In its current version, NEDUM-2D also relies on the hypothesis that all households within the urban area earn the same average income. It does not currently possess the sophistication to account for multiple household types and thus ignores income inequalities<sup>15</sup> . This simplification is acceptable for some indicators such as the extent of urbanization. However it can have some impacts for NEDUM-2D’s ability to capture the dispersion in rents and real estate prices around an average. Indeed, real estate prices are strongly affected by neighborhood characteristics among which social segregation can play a role. We will overcome this limitation to a certain degree by interacting our results with a map of current income dispersion.

# 10.2 Data and choice of parameters

We will detail in this section the data used and the choice of parameters for 1/ socio-economic and demographic inputs, 2/ household utility function 3/ developers’ behavior, the housing production function and construction costs, 4/ transport data and modelling choices and 5/ land use and building regulation data.

## 10.2.1 Socio-economic and demographic inputs

- <u>Population in the Greater Buenos Aires urban area:</u> Population for the GBA+ region in 2012 is estimated to be 13,744,044 in 2012 according to projections of the Censo Nacional de Poblacion, Hogares y Vivienda 2001 (CNPHyV 01) for 2012. With an average household size of 3.3, this corresponds to 4,106,479 households. The demographic scenario used in this exercise considers that the population will increase smoothly at the same pace that it evolved over the 2001 – 2012 period, reaching a little over 6.96 million in 2050. This corresponds to nearly a 70% increase compared to 2012 with an annual demographic growth rate of 1.40%. We equally use a second demographic scenario whereby the number of households doubles in 2050 compared to 2012. This second demographic scenario corresponds to an annual growth rate of 1.84%.

- <u>Income evolution of the average household: With the current version of the model in which we only</u> consider one income class, we need an average income per household. Data from INDEC gives us this information separately for Buenos Aires and the other ‘partidos’ of the Gran Buenos Aires at trimestral intervals. We thus calculate a weighted average based on their respective household populations for all semesters of year 2012. This yields an average yearly income of AR$ 85,540 or around US$ 18,140. In the baseline scenario, average incomes will grow at the same average annual rate as Argentina’s per capita net income over the 1970 – 2015 period (in constant 2010$), i.e. 1.24%, starting at US$ 18,140 in 2012 and reaching close to US$ 29,000 in 2050. In the sprawl scenario, average income doubles over the 2012 – 2050 period, increasing at an annual rate of 1.84% and reaching close to US$ 36,300.

## 10.2.2 Household utility

The household utility function reads as follows: The widespread choice of this Cobb-Douglas functional form in urban economics is consistent with findings that households devote a near constant budget to rental housing expenditures through time and space (Davis and Ortalo-Magné 2011). In this case, households spend a share � of their income net of transportation costs on housing. Indeed, maximizing (9) with respect to � and replacing � with the budget constraint by �+ �<sup>�</sup> −�(�)�(�) −�(�) , we find: �<sup>∗</sup> (�)�(�) = �(�+ �<sup>�</sup> −�(�)) . For the purpose of the

> 15 A version of NEDUM-2D that is able to represent multiple household types differing by annual average income is currently being developed in CIRED.

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