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This Handbook provides, for the first time, comprehensive guidelines for the compilation of Residential Property Price Indexes and explains in depth the methods and best practices used to calculate an RPPI.
For most citizens, buying a residential property (dwelling) is the most important transaction during their lifetime. Residential properties represent the most significant component of households' expenses and, at the same time, their most valuable assets. The Residential Property Prices Indices (RPPIs) are index numbers measuring the rate at which the prices of residential properties are changing over time. RPPIs are key statistics not only for citizens and households across the world, but also for economic and monetary policy makers. Among their professional uses, they serve, for example, to monitor macroeconomic imbalances and risk exposure of the financial sector. This Handbook provides, for the first time, comprehensive guidelines for the compilation of RPPIs and explains in depth the methods and best practices used to calculate an RPPI. It also examines the underlying economic and statistical concepts and defines the principles guiding the methodological and practical choices for the compilation of the indices. The Handbook primarily addresses official statisticians in charge of producing residential property price indices; at the same time, it addresses the overall requirement on RPPIs by providing a harmonised methodological and practical framework to all parties interested in the compilation of such indices. The RPPIs Handbook has been written by leading academics in index number theory and by recognised experts in RPPIs compilation. Its development has been coordinated by Eurostat, the statistical office of the European Union, with the collaboration of the International Labour Organization (ILO), International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), United Nations Economic Commission for Europe (UNECE) and the World Bank.
For most citizens, buying a residential property (dwelling) is the most important transaction during their lifetime. Residential properties represent the most significant component of households' expenses and, at the same time, their most valuable assets. The residential property prices indices (RPPIs) are index numbers measuring the rate at which the prices of residential properties are changing over time. RPPIs are key statistics not only for citizens and households across the world, but also for economic and monetary policy makers. Among their professional uses, they serve, for example, to monitor macroeconomic imbalances and risk exposure of the financial sector. This handbook provides, for the first time, comprehensive guidelines for the compilation of RPPIs and explains in depth the methods and best practices used to calculate an RPPI. It also examines the underlying economic and statistical concepts and defines the principles guiding the methodological and practical choices for the compilation of the indices. The handbook primarily addresses official statisticians in charge of producing residential property prices indices; at the same time, it addresses the overall requirement on RPPIs by providing a harmonised methodological and practical framework to all parties interested in the compilation of such indices.
Hedonic regressions are used for property price index measurement to control for changes in the quality-mix of properties transacted. The paper consolidates the hedonic time dummy approach, characteristics approach, and imputation approaches. A practical hedonic methodology is proposed that (i) is weighted at a basic level; (ii) has a new (quasi-) superlative form and thus mitigates substitution bias; (iii) is suitable for sparse data in thin markets; and (iv) only requires the periodic estimation of hedonic regressions for reference periods and is not subject to the vagrancies of misspecification and estimation issues.
The 2019 Financial Soundness Indicators Compilation Guide (2019 Guide) includes new indicators to expand the coverage of the financial sector, including other financial intermediaries, money market funds, insurance corporations, pension funds, nonfinancial corporations, and households. In all, the 2019 Guide recommends the compilation of 50 FSIs—13 of them new. Additions such as new capital, liquidity and asset quality metrics, and concentration and distribution measures will serve to enhance the forward-looking aspect of FSIs and contribute to increase policy focus on stability of the financial system.
The repercussions of the 2007–2008 financial crisis have acted as an impetus to improve the quality and availability of statistical information. One such initiative addresses the importance of compiling a complete accounting of a nation’s wealth, and especially the wealth of households. This is ...
This paper investigates how housing prices respond to economic, financial and demographic conditions in emerging markets in Europe. We use quarterly data covering 10 countries over the period 1998–2022 and implement a panel quantile regression approach to obtain a granular analysis of real estate markets. Overall, economic, financial and demographic factors explain the changes in real house prices in emerging Europe, with income growth having the most significant impact. Quantile regression estimations show that income growth matters more for higher housing prices than those at the lower quantiles of the property market. We also find that an increase in short-term or long-term interest rates have a price-dampening impact, indicating that a higher cost of borrowing is associated with lower real house prices. These results indicate that the downturn in house prices could deepen with the looming economic recession and soaring interest rates.
Transaction-price residential (house) and commercial property price indexes (RPPIs and CPPIs) have inherent problems of sparse data on heterogeneous properties, more so CPPIs. In an attempt to control for heterogeneity, (repeat-sales and hedonic) panel data regression frameworks are typically used for estimating overall price change. We address the problem of sparse data, demonstrate the need to include spatial price spillovers to remove bias, and propose an innovative approach to effectively weight regional CPPIs along with improvements to higher-level weighting systems. The study uses spatial panel regressions on granular CPPIs for the United States (US).
"Examines Asia's emerging markets, which survived the financial debacle of 2008-09 with only modest declines in growth; discusses activities that could dampen continuing development in these markets including inflation, surging capital inflows, asset and credit bubbles, and rapid currency appreciation; and offers strategies to promote financial stability"--Provided by publisher.