PurposeIn Vietnam, delays in disbursement for state-funded construction projects remain a persistent challenge, directly impacting stakeholder performance. This study aims to identify significant risks contributing to these delays and proposes a multi-criteria model to mitigate them.Design/methodology/approachAdopting a hybrid method that integrates literature review and expert surveys, the study identifies 17 risk factors grouped into three categories: human-related, policy-related and performance-related. Data was collected through a structured questionnaire and analyzed using Cronbach's alpha, mean score analysis, Spearman's rank correlation, Exploratory Factor Analysis and Fuzzy Synthetic Evaluation (FSE).FindingsThe analysis highlights the limited authority of investors, frequent legal policy changes and delays in site clearance as the most critical causes of slow disbursement. Human-related risks were found to have the greatest influence on this disbursement. Respondents with similar work experience shared consistent assessments, while those in different roles or positions showed divergent views. The FSE results confirm that these risks significantly hamper the disbursement process in public construction projects.Research limitations/implicationsEvaluating disbursement delays and developing a corresponding model are essential for future research on enhancing project performance. Nevertheless, further in-depth study is needed to understand the full impact of these delays.Practical implicationsStakeholders should prioritize improving the qualifications and authority of staff and managers involved in the disbursement process. Doing so will accelerate payment procedures and contribute to more stable cash flow for all parties, especially contractors.Originality/valueThe study's findings provide valuable insights for stakeholders, particularly contractors, to manage financial planning better in the face of disbursement delays.
PurposeThe purpose of this study is to identify the specific causes of variation orders that affect project costs, as perceived by relevant stakeholders, including contractors, consultants, and clients. Variation orders are considered one of the significant contributors to cost overruns in construction projects. That is why various research efforts have addressed this issue to tackle its impact on construction projects. However, most of these studies have focused on various project types, giving little attention to large-scale infrastructure projects such as bridges. As a result, to address this gap, this study adopts a structural equation modeling (SEM) to investigate the primary causes of variation orders that lead to cost overruns in construction bridge projects. This is accomplished by identifying variation order causes through a comprehensive literature review, in addition to collecting data from eight actual bridge projects in Egypt.Design/methodology/approachThe research methodology involves the identification of the main causes of variation orders on cost overrun in bridge construction projects using an extensive literature review and structured questionnaires. First, an initial set of 51 potential variation order causes was identified. Then, interviews were conducted with eight experts to discuss the relevance of the identified causes to the cost overruns in bridge construction projects in Egypt. Consequently, the refined list of 51 causes served as the foundation for a structured questionnaire survey, which was distributed to 95 experienced professionals specializing in bridge projects in Egypt. Consequently, the responses were analyzed to identify the most significant causes, which consist of thirty main causes, using mean scores as the criterion for ranking. To investigate the underlying relationships among these causes, SEM was conducted using AMOS software. This analysis determined the correlations and interactions of latent variables within a multidimensional performance framework, providing deeper insights into how these causes collectively contribute to project cost overruns.FindingsUsing SEM, 19 key variation order causes were identified as significantly affecting bridge construction cost overruns. The aforementioned causes were classified into four latent variables, which form the foundation for eight hypotheses aimed at examining both the interactions among the variables and their impact on cost overruns. The analysis confirms the importance of 19 factors contributing to rising project costs, underlining the complex nature of variation orders in Egyptian bridge construction. Additionally, a case study of a real bridge project is provided that illustrates how these latent factors impacted the cost overruns. This case study acts as a tangible example highlighting the importance of these factors in managing cost performance in bridge construction.Originality/valueThis study uses structural equation modeling to identify the primary causes of variation orders resulting in cost overruns within Egyptian bridge construction projects. In contrast to previous research efforts that focused on various types of construction projects, with minimal attention given to bridge construction projects, this research specifically examines bridge projects by integrating practical insights, expert evaluations and thorough statistical analyses. Its value lies in providing assistance to practitioners in improving decision-making, reducing risks and enhancing cost management in bridge projects.
PurposeThe purpose of this study is to evaluate that prefabricated buildings (PBs), which offer environmental, economic and logistical benefits, are constrained by substantial capital requirements. PB construction's complex, gray-box supply chain creates uncertainty in the bank's financing decisions. Recent advances in blockchain-driven supply chain finance can resolve trust issues to spawn a feasible multilevel finance system.Design/methodology/approachA blockchain-enabled financing model for the PB supply chain was established to characterize the interactions among contractors, suppliers and banks. An evolutionary game model was developed to analyze their strategic behaviors and equilibrium conditions under different incentive and penalty scenarios. A system dynamics simulation, using real project data, was conducted to verify the theoretical model and evaluate the effects of key parameters on system stability.FindingsThe proposed blockchain model effectively mitigates information asymmetry and lowers supplier financing costs within the prefabricated construction supply chain. Under conventional static incentive mechanisms, the tripartite evolutionary game lacks a stable equilibrium, leading to persistent behavioral fluctuations among contractors, suppliers and banks. By implementing a dynamic incentive mechanism that adjusts subsidies and penalties based on real-time compliance performance, the system converges to a stable state in which all participants consistently adhere to blockchain-based strategies. This approach not only enhances operational transparency and trust but also safeguards the interests of all stakeholders while promoting the practical adoption of blockchain technology.Originality/valueThe results provide theoretical support and practical guidance for refining financing arrangements for the complex PB supply chain.
PurposeAlthough artificial intelligence (AI) technologies are increasingly adopted worldwide, their diffusion in the Nigerian construction industry (NCI) remains limited. This study aims to investigate the transformative opportunities presented by AI and the critical challenges hindering its adoption in the NCI, framed by the technology acceptance model (TAM) and organisational change management theory (OCMT).Design/methodology/approachUsing a quantitative approach within a positivist paradigm, data were collected via an electronic survey distributed through convenience sampling to professionals across government agencies, consulting firms and contractors. The data were analysed using descriptive and inferential statistics.FindingsResults reveal AI's potential to revolutionise operational efficiency and decision-making within the NCI. However, adoption is constrained by insufficient digital infrastructure and limited technical expertise. Addressing these barriers, particularly from the perspectives of individual acceptance (TAM) and organisational readiness (OCMT), is essential to fully harness AI's transformative capabilities.Originality/valueThis study uniquely examines the socio-economic, technical and cultural factors shaping AI adoption in the NCI. By offering context-specific knowledge and strategic interventions, it informs policymakers, consultants and contractors of practical pathways for AI integration. The research contributes to the global discourse on AI in construction, especially in developing economies, providing a framework to foster innovation and sustainability in comparable contexts.
PurposeThis study aims to explore the market factors of carbon trading in the Australian construction industry and further develops an index for overall market factor level.Design/methodology/approachThirty market factors were identified from literature and ranked by experts in an expert forum. Fuzzy synthetic evaluation was used in developing the market index. Analyses were conducted using statistical package for social sciences version 27 and R software.FindingsFactor analysis was used to cluster the factors into five components, and they were used as the input variables for fuzzy analysis. The respective components were: gross domestic product factors, demand for carbon credits factors, profits and energy gap factors, trading factors and transaction costs factors.Originality/valueThe index developed can exist as a multidimensional framework for measuring market factors that relate to carbon trading in construction projects. For stakeholders and policymakers, the market model serves as a guide for the critical market factors to prioritise in carbon trading projects.
PurposeThe purpose of this study is to consider the major factors for the use of digital technology in construction project management, including Legislation and Complexity Management (LC), Cost and Culture Management (CC), Safety Management Resources (SMR), Safety and Interest Management and Technology (SI) and Expertise Management (TE).Design/methodology/approachA cross-sectional survey of 210 construction professionals was conducted. The analysis of the gathered data applied structural equation modeling within analysis of moment structures with the intention of measuring the influence of the five factors toward the acceptance of digital technology. The proposed framework also fit the tests with the aid of the RMSEA = 0.041, CFI = 0.945 and TLI = 0.926 model fit indices.FindingsThe results confirm that all five constructs significantly relate to digital technology adoption, with TE and SMR exhibiting the strongest effects. Findings highlight the importance of regulatory alignment, cost management and stakeholder engagement in accelerating digital transformation in construction firms.Research limitations/implicationsThe findings indicate that strengthening digital skills, improving safety-driven technology resources and managing organizational culture more effectively can substantially accelerate digital transformation in construction project management.Originality/valueThis study contributes to technology acceptance models with the merging of multiple managerial dimensions and the construction of a comprehensive framework for construction digital transformation. The study yields actionable advice that policymakers can use in making strategic digital initiatives.
PurposeThe accurate prediction of property prices remains a significant challenge in real estate market analysis. This study aims to compare the predictive performance of various machine learning (ML) models, including Random Forest, Gradient Boosting, Lasso Regression, Elastic Net, AdaBoost Regressor, Bayesian Regression, Bagging Regressor and Stacking Regressor.Design/methodology/approachUsing a comprehensive data set of 1,512 property transactions from Prishtina, Kosovo, spanning 2019-2023, this study applies a consistent methodological framework for evaluating each model's performance using Mean Squared Error, Coefficient of Determination (R-2), Mean Absolute Error and Root Mean Squared Error. The models were trained on 70% of the data, validated through k-fold cross-validation and tested on the remaining 30%.FindingsThe results indicate that ensemble methods, particularly Random Forest and Bagging Regressor, exhibit superior predictive power with low error metrics and high R-2 values. While complex models show high accuracy, simpler linear-based approaches like Lasso and Elastic Net provide benefits in interpretability. The findings highlight the need to balance predictive performance with interpretability and computational efficiency.Originality/valueThis study's insights support real estate practitioners seeking data-driven valuation methods and guide researchers aiming to enhance property price prediction in emerging markets. By offering a robust evaluation of diverse ML techniques in this context, the research contributes valuable knowledge for model selection and performance optimization.
PurposeThis study aims to deal with the taxation of immovable property in the Czech Republic. This tax is significant revenues for the budget of the municipalities where the immovable property is located.Design/methodology/approachTo achieve this paper's objective, the author used standard positivist economic methodology. For a dependency analysis between the examined factor, regression analyses were used.FindingsThe tax rates changed only twice over the whole analysed period 1993-2024. It also follows that the share of property tax in the total tax revenue is decreasing. Due to the increase in the market price of the property, there is tax regressivity. The real tax burden on building plot increased only in municipalities with up to 600 inhabitants; in all other municipalities and cities, there is a decrease. Municipalities have options to increase tax revenue and tax burden. Many municipalities do not use this provision, even though the regression analysis results valid a positive impact on tax revenue.Research limitations/implicationsA slight limitation of this study is the fact that the amount of the tax revenue for the year 2025 is not included in the input database. This data are not available.Practical implicationsIn this study, there are practical recommendations which could be implemented into legislation. These recommendations are in the area of property valuation or the area of implications of inflation to tax rates.Social implicationsProperty tax reflects social and societal aspects. This is characterised by the fact that permanent residential properties have lower tax rates. Furthermore, in municipalities with a smaller population, the tax burden on permanent residential properties is lower. The reason is the different levels of infrastructure.Originality/valueAnalysis long period from the setting up of the Czech Republic to the present time.
Purpose Accessing decent, affordable housing is becoming more and more difficult for all countries, but especially for those in the global south. This paper aims to identify factors influencing housing finance from a neoliberal viewpoint using Lusaka as a case study. Design/methodology/approach The study used a quantitative methodology and recruited 214 respondents from key institutions involved in housing planning and development in Lusaka, Zambia. A structured questionnaire containing 13 indicator variables informed by literature was used for data collection. The acquired data underwent exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), and goodness-of-fit was used to assess the model’s acceptability using a two-index technique. Findings Results revealed that housing finance is defined by five variables: low-interest commercial bank loans, public–private partnerships, employer-assisted (housing allowance), community financing initiatives and pension-backed housing finance. Research limitations/implications The research is limited to a specific geographical area (Lusaka, Zambia) and may only be generalisable to other locations with the same economic metrics. Practical implications This paper provides a guide for improving urban housing by highlighting the areas housing finance should concentrate on. The identified variables can inform policymakers and stakeholders in developing strategies to address affordability challenges and improve access to housing. Originality/value This research contributes to the understanding of housing finance models from a neoliberal perspective, particularly in the context of urban areas in the global south. The findings offer insights into the specific variables that define housing finance and can inform interventions to improve housing affordability and access.
PurposeThis paper aims to investigate the background to the introduction of a recurrent property tax on residential property in Ireland. Within the European Union only two countries did not have a recurrent property tax on residential property, namely, Ireland and Malta. However, as from 2013, Ireland took the important step to re-introduce a residential property tax known as the Local Property Tax (LPT). The implementation of a new recurrent property tax represented a significant reform and challenge for the authorities, particularly in terms of the valuation of properties, where initally some 1.9 million dwellings had to be valued.Design/methodology/approachThe paper is based on an extensive review of government led commissions and published reports on the Local Property Tax both prior to and post implementation. In addition, a detailed analytical review of published research has identified the thinking behind government decisions in relation to how the LPT could be implemented within a very short period of time.FindingsPreparatory groundwork to introduce a new recurrent property tax took about four years. However, once the signal by the government was given the actual implementation took less than one year. This major tax reform was successful if measured against efficiency of implementation, the use of a simplified valuation methodology and reliance of owners' declaration of value.Research limitations/implicationsThere were no research limitations encountered.Practical implicationsThe paper highlights a number of key policies that were critical to the successful implementation of the LPT in Ireland. In this regard, two policies were to the fore, namely, the use of simplified valuation structure based on value banding and in adopting self-declared valuations provided by property owners. The LPT has now been in place for some 12 years and has been successful in raising stable and predictable revenue for local government. The implementation strategy adopted by Ireland should be of interest to other countries and/or jurisdictions when reviewing their options for property tax reform.Social implicationsThe paper has outlined the implementation of a property tax that has a reach of some 1.4 million taxpayers. It clearly provides evidence of impact and tax liability.Originality/valueThe paper has provided some unique insights into the implementation and performance of the LPT. It has also contributed to the literature on the innovative use of value banding. In addition, the paper provides a solid basis upon which other countries/jurisdictions could evaluate this as a potential reform solution.
PurposeEffective risk management practices help organisations and project managers foresee future project problems and thus institute measures to ensure successful project completion. This study aims to identify factors hindering the Northern Cape Provincial Government Departments from implementing risk management practices for public infrastructure construction projects.Design/methodology/approachThis study uses a qualitative research methodology. Workers who deliver infrastructure projects in the Northern Cape Provincial Government Departments were purposively sampled and interviewed face-to-face using open-ended questions. The data gathered was analysed using content analysis to identify main and sub-themes, as well as their frequencies.FindingsThe results indicate that several obstacles hinder the effective implementation of risk management in construction project execution across departments. Some of these obstacles include poor project planning, inadequate communication, insufficient supervision and a lack of risk management culture. These obstacles were categorised into funding issues, management attitudes, personnel problems and documentation challenges.Research limitations/implicationsThe study is confined to a province in South Africa, making it difficult to generalise the findings. However, it provides an in-depth understanding of the risk management practices used in public infrastructure construction projects.Practical implicationsChallenges affecting risk management in public infrastructure construction projects within the department may negatively affect project outcomes, leading to the loss of public resources. There is, therefore, an urgent need to tackle these challenges to ensure effective project implementation and safeguard municipal meagre resources.Originality/valueThis study helps us understand the sources of risk challenges for public infrastructure project execution. The government can use the findings to strategise to eliminate these risk factors for project delivery, ensure projects are delivered as planned and reduce waste in public funds management.
PurposeThe purpose of this study is to investigate the impact of spatial justice factors-such as access to amenities, green spaces, socio-economic status, transportation and environmental quality-on apartment prices in Prishtina, Kosovo, thereby contributing to the understanding of urban development dynamics in rapidly urbanizing cities.Design/methodology/approachThe present study used multiple regression analysis to investigate the relationship between apartment prices and various quantitative variables. Additionally, this study used logarithmic transformations and normalization to delve deeper into spatial predictors that affect housing prices.FindingsThis study reveals that socio-economic status and access to emergency services are the most significant predictors of apartment prices. Furthermore, transportation and green spaces gained prominence after normalization, highlighting their context-sensitive roles in shaping housing prices.Originality/valueThe originality of this study lies in its exploration of spatial justice variables in the underrepresented context of Prishtina, Kosovo, a rapidly urbanizing post-conflict city. By offering novel insights into urban housing markets and spatial justice, this research enhances the global understanding of equitable urban development in emerging urban landscapes.
PurposeThis paper aims to apply gradient boosting (GB) to forecast apartment prices in Prishtina, evaluating its effectiveness through established evaluation metrics and identifying key price determinants via variable importance analysis.Design/methodology/approachThe research method comprises analyzing a dataset of 1,468 property transactions from 2019 to 2023, attained from the Kosovo Department of Property Taxes. The data were split into training and testing subsets, with normalization applied to provide consistency, and the GB model's performance was evaluated using Mean Squared Error, Coefficient of Determination, Mean Absolute Error and Root Mean Squared Error.FindingsFindings point out that the GB model shows noteworthy predictive accuracy, with the distance from the Central Business District identified as the primary influencer of apartment prices in Prishtina. These findings also point out the model's capability to effectively capture and reflect the nuances of the local real estate market.Practical implicationsImplications of this study advise the practical utility of machine learning, specifically GB, in enhancing real estate valuation and decision-making processes. By providing accurate and efficient market analyses, such models can enable more informed investment decisions, contribute to market transparency and help in stabilizing property prices.Originality/valueOriginality of this research lays in its focused application of Gradient Boosting within an emerging market context, enriching the empirical discourse on real estate valuation. By conducting a comprehensive variable importance analysis, the study offers fresh understanding into the dynamics influencing property prices, thereby advancing the field of real estate research.
PurposeEffective risk management involves a clear understanding and appreciation of the nature and the complex interactions among risks in international construction joint ventures (ICJV) projects. This study aims to explore the risk paths in ICJV projects, given that previous studies have not given enough attention to this critical aspect of ICJV risk management.Design/methodology/approachA comprehensive literature review led to the formulation of a risk register containing 74 factors. Subsequently, 30 risk paths were hypothesised, and, in a survey, data were collected from 94 foreign and local partners. The data was analysed using PLS-SEM.FindingsThis study adopted the PLS-SEM and established 20 risk interrelationships. Specifically, host government-related risks, macroeconomic risks, legal risks, poor market conditions and unavailability of resources, among others, were found to impact the productivity of ICJV projects. These were found to affect ICJV projects through various risk networks, confirming the interdependency, rather than the independence of risks.Practical implicationsThis study contributes knowledge by extending the scope of current risk management studies of ICJV project typologies by exploring risk paths using SEM. It is expected that, as found in this study, understanding the complex relationships among risks would contribute to the effective management of risks in ICJV projects by partners through the allocation of adequate resources.Originality/valueThis study improves on a major limitation of most ICJV studies where ICJV risks were identified and analyzed as independent risks. The failure to understand the interrelations among risk factors may lead to inaccurate risk assessment and flawed conclusions.
PurposeThe going concern for any company, and certainly a construction company, is profitability. More than a monetary measure, profitability considers the relationship with the company's revenues. Thus, the effective use of the company's working capital (current assets less current liabilities) and other financial metrics are studied for their impact on profitability. This study aims to examine the relationship between working capital management and profitability among U.S. construction companies.Design/methodology/approachThis study applies the well-documented methodology of studies from around the globe - but specifically within the US construction industry. A database of over 6,000 annual financial statements from construction companies was used to determine the commonly used financial ratios, while correlation and multiregression analyses were run. The test was subsequently performed on varied strata based on company revenue size.FindingsThe findings of this study are intuitively unconventional - suggesting that extending credit terms provides pecuniary benefits to companies. This study does present a fundamental relationship between working capital and profitability - but not without reasonable confounders.Originality/valueVariation in the comparative results from previous studies and this one provided sufficient new findings to support the research conclusions and subsequent discussion - adding to the body of knowledge on net working capital and profitability.
Purpose This study aims to investigate the disparity in corporate social responsibility (CSR) engagement between construction firms and other sectors in India. This paper explores the financial implications of CSR activities on company performance. Design/methodology/approach A literature review was first conducted to identify six key financial metrics (net profit, total assets, equity, income, share capital and R&D expenditure), which were used to assess the relationship between financial performance and CSR investment. After that, this paper analysed financial data from FY22 for construction and non-construction firms in India. Regression analysis is used to further explore these relationships. Findings The study reveals significant differences in CSR engagement. Non-construction firms exhibit higher net profits and a more substantial investment in CSR initiatives. Regression analysis confirms a positive association between net profit and CSR spending. Interestingly, other financial metrics have minimal impact on CSR activities. Furthermore, non-construction firms exhibit a stronger commitment to CSR across all thematic areas. Originality/value This research contributes to a deeper understanding of CSR practices and financial dynamics in the Indian business landscape, particularly within the construction sector. The findings on the connection between financial performance and CSR engagement offer valuable insights for policymakers and stakeholders. This knowledge can be used to develop strategies that promote sustainable practices and corporate responsibility in construction and other industries.
Purpose This paper aims to identify and assess the features and relationship of corruption in factors that influence the survival of construction firms, particularly micro, small and medium-sized construction firms (MSMCFs). Design/methodology/approach A total of 59 MSMCFs that were awarded building contracts in federal health and tertiary institutions were surveyed, and 31 firms first rated 37 criteria that affect the viability of construction enterprises. The mean item score and factor analysis were used to analyse the data. Based on existing publications on corruption in the construction sector, this study then reviewed and evaluated the elements determining MSMCFs’ viability and identified and explored in detail those that can be undermined by corruption. Findings Corruption in the construction industry can have an adverse effect on 25 out of 37 factors, including the top five factors influencing firms’ viability. The viability of MSMCFs was described by the eight component factors (CFs) that comprised all of the variable elements. According to the research currently available on corruption in the construction sector, corrupt acts and practices in the sector have the potential to compromise four of the CFs and at least two variables in each of the other four CFs. Research limitations/implications A limitation of this study is the constraint of accommodating construction stakeholders’ views on the impact of corruption on the viability of MSMCFs. Despite this, the study provides a foundation for future research on corruption and the viability of construction firms. Practical implications This paper concludes that construction project conception, inception and implementation were susceptible to the widespread corruption in construction. It recommends that for MSMCFs to survive, there must be a conscious effort made to reduce or eradicate corruption in the construction industry. Originality/value This paper fulfils the need for identifying and assessing factors influencing the viability of MSMCFs in relation to their susceptiveness to corruption endemic in the construction industry.
Purpose This study aims to identify the nature of intragroup conflicts between direct stakeholder’s teams during the construction stage in Sri Lankan construction projects and how to manage their impact on project deliverables. Design/methodology/approach This study used a qualitative research approach, adopting a case study-based research strategy. The required empirical data were collected using semi-structured interviews and a document review of four selected construction projects implemented in Sri Lanka. These selected projects are executed using a traditional separated procurement system with remeasurement contracts. A manual content analysis was used for data analysis. Findings This study highlighted three main types of intragroup conflicts between direct stakeholder’s teams that can arise during the construction stage of projects in Sri Lanka, as well as the causes and effects of those conflicts on project deliverables. The study also proposed proactive strategies to manage these intragroup conflicts. Originality/value The ability to effectively manage intragroup conflicts between direct stakeholder’s teams during construction stage directly impacts the project deliverables. However, how this can be explicitly managed during construction stage has not been adequately investigated in a Sri Lankan context. The existing study aims to bridge this gap. This study is of further originality as it analyzes the nature and extent of the impact of intragroup conflicts within direct stakeholder’s teams toward the end project deliverables at the construction stage and proposes proactive strategies to manage them.
Purpose Many construction companies use the diversification strategy to take advantage of its various advantages. However, previous studies on this strategy have neither analysed its management perspective nor considered the pre-diversification stage of construction companies. The purpose of this study is to provide a detailed discussion about the diversification motivations of construction companies and to evaluate the pre-diversification decision-making criteria at the corporate level in the construction industry from the perspective of senior managers. Design/methodology/approach The related literature was reviewed to determine the reasons for diversification. The data required to investigate the relationship between company profiles and reasons for diversification were then obtained through a semi-structured questionnaire survey conducted with 40 senior construction managers. The results were analysed through statistical tests. Findings Profiles of construction companies have no significant effect on reasons for diversification. The majority of contractors diversify regardless of typology, risk attitude or diversification modes and describe themselves as a prospector or an analyser, risk-averse and moderately diversified. Finally, the related diversification is more favourable than the unrelated one. Research limitations/implications The questionnaire survey was conducted with companies in Turkey. The scope of the study could be extended to other countries or more companies to increase its generalization capability. This study reveals managerial behaviours required for construction companies to overcome the challenges faced in making a decision to implement a diversification strategy, achieve a sustainable growth and gain a competitive advantage. The study argues for the necessity of establishing a relationship between company profiles and the reasons, advantages and disadvantages of diversification strategy. The findings and recommendations are a valuable source of information and guidance for practitioners, policymakers and researchers. Practical implications Decision-maker practitioners in the construction industry may benefit from the results of this research as a guide during the pre-diversification stage of the decision-making process for implementing the diversification strategy. Originality/value To the best of the authors’ knowledge, this study is the first attempt to present the approaches of senior managers of construction companies on reasons for diversification and the relationship between these reasons and typologies, risk attitudes and diversification modes of companies.
Purpose Since the Chinese real estate market has expanded so quickly over the past 10 years, investors and the government are both quite concerned about projecting future property prices. Design/methodology/approach This work aims to investigate monthly rental price index forecasts of residential properties for ten major Chinese cities from 3M2012 to 5M2020 by using Gaussian process regressions with a diverse variety of kernels and basis functions. The authors conduct forecast exercises through use of Bayesian optimizations and cross-validation. Findings With relative root mean square errors spanning the range of 0.0370%–0.8953%, the constructed models successfully forecast the ten price indices from 6M2019 to 5M2020 out of sample. Originality/value The findings might be used independently or in combination with other projections to create theories about the trends in the rental price index of the residential property and carry out additional policy analysis.
