should be noted here that while we explore the impacts of a wide range of parameter values on the model’s results, these results should not be interpreted as carrying the same weight. This is because many parameter values (or the combination of parameter values) that we test are highly unlikely and depart both significantly from our calibration values and in most cases from the values found in the literature. The robustness analysis is therefore geared more toward understanding how the model behaves in response to changes in parameter than it is toward understanding how optimistic/pessimistic our central results are.

||**Parameter**<br>**symbol**<br>|**Parameter**<br>**value**|**Uncertainty**<br>**range **|**Method of obtention of the central parameter**<br>**value and other relevant information**|
|---|---|---|---|---|
|**Housing**<br>**production**<br>**function scale**<br>**parameter**|A<br>|0.25|0.015 – 40|Calibrated based on construction costs for various<br>building heights available for 2012 from CLARIN<br>and INDEC.|
|**Elasticity of**<br>**housing**<br>**production with**<br>**respect to capital**|b<br>|0.7|0.4 – 0.95|Calibrated based on construction costs for various<br>building heights available for 2012 from CLARIN<br>and INDEC. The value of 0.7 calibrated is very<br>close to the value of 0.65 found for France in<br>(Combes,Duranton,and Gobillon 2021).|
|**Share of**<br>**household**<br>**income net of**<br>**transport costs**<br>**spent on housing**|β<br>|0.4|0.1 – 0.6|Calibrated. This figure is consistent with official<br>statistics from INDEC which place average<br>expenditure shares at 15.2% and 17.7%<br>respectively for transport and housing and<br>expenditures at 164% of income(INDEC 2013).|
|**Real interest rate:**<br>**opportunity cost**<br>**of capital**|i<br>|0.02|0.005 – 0.1|Assumed as a reasonable value. It is difficult to<br>ascertain a real interest rate that would reflect<br>the annual financial burden of construction in an<br>urban area which has developed over centuries.|
|**Unit cost of time**<br>**as a fraction of**<br>**hourly income**|t<br>|0.65|_|Calibrated. The value of 0.65 falls within the range<br>of values - [0.5, 1] - reported by most papers<br>(Small and Verhoef 2007; Glaeser, Kahn, and<br>Rappaport 2008).|
|**Depreciation rate**<br>**of capital**|ρ|0.01|_|Assumption that structures on average last for<br>100 years, which is consistent with (Hallegatte<br>2009)|

_Table 4: Main parameters in the NEDUM-2D model applied to Buenos Aires and sensitivity analyses ranges_ 6.1 How does land value creation change across all sensitivity scenarios?

We first observe the distribution of aggregate land value change across the 3,000 different parameterizations. As shown in Figure 7, aggregate land value change ranges from US$ -6 million to more than US$ 600 million, whereas the annualized land value creation ranges between US$ -0.5 million to more than US$ 15 million. The results found from using the calibrated parameters (US$ 379.04 million for aggregate land value creation and US$ 7.58 million for the annualized one) lie on the right side of the distribution (the 94<sup>th</sup> percentile of the distribution). Put differently, the outcomes from the calibrated parameters could be categorized as optimistic, as there are only 180 (of 3,000) scenarios for which outcomes are at least as high as these calibrated baseline outcomes. However, when looked across scenarios, the outcomes are quite promising. The average aggregate land value creation across all 3,000 scenarios is US$ 99.22 million, which amounts to almost one-third of the original investment. Further, it 26