Several recent articles have noted a sharp rise in the price of renting an apartment or house across the United States. Many have also argued that the rise in rents disproportionately affects lower and middle class renters. We decided to take look by examining the great data available on rents from Zillow.
The chart below shows general inflation (measured with PCE headline inflation) versus the increase in rents. Both series are indexed to be 100 as of November 2010 (the first month the Zillow data are available). The pattern is undeniable: rents are rising much more rapidly than other consumer prices.
So rents are rising rapidly. But where is the increase in rents the largest? Several stories suggest that the rise in rental prices has been largest for lower and middle income families. Zillow has zip code level data on rents, which we can use to evaluate this argument.
Here is the growth in rent from November 2010 to March 2014 across zip codes, where we split zip codes into five groups based on their average adjusted gross income as of 2006.
As the graph clearly shows, . . .
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So both John Cochrane and Martin Wolf are advocating 100% reserve banking. If these two agree on anything, it’s worth taking seriously! (It’s pretty amazing how advocates for narrow banking come from across the political spectrum.) We have some thoughts on banking and private money creation, but you’ll have to read our book to figure out what they are.
Instead, we wanted to provide some history behind the idea. One of the greatest economists of the 20th century (indeed, in our view, perhaps the greatest), Irving Fisher was a strong supporter of the so-called Chicago Plan which would implement 100% reserve banking. Here is a link to the one of the original documents from 1939. The Chicago Plan economists were living in the shadow of the Great Depression, and it had a very strong influence on their thinking.
Some of the document is a bit hard to get through, but we recommend starting on page 14 where the section entitled “The Fractional Reserve System” begins. The economists pulled no punches:
“A chief loose screw in our present American money and banking system is the requirement of only fractional reserves behind demand deposits. Fractional reserves give our thousands of commercial banks power . . .
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Exactly a year ago from today, one of us wrote a short piece entitled “Will Housing Save the U.S. Economy?” The conclusion was pessimistic:
“we need to temper our optimism on what a housing recovery can do. I agree that house prices will continue to rise and new residential construction will steadily increase from its current very low level … But we will not be returning to the boom years that preceded the Great Recession. The days when housing was the predominant force driving economic activity are gone…”
With an additional year of data, the prediction from a year ago seems spot on. Let’s take a closer look.
Rising house prices could potentially boost economic activity through two channels. First, and most importantly in our view, it could allow lower income, lower credit score households to borrow and spend. This has been historically called the “housing wealth effect,” but the “home equity withdrawal effect” is a more appropriate term. As our latest research shows, the housing wealth effect is driven entirely by lower income households who borrow against rising home equity to spend. This channel was huge during the 2002 to 2006 period, something our research quantifies.
Second, it could potentially . . .
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As we mentioned in our post yesterday, economists care much more about inequality in well-being rather than inequality in income or wealth. Data on well-being are more difficult to gather, but we discussed some evidence that inequality in consumption also increased from 1980 to 2010. Consumption directly affects the utility of an individual in most economic models. Income does not.
Here is some more evidence, with a particular focus on the last few years:
Aaron Cobet of the Bureau of Labor Statistics used information from the Consumer Expenditure Survey to plot the following chart, which measures the change in annual expenditures for the richest and poorest Americans between 2008 and 2012.
As it shows, spending increased the most from 2008 to 2012 for the richest households in the United States. So stronger income growth for rich Americans from 2008 to 2012 has translated into stronger spending growth as well. A working paper by Barry Cynamon and Steve Fazzari shows similar results.
The chart above is consistent with this fantastic New York Times article written by Nelson Schwartz earlier this year. He reports that high-end hotels and casinos that target rich customers are doing much better . . .
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There has been a lot of attention on income and wealth inequality in the past few years. But should we care about income and wealth inequality? Presumably we care because we believe that increases in income and wealth inequality lead to higher inequality in outcomes such as consumption, health, or overall well-being. In economics, it is these outcomes that actually enter the utility function, not income or wealth.
Income and wealth inequality in the United States have risen sharply since 1980. This is indisputable. But has this growth in income inequality led to an increase in consumption inequality? In other words, have the poor been buying fewer goods and services as their incomes have declined in relative terms?
At first glance, the answer to this question may seem obvious: if the poor are getting poorer, how could they avoid a reduction in spending? But the answer is not obvious. If government programs provide large amounts of assistance to the poor, then consumption inequality may be muted relative to income inequality. Or if the rich lend vast sums to the poor through the financial system, then the spending of the poor may remain high, at least in the short run until . . .
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Great reads from Nick Timiraos over at the Wall Street Journal:
Five Questions on the State of Mortgage Lending
Mortgage Lenders Ease Rules for Home Buyers in Hunt for Business
His central point is that the mortgage market is starting to direct credit toward lower credit quality buyers.
We still think that the mortgage market remains pretty conservative, especially compared to the auto and credit card loan market. House prices have risen sharply, especially out west, and many first-time potential buyers are priced out of the market.
There are many who think the mortgage market is excessively tight, but that isn’t obvious. The key question remains: can potential buyers service the payments on the large mortgages they need to buy pretty expensive homes? Logan Mohtashami is a loan manager in southern California who argues that income levels do not justify a lot more mortgage lending given how high prices are. His views are worth checking out: (@LoganMohtashami).
. . .
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In a series of speeches, Federal Reserve Governor Jeremy Stein emphasized the importance of financial stability concerns in monetary policy-making. But how does one measure whether threats to financial stability are lurking?
Put differently, can we know that there is a credit bubble about to burst?
In his speeches, Stein cites the work of two Harvard Business School professors, Robin Greenwood and Samuel Hanson. Their research argues that a good indicator of credit market overheating is the share of all new corporate debt issues coming from low-grade issuers.
This measure is based on the quantity of credit issued, not just interest rates. Others focus exclusively on credit spreads, or the interest rate differentials between, say, junk and investment grade firms. Greenwood and Hanson argue that quantities of credit issued by low-grade versus high-grade firms add a lot of power when predicting credit market crashes.
So how big is the risk of a credit market crash today? Robin and Sam were nice enough to send us the updated data through 2013. This chart shows the high yield share of corporate debt issues. Or in other words, the fraction of all corporate debt issues by high yield (or junk) firms:
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We hate to be the Debbie Downers of the day, but we just plotted the graph below and felt we should share it. It plots real GDP for the United States from 1970 to 2013 using a logarithmic scale. If the red line were straight, that would imply that real GDP grew at a constant rate from 1970 to 2013.
The black dots represent the trend using the pre-2007 data. In other words, we estimate the trend growth rate the U.S. economy was on prior to the 2007 recession. The difference between the red line and the black dots represents the deviation from trend for the U.S. economy.
Two things jump out. First, in all other recessions, we returned to trend pretty quickly. Second, the Great Recession has taken what looks like a permanent toll on the U.S. economy. We went way off trend. We are not catching up. And if anything, it looks like we are getting further away from trend.
We did a similar exercise using retail spending in a previous post. But the result is even more startling using GDP.
Like we said, sorry to be Debbie Downers.
. . .
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New housing starts for March are out today. Rather than focus on the short-term movements, it’s worth looking at the long run. Here is the graph from calculatedriskblog.com:
It’s really quite an amazing graph. We are now five full years from the end of the recession (if you buy NBER dating). And housing starts are still below any level we’ve seen since the early 1990s!
So who out there thinks we are ever going to get back to the 1.5 million annualized rate? When?
It may be time to start taking seriously the idea that the boost to the economy from new residential construction in the long-run may be much lower than it was in the 10 years prior to the Great Recession. The anomaly is not the weakness now, but the strength in the late 1990s and early 2000s.
. . .
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Chicago Fed President Charles Evans noted last week that “the U.S. consumer is slowly improving but is just a shadow of its former self.” We couldn’t have said it better. Retail sales for March is out this morning, and we thought it would be a good time to examine why household spending has been so weak using data that were just updated through 2013.
The chart below plots spending of U.S. households from 2006 to 2013. We split states into five groups, and we plot household spending for the 20% of states that had the worst housing crash from 2006 to 2009 and the 20% of states that had the smallest decline in house prices over the same period. Both groups contain 20% of the population, and the states in the worst housing crash group are Arizona, California, Florida, Michigan, and Nevada. The states where house prices fell the least include about 15 states scattered throughout the country. Both series are indexed to be 100 in 2006, which allows one to calculate the percentage change relative to 2006 for any year by simply taking that year’s spending position on the y axis and subtracting 100.
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