V. Beran, P. Dlask, D. Eaton, E. Hromada and O. Zindulka
This paper seeks to present an approach to the improvement of the risk analysis of construction projects by linking risk analysis to the potential variances in time and cost…
Abstract
Purpose
This paper seeks to present an approach to the improvement of the risk analysis of construction projects by linking risk analysis to the potential variances in time and cost performance of endogenous variables. This link is synchronous, such that changes in either the cost or time schedule variables are uniquely modelled to the overall time and cost profile of a project.
Design/methodology/approach
The presented approach is based on synchronous time and cost scheduling, simulation and the methodology presented here develops technical indicators on the basis of virtual management momentum (VMM).
Findings
The paper shows a potential improvement to management decision making by the use of VMM. Senior management can test alternative strategies and develop probability matrices defining the potential results.
Research limitations/implications
The current model simulates the impact of endogenous risks, and therefore by implication excludes all exogenous risks. This limitation can be removed by the introduction of other risk vectors, but is beyond the scope of the current research.
Originality/value
The mapping technique utilised to display these changes makes the appreciation of such impacts visually simple and is extremely effective in improving the management of endogenous project risk.
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Thiago Cesar de Oliveira, Lúcio de Medeiros and Daniel Henrique Marco Detzel
Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large…
Abstract
Purpose
Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large databases, there is a reduction in the predictive capacity when traditional methods, such as multiple linear regression (MLR), are used. This paper aims to determine whether in these cases the application of data mining algorithms can achieve superior statistical results. First, real estate appraisal databases from five towns and cities in the State of Paraná, Brazil, were obtained from Caixa Econômica Federal bank.
Design/methodology/approach
After initial validations, additional databases were generated with both real, transformed and nominal values, in clean and raw data. Each was assisted by the application of a wide range of data mining algorithms (multilayer perceptron, support vector regression, K-star, M5Rules and random forest), either isolated or combined (regression by discretization – logistic, bagging and stacking), with the use of 10-fold cross-validation in Weka software.
Findings
The results showed more varied incremental statistical results with the use of algorithms than those obtained by MLR, especially when combined algorithms were used. The largest increments were obtained in databases with a large amount of data and in those where minor initial data cleaning was carried out. The paper also conducts a further analysis, including an algorithmic ranking based on the number of significant results obtained.
Originality/value
The authors did not find similar studies or research studies conducted in Brazil.
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Evangelos Vasileiou, Elroi Hadad and Martha Oikonomou
We examine the aggregate price trend of the Greek housing market from a behavioral perspective.
Abstract
Purpose
We examine the aggregate price trend of the Greek housing market from a behavioral perspective.
Design/methodology/approach
We construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.
Findings
The results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.
Originality/value
To investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.
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Abdul Wahid, Oskar Kowalewski and Edmund H. Mantell
This research aims to identify the statistically significant characteristics of a hedonic model to explain the pricing of residential properties in two cities in Pakistan.
Abstract
Purpose
This research aims to identify the statistically significant characteristics of a hedonic model to explain the pricing of residential properties in two cities in Pakistan.
Design/methodology/approach
The research methodology applies extreme bounds analysis and the least absolute shrinkage and selection operator. Estimators of efficient pricing were measured via stochastic frontier analysis.
Findings
The study findings show that the market valuation of residential properties in Islamabad and Rawalpindi is systematically related to numerous factors, including property location, neighborhood characteristics, environmental characteristics, structural characteristics and administrative qualities of local housing societies. The authors also find statistical evidence that suggests that residential estate properties in the two cities are overpriced in the sense that the market transaction prices tend to be higher than the fair prices of the properties in the two cities.
Practical implications
In Pakistan, the term “real estate” is used rather synonymously with the word “investment.” The findings of this research will help investors to identify the measurable factors that affect the transaction prices of residential real estate. These identifications will facilitate the development of strategic plans toward achieving sustainable rates of return in residential real estate markets.
Social implications
The residential real estate sector in Pakistan is constantly changing. There are myriad causes for these changes, including changes in social structure, cultural attitudes and technology. Customary methods for forecasting market prices for residential properties have been rendered unreliable because of the dynamics of the market. This study will contribute to the understanding of the changing dynamics of residential real estate pricing.
Originality/value
Although Pakistan's residential real estate market is growing very rapidly, there is little published research identifying the drivers of this growth. This study covers these aspects to fill the theoretical gap in a real estate context.
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Evangelos Vasileiou, Elroi Hadad and Georgios Melekos
The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables…
Abstract
Purpose
The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.
Design/methodology/approach
In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.
Findings
Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.
Practical implications
The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.
Originality/value
This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.
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Fang-Jye Shiue, Hsin-Yun Lee, Meng-Cong Zheng, Akhmad F.K. Khitam and Sintayehu Assefa
For large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction…
Abstract
Purpose
For large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction projects, which frequently have large budgets, long durations and many activities with complex procedures, project segmentation involves complicated decision-making. To fill this gap, this study aims to develop an integrated model for planning project segmentation.
Design/methodology/approach
The proposed model integrates a simulation and multiple attribute decision-making method. The simulation is used to evaluate the bidding outcome of various project segmentations. The owner can then determine the bid-price behavior of contractors in response to varying work package sizes. The multiple attribute decision-making method is used to select the optimal segmentation solution from the simulated scenarios.
Findings
The proposed model is applied to a large road preservation project in Indonesia and incorporates bid participants and market conditions. The model provides seven scenarios for segmentation. The range of scenarios captures increasing competitiveness in the construction with the average bid price becoming gradually more beneficial for the owner. The model also utilizes a multiple attribute decision-making method to select the optimum scenario for the owner.
Originality/value
This study presents an applicable model for project segmentation that is useful for both project owners and contractors. By utilizing the proposed model, a project owner can segment a large project into smaller contract packages to create improved project pricing.
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Zhao Xu, Xiang Wang, Ya Xiao and Jingfeng Yuan
There is often a lack of accurate performance evaluation in Public–Private Partnership (PPP) projects. It is a challenging issue to effectively use Building Information Modeling…
Abstract
Purpose
There is often a lack of accurate performance evaluation in Public–Private Partnership (PPP) projects. It is a challenging issue to effectively use Building Information Modeling (BIM) for PPP project performance evaluation. The objective of this study is to develop a PPP project performance evaluation model based on Industry Foundation Classes (IFC) and an enhanced matter-element method to more precisely evaluate PPP project performance.
Design/methodology/approach
The performance evaluation of PPP projects in the construction and operation period was explored. The PPP project performance evaluation indicator system was first established based on a literature review and PPP project practice. Then, the evaluation indicator information was expressed through IFC mapping and extension. After that, an IFC-based PPP project performance evaluation model was developed, and a case study was provided to validate the use of the proposed performance evaluation model.
Findings
The results of the case study show that the proposed approach can accurately and efficiently evaluate PPP projects, and it could favorably contribute to performance evaluation in PPP projects.
Research limitations/implications
This study only concerns the performance evaluation of one type of PPP project. Further research is required to study different types of PPP projects; the model needs to be more efficient and intelligent.
Originality/value
The performance evaluation of PPP projects utilizing IFC extension and the enhanced matter-element method provides guidance for the government and private parties to accurately and efficiently evaluate PPP project performance.
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Mohsen Roohani Qadikolaei, Yaser Hatami, Sara Nikmard Namin and Ali Soltani
This study aims to explore the intricate relationship between housing prices and transaction volumes in Tehran, a city with diverse socioeconomic and regional characteristics…
Abstract
Purpose
This study aims to explore the intricate relationship between housing prices and transaction volumes in Tehran, a city with diverse socioeconomic and regional characteristics. This research addresses a critical gap in understanding the role of local spatial factors, which previous studies have often overlooked, focusing instead on macroeconomic variables.
Design/methodology/approach
Using a data set of housing transactions of Metropolitan Tehran from 2010 to 2020 sourced from secondary data, this study uses generalized linear mixed models and spatial clustering techniques. These methods enable an examination of geographical clustering and the effects of local contextual variables on the dynamics between housing prices and transaction volumes.
Findings
Results indicate significant spatial heterogeneity within Tehran’s housing market. Higher prices and transaction volumes are concentrated in the northern and western regions, influenced by factors such as employment rates, rental housing supply and the physical attributes of the housing stock. The findings suggest that macroeconomic policies alone are insufficient to address housing challenges in Tehran; targeted, localized interventions are necessary.
Research limitations/implications
This study’s reliance on secondary data and its focus on a single urban environment may limit the generalizability of the findings. Further research incorporating a wider range of local and macro variables could strengthen the applicability of the results across different contexts.
Practical implications
This study underscores the need for region-specific housing policies that consider local economic, social and spatial conditions. Policymakers could improve housing affordability and accessibility in Tehran by implementing tailored strategies to address the distinct needs of different districts.
Originality/value
This study offers a novel perspective by integrating spatial and contextual factors in housing market analysis, providing insights that challenge the traditional macroeconomic focus. The use of advanced statistical and spatial analysis techniques contributes to a deeper understanding of urban housing market dynamics.
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Renuganth Varatharajoo and Ralph Kahle
To compare the conventional reaction wheel and battery systems with the combined energy and attitude control system. The system mass, volume and power requirements are revealed…
Abstract
Purpose
To compare the conventional reaction wheel and battery systems with the combined energy and attitude control system. The system mass, volume and power requirements are revealed corresponding to the small satellite missions.
Design/methodology/approach
All the relevant system parametric equations are established. The system mass, volume and power are estimated accordingly for the conventional and the combined systems. Then, both systems are compared with respect to the typical small satellite missions.
Findings
The combined system outperforms the conventional system in most small satellite missions. However, there are some small satellite missions where the conventional systems are better in terms of the mass and volume budgets.
Research limitations/implications
This research work is exclusively for small satellites in the LEO orbits.
Practical implications
A reasonable information for sizing the combined energy and attitude control system is established. The system mass, volume and power budgets can be extracted from this research work.
Originality/value
The results provide an instant answer regarding the feasibility of the combined energy and attitude control system for small satellites.
Details
Keywords
An architecture is proposed based on the torque mode operation for the combined energy and attitude control system (CEACS). The CEACS energy storage and attitude control…
Abstract
Purpose
An architecture is proposed based on the torque mode operation for the combined energy and attitude control system (CEACS). The CEACS energy storage and attitude control performances are demonstrated.
Design/methodology/approach
All the relevant system equations are established. The torque mode‐based architecture is designed. The architecture is evaluated through numerical treatments.
Findings
The proposed CEACS architecture can simultaneously manage the energy storage and attitude control tasks.
Research limitations/implications
This research work is exclusively for small satellites in the LEO missions. However, the proposed CEACS architecture is applicable for other missions.
Practical implications
The torque mode operation will allow an ironless motor/generator design for CEACS.
Originality/value
The results demonstrate the CEACS operation in the torque mode.