Based on the given feature "housing" and task "Does this person receive a credit?", we can analyze the relationship between the feature and the target variable.

Analyzing the feature "housing" with respect to the task, we can make some assumptions:

1. If the housing is categorized as "own", there is a higher possibility that the person receives a credit. This is because owning a house signifies stability and financial security.
2. If the housing is categorized as "for free", it is difficult to predict whether the person receives a credit or not. While having a house for free might indicate a lower financial burden, it might also suggest a lack of regular income or other financial factors that could impact creditworthiness.
3. If the housing is categorized as "rent", there is a lower likelihood that the person receives a credit. Renting a house might imply unstable financial conditions or a lack of sufficient income or assets.

Based on this analysis, we can create the requested dictionary:

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

Please note that the analysis and the resulting dictionary are based on common assumptions and general knowledge. The actual relationship could be more complex and depend on various other factors.