Best imputation for principal component analysis - stata

Question

I have a list of assets. I use these assets to create an asset index using pca:
local assets newspaper magazine clock water borehole table bed study bicycle cart car motorcycle tractor electricity fridge airconditioner fan washing_machine vacuum
alpha $assets
pca $assets
predict asset_idx
label var asset_idx "pupil asset ownership index: created using pca"
egen asset_idx_std = std(asset_idx)
However, I am missing about 3% of assets for each variable. This amounts to 30% after pairwise deletion. As such, I wish to impute the missing variables so every student with less than 10% of observations missing is not deleted during PCA. I do not otherwise wish to mi set, but can't work out another way:
mi set wide
mi register imputed newspaper magazine clock water borehole table bed study bicycle cart car motorcycle tractor electricity fridge airconditioner fan washing_machine vacuum computer internet radio tv vcr dvd cd cassette camera digi_camera vid_cam landline phone
set seed 1234
mi impute logit newspaper magazine clock water borehole table bed study bicycle cart car motorcycle tractor electricity fridge airconditioner fan washing_machine vacuum computer internet radio tv vcr dvd cd cassette camera digi_camera vid_cam landline phone, force add(1)
Unfortunately, this is only successfully imputing a small fraction of missing observations:
------------------------------------------------------------------
| Observations per m
|----------------------------------------------
Variable | Complete Incomplete Imputed | Total
-------------------+-----------------------------------+----------
newspaper | 7007 110 4 | 7117
------------------------------------------------------------------
Any suggestions on appropriate imputation are much appreciated.