Borio, C., N. Kennedy, and S. Prowse, 1994, “Exploring Aggregate Asset Price Fluctuations Across Countries,” BIS Economic Papers No. 40, April.
Carey, M., 1990, “Feeding the Fad: the Federal Land Banks, Land Market Efficiency and the Farm Credit Crisis,” University of California, Berkeley.
Englund, Peter, 1997, “House Price Dynamics: An International Empirical Perspective,” Journal of Housing Economics No. 6, pp. 119-36.
Evans, Owen, Alfredo M. Leone, Mahinder Gill, and Paul Hilbers, 2000, Macroprudential Indicators of Financial System Soundness, IMF Occasional Paper No. 192 (Washington: International Monetary Fund).
Guerra de Luna, A., 1997, “La Relevancia Macroeconómica de los Bienes Raíces en Mexico,” Banco de México, Serie de Documentos de Investigación No. 9707.
Herring, R., and S. Wachter, 1999, Real Estate Booms and Banking Busts–An International Perspective, Group of Thirty Occasional Papers No. 58.
Higgins, M., and C. Osler, 1997, “Asset Market Hangovers and Economic Growth: The OECD during 1984-93,” Oxford Review of Economic Policy, Vol. 13, No. 3.
Hilbers, Paul, 1998, “Financial Sector Reform and Monetary Policy in the Netherlands,” IMF Working Paper 98/19 (Washington: International Monetary Fund).
Iacoviello, M., “House Prices and the Macroeconomy in Europe: Results from a Structural Var Analysis,” ECB Working Paper 18 (Frankfurt: European Central Bank).
International Monetary Fund, 2000b, Financial Sector Assessment Program—A Review—Lessons from the Pilot and Issues Going Forward, November.
Kalra, S., D. Mihaljek, and C. Duenwald, 2000, “Property Prices and Speculative Bubbles: Evidence from Hong Kong SAR,” IMF Working Paper 00/2 (Washington: International Monetary Fund).
Kaminsky, G., and C. Reinhart, 1999, “The Twin Crises: The Causes of Banking and Balance of Payments Problems,” American Economic Review, Vol. 89 (June), pp. 473-500.
Kanaya, A., and D. Woo, 2000, “The Japanese Banking Crisis of the 1990s: Sources and Lessons,” IMF Working Paper 00/7 (Washington: International Monetary Fund).
Kennedy, N., and P. Andersen, 1994, “Household Saving and Real House Prices: An International Perspective,” BIS Working Paper No. 20, January (Basel: Bank for International Settlements).
Mancera, M., 1997, “Problems of Bank Soundness: Mexico’s Recent Experience,” in: Banking Soundness and Monetary Policy, 1997, by C. Enoch and J. Green (eds.), pp. 228-41.
Miller, M., and P. Luangaram, 1998, “Financial Crisis in East Asia: Bank Runs, Asset Bubbles and Antidotes,” National Institute Economic Review.
Nagashima, A., 1997, “The Role of the Central Bank during Problems of Bank Soundness: Japan’s Experience,” in: Banking Soundness and Monetary Policy, 1997, by C. Enoch and J. Green (eds.), pp. 191-222.
Renaud, B., 1999, “Real Estate and the Asian Crisis: Lessons of the Thailand Experience,” paper presented at a conference on the dynamics of real estate cycles, New York University, March 1999.
Renaud, B., M. Zhang, and S. Koeberle, 1998, “How the Thai Real Estate Boom Undid Financial Institutions: What can be done now?,” Background paper for the Conference on Thailand’s Dynamic Economic Recovery and Competitiveness, Bangkok, May.
Rodriguez, J., 1989, “The Crisis in Spanish Private Banks: An Empirical Analysis,” Revista Internazionale di Scienze Economiche e Commerciali, Vol. 36.
Samiei, H., and G. Schinasi, “Real Estate Price Inflation, Monetary Policy and Expectations in the United States and Japan,” IMF Working Paper 94/12 (Washington: International Monetary Fund).
The authors are grateful to Huw Evans, Peter Hayward, Alfredo M. Leone, Pamela Madrid, Armando Morales, Marina Moretti, Abdelhak Senhadji, Mark Stone, Masahiko Takeda, and Delisle Worrell for helpful comments on an earlier draft. At the time of writing, Mr. Lei was with the IMF’s Monetary and Exchange Affairs Department; he is currently with the University of Michigan.
Borio, Kennedy, and Prowse (1994), European Central Bank (2000), Higgins and Osler (1997), Miller and Luangaram (1998), Allen and Gale (1998), Krugman (1998, mimeo), and Herring and Wachter (1999). The importance of asset market developments for financial system stability was also highlighted during a consultative meeting on macroprudential indicators organized by the IMF in September 1999 (Evans, Leone, Gill, and Hilbers (2000)).
DiPasquale and Wheaton (1996) present a textbook model for analyzing real estate markets; Herring and Wachter (1999) discuss a number of theories on real estate cycles with a focus on the role of banks and collateralized assets; Carey (1990) develops a model based on land prices and fixed supply of land; Allen and Gale (1998) emphasize the role of expectations for the supply of credit in the dynamics of real estate and equity prices; Krugman (1998) develops a model specific to the Asian crisis that deals with the implications of moral hazard; Kiyotaki and Moore (1995) present a model based on a two-way relationship between borrowers’ credit limits and the value of collateralized assets; Kennedy and Andersen (1994) focus on the relationship between house prices and household savings; Samiei and Schinasi (1994) analyze the impact of (changes in) monetary policy on real estate prices; and Iacoviello (2000) estimates the effects of macroeconomic shocks on house prices.
The empirical part of this paper generally covers developments up to end-1999.
By banking crisis or significant distress we refer to cases of runs or other substantial portfolio shifts, collapses of financial firms or massive government intervention. This study relies on existing, comparative studies of banking crises, particularly Kaminsky and Reinhart (1999), Lindgren, Garcia, and Saal (1996), and Enoch and Green (1997), as well as on studies for individual country cases. The beginning of distress refers to the first collapse or run, while the peak is defined as the period with the heaviest governmental intervention and/or bank closures.
In some cases, real estate prices already reached their peak about four years before the beginning of financial sector distress. This points to the possibility that when a real estate bubble is suspected, supervisory authorities could try to be more conservative on loan classification and provisioning in order to spread out the impact of the real estate downturn on earnings and capital, thus avoiding the need for fire sales later on.
Available Swedish bank statistics did not identify real estate lending as a separate category.
In Sweden’s case, the 1991 tax reform also played an important role by considerably raising housing costs (and reducing demand for housing) as a result of several factors: (i) higher indirect taxes, implying increased operating and maintenance costs for the entire housing stock; (ii) reduced interest deductions, involving higher costs for owners and tenants (depending on the pass-through); and (iii) a cut in interest subsidies for newly constructed rental apartments.
Using ARIMA models, Kalra, Mihaljek, and Duenwald (2000) estimate that in mid-1997 property prices were about 40 percent above levels reflecting fundamentals.
As mentioned in section III, we primarily relied on two earlier empirical studies in defining the periods of financial sector stress for the countries in our sample (Lindgren, Garcia, and Saal (1996); and Kaminsky and Reinhart (1999)).
This represents the ratio of M2 (IFS line 34 plus 35, or IFS line 35L if available) to base money (IFS line 14), representing monetary conditions.
This variable is derived from the deposit rate (IFS line 60) deflated by consumer prices (IFS line 64).
For most countries in our sample, there are only about eight annual observations for the real commercial property price index, which excludes its use in empirical tests. Also, large values of the explanatory variables would produce a problem of “underflow,” due to rounding errors. Therefore, in computing the maximum likelihood estimators, we have to readjust the price indices to make the computation feasible; this readjustment of the base for the price indices does not compromise our results.
If the real residential property price falls this period compared to the previous one, then DRER = 1 for this period.
We use estimators to approximate the asymptotic covariance matrix for the coefficient vector β and follow the equations (19-25) and (19-26) in Greene (1997) to obtain the asymptotic covariance matrix for the marginal effects, or slopes.
The likelihood ratio statistic LR is LR = –(ln L* – ln L), and the restricted log-likelihood for both the probit and logit models is given by ln L* = T[p ln p + (1–p)ln(1–p)], where p is the proportion of observed financial stress in the sample. LR is asymptotically distributed as a chi-square with degrees of freedom equal to the number of explanatory variables (excluding the constant term).
LRI is defined as LRI = 1–ln L/ln L*, where ln L* is given in the footnote above.