No coefficient size and standard error with Geographically Weighted Regression (R)

Question

I've attempted a geographically weighted regression but feel I might have performed some sought of stupidity. I am unable to harvest neither the coefficient size nor the standard error (I would like to calculate the t statistic and p-value).

library(sf)
library(sp)

#- read
col = st_read('./SpatialStats_CoCT_HusCrm.gpkg', layer='LatticeDataR')


#- interpolate NaN values
intp_col <- col
intp_col$CRIME <- na_interpolation(intp_col$CRIME, na.rm = FALSE)
intp_col$AVMEDINC <- na_interpolation(intp_col$AVMEDINC, na.rm = FALSE)
intp_col$GV2015VAL <- na_interpolation(intp_col$GV2015VAL, na.rm = FALSE)

#- adaptive
crime.bw03 <- gwr.sel(intp_col$CRIME ~ intp_col$AVMEDINC + intp_col$GV2015VAL, 
                  data=intp_col, adapt = TRUE, # data=col,
                  coords = cbind(intp_col$X, intp_col$Y))

#- fit
gwr.fit3 <- gwr(intp_col$CRIME ~ intp_col$AVMEDINC + intp_col$GV2015VAL,
                data = intp_col, 
                coords = cbind(intp_col$X, intp_col$Y),
                adapt= crime.bw03, se.fit=T, hatmatrix=T)

Call:
gwr(formula = intp_col$CRIME ~ intp_col$AVMEDINC + intp_col$GV2015VAL, 
    data = intp_col, adapt = crime.bw03, hatmatrix = T, se.fit = T)
Kernel function: gwr.Gauss 
Adaptive quantile: 0.00599 (about 4 of 777 data points)
Summary of GWR coefficient estimates at data points:
                        Min.   1st Qu.    Median   3rd Qu.      Max. Global
X.Intercept.       -1.65e+03  4.24e+02  6.21e+02  8.85e+02  1.71e+03    582
intp_col.AVMEDINC  -2.44e-02 -1.95e-04  2.40e-04  1.78e-03  2.31e-02      0
intp_col.GV2015VAL -4.21e-04 -1.36e-05  6.88e-07  2.48e-05  4.03e-04      0
Number of data points: 777
Effective number of parameters (residual: 2traceS - traceS'S): NA 
Effective degrees of freedom (residual: 2traceS - traceS'S): NA 
Sigma (residual: 2traceS - traceS'S): NA ...

Notice the zero Global and NAs.

I want to harvest the coeficients the standard error to calculate the t and p for an independent variable.

names(gwr.fit3$SDF)
 [1] "sum.w"                     "X.Intercept."             
 [3] "intp_col.AVMEDINC"         "intp_col.GV2015VAL"       
 [5] "X.Intercept._se"           "intp_col.AVMEDINC_se"     
 [7] "intp_col.GV2015VAL_se"     "gwr.e"                    
 [9] "pred"                      "pred.se"                  
[11] "localR2"                   "X.Intercept._se_EDF"      
[13] "intp_col.AVMEDINC_se_EDF"  "intp_col.GV2015VAL_se_EDF"
[15] "pred.se.1

gwr.fit3$SDF$intp_col.GV2015VAL_se
NULL

Also if I attempt to execute a spgwr significance test:

LMZ.F3GWR.test(gwr.fit3)
Error in solve.default(t(x) %*% diag(wj) %*% x) : 
  system is computationally singular: reciprocal condition number = 9.96923e-17

I don't know. The data is [-> here]. Your help is appreciated.