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The value of the greenbelt in Vienna: a spatial hedonic analysis

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Abstract

This paper employs the hedonic price method to examine whether the implicit value of the greenbelt is capitalized into apartment prices in the city of Vienna, Austria. We improve the traditional model using spatial econometric techniques and compare the estimates from different spatial models, namely the spatial lag model, the spatial error model, and the spatial Durbin model (SDM). While our use of spatial models addresses the common problem of omitted variable bias, the SDM specifically allows for controlling possible nearby proximity effects (i.e., small-scale neighborhood) that are rarely included in this type of analyses. Findings indicate that distance from the greenbelt is important in explaining apartment prices in Vienna and that the greenbelt exerts a centrifugal force. The SDM is found to be the best performing model indicating the existence of small-scale neighborhood effects and presenting a solid case for the consideration of this model in valuation of green amenities.

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Notes

  1. http://data.worldbank.org/.

  2. Maas et al. (2009) present the results of a study conducted in the Netherlands on 10,089 individuals. They find a correlation between lack of green spaces (within 3 km of the place of residence) and the feeling of loneliness and lack of social ties. Other studies also show that there is less domestic violence reported in green neighborhoods (Prow 1999; Kuo and Sullivan 2001). Witt and Crompton (1996) highlight a correlation between the presence of green spaces and crime: juvenile delinquency and domestic violence are mitigated in green neighborhoods.

  3. Ecological services in urban areas are air filtration, regulation of microclimate, noise reduction, water retention, water treatment, carbon sequestration, erosion control, and preservation of biodiversity.

  4. Willis and Osman (2005) present a review of the literature on the positive effects of green spaces on health. Proximity to green spaces promotes physical activities. Potential benefits include reduced risk of cardiovascular disease, certain cancers, and certain types of diabetes. A pleasant living environment has positive effects on mental health and well-being. The presence of a green space can reduce the risk of depression. They estimate that a 1 % permanent reduction in the sedentary population of the United Kingdom from 23 to 22 % would produce a social benefit of 1.44 billion pounds per year and 479 million if the elderly are excluded. This does not include benefits related to the reduction in psychological morbidity such as mental illness, domestic violence, or mental fatigue. These figures show that the benefits of green spaces are substantial.

  5. Urban containment policies have been classified by Pendall and Martin (2002) into three major forms: urban growth boundaries, urban service boundaries, and greenbelts.

  6. The city of Paris, France, has also laid plans to develop a greenbelt. This Greater Paris project was presented by the Government in 2009. It aims at portraying Paris as a model of a sustainable city with efficient transportation, a competitive economy, better quality of life, a stronger cultural life, and increased presence of nature in the city. While transportation is the key element of the project, the role of green spaces has also been recognized as important: beyond the preservation of the existing green spaces, it is intended to promote the development of a greenbelt inclusive of regional parks and forests to create ecological corridors for fauna and flora.

  7. http://www.wien.gv.at/english/environment/protection/reports/pdf/green-04.pdf.

  8. The reasons for estimating two different model specifications are detailed in Sect. 5

  9. If both LM tests are significant, then Robust LM (RLM) tests are carried out. In both these tests, a higher level of significance of the test statistic indicates the most likely data generation process. See Anselin et al. (1996) for details of these tests.

  10. SDM and SAR coefficient estimates cannot be interpreted as partial derivatives of respective explanatory variables.

  11. Note that the direct impact is represented by the average of diagonal elements of the matrix of partial derivatives, and the indirect effect is represented by the average of off-diagonal elements.

  12. Consistent with our use of SDM, LeSage (2014) has highlighted the practical validity of that model compared to SAR and SEM.

  13. See Cropper et al. (1988) for a discussion on this topic.

  14. Box–Cox tests were not consistent, not conclusive, and varied depending on the independent variables.

  15. Other spatial models tested using alternative spatial weights matrices are not shown due to space reasons and available on request.

  16. Having more than one toilet in a small apartment does not necessarily increase its value (one toilet is feasibly adequate for a small apartment and adding more could impose a maintenance burden): higher maintenance costs translate into lower apartment price.

  17. Note in the case of elevator, for example, the direct impact shows this positive effect on price (this is to state that an apartment’s own elevator has an impact on price).

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Acknowledgments

We thank the Editor and two anonymous referees for very helpful comments on a previous version of our paper. We are also grateful to ERES NETconsulting-Immobilien.NET GmbH for providing sample data. Johanna Choumert received support from the Agence Nationale de la Recherche of the French government through the program “Investissements d’avenir, ANR-10-LABX-14-01”.

Author information

Authors and Affiliations

  1. Faculty of the Built Environment, City Futures Research Centre, University of New South Wales, Sydney, 2052, Australia

    Shanaka Herath

  2. CERDI-CNRS, Université d’Auvergne, 65, Boulevard F. Mitterrand, 63000, Clermont-Ferrand, France

    Johanna Choumert

  3. Institute for Multilevel Governance and Development / Research Institute for Spatial and Real Estate Economics, WU-Vienna University of Economics and Business, Welthandelsplatz 1, 1020, Vienna, Austria

    Gunther Maier

Authors
  1. Shanaka Herath
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  2. Johanna Choumert
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  3. Gunther Maier
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Corresponding author

Correspondence to Johanna Choumert.

Appendix 1: Moran test (for base model residuals) and LM tests

Appendix 1: Moran test (for base model residuals) and LM tests

Model

Spatial weights matrix (criterion)

Moran statistic

LM error

LM lag

RLM error

RLM lag

Model 1

WDHALF (0.5 km)

\(-\)0.010

0.590

1.186

0.503

1.099

(0.703)

(0.442)

(0.276)

(0.478)

(0.295)

WD1 (1 km)

0.000

0.000

5.084

0.027

5.110

(0.056)

(0.991)

(0.024)

(0.870)

(0.024)

WD2 (2 km)

\(-\)0.004

1.008

2.837

0.853

2.682

(0.959)

(0.315)

(0.092)

(0.356)

(0.102)

W1 (\(k=1\))

\(-\)0.011

0.172

2.420

2.196

4.444

(0.582)

(0.678)

(0.120)

(0.138)

(0.035)

W3 (\(k=3\))

0.006

0.145

0.000

0.175

0.031

(0.042)

(0.704)

(0.985)

(0.676)

(0.861)

W5 (\(k=5\))

\(-\)0.003

0.070

0.946

0.607

1.482

(0.380)

(0.791)

(0.331)

(0.436)

(0.224)

Model 2

WDHALF (0.5 km)

0.085

39.729

5.216

41.693

7.180

(0.000)

(0.000)

(0.022)

(0.000)

(0.007)

WD1 (1 km)

0.061

40.771

2.229

39.437

0.895

(0.000)

(0.000)

(0.136)

(0.000)

(0.344)

WD2 (2 km)

0.020

22.628

5.707

23.979

7.059

(0.000)

(0.000)

(0.017)

(0.000)

(0.008)

W1 (\(k=1\))

0.139

27.260

59.824

1.143

33.707

(0.000)

(0.000)

(0.000)

(0.285)

(0.000)

W3 (\(k=3\))

0.083

27.064

71.715

2.033

46.685

(0.000)

(0.000)

(0.000)

(0.154)

(0.000)

W5 (\(k=5\))

0.054

18.284

119.551

0.176

101.444

(0.000)

(0.000)

(0.000)

(0.675)

(0.000)

  1. W (row-standardized) spatial weights matrix is used. p values follow in parentheses. k denotes the number of neighbors in cases where “nearest neighbors” are considered
  2. Source: Authors’ calculations

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Herath, S., Choumert, J. & Maier, G. The value of the greenbelt in Vienna: a spatial hedonic analysis. Ann Reg Sci 54, 349–374 (2015). https://doi.org/10.1007/s00168-015-0657-1

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