Analysis of United Kingdom off-highway construction machinery market and its consumers using new-sales data

Gary D. Holt, David J. Edwards

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

The off-highway construction machinery market and its consumers have attracted minimal previous research. This study addresses that void by analyzing annual United Kingdom (UK) (volume/portfolio) new-sales data for the 10 most popular products within that market, 1990-2010 inclusive. Graphical, descriptive statistical, Pearson-correlational, autocorrelational, and elementary modeling are employed to identify contrasts in sales regarding (1) high- and low-volume items; (2) growth trends and significant recessionary effects on volumes; (3) a demand change point circa 1997, since when annual product portfolio has changed little; and (4) product associations in consumer demand. Significant association is demonstrated between demand and construction output, especially with the value of new housing. Subsequently, consumption of wheeled loaders is modeled using construction volume, and demand for mini and crawler excavators is modeled using new-housing data. Time series trends for these machinery types are presented and forecast through 2015. The primary contribution of this study is a deeper understanding of the UK new-machinery market and the predilections of its consumers over the last two decades (to present).

Original languageEnglish
Pages (from-to)529-537
Number of pages9
JournalJournal of Construction Engineering and Management - ASCE
Volume139
Issue number5
DOIs
Publication statusPublished - 1 May 2013
Externally publishedYes

Keywords

  • Demand
  • Machinery
  • Market sector
  • Sales
  • Supply

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial Relations
  • Strategy and Management

Fingerprint

Dive into the research topics of 'Analysis of United Kingdom off-highway construction machinery market and its consumers using new-sales data'. Together they form a unique fingerprint.

Cite this