Machine Learning Valuation Models

Machine Learning Valuation Models is a computational valuation framework that employs machine learning algorithms to estimate market value by learning complex, non-linear relationships between property characteristics and transaction prices from large datasets, distinguishing it from traditional appraisal methods through its data-driven, adaptive prediction capability.

249

Publications

10.3K

Citations

598

Authors

312

Institutions

Publications per year

2017–2026

181

Authors

598

  1. RB
    Rotimi Boluwatife Abidoye

    UNSW Sydney, Australia

    7 publications · 326 citations

    7

    h-index

  2. AP
    Albert P.C. Chan

    Hong Kong Polytechnic University, Hong Kong SAR China

    5 publications · 284 citations

    5

    h-index

  3. PM
    Pierluigi Morano

    Polytechnic University of Bari, Italy

    3 publications · 93 citations

    3

    h-index

  4. CL
    Changro Lee

    Kangwon National University, South Korea

    3 publications · 36 citations

    3

    h-index

  5. MH
    Martin Hoesli

    University of Geneva, Switzerland

    3 publications · 278 citations

    3

    h-index

1–5 of 598

Venues

  1. SSRN Electronic Journal

    Repository · 10 publications · 174 citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 9 publications · 297 citations

    9

    h-index

  3. Journal of Property Research

    7 publications · 676 citations

    7

    h-index

  4. Land Use Policy

    6 publications · 531 citations

    6

    h-index

  5. Expert Systems with Applications

    5 publications · 814 citations

    5

    h-index

1–5