Handbook On Residential Property Price Indices

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.

This book contains a special issue of the Journal of Real Estate Finance and Economics, comprising thirteen articles on house price measurement. These articles address the various procedures used to compute cross-sectional or temporal house price indices. Specifically, these articles contain research that: (1) evaluates hedonic, repeat sales, or hybrid approaches to constructing house price indices; (2) evaluates alternative sources of data on house prices and corresponding housing characteristics; (3) identifies the most influential land, structural, neighborhood, and proximity determinants of house prices (and associated changes in house prices); (4) provides a methodology for identifying housing market segments; (5) incorporates spatial autocorrelation in house price indices; and (6) provides more accurate estimates of the variance in house prices.

The importance of house prices to households, real estate developers, banks and policy-makers cannot be overemphasised. House price changes affect consumer spending and business investment patterns, which in turn affect the wider macro economy and the entire business cycle. Measuring and understanding house prices is therefore essential to a functioning economy, but researchers continue to disagree on the best methodological approach for constructing real estate indices. This book argues the need for more accurate house price indices, outlines the various methods used to construct indices and discusses the existing house price indices around the globe. It shows how the raw data of property transactions can be prepared for the purpose of constructing indices, discusses various applications of property price indices and empirically demonstrates how the index numbers can be used to model the supply of new houses and to estimate the price elasticity of supply. Essential reading for economists, real estate professionals and researchers, and policy-makers.

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.

This booklet discusses some major methodological issues relating to the construction of house price models on a macro level. There is no single method that always produces the optimal results; the choice of a particular approach, method, theory, model and technique is context-dependent. This is especially true in housing markets, where a multitude of different submarkets exist. The methodology chosen should be based on sound theory, from which the basic concepts of analysis can be derived. This booklet discusses the use of potential models, which can be constructed using a general field theory, and which act as a theoretical foundation for further analysis. If we use potential models for house price analysis we can discover additional features from the data set that other approaches would simply miss. This e-book presents a pragmatic overview of key methodological concerns with the emphasis on the use of potential models. Theoretical methodological questions are left unanswered, and are not even presented in this text, since they have little relevancy to real-world modelling questions.

This book answers the question of how exactly property price indexes should be constructed. The formation and collapse of property bubbles has had a profound impact on the economic administration of many nations. The property price bubble that began around the mid-1980s in Japan has been called the 20th century's biggest bubble. In its aftermath, the country faced a period of long-term economic stagnation dubbed the "lost decade." Sweden and the United States have also faced collapses of property bubbles in the 20th and early 21st centuries, respectively. It has been pointed out that the "information gap" that existed between policy-making authorities and the property (including housing) and financial markets was a problem. In 2009, the IMF proposed the creation of a housing price index to the G20 in order to fill this information gap, and the proposal was adopted. Furthermore, in 2011, it was suggested that the next economic crisis would be caused by a bubble in commercial property prices, and it was decided to create a commercial property index as well. This book provides practical examples of how the theory of property price indexes can be applied to the issues of property as a non-homogenous good and a technological and environmental change.

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).