Search Results (147)

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume is represented by a dense set of 3D coordinate points arranged in a predefined order and is displayed alongside the parcel map for reference and utilization by readers. To store a 3D property volume in the database, it is essential to record the connectivity relationships among the original 3D coordinate points, the associations between points and lines for representing boundary lines, and the relationships between lines for defining surfaces. Only by preserving the data structure that represents the relationships among points, lines, and surfaces can the 3D property volume in a parcel map be fully reconstructed. This approach inevitably results in the database storage volume significantly exceeding the original size of the point set, thereby causing storage redundancy. Consequently, this paper introduces a reversible 3D property volume compression coding method (called 3DPV-CC) to address this issue. By analyzing the distribution characteristics of the coordinate points of the 3D property volume, a specific rule for sorting the coordinate points is designed, enabling the database to have the ability of data storage and recovery by merely storing a reordered point set. The experimental results show that the 3DPV-CC method has excellent support capabilities for 3D property volumes of the vertical and slopped types, and can compress and restore the coordinate point set of the 3D property volume for drawing 3D parcel maps. The compression capacity of our method in the test is between 23.66% and 38.42%, higher than the general data compression methods (ZIP/7Z/RAR: 8.37–10.32%). By means of this method, land or real estate administrators from government departments can store 3D property volume data at a lower cost. This is conducive to enhancing the informatization level of land management. Full article

In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions. Full article

This paper investigates the spillover effects and transmission networks of systemic risk within China’s national economic sectors under extreme conditions from both time and frequency domain perspectives, building upon the spillover index methodology and calculating the ∆CoVaR index for Chinese industries. The findings indicate the following: (1) Extreme-risk spillovers synchronize across industries but exhibit pronounced time-varying peaks during the 2008 Global Financial Crisis, the 2015 crash, and the COVID-19 pandemic. (2) Long-term spillovers dominate overall connectedness, highlighting the lasting impact of fundamentals and structural linkages. (3) In terms of risk volatility, Energy, Materials, Consumer Discretionary, and Financials are most sensitive to systemic market shocks. (4) On the risk spillover effect, Consumer Discretionary, Industrials, Healthcare, and Information Technology consistently act as net transmitters of extreme risk, while Energy, Materials, Consumer Staples, Financials, Telecom Services, Utilities, and Real Estate primarily serve as net receivers. Based on these findings, the paper suggests deepening the regulatory mechanisms for systemic risk, strengthening the synergistic effect of systemic risk measurement and early warning indicators, and coordinating risk monitoring, early warning, and risk prevention and mitigation. It further emphasizes the importance of avoiding fragmented regulation by establishing a joint risk prevention mechanism across sectors and departments, strengthening the supervision of inter-industry capital flows. Finally, it highlights the need to closely monitor the formation mechanisms and transmission paths of new financial risks under the influence of the pandemic to prevent the accumulation and eruption of risks in the post-pandemic era. Authorities must conduct annual “Industry Transmission Reviews” to map emerging risk nodes and supply-chain vulnerabilities, refine policy tools, and stabilize market expectations so as to forestall the build-up and sudden release of new systemic shocks. Full article

In the post-digital era, virtual reality (VR) technology is increasingly being utilized in the real estate industry. In this study, the influence of functional experience with VR technology (e.g., interactivity and flexibility) on consumers’ offline house viewing intentions is explored. On the basis of efficiency–flexibility ambidexterity and customer inspiration theory, a structural equation model was employed to analyze empirical data collected from 388 consumers in the Guangdong–Hong Kong–Macao Greater Bay Area. The key findings are as follows: (1) VR technology features have significant positive effects on customer inspiration, which in turn enhances customers’ willingness to view houses offline; (2) VR presence, enjoyment, interactivity, and flexibility all contribute to customer inspiration, with VR presence having the most substantial impact; and (3) VR knowledge and consumer demand for uniqueness significantly moderate the relationship between VR technology features and customer inspiration. For example, consumers with substantial VR knowledge can more effectively leverage VR technology, whereas those with a strong need for uniqueness are more likely to be inspired by the innovative aspects of VR. This research provides theoretical support for the application of VR technology in real estate marketing and practical guidance for enterprises to optimize VR marketing strategies, improve consumer experiences, and drive offline transactions. These insights can help companies better understand consumer psychology and behaviour in the digital marketing landscape. Full article

The real estate sector is increasingly recognizing facility management (FM) as a key driver of asset value. Among emerging technologies, Building Information Modeling (BIM) and Building Management Systems (BMSs) stand out for their potential to enhance FM efficiency by integrating design data with building operations across the entire lifecycle, from construction to maintenance, performance monitoring, and renovation. While their technical applications have been widely studied, the financial impact of these tools on FM remains underexplored. This paper addresses that gap by estimating the economic value generated by implementing BIM and BMS in real estate facility management. Based on thirteen semi-structured interviews with professionals from the Italian real estate sector, we identified and quantified cost-saving factors and challenges related to digital adoption. These cost efficiencies, when recurring and quantifiable, can improve net operating income (NOI), thereby supporting higher asset valuations under income-based real estate appraisal methods. The results show that integrating BIM and BMS in facility management may generate average annual cost savings of 5.81% relative to asset value, with coordination improvements alone accounting for up to 3.28% per year. Based on a 30-year simulation, these savings correspond to a positive Net Present Value (NPV), supporting the financial viability of digital FM adoption in real estate. This study offers empirical evidence to support investment decisions in digital FM technologies and contributes to bridging the gap between innovation and financial evaluation in the real estate sector. Full article

We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms. Full article

With the growing global emphasis on sustainable development goals, Environmental, Social, and Governance (ESG) factors have emerged as critical considerations in shaping economic policies and strategies. This study employs the ARMA-eGARCH-skewed t and Vine Copula models, combined with the CoVaR method, to investigate the dependence structure and risk spillover pathways across various industrial sectors in China within the ESG framework. By modeling the complex interdependencies among sectors, this research uncovers the relationships between individual industries and the ESG benchmark index, while also analyzing the correlations across different sectors. Furthermore, this study quantifies the risk contagion effects across distinct industries under extreme market conditions and maps the pathways of risk spillovers. The findings highlight the pivotal role of ESG considerations in shaping industrial structures. Empirical results demonstrate that industries such as agriculture, energy, and manufacturing exhibit significant systemic risk characteristics in response to ESG fluctuations. Specifically, the identified risk spillover pathway follows the sequence: agriculture → consumption → ESG → manufacturing → energy. The CoVaR values for agriculture, energy, and manufacturing indicate a significant potential for risk contagion. Moreover, sectors such as real estate, finance, and information technology exhibit significant risk spillover effects. These findings offer valuable empirical evidence and a theoretical foundation for formulating ESG-related policies. This study suggests that effective risk management, promoting green finance, encouraging technological innovation, and optimizing industrial structures can significantly mitigate systemic risks. These measures can contribute to maintaining industrial stability and fostering sustainable economic development. Full article

This study investigates the structural transformation of Shanghai’s economy (2004–2023), analyzing the interplay between industrial shifts and sustainable growth. While prior work has focused on short-term trends or isolated sectors, we provide the first comprehensive analysis of Shanghai’s two-decade transition from manufacturing to services, leveraging annual nominal GDP data and three forecasting models (Autoregressive Integrated Moving Average model ARIMA, Support Vector Machine SVM, and Grey Model GM). Our findings reveal that the tertiary sector’s contribution surged from 50.8% to 75.2% of GDP, driven by finance, technology, and real estate, while the secondary sector declined to 24.6%. The autoregressive integrated moving average ARIMA(1,1) model outperformed alternatives (mean absolute percentage error 2.97%), projecting GDP growth to CNY 60,321.54 trillion by 2026. Crucially, we demonstrate that Shanghai’s structural evolution aligns with advanced urban economies (e.g., New York, Tokyo), yet retains distinct industrial resilience due to China’s dual-circulation policy. These results challenge assumptions about manufacturing decline, instead highlighting a rebalancing toward high-value-added sectors. The study contributes (1) a validated framework for forecasting urban GDP in policy-stabilized economies and (2) empirical evidence for prioritizing tertiary innovation in sustainable development strategies. Policymakers and researchers can leverage these insights to navigate trade-offs between growth, equity, and environmental goals in rapidly urbanizing regions. Full article

While the influence of digital technologies on firms’ innovation performance has been examined in the digital transformation literature, the mechanism by which digital transformation affects innovation quality has remained largely unexplored. By analyzing a longitudinal sample of 17,216 China’s A-share listed companies from 2009 to 2021 (excluding real estate and financial firms), we employed a fixed-effects regression model to investigate the impact of digital transformation on strategic risk-taking behavior. The findings indicate that digital transformation significantly enhances innovation quality. Market competition enhances the positive effect of digital transformation on innovation quality. Further analysis reveals that digital transformation has a positive impact on dynamic capability, which in turn mediates the relationship between digital transformation and innovation quality. Furthermore, digital transformation breaks down the barriers to innovation quality by reducing financing costs and financing constraints. These findings have implications for firms’ digital strategy in emerging economies. Full article

Energy efficiency and CO₂ emission reduction are key objectives related to climate change mitigation, sustainable development, and energy resource management. In the Nordic context, energy consumption trends in both the residential and industrial sectors are closely linked to European Union policies, technological innovation, and real estate investments. In recent decades, the development and renovation of the real estate sector has become one of the most important factors determining changes in energy consumption, especially in residential buildings, which remain among the largest energy consumers and polluters. In this context, countries’ efforts to reduce CO₂ emissions and increase energy efficiency are inseparable from the real estate sector’s contribution to these processes, by promoting investments in building modernization and energy-saving technologies. However, the real estate sector remains a complex area where economic interests need to be reconciled with environmental objectives, especially in the context of EU strategies such as the Renovation Wave and the Energy Efficiency Directive. This article examines the links between real estate investment, energy efficiency, and CO₂ emission reduction, based on quantitative analysis, to assess how the development of the real estate sector and EU policy measures affect sustainable development in Northern Europe. This study uses advanced quantitative methods, including a panel regression model, which helps better reveal the long-term dependencies between investment, energy consumption, and emissions dynamics. This article highlights the importance of the real estate sector in implementing sustainability policies and suggests strategic solutions that can help reconcile economic and environmental priorities. Full article

The transition to energy-efficient buildings is a priority of the European EPBD (Energy Performance Building Directive) and requires deep retrofits to reduce consumption and emissions. However, their financial viability remains underexplored. This research assesses the financial feasibility of deep retrofit interventions through innovative business models, focusing on the Managed Energy Services Agreement (MESA), which is considered the most effective for residential buildings. Additionally, we integrate off-site production from the Energiesprong model, which optimizes costs and time through long-term contracts and industrialized retrofit technologies. The analysis targets two investment profiles—owner/tenant and developer/entrepreneur—in Italian urban contexts with different market dynamics. A static analysis evaluates retrofits based on existing costs and technologies, while a dynamic analysis considers future profitability improvements because of cost reductions enabled by off-site production. The results indicate that, under current conditions, residential retrofitting is not financially sustainable without public subsidies. However, cost reductions driven by off-site technologies improve profitability, making large-scale retrofits feasible. Moreover, real estate market characteristics affect financial sustainability: in smaller cities, deeper cost reductions are necessary for retrofit interventions to become viable. Full article

This paper constructs a theoretical framework integrating health risk transmission, remote work constraints, and spatial equilibrium to analyze the impact mechanisms of major public health events on the real estate market. This study finds that pandemics affect market equilibrium through multiple channels, including changes in residents’ utility functions, the reshaping of labor market structures, and adjustments in location choices. The model combines the SIR model from epidemiology with spatial economics to depict the endogenous decision mechanism of health risks. By constructing a two-sector general equilibrium model that includes remote work sectors, this study reveals the impact of technological change on the spatial structure of the real estate market. Based on the mobility preference theory, an asset pricing framework incorporating health risk premiums is established. Comparative static analysis shows that the health risk transmission coefficient influences housing prices through two channels: directly lowering willingness to pay and indirectly affecting the distribution of population density. Dynamic analysis indicates that, under specific parameter conditions, the market exhibits asymptotic stability. Policy simulation results show that the transmission effects of monetary and fiscal policies exhibit significant spatial heterogeneity, requiring policymakers to pay more attention to regional differences. This study not only enriches the analytical tools of real estate economics but also provides theoretical support for relevant policy formulation. Full article

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