise in safe assets as a percentage of assets to occur, a concurrent drop in some other category of assets should occur. It appears that category is loans and leases. Loans and leases dropped for the large domestic banks from over 60 percent before the recession to about 53 percent at the beginning of 2010. The trend steadied, and the level has hovered between 54 percent and 56 percent since. 2006 2007 2008 2009 2010 2011 2012 2013 Note: Shaded bar indicates a recession. Source: H.8 Statistical Release, Board of Governors of the Federal Reserve System. At large banks, other assets seem to have peaked in the crisis but returned recently to a slightly lower level than before the recession. Other securities seem to have been steady throughout. At small banks, both other assets and other securities have stayed fairly close to their averages since 2005. Other Assets of Small Domestic Banks Percentage of total assets We see that during and after the last recession, the percentage of bank assets held in cash, Treasury securities, and agency securities experienced a steep rise, while loans and leases dropped. Other assets and other securities seem to have stayed almost steady. 14.0 12.0 Other assets 10.0 8.0 6.0 Other securities 4.0 2.0 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 Note: Shaded bar indicates a recession. Source: H.8 Statistical Release, Board of Governors of the Federal Reserve System. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 3 Households and Consumers The Ever-Updated Personal Saving Rate 06.05.13 by Pedro Amaral and Sara Millington The Bureau of Economic Analysis (BEA) estimates that the personal saving rate for the first quarter of 2013 was 2.3 percent—a five-year low, and a substantial drop from the fourth quarter of 2012, when it stood at 5.3 percent. Since many economists think a healthy household balance sheet is a necessary condition to fuel a stronger economic recovery, should we be worried about how low this estimate of the saving rate is? Personal Saving Rate Percent 7 6 5 4 3 2 1 0 2000 2002 2004 2006 2008 2010 2012 Note: Shaded bar indicates a recession. Source: Bureau of Economic Analysis. Difference between First and Current Estimates for the Personal Saving Rate Percentage points 7 6 5 4 3 2 1 We argue that the answer to this question is no, at least not yet. Quarterly saving rates are fairly volatile, and even though the first estimate for April came in at an equally paltry 2.5 percent, we should wait to see whether such low readings are confirmed in the next few months. More importantly, though, initial estimates for the personal saving rate normally end up being substantially revised. Moreover, these revisions are overwhelmingly on the positive side; that is, the final estimates are usually a lot higher than the initial ones. How much higher? The initial estimate for the personal saving rate has averaged 4.9 percent since World War II, while the final (current) estimate is 7 percent. So when we say revisions are substantial, we are not exaggerating. Why is the personal saving rate so hard to estimate? The BEA computes the personal saving rate as part of its National Income and Product Accounts (NIPA) and defines it as the ratio of personal savings to disposable income. Personal savings, in turn, are obtained by subtracting personal outlays (consumption expenditures, interest payments, and current transfer payments) from disposable personal income, which is personal income minus personal current taxes. 0 -1 -2 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012 Sources: Bureau of Economic Analysis, Federal Reserve Bank of Philadelphia. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 4 This is where things get tricky. While the BEA has a very good handle on personal outlays, disposable income is considerably harder to define and estimate. Here are its main components: • Compensation of employees (wages and salaries plus employer contributions to pension plans and social insurance) • Proprietors’ income (the income of owners of nonincorporated businesses) • Rental income • Income receipts on assets (interest and dividend income) • Current transfer receipts (from Social Security, Medicare, etc., but also from businesses) net of contributions While some of these components are straightforward to estimate, particularly the ones involving government outlays and receipts, others are inherently hard to define. Moreover, some income sources that have become fairly important for households in the last 30 years, like capital gains on equity and real-estate, are excluded altogether. The revision process is typically a lengthy one. Data for a given quarter are first published in an advance release late in the first month of the following quarter. After that, the second and third (aka final) estimates are published one and two months after that, respectively. Then, usually in the following summer, the latest three years of data are revised, so that the estimates typically undergo three rounds of annual summer revisions. After that, estimates are only revised in benchmark revisions, when the BEA reconsiders its definitions and classifications to more accurately portray an ever-evolving economy, and it introduces new and improved statistical methodologies. Such benchmark revisions are usually very substantial and occur every four years. One is coming up in July this year. There is an alternative way of obtaining estimates for the personal saving rate using the Flow of Funds Accounts (FOFA) reported by the Federal Reserve Board. It is based on the fact that savings (income minus outlays) are simply changes in net worth. The FOFA and NIPA concepts of savings actually differ in that the former includes net Federal Reserve Bank of Cleveland, Economic Trends | June 2013 5 Measures of the Personal Saving Rate Percent (eight-quarter moving average) 12 Flow of Funds Accounts 10 8 6 4 The lesson is that we should be careful when making inferences about household deleveraging based on the latest BEA estimates for the saving rate. Not only are these usually subject to substantial revision, but at this time alternative measures of the saving rate are pointing in a different direction. National Income and Product Accounts 2 0 -2 2000 2002 2004 expenditures in consumer durables while the latter does not. Nonetheless, the FOFA also reports a NIPA-concept equivalent savings using FOFA data. The resulting saving rate is very noisy, so we show 8-quarter moving averages in the figure below. In contrast to the NIPA saving rate, the FOFA saving rate is not only higher, it has been increasing. 2006 2008 2010 2012 Note: Shaded bar indicates a recession. Sources: Bureau of Economic Analysis, National Income and Product Accounts, Federal Reserve Board, Flow of Funds Accounts. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 6 Inflation and Prices Recent Trends in Various CPI-Based Inflation Measures 05.28.13 by Todd Clark and Bill Bednar In the Bureau of Labor Statistics’ most recent release of the Consumer Price Index (CPI), the index declined in April at an annual rate of 4.3 percent. This follows a monthly decline of 2.2 percent in March. On a year-over-year basis, CPI inflation slowed from a recent peak of 2.0 percent in February to 1.1 percent in April. Taken at face value, this slowing of inflation suggests some further deceleration of inflationary pressure in the U.S. economy. CPI-Based Inflation Measures 12-month percent change A careful assessment of underlying price trends suggested by other, “core” measures of CPI inflation provides further evidence of this deceleration. CPI inflation can be significantly affected by large, temporary movements in volatile individual components of the price basket, and core measures—such as the CPI excluding food and energy, the median CPI, and the trimmed-mean CPI—are less affected by these temporary, idiosyncratic price changes. 7.0 6.0 5.0 Headline CPI 4.0 3.0 2.0 1.0 CPI excluding food and energy 0.0 Median CPI -1.0 Trimmed-mean CPI -2.0 -3.0 2005 2007 2009 2011 2013 Note: Shaded bar indicates a recession. Sources: Bureau of Labor Statistics; Federal Reserve Bank of Cleveland. Median and Trimmed Mean CPI 12-month percent change 6.0 5.0 75th percentile 4.0 Median CPI Inflation in the CPI excluding food and energy was 0.6 percent at an annual rate in April, down from 1.3 percent in March. Inflation in the median and trimmed mean CPIs came in at 1.8 and 1.0 percent, respectively, compared to 1.1 and 0.7 percent in March. On a year-over-year basis, inflation in the CPI-excluding-food-and-energy measure slowed from 1.9 percent in March to 1.7 percent in April. Year-over-year inflation in the trimmed-mean CPI fell from 1.7 percent in March to 1.6 percent. However, year-over-year inflation in the median CPI has continued to remain close to 2 percent, coming in at 2.1 percent in April. 3.0 2.0 Trimmed-mean CPI 1.0 0.0 25th percentile -1.0 -2.0 2005 2007 2009 2011 2013 Note: Shaded bar indicates a recession. Sources: Bureau of Labor Statistics; Federal Reserve Bank of Cleveland; authors’ calculations. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 While the recent discrepancies between these various measures are small in historical terms, over the past six months inflation measured by the median CPI has been consistently above inflation measured by the trimmed-mean or core CPI. The reason for this is that, among the components of the CPI, there has been a slight shifting of prices below the median. Looking at the boundary for the lower 25th percentile of price changes, it has shifted 7 downward slightly over this time period, while the boundary for the upper 25th percentile has remained relatively constant. This basically means that there is a wider distribution of price changes below the median than there is above. While this may not impact the median CPI, it would have the impact of pulling down the other measures, which has been the primary cause of the recent differences. Owner’s Equivalent Rent of Primary Residency 12-month percent change 6.0 5.0 4.0 3.0 2.0 1.0 0.0 -1.0 2005 2007 2009 2011 2013 Note: Shaded bar indicates a recession. Source: Bureau of Labor Statistics. Core CPI With and Without Shelter 12-month percent change 4.0 3.5 3.0 Core CPI 2.5 2.0 1.5 1.0 Core CPI excluding shelter 0.5 0.0 2005 2007 2009 2011 Note: Shaded bar indicates a recession. Sources: Bureau of Labor Statistics, Haver Analytics. 2013 One reason that median CPI inflation has been so stable is that a major component of the CPI, owner’s equivalent rent of primary residency (OER), has been increasing at a steady rate. Year-over-year inflation in the OER component has been 2.1 percent each month since last September. The weight (relative importance) of this component in the CPI is currently around 23 percent, much larger than any other single component, so OER has a large impact on all measures of CPI inflation. For example, excluding OER and other shelter components from the calculation of the core CPI in April gives a year-over-year change of 1.4 percent, compared with 1.7 percent when these shelter components are included. OER is particularly important for the median CPI because OER is often at or near the median of the distribution of CPI components. To shed more light on the recent behavior of core inflation, it is helpful to distinguish inflation in core services (services excluding energy services) and inflation in core goods (goods excluding food and energy goods). Normally, inflation in services exceeds inflation in goods. While this pattern broke down briefly following the last recession, it has returned in the last couple of years. Year-overyear inflation in core goods prices has averaged 1.0 percent since the beginning of 2012, while inflation in core services prices has averaged 2.4 percent. Recently, goods inflation has trended down sharply, actually reaching negative territory in April (around −0.1 percent), while services inflation has remained steady. This makes clear that the recent slowing of some measures of core inflation has been driven by deceleration in goods prices, not services. One variable that is positively correlated with the gap between services and goods inflation is the exchange rate. As the exchange rate appreciates, Federal Reserve Bank of Cleveland, Economic Trends | June 2013 8 imports tend to become less expensive. Since it is primarily goods that are imported rather than services, the downward pressure from exchange rate appreciation falls primarily on goods prices, boosting services inflation relative to goods inflation. Recently, there has been some appreciation of the exchange rate, which could explain some of the rewidening of the gap in goods and services inflation. Consistent with these developments, inflation in imported consumer goods (excluding autos) has been moving downward recently, similarly to inflation in core goods prices. Goods and Services Prices 12-month percent change 7.0 6.0 5.0 Core services 4.0 3.0 2.0 1.0 0.0 Core goods -1.0 -2.0 -3.0 1990 1995 2000 2005 Putting all of this together, it is clear that the CPI report for April revealed some further, modest slowing of inflation, reflecting stable inflation in services prices and additional declines in goods inflation. 2010 Note: Shaded bar indicates a recession. Source: Bureau of Labor Statistics. Inflation Gap and the Exchange Rate Difference, 12-month percent change Index (1984=100) 6.0 5.0 4.0 3.0 130 120 Difference between services and goods prices (left axis) 110 100 2.0 90 1.0 80 0.0 70 Exchange rate -1.0 60 -2.0 50 -3.0 1990 40 1995 2000 2005 2010 Note: Shaded bars indicate recessions. Source: Bureau of Labor Statistics, Board of Governors of the Federal Reserve System, authors’ calculations. Goods and Import Prices 12-month percent change 7.0 6.0 5.0 4.0 3.0 Core goods 2.0 1.0 0.0 -1.0 Imports of consumer goods -2.0 -3.0 1990 1995 2000 2005 2010 Note: Shaded bars indicate recessions. Source: Bureau of Labor Statistics. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 9 Monetary Policy Yield Curve and Predicted GDP Growth, May 2013 Covering April 16, 2012–May 20, 2013 by Joseph G. Haubrich and Patricia Waiwood Highlights Overview of the Latest Yield Curve Figures May April March Three-month Treasury bill rate (percent) 0.04 0.06 0.10 Ten-year Treasury bond rate (percent) 1.93 1.73 2.04 Yield curve slope (basis points) 189 167 194 Prediction for GDP growth (percent) 0.3 0.5 0.5 Probability of recession in one year (percent) 6.1 8.1 5.9 Sources: Board of Governors of the Federal Reserve System; authors’ calculations. Yield Curve Spread and Real GDP Growth Percent 10 GDP growth (year-over-year change) 8 6 4 2 0 10-year minus three-month yield spread -2 -4 -6 1953 1960 1967 1974 1981 1988 1995 2002 Note: Shaded bars indicate recessions. Source: Bureau of Economic Analysis, Federal Reserve Board. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 2009 The slope of the yield curve—the difference between the yields on short- and long-term maturity bonds—has achieved some notoriety as a simple forecaster of economic growth. The rule of thumb is that an inverted yield curve (short rates above long rates) indicates a recession in about a year, and yield curve inversions have preceded each of the last seven recessions (as defined by the NBER). One of the recessions predicted by the yield curve was the most recent one. The yield curve inverted in August 2006, a bit more than a year before the current recession started in December 2007. There have been two notable false positives: an inversion in late 1966 and a very flat curve in late 1998. More generally, a flat curve indicates weak growth, and conversely, a steep curve indicates strong growth. One measure of slope, the spread between ten-year Treasury bonds and three-month Treasury bills, bears out this relation, particularly when real GDP growth is lagged a year to line up growth with the spread that predicts it. The slope change had a bit more impact on the probability of a recession. Using the yield curve to predict whether or not the economy will be in recession in the future, we estimate that the expected chance of the economy being in a recession next May is 6.1 percent, down from April’s 8.1 percent, though up a bit from March’s 5.9 percent. So although our approach is somewhat pessimistic as regards the level of growth over the next year, it is quite optimistic about the recovery continuing. 10 The Yield Curve as a Predictor of Economic Growth Yield Spread and Lagged Real GDP Growth Percent 10 One-year lag of GDP growth (year-over-year change) 8 6 4 2 0 Ten-year minus three-month yield spread -2 -4 -6 1953 1960 1967 1974 1981 1988 1995 2002 2009 Note: Shaded bars indicate recessions. Sources: Bureau of Economic Analysis, Federal Reserve Board. The slope of the yield curve—the difference between the yields on short- and long-term maturity bonds—has achieved some notoriety as a simple forecaster of economic growth. The rule of thumb is that an inverted yield curve (short rates above long rates) indicates a recession in about a year, and yield curve inversions have preceded each of the last seven recessions (as defined by the NBER). One of the recessions predicted by the yield curve was the most recent one. The yield curve inverted in August 2006, a bit more than a year before the current recession started in December 2007. There have been two notable false positives: an inversion in late 1966 and a very flat curve in late 1998. More generally, a flat curve indicates weak growth, and conversely, a steep curve indicates strong growth. One measure of slope, the spread between ten-year Treasury bonds and three-month Treasury bills, bears out this relation, particularly when real GDP growth is lagged a year to line up growth with the spread that predicts it. Predicting GDP Growth We use past values of the yield spread and GDP growth to project what real GDP will be in the future. We typically calculate and post the prediction for real GDP growth one year forward. Yield Curve Predicted GDP Growth Percent Predicted GDP growth 4 2 0 -2 Ten-year minus three-month yield spread GDP growth (year-over-year change) -4 -6 2002 2004 2006 2008 2010 2012 Sources: Bureau of Economic Analysis, Federal Reserve Board, authors’ calculations. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 2014 Predicting the Probability of Recession While we can use the yield curve to predict whether future GDP growth will be above or below average, it does not do so well in predicting an actual number, especially in the case of recessions. Alternatively, we can employ features of the yield curve to predict whether or not the economy will be in a recession at a given point in the future. Typically, we calculate and post the probability of recession one year forward. Of course, it might not be advisable to take these numbers quite so literally, for two reasons. First, this probability is itself subject to error, as is the case with all statistical estimates. Second, other researchers have postulated that the underlying determinants of the yield spread today are materially 11 Recession Probability from Yield Curve Percent probability, as predicted by a probit model 100 90 80 Probability of recession 70 60 Forecast 50 40 30 20 10 0 1960 1966 1972 1978 1984 1990 1996 2002 2008 Note: Shaded bars indicate recessions. Sources: Bureau of Economic Analysis, Federal Reserve Board, authors’ calculations. different from the determinants that generated yield spreads during prior decades. Differences could arise from changes in international capital flows and inflation expectations, for example. The bottom line is that yield curves contain important information for business cycle analysis, but, like other indicators, should be interpreted with caution. For more detail on these and other issues related to using the yield curve to predict recessions, see the Commentary “Does the Yield Curve Signal Recession?” Our friends at the Federal Reserve Bank of New York also maintain a website with much useful information on the topic, including their own estimate of recession probabilities. 2014 For more on the yield curve, read the Economic Commentary “Does the Yield Curve Signal Recession?” at http://www.clevelandfed.org/ Research/Commentary/2006/0415.pdf. For more on the Federal Reserve Bank of New York’s estimate fo recession, visit http://www.newyorkfed.org/research/capital_markets/ycfaq.html. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 12 Regional Economics Housing Recovery? 05.22.13 by Kyle Fee and Daniel Hartley The lowest point of the housing bust was characterized by a glut of supply. Homes for sale remained on the market longer, and foreclosed homes and those with mortgages in default added to or threatened to add to this inventory. As a result, prices fell, and construction of new homes fell to extremely low levels. Housing Starts Thousands of units Thousands of units 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600 500 400 300 2005 500 450 400 350 300 Multifamily 250 Single-family 200 150 100 50 0 2006 2007 2008 2009 2010 2011 2012 2013 Note: Data are seasonally adjusted annual rates. Source: Census Bureau. For Sale Inventory Millions of units Months 6 14 5 12 10 4 8 Units 3 6 2 4 Months’ supply 1 0 2000 2 Recent data shows that construction activity of multifamily housing has recovered to around its pre-bust level, with roughly 300,000 to 400,000 units of new construction beginning each year. These levels were typical through the late 1990s and early 2000s. In contrast, single-family housing construction—even though it has increased from around 400,000 units per year to around 600,000 units per year—is still far below its peak during the boom (1.8 million units) and less than its average during the late 1990s (about 1.2 million units per year). After swelling from 2.5 million units in 2005 to 4 million units in 2007, the inventory of housing units for sale has fallen to 2 million units, back to its year 2000 level. This means that given the recent pace of home sales, the average time a home spends on the market has fallen from around one year to less than five months. Even the inventory of foreclosed homes, another source of units for sale, has started to fall in the past year. After 14 quarters above 4.0 percent, the inventory of foreclosed homes is currently 3.6 percent. Boding well for the future, foreclosure starts and the fraction of mortgage holders that are in default have also been falling recently The reduction in supply has been accompanied by increases in prices. Home prices grew by about 10 percent over the past year following four years of price declines or stagnation. 0 2002 2004 2006 2008 2010 2012 Source: National Association of Realtors. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 While there are many encouraging signs of recovery in housing markets at the national level, there is a 13 Mortgage Delinquencies and Foreclosures Percent 6 Delinquent: 90+ days 5 4 Foreclosure inventory 3 Foreclosure starts 2 1 0 1991 1995 1999 2003 2007 2011 Source: Mortgage Banker’s Association. Home Prices Year-over-year percent change 20 good amount of variation in the degree of recovery across the country. Home-price growth has been strongest over the past year in western states and in Atlanta and New York City. A number of factors may be driving this variation. Local economic conditions and population growth play a role in driving demand for housing. At the same time, states where foreclosures are processed through the judicial system have had much less of a reduction in their stocks of foreclosures than nonjudicial foreclosure states. Nonjudicial foreclosure states are able to process the stock of foreclosed homes faster, effectively shrinking the foreclosure inventory. There seems to be some correlation between recent price growth and the way foreclosures are processed. Additionally, there appears to be higher price growth in the past year in places where prices fell the most from the peak of the boom to the trough. 15 10 5 All sales Excluding distressed 0 -5 -10 -15 -20 -25 2000 2002 2004 2006 2008 2010 2012 Note: Shaded bars indicate recessions. Source: CoreLogic. House-Price Growth by State, 2012-2013 Less than 2.6% 2.6% - 4.3% 4.3% - 9.0% Greater than 9.0% Source: CoreLogic. Federal Reserve Bank of Cleveland, Economic Trends | June 2013 14 Economic Trends is published by the Research Department of the Federal Reserve Bank of Cleveland. Views stated in Economic Trends are those of individuals in the Research Department and not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of Governors of the Federal Reserve System. Materials may be reprinted provided that the source is credited. If you’d like to subscribe to a free e-mail service that tells you when Trends is updated, please send an empty email message to econpubs-on@mail-list.com. No commands in either the subject header or message body are required. ISSN 0748-2922 Federal Reserve Bank of Cleveland, Economic Trends | June 2013 15
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