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House Prices and Economic Growth

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Abstract

Using quarterly data for all 379 metropolitan statistic areas (MSAs) in the U.S. from 1980:1 to 2008:2, this paper empirically studies the effect of house prices on local Gross Metropolitan Product (GMP). We compare the effects of predictable and unpredictable house price changes, which we use to capture the collateral and wealth effects of house prices respectively. We further analyze the relationship between the effects and household borrowing constraints, as well as the temporal pattern of the effects. Our analysis provides the following findings. First, house price changes have significant effects on GMP growth, and the effect of predictable changes (the collateral effect) is about three times stronger than the effect of unpredictable changes (the wealth effect). Second, the persistent component of predictable changes has a stronger collateral effect than the novel component. Third, when households are more financially constrained, the collateral effect is stronger, the wealth effect is weaker, and the total effect remains unchanged. Finally, the effects last for eight quarters, and peak on the fourth quarter after house price changes take place.

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Notes

  1. On September 26th, 2005 Alan Greenspan in a speech to the American Bankers Association re-iterated the important role of house prices in the current economy and suggested that housing may have fueled consumption over the past few years.

  2. See Determination of the December 2007 Peak in Economic Activity, National Bureau of Economic Research.

  3. The definitions of the wealth effect and the collateral effect will be discussed in the literature review section.

  4. The common joke from 2000 through 2005 was that the American home was akin to an ATM machine.

  5. Carroll et al. (2006, working paper) highlight the important of the speed at which the economy adjusts to housing shocks. However, while they distinguish immediate consumption effect from total effect of housing shocks, they do not analyze the temporal pattern of the effect.

  6. Data providers, both economy.com and OFHEO, recalculate historical values of the series whenever the MSA definitions change. As a result, the time series of the variables are consistent geographically.

  7. We use the median single family home price instead of the OFHEO house price index to construct the housing collateral ratio for the OFHEO indexes are all normalized and not denominated with $.

  8. For example, Ortalo-Magné and Rady (2004) suggests that income shocks affect the housing market, and Gabriel et al. (1999) substantiate the effect of migrations on the housing market.

  9. See Clayton et al. (2009) for details regarding the positive relation between house permits and constructions.

  10. While the use on MSA-level data has advantages over international data, it is worth noting that there still could be significant variation in the tax code, political environment, etc. across the municipalities within an MSA.

  11. The first step estimates the “weighted” productivity for each NAICS Supersector industry (e.g., Manufacturing, Education & Health Services, etc) in the MSA by multiplying the U.S. level productivity for this industry with the ratio of industry employment to total employment in the MSA (data from BLS). The second step estimates the GMP with the product of the sum of “weighted” productivity with the total MSA employment. This procedure essentially sums up the estimated products of all NAICS Supersector industries in the MSA.

  12. Including the unemployment rate as an additional instrumental variable does not change the results in “Empirical Analysis”, but significantly reduces the sample size in the analysis.

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Acknowledgements

We thank the editor C. F. Sirmans and two anonymous referees for very constructive comments. We also thank Shaun Bond, Grace Wong, and other participants of 2006 AREUEA annual and mid-year conferences for insightful comments. All errors are ours.

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Authors and Affiliations

  1. School of Business Administration, University of San Diego, 5998 Alcalá Park, San Diego, CA, 92110-2492, USA

    Norman Miller

  2. Leeds School of Business, University of Colorado at Boulder, 419 UCB, Boulder, CO, 80309-0419, USA

    Liang Peng

  3. Collateral Analytics, 3465 Waialae Avenue, Suite 330, Honolulu, HI, 96816, USA

    Michael Sklarz

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  1. Norman Miller
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  2. Liang Peng
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  3. Michael Sklarz
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Correspondence to Liang Peng.

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Miller, N., Peng, L. & Sklarz, M. House Prices and Economic Growth. J Real Estate Finan Econ 42, 522–541 (2011). https://doi.org/10.1007/s11146-009-9197-8

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