The amount of data processed in high-performance computing has been growing rapidly. Accordingly, the cost of data movement in a large-scale computing system further increases, which has a huge effect on computing performance. To reduce the data movement cost, we have proposed hardware-based data compression for numerical data streams that can greatly reduce overhead has been proposed. Although our proposed hardware compressor works well for scientific computation in previous studies, the compression performance heavily depends on a type of target data. For practical use, we need to know the characteristics of the data compression and the relationship between data types and compression performance. In this paper, we clarify difference in compression performance among different data types by investigating hardware-based compression performance for data types of double, single, and half-precision floating-point and ffixed-point with results of numerical simulation. We also propose an area-saving hardware design for double-precision floating-point data.