```json
{
    "no": ["no"],
    "yes": ["yes"]
}
```

Based on prior knowledge, we can assume that clients who already have a personal loan may be less likely to subscribe to a term deposit, as they may have other financial obligations or higher debt-to-income ratios. Therefore, for the target class "no" (clients who do not subscribe to a term deposit), the possible values of the feature "loan" will mostly be "no".

On the other hand, for the target class "yes" (clients who do subscribe to a term deposit), the possible values of the feature "loan" could include both "yes" and "no". However, it is difficult to predict the exact values without further information on the dataset. Therefore, we include both "yes" and "no" as possible values for the target class "yes".

Note that in this analysis, we assume that personal loan refers to loans taken by the individual for personal purposes, such as a car loan or a home loan, rather than loans acquired by the client as a part of the banking relationship.