Skip to main content
SpringerLink
Log in

Predicting Residential Property Valuation in Major Towns and Cities on Mainland Fiji

  • Conference paper
  • First Online:
Big Data Intelligence and Computing (DataCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13864))

Included in the following conference series:

  • 568 Accesses

Abstract

The real estate sector plays a pivotal role towards the economy of every nation. Transaction of properties heavily rely on the valuation price as determined by the appraisers who use many variations of techniques to determine the valuation of respective properties. This value is used by investors, sellers, intermediary agencies such as real estate agencies and financial institutions as well as government entities. However, the value determined by the appraisers is just an approximation, excluding the accuracy and error rate with respect to the actual price. This study aims to integrate the capabilities of machine learning models and algorithms towards determining valuation price. Four algorithms were selected, namely, Multiple Linear Regression, Decision Tree, Random Forest, and Artificial Neural Network, for supervised learning against training and testing of a dataset acquired from real estate agencies based on residential properties in major towns and cities on mainland Fiji. Results show that Random Forest and Artificial Neural network produce high levels of accuracy based on Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RSME) values. The study has significantly contributed towards developing insights to developing accurate models which could enable users access to valuation of properties based on the input of property features through machine learning predictions.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Su, T., Li, H., An, Y.: A BIM and machine learning integration framework for automated property valuation. J. Build. Eng. 44(44), 102636–102661 (2021)

    Article  Google Scholar 

  2. Kabaivanov, S., Markovska, V.: Artificial intelligence in real estate market analysis. In: AIP Conference Proceedings, vol. 2333, no. 1, pp. 30001–30008 (2021)

    Google Scholar 

  3. Glumac, B., Des Rosiers, F.: Practice briefing – Automated valuation models (AVMs): their role, their advantages and their limitations. J. Property Investment Finan. 39(5), 481–491 (2020)

    Article  Google Scholar 

  4. 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(1), 71–83 (2018)

    Google Scholar 

  5. Valier, A.: Who performs better? AVMs vs hedonic models. J. Property Investment Finan. 38, 213–225 (2020)

    Article  Google Scholar 

  6. Wang, D., Li, V.J.: Mass appraisal models of real estate in the 21st century: a systematic literature review. Sustainability 11(24), 7006 (2019)

    Article  Google Scholar 

  7. Renigier-Bilozor, M., Zobrek, S., Walacik, M., Borst, R., Grover, R., d’Amoto, M.: International acceptance of automated modern tools use must-have for sustainable real estate market development. Land Use Policy 113, 105876 (2022)

    Article  Google Scholar 

  8. Yalpir, S., Sisman, S., Akar, A.U., Unel, F.B.: Feature selection applications and model validation for mass real estate valuation systems. Land Use Policy 108, 105539 (2021)

    Article  Google Scholar 

  9. Sakri, S.B., Ali, Z.: Analysis of the dimensionality issues in house price forecasting modeling. In: Fifth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU), pp. 13–19. IEEE (2022)

    Google Scholar 

  10. Lim, Z., Bellotti, A.: Normalized nonconformity measures for automated valuation models. Expert Syst. Appl. 180, 115165 (2021)

    Article  Google Scholar 

  11. Zhang, Q.: Housing Price Prediction Based on Multiple Linear Regression. Scientific Programming 2021, 1–9 (2021). https://doi.org/10.1155/2021/7678931

    Article  Google Scholar 

  12. Ghatnekar, A., Shanbhag, A.D.: Explainable, multi-region price prediction. In: International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–7. IEEE (2021)

    Google Scholar 

  13. Boström, H., Linusson, H., Löfström, T., Johansson, U.: Accelerating difficulty estimation for conformal regression forests. Ann. Math. Artif. Intell. 81(1–2), 125–144 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  14. Chanasit, K., Chuangsuwanich, E., Suchato, A., Punyabukkana, P.: A real estate valuation model using boosted feature selection. IEEE Access 9, 86938–86953 (2021)

    Article  Google Scholar 

  15. Wang, W.C., Chang, Y.J., Wang, H.C.: An application of the spatial autocorrelation method on the change of real estate prices in Taitung city. Int. J. Geo-Inform. 8(6), 249 (2019)

    Article  Google Scholar 

  16. Cheri, X.: Optimizations of training dataset on house price estimation. In: 2nd International Conference on Big Data Economy and Information Management (BDEIM), pp. 197–203. IEEE (2021)

    Google Scholar 

  17. Ghosalkar, N.N., Dhage, S.N.: Real estate value prediction using linear regression. In: Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. IEEE (2018)

    Google Scholar 

  18. He, K., He, C.: Housing price analysis using linear regression and logistic regression: a comprehensive explanation using melbourne real estate data. In: International Conference on Computing (ICOCO), pp. 241–246. IEEE (2021)

    Google Scholar 

  19. Sanyal, S., Biswas, S.K., Das, D., Chakraborty, M., Purkayastha, B.: Boston house price prediction using regression models. In: International Conference on Intelligent Technologies (CONIT), pp. 1–6. IEEE (2022)

    Google Scholar 

  20. Madhuri, C.R., Anuradha, G., Pujitha, M.V.: House price prediction using regression techniques: a comparative study. In: International Conference on Smart Structures and Systems (ICSSS), pp. 1–5. IEEE (2019)

    Google Scholar 

  21. Manasa, J., Gupta, R., Narahari, N.S.: Machine learning based predicting house prices using regression techniques. In: 2nd International conference on innovative mechanisms for industry applications (ICIMIA), pp. 624–630. IEEE (2020)

    Google Scholar 

  22. Gampala, V., Sai, N.Y., Bhavya, T.N.S.: Real-estate price prediction system using machine learning. In: International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 533–538. IEEE (2022)

    Google Scholar 

  23. Liu, Z., et al.: A bayesian approach to residential property valuation based on built environment and house characteristics. In: IEEE international conference on big data (big data), pp. 1455–1464. IEEE (2018)

    Google Scholar 

  24. Yee, W.L., Bakar, N.A.A., Hassan, N.H., Zainuddin, N.M.M., Yusoff, R.C.M., Ab Rahim, N.Z.: Using machine learning to forecast residential property prices in overcoming the property overhang issue. In: International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), pp. 1–6. IEEE (2021)

    Google Scholar 

  25. Baldominos, A., Blanco, I., Moreno, A.J., Iturrarte, R., Bernardez, O., Afonso, C.: Identifying real estate opportunities using machine learning. Appl. Sci. 8(11), 2321 (2018)

    Article  Google Scholar 

  26. Dey, S.K., Urolagin, S.: Real estate price prediction using data mining techniques. In: 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–4. IEEE (2021)

    Google Scholar 

  27. Li, Z.: Predictions of house price index based on machine learning methods. In: 2nd International Conference on Computing and Data Science (CDS), pp. 472–476. IEEE (2021)

    Google Scholar 

  28. Zhao, Y., Chetty, G., Tran, D.: Deep learning with XGBoost for real estate appraisal. In: IEEE symposium series on computational intelligence (SSCI), pp. 1396–1401. IEEE (2019)

    Google Scholar 

  29. Almaslukh, B.: A gradient boosting method for effective prediction of housing prices in complex real estate systems. In: International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 217–222. IEEE (2020)

    Google Scholar 

  30. Aithal, P.K., Acharya, U.D., Geetha, M., Sagar, R., Abraham, R.: A comparative study of deep neural network and statistical models for stock price prediction. In: 3rd International Conference for Emerging Technology (INCET), pp. 1–5. IEEE (2022)

    Google Scholar 

  31. Piao, Y., Chen, A., Shang, Z.: Housing price prediction based on CNN. In: 9th International Conference on Information Science and Technology (ICIST), pp. 491–495. IEEE (2019)

    Google Scholar 

  32. Zhan, C., Wu, Z., Liu, Y., Xie, Z., Chen, W.: Housing prices prediction with deep learning: an application for the real estate market in Taiwan. In: 18th International Conference on Industrial Informatics (INDIN), vol. 1, pp.719–724. IEEE (2020)

    Google Scholar 

  33. Al-Gbury, O., Kurnaz, S.: Real estate price range prediction using artificial neural network and grey wolf optimizer. In: 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–5. IEEE (2020)

    Google Scholar 

  34. Lee, C.: Forecasting spatially correlated targets: simultaneous prediction of housing market activity across multiple areas. Int. J. Strateg. Prop. Manag. 26(2), 119–126 (2022)

    Article  Google Scholar 

  35. Wang, F., Zou, Y., Zhang, H., Shi, H.: House price prediction approach based on deep learning and ARIMA model. In: 7th International Conference on Computer Science and Network Technology (ICCSNT), pp. 303–307. IEEE (2019)

    Google Scholar 

  36. Horvath, S., Scoot, M., Zaddach, S., Neuner, H., Weitkamp, A.: Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis. Land Use Policy 107, 105475 (2021)

    Article  Google Scholar 

  37. Stubnova, M., Urbanikova, M., Hudakova, J., Papcunova, V.: Estimation of residential property market price: comparison of artificial neural networks and hedonic pricing model. Emerg. Sci. J. 4(6), 530–538 (2020)

    Article  Google Scholar 

  38. Peterson, S., Flanagan, A.: Neural network hedonic pricing models in mass real estate appraisal. J. Real Estate Res. 31(2), 147–164 (2009)

    Article  Google Scholar 

  39. Kato, N., Yamasaki, T., Aizawa, K., Ohama, T.: Users’ preference prediction of real estates featuring floor plan analysis using FloorNet. In: Proceedings of the 2018 ACM Workshop on Multimedia for Real Estate Tech, pp. 7–11 (2018)

    Google Scholar 

  40. Wang, P.Y., Chen, C.T., Su, J.W., Wang, T.Y., Huang, S.H.: Deep learning model for house price prediction using eterogeneous data analysis along with joint self-attention mechanism. IEEE Access 9, 55244–55259 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fiji National University, Lautoka, Fiji

    Kunal Maharaj & Neeraj Sharma

  2. University of Fiji, Lautoka, Fiji

    Kunal Kumar & Neeraj Sharma

Authors
  1. Kunal Maharaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kunal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Neeraj Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kunal Maharaj .

Editor information

Editors and Affiliations

  1. Asia University, Taichung, Taiwan

    Ching-Hsien Hsu

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Mengwei Xu

  3. University of New Brunswick, Fredericton, NB, Canada

    Hung Cao

  4. Asia University, Taichung, Taiwan

    Hojjat Baghban

  5. University of Fiji, Samabula, Fiji

    A. B. M. Shawkat Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maharaj, K., Kumar, K., Sharma, N. (2023). Predicting Residential Property Valuation in Major Towns and Cities on Mainland Fiji. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-2233-8_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2232-1

  • Online ISBN: 978-981-99-2233-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation