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<doi>/ISEC.res.2017.42</doi>
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<article-title>SPATIAL REGRESSION ANALYSIS FOR<br/>
MODELING THE SPATIAL VARIATION IN<br/>
HIGHWAY CONSTRUCTION COSTS</article-title>
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<author>MINSOO BAEK<sup>1</sup> and BAABAK ASHURI<sup>2</sup></author>

<aff><sup>1</sup>School of Building Construction, Georgia Institute of Technology, Atlanta, USA<br/>
<sup>2</sup>School of Building Construction and School of Civil &amp; Environmental Engineering, Georgia<br/>
Institute of Technology, Atlanta, USA</aff>


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<title>ABSTRACT</title>
<p>Price volatility in wages, materials, and equipment has a significant impact on highway
construction costs. As the construction market and economy have experienced
dynamic changes in prices, the price volatility becomes less predictable. In addition,
various levels of the price volatility in different market locations aggravate the
prediction. Thus, in developing highway construction costs, transportation agencies
should consider geographical location of construction projects and market conditions of
the locations. Transportation agencies face significant uncertainties in price volatility
across different geographical locations. This volatility may not be uniformly
distributed across different geographical locations due to changes in the availability of
local contractors, materials, equipment, and labor. The objective of this research is to
develop statistical models that are capable to explain spatial variations in submitted
unit prices for asphalt line items in highway projects considering local market
condition factors. Historical bid data used in this research consist of resurfacing and
widening projects let in the state of Georgia, the United States, between 2008 and 2015.
The methodology of this research is a spatial regression analysis to explain the spatial
variation in the submitted unit prices for asphalt line items. The findings of this
research indicate that volatility in submitted bid prices is not uniformly distributed
across different geographical locations within the same transportation agency. The
contribution to the body of knowledge of this research is an improved understanding of
the role of local construction market and macroeconomic conditions to explain
geographic variability in construction costs.</p>
<p><italic>Keywords: </italic>Spatial variation, Unit price, Uncertainty, Geographically weighted
regression, Spatial analysis.</p>
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