Search Results (82)

Land Value Capture (LVC) is increasingly being emphasized as a key mechanism for financing mass transit systems, promoted as a sustainability-oriented policy tool amid tightening public budgets. China has adopted a development-led approach to value capture through the “Rail plus Property (R + P)” model, drawing inspiration from the Hong Kong experience. The Shenzhen Metro’s “R + P” strategy has been widely acclaimed as the key to its reputation as “the only profitable transit company in mainland China without subsidies.” This paper questions this assumption and argues that the Shenzhen model is neither sustainable nor replicable, as its past performance depended on two exceptional conditions: an ascending phase of a real-estate cycle and unique institutional concessions from the central state. To substantiate this argument, we contrast Shenzhen’s value capture strategy with that of Nanjing—a provincial capital operating under routine institutional conditions, with governance and spatial structures broadly reflecting the prevailing urban development model in China. Using a comparative framework structured around three key dimensions of LVC—urban governance, risk management, and the transit company’s shift toward real estate—this paper reveals how distinct urban political economies give rise to contrasting value capture approaches: one expansionary, prioritizing short-term profit and rapid scale-up while downplaying risk management (Shenzhen); the other conservative, shaped by institutional constraints and characterized by reactive, incremental adjustments (Nanjing). These findings suggest that while LVC instruments offer valuable potential as a funding source for public transit, their long-term viability depends on early institutional embedding that aligns spatial, fiscal, and political interests, alongside well-developed project planning and capacity support in real estate expertise. Full article

As a classical statistical method, multiple regression is widely used for forecasting tasks in power, medicine, finance, and other fields. The rise of machine learning has led to the adoption of neural networks, particularly Long Short-Term Memory (LSTM) models, for handling complex forecasting problems, owing to their strong ability to capture temporal dependencies in sequential data. Nevertheless, the performance of LSTM models is highly sensitive to hyperparameter configuration. Traditional manual tuning methods suffer from inefficiency, excessive reliance on expert experience, and poor generalization. Aiming to address the challenges of complex hyperparameter spaces and the limitations of manual adjustment, an enhanced sparrow search algorithm (ISSA) with adaptive parameter configuration was developed for LSTM-based multivariate regression frameworks, where systematic optimization of hidden layer dimensionality, learning rate scheduling, and iterative training thresholds enhances its model generalization capability. In terms of SSA improvement, first, the population is initialized by the reverse learning strategy to increase the diversity of the population. Second, the mechanism for updating the positions of producer sparrows is improved, and different update formulas are selected based on the sizes of random numbers to avoid convergence to the origin and improve search flexibility. Then, the step factor is dynamically adjusted to improve the accuracy of the solution. To improve the algorithm’s global search capability and escape local optima, the sparrow search algorithm’s position update mechanism integrates Lévy flight for detection and early warning. Experimental evaluations using benchmark functions from the CEC2005 test set demonstrated that the ISSA outperforms PSO, the SSA, and other algorithms in optimization performance. Further validation with power load and real estate datasets revealed that the ISSA-LSTM model achieves superior prediction accuracy compared to existing approaches, achieving an RMSE of 83.102 and an $R^2$ of 0.550 during electric load forecasting and an RMSE of 18.822 and an $R^2$ of 0.522 during real estate price prediction. Future research will explore the integration of the ISSA with alternative neural architectures such as GRUs and Transformers to assess its flexibility and effectiveness across different sequence modeling paradigms. 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

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. 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

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income workers, which likely contributed to shorter commutes. On the other hand, the positive relationship between housing prices and commuting times highlights the need for affordable housing near employment centers to reduce commuting times. These findings underscore the complex interactions between each employment center’s characteristics and workers’ socioeconomic factors in shaping commuting behavior. Full article

The dynamic fluctuations in the real estate market significantly impact the development of the national economy. Investigating the spatiotemporal characteristics of housing prices can assist the government in formulating rational regulatory policies. Taking Zhengzhou City as the research subject, this study analyzed the spatiotemporal characteristics of housing prices based on housing price data and POI (Point of Interest) data from January 2022 to March 2024, utilizing a spatial scale of 500 m × 500 m grids. A hedonic price model and a geographically weighted regression (GWR) model were constructed to examine the mechanisms of 12 influencing factors on housing prices. The results indicate that housing prices in the eastern part of Zhengzhou are higher than those in the west, with an overall declining trend observed in Zhengzhou’s housing prices. Among the influencing factors, the age of the house exerts the greatest impact on housing prices, while finance has the least influence. The GWR model demonstrates superior fitting performance compared to the hedonic price model. The mechanisms of the influencing factors exhibit spatial heterogeneity. This study provides valuable insights for relevant government departments in Zhengzhou City, contributing to the optimization of urban planning and the regulation of the real estate market. 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

As the scale of real estate project financing (PF) of large construction companies in South Korea increase, discontinued construction projects and PF default rates in the financial world are also rapidly increasing. Furthermore, the percentage of PF bad debts in South Korea today has increased as much as about three times compared to that in 2023. The increase in bad debt rates results mainly from the moderate supply of new funds, delays in non-performing PF arrangements, and so forth. To address this problem, it is necessary to restart the development of non-performing real estate PF development sites through successful bidding and to review the valuation basis for development projects. Therefore, this study aims to derive internal and external characteristics of non-performing real estate PF development sites in South Korea and examine the effects of specific factors on their successful bidding. In addition, significant variables are selected based on the analysis result; the analytic hierarchy process (AHP) analysis is performed to establish a new valuation system for real estate development projects. After careful consideration of various literature reviews and expert opinions, an analysis model is established to ensure the suitability of the study model with the error range minimized. As AHP was performed based on the newly established hierarchy, the higher ranks of each valuation factor were derived based on priority and importance, and the valuation basis was rearranged accordingly. The conclusion was derived through a comprehensive review of the results of the two analyses above. It was verified that certain factors—business feasibility assessment, work performance assessment, and basic evaluation—played key roles in the success and successful bidding of real estate projects. This point suggests that strict project management and performance standards must be set based on the economic achievements of financial validity indexes and business performance capabilities. Stable profit distribution and business transparency are also viewed as vital factors for the success of projects. Therefore, this study reestablishes the valuation basis for development projects in South Korea and presents policy suggestions on location propriety and business advancement based on the analysis of non-performing PF bid decision factors and the development project valuation basis. 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

Since 2016, strict regulatory policies have limited the size and financing channels of China’s urban real estate market. To adapt to the new situation, real estate developers need to adjust their development strategies and adopt more precise investment methods. In this respect, cost reduction, an accurate market positioning, and conducting detailed market research are particularly crucial. Given that the uneven development of the urban housing market has exacerbated urban segregation, urban residential space was subdivided into multiple potential submarkets. This study focuses on the land acquisition and the development of commercial residential projects in Hangzhou, China, from 2003 to 2022. Using the latent class analysis method based on the discrete choice model, the potential submarkets of Hangzhou’s housing development market are identified, and the project positioning and land selection preferences of developers are assessed. The results show that Hangzhou’s residential projects can be divided into five potential market categories: high-rise basic demand dwellings, high-end-improvement-type dwellings, luxury low-density dwellings, primary-improvement-type dwellings, and large-scale mixed-density dwellings. The importance of different land elements for developers when developing various types of projects is evaluated by calculating the willingness-to-pay coefficient. The findings of this study provide a comprehensive perspective for the government, real estate developers, and property owners to better understand the development dynamics on the supply side of the real estate market. Full article

This study investigates the influence of leverage and managerial overconfidence on the decision-making process regarding real estate project financing (PF) guarantees in South Korea. Utilizing a dataset of 570 firm-year observations from construction companies listed on the South Korean stock market from 2007 to 2022, the analysis reveals that more highly leveraged companies are more likely to engage in real estate PF investments. These investments are preferred by financially strained constructors because they can use PF investments to record guarantees as contingent liabilities, avoiding the recognition of additional debt on their financial statements. This study further finds that the positive correlation between leverage and real estate PF investments strengthens with increasing managerial overconfidence, indicating that overconfident managers are prone to overestimate future project revenues and the positive impacts of potential business developments, thereby making riskier investment decisions under unfavorable borrowing conditions. Full article

This study examines whether and through which channels ESG ratings influence key metrics in the real estate industry. Focusing on Switzerland as a case study and concentrating on commercial real estate investors and their income properties, we utilize unique datasets and employ an OLS post-LASSO estimation procedure to identify and quantify the associations between ESG ratings and four key metrics: appraisal-based and transaction-based discount rates, rental incomes, and vacancy rates. Our results demonstrate that ESG ratings maintain a significant association with all four key metrics even after undergoing robustness checks. When dissecting the total ESG rating into its components, the environmental rating stands out as the most significant. While largely dependent on the specific metric being analyzed, the association of social and governance ratings tends to be less pronounced. Delving deeper into individual ESG rating levels, our findings suggest potential signaling effects, as properties with higher ESG ratings demonstrate heightened sensitivity to both types of discount rates and vacancy rates. Overall, our findings deepen the understanding of the association between ESG ratings and real estate markets, illuminating the intersection of sustainability and financial relevance. Full article

As a crucial form of public transportation, subways are becoming essential infrastructure that cities in China increasingly prioritize for development. However, there is a lack of effective risk assessment methods for subway station and line siting. To address this gap, this paper uses the subway system in Kunming, China, as a case study, establishing a subway site risk evaluation framework (SIRE-BAF) that integrates three dimensions: balance (B), attractiveness (A), and financing mode (F). An extended NP-RV model is proposed to assess the balance (or imbalance) characteristics of subway stations based on sub-dimensions of traffic supply, land use, and urban vitality. Findings indicate that (1) the balance (or imbalance) of subway stations is distinctly distributed along the line and simultaneously exhibits a spatial pattern radiating from the urban core to the periphery. (2) Stations with high urban vitality and minimal imbalance are highly attractive and tend to face “undersupply” during operation, whereas stations with lower attractiveness are more prone to “oversupply”. A higher level of BAF coupling coordination suggests a more suitable subway site selection and lower investment risk, while lower coupling coordination indicates increased risk. (3) Excessive reliance on the “subway + real estate” model, without considering urban vitality, may lead to high vacancy rates and reduced efficiency in subway service. This paper further assesses the site selection risks for the proposed Kunming subway. This study contributes to risk assessments of existing subway operations and maintenance in Chinese cities, enhances planning rationality and site selection for proposed subways, and holds potential applicability for other cities. Full article