Skip to main content
SpringerLink
Log in

Blockchain and Artificial Intelligence in Real Estate

  • Conference paper
  • First Online:
Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs (ICDSST 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 447))

Included in the following conference series:

  • 696 Accesses

Abstract

Since their development, blockchain and artificial intelligence (AI) technologies have gained substantial momentum and immense adoption in different industries worldwide. The innovations of cryptocurrencies and machine learning algorithms have had significant implications for the growth and advancement of these technologies. The combination of the two presents incredible benefits to organizations in various sectors in terms of harnessing existing data for pattern recognition and insight identification. The technologies have impacted how industries do their businesses. This study includes a systematic review that explores how blockchain and AI, have changed the real estate industry, as well as the way the related businesses can take advantage of the technologies’ capabilities to stay afloat within this new technological development. This research adopts the Prisma methodology to explore how the application of blockchain and AI has impacted the real estate sector. The main finding is that in real estate, the combination of blockchain and AI has great potential, especially in modeling data and valuation, storing information in digital formats and securing transactions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Treiblmaier, H.: Toward more rigorous blockchain research: recommendations for writing blockchain case studies. In: Treiblmaier, H., Clohessy, T. (eds.) Blockchain and Distributed Ledger Technology Use Cases. PI, pp. 1–31. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44337-5_1

    Chapter  Google Scholar 

  2. Alladi, T., Chamola, V., Parizi, R.M., Choo, K.-K.R.: Blockchain applications for industry 4.0 and Industrial IoT: a review. IEEE Access 7, 176935–176951 (2019)

    Google Scholar 

  3. Angraal, S., Krumholz, H.M., Schulz, W.L.: Blockchain technology: applications in health care. Circ. Cardiovasc. Qual. Outcomes 10, e003800 (2017)

    Article  Google Scholar 

  4. Bozkir, E., Eivazi, S., Akgün, M., Kasneci, E.: Eye tracking data collection protocol for VR for remotely located subjects using blockchain and smart contracts. In: Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp. 397–401. IEEE (2020)

    Google Scholar 

  5. del Castillo, M.: Forbes Blockchain 50 (2022). https://www.forbes.com/sites/michaeldelcastillo/2022/02/08/forbes-blockchain-50-2022/. Accessed 15 Feb 2022

  6. Chen, G., Xu, B., Lu, M., Chen, N.-S.: Exploring blockchain technology and its potential applications for education. Smart Learn. Environ. 5, 1–10 (2018)

    Article  Google Scholar 

  7. Chen, J., Lv, Z., Song, H.: Design of Personnel big data management system based on blockchain. Future Gener. Comput. Syst. 101, 1122–1129 (2019)

    Article  Google Scholar 

  8. Bachute, M.R., Subhedar, J.M.: Autonomous driving architectures: insights of machine learning and deep learning algorithms. Mach. Learn. Appl. 6, 100164 (2021)

    Google Scholar 

  9. Mallick, A., Dhara, S., Rath, S.: Application of machine learning algorithms for prediction of sinter machine productivity. Mach. Learn. Appl. 6, 100186 (2021)

    Google Scholar 

  10. Abou Jaoude, J., Saade, R.G.: Blockchain applications-usage in different domains. IEEE Access 7, 45360–45381 (2019)

    Article  Google Scholar 

  11. Aggarwal, S., Chaudhary, R., Aujla, G.S., Kumar, N., Choo, K.-K.R., Zomaya, A.Y.: Blockchain for smart communities: applications, challenges and opportunities. J. Netw. Comput. Appl. 144, 13–48 (2019)

    Article  Google Scholar 

  12. Briner, R.B., Denyer, D.: Systematic review and evidence synthesis as a practice and scholarship tool. In: Oxford Handbook of Evidence-Based Management: Companies, Classrooms and Research, pp. 112–129 (2012)

    Google Scholar 

  13. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., Group, P.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009)

    Google Scholar 

  14. Maesa, D.D.F., Mori, P.: Blockchain 3.0 applications survey. J. Parallel Distrib. Comput., 138, 99–114 (2020)

    Google Scholar 

  15. McGhin, T., Choo, K.-K.R., Liu, C.Z., He, D.: Blockchain in healthcare applications: research challenges and opportunities. J. Netw. Comput. Appl. 135, 62–75 (2019). https://doi.org/10.1016/j.jnca.2019.02.027

    Article  Google Scholar 

  16. Honkanen, P., Nylund, M., Westerlund, M.: Organizational building blocks for blockchain governance: a survey of 241 blockchain white papers (2021)

    Google Scholar 

  17. Leible, S., Schlager, S., Schubotz, M., Gipp, B.: A review on blockchain technology and blockchain projects fostering open science. Front. Blockchain 16 (2019)

    Google Scholar 

  18. Hampton, N.: Understanding the blockchain hype: why much of it is nothing more than snake oil and spin. https://www2.computerworld.com.au/article/606253/understanding-blockchain-hype-why-much-it-nothing-more-than-snake-oil-spin/. Accessed 15 Feb 2022

  19. Malhotra, Y.: AI, machine learning & deep learning risk management & controls: beyond deep learning and generative adversarial networks: model risk management in AI, machine learning & deep learning: princeton presentations in AI-ML risk management & control systems (presentation slides). In: Proceedings of the Machine Learning & Deep Learning: Princeton Presentations in AI-ML Risk Management & Control Systems (Presentation Slides). In: Princeton Presentations in AI & Machine Learning Risk Management & Control Systems, 2018 Princeton Fintech & Quant Conference, Princeton University (2018)

    Google Scholar 

  20. Sokolov, V.: Discussion of ‘deep learning for finance: deep portfolios.’ Appl. Stoch. Models Bus. Ind. 33, 16–18 (2017). https://doi.org/10.1002/asmb.2228

    Article  MathSciNet  MATH  Google Scholar 

  21. Cuturi, M.P., Etchebarne, G.: Real estate pricing with machine learning & non-traditional data sources. https://tryolabs.com/blog/2021/06/25/real-estate-pricing-with-machine-learning-non-traditional-data-sources. Accessed 15 Feb 2022

  22. Erika Blockchain In Real Estate: 8 Things (2022) You Should Know. Gokce Cap. We Buy Sell Land (2021)

    Google Scholar 

  23. Yadav, A.S., Kushwaha, D.S.: Blockchain-based digitization of land record through trust value-based consensus algorithm. Peer-to-Peer Network. Appl. 14(6), 3540–3558 (2021). https://doi.org/10.1007/s12083-021-01207-1

    Article  Google Scholar 

  24. Blockchain Real Estate: Smart Contracts and Their Potential Impact on RE! Rebellion Res. (2021)

    Google Scholar 

  25. Blockchain in Real-Estate: How Technology Can Revolutionize the Industry. https://www.blockchain-council.org/blockchain/blockchain-in-real-estate-how-technology-can-revolutionize-the-industry/. Accessed 16 Feb 2022

  26. Dragov, R., Siviero, A., Micheletti, G., Butiniello, L., Magnani, I.: Advanced Technologies for Industry: AT Watch : Technology Focus on Blockchain. Publications Office, LU (2021)

    Google Scholar 

  27. PricewaterhouseCoopers Blockchain in Real Estate. https://www.pwc.de/en/real-estate/digital-real-estate/blockchain-in-real-estate.html. Accessed 16 Feb 2022

  28. Blockchain in Real Estate: Use Cases and Implementations. https://consensys.net/blockchain-use-cases/real-estate/. Accessed 16 Feb 2022

  29. Liebkind, J.: How blockchain technology is changing real estate. https://www.investopedia.com/news/how-blockchain-technology-changing-real-estate/. Accessed 16 Feb 2022

  30. Daley, S.: 19 top blockchain real estate companies to know 2022 | built in. https://builtin.com/blockchain/blockchain-real-estate-companies. Accessed 16 Feb 2022

  31. Ullah, F., Al-Turjman, F.: A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities. Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-021-05800-6

  32. Hayes, A.: Blockchain Explained. https://www.investopedia.com/terms/b/blockchain.asp. Accessed 15 Feb 2022

  33. Tajani, F., Morano, P., Ntalianis, K.: Automated valuation models for real estate portfolios: a method for the value updates of the property assets. J. Prop. Invest. Finance 36, 324–347 (2018). https://doi.org/10.1108/JPIF-10-2017-0067

    Article  Google Scholar 

  34. Hoksbergen, M., Chan, J., Peko, G., Sundaram, D.: Asymmetric information in high-value low-frequency transactions: mitigation in real estate using blockchain. In: Doss, R., Piramuthu, S., Zhou, W. (eds.) FNSS 2019. CCIS, vol. 1113, pp. 225–239. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34353-8_17

    Chapter  Google Scholar 

  35. Abidoye, R.B., Chan, A.P.C.: Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network. Pac. Rim Prop. Res. J. 24, 71–83 (2018). https://doi.org/10.1080/14445921.2018.1436306

    Article  Google Scholar 

  36. Soper, T.: Zillow group uses machine learning to improve zestimate algorithm for changing market trends. https://www.geekwire.com/2021/zillow-group-uses-machine-learning-improve-zestimate-algorithm-dynamic-market-conditions/. Accessed 15 Feb 2022

  37. RENTestimate: Rent Estimate Calculator by Address. https://www.homeunion.com/rentestimate/. Accessed 15 Feb 2022

  38. Constantinescu, M.: Machine-learning real estate valuation: not only a data affair. https://towardsdatascience.com/machine-learning-real-estate-valuation-not-only-a-data-affair-99d36c92d263. Accessed 15 Feb 2022

Download references

Funding Acknowledgement

This research was supported by Flexice PC as part of the project “Business Intelligence Network of Real Estate Consultants – BIRN”, praxis code KMP6-0286090 and was co-financed by the European Regional Development Fund (ERDF) of the European Union (ΕΕ) under the action “Strengthening Research, Technological Development and Innovation under the framework “Investment Innovation Plans” with code OPS 4228 of the Operational Program «Central Macedonia» 2014–2020.

Author information

Authors and Affiliations

  1. Department of Economic Sciences, International Hellenic University, 62124, Serres, Greece

    Christos Ziakis

Authors
  1. Christos Ziakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Ziakis .

Editor information

Editors and Affiliations

  1. Federal University of Pernambuco, Recife, Brazil

    Ana Paula Cabral Seixas Costa

  2. University of Macedonia, Thessaloniki, Greece

    Jason Papathanasiou

  3. Loughborough University, Loughborough, UK

    Uchitha Jayawickrama

  4. Ecole des Mines d'Albi, Albi, France

    Daouda Kamissoko

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ziakis, C. (2022). Blockchain and Artificial Intelligence in Real Estate. In: Cabral Seixas Costa, A.P., Papathanasiou, J., Jayawickrama, U., Kamissoko, D. (eds) Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs. ICDSST 2022. Lecture Notes in Business Information Processing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-031-06530-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06530-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06529-3

  • Online ISBN: 978-3-031-06530-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation