Based on prior knowledge and experience, the relationship between the feature "housing" and the target variable "Does this person receive a credit? Yes or no?" can be analyzed as follows:

Intuitively, it can be assumed that the type of housing a person has may play a role in determining whether they receive credit or not. People who own their homes and have stable housing may have a higher likelihood of being approved for credit, while those who live in rented accommodation or receive housing for free may have a lower likelihood.

Based on this analysis, the dictionary with the specific details can be generated as follows:

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

The values "own" correspond to the target class "yes" as individuals who own their homes may be more likely to receive credit. The values "for free" and "rent" correspond to the target class "no" as individuals who live in rented accommodations or receive housing for free may be less likely to receive credit.

Note: This analysis is based on general intuition, and the actual relationship between the feature and the target variable may vary depending on the specific dataset and context. It is always recommended to perform a thorough analysis on the actual data to confirm these assumptions.