Based on prior knowledge, we can analyze the relationship between the feature "property_magnitude" and the task of determining whether the person receives a credit.

Looking at the feature "property_magnitude" with categories ['real estate', 'life insurance', 'no known property', 'car'], we can make some assumptions:

1. People who own real estate may have a higher chance of receiving credit.
2. People with life insurance may also have a higher chance of receiving credit, but it might be less significant compared to real estate.
3. People with no known property may have a lower chance of receiving credit, as they don't have any assets to offer as collateral.
4. People who own a car may have a moderate chance of receiving credit, but it might differ based on factors like the car's value or condition.

Based on these assumptions, we can generate a dictionary:

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

Please note that this analysis is based on general assumptions and might vary based on specific data and additional factors.