The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.
Yescha Nuradisa Ekarachmi Danandjojo, Samira Ramezani, Johan Woltjer, Taede Tillema
⢠Policies both enable and constrain LVC, requiring flexible regulatory alignment. ⢠Limited local fiscal authority weakens LVC use for transport infrastructure funding. ⢠MRT Jakarta shows transit agencies need clear mandates and institutional support. ⢠Intergovernmental collaboration is essential for effective LVC in multi-level systems. ⢠Effective LVC needs risk sharing, incentives, and non-fiscal tools for private actors. Discussions of stakeholder relationships in land value capture (LVC) for transport infrastructure development remain limited, particularly within decentralized systems in the Global South and in multi-level government contexts, where strong government control is present. This paper examines the factors affecting stakeholder relationships and how these relationships influence the implementation of LVC. The case study focuses on Jakartaâs Mass Rapid Transit (MRT) in Indonesia, where LVC is considered a promising financing tool. The findings highlight that in the context of Jakarta, policy and regulations, institutional arrangements, and risk mitigation are the most influential factors. First, while policies and regulations are essential in defining stakeholder responsibilities, they also create rigid boundaries that can limit flexibility for local innovation in exploring LVC instruments. Second, the limited authority of the transit agency indicates the need for more explicit mandates and greater support from governing bodies. Third, public agencies need to take a more proactive role in risk mitigation by developing mutually beneficial partnerships with private entities. Overall, this study bridges theory and practice by placing LVC within a multi-level governance framework that links the governance of transport infrastructure development and land-use management. It shows that successful LVC implementation depends on collaboration among stakeholders from different sectors and requires institutional flexibility and adaptive governance that balance national policy coherence with local discretion. By highlighting these cross-sector and governance dynamics, the study contributes to wider discussions on urban development, transport infrastructure governance, and publicâprivate collaboration, making it relevant to both scholars and practitioners across multiple disciplines.
The tokenization of Real-World Assets (RWAs) via Decentralized Finance (DeFi) protocols promises fractional ownership and continuous liquidity for traditionally illiquid asset classes, yet the market microstructure governing on-chain RWA pricing efficiency and pool liquidity remains under-theorised and empirically unresolved. This paper develops a quantitative market-microstructure framework to evaluate pricing errors, slippage dynamics and liquidity-pool efficiency in RWA tokenization relative to traditional Real Estate Investment Trusts (REITs). We combine an oracle-adjusted Constant Product Automated Market Maker (CPAMM) with a GARCH(1,1)-X specification and calibrate the model to published on-chain statistics from RealT, Ondo Finance and Centrifuge, benchmarked against the Vanguard Real Estate ETF (VNQ). Simulation-based evidence indicates that (i) RWA tokenization lowers the implied cost of capital by 115-140 basis points; (ii) asset-level idiosyncratic volatility induces nonlinear slippage in constant-product pools during stress regimes; and (iii) oracle latency dominates the persistence of pricing deviation (PEâ). We propose an oracle-conditioned hybrid liquidity architecture that significantly mitigates pricing deviation and enhances market efficiency.
Property valuation, a foundational method for governments, financial institutions, and insurers to gauge economic stability, remains hindered by opaque, fragmented data practices. Despite technological advancements like Artificial Inteligence (AI) and Web3, valuation processes rely on siloed, non-standardized data that institutions rarely shareâeven internally. This paper identifies systemic barriers to global transparency and proposes a Proptech framework to resolve this disconnect. Unlike market valuation, which leverages AI and algorithms to predict prices, housing valuation depends on manual audits and confidential metrics. This lack of transparency limits governments' capacity to preempt real estate crises or curb speculative risks. By integrating blockchain-enabled data sharing and AI analytics, a decentralized Proptech platform, sharing a global network, could standardize and democratize valuation data, enabling real-time insights for crisis management and evidence-based policymaking. The study highlights how such innovation could transform urban planning, financial markets, and economic resilience, positioning Proptech as a catalyst for equitable, transparent valuation ecosystems. Received: 7 April 2025 | Revised: 29 July 2025 | Accepted: 14 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Pedro Faria: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing â original draft, Writing â review & editing, Visualization, Supervision, Project administration. Peter Finn: Conceptualization, Validation, Investigation, Resources, Writing â review & editing. Tiago Navarro: Conceptualization, Resources, Writing â review & editing, Visualization, Supervision.
This study builds on the fiscal decentralization and promotion tournament theories. Utilizing 12 years of panel data from 108 cities in the Yangtze River Economic Belt, we measure the urban carbon productivity index through a super-efficient SBM model that incorporates undesired outputs. We then analyze the effect of local land finance strategy interaction on urban carbon productivity and its mechanism using a spatial self-lagging model. Our key findings reveal: (1) Local governments exhibit mimetic spatial strategy interactions in land finance behavior, both from a geographical distance perspective and when combining economic development levels with geographical distance factors; (2) the interactive behavior of local land finance strategy has a significant inhibitory effect on urban carbon productivity, thereby leading to a loss of urban carbon productivity; (3) the local land finance strategy interaction causes a reduction in urban carbon productivity by changing the cross-city foreign direct investment strategy interaction, environmental regulation strategy interaction, and industrial structure strategy interaction. To achieve these goals, China should foster healthy competition among cities regarding land finance and promote âthree-way synergyâ between opening up, environmental protection, and industrial upgrading. These coordinated efforts aim to boost urban carbon productivity in the Yangtze Basin while offering developing countries a fresh approach to watershed governance focused on carbon reduction goals.
This study utilizes version 6 of the regression analysis of time series (RATS) software package to implement the estimation of the bivariate diagonal generalized autoregressive conditional heteroscedasticity (GARCH) model combined with a composite asset selection approach including two hybrid performance measures to solve âthe trade-off problem between return and riskâ and âthe inconsistent results from different performance measuresâ in the problem of asset allocation within a group of minimum variance portfolios during the pre-COVID-19 and COVID-19 periods. Empirical results show that the optimal portfolios obtained from this approach and the assets added to a portfolio to achieve better performance differ between the pre-COVID-19 and COVID-19 periods. For instance, the optimal portfolios are the Chinese yuan-Ethereum and Bitcoin-Ethereum for the pre-COVID-19 period, but the WTI-Ethereum for the COVID-19 period. To achieve better performance, we added Ethereum to our portfolio during the pre-COVID-19 period, while WTI and Bitcoin were added during the COVID-19 period. Thus, the COVID-19 pandemic had a significant impact on the performance of asset allocation in the three markets. The proposed approaches in this study can be embedded in a computer as an asset allocation algorithm of Robo-advisers.
Urban local bodies across India struggle to mobilize adequate revenues despite the informal sector's significant economic contribution, employing 80% of the workforce while contributing minimally to municipal coffers. This research examines how Indian cities can better harness this untapped fiscal resource through an integrated analysis of municipal finance data and urban case studies. The investigation reveals systemic obstacles including outdated property tax systems, complex licensing procedures, excessive transfer dependency, and political interference that collectively constrain revenue potential. Findings demonstrate that strategic interventions - particularly digital governance tools, streamlined regulations, and incentive-based fiscal policies - can dramatically improve collection efficiency while safeguarding vulnerable informal workers. Evidence from pioneering cities highlights successful approaches such as GIS-based property mapping, single-window licensing systems, and performance-linked transfers that have boosted revenues by 20-25%. The study develops a comprehensive policy framework that balances revenue generation with inclusive development, offering scalable solutions for municipal finance reform. These insights provide valuable guidance for urban governance in developing economies facing similar challenges of informality and fiscal decentralization.
A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated.
We analyze the vulnerability of decentralized autonomous organizations (DAOs) to speculative exploitation via their redemption mechanisms. Studying a game-theoretic model of repeated auctions for governance shares with speculators, we characterize the conditions under which -- in equilibrium -- an exploitative exit is guaranteed to occur, occurs in expectation, or never occurs. We evaluate four redemption mechanisms and extend our model to include atomic exits, time delays, and DAO spending strategies. Our results highlight an inherent tension in DAO design: mechanisms intended to protect members from majority attacks can inadvertently create opportunities for costly speculative exploitation. We highlight governance mechanisms that can be used to prevent speculation.
BADADHE SHIVAJI, VENKATESH IYER, SAMI SHAIKH, ARUN GHANDAT
The real estate sector grapples with the persistent issues of inconsistent property appraisals, a lack of transparency in valuation methodologies, and a reliance on outdated pricing frameworks. This project introduces an innovative solution: a distributed ledger-based real estate valuation system. This system leverages self-executing digital agreements and spatial data analytics to deliver dynamic, transparent, and data-driven property assessments. By incorporating OpenStreetMap APIs, the system automates the acquisition of real-time data pertaining to proximate community resources, such as educational institutions, healthcare facilities, recreational spaces, and public transit networks. A weighted valuation algorithm processes this information to derive a contextual relevance score, quantifying the spatial influence and impact of these factors on property values. The computed scores, along with pertinent property details, are securely stored and managed on the Ethereum network via smart contracts, ensuring data integrity, immutability, and enhanced stakeholder trust. Furthermore, the system automates the entire valuation workflow through a Python-based backend, which serves as an intermediary between distributed ledger interactions and spatial data acquisition. Designed for scalability, transparency, and operational efficiency, this project aims to modernize conventional property valuation practices by addressing inherent inefficiencies and empowering stakeholders with access to reliable, up-to-the-minute valuation data. By redefining the paradigm of property value assessment, this system offers a transformative approach to real estate pricing, harmonizing cutting-edge distributed ledger technology with advanced spatial data analysis.
Spatial economics deals with the mutual socioeconomic influence of the geographical boundary of an administrative body on the neighboring entities- municipalities, districts, states, and countries. Researchers have conducted spatial analyses to solve a variety of economic problems like labor dynamics, wage equilibrium, capital formation, and demographic agglomeration/dispersion, among others. However, the application of spatial economics in public finance, despite being a pressing priority, has not been extensively explored. With India being the largest democracy in the world and having a decentralized state budget mechanism in place, focused attention is required to measure the contiguity effect in state finance. I find strong spatial dependence by implementing a fixed effect panel regression design followed by a spatial regression approach to assess fiscal health in Indian states over 22 years. The analysis reveals spatial dependence on both the income and expenditure sides of state budgetary fiscal and primary deficits. I also analyze the dynamics of the capital budget revenue and its idiosyncrasies in determining the spatial roles that govern state deficits. The empirical results underscore that fiscal policymaking through budget preparation for an Indian state must account for major fiscal components of bordering states to achieve targeted fiscal objectives.
In this study, we propose a valuation methodology for Non-Fungible Tokens (NFTs), focusing on the profile picture (PFP) NFT category represented by the Bored Ape Yacht Club (BAYC). To identify the attributes that influence the value of individual BAYC NFTs, we develop a hedonic pricing model that uses the NFTâs value as the dependent variable and its properties as independent variables. We apply Term Frequency-Inverse Document Frequency (TF-IDF) to quantify attributes of NFTs. Three hedonic modelsâlinear, quadratic, and full quadraticâare proposed. For the full quadratic model, we introduce a systematic procedure to select first-order, second-order, and interaction terms in the model. To evaluate the performance of the proposed models, we carried out comparative computational experiments. We collected actual BAYC transaction data and split it into a training set (70%) and a validation set (30%). For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. The machine learning models perform well on the training set, however, this was largely due to overfitting. In contrast, the proposed hedonic models maintained consistent performance with minimal degradation from the training to the validation set. Among them, the full quadratic model demonstrates the highest explanatory power on the validation set in terms of adjusted R² and other evaluation metrics.
Blockchain-based tokenization is transforming the real estate sector, presenting a compelling alternative to the traditional model of Real Estate Investment Trusts (REITs). As the industry shifts from financialization to decentralization, driven by technological advancements, these two models offer different approaches to democratizing real estate investment.REITs have been a foundational aspect of real estate financialization, enabling individual investors to participate in large-scale real estate ventures through fractional ownership of diversified property portfolios. This has broadened the investor base and improved market liquidity. However, the emergence of blockchain technology and decentralized finance (DeFi) introduces a new paradigm: real estate ownership can now be fractionalized into digital tokens. This enhances liquidity, transparency, and accessibility through global 24/7 trading platforms. While REITs have made significant strides in expanding access to real estate investment, blockchain-based tokenization can further enhance these achievements by lowering entry barriers, reducing transaction costs, and decentralizing market operations. Nevertheless, the adoption of blockchain technology in real estate also comes with challenges, including regulatory uncertainties, technological risks, and the need for robust governance frameworks. As the lines between finance and technology continue to blur, it is essential to adapt regulatory frameworks and investment strategies to navigate this evolving landscape. The critical review highlights the future implications of these trends, emphasizing the importance of continued research and regulatory innovation to fully realize the potential of decentralized real estate markets. This is particularly relevant in addressing issues of housing inequality and affordability, as housing serves not only as an investment vehicle but also as a fundamental shelter for people.
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Advanced Research in Systems and Signal Processing
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
The block-building process on the Ethereum network has changed significantly with an upgrade of its consensus protocol. Network participants access blocks through block building auctions at a decentralized financial market, termed builder market, where builders vie for the right to build blocks and earn Maximal Extractable Value (MEV) rewards. This paper employs empirical game-theoretic analysis to examine buildersâ strategic bidding incentives in the Ethereum block building auctions, termed MEV-Boost auctions. We study various scenarios with different auction game settings and evaluate how critical elements such as network connectivity and access to MEV opportunities impact buildersâ strategic bidding incentives. Through our analyses, we highlight the challenge of creating a decentralized yet competitive builder market.
Huned Materwala, Shraddha M. Naik, Ali S. Taha, Tala Abdulrahman Abed ¡ 5 authors
Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.
This research study offers a comprehensive overview of current advancements in financial practices in the United States. This research will examine recent shifts in American financial habits and offer stakeholders guidance on how to effectively manage the evolving financial landscape. A thorough assessment of prior literature reviews and empirical studies on digital finance in the US is part of the research methodology.The literature review focuses on how developments in financial technology (FinTech), regulatory changes, a growing emphasis on sustainability, and shifting consumer behavior have significantly altered the financial sector.. The influence of regulatory barriers, ESG integration, evolving consumer behavior, and the complex interactions affecting US financial practices are the main topics of discussion. The results underscore the significance of digital transformation, regulatory impediments and campaigns, consumer inclinations, the advantages and challenges of decentralized financing (DeFi), and cybersecurity and privacy issues. Recommendations are provided based on the results to enhance regulatory flexibility, raise financial literacy and awareness, fund cybersecurity infrastructure, encourage cooperation and information exchange, welcome responsible innovation, and track and react to market dynamics. By putting these recommendations into practice, stakeholders can better navigate the complexity of digital banking in the US and foster innovation, inclusion, and trust in the digital financial ecosystem while averting the dangers and difficulties that come with it.
Ethereum, as a representative of Web3, adopts a novel framework called Proposer Builder Separation (PBS) to prevent the centralization of block profits in the hands of institutional Ethereum stakers. Introducing builders to generate blocks based on public transactions, PBS aims to ensure that block profits are distributed among all stakers. Through the auction among builders, only one will win the block in each slot. Ideally, the equilibrium strategy of builders under public information would lead them to bid all block profits. However, builders are now capable of extracting profits from private order flows. In this paper, we explore the effect of PBS with private order flows. Specifically, we propose the asymmetry auction model of MEV-Boost auction. Moreover, we conduct empirical study on Ethereum blocks from January 2023 to May 2024. Our analysis indicates that private order flows contribute to 54.59% of the block value, indicating that different builders will build blocks with different valuations. Interestingly, we find that builders with more private order flows (i.e., higher block valuations) are more likely to win the block, while retain larger proportion of profits. In return, such builders will further attract more private order flows, resulting in a monopolistic market gradually. Our findings reveal that PBS in current stage is unable to balance the profit distribution, which just transits the centralization of block profits from institutional stakers to the monopolistic builder.
This study explores the emerging virtual property market within the digital world, with a focus on identifying the key factors influencing property prices, market activity, and sales volume. Using a dataset of 2,000 virtual property transactions, the research provides a comprehensive analysis of market dynamics in this new frontier of digital real estate. The findings reveal significant volatility in transaction activity, with a peak of 1,222 transactions in January 2022 followed by a sharp decline to 539 in February 2022 and just 24 in March 2022, indicative of a nascent and speculative market. The analysis identifies land price as the most significant determinant of virtual property values, showing a near-perfect correlation of 0.992 with sales prices. This highlights the critical role of location and land value, similar to traditional real estate markets. Additionally, the study finds that properties attracting more bids tend to sell at higher prices, with a moderate correlation of 0.380 between bids count and sales price, reflecting the impact of competitive bidding in driving up values. However, the market is relatively illiquid, with a mean sales count of just 1.79, indicating that most properties are held as long-term investments rather than frequently traded assets. Interestingly, the research also uncovers a weak negative correlation of -0.051 between sales price and the underlying cryptocurrency, MANA, suggesting that the value of virtual properties may be increasingly decoupled from cryptocurrency volatility as the market matures. These insights provide valuable guidance for investors, developers, and policymakers navigating the evolving landscape of virtual real estate. The study concludes with a discussion of the implications for future market stability and potential areas for further research.
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Manoel Fernando Alonso Gadi, Maximilian Schmidt, Noah Ruemmele, MiguelâĂngel Sicilia
Stock market indices are pivotal tools for establishing market benchmarks, enabling investors to navigate risk and volatility while capitalizing on the stock market's prospects through index funds. For participants in decentralized finance (DeFi), the formulation of a token index emerges as a vital resource. Nevertheless, this endeavor is complex, encompassing challenges such as transaction fees and the variable availability of tokens, attributed to their brief history or limited liquidity. This research introduces an index tailored for the Ethereum ecosystem, the leading smart contract platform, and conducts a comparative analysis of capitalization-weighted (CW) and equal-weighted (EW) index performances. The article delineates exhaustive criteria for token eligibility, intending to serve as a comprehensive guide for fellow researchers. The results indicate a consistent superior performance of CW indices over EW indices in terms of return and risk metrics, with a 30-constituent CW index outshining its counterparts with varied constituent numbers. The recommended CW30 index demonstrates substantial advantages in comparison to established benchmarks, including prominent indices like DeFi Pulse Index (DPI) and CRypto IndeX (CRIX). Additionally, the article explores the practicality of implementing the CW30 in Layer 2 networks of the Ethereum Ecosystem, advocating for the Arbitrum infrastructure as the optimal choice for the decentralized crypto index protocol herein referred to as the Ethereum Ecosystem Index (EEI). The study's insights aspire to enrich the DeFi ecosystem, offering a nuanced understanding of network selection and a strategic framework for implementation. This research significantly enhances the existing literature on index construction and performance within the Ethereum ecosystem. To our knowledge, it represents a pioneering comprehensive analysis of an index that accurately mirrors the Ethereum market, advancing our comprehension of its intricacies and wider ramifications. Moreover, this study stands as one of the initial thorough examinations of index construction methodologies within the nascent asset class of crypto. The insights gleaned provide a pragmatic approach to index construction and introduce an index poised to serve as a benchmark for index products. In illuminating the unique facets of the Ethereum ecosystem, this research makes a substantial contribution to the current discourse on crypto, offering valuable perspectives for investors, market stakeholders, and the ongoing exploration of digital assets.