Today I presented on the topic of real options analysis using an approximate dynamic programming technique called the Least Squares Monte Carlo algorithm. The method was applied to real estate development. The model can be downloaded here (Update the talk is on YouTube here):

The default inputs and parameters of the model are set to those of this paper. However that particular paper solves the model using a binomial tree with a single source of uncertainty (which I demonstrate in Analytica here). Since real estate development is fraught with multiple sources of uncertainty this model instead uses Least Squares Monte Carlo and demonstrates how to solve the model with two sources of uncertainty. The model can be adapted to add further sources of uncertainty.

For further background on real options, dynamic programming, and an application to a mining concession see this post. Additionally in many cases a decision maker may have a limited number of option rights to exercise over a much longer time period. This is the swing option structure which I have demonstrated in this post. Also abandonment options in R&D can be important and I show a basic model in this post. Here is a real options model applied to a stochastic storage problem, specifically the control and valuation of a Natural Gas storage facility.

While all of these topics are different, they are all solved using dynamic programming and the LSM method. Thus the approach is very general. Variants of the LSM method have even been used for the control of unmanned aerial vehicles in a target tracking situation, see this interesting paper.

One the participants asked about how the LSM approach can be used for the valuation of pharmaceuticals. This paper is one such approach (and it is by one of the authors that devised the LSM algorithm). A scaled down pharmaceutical model is the subject of the R&D post mentioned above.

Feel free to reach out with any questions.

The slides from the talk are here:

Post Updated 9/22/21 to add additional links.