Building development cost drivers in the New Zealand construction industry : a multilevel analysis of the causal relationships : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) in Construction, School of Engineering & Advanced Technology, Massey University, Albany, New Zealand

Building development cost is influenced by a raft of complex factors which range from project characteristics to the operating environment and external dynamics. It is not yet clearly understood how these factors interact with each other and individually to influence building cost. This gap in knowledge has resulted in inaccuracies in estimates, improper cost management and control, and poor project cost performance. This study aims to bridge the knowledge gap by developing and validating a multilevel model of the key drivers of building development cost (BDC) and their causal relationships. Based on literature insights and feedback from a survey of industry practitioners, some hypotheses were put forward in regards to the causal relationships between the BDC and the following key drivers as latent constructs: project component costs factor, project characteristics factor, project stakeholders’ influences factor, property market and construction industry factor, statutory and regulatory factor, national and global dynamics, and socio-economic factor. Observed indicators of the model's latent constructs were identified and measured using a mixed methods research design. Results showed that property market and construction industry factor was the most significant predictor of building development cost in New Zealand, while project component cost factor has the least impact. The model’s fit to the empirical dataset, and its predictive reliability, was validated using structural equation modelling. Results of an additional model validation test by a panel of experts further confirmed its efficacy. Overall, the results suggest that sole reliance on the immediate project component costs without due consideration of the wider and more influencing effects of the external factors could result in inaccurate estimates of building development cost. Key recommendations included addressing the priority observed indicators of the most significant latent variables in cost studies and analysis. Keywords: Building development cost, cost drivers, cost modelling, cost predictio

Development of Intelligent Prefabs Using IoT Technology to Improve the Performance of Prefabricated Construction Projects

Prefabrication (PC) projects have many advantages, such as cost and energy savings and waste reduction. However, some problems still exist that hamper the development of prefabrication projects. To improve PC project performance and advance innovation in construction, this study introduces an innovative method that incorporates Radio Frequency Identification (RFID) and Long Range (LoRa) technologies, sensor networks, the BIM model and cloud computing to automatically collect, analyze and display real-time information about PC components. It can locate PC components on a construction site and monitor their structural performance during the installation process. RFID technology and strain sensors were used to collect the required data on a construction site. All the data was transmitted to a server using LoRa technology. Then, the cloud-based Building Information Modelling (BIM) model of the project was developed to store and vividly present project information and real-time onsite data. Moreover, the cloud-based BIM model enables project team members to access the project information from anywhere by using mobile devices. The proposed system was tested on a real PC project to validate its effectiveness. The results indicate that the sensor network can provide reliable data via LoRa technology, and a PC component can be accurately located on site. Also, the monitoring data of structural performance for the PC component during the installation process is acceptable. The proposed method using innovative technologies can improve PC project performance and help industry professionals by providing sufficient required information

Energy Management through Cost Forecasting for Residential Buildings in New Zealand

Over the last two decades, the residential building sector has been one of the largest energy consumption sectors in New Zealand. The relationship between that sector and household energy consumption should be carefully studied in order to optimize the energy consumption structure and satisfy energy demands. Researchers have made efforts in this field; however, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the data indicates that they are significantly correlated. Hence, this study proposes time series methods, including the exponential smoothing method and the autoregressive integrated moving average (ARIMA) model for forecasting residential building costs of five categories of residential buildings (one-storey house, two-storey house, townhouse, residential apartment and retirement village building) in New Zealand. Moreover, the artificial neutral networks (ANNs) model was used to forecast the future usage of three types of household energy (electricity, gas and petrol) using the residential building costs. The t-test was used to validate the effectiveness of the obtained ANN models. The results indicate that the ANN models can generate acceptable forecasts. The primary contributions of this paper are twofold: (1) Identify the close correlation between household energy use and residential building costs; (2) provide a new clue for optimizing energy management

An Integrated BIM-GIS Method for Planning of Water Distribution System

An important function of a water distribution system (WDS) is to supply drinking water to each demand point using a pipe network that has minimal impact on the surroundings. To produce a reliable WDS, planning usually requires a significant amount of geo-spatial information. Current planning practices for pipeline systems, which gather geographic information based on maps, are time-consuming and cumbersome. With the rapid developments in computer and information technology, it is necessary to propose a new WDS planning method that enhances the current planning practices and facilitates the decision-making process. The proposed method allows project information in building information modeling (BIM) to be incorporated into a geographic information system (GIS) model, using semantic mapping to incorporate WDS project data and geo-spatial information to facilitate the WDS planning process. Moreover, a 3D visualization model of the proposed WDS project and its surroundings is provided. In addition, topological rules are set to identify any conflicts between the WDS project and its surroundings. A real WDS project was used to validate the method. The proposed method can help project participants better understand the WDS project and its surroundings and identify any errors in the planning process, thus improving sustainable development

Exploring the trend of New Zealand housing prices to support sustainable development

The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions

Optimization of the supplier selection process in prefabrication using BIM

Prefabrication offers substantial benefits including reduction in construction waste, material waste, energy use, labor demands, and delivery time, and an improvement in project constructability and cost certainty. As the material cost accounts for nearly 70% of the total cost of the prefabrication project, to select a suitable material supplier plays an important role in such a project. The purpose of this study is to present a method for supporting supplier selection of a prefabrication project. The proposed method consists of three parts. First, a list of assessment criteria was established to evaluate the suitability of supplier alternatives. Second, Building Information Modelling (BIM) was adopted to provide sufficient information about the project requirements and suppliers’ profiles, which facilitates the storage and sharing of information. Finally, the Analytic Hierarchy Process (AHP) was used to rank the importance of the assessment criteria and obtain the score of supplier alternatives. The suppliers were ranked based on the total scores. To illustrate how to use the proposed method, it was applied to a real prefabrication project. The proposed method facilitates the supplier selection process by providing sufficient information in an effective way and by improving the understanding of the project requirements

Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project