choose the transportation mode that minimizes their transportation costs, and the probability to choose each employment subcenter depends on income net of commuting cost.

- iii. _Rents are adjusted_ , as changes in transportation costs modify the relative attractiveness of locations in the urban area. All else equal, locations that are furthest from jobs and where households largely commute by transportation modes subject to the tax become less attractive because they are tied with higher transportation costs. As a result, housing rents in these locations will decrease proportionally more than rents in more central or more job-dense locations.

- iv. _Households adjust their dwelling sizes_ in reaction to rents changes, if they live in the formal private sector (as we assume that dwellings have fixed size in informal housing, backyarding, and formal subsidized housing).

- v. _Households’ locations change_ , i.e. they move within the urban area to maximize their utility.

# 4. Data, calibration, and validation

## 4.1. Data and calibration

We use the same data as Pfeiffer et al. (2019). The spatial distribution of the population is taken from National Censuses for the years 2001 and 2011. Incomes are also from the 2011 Census, and reclassified in four groups. For transportation, we use the transport model used by the City of Cape Town to retrieve transportation times between pairs of transport zones for each transport mode and jobs’ locations. We also use aggregated statistics on modal shares and residence-workplace distances in Cape Town derived from Cape Town’s 2013 Transport Survey. We use housing prices from the housing sales registry of the City of Cape Town. Concerning land use, areas of subsidized housing are identified from the cadastre of the City of Cape Town. The area available for backyard housing is estimated as the yard size of these units. Informal settlements areas are taken from the 2011 census. Finally, amenities are taken from various sources (see Pfeiffer et al., 2019). For the estimation of the model’s parameters, we also use property price data extracted from the City of Cape Town’s geocoded data set on property transactions for 2011, as well as data on dwelling sizes made available to us by the City of Cape Town.

As described in Pfeiffer et al. (2019), three sets of parameters need to be estimated. The first set consists of the minimum lot size, the size of the backyards of informal dwellings, and agricultural rents, and are directly derived from the available data without solving the model. The second set consists of the housing production function parameters, the utility function parameters, the wages, and the amenities, that had been calibrated in Pfeiffer et al. (2019) using partial relations from the model. Finally, we spatially calibrate the disamenity parameters for informal housing and informal settlements by running the entire model in order to replicate the population distribution by housing type from the 2011 Census.

## 4.2. Validation and baseline

We performed some validation checks to assess the validity of our modeling approach. All results can be found in Appendix A. First, at the aggregated level, we checked that the number of households per income class and per housing type simulated by the model in 2011 matches the data from the Census (Appendix A1). Then, we compared simulated population spatial distribution for each housing type in 2011 with the data from the Census (Appendix A2). Finally, we compared simulated rents and housing prices data (Appendix A3).

Regarding housing types, our simulations fit the data: we slightly overestimate the number of households living in formal private housing and informal housing in backyards, and underestimate the number of 7