Smart Property Valuation via Blockchain-Based Data Platforms

Thesis Abstract

This study investigates the limitations of traditional property valuation in the face of dynamic market information and opaque data sources, and proposes a blockchain-based data platform to enable transparent, tamper-evident, and real-time valuation processes. The aim is to develop and validate a technology-enabled valuation framework that integrates multiple data streams, including public records, transactional data, and sensor-based property metrics, into a unified blockchain environment to improve accuracy, reproducibility, and stakeholder trust. Specific objectives are (1) to design a governance and data-availability model for a decentralized valuation platform; (2) to evaluate the predictive accuracy of blockchain-augmented valuation relative to conventional valuation methods; (3) to identify data quality, interoperability, and regulatory challenges; (4) to assess user acceptance among real estate professionals, investors, and lenders; and (5) to offer policy and practice recommendations for scalable deployment. The methodology adopts a mixed-methods research design. The population comprises property valuations and market transactions within a metropolitan region over the past five years. A purposive sample of 400 residential and 200 commercial property records will be drawn from public cadaster datasets, MLS-like transactional databases, and bank-secured appraisal files, supplemented by 60 in-depth interviews with estate surveyors, valuers, blockchain developers, and regulatory officers. Data collection instruments include (i) a structured valuation dataset capturing property characteristics, transaction prices, timing, and macroeconomic indicators; (ii) a blockchain-smart contract log for provenance and governance metadata; (iii) a semi-structured interview guide for stakeholder perspectives; and (iv) a questionnaire assessing perceived usefulness and ease of use. The study employs content analysis for interview data, and quantitative analysis using multiple regression and machine learning techniques to compare predictive accuracy. The primary analytical framework integrates theoretical models from the Efficient Market Hypothesis and Technology Acceptance Model, augmented with a blockchain-specific governance theory to explain information asymmetry reduction and trust mechanisms. Key expected findings include (1) improved valuation accuracy and reduced mean absolute percentage error when incorporating blockchain-verified data and multi-source feeds, relative to baseline hedonic models; (2) statistically significant gains in out-of-sample predictive performance for residential and commercial property segments; (3) positive correlations between data provenance trust metrics and user acceptance among professional stakeholders; (4) identification of critical data interoperability barriers and regulatory constraints that influence platform adoption; and (5) evidence that smart-contract-enabled automations reduce transaction latency and archival risk in valuation records. Analytical techniques will include OLS and robust regression to quantify predictive improvements, random forest and gradient boosting for feature importance, and difference-in-differences tests to evaluate platform impact over time; thematic analysis will be applied to interview transcripts to extract governance, trust, and usability themes. The study contributes to knowledge by bridging property valuation theory with blockchain-enabled data governance, demonstrating how tamper-evident, interoperable data platforms can enhance valuation transparency and reproducibility. It advances methodological practices by detailing an integrated framework that combines econometric forecasting with blockchain provenance analytics and user-centered evaluation. The implications extend to practice for valuers, lenders, and regulators, offering an empirically validated blueprint for scalable deployment, data standards, and governance policies. The conclusion anticipates that the proposed platform will reduce information asymmetry, improve valuation fairness, and support more efficient capital allocation, while highlighting requirements for regulatory alignment, data interoperability, and ethical safeguards. Recommendations include developing sector-wide data standards, establishing independent audit mechanisms for valuation algorithms, and pursuing pilot implementations across multi-jurisdictional datasets to test scalability and resilience.