1. Challenge the future Delft University of Technology The Added Value of Image A Hedonic Office Rent Analysis Philip Koppels, Hilde Remøy, Hans de Jonge and Anet Weterings

2. 2/18 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

3. 3/18 Previous Hedonic Studies Variables: 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

4. 4/18 Previous Hedonic Studies Variables: 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

5. 5/18 Delphi-Expert panel

6. 6/18 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

7. 7/18 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

8. 8/18 Descriptive Statistics Building size Building period Structural Characteristics 172 office buildings 517 lease transactions

9. 9/18 Descriptive Statistics Spatial Distribution Sources: Map material: © Amsterdam, Geo en Real Estate information TU Delft, department of Real Estate & Housing

10. 10/18 Results R square change Model summary R0.812 R Square0.660 Adj R Square 0.637 Std Err.0.177 F28.169 Sig.0.000

11. 11/18 Results Significance Variables

12. 12/18 Results Significance Variables

13. 13/18 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

14. 14/18 Discussion The 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

15. 15/18 Questions? Contact author: p.w.koppels@tudelft.nl

16. 16/18 Checking Assumptions Heteroscedasticity and Normality of Residuals Heteroscedasticity Normality of Residuals

17. 17/18 Discussion Sources: Map material: © Amsterdam, Geo en Real Estate information Data: Locatus TU Delft, department of Real Estate & Housing

18. 18/18 Discussion Sources: Map material: © Amsterdam, Geo en Real Estate information Data: Bak 2008 TU Delft, department of Real Estate & Housing