How to deal with Missing Not at Random Data for k-means clustering? I am running k-means clustering on a customer dataset. One of the available demographic fields is inferred homevalue, represented as an integer. 
This field has value 0 when it's inferred that the customer is not a homeowner at all (they are more likely a renter, live with relatives, etc).
I'm struggling to think of a good way to treat this value. 

Does it make sense to keep this value as 0? Then my understanding is that the algorithm will interpret this as someone who doesn't own property is closely related to someone who owns an extremely low value property which doesn't seem intuitively right. 

Is there a better way of dealing with this?
I think there are two approaches you can take:

1) Square the 'homevalue'

Assuming that homevalue is a numeric you can square this value.

Then, you will increase the difference between $0$-valued homes and low-valued homes. Further, it will make the difference between high-valued homes to low-valued homes also larger.

2) Split into two features

You can also split this information into two different features:


Home value $v$, where $v \in \mathbb{R}^+$
Owns a home $h$, where $h \in \{true, false\}$ and $h = \substack{false, \quad if \, v \, equals \, 0\\true, \quad \ otherwise}$


This way, your model has a way to explicitly distinguish between people that own or not a home. 

Then hopefully you have another feature indicating the lifestyle or earnings of a person that doesn't own a home.