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Digital Transactions in Real Estate Marketing

Received: 14 September 2020    Accepted: 22 October 2020    Published: 4 November 2020
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Abstract

Identity theft and tax fraud have been leading white-collar crimes for over a decade. Emerging trends have seen the rise in white-collar crime to now include deed theft. This type of theft marries identity theft and mortgage fraud, and it is robbing the hopes of many people who aspire to own a home. This whitepaper identifies how deed theft takes place through targeting homes with deceased owners, house flipping fraud and fake forensic auditors. It explores this evolved white-collar crime and its dangerous effects, while also revealing the best ways to mitigate the risk of deed theft. Furthermore, this paper provides the background of the crime and how the crime correlates with identify theft. Gaining control of this issue is challenging due to the existing infrastructure that was introduced in 1920. This leaves few country clerks with any response to the issue surrounding identity theft and mortgage fraud. To mitigate this challenge, devices have been developed to track the behaviour of people to ensure that their residences are not ‘stolen’. Although these solutions are available, few individuals learn about deed theft so that the solutions can be used to protect themselves against it. This whitepaper presents DeedLock as the most effective solution to address the looming challenge of property theft.

Published in Journal of World Economic Research (Volume 9, Issue 2)
DOI 10.11648/j.jwer.20200902.15
Page(s) 110-114
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Deed Theft, DeedLock, Real Estate, Blockchain

References
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[2] Moosavi, V. (2017). Urban data streams and machine learning: a case of swiss real estate market. arXiv preprint arXiv: 1704.04979.
[3] Fan, K., Sun, S., Yan, Z., Pan, Q., Li, H., & Yang, Y. (2019). A blockchain-based clock synchronization scheme in IoT. Future Generation Computer Systems, 101, 524-533.
[4] Toradmalle, D., Muthukuru, J., & Sathyanarayana, B. (2019). Certificateless and a provably-secure digital signature scheme based on an elliptic curve. International Journal of Electrical & Computer Engineering (2088-8708), 9.
[5] Ismail, L., & Materwala, H. (2019). A Review of Blockchain Architecture and Consensus Protocols: Use Cases, Challenges, and Solutions. Symmetry, 11 (10), 1198.
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[7] Frost, M. (2019, April 8). Relief could be coming soon for victims of deed theft. Retrieved from Brooklyn Daily Eagle: https://brooklyneagle.com/articles/2019/04/08/relief-could-be-coming-soon-for-victims-of-deed-theft/
[8] Johnson, D., Menezes, A., & Vanstone, S. (2001). The elliptic curve digital signature algorithm (ECDSA). International journal of information security, 1 (1), 36-63.
[9] Kittur, A. S., & Pais, A. R. (2017). Batch verification of digital signatures: approaches and challenges. Journal of information security and applications, 37, 15-27.
[10] Weider, D. Y., Gole, M., Prabhuswamy, N., Prakash, S., & Shankaramurthy, V. G. (2016, June). An Approach to Design and Analyze the Framework for Preventing Cyberbullying. In 2016 IEEE International Conference on Services Computing (SCC) (pp. 864-867). IEEE.
[11] Schernthanner, H., Asche, H., Gonschorek, J., & Scheele, L. (2020). Spatial modeling and geo-visualization of rental prices for real estate portals. In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 962-977). IGI Global.
[12] Sadikin, M. A., & Wardhani, R. W. (2016, July). Implementation of RSA 2048-bit and AES 256-bit with digital signature for secure electronic health record application. In 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA) (pp. 387-392). IEEE.
[13] Wadley, C. D., Dintenfass, K., Missouri, D. C., Wittkowski, A. C., Jones-McFadden, A. C., & Thompson, A. F. (2017). U.S. Patent Application No. 14/851, 758.
[14] Li, Y., Wang, S., Yang, T., Pan, Q., & Tang, J. (2017, June). Price Recommendation on Vacation Rental Websites. In Proceedings of the 2017 SIAM International Conference on Data Mining (pp. 399-407). Society for Industrial and Applied Mathematics.
[15] Pornin, T. (2013). Deterministic usage of the digital signature algorithm (DSA) and elliptic curve digital signature algorithm (ECDSA). Internet Engineering Task Force RFC, 6979, 1-79.
[16] Knoll, J., Groß, R., Schwanke, A., Rinn, B., & Schreyer, M. (2018, June). Applying recommender approaches to the real estate e-commerce market. In International Conference on Innovations for Community Services (pp. 111-126). Springer, Cham.
[17] Levin, B. (2018). Potential for Cryptocurrency to Fund Investment in Sustainable Real Assets (Doctoral dissertation, Duke University).
[18] Morena, M., Truppi, T., Pavesi, A. S., Cia, G., Giannelli, J., & Tavoni, M. (2020). Blockchain and real estate: Dopo di Noi project. Property Management.
[19] Pindado, G., & Álvarez, J. M. Á. (2018). The GJ-STATIC securitization fund on property rentals. An instrument to help struggling real estate and construction companies in Spain. Aestimatio: The IEB International Journal of Finance, (16), 2-5.
[20] Kundu, D. (2019). Blockchain and trust in a smart city. Environment and Urbanization ASIA, 10 (1), 31-43.
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Cite This Article
  • APA Style

    Jeffrey Shepard. (2020). Digital Transactions in Real Estate Marketing. Journal of World Economic Research, 9(2), 110-114. https://doi.org/10.11648/j.jwer.20200902.15

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    Jeffrey Shepard. Digital Transactions in Real Estate Marketing. J. World Econ. Res. 2020, 9(2), 110-114. doi: 10.11648/j.jwer.20200902.15

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    AMA Style

    Jeffrey Shepard. Digital Transactions in Real Estate Marketing. J World Econ Res. 2020;9(2):110-114. doi: 10.11648/j.jwer.20200902.15

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  • @article{10.11648/j.jwer.20200902.15,
      author = {Jeffrey Shepard},
      title = {Digital Transactions in Real Estate Marketing},
      journal = {Journal of World Economic Research},
      volume = {9},
      number = {2},
      pages = {110-114},
      doi = {10.11648/j.jwer.20200902.15},
      url = {https://doi.org/10.11648/j.jwer.20200902.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jwer.20200902.15},
      abstract = {Identity theft and tax fraud have been leading white-collar crimes for over a decade. Emerging trends have seen the rise in white-collar crime to now include deed theft. This type of theft marries identity theft and mortgage fraud, and it is robbing the hopes of many people who aspire to own a home. This whitepaper identifies how deed theft takes place through targeting homes with deceased owners, house flipping fraud and fake forensic auditors. It explores this evolved white-collar crime and its dangerous effects, while also revealing the best ways to mitigate the risk of deed theft. Furthermore, this paper provides the background of the crime and how the crime correlates with identify theft. Gaining control of this issue is challenging due to the existing infrastructure that was introduced in 1920. This leaves few country clerks with any response to the issue surrounding identity theft and mortgage fraud. To mitigate this challenge, devices have been developed to track the behaviour of people to ensure that their residences are not ‘stolen’. Although these solutions are available, few individuals learn about deed theft so that the solutions can be used to protect themselves against it. This whitepaper presents DeedLock as the most effective solution to address the looming challenge of property theft.},
     year = {2020}
    }
    

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    AB  - Identity theft and tax fraud have been leading white-collar crimes for over a decade. Emerging trends have seen the rise in white-collar crime to now include deed theft. This type of theft marries identity theft and mortgage fraud, and it is robbing the hopes of many people who aspire to own a home. This whitepaper identifies how deed theft takes place through targeting homes with deceased owners, house flipping fraud and fake forensic auditors. It explores this evolved white-collar crime and its dangerous effects, while also revealing the best ways to mitigate the risk of deed theft. Furthermore, this paper provides the background of the crime and how the crime correlates with identify theft. Gaining control of this issue is challenging due to the existing infrastructure that was introduced in 1920. This leaves few country clerks with any response to the issue surrounding identity theft and mortgage fraud. To mitigate this challenge, devices have been developed to track the behaviour of people to ensure that their residences are not ‘stolen’. Although these solutions are available, few individuals learn about deed theft so that the solutions can be used to protect themselves against it. This whitepaper presents DeedLock as the most effective solution to address the looming challenge of property theft.
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Author Information
  • Union Institute and University, Trustee of The University, Cincinnati, Ohio, United States of America

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