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The purpose of this paper is to examine whether mandatory disclosure of information accompanying the sale of real estate achieves its aim of informed purchasers.

Using a case study approach focused on mandatory disclosure in South Australia data was collected from interviews and focus groups with key personnel in the property industry involved in the production of information required to fulfil vendors’ disclosure obligations.

The authors found that purchasers are ill-served by a long and complex form of mandatory disclosure with a short time frame that prevents the use of the information provided. Without good form design and increased digital affordances provided by the cadastral and conveyancing systems, mandatory disclosure is insufficient to ensure minimisation of information asymmetry between vendor and purchaser.

To the best of the authors’ knowledge, this is the first Australian qualitative study that examines the utility of mandatory vendor disclosure in real estate sales and the first to consider the impact of the digitalisation of cadastral and conveyancing systems upon the efficacy of mandatory disclosure regimes.

This paper aims to assess the current and future dynamics of land cover transitions and analyze the vegetation conditions in Abuja city since its establishment as the capital of Nigeria in 1991.

A random forest classifier embedded in the Google Earth Engine platform was used to classify Landsat imagery for the years 1990, 2001, 2014 and 2020. A post-classification comparison was used to detect the dynamics of land cover transitions. A hybrid simulation model that comprised cellular automata and Markovian was used to model the probable scenario of land cover changes for 2050. The trend of Normalized Difference Vegetation Index was examined using Mann–Kendall and Theil Sen’s from 2014 to 2022. Nighttime band data from the National Oceanic and Atmospheric Administration were obtained to analyze the trend of urbanization from 2014 to 2022.

The findings show that built-up areas increased by 40%, while vegetation, bare land and agricultural land decreased by 27%, 7% and 8%, respectively. Vegetation had the highest declining rate at 3.15% per annum. Built-up areas are expected to increase by 17.1% between 2020 and 2050 in contrast with other land cover. The proportion of areas with moderate vegetation improvement is estimated to be 15.10%, while the proportion of areas with no significant change was 38.10%. The overall proportion of degraded areas stands at 46.8% due to urbanization.

The findings provide a comprehensive insight into the dynamics of land cover transitions and vegetation variability induced by rapid urbanization in Abuja city, Nigeria. In addition, the findings provide valuable insights for policymakers and urban planners to develop a sustainable land use policy that promotes inclusivity, safety and resilience.

Due to the lack of consensus on influential variables for real estate appraisal, which varies from one country to another based on the national preferences and customs of each country, this study aims to identify the most influential variables affecting condominium apartment real estate appraisal within the context of Al Bireh city, Palestine.

The methodology adopts a cross-sectional quantitative approach, entailing the administration of an online questionnaire survey to 103 buyers and appraisers. The questionnaire aims to evaluate 32 variables concerning their impact on condominium real estate appraisal. Out of these, 25 are derived from three specific previous studies, and the remaining 7 are identified through various studies or by the authors, taking into account the local context and the geopolitical situation in Palestine. Respondents assign scores to these variables on a five-point Likert scale, ranging from 1 to 5, where 1 indicates less influence and 5 signifies the most influence. Variables with an arithmetic mean score exceeding 4 are deemed the most influential.

The findings underscore 16 and 17 influential variables as perceived by buyers and appraisers, respectively. Notably, 13 variables are common, including aspects such as parking, elevator, neighborhood, floor apartments, finishing quality, construction material, condition, building apartments, area, open sides, building floors, colonies and age.

The primary constraint of this study is its dependence on insights solely from buyers and appraisers, disregarding input from other stakeholders like investors or developers. The questionnaire lacks vital respondent characteristics, such as gender and occupation, impeding the analysis of variable dependence on participant attributes. Although some additional influential variables are suggested through the responses of an open-ended question, the questionnaire is not repeated, leaving their influence unassessed. This study's focus on Al Bireh city limits the opportunity for result comparison with other cities, diminishing its generalizability.

The implications of this research are twofold: to provide stakeholders with a checklist for variables influencing apartment price value and to guide data collection related to the real estate appraisal sector, facilitating its use as input in advanced appraisal methods such as artificial intelligence with a view to improving overall performance. Obtaining an informed, mature and accurate appraisal has direct economic, business and financial impacts at the level of policymakers and individuals.

To the best of the authors' knowledge, this study is the inaugural endeavor in Palestine focusing on identifying pivotal factors influencing condominium apartment appraisal values. This study concludes by presenting a checklist comprising the most influential variables, offering utility to various stakeholders, including buyers, appraisers and developers. In addition, the questionnaire incorporates an open-ended question, soliciting respondents' input on additional variables they believe impact the appraisal process.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Demand 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.

The 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.

Nine 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.

The 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.

The real estate industry has rapidly changed due to technological advances across residential and commercial real estate from the perspective of occupiers, investors, and service providers. Owners and buyers of properties have access to increasing information in the marketplace, including access to residential real estate platforms such as Zillow. Automated appraisals and artificial intelligence (AI) in the mortgage application process speed up home buying. Commercial real estate uses fintech to source deals, perform due diligence, and execute property management requests. This chapter includes a practitioner's view of the current and future information data needs, processes, and point solutions in the evolving technology landscape, including how tools such as ChatGPT apply. It concludes that the real estate fintech revolution has only begun, as data gaps in the real estate market require resolution before yielding better process automation and as the business model of real estate service providers shifts to strategic advisory roles.

A partnership model is necessary for palm oil plantations’ sustainability. The developed model does not identify the optimal technique for smallholder palm oil because it faces complex challenges. This study aims to determine a partnership model for sustainable palm oil plantation business in Indonesia.

Qualitative research methods were used, and data analysis was performed using NVivo 12 Plus software, which helps improve the accuracy of qualitative studies and provides implications for evidence-based studies. All respondents, whether through surveys, interviews or focus group discussions, understood their contributions to this research and provided consent.

The results indicate that the core–plasma partnership pattern implemented by companies needs to be considered a relevant model for partnerships in the Indonesian palm oil industry. Social networks play a role in implementing this partnership pattern, which is influenced by the diversity of the actors involved. However, complexity arises from the dynamics of power and position among these actors, which demands increased interaction and mutual trust. Therefore, various dimensions must be considered, including plantation management, product marketing, cultivation techniques and sustainable development.

The research results have limitations, particularly regarding access to information for company policymakers, because there remains complexity related to the position and dominance of power between actors, which influences the achievement of common goals. A more complex analysis is needed to produce complete research. Further studies are required to provide a more comprehensive explanation of the humanist approach in the context of palm plantations.

This study provides an important theoretical implementation: a more humane approach through a partnership model that adds value and is based on aspects of morality in implementing partnerships in the palm oil plantation sector. It also provides new and substantial insights regarding practical implementation for policymakers and practitioners who want to improve partnership practices in sustainable palm oil businesses by implementing value-added and morality-based partnership models in Indonesia as well as other developing countries.

Reducing carbon emissions in agriculture is vital for fostering sustainable agricultural growth and promoting ecological well-being in rural areas. The adoption of Low-Carbon Agriculture (LCA) by farmers holds great potential to accomplish substantial reductions in carbon emissions. The purpose of this study is to explore the farmers' preference and willingness to engage in LCA.

This study employs the Choice Experiment (CE) method to examine farmers' preferences and willingness to adopt LCA, using field survey data of 544 rural farmers in the Weihe River Basin between June and July 2023. We further investigate differences in willingness to pay (WTP) and personal characteristics among different farmer categories.

The empirical results reveal that farmers prioritize government-led initiatives providing pertinent technical training as a key aspect of the LCA program. Farmers' decisions to participate in LCA are influenced by factors including age, gender, education and the proportion of farm income in household income, with their evaluations further shaped by subjective attitudes and habits. Notably, we discovered that nearly half of the farmers exhibit indifference towards LCA attributes.

To the best of the authors' knowledge, this study is the first to investigate farmers' attitudes toward LCA from their own perspectives and to analyze the factors influencing them from both subjective and objective standpoints. This study presents a fresh perspective for advocating LCA, bolstering rural ecology and nurturing sustainable development in developing nations.

There is an urgent need to translate climate change awareness into tangible climate adaptation strategies. The built environment is identified as one of the kick-off points in making climate change adaptation as the built environment shares a dual-way relationship. While the built environment largely contributes to the climate change-triggering factors, it also becomes highly vulnerable in the face of climate change impacts. Tied up with the interconnectedness of the built environment processes and associated systems, the involvement of numerous stakeholders from different spectrums creates the need for a holistic and multi-stakeholder approach in developing climate response strategies for the built environment. Accordingly, this study aims to identify the roles and responsibilities of the different built environment stakeholders in climate change adaptation.

The study consisted of a scoping review at the initial stage, contextualising studies based on secondary data, and semi-structured expert interviews in five different countries: the UK, Sweden, Malta, Spain and Sri Lanka. The paper summarises the findings of the individual country-level desk studies and 65 built environment stakeholder interviews representing national and local governments, communities, academia and research organisations, civil organisations, professional bodies and the private sector. The findings were validated through focus group discussions in two stakeholder seminars.

The findings summarised a set of key roles and sub-roles for each stakeholder category, considering the current status and needs. The national governments need to set a long-term vision, enabling multi-sector interventions while promoting investment and innovation in climate change adaptation. The local governments overlook local adaptation plans, while the community is responsible for decarbonising operations and practising adaptation at the local level. Civil organisations and professional bodies are the voice of the community, linking policy and practice. Academia and research are responsible for nurturing skills and new knowledge, and the private sector must contribute by adopting climate resilience into their business portfolio and corporate social responsibility.

This research is part of an Europe-Union-funded research project, Built Environment leArning for Climate Adaptation (BEACON), which aims to develop skills and competencies of the built environment professionals so that they will be adequately equipped to handle the adaptation process of the built environment needs to adapt in facing the climate change impacts.

The paper provides an in-depth analysis of the roles and responsibilities pertaining to each category of the different stakeholders in effectively adapting the built environment to withstand the climate change consequences. Demarcation of each stakeholder’s roles and responsibilities separately facilitates collaboration and coordination between the different parties and provides a more holistic approach to climate change adaptation in the built environment.