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A Spatial Hedonic Analysis of the Value of the Greenbelt in the City of Vienna, Austria

A Spatial Hedonic Analysis of the Value of the Greenbelt in the City of Vienna, Austria. Shanaka Herath , Johanna Choumert , Gunther Maier. Introduction. Greenbelts are important features of various cities – also Vienna Proximity to greenbelt is attractive for housing

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A Spatial Hedonic Analysis of the Value of the Greenbelt in the City of Vienna, Austria

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  1. A Spatial Hedonic Analysis of the Value of the Greenbelt in the City of Vienna, Austria ShanakaHerath, Johanna Choumert, Gunther Maier

  2. Introduction • Greenbelts are important features of various cities – also Vienna • Proximity to greenbelt is attractive for housing • Counter effect to proximity to the city center • What effect does proximity to the greenbelt have on housing prices in Vienna? • Spatially located observations – spatial analysis

  3. Structure • Introduction • Greenbelt in Vienna • Data and variables • Empirical strategy • Estimation results • Conclusion

  4. Greenbelt in Vienna • Most important: Wienerwald (N – W) • Over 1,000 km2 • Mainly woodland • In the city many trails for hiking and mountain biking (accessible by tram or bus) • Secondary green area: Prater (2nd district) • Approx. 6 km2 • Mainly woods, wetlands • Amusement park, sports facilities

  5. Data and variables • Data provided by ERES.NET GmbH based on Immobilien.net • Dec. 11, 2009 – Mar. 25, 2010 • Apartment sales (1651) • Only those with location information • Asking price • Size • # of rooms, bathrooms, toilets • Condition • Features (balcony, terrace, elevator, parquet flooring)

  6. Data and variables • Geocoding of addresses via Google maps • Geocoding of the boundary of Wienerwald (in the city) and of Prater • For every apartment in the dataset we calculate the minimum distance to Wienerwald (greenbelt, dis_g) and Prater (dis_p) in addition to distance to city center (dis_c)

  7. Empirical strategy • Standard hedonic price theory: • … vector (nx1) of housing prices • … matrix (nxj) of housing characteristics (explanatory variables) • … unknown parameter vector (jx1) • … vector of random error terms (nx1) • If we assume to be iid distributed: standard OLS estimation – in case of spatial correlation: inefficient and maybe biased estimates.

  8. Empirical strategy • When iid-assumption does not hold, neighborhood relations have to be taken into account • Neighborhood characterized by W (spatial weight matrix or neighborhood matrix) • 3 types of spatial model: • Spatial lag model: • Spatial error model: • Spatial Durbin model: • SDM generalizes SLM and SEM

  9. Empirical strategy • Semi-log specification • Two variants of “location in the city”: • District dummies • Distance from CBD • OLS estimation • LM tests for spatial autocorrelation in the residuals – verified • LM tests for different spatial weight matrices (distance, nearest neighbors) • Test of SDM against SLM and SEM

  10. Empirical strategy • Parameters of the SLM, SDM cannot be interpreted directly or compared to those of OLS or SEM • Average effect of a marginal change: • Direct effect + • Indirect effect = • Total effect

  11. Estimation results • Testing for spatial autocorrelation • Verified in all cases • We need to apply a spatial model rather than just OLS • Tests also used to find the best W matrix for SLM and SEM

  12. Estimation results • Testing the SDM against the SEM • For both model specifications the SDM is superior to the SEM

  13. Estimation results • Intrinsic characteristics • expected signs • generally consistent across the models • District dummies: • All negative significant (relative to CBD) • Show the expected pattern (attractive vs. unattractive districts) • OLS estimates seem to be inflated (upward biased) • Seem to pick up spatial effects

  14. Estimation results • Distance to city center • negative and significant • Distance to green belt • negative and significant • Effect is smaller than that of distance to CBD • Distance to Prater • Negative and significant in 3 of four estimations • Effect is stronger for model with district dummies (picks up some of the distance decay)

  15. Estimation results • For the SDM no significant total effects for Model 1 • The previous results are confirmed for Model 2 • Distance to CBD negative and significant due to direct effect • Distance to green belt is negative and significant due to indirect effect – smaller than CBD coefficient • Distance to Prater is negative, but insignificant

  16. Conclusion • The impact of proximity to the green belt is verified for the Viennese housing market (sales of apartments). • Distance to green areas in the city is of lower importance. • Natural amenities are important factors in the residential choice of households. • But, they are clearly dominated by distance to the city center.

  17. Conclusion • Spatial effects seem to be important in residential hedonic price models. • Cannot be fully accounted for in the structural part of the model – spatial autocorrelation in residuals remains. • OLS estimates are upward biased. • SDM needs special treatment for interpretation

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