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Construction is repeatedly criticised for its low productivity based on statistical data that do not represent the output of construction adequately. The purpose of this paper is to improve the understanding of construction output – being the numerator in construction productivity calculations – by focussing on changes in quantity of the products, product characteristics and composition of the aggregate rather than as changes in price.

The research design of this study applies statistical data from the national accounts along with data from four paradigmatic case studies of social housing projects covering a period of 50 years.

The results indicate that while construction output prices have increased threefold over the past 50 years, improvements in performance can only explain approximately 20 per cent.

The developed four-step method has demonstrated its value as a means to measure changes in the characteristics of the product, but more studies on the actual figures and results over time and regions are required before solid conclusions can be drawn.

This study has added new knowledge of construction output that supports the development of a more accurate construction statistics, which in turn can assist the design of more effective and evidence-based policies for improving construction productivity.

This paper describes and demonstrates a novel performance-based methodology for addressing changes in the characteristics of the products in a longitudinally perspective, which can potentially provide a better understanding of changes in productivity.

Due to the complicated nature of houses, the driving factors of the residential construction output can be investigated from different perspectives of interests. However, little research has provided an insight of the trend of the residential construction output from a cross-disciplinary perspective. The purpose of this paper is to identify the long-run equilibrium types of residential construction output, including external equilibrium, solo-market equilibrium and dual-market equilibrium.

A vector error correction model is applied into longitudinal data in the eight Australian states and territories to overview the regional variations of the residential construction output.

The empirical results show that the equilibrium of regional residential construction outputs in New South Wales and Victoria are determined by the external factors; the equilibrium in Western Australia is dominated by the construction market; and the equilibriums in the other five states and territories are influenced by both construction and house markets.

The simplified approach may overlook the detailed explanation of the external factors, such as regional population, economy, policy and so forth. Given this limitation, future studies can introduce the correspondingly variables as per research interests.

Implementing the existing research into residential construction output and house supply, this research provides a simplified approach that demonstrates the linkage between construction and real estate sectors to identify the long-run equilibriums across regions. The underlying research sheds light in delivering inter-disciplinary research into the residential construction output.

Construction economics scholars have emphasised the importance of construction output forecasting and have called for increased investment in infrastructure projects due to the positive relationship between construction output and economic growth. However, construction output tends to fluctuate over time. Excessive changes in the volume of construction output have a negative impact upon the construction sector, such as liquidation of construction companies and job losses. Information gleaned from extant literature suggests that fluctuation in construction output is a global problem. Evidence indicates that modelling of construction output provides information for understanding the factors responsible for these changes.

An interpretivist epistemological lens is adopted to conduct a systematic review of published studies on modelling of construction output. A thematic analysis is then presented, and the trends and gaps in current knowledge are highlighted.

It is observed that interest rate is the most common determinant of construction output. Also revealed is that very little is known about the underlying factors stimulating growth in the volume of investment in maintenance construction works. Further work is required to investigate the efficacy of using non-linear techniques for construction output modelling.

This study provides a contemporary mapping of existing knowledge relating to construction output and provides insights into gaps in current understanding that can be explored by future researchers.

Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR).

Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models.

The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy.

The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability.

The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy.

This is the first study to apply the NNAR model to construction output forecasting research.

Because of the importance and potential usefulness of construction market statistics to firms and government, consistency between different sources of data is examined with a view to building a predictive model of construction output using construction data alone. However, a comparison of Department of Trade and Industry (DTI) and Office for National Statistics (ONS) series shows that the correlation coefcient (used as a measure of consistency) of the DTI output and DTI orders data and the correlation coefficient of the DTI output and ONS output data are low. It is not possible to derive a predictive model of DTI output based on DTI orders data alone. The question arises whether or not an alternative independent source of data may be used to predict DTI output data. Independent data produced by Emap Glenigan (EG), based on planning applications, potentially offers such a source of information. The EG data records the value of planning applications and their planned start and finish dates. However, as this data is ex ante and is not correlated with DTI output it is not possible to use this data to describe the volume of actual construction output. Nor is it possible to use the EG planning data to predict DTI construc‐tion orders data. Further consideration of the issues raised reveal that it is not practically possible to develop a consistent predictive model of construction output using construction statistics gathered at different stages in the development process.

Safety efficiency is the key to balance safety and production in construction industry; but the existing safety efficiency evaluation methods have the limitations of overestimating efficiency and ignoring undesirable outputs; therefore, according to the characteristics of safety production in construction industry, this paper innovatively develops a new cross-efficiency data envelopment analysis method to analyze safety efficiency, which can solve the limitations of traditional methods; and then the safety efficiency and its influencing factors of China's construction industry are analyzed, and some useful conclusions are obtained to improve its safety efficiency.

A new cross-efficiency data envelopment analysis method with undesirable outputs is proposed; and the two-stage efficiency analysis framework is designed.

First, the construction industries in different areas have different reasons for affecting their safety efficiency; second, the evaluation results of global safety priority tend to be more acceptable; third, frequent safety accidents and low resource utilization lead to a slow downward trend of the safety efficiency of China's construction industry in the long run; fourth, construction engineering supervision, construction industrial scale, and construction industrial structure have the significant impact on safety efficiency.

Theoretically, a new cross-efficiency data envelopment analysis method with undesirable outputs is proposed for evaluating safety efficiency; practically, the safety efficiency and its influencing factors of China's construction industry are analyzed.

The aim of this research is to answer whether or not wholesale interest rates, such as the London Interbank Offered Rate (LIBOR), can be used as an effective policy instrument to influence construction output. Developers and contractors borrow to finance construction and are charged retail interest rates, determined by the lending bank. The study investigated the relationship between LIBOR and construction industry output.

The study identified two time series, LIBOR and annual construction output and a number of regressions were run using the first differences to observe whether a change in LIBOR alone had a significant influence on construction output lagged by one to four years.

No significant relationship was found between changes in LIBOR and the annual change in construction output, regardless of the number of years lagged.

The policy implication of this research shows that control of demand for construction by government using wholesale interest rates is unlikely to succeed. Banks' lending to developers depends on other factors, such as retail interest rates, risk management and expectations.

The value of this research is that it supports the view that government policy needs to focus on stimulating construction demand, using real projects rather than monetary policies, such as interest rate manipulation.

The prices of construction resources (construction cost) have been increasing faster than construction output prices (construction price) in Hong Kong since the mid‐1970s, giving rise to a long‐term divergence between the two price trends. As the difference has existed for quite a long time, it cannot be adequately explained by a short‐term change in supply and demand conditions. The present paper introduces the major indices that reflect the trends of the prices of construction resources and outputs in Hong Kong. It also attempts to explain, from an economic point of view, the major factors which contributed to the divergence between the long‐term trends of the prices of construction resources and outputs. One of the conclusions is that, for the past 25 years, the productive efficiency of the Hong Kong building industry has benefited from and been greatly upgraded by imported construction technologies, as well as by a burgeoning quality of human resources. The data and examples quoted in the present paper are sided towards building construction. Therefore, the scope of investigation of this paper, strictly speaking, is confined to the building industry, and does not include the building services and civil engineering subsectors. However, because of the higher degree of mechanization and faster technological progress in the civil engineering and building services subsectors, the present author believes that the results and conclusions should also be applicable to the whole construction industry.

As one of the pillar sectors, China’s construction industry (CE) is not efficient in productivity with large regional gaps over the past decades. It is crucial for stakeholders to have insightful information on regional input of resources and output of productive efficiency for making policies and investment decisions. The purpose of this paper is to develop an efficiency measurement for the CE and explore the regional differences of construction productive efficiency across the three regions of China.

Data envelopment analysis (DEA) is an objective benchmarking methodology used for measuring the performance of construction productivity. Distance friction minimization (DFM) approach, based on DEA model, is applied to identify the causes of inefficiency, sources of growth and the optimal paths to efficient frontier for regional CE. Further studies are conducted to provide insightful information for efficiency improvement, according to DFM modeling results and empirical analysis.

The results indicate that eastern region leads construction development due to strong performance of coastal provinces. Faced with decreasing supply of skilled workers in developed region, investing more on construction plants and equipment for labor savings is more efficient to the long-term productivity growth of CE in the east. For developing midland region, heavy reliance on cheap manpower should be gradually relieved by allocating more budgets to vocational training and education program to boost quality labor supply, as well as making steady investment on construction equipment and advanced technology. In underdeveloped western region, raising construction labor wages is recommended to attract more workers to meet the market demand and achieve an optimal production efficiency in the CE.

The findings provide insights into the causes of inefficiency, the sources of growth and the best strategies for efficiency improvement in regional CE, recommendations are made for policy making and strategic planning to enhance the overall performance of China’s construction productive efficiency.

Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output.

The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016.

The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed.

The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions.

This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity.

This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.