Based on the hint provided and the analysis of the dataset, I have identified the following issues:

1. **Issue with 'date' column datatype**

- **Issue**: The 'date' column is represented as an object datatype.
- **Evidence**: The column 'date' is listed with datatype `object` in the dataframe info.
- **Description**: The 'date' column is expected to represent dates for when a house was sold. However, this column is currently treated as a generic object (string) type rather than a specific date type. For accurate date manipulation and analysis (such as computing time intervals or filtering by specific date ranges), it would be more appropriate for this column to be in a date/datetime format.

2. **Issue with 'waterfront', 'view', 'condition', 'grade' being integer but possibly categorical**

- **Issue**: Columns like 'waterfront', 'view', 'condition', and 'grade' are represented with integer datatypes.
- **Evidence**: Columns 'waterfront', 'view', 'condition', 'grade' are listed with datatype `int64` in the dataframe info.
- **Description**: These columns represent attributes of houses that are naturally categorical or ordinal in nature. Representing these features as integers might be confusing as it implies a quantitative relationship between the values that might not accurately represent the underlying data. For example, 'waterfront' could logically be a binary indicating the presence or absence of a feature, 'view', 'condition', and 'grade' read more as categories which rate the house on different scales. It might be more meaningful to handle these columns as categorical variables to better represent the data's nature and intend viewership.

Each of these issues deviates from expected standards for representing specific types of data, particularly when considering the context given in the hint about "confusing data type for a feature". Transformations or adjustments based on these observations could enhance the dataset's usability for analysis purposes.