Recursos Materiales y gastos iniciales del proyecto

From 2006 to 2012, a total of 520 branded and 3,713 non-branded observations were covered in the final dataset, equaling 10.6 million square meter of usable area and a total market value of 27.3 billion Euros. For each year of the study horizon, this corresponds to an average of 74 branded and 530 non-branded observations, a usable area of approx. 1.5 million square meters,447 and a market value of approximately 3.9 billion Euros.448 From a structural perspective, a hierarchy of nested groups was identified. There are 4,233 observations at the measurement occasion level that belong to 1,118 properties. The properties are located in 273 different postcode areas from 20 cities in Germany. Ta- ble summarizes the number of groups on the four levels and the corresponding number of single observations.

Table 6: Overview – Number of Groups and Observations

Source: Own illustration.

Table provides an overview of the dataset variables and Figure contains the correspond- ing histograms. For reasons of interpretability, parameter values refer to the uncentered and untransformed variables at this point.

Table 7: Overview – IPD Data Set after Elimination Procedure

445

See HAIR et al. (2010b), p. 204.

446

See HAIR et al. (2014), p. 125; LAWRY/GASKIN (2014), p. 137; GARSON (2012), p. 45; DIAMANTO-

POULOS/SIGUAW (2006), p. 270.

447

The total amount of office space in Germany was estimated with 311.2 million square meters. See Deutscher Verband für Wohnungswesen, Städtebau und Raumordnung e.V./Gesellschaft für Immobilienwirtschaftliche Forschung e.V. (2012), p. 27.

448

The exact size of the properties was unknown due to data security standards. For this reason, the estimation of the total market value in the dataset was based on the mean category ranges. An average usable area of 30,000 square meters was assumed for the category ≥15,000 square meter.

Level No. of groups Observations per group

Min. Max. Avg.

4th level (cities) 20 17 746 211.7

3rd level (postcode areas) 273 1 128 15.5

2nd level (properties) 1,118 1 7 3.8

Source: Own illustration.

Figure 15: Histograms of Data Set Variables

Source: Own illustration.

Property market values show a broad spectrum between 518 EUR/m² up to 11,470 EUR/m² with a mean of app. 2,575 EUR/m². This span seems realistic from a real estate point of view, since a range of office properties in different cities are covered in the da- taset. The frequency distribution of the year of observation shows that the number of ob- servations varies from year to year and decreases across the study horizon. Changes in the overall number of database participants or in their willingness to contribute might be potential reasons for this development. Most likely, the limited number of observations ob- tained in the last year (2012) can be attributed to delays of participants’ data delivery. Building Age varies between 1 and 129 years with a mean of app. 21 years. The histo- gram indicates that properties that were built before the year 1990 only represent a minor share (app. 32.5%) of the observations. Obviously, the IPD Investment Property Databank sample consists of relatively modern office buildings in comparison to the German mar- ket. According to estimations for the total office market, a share of 70.6% was built before 1990.449

449

See Deutsche Hypothekenbank (2013), p. 10.

Variable Min. Max. Mean Standard

deviation Value 518.00 11,470.00 2,574.512 1,257.720 Year of observation 0 6 2.798 1.998 Building age 1 129 20.990 18.698 Rent 0.5 497.7 164.270 70.770 Brand status 0 1 0.122 0.328 Usable area 1 4 2.186 1.133 City size 1 4 3.346 0.772 0 100 200 300 400 500 F re q u e n cy 0 100 200 300 400 500

Rent (in EUR/sqm)

0 200 400 600 F re q u e n cy 0 5000 10000 15000

Value (in EUR/sqm)

0 200 400 600 F re q u e n cy 0 50 100 150

Building Age (in years)

0 200 400 600 800 F re q u e n cy 1 3 5 0 2 4 6 Year 0 500 1000 1500 2000 F re q u e n cy 1 2 3 4 City Size 0 500 1000 1500 F re q u e n cy 1 2 3 4 Usable Area

Parameter values for the variable ‘Rent’ lie between 0.5 EUR/m² and 497.7 EUR/m² with a mean of 164.3 EUR/m². The histogram shows that a majority (59.9%) of the observations is below the mean rent. Across the horizon of the study, average office rents in the Ger- man market varied between app. 148.8 EUR/m² and app. 154.8 EUR/m², suggesting that the sample mean is approximately in line with the mean of the total market.450 Looking at the mean parameter value of ‘Brand Status’, one can state that app. 12.2% of the obser- vations refer to branded properties. Regarding the usable area, properties with a usable floor space of less than 10,000 square meters apparently account for 65.6%.

The frequency distribution of the city size variable implies that the sample is mainly based on observations from larger cities. In fact, this generally corresponds with the focus of the German office market that is clearly dominated by the seven largest sub-markets of Berlin, Düsseldorf, Stuttgart, Frankfurt/Main, Hamburg, Munich, and Cologne. However, with a share of 80.9% of the total number of observations, these cities are overrepresented in comparison to the German office market where these cities account for app. 28.5% (88.6 million square meters).451 A detailed overview of the observations per city is provided in Table .

Altogether, the data sample seems to be an appropriate representation of the office mar- ket in larger German cities. However, relatively young properties and buildings with a usa- ble area below 10,000 square meters seem to be overrepresented. Some of the original variables apparently deviate from normal distribution. This does not necessarily endanger the applicability of multilevel analysis, since only residuals are assumed to approximate normality. The overall appropriateness of the dataset regarding the requirements of multi- level modeling is discussed in Section Fehler! Verweisquelle konnte nicht gefunden

werden.. Post estimation residual tests are outlined in Section Fehler! Verweisquelle konnte nicht gefunden werden..

Table 8: Overview – Number of Observations per City

450

See IPD Investment Property Databank (2014), p. 2.

451

See Deutscher Verband für Wohnungswesen, Städtebau und Raumordnung e.V./Gesellschaft für Immobilienwirtschaftliche Forschung e.V. (2012), p. 27 and Jones Lang LaSalle (2014), p.3.

Source: Own illustration.