# **1. Introduction**

Urban planning requires ex-ante assessments of potential policy impacts. These impacts need to be assessed at the scale of a whole city, with the understanding that a city forms a system and that policies can have systemic consequences. For instance, urban planners may try to anticipate how land markets could respond to a major transport infrastructure investment, potentially modifying the spatial organization of a city and its footprint over the long term. Doing so requires an understanding of the market forces that drive city structure, including housing construction decisions, household location and transport mode choice, subject to physical constraints and zoning. To answer such questions, urban economists have come up with sophisticated spatial simulation tools which are grounded in urban economic theory and which can be used to support urban planning at the local government level. Land use and transport integrated (LUTI) simulation models which simultaneously predict land use and transport decisions have been designed for a few major cities in the world, and various modeling approaches have emerged, including TRANUS (de la Barra, 1989), UrbanSim (Wadell, 2000) and RELU-TRAN (Anas and Liu, 2007) among others (see Acheampong and Silva, 2015, for a full literature review of such models). Although these simulation models have mostly been applied to cities in the United States and Europe, they are increasingly being applied to metropolitan areas elsewhere (see for instance the recent applications of the RELU-TRAN model to Beijing (Anas and Timilsina, 2015) or Cairo (Anas et al., 2017)). In the case of South Africa, a local version of UrbanSim was developed by the Centre for Scientific and Industrial Research (CSIR) for the East Rand area, Durban and Nelson Mandela Bay (see Wray and Cheruiyot, 2015, for a survey of land use modeling in South Africa).<sup>1</sup> Although the development of LUTI models for developing countries is facilitated by improved data availability, it also faces at least three major challenges: First, these models are often complex (involving hundreds of equations) and computationally intensive, which can make them cumbersome to design, difficult to operate, and intractable. Second, because of their complexity and the skills required to operate them, these models may be out of reach and unaffordable to local authorities with limited fiscal resources. Finally, the existing models overlook a key feature of land markets in developing countries: the presence of _a large informal housing sector which coexists and interacts with the formal housing sector_ (Durand-Lasserve and Selod, 2007, Napier et al. 2013). This gap precludes analyses of informal housing (which hosts the poor and often a large fraction of the middle class) and of how and the degree to which informal housing affects the whole system. More importantly, because, for many cities, housing informality is far from being a marginal phenomenon, it is possible and even likely that the predictions from models that lack an informal housing sector would not hold if the informal sector were accounted for.

In the face of these challenges, simple urban simulation models that are based in urban economic theory emerge as a less costly alternative to previously developed models (see Arnott, 2012). They are useful for urban planning as they can account for broad patterns of urban development over the long term, while remaining tractable so that users understand the forces at play. The

> 1The South African initiatives include a simple cellular-automata model (Dyna-CLUE) that was developed for Johannesburg to investigate land-use conversion processes (Le Roux and Augustijn, 2017). A conceptual framework for an agent-based model of slum evolution was introduced in Shoko and Smit (2013).

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