The global financial crisis (GFC) of 2008‐2009 has highlighted the need for understanding fluctuations in housing variables and how, as such, they contribute to understanding how…
Abstract
Purpose
The global financial crisis (GFC) of 2008‐2009 has highlighted the need for understanding fluctuations in housing variables and how, as such, they contribute to understanding how housing markets work. The contention of this paper is to present a univariate structural time series analysis of the Australian Housing Finance Commitments (HFCs) covering the period 1988:6‐2009:5. The empirical analysis aims to focus on establishing whether monthly HFCs exhibit the expected cyclical and seasonal variations. The presence of a monthly seasonal pattern in HFCs is to be ascertained by way of testing possible hypotheses that explain such a pattern.
Design/methodology/approach
A structural time series framework approach, used in this paper, is in line with that promulgated by Harvey. Such models can be interpreted as regressions on functions of time in which the parameters are time‐varying. This makes them a natural vehicle for handling changing seasonality of a complex form. The structural time series model is applied to seasonally unadjusted monthly HFCs, between 1988:6 and 2009:5. The data have been sourced from the ABS. For consistency, the sample for each variable is standardised to start with the first available July observation and end with the latest available June observation.
Findings
The modelling results confirm the presence of cyclicality in HFCs. The magnitude of the observed cycle‐related changes is A$817m. A structural time series model incorporating trigonometric specification reveals that seasonality is also present and that it is stochastic (as implied by the inconsistency of the monthly seasonal factors over the sample period). The magnitude of monthly seasonal changes is A$435.8m. The results show the presence of statistically significant factors for January, February, March, April, May, September, October and November, which are attributed to “spring”, “summer” and “autumn” seasonal effects.
Originality/value
Empirical evidence of variations in housing‐related variables is relatively limited. A study of the literature uncovered that most studies focus on house prices and found no empirical research focusing on fluctuations in HFCs. Consequently, this research aims to be the first to explain the presence of seasonal and cyclical fluctuations in such an important housing variable as HFCs. Moreover, the paper aims to enhance the practice of modelling seasonal influences on housing variables.
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Falilat Yetunde Olowu, Hafeez Idowu Agbabiaka, Emmanuel Babatunde Jaiyeoba and Abiola Aminat Adesanya
The study had examined the dynamism in rental housing characteristic in Ile-Ife, Nigeria.
Abstract
Purpose
The study had examined the dynamism in rental housing characteristic in Ile-Ife, Nigeria.
Design/methodology/approach
Data were collected through questionnaire administration on 550 tenants selected across high, medium and low density areas, using systematic random sampling.
Findings
Findings revealed that rented apartments in the traditional town are built with modern materials like sandcrete blocks, cement, corrugated roofing sheet and aluminium. Further findings revealed a statistical significant variation in the rental housing typologies across the residential densities (χ2 = 94.732a, df = 10 and p = 0.000). The dominant housing typology in the low income earners areas is rooming apartments known traditionally as (face-to-face), in the middle income earners areas detached and semi-detached bungalows (Mini, 2bedroms and 3 bedrooms flat); and lastly, bungalows and duplexes dominates the high income earners areas. Therefore, the study likened the variation across the income areas to deferential in socioeconomic characteristics of tenants, surroundings peculiarities and the landlord and tenant relationships.
Originality/value
The outcome of this study could strengthen policies in creating design standards for construction of housing for renters; this is step towards achieving Sustainable Development Goal (SDG) 11, creating an inclusive communities.
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Olalekan Oshodi, David J. Edwards, Ka Chi lam, Ayokunle Olubunmi Olanipekun and Clinton Ohis Aigbavboa
Construction economics scholars have emphasised the importance of construction output forecasting and have called for increased investment in infrastructure projects due to the…
Abstract
Purpose
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.
Methodology
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.
Findings
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.
Originality
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.
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Ka Chi Lam and Olalekan Shamsideen Oshodi
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
This is the first study to apply the NNAR model to construction output forecasting research.
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Olivia McDermott, Cian Moloney, John Noonan and Angelo Rosa
The current paper aims to discuss the implementation of Green Lean Six Sigma (GLSS) in the food industry to improve sustainable practices. The focus is more specifically on dairy…
Abstract
Purpose
The current paper aims to discuss the implementation of Green Lean Six Sigma (GLSS) in the food industry to improve sustainable practices. The focus is more specifically on dairy processors to ascertain the current state of the literature and aid future research direction.
Design/methodology/approach
Utilising a systematic literature review (SLR), the paper addresses various terms and different written forms in the literature. The study characterises the current deployment of GLSS in the food industry and explains the reported benefits of this approach.
Findings
GLSS, a concept that has yet to be fully explored in the food industry, as in other sectors, holds significant potential to enhance the food industry’s sustainability practices. The dairy sector, a subsector of the food industry known for its high greenhouse gas emissions, is a prime candidate for the application of GLSS. In instances where it has been applied, GLSS has demonstrated its effectiveness in improving sustainability, reducing waste, lowering greenhouse gas emissions and minimising water usage. However, the specific tools used and the model for GLSS implementation are areas that require further study, as they have the potential to revolutionise food industry operations and reduce their environmental impacts.
Practical implications
Benchmarking of this research by the food industry sector and by academics can aid understanding of the practical application of GLSS tools and aid implementation of these practices to evolve the dairy processing sector in the next decade as sustainability champions in the sector.
Originality/value
This study extensively analyses GLSS in the food industry, with a particular focus on dairy processors.