Search Results (134)

With global climate challenges intensifying, the cement industry, as a major CO₂ emitter, has attracted significant attention regarding its emission reduction potential and strategies. Advanced economies like the European Union use carbon pricing to spur innovation, while emerging countries focus on incremental solutions, such as fuel substitution. Combining LMDI decomposition and the LEAP model, this study examines Jiangsu Province as a test bed for China’s decarbonization strategy, a highly efficient region with carbon intensity 8% lower than the national average. Historical analysis identifies carbon intensity, energy mix, energy intensity, output scale, and economic effects as key drivers of emission changes. Specifically, the reduction in cement production, real estate contraction, lower housing construction, and reduced production capacity are the main factors curbing emissions. Under an integrated technology strategy—including energy efficiency, fuel and clinker substitution, and CCS—CO₂ emissions from Jiangsu’s cement sector are projected to decrease to 17.28 million tons and 10.9 million tons by 2060 under high- and low-demand scenarios, respectively. Clinker substitution is the most significant CO₂ reduction technology, contributing about 60%, while energy efficiency gains contribute only 3.4%. Despite the full deployment of existing reduction methods, Jiangsu’s cement industry is expected to face an emissions gap of approximately 10 million tons to achieve carbon neutrality by 2060, highlighting the need for innovative emission reduction technologies or carbon trading to meet carbon neutrality goals. Full article

As Hong Kong faces increasing pressure on resources and environmental sustainability, there is a growing need to shift towards circular building practices. The ever-increasing demand for sustainable urban development necessitates innovative approaches towards greener and more sustainable building design and construction. This paper introduces a new integrated Technology–Material–Design (TMD) Circularity Assessment Framework, a three-dimensional and comprehensive tool designed to evaluate and enhance the circularity level of buildings in Hong Kong. Through an extensive literature review, the research study identifies a new perspective with key metrics and best practices that inform the new assessment framework, enabling various key stakeholders to pinpoint effective strategies for overcoming profound challenges and seizing timely opportunities to foster a more sustainable and resilient built environment. This paper successfully categorises all circularity assessment frameworks into three perspectives, i.e., material-based, technology-oriented, and design-supported. Future research could apply BIM technology to automate and circularise the new assessment framework. Another significant contribution of this paper is the derivation of a new formula for the Building Circularity Index (BCI) for Hong Kong, which quantifies building circularity levels using a set of defined measurement metrics. By providing a robust assessment method, the TMD Circularity Assessment Framework facilitates informed decision making for architects, engineers, governments, developers, policymakers, and other stakeholders in a new horizon. The review findings underscore the potential of the TMD Framework to guide the transition towards more circular buildings, ultimately contributing to the broader goals of environmental sustainability and resource efficiency in Hong Kong’s construction and real estate sector. Full article

This study addresses the vast amount of information held by the banking sector, especially regarding opportunities in tourism development, production, and large residential projects. With advancements in information technology and databases, data mining has become essential for banks to optimally utilize available data. From January 2023 to July 2024, data from the Egyptian Arab Land Bank (EALB) were analyzed using data mining techniques, including rough set theory and the Weka version 3.0 program. The aim was to identify potential units for targeted marketing, improve customer satisfaction, and contribute to sustainable development goals. By integrating sustainability principles into financing approaches, this research promotes green banking, encouraging environmentally friendly and socially responsible investments. A survey of EALB customers assessed their interest in purchasing homes under the real estate financing program. The results were analyzed with GraphPad Prism version 9.0, with 95% confidence intervals and an R-squared value close to 1, and we identified 13 units (43% of the total units) as having the highest marketing potential. This study highlights data mining’s role in enhancing marketing for the EALB’s residential projects. Combining sustainable financing with data insights promotes green banking, aligning with customer preferences and boosting satisfaction and profitability. Full article

This paper employs bootstrap rolling-window tests to investigate the dynamic causal relationship between the housing boom (HB) and technological innovation (TI) in China. Through sub-sample analysis, we reveal the dual impact of the HB on TI: during periods dominated by the liquidity effect, the HB exerts a positive influence on TI, whereas during periods dominated by the crowding-out effect, the HB negatively affects TI. Furthermore, this study identifies the significant positive effect of TI on the HB, suggesting that TI serves as a predictor of real estate development trends. This research not only provides empirical evidence on the bidirectional interaction between the HB and TI, but also offers valuable insights for policymakers in terms of balancing the development of the real estate market and TI. Full article

This study examines the integration of multi-real-option valuation and security token offering (STO) as an innovative approach to real estate project financing. The case study of Aspen Resort Development serves to illustrate this methodology. The traditional discounted cash flow (DCF) method is frequently ill-suited to the dynamic and uncertain nature of long-term real estate projects, particularly in regard to the ability to adapt to market fluctuations. In order to address these limitations, this study employs a multi-real-option model with a binomial lattice framework, thereby facilitating flexible decision-making in various investment stages. The analysis demonstrates that the STO-based project financing (STO-PF) model offers enhanced financial performance and strategic advantages in comparison to the conventional DCF approach. Furthermore, the STO-PF model has the effect of increasing liquidity, expanding investment accessibility, and improving risk management through the utilization of digital platforms. By quantifying the project’s extended net present value (ENPV), the integration of STOs with real-options models can facilitate optimal investment decisions in the context of a high level of market volatility. Consequently, the STO-PF model is determined to yield a project value (E) of USD 7.34 million and a real-options value (ROV) of USD 3.69 million. This is markedly higher than the net present value (NPV) of USD 3.65 million derived from the traditional project finance (PF) model. Furthermore, the put option for the second investment stage contributes USD 16.45 million to the overall value of the project, thereby demonstrating the flexibility and strategic advantages of the STO framework in comparison to static NPV analysis. The Aspen project serves as a case study, demonstrating the financial viability of phased investments in dynamic market conditions. It contributes to the theoretical understanding of STO-based financing and provides practical insights for developers seeking flexible and innovative financing solutions in the real estate sector. Further research is required to confirm the applicability of STOs in diverse market environments and regulatory contexts. Additionally, in-depth research is necessary to integrate emerging technologies, such as artificial intelligence and machine learning, into multi-real-option-based financial platforms. This integration aims to enhance financial modeling and decision-making processes, as well as to facilitate the integration of digital technologies in this field. Only then can the development and implementation of smart construction development advance. Full article

This study aims to identify the added value of smart city technologies in real estate development, one of the most significant factors that would transform traditional real estate into smart ones. In total, 16 technologies utilized at both levels have been investigated. The research followed an integrative review methodology; the review is based on 168 publications. The compiled results based on metadata analysis displayed the state of each technology’s added values and usage in both scales. A total of 131 added values were identified. These added values were categorized based on the real estate life cycle sub-phases and processes. Moreover, the value of the integration between these technologies was revealed. The review and results proved that these technologies are mature enough for practical use; therefore, real estate developers, city management, planners, and experts should focus on implementing them. City management should invest in Big Data and geodata and adopt several technologies based on the aspects required for development. This study can influence stakeholders, enhance their decision-making on which technology would suit their needs, and provide recommendations on who to utilize them. Also, it provides a starting point for stakeholders who aim to establish a road map for incorporating smart technologies in future smart real estate. Full article

Real estate plays a pivotal role in the contemporary world, accounting for over half of global wealth and significant employment and GDP shares. This essay examines three key events—the 2007–2008 Global Financial Crisis (GFC), the COVID-19 pandemic, and recent technological revolutions—to place real estate’s centrality. By analyzing housing price indexes in major economies, the paper identifies global trends and regional nuances, as well as highlights real estate’s dual role as both a reflection and a driver of economic cycles. Then, I explore in detail the GFC, the urban roots of COVID-19 and its effects on real estate markets, and the relationship between new techno-economic paradigms and cities and real estate. Future research directions on real estate are also pointed out. Full article

The visual perception of interior architecture plays a crucial role in real estate marketing, influencing the decisions of buyers, interior architects, and real estate agents. These professionals rely on personal assessments of space, often drawing from their experience of using décor to influence how interiors are perceived. While intuition may validate some approaches, this study explores an under-examined aspect of interior design using a mobile eye-tracking device. It investigates how decorative elements affect spatial perception and offers insights into how individuals visually engage with interior environments. By integrating décor into the analysis of interior architecture, this study broadens the traditional scope of the field, demonstrating how décor composition can modulate spatial perception using eye-tracking technology. Results show that effective styling can redirect attention from key architectural elements, sometimes causing them to be overlooked during the critical first moments of observation commonly known as the “first impression”. These findings have important implications for interior design practice and architectural education. Full article

One of the key elements in real estate appraisal of residential buildings is the usable area. To determine the monetary value of real estate, appraisers in Poland often rely on transaction data registered in the Real Estate Price Register (REPR). However, the REPR may contain meaningful gaps, particularly on information concerning usable areas. This may lead to difficulties in finding suitable comparative properties, resulting in mispricing of the property. To address this problem, we used linear and nonlinear models to estimate the usable area of buildings with multi-pitched roofs. Utilizing widely available data from the Topographic Objects Database (BDOT10k) based on LiDAR technology, we have shown that three parameters (building’s covered area, building’s height, and optionally the number of storeys) are sufficient for a reliable estimate of the usable area of a building. The best linear model, using design data from architectural offices, achieved a fit of 95%, while the best model based on real data of existing buildings in the city of Koszalin, Poland achieved 92% fit. The best nonlinear model achieved slightly better results than the linear model in the case of design data (better fit by approximately 0.2%). In the case of existing buildings in Koszalin, the best fit was at 93%. The proposed method may help property appraisers determine a more accurate estimation of the usable area of comparative buildings in the absence of this information in the REPR. Full article

The Green Building Evaluation Standard (ESGB) has become an important support for China’s building sector in realizing the “double carbon” goal. However, there remains a lack of comprehensive research on the historical evolution and development status of the ESGB. This study firstly analyzes the updating logic and development strategy of the three versions of the ESGB, then compares the differences between ESGB 2019, CASBEE-NC 2014, and LEED O+M v4.1 from the perspective of the index system, and further examines the current international application status of the ESGB. The results show that LEED focuses on decarbonization and ecological protection, while CASEBB focuses on the concept of humanization and positively influences the local real estate market, and ESGB 2019 contains more health and comfort considerations than its previous version and is close to the internationally advanced level in terms of provision setting and international application. This study offers valuable insights into the potential for further refinement of green building standards in China and highlights areas for future research, including enhancing the ESGB’s adaptability and integration with emerging technologies to promote global sustainable development. Full article

This paper explores different building maintenance strategies in commercial buildings in Sydney, Australia, focusing on corrective maintenance (CM) and preventive maintenance (PM). While CM involves rectifying issues after they occur, PM aims to enhance productivity by anticipating potential issues. Although PM seems more logical, the decision to implement this type of maintenance strategy are typically made based on item reliability, failure frequency, and downtime cost, commonly found in manufacturing facilities or critical environments. However, as found in the selected/surveyed commercial real estate buildings, CM was more frequently adopted in aged facilities with older infrastructure, and PM was favoured for buildings without structural deficiencies; however, operating equipment failures were common. However, in many cases, decision makers did not consider the broader effects of downtime beyond direct financial losses, costs associated with customer satisfaction, worker efficiency, rent abatements, and reputation damage. While each building is unique and may require a bespoke maintenance schedule, this study’s insights may help managers select the most appropriate maintenance strategy. Nonetheless, further research is needed to investigate the role of innovative technologies (such as machine learning and artificial intelligence) in enhancing maintenance efficacy and explore the influences of economic shifts, corporate and financial objectives, and the availability of technical resources. Full article

The traditional concept of real estate, rooted in physical attributes—including land, buildings, and infrastructure—has undergone significant transformation in the digital age. This paper repositions real estate as a foundational platform within the broader digital ecosystem, serving as an integrative node that connects various digital platforms. By examining processes such as informatization, servicization, automatization, e-spatialization, dematerialization, and humanization, this study demonstrates how these elements collectively embed economic, social, and environmental value states into the digital framework of real estate. Leveraging advanced technologies and interdisciplinary methodologies, this paper constructs a comprehensive framework for understanding real estate’s digital evolution. The findings highlight real estate’s pivotal role in providing essential spatial and digital infrastructure that support the integration and operation of other platforms. It contributes to the discourse on platformization by offering a new model for understanding and utilizing real estate in a digitally driven society while proposing strategies crucial for its successful implementation in the ongoing digital transformation. Full article

Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal. Full article

The concept of homogeneity in the real estate market is a well-known analysis aspect, yet it remains a significant challenge in practical implementation. This study aims to fill this research gap by introducing the HELIOS concept (Homogeneity Estate Linguistic Intelligence Omniscient Support), presenting a new approach to real estate market analyses. In a world increasingly mindful of environmental, social, and economic concerns, HELIOS is a novel concept grounded in linguistic intelligence and machine learning to reshape how we perceive and analyze real estate data. By exploring the synergies between human expertise and technological capabilities, HELIOS aims not only to enhance the efficiency of real estate analyses but also to contribute to the broader goal of sustainable and responsible data practices in the dynamic landscape of property markets. Additionally, the article formulates a set of assumptions and suggestions to improve the effectiveness and efficiency of homogeneity analysis in mass valuation, emphasizing the synergy between human knowledge and the potential of machine technology. Full article

Traditionally, studies analyzing consumer preferences in real estate have focused on measuring subjective user responses, neglecting associated physiological responses. This study investigates the relationship between visual attention patterns and subjective evaluations of home sales information. Eye-tracking technology was used to record two characteristics of 21 participants’ visual behavior while viewing stimuli containing information about home sales (First Fixation Time and Total Fixation Duration). Additionally, participants evaluated their level of satisfaction with nine dwelling characteristics based on a questionnaire adapted for this research (1, quality and finishes of the dwelling; 2, size of the dwelling; 3, dwelling floor level; 4, bright and outdoor orientation of the dwelling; 5, flexible layout; 6, peaceful atmosphere; 7, smart and secure character; 8, privacy; and 9, original and luxurious character). The results demonstrated significant correlations between fixation time on specific areas of the stimulus and subjective ratings of features such as size, quality, finishes, layout, and orientation of the homes. Furthermore, differences based on gender and participants’ architecture experience level were observed in visual behavior during image observation. These findings underscore the importance of visual design and presentation in home marketing, as visual attention can influence perceptions of home characteristics and, ultimately, purchase decisions. Full article