E. AnalysesA discussion of the analysis of each goal for this project requires us to first establish metrics. Shown in the table below are the metrics for each goal:Listed in the table are the environmental goals in green, the economic goals in purple, and the social goals in red. All the goals are measured from the baseline, or Option 1, and these are assigned a value of zero. Option 2 is assigned metrics based on how it measures off of the baseline. It should be noted that all of the scores for Option 2 were assigned qualitatively, rather than quantitatively, due to the malfunction of the DSS modeler (more on this in the innovation section). The environmental and social goals are all assigned -3 to +3 metrics ranging from -15% to +15%, in increments of 5%. The performance of Option 2, with the exception of green space creation, all performed negatively as compared to the baseline. For social goals, Option 2 had scores that were all positive, with the exception of the connectivity goal, which did not differ significantly from the baseline. The economic goals, valued most highly by our mentor, were also assigned qualitatively, and evaluated with similar +3 to -3 metrics with associated ranges from the baseline. Not surprisingly, Option 2 scored high in terms of revenues but low in terms of costs. The chart below graphically illustrates how each goal was scored as compared to the baseline, or Option 1.Because of the importance of the economic goals, however, our team decided to quantitatively evaluate the Time to Profit goal to determine the time it would take for developers to get a return on their investment. To do this, first the costs of construction and operation were complied for the different types of development (commercial, residential, etc) for each of the development scenarios. The costs of construction were determined by first estimating the cost per square foot for the different types and multiplying this value by the total building footprint. The costs per square foot were estimated with the help of our mentor, and all of the footprints were givens. For Option 2, the costs per square foot for construction were estimated by increasing the price of the same types for Option 1 by 25%. The total costs for construction of the buildings comes to $475,881,600 for Option 1 and $590,822,700 for Option 2.The next step requires the calculation of the revenues per year for each option. The revenues for each were determined based on an estimate of average rent per unit per month for each type of space. This was multiplied by the number of units which was either given or estimated from the building footprint to get the costs of rent per month. It was assumed that a building that doubled in height would also double in the number of units available. The total reveunue per year, for Option 1, was approximately $71M dollars, while Option 2 generated about $142M per year.Once costs and revenues were established, the time to profit could be calculated. Operating costs were estimated for each type of building and then summed up and added to the total costs for each year. The operating costs were also assumed to be the same every year. Additionally, to the construction cost calculated in the first step is the cost of the air rights to develop. All of these together make up the total construction costs. To determine the time to profit, this value is reduced by the revenues and increased by the operating costs for each year until they intersect. To find the intersection point, a simple linear regression was performed. It was found that for Option 1, the time to profit was 13.7 years, while for Option 2, the time to profit was 7.4 years. The graph below illustrates this point. For the calculations, see the “215 Economic goals” spreadsheet in our AFS folder. In the process of building the analysis model, it was realized that some of the metrics we had established for our goals were not entirely applicable or not within the right ranges for the actual data. For example, the time to profit metric is listed in our table as being measured in unitsof months. The hand calculations show that this number should clearly be in years. Additionally, while we calculated the time to profit for developers, it seems like it would be moreuseful to conduct this analysis for all the stakeholders, but in particular the City of Palo Alto.G. ValueTo determine the value for each option, the assigned metrics generated from analysis is combined with stakeholder preferences to evaluate our options. Because we assigned Option 1 as the baseline, all of the metrics for that option were zero. As a result, Option 1 seemingly has no value. This set up, however, allows us to evaluate user preference of Option 2 over Option 1. The chart below illustrates the average value for each goal for Option 2. The chart shows that social goals performed well, as did time to profit and revenues.Another chart that explains the value of the options is the aggregate valuation of grouped by each stakeholder, rather than by each goal. While Option 1 is, once again, zero, it is extremelynotable that every relevant stakeholder group found positive valuation in Option 2. This means, in simple terms, that stakeholders liked Option 2 over Option 1, and more generally, that they do not object to increases in density.It should be noted, however, that this exercise also revealed that our stakeholders were somewhat unbalanced, and would most likely always prefer higher density, which isn’t entirely accurate. The majority of the stakeholders stand to make money and also make this their primarygoal. Increasing density increases revenues which makes those stakeholders with strong economic preferences dominate over the Palo Alto residents, who stand as the only stakeholder not directly making money off of the deal. In the future, it may have been useful to give the residents more weight to balance out the developers and land