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Appendix 1. Data Sources and Country Coverage
Data sources: The data for house prices are residential property prices (seasonally adjusted) at country level (also at city level) deflated by CPI inflation in each country. They are compiled by the data form multiple sources including Bank for International Settlements; CEIC Data Co. Ltd; Emerging Markets Economic Data Ltd; Global Financial Data Solutions; Global Property Guide; Haver Analytics; IMF, Research Department house price data set; Organisation for Economic Co-operation and Development; Thomson Reuters Datastream; IMF staff calculation.
Country Coverage: The country coverage for country-level data is different for the long dataset from 1971Q1 and the short dataset from 2002Q1, due to the avialability of data by country. The long dataset covers only 19 countries including AUS, BEL, CAN, CHE, DEU, DNK, ESP, FIN, FRA, GBR, IRL, ITA, JPN, NLD, NOR, NZL, SWE, USA, ZAF while the short dataset covers 27 countries and area including AUS, CAN, CHE, CHL, CHN, COL, CZE, DNK, Euro Area, GBR, HKG, HUN, IDN, ISR, JPN, KOR, MYS, NOR, NZL, RUS, SGP, SRB, SWE, THA, TWN, USA, ZAF. Please note that the short dataset does not use individual data for European countries in the euro area but treat them as a one country.
The views expressed here are those of the author and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. The author thanks staff of IMF for valuable comments.
Actually, Hirata et al. (2012) raises the analysis on determinants of cross-country differences in the degree of synchronization as one of possible avenues for future research. In their conclusion, they note that “we do not articulate why house prices have become more synchronized over time. A natural next topic to explore is a deeper analysis of differential effects of shocks and structural features of countries, including their linkages through the banking system, on the temporal changes in the degree of synchronization of house prices.”
The two-step approach is econometrically less rigorous than the traditional approach, which simultaneously estimates factor loadings and the global (and regional) factors by a Bayesian method (e.g., Kose et al. 2003). The two-step approach is, however, much more tractable in practice and gives almost the same results as in the traditional approach for most cases. See Doz, Giannone, and Reichlin (2011) and Koop and Korobilis (2013) for more detailed discussion on the two-step approach to estimate a dynamic factor model. The estimation by the two-step approach in this paper is conducted by modifying the Matlab programs available at Dimitris Korobilis’ webpage (https://sites.google.com/site/dimitriskorobilis/matlab).
See Annex 1 for the list of countries covered by the dataset.
For instance, even if we had the result that house prices in French cities are more synchronized with the global trend compared with Chinese cities, such a result could be induced by the fact that the sample for French cities contain only large cities, and so it could not necessarily mean that French cities in general are more exposed to the global trend than Chinese cities.
In the estimation using the long dataset, the regional factor is not included in the estimation because the sample countries are dominated by European countries and there is not sufficient regional diversity.
In the analysis using the short dataset, the Euro area is included in the sample and countries in the Euro area are dropped from the sample. Otherwise, the sample is dominated by too many European countries. The main conclusion is, however, almost unchanged even if we use individual country’s data rather than the Euro area data.
While we do not have city-level data granular enough to explore this issue, there are some possibilities to reconcile those findings. First, if the city specific idiosyncratic factors are independent with each other, they are expected to wash out in the aggregate. Thus, the country level house prices are expected to be mainly driven by common factors across cities, including the global factor and the country factor. Second, when making the country level house prices, the weight for large cities may be larger than others. Thus, the country level house prices are highly correlated with large cities’ house prices compared with small cities’ house prices.
The Chinn-Ito index is constructed by de-jure financial openness in the IMF indexes (See Chinn and Ito (2006) for more details about the methodology to construct the index). Hence, the literature often uses gross foreign asset and liability as a proxy for de-facto financial openness, but such a proxy for de-facto financial openness does not have any significant effects on house price synchronization in this paper’s framework (The estimation results are not shown in the main text). This is probably because the amount of foreign asset and liability is driven by not only financial openness but also other factors including economic developments etc.
When output growth and inflation are added as control variables, all the statistical significance is lost for both cases of synchi. This is probably caused by the fact that financial openness is highly correlated with economic developments, and that the cross-sectional regression cannot identify the effects of financial openness orthogonal to economic developments, given the limited sample size in this analysis.
For instance, in a financially integrated world, global investors can continue to sell housing assets in countries associated with lower expected returns and buy housing assets in other countries, until the expected returns across countries converge to the same level. Such substitution effects can simultaneously lead to a collapse of housing prices in some countries and a surge of housing prices in other countries, thus damping the synchronization of house prices across countries. See Kalemli-Ozcan et al. (2013) for an analogous discussion for business cycle synchronicity.