The same building geometry from the 3 Unit part of the Module 5 assignment was used as the basis for this analysis (See Figure 1 below):

The process began by trying to determine two evaluation metrics that could be useful in analyzing potential building designs. There were many options that I considered such as energy use, solar panel potential, daylighting, real estate value, construction costs, view quality, view obstructions, etc. I decided to choose two evaluation metrics that are somewhat related, so they can help create an informed decision on one particular topic. In the end I decided to make nodes for estimated real estate value, where the building use types can be changed for particular floors. In addition, I decided to evaluate view quality for each floor. Together, I think these are two important metrics when it comes to evaluating buildings and they can work together to create a more holistic picture of real estate potential. As we all know, without clear real estate value and appeal many buildings could be ruled out and never built.

Since the geometry I built stemmed from Dynamo exclusively, both these nodes were fully developed from scratch. The details for each node and how they were built can be seen below:

(1) Real Estate Value by Floor Height and Use Case

The purpose of this node is to estimate the real estate of a multi-use building by the floor level and use case. Many buildings have been following these multi-use cases, where the lower floors are commercial, whereas the higher floors take advantage of the views and are residential. This is a main reason why this evaluation node and the view quality are so intertwined. This node was fully-customized and did not use any template. See the node below (Figure 2 and 3)

This custom node works in the following way. First it takes input from the ‘TwistwithRealEstateandViewQuality’ (Twist) custom node that creates the custom geometry (See Figure 4). This added Residential Scaling Factor and Number of Commercial Floors as new inputs for the ‘Twist’ node, the rest were existing inputs for the ‘Twist’ node

From here, the number of Commercial Floors was calculated so that the list of values could be scaled later on (See Figure 5).

Then, the user-inputted real estate values were used. This utilized a linear scale from X1 to X2 based on the first to highest floor ($800 to $1600 per SQFT in this case). As seen in Figure 6 below, two lists were created. The first took the commercial floors (from the user input) and simply applied the linearly increasing value function. The second took the residential floors and multiplied the values by a factor (user-inputted). Then the lists were merged again so the total real estate value could be calculated. The node could be adapted if commercial is above the residential floors simply by applying a different factor above the level where the use changes (ex. factor = 0.8 if commercial above residential).

(2) View Quality Node

The intent of this node was to determine how many square feet in the building could take advantage of the peak views from the site at the Transbay Terminal. Once again, this was a custom node and required some assumptions to get it to work. The node works in a way where it takes the overall floor area for each floor, offsets the exterior curve inwards X feet, then calculates the SQFT differential between the two — ultimately resulting in the “view quality” SQFT per floor. See the custom node logic below (Figure 7):

The first step took the inputs from the ‘Twist’ node (gross floor surface areas, floor surface geometry, and offset distance), see Figure 8. From here, the perimeter curves are selected and a set of points are placed on each surface curve (See Figure 9 and 10). This is done so that a polyCurve can be fitted to each. The “offset” function works in a way that PolyCurves create the most reliable results (without nulls).

From here, there was another issue with the offset function. Depending on the “direction” the curve was drawn, it would offset either inside or outside of the existing curve. The workaround to this was offsetting each curve with a positive and negative value, then selecting the shorter curve (which is the interior one). Ultimately, this reliably outputted the interior offset curve (See Figure 11).

In order to recreate the surface, points were once again added back to the interior PolyCurve. Then, a nurbs curve was fit to it so that a curved surface could be created again. This resulted in increased accuracy over the straight edge PolyCurve (See Figure 12). Then the area was found for each level and subtracted from the gross floor area. Ultimately, the view quality area could be calculated (See Figure 13).

As for the integration into the existing ‘TwistwithRealEstateandViewQuality’ node, I created another parallel stream to evaluate each new node. The node still sits in the logic (See Figure 14 red), so it can be evaluated with the same test alternatives. I will not re-explain how I created the ‘Twist’ node, but I will explain the additions to the existing geometry below (See Figure 15 for the entire nodal logic).

The main difference in Figure 15 is the addition of the View Quality and Real Estate custom nodes outlined above. The Real Estate Node (bottom left green node above) is combined with the mass floor areas to ultimately calculate the total value (Figure 16 and 17). Similarly, the View Quality node is intertwined using other inputs from the ‘Twist’ node (Figure 18). Ultimately, the outputs adds two new layers. Previously, it was only combining the ‘Solid.Volume’, ‘Surface.Area’ and first ‘Math.sum’. Now, the total real estate value and view quality are added in parallel as the last two “Math.sums’ (Figure 19).

As a result, multiple test inputs can be tested for all five parameters (Volume, Surface Area, Floor Surface Area, Real Estate Value, and View Quality). The Excel output can be seen in Figure 21 below:

With the evaluation nodes that I decided to implement (real estate value and view quality), it made sense to consider similarly motivated metrics. The most important metrics would be those that could be used as a measure of “real estate success”. In other words, things related to initial cost estimates, marketability, eventual real estate value, and so on. These metrics are somewhat interconnected at first glance. For example, real estate value (on a square foot basis) and gross floor square footage seem like the same thing. However, with the way the metrics were set up, the real estate actually builds off gross floor area and adds building height and use characteristics into the calculation. Similarly, view quality and exterior surface area are somewhat related. As the exterior surface area gets larger, it creates floor surface profiles with larger perimeters — and with the way the view quality offset was designed, this would also create a larger view quality value. There is an important tradeoff between real estate metrics (view quality and the real estate value) and the more construction focussed metrics (like gross floor area and surface area). These dependencies and connections were kept in mind when creating the evaluation scheme. In addition, these are just four of the many metrics that can evaluate buildings. Future iterations of this assignment could consider aspects like energy use, solar insolation (daylighting), solar panel potential, view quality of specific elements, panel attributes, etc.

For this reason, I chose to use a weighting scale to balance these four metrics to create a single evaluation score. The overall nodal logic can be seen below in Figure 22.

First, the maximums and minimums of all the evaluation criteria was determined so that the values for each iteration could be normalised.

From here, an evaluation function node was created to incorporate the weights into a combined score. As seen below in Figure 24, the evaluation weightings were selected. Similar to the description above, real estate value and view quality were weighted the highest (at 3). This is a result of the intention of this score, which is essentially a combined score for real estate potential. View quality is a large selling point for premium units, and especially with the prime views from this site, it should be especially important. In addition, understanding the use of the building, and how the potential use cases change the value of the building is very important. Some people may not want to live on lower floors with obstructed views, so creating some commercial units on lower levels and residential above may be a good solution. With this, the ability to change the number of floors and quickly re-evaluate these metrics can help optimize the use cases for the potential building. In terms of facade costs and eventual heating/cooling, exterior surface area is an important consideration, and is why it has a weighting of 2. Lastly, since the view quality and real estate value were already somewhat dependent on the gross floor area, it was weighted at a 1 to reduce redundancies. The custom node logic can be seen below in Figure 25, but is roughly based off the one in the course resources.

Lastly, the original results were combined with the “scores” so that the top three results could be highlighted. In Figure 26, the original results were combined with the scores, so the custom node in Figure 27 and 28 could be used. The custom node was taken from the CEE 220C course library. Finally, the preferred alternative could be selected in Figure 29.

In the end, the final geometry and results could be exported to an Excel sheet and seen visually in Dynamo (Figure 30). The top three alternatives can be seen below in Figure 31 in terms of their final score (in decreasing shades of green). I chose the best design to the be ‘Test 13’, as it had a perfect score with the criteria that were used in this evaluation (Height = 430ft, Floor Height = 11ft). This design creates the most real estate value by building the highest building, with therefore the most view quality area and floor surface area. However, it is important to note that the best options were the ones with the tallest height and the shortest floor height. This could be a flaw in the analysis, and in future iterations higher floor heights could be treated as a positive for real estate value (as users generally like higher ceilings). Nonetheless, the design process was very interesting and useful in evaluating real estate value overall.