Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Explaining the Boom-Bust Cycle in the U.S. Housing Market: A Reverse-Engineering Approach ∗ Paolo Gelain Kevin J. Lansing Gisle J. Navik Norges Bank FRBSF Norges Bank October 22, 2014 RBNZ Workshop The Interaction of Monetary and Macroprudential Policy ∗ Any opinions expressed here do not necessarily reflect the views of the managements of the Norges Bank, the Federal Reserve Bank of San Francisco, or the Board of Governors of the Federal Reserve System.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras U.S. housing market boom and bust, 1995 to 2012 Correlated booms and busts in house prices, mortgage debt, and consumption. Housing rent-income ratio did not increase during the boom. Start of NBER recession = 2007.Q4.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What explains the U.S. housing market boom and bust? Rational explanations of the boom often appeal to exogenous “housing demand shocks” (increase in housing preference). Problem with this story: An increase in housing preference would increase the housing service flow, as measured by the imputed rent. But this did not happen in the data.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What explains the U.S. housing market boom and bust? Rational explanations of the boom often appeal to exogenous “housing demand shocks” (increase in housing preference). Problem with this story: An increase in housing preference would increase the housing service flow, as measured by the imputed rent. But this did not happen in the data. Empirical evidence: Changes in lending standards contributed to the boom-bust cycle.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What explains the U.S. housing market boom and bust? Rational explanations of the boom often appeal to exogenous “housing demand shocks” (increase in housing preference). Problem with this story: An increase in housing preference would increase the housing service flow, as measured by the imputed rent. But this did not happen in the data. Empirical evidence: Changes in lending standards contributed to the boom-bust cycle. This Paper: Investigate how a simple asset pricing model with 4 shocks can account for the boom-bust patterns in U.S. data over the period 1995 to 2012.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What explains the U.S. housing market boom and bust? Rational explanations of the boom often appeal to exogenous “housing demand shocks” (increase in housing preference). Problem with this story: An increase in housing preference would increase the housing service flow, as measured by the imputed rent. But this did not happen in the data. Empirical evidence: Changes in lending standards contributed to the boom-bust cycle. This Paper: Investigate how a simple asset pricing model with 4 shocks can account for the boom-bust patterns in U.S. data over the period 1995 to 2012. Model with moving-average forecast rules and long-term mortgages does best in matching the U.S. data. Matches data with small housing preference shocks and plausible lending standard shocks.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Competing explanations for the boom-bust episode Interest rates too low (empirical). Taylor (2007, Jackson Hole )
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Competing explanations for the boom-bust episode Interest rates too low (empirical). Taylor (2007, Jackson Hole ) Shifting lending standards (empirical). Mian and Sufi (2009, IMF Review ) Demyanyk and Van Hemert (2009, Rev. Financial Studies ) Duca, Muellbauer & Murphy (2011, Economic Journal ) Dokko, et al. (2011, Economic Policy )
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Competing explanations for the boom-bust episode Interest rates too low (empirical). Taylor (2007, Jackson Hole ) Shifting lending standards (empirical). Mian and Sufi (2009, IMF Review ) Demyanyk and Van Hemert (2009, Rev. Financial Studies ) Duca, Muellbauer & Murphy (2011, Economic Journal ) Dokko, et al. (2011, Economic Policy ) Shifting lending standards plus rational learning (model). Boz and Mendoza (2012, JME )
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Competing explanations for the boom-bust episode Interest rates too low (empirical). Taylor (2007, Jackson Hole ) Shifting lending standards (empirical). Mian and Sufi (2009, IMF Review ) Demyanyk and Van Hemert (2009, Rev. Financial Studies ) Duca, Muellbauer & Murphy (2011, Economic Journal ) Dokko, et al. (2011, Economic Policy ) Shifting lending standards plus rational learning (model). Boz and Mendoza (2012, JME ) Housing preference shocks, not LTV shocks (model). Justiniano, Primiceri & Tambalotti (2013, NBER WP 18941)
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Competing explanations for the boom-bust episode Interest rates too low (empirical). Taylor (2007, Jackson Hole ) Shifting lending standards (empirical). Mian and Sufi (2009, IMF Review ) Demyanyk and Van Hemert (2009, Rev. Financial Studies ) Duca, Muellbauer & Murphy (2011, Economic Journal ) Dokko, et al. (2011, Economic Policy ) Shifting lending standards plus rational learning (model). Boz and Mendoza (2012, JME ) Housing preference shocks, not LTV shocks (model). Justiniano, Primiceri & Tambalotti (2013, NBER WP 18941) Looser saving constraints –> lower interest rates (model). Justiniano, Primiceri & Tambalotti (2014, WP)
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Competing explanations for the boom-bust episode Interest rates too low (empirical). Taylor (2007, Jackson Hole ) Shifting lending standards (empirical). Mian and Sufi (2009, IMF Review ) Demyanyk and Van Hemert (2009, Rev. Financial Studies ) Duca, Muellbauer & Murphy (2011, Economic Journal ) Dokko, et al. (2011, Economic Policy ) Shifting lending standards plus rational learning (model). Boz and Mendoza (2012, JME ) Housing preference shocks, not LTV shocks (model). Justiniano, Primiceri & Tambalotti (2013, NBER WP 18941) Looser saving constraints –> lower interest rates (model). Justiniano, Primiceri & Tambalotti (2014, WP) “Bubble”: Departure from rational expectations Adam, Kuang & Marcet (2012, NBER Macro Annual ) Gelain, Lansing & Mendicino (2013, IJCB )
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Relaxed lending standards and the run-up in house prices Source: B. Tal (2006), CIBC World Markets, Consumer Watch U.S. (October 18). House prices rose faster where lending standards were weaker.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What motivated the change in lending standards? “[T]he financial services sector has been dramatically transformed by technology......Where once more-marginal applicants would simply have been denied credit, lenders are now able to quite efficiently judge the risk posed by individual applicants and to price that risk appropriately. These [technology] improvements have led to rapid growth in subprime mortgage lending.”
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What motivated the change in lending standards? “[T]he financial services sector has been dramatically transformed by technology......Where once more-marginal applicants would simply have been denied credit, lenders are now able to quite efficiently judge the risk posed by individual applicants and to price that risk appropriately. These [technology] improvements have led to rapid growth in subprime mortgage lending.” Mortgage Delinquencies by Loan Type
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras What motivated the change in lending standards? “[T]he financial services sector has been dramatically transformed by technology......Where once more-marginal applicants would simply have been denied credit, lenders are now able to quite efficiently judge the risk posed by individual applicants and to price that risk appropriately. These [technology] improvements have led to rapid growth in subprime mortgage lending.” Mortgage Delinquencies by Loan Type Fed Chairman Alan Greenspan, April 8, 2005 .
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras House price changes and their expectations in four cities Source: Case, Shiller, and Thompson (2012), Brookings Papers on Economic Activity.
Overview Evidence Model Calibration Quantitative Results Reverse Engineered Shocks Conclusion Extras Expected house price changes vs. lagged price changes Source: Case, Shiller, and Thompson (2012), Brookings Papers on Economic Activity. “12-month expectations are fairly well described as attenuated versions of lagged actual 12-month price changes.”
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