House Prices and Productivity Growth

Many economists, myself included, refer to the recent boom and bust in house prices as a bubble, whose foundation lay in a combination of credit market excesses and human imperfections. Fundamentals certainly played a role as well, but bubble forces were particularly important.

In a short paper recently published by the New York Federal Reserve, Jim Kahn makes a very different argument: that the boom and bust in house prices can largely be explained by a boom and bust in productivity growth:

The housing boom and bust of the last decade, often attributed to “bubbles” and credit market irregularities, may owe much to shifts in economic fundamentals. A resurgence in productivity that began in the mid-1990s contributed to a sense of optimism about future income that likely encouraged many consumers to pay high prices for housing. The optimism continued until 2007, when accumulating evidence of a slowdown in productivity helped dash expectations of further income growth and stifle the boom in residential real estate.

Jim’s argument depends on several related lines of reasoning:

  • First, he notes that productivity drives long-term income growth and that incomes determine how much families can pay for homes. He then argues that the demand and supply for housing are inelastic and, as a result, rising incomes imply rising house prices. Putting these pieces together, he concludes that faster productivity growth implies faster house price appreciation.
  • Second, he notes that productivity growth accelerated in the mid-to-late 1990s and then slowed around 2004. The productivity acceleration thus began shortly before house price took off, and the productivity slowdown began shortly before house prices began to collapse.

Jim’s chart shows his estimate of whether the United States is in a high-productivity-growth regime over time. That probability was essentially zero in the early 1990s but jumped to 100% by the late 1990s. It then fell close to zero again in the mid-2000s.

  • Third, Jim argues that it takes time for people to recognize that a productivity shift has occurred. Although the recent productivity slowdown began, according to his estimates, in 2004, observers wouldn’t recognize that for some time. His model suggests, in fact, that observers wouldn’t have been able to figure out that productivity had slowed until the release of revised data in the summer of 2007. Forecasts before then (see the blue line labeled June 2007) would likely have continued to presume solid productivity growth. But subsequent forecasts (the orange line labeled August 2007) would have assumed lower productivity growth.

In his view, that recognition lag explains why house prices continued to grow rapidly for a few years after the productivity slow down began. In short, where many observers see bubble-like thinking, he sees a time lag in understanding the state of the economy.

P.S. Jim’s paper also provides some interesting projections of productivity growth. The following graph, for example, shows the difference between projections that assume the U.S. is in a high-productivity-growth regime (blue line from June 2007) and projections that assume we are in a low-productivity-growth regime (orange and green):

About Donald Marron 294 Articles

Donald Marron is an economist in the Washington, DC area. He currently speaks, writes, and consults about economic, budget, and financial issues.

From 2002 to early 2009, he served in various senior positions in the White House and Congress including: * Member of the President’s Council of Economic Advisers (CEA) * Acting Director of the Congressional Budget Office (CBO) * Executive Director of Congress’s Joint Economic Committee (JEC)

Before his government service, Donald had a varied career as a professor, consultant, and entrepreneur. In the mid-1990s, he taught economics and finance at the University of Chicago Graduate School of Business. He then spent about a year-and-a-half managing large antitrust cases (e.g., Pepsi vs. Coke) at Charles River Associates in Washington, DC. After that, he took the plunge into the world of new ventures, serving as Chief Financial Officer of a health care software start-up in Austin, TX. After that fascinating experience, he started his career in public service.

Donald received his Ph.D. in Economics from the Massachusetts Institute of Technology and his B.A. in Mathematics a couple miles down the road at Harvard.

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