Based on prior knowledge, it is likely that the housing feature will have some relationship with the task of whether a person receives credit or not. 

Now, let's analyze the relationship between the housing feature and the target variable of whether a person receives credit or not:
- It is possible that people who own their own homes have a higher chance of receiving credit, as they have a stable housing situation and are seen as more financially responsible.
- People who live in rented housing may have a lower chance of receiving credit, as they may be seen as less financially stable or more likely to move frequently.
- People who live in housing for free may have varying chances of receiving credit, as this category could include situations where the person is living with family or friends and not paying rent or situations where the housing is provided by an employer or a government program. 

Based on this analysis, let's create a dictionary with the following format:

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

In this case, we include the "own" category in the "yes" list as people who own their homes are more likely to receive credit. For the "no" list, we include the "for free" and "rent" categories as these are housing situations that may indicate a lower chance of receiving credit.

This analysis may vary depending on the specific dataset and context of the problem, but this is a general analysis based on prior knowledge.