Based on prior knowledge, it can be assumed that the feature "housing" may have some relation to the task of determining whether a person receives a credit or not. The different categories of the feature include 'own', 'for free', and 'rent'. 

To analyze the relationship between the feature and the target, we can look at the distribution of housing types for each target class. By examining the frequencies of the different housing categories for the 'yes' and 'no' target classes, we can determine if any patterns or tendencies exist.

Here is the dictionary based on the analysis:

```json
{
	"yes": ["own", "for free", "rent"],
	"no": ["own", "rent"]
}
```

This indicates that for the 'yes' class, all three housing categories can be possible values, whereas for the 'no' class, only the 'own' and 'rent' categories are present.

Note that the 'for free' category of housing is included in the 'yes' class, but it's not present in the 'no' class.