# RECORD b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6 # TITLE: Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria # YEAR: 2014.0 VENUE: nan DOI: 10.1177/0042098013492234 # AUTHORS: M. Helbich; Wolfgang A. Brunauer; E. Vaz; P. Nijkamp # PAGES: 24 CANDIDATE TABLE PAGES (1-based): [14, 16] # ABSTRACT: nan ===== PAGE 1 (score 1, numeric-paren 0, table-words 0, var-words 1) ===== TI 2013-171/VIII Tinbergen Institute Discussion Paper Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria Marco Helbich1 Wolfgang Brunauer2 Eric Vaz3 Peter Nijkamp4 1 University of Heidelberg, Germany; 2 UniCredit Bank Austria AG, Austria; 3 Ryerson University, Canada; 4 Faculty of Economics and Business Administration, VU University Amsterdam, and Tinbergen Institute. ===== PAGE 14 (score 76, numeric-paren 70, table-words 1, var-words 4) ===== 12 Burgenland have a significantly lower price compared with houses situated in Lower Austria, serving as reference category. Tab. 2: Estimation results of the global models. OLS SAR S2SLS Coeff. HC Std. err. t-val. Coeff. Std. err. z-val. Coeff. SHAC Std. err. t-val. Intercept 9.391 0.195 48.278*** 6.872 0.328 20.969*** 6.542 0.548 11.939** * lnarea_total 0.410 0.017 23.988*** 0.409 0.018 22.825*** 0.411 0.017 23.864** * lnarea_plot 0.111 0.012 8.945*** 0.111 0.013 8.855*** 0.111 0.012 8.891*** cond_house_1 - 0.052 0.014 -3.742*** - 0.045 0.014 -3.330** - 0.044 0.014 -3.229** heat_1 - 0.102 0.020 -5.018*** - 0.107 0.023 -4.602*** - 0.105 0.021 -5.005*** bath_1 - 0.076 0.019 -3.910*** - 0.074 0.022 -3.339*** - 0.075 0.019 -3.985*** attic_1 - 0.024 0.012 -2.018* - 0.024 0.011 -2.077* - 0.024 0.012 -2.020* basement_1 0.132 0.013 9.936*** 0.126 0.013 9.597*** 0.124 0.014 9.069*** garage_1 - 0.082 0.012 -6.899*** - 0.084 0.012 -7.162*** - 0.084 0.012 -7.064*** terr_1 0.070 0.012 5.893*** 0.066 0.012 5.673*** 0.066 0.012 5.574*** age - 0.006 0.000 - 16.976*** - 0.006 0.000 - 17.617*** - 0.006 0.000 -16.434 time 0.009 0.003 3.550*** 0.010 0.002 4.100*** 0.010 0.003 3.925*** muni_pp_ind 0.005 0.001 5.800*** 0.005 0.001 5.890*** 0.005 0.001 5.709*** muni_acad 0.011 0.002 5.695*** 0.009 0.002 4.384*** 0.008 0.002 3.692*** muni_unempl - 1.454 0.649 -2.241* muni_age_ind - 0.020 0.004 -5.538*** - 0.017 0.004 -4.788*** - 0.016 0.004 -4.255*** ln_muni_popd 0.033 0.007 4.518*** 0.020 0.006 3.153** 0.020 0.006 3.245 enumd_acad_dmuni 0.008 0.001 6.072*** 0.006 0.001 4.997*** 0.005 0.001 4.232** enumd_unempl_dmun i 1.758 0.607 2.898** enumd_age_ind_dmu ni 0.004 0.003 1.568*** Burgenland - 0.129 0.023 -5.644*** - 0.096 0.025 -3.905*** - 0.087 0.024 -3.671*** Carinthia - 0.082 0.024 -3.397*** - 0.093 0.024 -3.912*** - 0.092 0.024 -3.801*** Upper Austria 0.018 0.021 0.870 0.016 0.019 0.832 0.012 0.019 0.648 Vorarlberg & Tyrol 0.241 0.042 5.791*** 0.182 0.036 5.023*** 0.179 0.041 4.340*** Salzburg 0.231 0.030 7.748*** 0.190 0.028 6.796*** 0.185 0.031 6.054*** Styria - 0.055 0.020 -2.760** - 0.056 0.020 -2.805** - 0.055 0.020 -2.759** Vienna 0.266 0.034 7.759*** 0.220 0.031 7.011*** 0.215 0.034 6.401*** šœŒ 0.205 p < 0.001 0.229 p < 0.001 Adj. RĀ² 0.473 0.486 AIC 2783 2707 AIC without regions 2988 2850 RMSE 0.344 0.339 0.339 RMSE without regions 0.353 0.345 0.345 Signif.: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.10 ===== PAGE 15 (score 8, numeric-paren 1, table-words 1, var-words 5) ===== 13 5.3 Local and semi-local model Next, GWR and MGWR are estimated. A data-driven approach to explore functional instability replaces the federal states dummies. For both models the analysis is restricted exclusively to the main effects. Otherwise, compositional effects would result in complex and difficult interpretational local effects. Because of a significant compositional effect of the unemployment rate and an insignificant corresponding main effect in the S2SLS model, the unemployment rate is additionally considered at the enumeration district level. To achieve a trade-off between computational burden and estimation accuracy, (a) a random sample of 50% of the overall data set is used and (b) only those variables are chosen that are significant in the global S2SLS model (Table 1). First, it is necessary to distinguish between global and local effects using non-parametric GWR with a Gaussian kernel. CV determines an optimal adaptive bandwidth that includes 227 neighboring houses. The test statistic after Leung et al. (2000a) does not reject the H0 of stationary parameters for six out of 16 variables. In a MGWR specification, those six variables are thus held spatially constant, while the remaining 10 variables are allowed to vary. Again, applying a Gaussian kernel function, CV results in an optimal bandwidth of 177 neighboring houses for the MGWR. Compared to the GWR the AIC score is noticeably reduced from 1,396 (GWR) to 1,381 (MGWR), while the RMSE for both models is quasi similar (0.338). Statistically the MGWR is favored. Sensitivity analysis with alternative kernels (i.e. bi-square kernel) shows no significant differences. The spatial variation of the model fit ranges from 0.24 to 0.49. The lowest values are located in Vienna plus its surroundings and in the south of Austria. The best fit is achieved in the northern parts of Austria. The reason for the low fit in Vienna might be that flats dominate the market in the capital. Figure 2 visualizes the parameter surfaces of the local RĀ² and the predicted dwelling prices in Euro, achieved through ordinary kriging (Pebesma, 2004). The F(3)-test (Leung et al., 2000a) confirms that now all spatially varying covariates are statistically significant. No significant residual dependence on a 0.01 level is detected by the Moran's š¼ (Leung et al., 2000b). Fig.2: Local model performance and the predicted house prices. ===== PAGE 16 (score 35, numeric-paren 24, table-words 2, var-words 7) ===== 14 Table 3 lists the MGWR results and its global counterparts. Additionally, Figures 3 through 5 visualize the parameter surfaces. Based on pseudo-t-values, only significant coefficients on š›¼= 0.1 have gray tones. Tab. 3: Estimation results of the semi-local model. Global coeff. Local coeff. Estim. Std.err. t-val. 1. quantile Median 3. quantile F(3)-test lnarea_total 0.422 0.024 17.453*** 0.367 0.398 0.458 *** lnarea_plot 0.077 0.018 4.268*** 0.061 0.087 0.111 *** heat_1 -0.111 0.031 -3.544*** -0.163 -0.125 -0.072 * Age -0.005 0.000 -11.441*** -0.006 -0.005 -0.004 *** Time 0.006 0.003 1.990* 0.007 0.011 0.013 *** enumd_unempl -2.103 0.587 3.581*** -2.335 -1.520 -2.236 *** muni_pp_ind 0.004 0.001 4.442*** -0.001 0.006 0.007 *** muni_acad 0.012 0.003 4.567*** 0.007 0.015 0.022 *** muni_age_ind -0.038 0.005 -8.260*** -0.037 -0.028 -0.022 *** ln_muni_popd 0.067 0.008 8.623*** 0.036 0.065 0.079 *** cond_house_1 -0.038 0.020 -1.953. bath_1 -0.064 0.031 -2.029* attic_1 -0.028 0.016 -1.674. basement_1 0.144 0.019 7.665*** garage_1 -0.068 0.017 -4.060*** terr_1 0.076 0.017 4.587*** Signif.: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.10 Fig. 3: Physical covariates and temporal effect. ===== PAGE 17 (score 4, numeric-paren 0, table-words 0, var-words 4) ===== 15 Fig. 4: Temporal effect and neighborhood covariates. Fig. 5: Neighborhood covariates. The negative MGWR intercept is not, by itself, meaningful. The spatially fixed coefficients consist solely of physical covariates, meaning buyers value these housing characteristics equal across space. Furthermore, these stationary coefficients illustrate that real estate markets are economically connected. Compared to the S2SLS estimates, the MGWR coefficients do not considerably deviate, which is an indication of model suitability. The total floor area is statistically significant across the entire study site and shows strong spatial variation (Figure 3). Its positive effect on house prices is less pronounced closer to Vienna and reaches its largest positive effects in parts of the federal states of Salzburg and Tyrol. These areas include well-established winter sports sites (e.g., KitzbĆ¼hel) where tourism and vacation homes, secondary residences etc. increase house prices. Particularly, the scarcity of space leads to higher prices for additional floor area in these regions. In contrast, a different pattern can be found for