How to deal with Missing Not at Random Data for k-means clustering?
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.