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https://hdl.handle.net/10356/166042
Title: | Asset tokenization in real estate | Authors: | Tan, Gordon Leng Hwee | Keywords: | Engineering::Computer science and engineering::Software::Software engineering | Issue Date: | 2023 | Publisher: | Nanyang Technological University | Source: | Tan, G. L. H. (2023). Asset tokenization in real estate. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166042 | Abstract: | Over the years, Real Estate has proven to generate profits for investors and is a popular source of investment. Despite the proven profits, participation in real estate investment has usually been reserved for wealthy individuals as direct investment in real estate requires a large flow of capital funds that are usually not readily accessible for most individuals. REITs have been the solution to this problem by offering fractional shares as a way to increase accessibility for all. However, the current system in place involve many intermediaries and opaque processes which lowers profit for investors. Asset tokenization has been gaining traction in recent years as it allows traditional assets to be tokenized on the blockchain opening up to new functionalities and workflows that were previously not possible. As such, this project aims to explore asset tokenization in the field of real estate by implementing a decentralised application that facilitates tokenization of real estate and supports real estate related activities through the use of blockchain. This project displays how asset tokenization is able to lower barriers of entry, eliminate intermediaries and provide more liquidity when investing in real estate thus creating an improved investment landscape for prospective investors in real estate. | URI: | https://hdl.handle.net/10356/166042 | Schools: | School of Computer Science and Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
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FYP_Final.pdf Restricted Access | 21.98 MB | Adobe PDF | View/Open |
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