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  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

Structure  Introduction  Greenbelt in Vienna  Data and variables  Empirical strategy  Estimation results  Conclusion

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 (2 nd district)  Approx. 6 km2  Mainly woods, wetlands  Amusement park, sports facilities

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)

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)

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

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 OLSSEMSLMSDM Districts Distance

Estimation results Model Spatial weights matrix (criterion) Moran statistic LM errorLM lag RLM error RLM lag Model 1WDHALF (0.5 km) (0.000) (0.000) (0.068) (0.000) (0.049) WD1 (1 km) (0.000) (0.002) (0.184) (0.003) (0.239) WD2 (2 km) (1.000) (0.000) (0.442) (0.000) (0.556) W1 (k = 1) (0.000) (0.000) (0.043) (0.001) (0.569) W3 (k = 3) (0.000) (0.000) (0.005) (0.005) (0.114) W5 (k = 5) (0.000) (0.002) (0.000) (0.059) (0.008) Model 2WDHALF (0.5 km) (0.000) (0.000) (0.038) (0.000) (0.005) WD1 (1 km) (0.000) (0.000) (0.487) (0.000) (0.352) WD2 (2 km) (0.000) (0.000) (0.041) (0.000) (0.001) W1 (k = 1) (0.000) (0.000) (0.000) (0.000) (0.002) W3 (k = 3) (0.000) (0.000) (0.000) (0.000) (0.000) W5 (k = 5) (0.000) (0.000) (0.000) (0.000) (0.000) Notes: W (row standardised) spatial weights matrix is used. p-values follow in parentheses. k denotes number of neighbours in cases where "nearest neighbours" are considered  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

Estimation results  Testing the SDM against the SEM  For both model specifications the SDM is superior to the SEM Specification… DfLLChi 2 Prob > χ 2 with districtSpatial error model -> spatial Durbin model Spatial error model LOG (lagged dependent variable) (Df = 46)0.00*** with distance from the city centre Spatial error model -> spatial Durbin model Spatial error model LOG (lagged dependent variable) (Df = 25)0.00***

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

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) VariableOLS 1OLS 2SEM 1SEM 2 LOGdis_c *** (0.015) *** (0.027) LOGdis_g ** (0.015) *** (0.013) *** (0.012) ** (0.022) LOGdis_p *** (0.024) *** (0.012) ** (0.022) (0.023) R2R Adj. R F-statistic Prob (F-stat) 0.00 λ LR test value 34.16*** *** Log likelihood AIC Breusch-Pagan test No. of observations 1651

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 Variable Specification 1Specification 2 Direct effects Indirect effects Total effects Direct effects Indirect effects Total effects LOGdis_c ** (-3.149) (-1.246) *** (-8.254) LOGdis_g 0.083* (2.026) (-1.245) (-0.600) (-0.739) ** (-2.598) *** (-5.488) LOGdis_p * (-2.554) (0.196) (-0.255) (0.339) (-0.482) (-0.437) Notes: z values are in parenthesis.

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.

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