Cost estimation in an early design phase is not an easy task due to the incompleteness of the physical characteristic of the building. In Honduras, little considerations are made to improve the condition of the cost estimation procedures. It can be seen due to the fact that the Honduran Construction Code does not provide any guidelines for developing cost estimation in any stage of design.
This paper attends to develop a cost estimation model based on current condition of the Honduran construction industry. With this model, owners could define the physical characteristics of the building by knowing how much the cost will be, depending on the modifications of these physical characteristics.
Several researchers have developed cost estimation models for an early design phase. They defined the construction cost by using their home country construction code, and collecting the cost factors from literature review. Regression models and Artificial Intelligence are the most common approaches that researchers used to develop the cost estimation models.
For this research an extensive literature review was carried out in order to determinate the cost factors for an early design phase. Based on that, 52 cost factors were selected. These cost factors were categorized into three different areas: external, internal and contingency. From these 52 cost factors 9 were selected in order to develop a cost estimation model. These 9 cost factors are: inflation, region of construction, supply condition and site access for external factors; type of building, type of structure, total area, number of floors and number of elevators for internal factors; from contingency no factors were selected.
For developing the cost estimation model a multiple regression analysis with SPSS 17 was used. The sample size is composed by 17 Honduran building properties, but only 15 were used to develop the regression analysis. The other two properties were used to carry out a verification test. Total cost (Lps), total cost/area (Lps/m2), log of total cost and log of total cost/area (Lps/m2) were used as dependent variables for generating four different regression models.
The Non-Linear model with total cost/area (Lps/m2) as dependent variable provides better results in the verification test (MAPE= 13% and predicted cost variation between -1% and 7%). From the model’s equation, it can be concluded that a variation in the type of structure and number of elevators provide significant contributions for the construction cost of the building. On the other hand, variations in the total area and number of floors do not provide a significant contribution for the cost of the building.

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