There are several methods of valuating an Intellectual Property. Below are a summary of each methods, their use scenarios, advantages, disadvantages

Income Based Method:
The value of the IP is calculated by the future cash flow generated by the IP over its economic life (includes the product lifetime of the products where the IP gets used). The typical models used in this method are discounted cash flow model, venture capital model and relief from royalty model. The method is more analytical in nature and looks at the economical benefit, economic life and appropriate discount rate. This method can be used for raising fund for IP development, for IP transaction etc. But this method is subjected to lot of assumptions and the valuation can vary significantly on those assumptions

Cost Based Method:
Here the value of IP is determined from the cost one needs to incur to develop similar kind of asset. The typical models used are replacement cost model and reproduction cost model. The advantage is the required information can be gathered easily and not influenced by too many assumptions. This is used mostly for accounting or tax purpose. But this does not include the economic benefit arises out of the IP and the duration of the economic benefit. Also it does not include the risk involves in future

Market Based Method:
Here the value of IP is calculated by the amount needed to acquire similar asset in the market in similar business scenario. It gives more accurate number on the economic benefit that the IP will be able to generate and is used in IP transaction and IP litigation. The challenge is it is difficult to gather data for comparable transaction as most of the cases those are confidential in nature

Option Based Method:
It uses option pricing to determine value of IP. Several models like real option pricing, monte carlo simulation, binomial tree, black scholes model are used. The advantage is it uses much deeper analysis techniques and takes care of uncertainty of potential cash flow, hence it is quite suitable in the sectors where uncertainty is very high. The challenge is it is very complex in nature and needs strong mathematical understanding