### DEFINITION of Capital Investment Analysis

Capital investment analysis is a budgeting procedure that companies and government agencies use to assess the potential profitability of a long-term investment. Capital investment analysis assesses long-term investments, which might include fixed assets like equipment, machinery or real estate. The goal of this process is to identify the option that can yield the highest return on invested capital. Businesses may use techniques such as net present value (NPV) analysis, discounted cash flow (DCF) analysis, risk-return analysis and risk-neutral valuation in a capital investment analysis.

### BREAKING DOWN Capital Investment Analysis

Capital investments are risky because they involve large, up-front expenditures on assets intended for many years of service and that will take a long time to pay for themselves. One of the basic requirements of a firm evaluating a capital project is an investment return greater than the hurdle rate, or required rate of return, for shareholders of the firm. The two most common tools for capital investment analysis are the NPV and DCF models. These models map out the initial outlay of capital and all subsequent cash flows from the project. An appropriate discount rate is used to calculate the present value of the cash flows. If this present value is greater than the initial investment cost, the project could get the green light.

### Doing Capital Investment Analysis the Right Way

Capital investment decisions are not made lightly. Analytical models are easy to set up. The inputs, however, drive model results; therefore, reasonable assumptions are critical for determining whether a contemplated investment goes forward. Cash flows beyond, say, three or five years can be difficult to project. The discount rate, when applied to years far into the future, has a substantial impact on the present value calculation. Sensitivity analysis, whereby varying inputs are plugged into the model to gauge changes in value, should be performed. But even then, unexpected events can upset the best designed model with the most reasonable assumptions, in which case the modeler may decide to integrate contingency factors into the analysis.