Table 8 Differences in estimated housing prices in Daejeon.

From: Asymmetric impacts of artificial intelligence on housing price valuation across education levels

 

0–5%

95–100%

0–10%

90–100%

0–20%

80–100%

Panel A.1: Univ. grad. is omitted.

RF-OLS

\(-0.042\)

\(0.022\)

\(0.013\)

\(-0.038\)

\(0.001\)

\(0.021\)

 

\(-{2.924}^{\ddagger }\)

\({3.562}^{\ddagger }\)

\(-{1.896}^{* }\)

RF-SLR

\(-0.038\)

\(0.072\)

\(-0.016\)

\(0.002\)

\(0.016\)

\(0.013\)

 

\(-{5.794}^{\ddagger }\)

\(-1.416\)

\(0.351\)

XGB-OLS

\(-0.048\)

\(0.019\)

\(0.010\)

\(-0.040\)

\(0.001\)

\(0.018\)

 

\(-{3.012}^{\ddagger }\)

\({3.538}^{\ddagger }\)

\(-{1.644}^{* }\)

XGB-SLR

\(-0.043\)

\(0.068\)

\(-0.020\)

\(0.000\)

\(0.016\)

\(0.009\)

 

\(-{5.926}^{\ddagger }\)

\(-1.493\)

\(0.676\)

Panel: A.2: Univ. grad. and Top school are omitted.

RF-OLS

\(-{0.091}^{\dagger }\)

\({0.085}^{\dagger }\)

\(-0.039\)

\({0.062}^{\dagger }\)

\(-{0.044}^{\ddagger }\)

\({0.068}^{\ddagger }\)

 

\(-{6.966}^{\ddagger }\)

\(-{6.594}^{\ddagger }\)

\(-{9.617}^{\ddagger }\)

RF-SLR

\(-{0.074}^{\dagger }\)

\({0.123}^{* }\)

\(-{0.058}^{\dagger }\)

\({0.080}^{\ddagger }\)

\(-0.016\)

\({0.046}^{\dagger }\)

 

\(-{9.559}^{\ddagger }\)

\(-{10.112}^{\ddagger }\)

\(-{6.005}^{\ddagger }\)

XGB-OLS

\(-{0.096}^{\ddagger }\)

\({0.085}^{\dagger }\)

\(-{0.042}^{* }\)

\({0.062}^{\dagger }\)

\(-{0.045}^{\ddagger }\)

\({0.067}^{\ddagger }\)

 

\(-{7.147}^{\ddagger }\)

\(-{6.829}^{\ddagger }\)

\(-{9.728}^{\ddagger }\)

XGB-SLR

\(-{0.078}^{\dagger }\)

\({0.123}^{* }\)

\(-{0.061}^{\dagger }\)

\({0.080}^{\ddagger }\)

\(-0.017\)

\({0.045}^{\dagger }\)

 

\(-{9.769}^{\ddagger }\)

\(-{10.410}^{\ddagger }\)

\(-{6.061}^{\ddagger }\)

Panel B.1: Top school is omitted.

RF-OLS

\(-{0.188}^{\ddagger }\)

\({0.134}^{\ddagger }\)

\(-{0.111}^{\ddagger }\)

\({0.056}^{\dagger }\)

\(-{0.060}^{\ddagger }\)

\({0.084}^{\ddagger }\)

 

\(-{17.143}^{\ddagger }\)

\(-{12.043}^{\ddagger }\)

\(-{14.866}^{\ddagger }\)

RF-SLR

\(-0.025\)

\(0.005\)

\(-{0.041}^{* }\)

\(0.042\)

\(-{0.059}^{\ddagger }\)

\({0.065}^{\ddagger }\)

 

\(-{1.934}^{* }\)

\(-{6.988}^{\ddagger }\)

\(-{15.137}^{\ddagger }\)

XGB-OLS

\(-{0.182}^{\ddagger }\)

\({0.137}^{\ddagger }\)

\(-{0.107}^{\ddagger }\)

\({0.056}^{\dagger }\)

\(-{0.058}^{\ddagger }\)

\({0.081}^{\ddagger }\)

 

\(-{17.366}^{\ddagger }\)

\(-{11.870}^{\ddagger }\)

\(-{14.601}^{\ddagger }\)

XGB-SLR

\(-0.020\)

\(0.008\)

\(-0.038\)

\(0.041\)

\(-{0.057}^{\ddagger }\)

\({0.063}^{\ddagger }\)

 

\(-{1.826}^{* }\)

\(-{6.750}^{\ddagger }\)

\(-{14.830}^{\ddagger }\)

Panel B.2: Univ. grad. and Top school are omitted.

RF-OLS

\(-{0.215}^{\ddagger }\)

\({0.163}^{\ddagger }\)

\(-{0.132}^{\ddagger }\)

\({0.087}^{\ddagger }\)

\(-{0.081}^{\ddagger }\)

\({0.120}^{\ddagger }\)

 

\(-{19.804}^{\ddagger }\)

\(-{15.775}^{\ddagger }\)

\(-{21.031}^{\ddagger }\)

RF-SLR

\(-0.033\)

\(0.016\)

\(-{0.050}^{\dagger }\)

\({0.059}^{\dagger }\)

\(-{0.072}^{\ddagger }\)

\({0.086}^{\ddagger }\)

 

\(-{3.077}^{\ddagger }\)

\(-{9.108}^{\ddagger }\)

\(-{19.078}^{\ddagger }\)

XGB-OLS

\(-{0.218}^{\ddagger }\)

\({0.164}^{\ddagger }\)

\(-{0.132}^{\ddagger }\)

\({0.086}^{\ddagger }\)

\(-{0.083}^{\ddagger }\)

\({0.117}^{\ddagger }\)

 

\(-{20.147}^{\ddagger }\)

\(-{15.749}^{\ddagger }\)

\(-{21.009}^{\ddagger }\)

XGB-SLR

\(-0.036\)

\(0.016\)

\(-{0.051}^{\dagger }\)

\({0.058}^{\dagger }\)

\(-{0.073}^{\ddagger }\)

\({0.083}^{\ddagger }\)

 

\(-{3.350}^{\ddagger }\)

\(-{9.090}^{\ddagger }\)

\(-{19.031}^{\ddagger }\)

  1. \(\ddagger\), \(\dagger\), and \(*\) indicate significance at the 1%, 5%, and 10% levels, respectively.