Adam, K., Kuang, P., and Marcet, A. (2012). House Price Booms and the Current Account. NBER Macroeconomics Annual, 26(1):77–122.
Adelino, M., Schoar, A., and Severino, F. (2012). Credit Supply and House Prices: Evidence from Mortgage Market Segmentation. NBER Working Papers 17832, National Bureau of Economic Research, Inc.
Ariccia, G. D., Igan, D., and Laeven, L. (2012). Credit Booms and Lending Standards: Evidence from the Subprime Mortgage Market. Journal of Money, Credit and Banking, 44:367–384.
Bayer, P., Geissler, C., and Roberts, J. W. (2011). Speculators and Middlemen: The Role of Intermediaries in the Housing Market. NBER Working Papers 16784, National Bureau of Economic Research, Inc.
Bernanke, B. S. (2005). The Global Saving Glut and the U.S. Current Account Deficit. Speech 77, Board of Governors of the Federal Reserve System (U.S.).
Burnside, C., Eichenbaum, M., and Rebelo, S. (2011). Understanding Booms and Busts in Housing Markets. NBER Working Papers 16734, National Bureau of Economic Research, Inc.
Caballero, R. J., Farhi, E., and Gourinchas, P.-O. (2008). An Equilibrium Model of Global Imbalances and Low Interest Rates. American Economic Review, 98(1):358–93.
Calza, A., Monacelli, T., and Stracca, L. (2013). Housing Finance And Monetary Policy. Journal of the European Economic Association, 11:101–122.
Campbell, J. Y. and Shiller, R. J. (1988). The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors. Review of Financial Studies, 1(3):195–228.
Campbell, S. D., Davis, M. A., Gallin, J., and Martin, R. F. (2009). What Moves Housing Markets: A Variance Decomposition of the Rent-Price Ratio. Journal of Urban Economics, 66(2):90–102.
Canova, F. and De Nicolo, G. (2002). Monetary Disturbances Matter for Business Fluctuations in the G-7. Journal of Monetary Economics, 49(6):1131–1159.
Case, K. E. and Shiller, R. J. (2003). Is There a Bubble in the Housing Market? Brookings Papers on Economic Activity, 34(2):299–342.
Chinco, A. and Mayer, C. (2014). Misinformed Speculators and Mispricing in the Housing Market. NBER Working Papers 19817, National Bureau of Economic Research, Inc.
Dokko, J., Doyle, B. M., Kiley, M. T., Kim, J., Sherlund, S., Sim, J., and Heuvel, S. V. D. (2011). Monetary Policy and the Global Housing Bubble. Economic Policy, 26(66):233–283.
Duca, J. V., Muellbauer, J., and Murphy, A. (2011). House Prices and Credit Constraints: Making Sense of the US Experience. Economic Journal, 121(552):533–551.
Fry, R. and Pagan, A. (2011). Sign Restrictions in Structural Vector Autoregressions: A Critical Review. Journal of Economic Literature, 49(4):938–60.
Gete, P. (2015). Housing Markets and Current Account Dynamics. Globalization and Monetary Policy Institute Working Paper 221, Federal Reserve Bank of Dallas.
Glaeser, E. L., Gottlieb, J. D., and Gyourko, J. (2012). Can Cheap Credit Explain the Housing Boom? In Housing and the Financial Crisis, NBER Chapters, pages 301–359. National Bureau of Economic Research, Inc.
Glaeser, E. L., Gyourko, J., and Saiz, A. (2008). Housing Supply and Housing Bubbles. Journal of Urban Economics, 64(2):198–217.
Head, A., Lloyd-Ellis, H.,, and Sun, H. (2014). Search, Liquidity, and the Dynamics of House Prices and Construction. American Economic Review, 104(4):1172–1210.
Himmelberg, C., Mayer, C., and Sinai, T. (2005). Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions. NBER Working Papers 11643, National Bureau of Economic Research, Inc.
Huang, H. and Tang, Y. (2012). Residential Land Use Regulation and the US Housing Price Cycle Between 2000 and 2009. Journal of Urban Economics, 71(1):93–99.
Iacoviello, M. and Neri, S. (2010). Housing Market Spillovers: Evidence from an Estimated DSGE Model. American Economic Journal: Macroeconomics, 2(2):125–64.
Inoue, A. and Kilian, L. (2013). Inference on Impulse Response Functions in Structural VAR Models. Journal of Econometrics, 177(1):1–13.
Jarocinski, M. and Smets, F. R. (2008). House Prices and the Stance of Monetary Policy. Review, Federal Reserve Bank of St. Louis.
Justiniano, A., Primiceri, G. E., and Tambalotti, A. (2015). Credit Supply and the Housing Boom. NBER Working Papers 20874, National Bureau of Economic Research, Inc.
Kilian, L. and Murphy, D. P. (2014). The Role of Inventories and Speculative Trading in the Global Market for Crude Oil. Journal of Applied Econometrics, 29(3):454–478.
King, R. G., Plosser, C. I., Stock, J. H., and Watson, M. W. (1991). Stochastic Trends and Economic Fluctuations. American Economic Review, 81(4):819–40.
Knittel, C. R. and Pindyck, R. S. (2013). The Simple Economics of Commodity Price Speculation. NBER Working Papers 18951, National Bureau of Economic Research, Inc.
Kuttner, K. (2012). Low Interest Rates and Housing Bubbles: Still No Smoking Gun. Department of Economics Working Papers 2012-01, Department of Economics, Williams College.
Lambertini, L., Mendicino, C., and Punzi, M. T. (2013). Expectation-Driven Cycles in the Housing Market: Evidence from Survey Data. Journal of Financial Stability, 9(3):518–529.
Leung, C. K. Y. and Tse, C.-Y. (2012). Flippers in Housing Market Search. 2012 Meeting Papers 434, Society for Economic Dynamics.
Ling, D. C., Ooi, J. T., and Le, T. T. (2015). Explaining House Price Dynamics: Isolating the Role of Nonfundamentals. Journal of Money, Credit and Banking, 47(S1):87–125.
Mian, A. and Sufi, A. (2009). The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis. The Quarterly Journal of Economics, 124(4):1449–1496.
Poterba, J. M. (1984). Tax Subsidies to Owner-occupied Housing: An Asset-Market Approach. The Quarterly Journal of Economics, 99(4):729–52.
Quigley, J. (2003). Comment on Case and Shiller: Is There a Bubble in the Housing Market? Brookings Papers on Economic Activity, 34(2):343–362.
Rubio-Ramirez, J. F., Waggoner, D. F., and Zha, T. (2010). Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference. Review of Economic Studies, 77(2):665–696.
Sá, F., Towbin, P., and Wieladek, T. (2014). Capital Inflows, Financial Structure and Housing Booms. Journal of the European Economic Association, 12:522–546.
Sá, F. and Wieladek, T. (2015). Capital Inflows and the U.S. Housing Boom. Journal of Money, Credit and Banking, 47(S1):221–256.
Shiller, R. J. (2007). Understanding Recent Trends in House Prices and Homeownership. Proceedings - Economic Policy Symposium - Jackson Hole, pages 89–123.
Smith, M. H. and Smith, G. (2006). Bubble, Bubble, Where’s the Housing Bubble? Brookings Papers on Economic Activity, 37(1):1–68.
Uhlig, H. (2005). What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure. Journal of Monetary Economics, 52(2):381–419.
Wheaton, W. C. (1990). Vacancy, Search, and Prices in a Housing Market Matching Model. Journal of Political Economy, 98(6):1270–92.
Annex I. Historical Contribution of All Shocks to House Price Developments, Alternative Models
Annex II. Survey-based Expectations versus Shocks
We are grateful to Olivier Blanchard, Hamid Faruqee, Maja Ganarin, Ayhan Kose, Kenneth Kuttner, John Muellbauer, Prakash Loungani, Emil Stavrev, Martin Straub, participants at the Bundesbank-IMF-DFG conference, the SSES congress, and the IMF and SNB Research seminars for very helpful comments. The views expressed here are those of the authors and not necessarily those of the IMF or the SNB.
This value includes housing held off the market.
The average maturity of the interest rate is 27 years and hence does not coincide exactly with the horizon of the inflation expectations. However, it is plausible that the expectation 10 year ahead accurately reflect the expectations of inflation at longer horizon.
This result is obtained for example if we consider the current house price Pt as the expected present discounted value of future rents Rt+1, discounted at rate
Σ is drawn from an Inverted-Wishart Distribution IW(ΣOLS, T), and coefficient matrices Ai from a Normal Distribution
We compute Q by drawing an independent standard normal matrix X and apply the QR decomposition X = QR.
The number of rotations needed to obtain 10000 acceptances varies with the specification. In the baseline model about 2 percent of the rotations are accepted.
Igan and Loungani (2012) using a standard VAR framework for individual countries, find that the average impact of a 1 percent increase in housing prices is a 0.2 percent increase in GDP growth.
Table 2 reports additionally the contribution of the deterministic component, i.e. the path the house prices would have taken if no shocks had occurred after 1996Q4. It is influenced by the initial conditions and the estimated long term growth rate of real house prices.
As discussed in the identification section, mortgage rate shocks capture, in addition to monetary policy shocks also shocks to risk free long term rates as well as shocks to mortgage risk premia.
The deterministic component explains a negligibly small part.
Hence, the identification follows closely Jarocinski and Smets (2008) and would be identical to the one described in Table (1) without any constraints on the vacancy rate and no identification of the price expectation shock.
Respondents include both, those saying that it is a good moment to buy because prices will rise and those who believe it is a good moment to buy because prices are low, thus implicitly judging prices to be higher in the future.
An exception is the peak in the late seventies. The marked real house price peak occurs because of high inflation, while nominal house prices do not reach a peak.
The corresponding figures are in an appendix.
We fix ρ = 0.98 based on the median quarterly rent-to-price ratio in the U.S. in 1996, which is close to 1.3% (see Campbell et al., 2009).
In practice, we constrain the relationship to hold for all quarters from 40 quarters to 200 quarters after the shock, when the respective npv’s have effectively converged to the long term level.
Thus, the x-axis for NPV refers to the expanding investment horizon k=T. For example, in the first period the ex post NPV is based on rental income R earned in that period plus the discounted house price P/(1+i). In period k=2, the NPV is based on discounted rental income in period 1 and 2 and the discounted house price in Period 2 and so on.
The closest to mean model is obtained by finding the rotation which minimizes the squared percent difference between the point-wise mean impulse response functions and the respective model’s impulse response to all identified shocks for all seven response variables for the first two quarter, i.e. the length of the imposed sign restriction.
Non-interest rate related lending standards are a broader concept than LTV and include for example debtor screening standards. Thus, LTV cannot fully capture all dimensions of lower lending standards. It is not evident, which measure best to use to reflect the overall ease of lending standards and a full treatment of the issues is beyond the scope of this paper. For a careful discussion of the role of lending standards see for instance Mian and Sufi (2009) or Ariccia et al. (2012), who use micro-level data.
We would like to thank the authors for sharing the series with us. Another study confirms that first time buyers tend to have a higher average LTV share (Patrabansh, 2013). However, this study finds little difference in the changes over time of the first-time buyer LTV relative to the repeat buyer LTV ratio.