Inflation, the Stock Market and Owner Occupied Housing

Inflation, the Stock Market and Owner Occupied Housing
Author: Lawrence H. Summers
Publisher:
Total Pages: 11
Release: 1980
Genre: Housing
ISBN:
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This paper suggests that to a large extent. the increases in the value of housing and decreases in the value of corporate capital may have a common explanation, the inter- action of inflation and a nonindexed tax system. The acceleration of inflation has sharply increased the effective rate of taxation of corporate capital income, while reducing the effective taxation of owner- occupied housing. These changes have been capitalized in the form of changing asset prices. In the long run, they will lead to significant changes in the size and composition of the capital stock. The first section of the paper describes in more detail the nonneutralities caused by inflation. A simple model showing how inflation and taxation interact to determine asset prices is presented in the second section. The third section presents some crude empirical tests suggesting that increases in the expected rate of inflation may account for a significant part of the asset price changes which have been observed. A final section concludes the paper by commenting on some implications of the results

International Encyclopedia of Housing and Home

International Encyclopedia of Housing and Home
Author:
Publisher: Elsevier
Total Pages: 3870
Release: 2012-10-09
Genre: Social Science
ISBN: 0080471714
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Available online via SciVerse ScienceDirect, or in print for a limited time only, The International Encyclopedia of Housing and Home, Seven Volume Set is the first international reference work for housing scholars and professionals, that uses studies in economics and finance, psychology, social policy, sociology, anthropology, geography, architecture, law, and other disciplines to create an international portrait of housing in all its facets: from meanings of home at the microscale, to impacts on macro-economy. This comprehensive work is edited by distinguished housing expert Susan J. Smith, together with Marja Elsinga, Ong Seow Eng, Lorna Fox O'Mahony and Susan Wachter, and a multi-disciplinary editorial team of 20 world-class scholars in all. Working at the cutting edge of their subject, liaising with an expert editorial advisory board, and engaging with policy-makers and professionals, the editors have worked for almost five years to secure the quality, reach, relevance and coherence of this work. A broad and inclusive table of contents signals (or tesitifes to) detailed investigation of historical and theoretical material as well as in-depth analysis of current issues. This seven-volume set contains over 500 entries, listed alphabetically, but grouped into seven thematic sections including methods and approaches; economics and finance; environments; home and homelessness; institutions; policy; and welfare and well-being. Housing professionals, both academics and practitioners, will find The International Encyclopedia of Housing and Home useful for teaching, discovery, and research needs. International in scope, engaging with trends in every world region The editorial board and contributors are drawn from a wide constituency, collating expertise from academics, policy makers, professionals and practitioners, and from every key center for housing research Every entry stands alone on its merits and is accessed alphabetically, yet each is fully cross-referenced, and attached to one of seven thematic categories whose ‘wholes' far exceed the sum of their parts

Housing Boom and Headline Inflation: Insights from Machine Learning

Housing Boom and Headline Inflation: Insights from Machine Learning
Author: Yang Liu
Publisher: International Monetary Fund
Total Pages: 45
Release: 2022-07-28
Genre: Business & Economics
ISBN:
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Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.