# _4.3. Benchmark simulation and retrospective fit_

We run a benchmark simulation to compare the outputs of the model with the data. Past evolution of average income, total population and income distribution are derived from Census data. Results are presented on Figure D.8 of Appendix D, which graphically shows that the overall fit is reasonable. We run a retrospective simulation (i.e., running the model “backwards”) starting in 2011, and compare the outputs of the model 2001 estimation with Census and property price data for the same year. We present the details and results for this retrospective simulation in Appendix D, which shows that the model appropriately replicates changes in housing prices over time. The good fit provides confidence that the model can reasonably be used to simulate the future evolution of Cape Town’s spatial structure.

# **5. Scenarios and prospective simulations**

We consider scenarios of income growth and population trends aligned to those which inform the City’s Land Use Scenarios underpinning its medium-term infrastructure master plans (City of Cape Town 2017, Medium Term Infrastructure Investment Framework). The anticipated twentyyear supply of State-subsidized housing is based on the City of Cape Town’s Housing Pipeline as contained in its Integrated Human Settlement Framework (2013) (see Appendix F). All the other parameters remain constant.<sup>35</sup> We prospectively run simulations for the period 2011-2040.

# _5.1. Urban growth boundary_

There has long been discussions in policy circles of Cape Town being a sprawling city. Against a backdrop where Cape Town’s urban footprint was estimated to have expanded by over 1,000 hectares a year during an unprecedented housing boom during the late 1990s and early 2000s, the City introduced an Urban Growth Boundary (or ’Urban Edge’) as a policy guideline and then, in 2012, as a statutory instrument. It was delineated to include sufficient developable land to accommodate future growth for at least 10 years and was thus not immediately binding. We simulate two scenarios: the ’No Urban Edge’ scenario where the Urban Edge constraint is absent (and the city’s urban footprint is permitted to expand unhindered into its rural hinterland) and the ’Urban Edge’ scenario where it continues to be present (see Figure F.12 representing the Urban Edge).<sup>36</sup> 35In our dynamic simulations, we assume that, for each income group, the wage ratios across job sub-centers remain constant over time ( _wic/wi′c_ is the same for any _i_ , _i_<sup>_′_</sup> and _c_ , and _wic/wic′_ is the same for any _i_ , _c_ and _c_<sup>_′_</sup> ). In levels, the mean wage for each income group grows at a constant rate.

> 36In South Africa, the Urban Edge enjoyed support from policymakers, academics and environmentalists alike as a means to protect valuable agricultural land, natural amenities and the functioning of ecological services, while supporting a more compact urban environment. It has been opposed by local politicians and property developers. Developers claimed that the urban growth boundary would generate regressive distributive effects since the restriction of land supply to the housing market raises the cost of housing. Politicians claimed that, by encumbering greenfield development, the growth boundary invariably stifles economic growth and job creation. The Urban Edge is not mentioned anymore in the City of Cape Town’s current spatial development framework.

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