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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 - PowerPoint PPT Presentation
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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 (2nd 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
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.
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
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
OLS SEM SLM SDMDistrictsDistance
Estimation results
Model Spatial weights matrix (criterion)
Moranstatistic
LM error LM lag RLM error
RLM lag
Model 1 WDHALF (0.5 km) 0.056(0.000)
17.014(0.000)
3.332(0.068)
17.552(0.000)
3.870(0.049)
WD1 (1 km) 0.029(0.000)
9.347(0.002)
1.767(0.184)
8.969(0.003)
1.389(0.239)
WD2 (2 km) -0.025 (1.000)
37.417(0.000)
0.592(0.442)
37.172(0.000)
0.347(0.556)
W1 (k = 1) 0.104(0.000)
15.425(0.000)
4.089(0.043)
11.661(0.001)
0.324(0.569)
W3 (k = 3) 0.058(0.000)
13.227(0.000)
7.730(0.005)
8.003(0.005)
2.505(0.114)
W5 (k = 5) 0.039(0.000)
9.452(0.002)
12.819(0.000)
3.569(0.059)
6.937(0.008)
Model 2 WDHALF (0.5 km) 0.260(0.000)
370.493(0.000)
4.298(0.038)
374.046(0.000)
7.851(0.005)
WD1 (1 km) 0.279(0.000)
847.553(0.000)
0.483(0.487)
847.937(0.000)
0.867(0.352)
WD2 (2 km) 0.143(0.000)
1204.118(0.000)
4.187(0.041)
1210.248(0.000)
10.317(0.001)
W1 (k = 1) 0.390(0.000)
214.472(0.000)
78.341(0.000)
145.961(0.000)
9.829(0.002)
W3 (k = 3) 0.296(0.000)
339.875(0.000)
147.802(0.000)
224.335(0.000)
32.262(0.000)
W5 (k = 5) 0.249(0.000)
385.522(0.000)
221.050(0.000)
231.811(0.000)
67.339(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… Df LL Chi2 Prob > χ2
with district Spatial error model -> spatial Durbin model
Spatial error model 49 281.21 - + LOG (lagged dependent variable) 95 395.53 228.63 (Df = 46) 0.00***
with distance from the city centre
Spatial error model -> spatial Durbin model
Spatial error model 28 129.49 - + LOG (lagged dependent variable) 53 164.97 70.96 (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)
Variable OLS 1 OLS 2 SEM 1 SEM 2
LOGdis_c -0.200***(0.015)
-0.228***(0.027)
LOGdis_g -0.044**(0.015)
-0.150***(0.013)
-0.040***(0.012)
-0.062**(0.022)
LOGdis_p -0.084***(0.024)
-0.060***(0.012)
-0.060**(0.022)
0.012(0.023)
R2 0.90 0.85 Adj. R2 0.90 0.85 F-statistic 318.7 375.9 Prob (F-stat) 0.00 0.00 λ -0.75 0.70LR test value 34.16*** 394.72***
Log likelihood 281.21 129.49AIC -432.26 189.75 -464.42 -202.97Breusch-Pagan test
96.89 56.58 95.61 64.90
No. of observations
1651 1651 1651 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 1 Specification 2Direct effects
Indirect effects
Total effects
Direct effects
Indirect effects
Total effects
LOGdis_c
-0.188**(-3.149)
-0.082(-1.246)
-0.271***(-8.254)
LOGdis_g 0.083*(2.026)
-0.141(-1.245)
-0.058(-0.600)
-0.022(-0.739)
-0.097**(-2.598)
-0.120***(-5.488)
LOGdis_p -0.094*(-2.554)
0.044(0.196)
-0.049(-0.255)
0.015(0.339)
-0.025(-0.482)
-0.009(-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