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Table 4 Autoregressive parameter \(\rho \) and regression coefficients \(\beta _j\) estimates of a SAR model on log-transformed prices of 361 houses in Beijing

From: Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data

Regressor DME SPDM CIP
Autoregressive parameter \(\rho \) \(0.575^{***}\) 0.398 \(0.500^{***}\)
Intercept \(1.325^{***}\) \(2.411^{***}\) \(1.729^{***}\)
Two living rooms \(0.299^{***}\) \(0.258^{***}\) \(0.361^{***}\)
More than two living rooms 0.608 \(0.519^{***}\) 0.602
Two drawing rooms 0.108 0.079 0.087
More than two drawing rooms \(0.773^{***}\) \(0.742^{**}\) \(0.577^{*}\)
More than one bathroom \(0.319^{***}\) \(0.391^{***}\) \(0.379^{***}\)
Building type 2 0.111 0.252 0.169
Building type 3 0.000 \(-\) 0.002 \(-\) 0.017
Building type 4 0.020 0.014 0.048
Building type “other” 0.367 0.352 \(0.428^{*}\)
Construction time in (1989, 1999] 0.109 0.094 \(0.145^{***}\)
Construction time in (1999, 2019] \(-\) 0.006 \(-\) 0.013 0.038
Renovation condition 2 \(0.441^{**}\) \(0.434^{*}\) \(0.400^{***}\)
Renovation condition 3 \(0.411^{***}\) \(0.377^{***}\) \(0.346^{***}\)
Renovation condition 4 \(0.506^{***}\) 0.\(477^{***}\) \(0.434^{***}\)
Building structure type 6 0.084 0.080 0.105
Other type of building structure 0.183 0.169 0.135
Elevator \(0.356^{***}\) \(0.321^{***}\) \(0.273^{**}\)
Subway station \(0.185^{**}\) \(0.224^{***}\) \(0.228^{***}\)
  1. The locations of 65 houses (\(18\%\)) are coarsened, thus the model has been fitted through DME, SPDM and CIP
  2. \(^{*}p<0.1\); \(^{**}p<0.05\); \(^{***}p<0.01\)