Capturing loan-to-value data in New Zealand – challenges and opportunities
Like many countries, New Zealand experienced rapid increases in both house prices and household debt in the decade leading up to the Global Financial Crisis. While house prices fell modestly during the recession of 2009/10, they remained elevated relative to fundamental metrics such as rents or incomes. By 2013, house prices were rising rapidly again in some parts of the country, particularly Auckland and Canterbury, reflecting both stronger demand and housing shortages. Although the rise in house prices was accompanied by only moderate growth in total household credit, lending undertaken at high loan-to-value ratios (LVRs in excess of 80 percent) had risen to nearly a third of all new housing loan commitments.
An A to Z of loan-to-value ratio (LVR) restrictions
The Reserve Bank recently announced restrictions on high loan-to-value ratio (LVR) lending. The restrictions take the form of a ‘speed limit’ that requires banks to restrict new residential mortgage lending at LVRs of over 80 percent to no more than 10 percent of the dollar value of their new residential mortgage lending. The speed limit on high-LVR lending is designed to slow the growth in house prices and housing credit, and mitigate associated risks to the financial system and the broader economy. This article sets out the framework for LVR restrictions, and explores the Reserve Bank’s early experience in operating them.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.