Based on the given feature "property_magnitude" and the task of determining whether a person receives a credit or not, we can analyze the relationship between the feature and the target variable.

Since "property_magnitude" represents the magnitude of a person's property (e.g., real estate, life insurance, car, or no known property), it can be assumed that individuals with higher property magnitudes are more likely to receive credit. Conversely, individuals with lower property magnitudes may be less likely to receive credit.

Based on this analysis, we can generate the following dictionary:

```json
{
	"yes": ["real estate", "life insurance", "car"], 
	"no": ["no known property"]
}
```

This dictionary indicates that individuals who have any of the property magnitudes of "real estate", "life insurance", or "car" are more likely to receive credit. On the other hand, individuals with the property magnitude of "no known property" may be less likely to receive credit.

Note that the dictionary includes all possible values of the feature property_magnitude for each target class, ensuring that the list of each target class is not empty.