Integrating Cloud Technology and Artificial Intelligence for Enhanced Real Estate Investment Trusts (REITS) and Crowdfunding Synergy
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Abstract
The integration of Cloud technology (CT) and Artificial Intelligence (AI) presents significant potential benefits for the real estate industry, particularly in the areas of REITs and crowdfunding. By providing greater transparency, reducing costs, and increasing liquidity, the cloud can make real estate investments more accessible and appealing to individuals. Moreover, AI can assist with decision-making processes and data analysis, leading to better investment strategies and higher returns. To measure individual perspectives on this integration in India, a survey questionnaire involving 236 individuals will be designed using a Likert scale to measure attitudes and opinions on this topic. This will be followed by data collection through a convenient sampling method and analysis using the SPSS tool. The survey aims to identify the level of awareness and adoption of integrating cloud and AI among real estate professionals and investors, intending to enhance REITs and Crowdfunding Synergy (CS). The findings of the study revealed that the use of CT can enhance transparency in real estate investments, making it easier for investors to understand how their money is being used and what returns they can expect.
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Gibilaro, L. and Mattarocci, G., 2021. Crowdfunding REITs: a new asset class for the real estate industry?. Journal of Property Investment & Finance, 39(2), pp.84-96.
Saiz, A., 2020. Bricks, mortar, and proptech: The economics of IT in brokerage, space utilization and commercial real estate finance. Journal of Property Investment & Finance.
Shayan, J., Azarnik, A., Chuprat, S., Karamizadeh, S. and Alizadeh, M., 2014. Identifying Benefits and risks associated with utilizing cloud computing. arXiv preprint arXiv:1401.5155.
Rashid, A. and Chaturvedi, A., 2019. Cloud computing characteristics and services: a brief review. International Journal of Computer Sciences and Engineering, 7(2), pp.421-426.
Dutt, M., 2015. Cloud computing and its application in libraries. International Journal of Librarianship and Administration, 6(1), pp.19-31.
Bhatnagar, R., Prof (Dr.) Paramjeet, R. and Dr Amit, G., 2022. Cloud IoT: An Emerging Computing Paradigm for Smart World. Emerging Computing Paradigms: Principles, Advances and Applications, pp.19-40.
Niaki, M.K., Torabi, S.A. and Nonino, F., 2019. Why manufacturers adopt additive manufacturing technologies: The role of sustainability. Journal of cleaner production, 222, pp.381-392.
Savolainen, J. and Collan, M., 2020. How additive manufacturing technology changes business models?–review of literature. Additive manufacturing, 32, p.101070.
Sánchez, M.A., 2022. A multi-level perspective on financial technology transitions. Technological Forecasting and Social Change, 181, p.121766.
Handfield, R., Jeong, S. and Choi, T., 2019. Emerging procurement technology: data analytics and cognitive analytics. International Journal of Physical Distribution & Logistics Management, 49(10), pp.972-1002.
Kwon, M.-S. ., Park, S.-B. ., Kim, D.-B. ., & Hong, S.-D. . (2023). A Study on Effective Color Production of Outdoor Digital Theme Park. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 146–149. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2581
Ikegwu, A.C., Nweke, H.F., Anikwe, C.V., Alo, U.R. and Okonkwo, O.R., 2022. Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions. Cluster Computing, 25(5), pp.3343-3387.
Saxena, A., Singh, R., Gehlot, A., Akram, S.V., Twala, B., Singh, A., Montero, E.C. and Priyadarshi, N., 2022. Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape. Sustainability, 15(1), p.309.
Kang, Y., Cai, Z., Tan, C.W., Huang, Q. and Liu, H., 2020. Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), pp.139-172.
Perifanis, N.A. and Kitsios, F., 2023. Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), p.85.
Drydakis, N., 2022. Artificial Intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID-19 pandemic. Information Systems Frontiers, 24(4), pp.1223-1247.
Gupta, A., Rathod, J., Patel, D., Bothra, J., Shanbhag, S. and Bhalerao, T., 2020. Tokenization of real estate using cloud technology. In Applied Cryptography and Network Security: ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA, Rome, Italy, October 19–22, 2020, Proceedings 18 (pp. 77-90). Springer International Publishing.
Belgaum, M.R., Alansari, Z., Musa, S., Alam, M.M. and Mazliham, M.S., 2021. Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues. International Journal of Electrical and Computer Engineering, 11(5), p.4458.
Gill, S.S., Tuli, S., Xu, M., Singh, I., Singh, K.V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U. and Pervaiz, H., 2019. Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, p.100118.
Prof. Sagar Kothawade. (2020). Scale Space Based Object-Oriented Shadow Detection and Removal from Urban High-Resolution Remote Sensing Images. International Journal of New Practices in Management and Engineering, 9(04), 17 - 23. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/95
Kang, J., Lee, H.J., Jeong, S.H., Lee, H.S. and Oh, K.J., 2020. Developing a forecasting model for real estate auction prices using artificial intelligence. Sustainability, 12(7), p.2899.
Qureshi, K.N., Jeon, G. and Piccialli, F., 2021. Anomaly detection and trust authority in artificial intelligence and cloud computing. Computer Networks, 184, p.107647.
Behl, A., Dutta, P., Luo, Z. and Sheorey, P., 2021. Enabling artificial intelligence on a donation-based crowdfunding platform: a theoretical approach. Annals of Operations Research, pp.1-29.
Yeh, J.Y. and Chen, C.H., 2020. A machine learning approach to predict the success of crowdfunding fintech project. Journal of Enterprise Information Management, (ahead-of-print).
Saadat, M.N., Halim, S.A., Osman, H., Nassr, R.M. and Zuhairi, M.F., 2019. Cloud based crowdfunding systems. Indonesian Journal of Electrical Engineering and Computer Science, 15(1), pp.409-413.
Starr, C.W., Saginor, J. and Worzala, E., 2021. The rise of PropTech: Emerging industrial technologies and their impact on real estate. Journal of Property Investment & Finance, 39(2), pp.157-169.
Yathiraju, N., 2022. Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System. International Journal of Electrical, Electronics and Computers, 7(2), pp.1-26.
Rodríguez, M., Jovanovi?, A., Petrova, M., Merwe, M. van der, & Levi, S. Predicting Customer Lifetime Value with Regression Models. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/144
Hoesli, M. and Malle, R., 2022. Commercial real estate prices and COVID-19. Journal of European Real Estate Research, 15(2), pp.295-306.
Banka, M., Tien, N.H., Dao, M.T.H. and Minh, D.T., 2022. Analysis of business strategy of real estate developers in Vietnam: the application of QSPM matrix. International journal of multidisciplinary research and growth evaluation, 3(1), pp.188-196.
Kieltyka, L., Hiep, P.M., Dao, M.T.H. and Minh, D.T., 2022. Comparative analysis of business strategy of Hung Thinh and Novaland real estate developers using McKinsey matrix. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), pp.175-180.
Ngoc, N.M., Tien, N.H. and Anh, D.B.H., 2020. Opportunities and challenges for real estate brokers in post Covid-19 period. Journal of Science and Technology, 170(10), pp.203-208.