Based on prior knowledge, we can analyze the relationship between the feature "property_magnitude" and the task of whether the person receives a credit or not. To do this, we need to look at the distribution of property magnitudes for both the "yes" and "no" classes.

Assuming we have a dataset with information about whether a person receives a credit or not, and the corresponding property magnitudes, we can analyze the relationship as follows:

1. We would first separate the dataset into two subsets based on the target class "yes" and "no".
2. Then, for each subset, we would calculate the distribution of property magnitudes.
3. Based on the distribution, we would identify the possible values of property magnitudes for each target class.

Here's an example of what the dictionary could look like based on the analysis:

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

In this example, the "yes" class includes property magnitudes such as "real estate", "car", and "life insurance", while the "no" class includes the value "no known property". Any other values of property_magnitude not mentioned in the dictionary are either hard to predict or not present in the dataset.

It's important to note that the actual values and their distribution may vary depending on the dataset and the specific task at hand. The provided example is just a hypothetical illustration.