Background:

Life-cycle costing (LCC) is an investment calculation that takes into

account operating costs throughout the investee lifecycle. LCC calculations are mainly used

to evaluate investments that have no revenue side. Attempts to apply the LCC calculations for

building-related investments have occurred since the 1980s. Despite the advantages of

using LCC calculations, use has not passed through in full. The main obstacles have been

identified as a lack of relevant input data, and insufficient experience of working

with LCC calculations. The results of calculations are perceived as being unreliable.

Purpose

: The purpose of this thesis is to investigate problems related to the input data of LCC

calculations and interpretation of their results and to propose measures that will reduce the

extent of these problems.

Method:

The thesis is based on an abductive approach, where the interaction between theory

and empirical data was the starting point. Collected data have been qualitative, both primary

and secondary. Secondary data consisted of performed LCC calculations and input data, and

primary data consisted of semi-structured interviews with five respondents working

at constructors in the construction industry.

Results, conclusions:

The result of this thesis show large differences in how clients are

working with LCC calculations within the construction industry. The concept

of LCC calculation is not completely established or standardized, and many in the

industry lack knowledge of how the estimates should be presented and interpreted. There is

a lack of knowledge on how the discount rate used in the calculations is determined. In

many cases this includes those who perform the calculations. The conclusions include that

the use of default values for input in the LCC calculations do not sufficiently take into

account the complexity of the input data. To improve the performance calculation must be

based on actual conditions in each case, such as energy prices and energy price

increases. Analysis of results in the form of sensitivity analysis, today performed in many

different ways, can be significantly improved.