All courses are scheduled to take place at the JVI. Courses may have to be delivered virtually in case safe travel to and in-person training in Vienna is not possible. The decision to offer virtual instead of onsite training will be made within 5 weeks of the course start date.
TARGET GROUP | Mid-level to senior officials and compilers responsible for, or planning to introduce or develop residential property price indexes (RPPI). Participants are expected to have a degree in economics or statistics; or equivalent experience.
DESCRIPTION | This course, presented by the IMF Statistics Department, identifies the key uses of RPPIs; reviews data sources and methods for compiling RPPIs; and outlines strategic issues for country-specific application. Emphasis is given to the importance of evaluating alternative data sources for compiling RPPIs in terms of coverage, timeliness, richness in terms of supporting a quality-mix methodology, suitability of a price measure, and weighting. Trade-offs involved in selecting data sources are considered, as are strategies for longer-run development of data sources. The methodological component of the course emphasizes the quality-mix problem: a change in the mix of properties transacted each period can bias measures of change in average prices. Mix-adjustment by stratification and hedonic regression are the main methods used to deal with this issue and interactive workshops deal with these topics. The course also highlights how data source and methodological issues are intertwined and follows the principles of the 2013 Handbook on RPPIs published by Eurostat, International Labor Organization (ILO), IMF, Organization for Economic Co-operation and Development (OECD), United Nations Economic Commission for Europe (UNECE), and the World Bank. Practical advice on RPPI compilation will draw on the 2020 RPPI Practical Compilation Guide published by the IMF.
OBJECTIVES | Upon completion of this course, participants should be able to:
• Explain the nature and uses of RPPIs.
• Identify the strengths and weaknesses of possible data sources for RPPIs.
• Select the most appropriate method for RPPI compilation based on the availability of data.
• Apply different methods for compiling RPPIs.
• Make recommendations, where necessary, for the further development of data sources.