Based on prior knowledge, we can analyze the relationship between the "residence_since" feature and the target variable "Does this person receive a credit?". 

Since the feature represents the number of years a person has been living at their present residence, it is reasonable to assume that individuals who have been living at the same residence for a longer period of time may have a higher likelihood of receiving a credit. 

To create the dictionary, we will need to identify typical "residence_since" values for both the "yes" and "no" classes.

Here is the dictionary with typical values for the "residence_since" feature for each target class:

```json
{
	"yes": [2.0, 3.0, 4.0, 5.0, 6.0],
	"no": [1.0, 3.5, 4.5, 6.5, 8.0]
}
```
Note that these values are just examples and may not reflect the actual data.