It has fat-tails, with the probability  25 20  mass of big rises or falls larger than implied by a normal distribution. For example, the  15  probability of a 10% movement in house prices  10  in any given year is twice as large as normality  5  would imply. 0 -5  Capturing these cyclical dynamics, and fattailed properties, of the housing market is not straightforward using aggregate models. These models typically rely, as inputs, on a small number of macro-economic variables, such as incomes and interest rates. They have  -10 1846 1866 1886 1906 1926 1946 1966 1986 2006 Source: Hills, Thomas and Dimsdale (2016); Bank calculations. Notes: The chart shows the Hodrick-Prescott trend in annual house price growth data (where lambda=6.25). Data during WWI and WWII are interpolated. a mixed track record in explaining and predicting housing market behaviour. One reason for this poor performance may be that the housing market comprises not one but many sub-markets - a rental market, sales market, a mortgage market etc. Moreover, there are multiple players operating in these markets - renters, landlords, owner-occupiers, mortgage lenders and regulators – each with distinctive characteristics, such as age, income, gearing and location. 27  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  27  It is the interaction between these multiple agents in multiple markets which shapes the dynamics of the housing market. Aggregate models suppress these within-system interactions.
In general, a system comprising three bodies undergoing motion via a force cannot be solved analytically – the “three-body problem”. And there are many problems in physics which involve the interaction of “particles” in ways which are complex and non-deterministic. 13  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  13  These features have resulted in ABMs being extremely widely used in the physical sciences, at various different levels of resolution. At the micro scale, some of the earliest applications of ABMs were to study plasmas – charged particles which constantly interact. Plasmas are the fourth state of matter after solids, liquids and gases. They are prime ABM candidates because they exhibit rich, non-linear, complex behaviour. In 1966, Edward Teller wrote a paper which described how to build a plasma ABM with 32 interacting particles (Brush, Sahlin and Teller (1966)). Even with “only” 32 interacting agents, this was a formidable computational challenge. But the advent of parallel and high performance computing since the 1990s has changed the scale of plasma particle simulations. Simulations today regularly use as many as 10  9  computational agents (Turrell et al (2015)). These plasma ABMs have found widespread application, micro and macro. At the micro level, they have been used to help identify and kill cancer cells with pinpoint accuracy, while leaving healthy surrounding cells unharmed (Bulanov et al (2002)).