Is the Real Estate Market of New Housing Stock Influenced by Urban Vibrancy?

Table 4

Spatial lag model (SLM) to assess the relationship between NLP and extrinsic and intrinsic characteristics of the new housing stock (source: authors’ elaboration).

Variable	New housing listing prices per square meter (LOG NLP)
	Spatial lag model (SLM)
	Coefficients	Probability
Spatial coefficient (W)	0.704	0.000
CST1-newly constructed	Omitted
CST2-totally refurbished	0.038	0.055	.
CY-2011	0.179	0.000
CY-2012	0.163	0.000
CY-2013	0.119	0.005
CY-2014	0.119	0.001
CY-2015	0.059	0.105
CY-2016	0.080	0.031
CY-2017	Omitted
CY-2018	0.879	0.034
BC1-economic	Omitted
BC2-medium	0.073	0.075	.
BC3-noble	0.193	0.000
BC4-prestigious	0.389	0.000
GRG0-box or private car park absent	Omitted
GRG1-box or private car park present	0.064	0.001
NeSI (numerical)	0.020	0.000
Constant	2.079	0.000
Number of observations	351
Log likelihood	129.905
AIC	−229.81
R square	0.70
Breush–Pagan test	24.766	0.024
Likelihood ratio test	189.608	0.000

Signif. codes: “”; “”; “”; “.”; “”