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DelftUniversity ofTechnology

The Added Value of ImageA Hedonic Office Rent Analysis

Philip Koppels, Hilde Remøy, Hans de Jonge and Anet Weterings

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Introduction

• Office location choice: focus on Face-to-Face contacts• Inter-industry linkages: complex information

• Presentation or image effects• ‘the right address’ and ‘by the company it keeps’ • Rational: lower marketing costs

• Previous hedonic studies: employed variables

A Urban Model of Office Rents

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Previous Hedonic StudiesVariables: Accessibility & Business Environment

• Accessibility• Proximity airports• Proximity highways• Proximity train station• Proximity subway station• Street integration index• Number of lanes

• Parking facilities• Covered deck parking• Parking on site• Number of parking spaces

• Office density• Distance to CBD• Proximity secondary centre• Office employment density• Cluster size• Office space density

• Worker amenities• Proximity shopping centre• Retail employment

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Previous Hedonic StudiesVariables: Prestige and Image

• Exterior appearance• Building class• Building status• Building size• Number of floors• Cladding• Design quality indicator• Landmark

• Company logo• Office unit location

• Interior appearance• Atrium / reception area• % common space• Quality of space

• Neighbourhood prestige• Household expenditure• Manufacturing output• Land use• Quality of landscape• Proximity public square• Proximity park

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Delphi-Expert panel

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Hedonic Pricing

• Heterogeneity • a bundle of attributes• implicit markets

• Dependent variable• asking rent• (base) contract rent

• Functional model form• natural log of rent/m2• independent variables: log, linear and quadratic

Methodology

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Data collection

• Selection criteria

• Lease transactions:• DTZ Zadelhoff, Dynamis and Strabo

• Building and location characteristics• Geographic information systems:

• Sources: National road database,

NAVTEQ, LISA, Locatus, CBS,

Bak 2008, Municipality of Amsterdam

• Document analysis• Field work

Methods and sources

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Descriptive Statistics

13%

13%

43%

31%1950-1964

1965-1979

1980-1994

1995-2007

14%

34%23%

29%> 2500 m2

2500-5000 m2

5000-1000 m2

>10000 m2

Building size

Building period

Structural Characteristics

0

10

20

30

40

50

60

70

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

172 office buildings

517 lease transactions

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Descriptive StatisticsSpatial Distribution

Sources:

Map material: © Amsterdam, Geo en Real Estate information

TU Delft, department of Real Estate & Housing

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ResultsR square change

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Block 1 Block 2 Block 3 Block 4 Block 5

Model summary

R 0.812R Square 0.660Adj R Square 0.637

Std Err. 0.177F

28.169Sig. 0.000

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Variables B Std. Err. VIF

(Constant) 5.324 .234 ***IC Station -.020 .018 3.619Highway -.035 .015 ** 2.121Metro -.045 .014 *** 1.747Employment F&B .063 .015 *** 2.843Employment logistic -.054 .011 *** 2.231Employment manufacturing -.039 .008 *** 1.821Water .009 .004 ** 1.838Squares .001 .003 1.885Green .002 .002 1.657Facilities .017 .004 *** 1.803

ResultsSignificance Variables

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Variables B Std. Err. VIF

Garage .094 .021 *** 1.640Parking lots .038 .012 *** 1.575Age -.031 .010 *** 1.530Cladding Natural .132 .028 *** 2.168Cladding Glass .079 .030 *** 1.861Cladding Metal .064 .029 ** 1.985Cladding Brick -.016 .028 2.156Floors -.002 .005 14.535Sq. Floors .000 .000 ** 13.342Logo .085 .018 *** 1.284Reception Area % .038 .015 *** 2.509Reception Spatial dimensions -.023 .016 2.532

ResultsSignificance Variables

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Checking Assumptions

• Multicollinearity• Variance Inflation Factor (VIF)• Eigenvalues and variance proportions

• Distance to intercity station

• Employment in financial and business services

• Employment in logistic and transport services

• Pearson correlations up to 0.635

• Independent Residuals• Durbin-Watson: 1.204

Multicollinearity and Independent Residuals

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DiscussionThe Added Value of Image

• Good explanatory power: adjusted R square 0.637

• Difficult to distinguish separate effects• Image: adjusted R square change 0.25• Other: adjusted R square change 0.21• Time-dummies: adjusted R square change 0.18

• Importance of including the ‘right’ variables

• Future research

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Questions?Contact author:

p.w.koppels@tudelft.nl

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Checking AssumptionsHeteroscedasticity and Normality of Residuals

Heteroscedasticity Normality of Residuals

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Discussion

Sources:

Map material: © Amsterdam, Geo en Real Estate information

Data: Locatus

TU Delft, department of Real Estate & Housing

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Discussion

Sources:

Map material: © Amsterdam, Geo en Real Estate information

Data: Bak 2008

TU Delft, department of Real Estate & Housing