Challenge the future Delft University of Technology Negative Externalities of Structural Vacant Offices Philip Koppels and Hilde Remøy

2/27 Introduction High levels of (structural) vacancy Concentration in specific areas and buildings Social security & criminality Negative externalities related to structural vacant buildings? Positive externalities are often assumed Landmark buildings Public space: proximity to parks & water bodies Difficult to measure and quantify: spatially lagged variables Function of distance Negative Externalities and Structural Vacancy

3/27 Introduction Negative Externalities and Structural Vacancy Source: Thibodeau (1990)

4/27 Introduction Isolating the effect: Market conditions: rental adjustment equation Omitted variables: proxy for neighborhood quality Negative Externalities and Structural Vacancy

5/27 Structural Vacancy “Structural” nature: three consecutive years Visibility of vacancy: at least 50% of the building is vacant Operationalization

6/ Number of Structural vacant buildings = 1

7/ Number of Structural vacant buildings = 8

8/ Number of Structural vacant buildings = 17

9/ Number of Structural vacant buildings = 34

10/ Number of Structural vacant buildings = 32

11/ Number of Structural vacant buildings = 29

12/ Number of Structural vacant buildings = 38

13/ Number of Structural vacant buildings = 54

14/27 Research Method Data sample: 152 buildings 334 transactions Hedonic Pricing

15/27 Model Specification Spatial Segmentation  Discrete spatial Heterogeneity  12 submarkets  Modelled by random effects

16/27 Model Specification Rent X coordinate Discrete Spatial Heterogeneity Submarket A Submarket B Submarket C

17/27 Model Specification Omitted Variables  Nested model:  Submarkets  Postcode area’s  Buildings  Modelled by random effects

18/27 Model Specification General Economic Trend (City-wide) Nominal Rent Level

19/27 Sub-market Specific Economic Trends Nominal Rent Level Model Specification

m²500 m² Building size (GFA) office buildings structural vacant office buildings 50 m r=500m 450 m 300 m

21/27 Weight factor Spatially Lagged Variable Model Specification Distance 0 R=250m. R=500m. R=750m. 1 R=150m. How to measure distance:  As the crow-flies  Along the road network

22/27 Distance Weight factor 0 R=250m. R=500m. R=750m. 1 R=150m. Spatially Lagged Variable Model Specification

50 m r=500m 450 m 300 m Impact depends on:  Distance  Building size

24/29 Negative Externalities of Structural Vacant Offices Best performing model assumes: Constant distance relationship with threshold distance at 250m. Distance measured: as the crow-flies 0 R=250m. 1 Distance Impact: independent of the size of the structural vacant building

25/29 Solution for Fixed Effects EffectTransformationEstimateStd. Err.Pr > |t| Intercept <.0001 Building ageLogarithmic Travel time to highwayLogarithmic Intercity location (r=250m): Yes Intercity location (r=250m): No--- Distance tram/metro stopLogarithmic Daily amenitiesLogarithmic Employment industryLogarithmic Employment F&BLogarithmic Inverse parking ratioLogarithmic Garage: No Garage: Yes--- Number of floorsLogarithmic Façade: Low/Standard Façade: High--- Company logo: No Company logo: Yes--- Size of entrance as % of total LFALogarithmic Numb. Structural Vacant offices (r=250m)

26/27 Conclusion & Discussion Base model seems plausible Significant relationship structural vacant offices and rent prices nearby buildings Vicious circle? Collective action Some issues to consider Proxy Weight matrix specification Submarket dummies Negative Externalities and Structural Vacancy

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