How to identify UK housing bubbles? A decision support model

Abstract

The purpose of this thesis is to provide a decision support model for the early diagnosis of housing bubbles in the UK during the phenomenon’s maturity process. The development process of the model is divided into four stages. These stages are driven by the normal distribution theorem coupled with the case study approach. The application of normal distribution theory is allowed through the usage of several parametric tools. An empirical application of the model is conducted using UK housing market data for the period of 1983-2011; and by placing particular emphasis on the last two UK housing bubble case studies, 1986 to 1989 and 2001/2 to 2007. The central hypothesis of the model is that during housing bubbles, all speculative activities of market participants follow an approximate synchronisation. The new algorithmic approach successfully identifies the well-known historical UK bubble episodes over the period of 1983-2011. The proposed algorithm acts like an index or a thermometer to gauge the ‘‘fever’’ of a housing bubble in the UK at any point in time. In this approach, the housing bubble is no longer invisible until the crash, and as such can be monitored over time. The study further determines that for uncovering housing bubbles in the UK, house price changes have the same weight as the debt-burden ratio when their velocity is positive. The application of this model-algorithm has led us to conclude that the model’s outputs fluctuate approximately in line with phases of the UK real estate cycle. Finally, the research has provided a new and more technical definition of housing bubbles. The phenomenon is defined as a situation in which all speculative activities of market participants achieve an approximate synchronisation. Consequently, under such regime, the model expects that (during housing bubbles) an irrational, synchronised and periodic increase in a wide range of relevant variables must occur to anticipate a bubble component. In this definition, the relevant variables are those that exhibit a periodic and irrational acceleration in the rate of change, which, in turn, is synchronised with other relevant variables. Therefore, the model views such variables as symptoms for identifying housing bubbles. This thesis proposes a new measure for studying the presence of irrational housing bubbles. This measure is not simply an ex post detection technique but employs dating algorithms that use data only up to the point of analysis for an on-going bubble assessment, giving an early warning diagnostic that can assist market participants and regulators in market monitoring