Valuing Intellectual Property assets is increasingly critical in today’s economy, where intangible assets constitute a significant portion of business value. This thesis addresses the challenges inherent in the IP valuation process, particularly the subjectivity and variability associated with different IP types and valuation methodologies. It proposes a new way to value IP assets, by building upon existing disaggregation methods, and by introducing the IP-heaviness classification system. The study aims to develop an objective valuation model for IP assets by introducing the IP-heaviness classification system. The goal of the model is to estimate the range of IP Contribution (IPC) to company value across different industry groups. Our study employed Kernel Density Estimation and Monte Carlo Simulation to analyze the dataset and generate a larger data sample. We then developed the IPH classification system, which categorizes industries based on their reliance on IP as a value contributor, grouping them by similar levels of IP dependence. This structured approach allows for a preliminary estimation of the IP contribution for each group, providing a standardized framework for IP valuation. Each IPH group was assigned its own probability density curve to represent its potential IPC value. Ultimately, our model produced confidence intervals for each IPH group, offering a reliable measure of the IP contribution within each category. Our findings reveal significant variability in the impact of IP on company value across different industries. Higher IPH groups, representing industries with substantial IP reliance, show a greater proportion of their value attributed to IP assets. Conversely, lower IPH groups, with less reliance on IP, exhibit lower IP contributions. The IPH classification system addresses the challenges of traditional IP valuation methods by providing a more objective and transparent approach. It enhances the comparability of companies within and across IPH groups and reduces subjectivity in the valuation process.