Location factors are used to adjust conceptual cost estimates by project location. Presently, the construction industry has adopted a simple, proximity-based interpolation method which uses the “nearest neighbor” location factor to estimate unknown location factors. Although this approach is widely accepted, its validity has not been statistically substantiated. This study assessed the current method of adjusting conceptual cost estimates by project location. An evaluation of 14 alternative spatial estimation methods was also conducted. These methods were based on different approaches for combining 4 criteria: proximity, state boundary, home value, and income. This study used the 2006 RSMeans city cost index (CCI) dataset to conduct the evaluation. Geographic information systems (GIS) were used to visualize data and conduct spatial-statistical evaluations. The Global Moran’s I test was used to assess proximity-based spatial interpolation, which was implemented in the current method. In addition, comparisons of the current method and alternative methods were statistically assessed. The statistical analysis consisted of box plots, histograms, homogeneity of variance tests (Levene’s Statistic), and equality of sample distribution medians tests (Mann-Whitney). From interpretations of results, it was concluded that the Moran’s I test provided statistical justification for the current method. In addition, an alternative method was statistically proven to outperform the current method. This alternative method was the conditional nearest neighbor (CNN). Moreover, an additional alternative method which incorporated the ranking of proximity, median home values, and state boundaries could potentially outperform the current method as well as the CNN method. Future research is needed to fully substantiate the additional alternative method.
","abstract_html":"<p>Location factors are used to adjust conceptual cost estimates by project location. Presently, the construction industry has adopted a simple, proximity-based interpolation method which uses the “nearest neighbor” location factor to estimate unknown location factors. Although this approach is widely accepted, its validity has not been statistically substantiated. This study assessed the current method of adjusting conceptual cost estimates by project location. An evaluation of 14 alternative spatial estimation methods was also conducted. These methods were based on different approaches for combining 4 criteria: proximity, state boundary, home value, and income. This study used the 2006 RSMeans city cost index (CCI) dataset to conduct the evaluation. Geographic information systems (GIS) were used to visualize data and conduct spatial-statistical evaluations. The Global Moran’s I test was used to assess proximity-based spatial interpolation, which was implemented in the current method. In addition, comparisons of the current method and alternative methods were statistically assessed. The statistical analysis consisted of box plots, histograms, homogeneity of variance tests (Levene’s Statistic), and equality of sample distribution medians tests (Mann-Whitney). From interpretations of results, it was concluded that the Moran’s I test provided statistical justification for the current method. In addition, an alternative method was statistically proven to outperform the current method. This alternative method was the conditional nearest neighbor (CNN). Moreover, an additional alternative method which incorporated the ranking of proximity, median home values, and state boundaries could potentially outperform the current method as well as the CNN method. Future research is needed to fully substantiate the additional alternative method.</p>","abstract_has_math":false,"creators":["Martinez, Adam"],"institution":null,"degree_name":"Construction Management","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Migliaccio, Giovanni","Zandbergen, Paul","Rounds, Jerald"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-06-25T07:00:00Z","date_published":"2010-06-25T07:00:00Z","updated_at":"2026-07-24T05:26:42Z","subjects":["Construction industry--Location--Economic aspects.","Building--Estimates--Statistical methods.","Civil and Environmental Engineering"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalrepository.unm.edu/ce_etds/119"],"render_values":[{"text":"https://digitalrepository.unm.edu/ce_etds/119","href":"https://digitalrepository.unm.edu/ce_etds/119","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1928/10836","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Migliaccio, Giovanni","Zandbergen, Paul","Rounds, Jerald"]},{"key":"dc:creator","label":"Author","values":["Martinez, Adam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2010-06-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis","Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Construction Management"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Construction industry--Location--Economic aspects.","Building--Estimates--Statistical methods.","Civil and Environmental Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/1928/10836","https://digitalrepository.unm.edu/ce_etds/119"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Location factors are used to adjust conceptual cost estimates by project location. Presently, the construction industry has adopted a simple, proximity-based interpolation method which uses the “nearest neighbor” location factor to estimate unknown location factors. Although this approach is widely accepted, its validity has not been statistically substantiated. This study assessed the current method of adjusting conceptual cost estimates by project location. An evaluation of 14 alternative spatial estimation methods was also conducted. These methods were based on different approaches for combining 4 criteria: proximity, state boundary, home value, and income. This study used the 2006 RSMeans city cost index (CCI) dataset to conduct the evaluation. Geographic information systems (GIS) were used to visualize data and conduct spatial-statistical evaluations. The Global Moran’s I test was used to assess proximity-based spatial interpolation, which was implemented in the current method. In addition, comparisons of the current method and alternative methods were statistically assessed. The statistical analysis consisted of box plots, histograms, homogeneity of variance tests (Levene’s Statistic), and equality of sample distribution medians tests (Mann-Whitney). From interpretations of results, it was concluded that the Moran’s I test provided statistical justification for the current method. In addition, an alternative method was statistically proven to outperform the current method. This alternative method was the conditional nearest neighbor (CNN). Moreover, an additional alternative method which incorporated the ranking of proximity, median home values, and state boundaries could potentially outperform the current method as well as the CNN method. Future research is needed to fully substantiate the additional alternative method.
"]},{"key":"dc:title","label":"Title","values":["Validation of methods for adjusting construction cost estimates by project location"]}]}],"canonical_facts":{"dc:contributor":["Migliaccio, Giovanni","Zandbergen, Paul","Rounds, Jerald"],"dc:creator":["Martinez, Adam"],"dc:date.available":["2010-06-25T07:00:00Z"],"dc:description.abstract":["Location factors are used to adjust conceptual cost estimates by project location. Presently, the construction industry has adopted a simple, proximity-based interpolation method which uses the “nearest neighbor” location factor to estimate unknown location factors. Although this approach is widely accepted, its validity has not been statistically substantiated. This study assessed the current method of adjusting conceptual cost estimates by project location. An evaluation of 14 alternative spatial estimation methods was also conducted. These methods were based on different approaches for combining 4 criteria: proximity, state boundary, home value, and income. This study used the 2006 RSMeans city cost index (CCI) dataset to conduct the evaluation. Geographic information systems (GIS) were used to visualize data and conduct spatial-statistical evaluations. The Global Moran’s I test was used to assess proximity-based spatial interpolation, which was implemented in the current method. In addition, comparisons of the current method and alternative methods were statistically assessed. The statistical analysis consisted of box plots, histograms, homogeneity of variance tests (Levene’s Statistic), and equality of sample distribution medians tests (Mann-Whitney). From interpretations of results, it was concluded that the Moran’s I test provided statistical justification for the current method. In addition, an alternative method was statistically proven to outperform the current method. This alternative method was the conditional nearest neighbor (CNN). Moreover, an additional alternative method which incorporated the ranking of proximity, median home values, and state boundaries could potentially outperform the current method as well as the CNN method. Future research is needed to fully substantiate the additional alternative method.
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