Center for Artificial Intelligence in Business Analytics and Financial Technology
Center for Artificial Intelligence in Business Analytics and Financial Technology
Center for Artificial Intelligence in Business Analytics and Financial Technology
It is well-known that all six factors above contribute to the long-term investment returns of real estate and real estate related securities. However, this has not been quantified at a level that incorporates not only the movements of each level, but also the relationships, dynamics, and causal factors that each level has on other levels of the valuation framework. This can be thought of as in the graphic below where each level exerts influence on a lower level of the framework.
Goal: The goal of this internal Center project is to develop a new framework for the valuation of real estate that accounts for 1) global and 2) national macroeconomic conditions, 3) regional economic conditions (MSA level), 4) micro economic conditions (at the individual neighborhood level), and on to 5) building and 6) tenant specific factors.
For example, global economic conditions are likely to impact economic conditions in individual countries given the interconnectedness of the economy. However, each country will have slightly different reactions to global economic conditions. Likewise, though individual metropolitan areas (MSA’s) will be impacted by national economic conditions, some factors, such as industry concentration, geographic constraints, etc. will lead to different price elasticities in each MSA.
The goal is to quantitatively determine the principal features driving change at each level and then determine the dynamics between levels so we can better predict future (even if short-term) movements in real estate and real estate related asset classes.
