Based on prior knowledge, we can analyze the relationship between the feature "loan" and the target variable "subscribes to a term deposit".

In this case, the feature "loan" represents whether the client has a personal loan or not. The possible values for this feature are "yes" and "no".

To determine the relationship between the feature and the target, we can examine the distribution of the target variable for each value of the feature.

If a client has a personal loan, it is possible that they have less disposable income available, which might decrease the likelihood of subscribing to a term deposit. On the other hand, if a client does not have a personal loan, they may have more financial stability and therefore be more likely to subscribe to a term deposit.

To create the dictionary with the requested format, we need to analyze the possible values of the feature "loan" for each target class:

- For the target class "no" (when the client does not subscribe to a term deposit), the possible values of the feature "loan" can be either "yes" or "no".
- For the target class "yes" (when the client subscribes to a term deposit), the possible values of the feature "loan" can also be either "yes" or "no".

Even though it is hard to predict the specific loan values that are possible for each target class, we should ensure that the list of each target class is not empty.

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

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

Please note that the possible values of the feature "loan" may vary depending on the specific dataset being analyzed. This analysis assumes that the feature can only take the values "yes" or "no".