The dataset contains 50,000 entries and 6 columns: `SquareFeet`, `Bedrooms`, `Bathrooms`, `Neighborhood`, `YearBuilt`, and `Price`. 

The datatype and initial rows seem to be appropriate to the column names. 

Next, I will check for any negative values in the 'Price' column as suggested by the hint.I have found two issues according to the given hint:

1. **Negative Prices in Data**

- **Issue**: Negative values in the 'Price' column.
- **Evidence**: Examples include prices of -24715.242482 for a 1024 SquareFeet property in Urban area, built in 2006, and -7550.504574 for a 1036 SquareFeet property in Suburb area, built in 1983.
- **Description**: There are a total of 22 entries with negative price values, which are unrealistic and possibly indicate data errors or anomalies in the 'Price' column. A price, representing the cost of a housing property, should not be negative under normal circumstances. This suggests the need for data validation or cleaning to ensure accuracy of housing price information.