Core-Real Estate Investments in times of crisis: Exemplified by the Frankfurt and London office mark

Currently, the crisis situation is driving the real estate markets around the world. Mainly in focus are so called ,core’ real estate investments: the most stable, nicest looking, most rented out and most ideally located investments. With these investments, investors intend to eliminate almost all risks. But are they really able to do so?

This book takes a closer look at the asset class and investment strategy ,core’ and tries to promote a clearer understanding of what ,core’ really is and what requirements this investment category has to fulfill. Furthermore, a new detention has been developed to narrow down a globally working detention throughout all asset types but mainly focusing on offices exemplified by the London and Frankfurt commercial real estate market.

In further chapters, risks around the ,core’ strategy are analyzed, and the current and past crisis situation's connection with these investments is discussed.

This work is intended to help all real estate professionals, such as investors, fund managers, financial experts and all professionals having to do with major ,core’ real estate investments around the world who would like to get a clearer and more precise comprehension of the matter.



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Chapter 2.2, General Concepts and Terminologies:

The general purpose of the analysis of event history data is to explain why certain individuals are at a higher risk of experiencing the event(s) of interest than others. In general, this can be accomplished by using special types of methods which, depending on the field in which they are applied, are called failure-time models, life-time models, survival models, transition rate models, response-time models or hazard models. It should be noted, however, that the origin of event history data modelling lies in the area of medical science. For this reason and for the continuing dominance of survival analysis within the area of event history data modelling, it is not surprising that all of the models that will be introduced shortly have been developed in this area and thus carry respective denominations.

In hazard models the risk of experiencing an event within a short time interval is regressed on a set of covariates. Two special features distinguish hazard rate models from other types of regression models: They make it possible to include censored observations in the analysis and to use time-varying explanatory variables. Censoring is, in fact, a form of partially missing information: On the one hand, it is known that the event did not occur during a given period of time, but on the other hand, the time at which the event occurred is unknown. Time varying covariates may change their value during the observation period. The ability of including covariates that may change their value in the regression makes it a truly dynamic analysis.

In order to understand the nature of event history data and the purpose of event history analysis, it is important to understand the following four elementary concepts: state, event, duration, and risk period. These concepts are illustrated below using first an example from the analysis of unemployment histories.

The first step in event history analysis is to define the relevant states which can be distinguished. The states are the categories of the dependent variable, the dynamics of which we want to explain. At every particular point in time, each person occupies exactly one state. In the analysis of unemployment histories, four states are generally distinguished: employment, part-time employment, re-training, and unemployment. The set of possible states is sometimes called the state space.

An event is a transition from one state to another, that is, from an origin state to a destination state. In this context, a possible event is ,first employment’, which can be defined as the transition from the origin state, unemployed, to the destination state, employed. Other possible events are: taking a part-time employment or a job re-training. It is important to note that the states which are distinguished determine the definition of possible events. If only the states employment and unemployment were distinguished, none of the above mentioned events could have been defined. In that case, the only events that could have been defined would be becoming employed or unemployed.

Another important concept is the risk period. Clearly, not all persons can experience each of the events under study at every point in time. To be able to experience a particular event, one must occupy the origin state defining the event, that is, one must be at risk of the event concerned. The period that someone is at risk of a particular event, or exposed to a particular event, is called the risk period. For example, someone can only experience to become unemployed when one was employed before. A strongly related concept is the risk set. The risk set at a particular point in time is formed by all subjects who are at risk at experiencing the event open at that point in time.

Using the concepts, event history analysis can de defined ,as the analysis of the duration of the non-occurrence of an event during the risk period’. This duration is usually labelled by the term episode. When the event of interest is ,first employment’, the analysis concerns the duration of non-occurrence of a first employment. In practice, as will be shown below, the dependent variable in event history models is not duration or time itself but a rate.

Therefore, event history analysis can also be defined as the analysis of rates of occurrence of the event during the risk period. In the first employment example, an event history model concerns a person’s employment rate during the period that he or she is in the state of never having been employed.

A strong point of hazard models is that one can use time-varying covariates. These are covariates that may change their value over time. Examples of interesting time varying covariates are, in the employment history example, an individual’s financial status or health status. As a matter of fact, the time variable and interactions between time and time-constant covariates are time-varying covariates as well.

We now do have to fit the above described concepts into the area of ADT:

In ADT one generally distinguishes between two states: ,adoption’ or ,non-adoption’ of an innovation. The event will be described by the adoption an innovation, which can be defined as the transition from the origin state, non-adoption, to the destination, adoption. This event pattern is called a ,single non-repeatable event’ where the term single reflects that the origin state, non-adoption, can only be left by one type of event, and the term, non-repeatable, indicates that the event can occur only once. Models that have been developed for this type of event pattern, we will call single risk models. The duration measures the time until an individual adopts an innovation. Logically, an individual does not necessarily have to adopt within the observation period or further beyond it. Individuals that do not adopt within the observation period and of which we do not when or whether at all they will adopt in the time after produce censored data. I will describe this phenomenon later within the current context.

By and large, this is the sort of event history pattern that is known from the area of survival analysis. In both fields, we observe the duration that lies between some predefined point in time and one single (absorbing) event. In most cases, survival analysis deals with the investigation of the duration between the beginning of treatment or hospitalization and the death of an individual. Ironically enough, both the adoption decision and the death of an individual are single non-repeatable events.

As both processes are equal in terms of their general event pattern, survival models represent likely alternatives for modelling and analyzing adoption- and diffusion processes. It should thus not surprise that all models that will be introduced come from the area of survival analysis. In effect, the vast number of models in survival analysis has been developed to model this type of event pattern.

There are, indeed, other alternative concepts, some of which may also be used in the context of ADT. I want to shortly introduce these for a more conclusive introduction.

Sometimes, it may prove necessary or is simply wanted to distinguish between different types of events or risks. In the analysis of death rates, one may, for example, want to distinguish between different causes of deaths. In ADT a distinction between various causes of adoption decisions is equally conceivable.

The standard method for dealing with situations where, as a result of the fact that there is more than one possible destination state, individuals may experience different types of events is the use of multiple risk or competing risk models.

Most events studied in social sciences are repeatable, and even most event history data contains information on repeatable events for each individual. This is in contrast to medical research and to ADT where the event of greatest interest is death or adoption, respectively. Events of repeatable events could be job changes, having children, arrests, or promotions. In an economic context, the investigation of (repeated) product buying decisions may prove interesting. Often events are not only repeatable but also of different types, that is, we have a multiple state situation. When people can move through a sequence of states, events cannot only be characterized by their destination states, as in competing risk models, but they may also differ with respect to their origin state and destination states. An example is, once again, an individual’s employment history: An individual can move through the states of employment, unemployment, and out of the labour force. In that case six different kinds of transitions can be distinguished which differ with regard to their origin and destination states.

Hazard models for analyzing data on repeatable events and multiple-state data are special cases of the general family of multivariate hazard models. Another application of multivariate hazard models is the analysis of dependent or clustered observations. Examples are the occupational careers of spouses, educational careers of brothers, child mortality of children in the same family. Hazard rate models can be easily generalized to situations in which there are several origin and destination states and in which there may be more than one event per observational unit.

After this general overview to other event history concepts, it is important to stress again that, in the course of this thesis, I will exclusively introduce and apply models for the analysis of single non-repeatable events (single risk models). Moreover, the integration of time varying explanatory variables will not be considered.

Therefore, I will model the adoption- and diffusion process as having one origin state, non-adoption, and one final non-repeatable event, adoption. Hereby, I will analyse the impact that time-constant explanatory variables have on the dynamics of this process.



David A. Pieper is a young real estate professional from Berlin, Germany. After finishing his Bachelor in Real Estate Economy at the Berlin School of Economics and Law, he completed his Real Estate Economist degree at the International Real Estate Business School (IREBS). He graduated in early 2012 as Executive MBA in Real Estate, cooperating among others with the Harvard Graduate School of Design and the University of Hong Kong.

Besides the topic of ,core' real estate investments, David A. Pieper focuses on the German residential real estate market, in particular Berlin and luxury real estate.