ABSTRACT Real Estate Tokenisation started with the introduction of blockchain technology. It is changing the world’s way of investing in assets by providing proportional ownership, improving liquidity and basically giving retail investors access to HNI investment opportunities they didn't have before. This technology is getting a lot of global attention but institutional adoption is largely dependent on the environment that regulatory bodies set for digital assets and tokenized securities. This study help to present the role of regulation in institutional adoption of real estate tokenization through a comparative analysis of three main jurisdictions India, IFSC GIFT City and the United Arab Emirates (UAE). Using secondary data the study analysed regulatory documents, policy reports and academic literature through a qualitative comparative policy analysis and thematic content analysis. The research looks at the impact of regulatory certainty, recognition through laws, investor protection, licensing, digital marketplace infrastructure and frameworks for foreign investments on institutional confidence. The finding show that institutions are more likely to participate where regulatory certainty, licensing frameworks and innovation-friendly policies can coexist. India has shown increasing regulatory engagement with digital asset innovation with IFSC GIFT City has given a more progressive regulatory environment for international financial activities whereas the UAE has supported itself by dedicated virtual asset regulations and innovation based policies and has established itself as a leading jurisdiction for institutional tokenization initiatives. Proposed by this study is the Institutional Regulatory Readiness Framework (IRRF) which is an six dimensional conceptual framework for measuring the institutional readiness towards real estate tokenization in various jurisdictions. The paper proposes a Regulatory Readiness Framework that combines legal, technological and institutional dimensions to provide knowledge of jurisdictions for large-scale real estate tokenization. By comparing three regulatory ecosystems, the study offers practical advice for policymakers, regulators, financial institutions and market participants who are seeking to improve institutional adoption. Index Terms: Real Estate Tokenization, Institutional Adoption, Blockchain, Regulation, IFSC GIFT City, United Arab Emirates, India, Real-World Assets (RWA), Digital Assets, Regulatory Readiness.
Doan B. L. Nguyen, Pham Khanh Nam, Nguyen Thi Hong Thu
This study examines factors considered in investor decisions to invest in physical and tokenized real estate in Vietnam using discrete choice modeling on data from 413 participants in Ho Chi Minh City. Results show that legality, transparency, transaction fees, and expected returns are key determinants of investment consideration. Older and higher-income investors exhibit lower preference for tokenized assets, while female, more educated, and blockchain-familiar investors show greater adoption tendencies. The findings highlight how legality and institutional quality shape emerging digital asset markets, underscoring the need for legal clarity, transparent data, and targeted education to foster tokenized real estate development.
In Germany, real estate is commonly referred to as ‘Betongold‘ (English: ‘concrete gold’). This term reflects the fact that real estate is seen as a store of value and inflation hedge, similar to the commodity gold. These attributes have led to the transformation of real estate from a utility object into a capital investment and, in some cases, into a speculative asset. As a consequence of this transformation, macroeconomic developments on the capital market have significant impact on the real estate market (e. g. the past phase of low interest rates led to a historic real estate boom in Germany). The recent rise in interest rates is changing the preferences of private and institutional investors with regard to investment opportunities. In this context, the asset class real estate is competing with other asset classes. In this paper the value storage capacity of real estate and its transformation into a financial asset is explored. In particular, the effects of monetary policy on the real estate markets and valuation of real estate are considered. Furthermore, real estate is compared with the asset classes gold and bitcoin, which are mainly used for their capability to store value and preserve purchasing power in inflationary monetary systems. The aim is to assess the suitability of these asset classes as a store of value and inflation protection based on characteristics such as durability, preciousness, ease of storage, stability of value, rarity/scarcity and liquidity. The specific characteristics (and advantages and disadvantages) of the individual asset classes are analysed and compared.
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 Jakarta’s 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 public–private 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 (PEₜ). We propose an oracle-conditioned hybrid liquidity architecture that significantly mitigates pricing deviation and enhances market efficiency.
The aim of this bachelor’s thesis is to clarify the key similarities and differences between physical and virtual real estate markets, focusing on marketplaces, transaction processes, market participants and value formation. The study is conducted as a literature review comparing the established, nationally regulated practices of traditional real estate markets with the global and still developing structure of blockchain-based metaverse markets. The findings show that although both markets share fundamental value drivers such as location, scarcity and income potential, the mechanisms behind these factors differ significantly. Institutional investors play a stabilizing role in physical markets, whereas metaverse markets remain fragmented and speculative. The results complement existing research and provide practical insights for professionals in both real estate and Web3 environments.
Real estate in smart cities and the metaverse is being reshaped by NFTs, tokenization, blockchain, AI valuation models, digital twins, and cybersecurity. Tokenization enables fractional ownership, access, and liquidity, while NFTs provide immutable rights that reduce fraud and enhance transparency. AI valuation uses machine learning, predictive analytics, and computer vision to improve pricing and integrate with blockchain for auditability. Digital twins link physical and virtual assets, supporting predictive maintenance, energy efficiency, and immersive walkthroughs. Yet adoption faces risks from smart contract exploits, market manipulation, and quantum computing, requiring quantum-resistant cryptography and privacy tools like zero-knowledge proofs. Case studies highlight Dubai's NFT registry, U.S. pilots, Europe's blockchain registries, and Asia's metaverse platforms. Economically, the market is projected to grow from USD 2.33 billion in 2025 to USD 67.40 billion by 2034, underscoring the need for harmonized laws, ethical AI, and sustainable frameworks.
Abdullah Jameel Abualhamayl, Mohanad A. Almalki, Firas Al-Doghman, Abdulmajeed A. Alyoubi · 5 authors
Traditional real estate markets are often dominated by institutional investors, which may limit access for small investors due to high entry costs and limited opportunities for fractional participation. Such market dynamics create slow, expensive, and inflexible transaction processes that limit liquidity and prevent broader participation in property ownership. To address these challenges, we propose a blockchain-based framework using fractional non-fungible tokens (F-NFTs) to digitally certify and manage shared real estate ownership. Our approach involves improvements to the ERC-721 token standard to support fractional ownership, development of smart contract algorithms for property registration and transfer, implementation of a prototype, and deployment on the Ethereum Sepolia testnet. The performance evaluation reveals that the complete certification process costs around $47.16 USD and can be completed in roughly 264 seconds, which reflects notable enhancements in transaction efficiency compared to traditional systems. By enabling costeffective and transparent property certification, this framework enhances transparency, democratizes property investment, and broadens market accessibility.
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 share—even 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 – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Peter Finn: Conceptualization, Validation, Investigation, Resources, Writing – review & editing. Tiago Navarro: Conceptualization, Resources, Writing – 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 “three-way synergy” 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.
Few studies examine whether the gap-filling effect, whereby the maturity structure of local government debt shapes corporate debt maturity, also holds in emerging, bank-dominated economies. Using data on Chinese local government financing vehicle (LGFV) bonds from 2009 to 2023, this study examines how the maturity structure of local government debt influences that of corporate debt. The results reveal a pronounced gap-filling effect, whereby longer local government debt maturities systematically crowd out long-term credit resources, raise corporate financing costs, and prompt firms to shorten their debt maturities. This effect is more salient during economic downturns and when local government debt levels are elevated. Heterogeneity analysis shows substantial variation in the gap-filling effect across regions, industries, and firm characteristics. In addition, firms with shorter debt maturities are found to face higher financial risks. This study contributes by providing policy evidence for fiscally decentralized emerging markets: local government maturity choices transmit to firms’ financing structure, implying that local debt management is instrumental for financial stability.
Given the Chinese government’s aim of high-quality development and under the double constraints of incremental land and limited local revenue, Chinese local governments' path-dependence on land finance is confronting significant challenges. Guangdong Province has implemented a series of urban renewal programs called Three Olds Redevelopment (TOR) projects, providing a potential alternative for financially sustainable development. Using panel data covering 21 prefecture-level cities in Guangdong during 2009–2018, this study constructed a quantitative analysis of the fiscal revenue effect of TOR. The results illustrate that TOR reduces fiscal revenues, including tax revenue and land-transfer fees, in the short term but increases them in the long term. Higher fiscal decentralization tends to amplify the fiscal effects, while market power and dependence on land finance may weaken the impact. As for the mechanism, TOR projects can increase long-term fiscal revenue because of social capital involvement and functional upgrading effects. We suggest urban renewal and policy innovation to transcend land finance path-dependence and realize sustainable local finance.
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 ‘the trade-off problem between return and risk’ and ‘the inconsistent results from different performance measures’ 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.
N Meghana, Neha N Rao, Nishitha Suvarna, Pranathi Bhat
Blockchain technology, a distributed and unalterable ledger system, can potentially change the real estate sector, and especially property title recording. The paper discusses the benefits and restrictions of blockchain title recording systems relative to the present U.S. title registry system, with reference to Nashville (Davidson County), Tennessee. With the benefits of enhanced security, efficiency, and fraud protection, challenges to implementation in the form of high cost, legal compliance, and scalability are yet to be overcome. Comparative reviews of international blockchain application in real estate, using examples from India and Russia, describe both successes and limitations of the technology. Finally, permissioned and private DLTs are discussed, noting that though more efficient, they might not represent a radical overhaul of government-sponsored registries. The results indicate that while blockchain-based title recording systems have direct advantages, they are not yet economically viable for mass implementation.
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.
The chapter delves into the emergence of digital assets as a distinct and transformative asset class within the financial landscape. This chapter explores the foundational elements that have contributed to the recognition of digital assets, such as cryptocurrencies, tokenized assets, and non-fungible tokens (NFTs), as legitimate financial instruments. The chapter begins by examining the historical context and key milestones that have marked the evolution of digital assets, from the creation of Bitcoin to the proliferation of various blockchain-based assets. It discusses the unique characteristics that differentiate digital assets from traditional asset classes, including their decentralized nature, programmability, and potential for fractional ownership. Furthermore, the chapter analyzes the factors driving the adoption and integration of digital assets into mainstream finance, such as technological advancements, regulatory developments, and growing institutional interest.
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.