The core ideas involve:
- The interdependence of the different elements or nodes within a system, directing us to the nature and strength of the interconnections or interrelationships between the elements.
- Emergence, that is, the unpredictability of complex systems wherein interactions between nodes or elements imply complex causal relationships that are often locally initiated and self-organising.
- Dynamics – system relationships change over time, sometimes predictably and at other times, sharply and unexpectedly. Lags and uncertainty are also typical.
- Initial conditions – the local context, history and hard-wired institutional features of systems matter materially, reducing the capacity to change, also known as path dependency.
- Evolution, learning and adaptation are important ideas – actors in systems receive feedback from their actions and this leads to changing incentives and adaptive behaviour which in turn transforms the system.
- Causal feedback loops amplifying or balancing initial shocks/trends. Positive feedback loops amplify or reinforce second-round effects which can also destabilise the system, whereas negative feedback loops mitigate or reduce initial effects (and may hinder the effectiveness of interventions, for instance).
Icebergs and bathtubs
Four mental models of direct interest can also be introduced. The iceberg model distinguishes between visible short run observational data, events or crises that often initiate crisis policy responses, as opposed to underlying trends and patterns beneath the surface and deep below, more fundamental structures and causal relationships. The bathtub analogy contrasts the stock of water in the bath from inflows via taps and outflows via the plug – helping us to think more explicitly about stocks and flows. Third, a number of system archetypes – or recurring models of different causal loop diagram structures have been established to chart specific societal problems of incentives and unintended consequences (among other things), such as the tragedy of the commons, limits to growth and many others. Within this mode of systems thinking, applied analysts have stressed the importance of leverage points – points of intervention that can have maximum impact on a system, but stressing that one needs to both know the system well to identify these and also recognise that maximum leverage may be located at a remote location and at a difficult part of the system in terms of influence.
How does this translate to the housing system? Housing is a complex system –
- Interdependence is all around us: private renting is a substitute for social renting and also for potential home ownership, in general, the demand for housing in one tenure is not independent of other tenures. Housing type/size configurations, sometimes call product groups, are also substitutes and these may be spread spatially across a housing system.
- Emergence locally can be hard to predict for instance when policies set of an unanticipated chain reaction of effects. Tax changes aimed at reducing buy to let investment may in some locations push landlords into not selling the property off but rather moving into the unregulated short term lets market, perhaps causing different problems in specific neighbourhoods.
- Dynamics – housing supply operates with a lag wherein market and development cycles can be large and long. Housing tax policies also can be blunt and slow acting, eg, tinkering with stamp duty transaction taxes.
- Initial conditions are important. For instance, the existing housing stock dominates new supply which means, among other things, that new house prices are shaped by second-hand prices (plus a premium). Also, intended policy changes may be too small relative to the large long-term drivers that shape the unfolding of major metropolitan housing systems (eg, wars, new technology, large scale migration, etc).
- Evolution, learning and adaptation – the housing market operates in real-time so learning and acting on feedback is essential to individual buyers and sellers. Information and its use are critical to securing better outcomes.
- Causal feedback loops. Housing market downturns accompanying more general recessions can deepen the recession when prices fall and job losses stoke arrears and possessions further reducing economic activity. Equally, social housing investment in a recession may stimulate and help balance the real economy.
There are a host of interesting ideas and applications that might be relevant to someone contemplating injecting innovation or reform into a local, regional or national housing system.
Key lessons include recognising the possibility of a complexity gap when contemplating transformative initiatives for housing, the scope for different forms of complexity or indeed systems archetypes to be at play (ie, different arrangements of causal loop diagrams). Housing reform is also often about social change and working in partnership with multiple interests is, of course, another form of system that needs to be managed understood and corralled. A great way to approach the application of these systems ideas to the housing sector is to apply the ideas of a checklist. The checklist mode of thinking is a natural complexity mode of thinking for engineers and scientists but also needs to be for policymakers, analysists and social scientists.
Ken Gibb, Director at UK Collaborative Centre for Housing Evidence (CaCHE). You can follow CaCHE on Twitter @housingevidence.
This guest blog is one in a series and is part of our new Future of Housing programme. Find out more about our work in this area by visiting our new Future of Housing knowledge hub.
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Our Future of Housing blog series is intended as a platform for open debate. Views expressed are not necessarily those of the Connected Places Catapult.