Guest editorial

Citation

Yates, S.R. (2013), "Guest editorial", International Journal of Housing Markets and Analysis, Vol. 6 No. 4. https://doi.org/10.1108/IJHMA-06-2013-0041

Guest editorial

Article Type: Guest editorial From: International Journal of Housing Markets and Analysis, Volume 6, Issue 4

The theory of complexity economics seeks to understand how order emerges in complex, non-linear systems. These systems can be characterized as complex, self-organizing, adaptive, dynamic and co-evolving. Complex adaptive systems have many parts – or agents – interacting with each other in many different ways. New agents can emerge spontaneously and change their behavior based on most recent experiences. These agents can also undergo rapid and unpredictable periods of change and evolve together with the systems with which they interact.

In recent years, real estate (property) researchers have begun to draw from complexity economics to explain the crises in the housing markets. The idea is to use alternative methods from other disciplines (like the medical sciences or behavioral psychology) that take into consideration the dynamic, adaptive nature of housing markets in order to better explain what happened in the most recent global financial crisis as well as predict market behavior in the future. The papers in this issue have all been written with this in mind, using alternative data and/or models from the traditional neoclassical models that are commonly applied to real estate research questions and published in this and other academic journals.

Follain’s article, “The search for capital adequacy in the mortgage market: a case of black swan blindness” sets the stage for the application of complexity economics to better understand the housing market. This paper serves several purposes to lay the stage for this special issue. First, it provides a case study to test Nassim Taleb’s hypothesis regarding “black swan blindness” as Follain searches for extreme events in capital adequacy prior to the recent housing crisis. Second, Follain reviews the economic and financial literature regarding the crisis clearly illustrating the complexity of the housing markets and the myriad of agents that made up the financial markets. Finally, he offers some concrete ideas on ways to improve analysis of the risk associated with housing and mortgage markets including the importance of exploring not ignoring extreme outliers, focusing on micro-markets and micro-level data rather than one national housing market, as well as encouraging housing economists to be more open to the expansion of traditional models in order to better capture the complexity of the markets and to more explicitly incorporate human behavior.

Wyman et al. extend Follain’s analysis and also make the case for explicitly recognizing the complexity that exists in the housing market in their paper, “Hidden complexity in housing markets: a case for alternative models and techniques”. These authors examine the lack of reliability created from the results of traditional neo-classical models and argue that the lack of fit of many of the models is due to the hidden complexity and non-linear data found within the housing markets, particularly over the last cycle. These authors argue that an alternative framework – complexity economics – may be a more appropriate method to model the discontinuities and imbalances in the markets. This paper makes the case that policy makers would benefit by adopting an analytical framework that incorporates the key elements of complexity (non-linearity, emergence, etc.) when constructing financial models aimed to explain systemic imbalances that afflict residential housing markets.

Liu and Ma present a methods piece in “A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities”. This paper develops a panel error correction model to investigate the spatial correlation patterns among house prices in order to identify a dominant housing market using the ripple down process. Liu develops a model that appears to address the dynamic, adaptive nature of housing markets and demonstrates a contagion effect. The author finds that Melbourne emerges as the dominant housing market in Australia and that what happens in Melbourne directly impacts the major housing markets in the rest of the country.

Collins et al. also present a methods piece focused on examining contagion in “Applying Latin hypercube sampling to agent-based models: understanding foreclosure contagion effects”. Recognizing that complex, adaptive systems exist within the real estate markets and that agents within the market interact with each other in different ways, these authors utilize an agent-based modeling and simulation (ABMS) technique to model foreclosure contagion. One of their major contributions is recognizing that as the complexity of the system increases, uncertainty surrounding the simulation inputs also increases. The researchers apply a new technique, Latin hypercube sampling (LHS). LHS offers a way to efficiently sample for sensitivity analysis when multiple input parameters are uncertain. The researchers found the foreclosure discount and the time it takes to complete the foreclosure process are the most important variables in terms of contagion so they suggest that policymakers should work hard to avoid regulation that increases this time and that foreclosure moratoriums are probably not the best solution for avoiding the downward spiral of contagion in a marketplace.

In “A general model of mortgage failure tipping point with an example from Southern California 2006-2007”, Huang et al. also investigate foreclosure contagion. They present a theory for identifying a mortgage default tipping point in a neighborhood whereby disinvestment and deterioration in surrounding neighborhoods begins once a neighborhood has a certain level of foreclosures. In addition, they apply their model to micro data for the Los Angeles metropolitan area for the period 2006-2007 rather than more broadly defined census tract data. These authors find evidence of a tipping point where 6 percent of the properties have gone into mortgage default. This leads the authors to suggest that as a matter of policy, government entities should take preemptive action before a neighborhood reaches the 4-5 percent mortgage default rate.

It would be impossible to cover all of the ways in which complexity economics can be applied to the housing markets and used to better understand them. However, this special issue is a starting point and the five papers discussed above address the heart of the issue. Each paper illustrates – either analytically or in an exploratory fashion – that housing markets are indeed complex adaptive systems that can best be characterized by going beyond the traditional, neo-classical models. Future researchers need to recognize the need to expand their horizons and to incorporate some of these alternative techniques from other disciplines into their current research projects. This journal will continue to work with researchers interested in pushing the envelope and relaxing some of the more unrealistic assumptions that have been used in the past and embracing the complexity of the housing markets.

Stephanie Rozelle Yates, Elaine M. Worzala
Guest Editors