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Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions

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  • Silviu-Ionut BABTAN

    (Accounting and Audit Department, The Faculty of Economics and Business Administration, Babes-Bolyai University, Cluj-Napoca, Romania)

Abstract

Artificial Intelligence (AI) is changing real estate valuation with innovative approaches. This article examines several AI methods – Regression Models, Decision Trees, Random Forests, Artificial Neural Networks, and XGBoost – and explores their applications for improving property valuation accuracy and efficiency, with implications for other professions involved, e.g. audit. The author starts by investigating traditional valuation methods' limitations, such as data constraints and subjectivity, and presents how these AI techniques, which are translated in property valuation field as automated valuation methods, tackle these challenges. Regression Models quantify attributes, Decision Trees provide clear insights, Random Forests improve predictions, Artificial Neural Networks design elaborate relationships, and XGBoost furnishes advanced boosting techniques for higher performance. Underscoring that AI is meant to support, not substitute, human assessors, the paper presents how these methods can enhance valuation processes, deliver more reliable valuation reports, and decrease errors, while also exploring future innovations and evolving trends in artificial intelligence for real estate industry and related professions.

Suggested Citation

  • Silviu-Ionut BABTAN, 2025. "Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 23(177), pages 180-196, February.
  • Handle: RePEc:aud:audfin:v:23:y:2025:i:177:p:180
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    References listed on IDEAS

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    1. Jha, Girish K. & Sinha, Kanchan, 2013. "Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 26(2).
    2. Jakub Drahokoupil, 2022. "Application of the XGBoost algorithm and Bayesian optimization for the Bitcoin price prediction during the COVID-19 period," FFA Working Papers 4.006, Prague University of Economics and Business, revised 09 May 2022.
    3. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    4. Jiahui Huang & Salmiza Saleh & Yufei Liu, 2021. "A Review on Artificial Intelligence in Education," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, May.
    5. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    6. Carmona, Pedro & Climent, Francisco & Momparler, Alexandre, 2019. "Predicting failure in the U.S. banking sector: An extreme gradient boosting approach," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 304-323.
    7. Masudul Choudhury, 2015. "Subjective probability and financial valuation: contrasting paradigms," Journal of Financial Reporting and Accounting, Emerald Group Publishing Limited, vol. 13(1), pages 20-38, July.
    8. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    artificial intelligence; real estate valuation; audit; automated valuation techniques methods;
    All these keywords.

    JEL classification:

    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General

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