Demand For Residential Construction Research Articles

Embodied Carbon in Australian Residential Houses: A Preliminary Study

Embodied carbon is a buzzword in the construction industry. Australia is committed to achieving Net Zero 2050 targets, and minimizing embodied carbon (EC) is inevitable. Owing to the population growth, there will be a significant demand for residential construction. Therefore, the material consumption in residential construction should be evaluated and proper strategies should be in place to minimize EC. The aim of this research is to undertake a preliminary study of EC in the Australian residential sector, with an emphasis on new residential home construction. This research presents a preliminary study on EC in residential buildings in Australia. Three case study residential buildings were used in this study. All three case studies are single -story residential units, with a gross floor area between 200 and 240 m2. One Click LCA software was used to calculate the EC. The EC of three case study residential homes is between 193 and 233 kgCO2e/m2. Based on the findings of this study, ‘other structures and materials’ contribute to a large amount of EC in residential construction. Concrete and aluminum are considered significant contributors to EC. Therefore, it is vital to either introduce low-EC material to replace aluminum windows or introduce various design options to minimize the use of aluminum in windows. There are various sustainable concretes available with low EC. It is essential to explore these low-EC concretes in residential homes as well. This research identifies the importance of adopting strategies to reduce the carbon impact from other sources, including concrete. It is also essential to consider the EC through transportation related to construction and promote locally sourced building materials in residential construction. Therefore, the results of this research indicate the necessity of reducing raw material consumption in Australian residential construction by implementing approaches such as a circular economy in order to circulate building materials throughout the construction supply chain and reduce raw material extraction.

Forecasting demand in the residential construction industry using machine learning algorithms in Jordan

PurposeDemand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.Design/methodology/approachThe identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.FindingsNine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.Originality/valueThe results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

Carbon dynamics and GHG implications of increasing wood construction: long-term scenarios for residential buildings in Austria

ABSTRACTWooden construction elements often exhibit lower life cycle greenhouse gas (GHG) emissions than conventional counterparts (‘material substitution effect’). Moreover, the building stock represents a carbon (C) sink if timber inflows (construction) surpass outflows (demolition) (‘C-stock effect’). A dynamic stock model incorporating these effects is applied to quantify potential climate benefits of wood construction in Austria's residential building sector. If present trends are maintained, culminating in a wood construction share (WCS) of 50% during 2050-2100, building shells could contain three times as much C in 2100 as today. Annual timber demand for residential construction could double, but would remain well below Austria's current net exports. Compared to a baseline scenario with constant WCS (22%), cumulated GHG savings from material substitution until 2050 are estimated 2 to 4.2 Tg CO2-equivalent – clearly less than savings from C-stock expansion (9.2 Tg). Savings from both effects would double in a highly ambitious scenario (WCS=80% during 2050-2100). The applied ’Stock Change Approach’ is consistent with IPCC Guidelines, but the above-mentioned savings from C-stock changes would not materialize under the current default GHG inventory accounting approach. Moreover, savings from C-stock effects must eventually be weighed against forest C-stock changes, as growing domestic demand might stimulate wood harvesting.

The effects of the late 2000s global financial crisis on Australia’s construction demand

An accurate measurement of the impacts of external shocks on construction demand will enable construction industry policymakers and developers to make allowances for future occurrences and advance the construction industry in a sustainable manner. This paper aims to measurethe dynamic effects of the late 2000s global financial crisis on the level of demand in the Australian construction industry. The vector error correction (VEC) model with intervention indicators is employed to estimate the external impact from the crisis on a macro-level construction economic indicator, namely construction demand. The methodology comprises six main stages to produce appropriate VEC models that describe the characteristics of the underlying process. Research findings suggestthat overall residential and non-residential construction demand were affected significantly by the recent crisis and seasonality. Non-residentialconstruction demand was disrupted more than residential construction demand at the crisis onset. The residential constructionindustry is more reactive and is able to recover faster following the crisis in comparison with the non-residential industry. The VEC model with intervention indicators developed in this study can be used as an experiment for an advanced econometric method. This can be used to analyse the effects of special eventsand factors not only on construction but also on other industries.

Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector

In recent years, forecasting demand for residential construction in Singapore has become more vital, since it is widely perceived that the next trough of the real estate cycle is approaching. This paper evaluates the use of a combination of neural networks (NNs) and genetic algorithms (GAs) to forecast residential construction demand in Singapore. Successful applications of NNs, especially in solving complex non-linear problems, have since stimulated interest in exploring the capabilities of other biological-based methods such as GAs, and in exploiting the synergy of these two techniques to create more problem-solving power. In the study, a basic NN model is used as a benchmark to gauge the performance of the combined NN-GA model. A relative measure of forecasting accuracy, known as the mean absolute percentage error (MAPE), is used for the comparison. The models are checked also for internal validity by allowing each to be trained twice and having a set of forecasts generated after each training. Both models are found to produce accurate forecasts, because their MAPE values consistently fall within the acceptable limit of 10%. However, the combined model out-performs the basis model remarkably by reducing the average MAPE from about 6% to a mere 1%. For each model, the marginal difference in the MAPE values (i.e., 0.5% for the NN model and 0.06% for the NN-GA model) of its two forecasts indicates consistency in performance, hence establishing internal validity as well. The findings reinforce the reliability of using NNs to model construction demand and reveal the benefit of combining NNs and GAs to produce more accurate models.

Forecasting construction durations for public housing projects: a Hong Kong perspective

The past thirty years have seen a great surge of international attention and discussion in the domain of construction time performance of building projects. Demand for residential construction has been continually increasing in Hong Kong since the 1950s, while an accelerated demand has been envisaged in the next few years up to 2001. This paper is based on the findings of the third survey in an investigation which aimed at determining a group of critical factors that affect construction durations of building projects in Hong Kong. This stage of the study attempted to generate standards (norms) for overall completion periods of public housing blocks, by modelling the durations of the primary work packages in the building construction process, i.e. piling, pile caps/raft, superstructure, EandM services, finishes and their respective sequential start-start lag times, in terms of the identified sets of critical factors (significant variables). Data from 15 case studies of standard New Cruciform type housing blocks was collected from the client and contractors. Multiple Linear Regression was applied in analyzing the project data and formulating a construction duration model. The forecasting performance of the model was next evaluated using an independent (new) set of project information from the client, in terms of a relative measure known as the Percentage Error. Both the general applications and drawbacks of the model are briefly presented and examined herein. The low values of percentage error (less than about ±10%) for overall construction durations confirmed that the model can be a reliable tool for predicting the construction durations of public housing projects in Hong Kong; and the research methodology used could well be extended to similar studies and duration forecasts in other sub-sectors in Hong Kong, as well as in other countries.

Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques

Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques

Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques

Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques

Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques

It is widely believed that the construction industry is more volatile than other sectors of the economy. Accurate predictions of the level of aggregate demand for construction are of vital importance to all sectors of this industry (e.g. developers, builders and consultants). Empirical studies have shown that accuracy performance varies according to the type of forecasting technique and the variable to be forecast. Hence, there is a need to gain useful insights into how different techniques perform, in terms of accuracy, in the prediction of demand for construction. In Singapore, the residential sector has often been regarded as one of the most important owing to its large percentage share in the total value of construction contracts awarded per year. In view of this, there is an increasing need to objectively identify a forecasting technique which can produce accurate demand forecasts for this vital sector of the economy. The three techniques examined in the present study are the univariate Box‐Jenkins approach, the multiple loglinear regression and artificial neural networks. A comparison of the accuracy of the demand models developed shows that the artificial neural network model performs best overall. The univariate Box‐Jenkins model is the next best, while the multiple loglinear regression model is the least accurate. Relative measures of forecasting accuracy dealing with percentage errors are used to compare the forecasting accuracy of the three different techniques.

Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression

In recent years, demand for residential construction has been growing rapidly in Singapore. This paper proposes the use of economic indicators to predict demand for residential construction in Singapore. At the same time, two forecasting techniques are applied, namely, Artificial Neural Networks (ANN) and Multiple Regression (MR), the former being a state-of-the-art technique while the latter a conventional one. A comparative study is carried out to determine whether the use of economic indicators with the application of the ANN technique can produce better predictions than with the MR method. A total of 12 economic indicators are identified as significantly related to demand for residential construction. Quarterly data from these 12 indicators are used to develop the ANN model. In order to assess the forecasting performance of this state-of-the-art technique, the same set of data is used to develop a conventional MR model. A comparison is made between the two models, in terms of their forecasting accurac...

Thai construction industry: Demand and projection

Thailand is fast becoming a newly industrializing country and, consequently, a large number of construction works are projected for the future. This study is an attempt to gain further insight into the demand for construction activities in Thailand. Three types of construction - residential, non-residential and ‘other’ (mainly public projects) - were considered. The demand function for each type of construction was estimated using regression analysis. Results indicate that rising per capita income, the ratio of consumer price index to the construction cost index, and population are the major determinants of demand for residential construction. The expansion of industrial production capacity and the ratio of corporate savings to the construction cost index are the most significant factors affecting the demand for non-residential construction. The demand of other construction is found to be largely a function of rising revenues of government and of public utilities. Further, projection results indicate that...