Arnold, N., J. Chen, F. Columba, G. Ho, and B. Mircheva, 2015, “The Nordic Financial Sector, Household Debt, and the Housing Market,” forthcoming.
Caldera Sanchez, A. and A. Johansson, 2011, “The Price Responsiveness of Housing Supply in OECD Countries, OECD Economics Department Working Paper.
Cardarelli, R., T. Monacelli, A. Rebucci, and L. Sala, 2009, “Housing Finance, Housing Shocks, and the Business Cycle: Evidence from OECD Countries,” IADB Working Paper.
International Monetary Fund, 2008, “The Changing Housing Cycle and the Implications for Monetary Policy,” World Economic Outlook Chapter 3 (April).
International Monetary Fund, Multi-Country Report, Housing Recoveries: Cluster Report on Denmark, Ireland, Kingdom of the Netherlands – the Netherlands, and Spain, IMF Country Report No. 15/1, 2015.
Lindquist, M., M. D. Riiser, H. Solheim, and B. H. Vatne, “Ten Years of Household Micro Data: What Have We Learned?” Staff Memo 8/2014.
Prepared by Giang Ho and Kazuko Shirono.
These estimates are calculated from deviations in price-to-income ratio and price-to-rent ratio, and also based on a model used in the early warning exercise. See IMF (2013) for more details on the methodology.
The price-to-rent ratio is often used to gauge house price misalignment, but rent series tend to imperfectly capture rent developments in practice. In Norway, the rent series is thought to capture mostly the rent developments of existing rental contracts, which tend to move at the rate of CPI inflation due to regulations. This tendency could lead to an overestimation of house price gaps using the price-to-rent ratio. In addition, the rental market in Norway, being relatively small and very different from the owner-occupied market, provides limited substitutes for the owneroccupied housing market, which makes the price-to-rent ratio an imperfect measure of house price valuation particularly for Norway.
The National Budget 2015.
Non-financial assets from the household level data are significantly larger than non-financial assets reported in aggregate OECD data. The difference is due to different valuations of real assets. Household level data use market values to evaluate real assets.
The indicator takes into account if interest payments on mortgage debt are deductible from taxable income and if there are any limits on the allowed period of deduction or the deductible amount, and if tax credits for loans are available.
See Arnold and others (forthcoming).
See Finanstilsynet, Risk Outlook 2014, and Risk Outlook 2013.
Norges Banks also conducts various sensitivity analyses using this framework. See, for example, Lindquist et al. (2014).
Deposit rates are also assumed to rise by 3 percent at the same time.
Parameters are calibrated based on similar shocks assumed in the bank stress test in the 2015 Norway FSAP exercise.
These results need to be interpreted with caution. About 40 percent of the debt held by household younger than 24 years old is vulnerable under the combined shock scenario, but these households hold less than 3.5 percent of total household debt, and close to 30 percent of their debt is student loans, which are interest free as long as borrowers remain in school. On the other hand, households aged 25-34 years hold more than 20 percent of total household debt. Thus the vulnerability of the latter group of households is likely to be more significant than the very young households.
In particular, the ordering of variables in the VAR is as listed above. An assumption is that macroeconomic variables are affected by monetary policy only with a lag while monetary policy responds contemporaneously to changes in all variables in the system. The house price variable enters last, allowing house prices to respond instantly to macroeconomic variables and monetary policy.