The Future Value of Investing in Adaptivity in Offices

The Future Value of Investing in Adaptivity in Offices: A financial decision model for an investor to value the future value of adaptivity in an office building in the Netherlands by using the Real Options Analysis

van Eerden, Tom (TU Delft Architecture and the Built Environment; TU Delft Management in the Built Environment; TU Delft Real Estate Management)

Hermans, M.H. (mentor)
Binnekamp, R. (graduation committee)
Koppels, P.W. (mentor)
Volker, L. (graduation committee)
Basdogan, S. (graduation committee)

Delft University of Technology

2018-04-10

With the rapid development of real estate market, due to trends as the change in the way of working and automation/digitalisation of working processes, flexibility is one of the most important tasks for owner/investors of office buildings to invest in. Currently, 60% of the vacant offices are structural vacant. Flexibility could be used as an instrument to prevent future vacant office space. The flexibility to change characteristics of a building during its life cycle is called the adaptive capacity of a building. Adaptability can be regarded as way to make the real estate supply more dynamic and better able to cope with demand dynamics (static supply and dynamic demand) and in this way extend the functional lifespan of buildings. The problem is that real estate investors are hardly interested in investing in office buildings to increase the adaptive capacity. Thereby, is the most used financial method the DCF method which is currently insufficiently used to simulate the future uncertainty in an investment in flexibility on the longer term. This aim of this research is to illustrate how to cope with the future uncertainty in a new financial method. By using a stochastic approach with the Decision Tree Analysis the investors could be stimulated to invest in the adaptive capacity of office buildings and increase the adaptive capacity of a building to respond if the functional demand changes.

Adaptivity
Flexibility
Real Estate Investor
Decision Tree Analysis
DCF method

http://resolver.tudelft.nl/uuid:dfef950f-ab5d-4f2f-bd3c-b3fb6344cddd

Student theses

master thesis

© 2018 Tom van Eerden