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Amar Shanghavi, who worked on the paper while at the IMF, is presently a graduate student at the London School of Economics. We thank Angana Banerji, Cyrille Briançon, Roberto Cardarelli, Jörg Decressin, Marco Espinosa, Daniel Hardy, Paul Hilbers, Alexander Hoffmaister, and Emil Stavrev for useful comments. The usual disclaimers apply.
If competition in housing construction is less than perfect, residential housing prices depend also on the degree of market power enjoyed by the construction companies.
Stiglitz (1990) defines asset bubbles as arising when “…the reason that the price is high today is only because investors believe that the selling price is high tomorrow—when ‘fundamental’ factors do not seem to justify such a price—then a bubble exists.”
In the base year, housing prices expressed in comparable units (e.g., US$) range widely across countries, whereas when expressed in index values they are the same (i.e., 100).
Our starting sample covers Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and the United States. The time span is from 1980 to 2007, but for many countries in the sample data are available only for shorter periods, generally since 1988. The sample is determined by the coverage of national data sources detailed in Appendix II and the residential property price database maintained by the Bank for International Settlements (BIS, 2005). Like most other authors, we do not perform formal “poolability” tests of the countries in the sample, relying instead on their common socioeconomic characteristics as OECD countries.
Data was available for 118 countries, but our starting sample consisted of 93 countries because of lack of data on explanatory variables. In the course of the exploratory stage of the regression analysis, we further dropped Ecuador, India, The Gambia and Paraguay based on outlier analysis.
Throughout the paper, the term housing is used in reference to residential housing (i.e., not commercial and office buildings).
The 25+ years span of the cross-country panel dataset certainly stretches the definition of short-run. However, data on supply-side fundamentals, as well as the characteristics of the rental markets, does not exist at the frequency, time span, and country coverage of our datasets, which is why we do not include such variables in the analysis. This is a shared weakness with most empirical studies on the subject.
In the regression analysis, all variables in levels are entered in real terms to ensure cross-country comparability by removing the scale effect due to diminishing purchasing power of the domestic currency.
We have also experimented with the growth rate of total population, as an alternative variable to capture demographic differences. We present only regressions with shpopa, because it turned out to be statistically significant in many more regression specifications than the growth rate of total population.
All variables in levels are entered in real terms to ensure cross-country comparability by removing the scale effect due to diminishing purchasing power of the domestic currency.
In a user-defined STATA macro build around the reg3 STATA procedure.
In the case of Germany, the data before and after the country’s reunification is not comparable, so observations prior to 1991 are discarded. In the case of the other two countries, the missing observations are for the residential property price index.
The principal components are linear combinations of all variables in the dataset, the values of which capture best the variability of the original data. See the documentation of the PCA procedure in STATA for more information.
We do not present the 2SLS estimates, which were qualitatively similar to the 3SLS results.
In other words, the model explains more than 33 percent of the variability of the dependent variable in half of the sample countries and less than 33 percent in the other half.
The long-run elasticities are obtained by dividing the 3SLS coefficient estimates, excluding that of the lagged dependent variable, by one minus the coefficient of the lagged dependent variable.
A lower rank is associated with greater efficiency in the business environment. Source: World Bank, Doing Business Database, www.doingbusiness.org/.
A lower rank implies better control of corruption. Source: World Bank, Worldwide Governance Indicators, http://info.worldbank.org/governance/wgi/index.asp.
Average rank on six governance indicators (control of corruption, rule of law, political stability and absence of violence, voice and accountability, regulatory quality and government effectiveness) with a lower rank indicating better governance. Source: World Bank, Worldwide Governance Indicators, http://info.worldbank.org/governance/wgi/index.asp.
The second to sixth lags of each explanatory variable except the lagged dependent one, the rate of inflation, the unemployment rate and the current account balance are jointly statistically insignificant at the 99 percent level of confidence.