IDEAS home Printed from https://ideas.repec.org/a/kap/jrefec/v65y2022i4d10.1007_s11146-021-09861-1.html
   My bibliography  Save this article

Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)

Author

Listed:
  • Tien Foo Sing

    (National University of Singapore
    National University of Singapore)

  • Jesse Jingye Yang

    (National University of Singapore)

  • Shi Ming Yu

    (National University of Singapore)

Abstract

This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%.

Suggested Citation

  • Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
    • Handle: RePEc:kap:jrefec:v:65:y:2022:i:4:d:10.1007_s11146-021-09861-1
    DOI: 10.1007/s11146-021-09861-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11146-021-09861-1
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11146-021-09861-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sumit Agarwal & Itzhak Ben-David & Vincent Yao, 2015. "Collateral Valuation and Borrower Financial Constraints: Evidence from the Residential Real Estate Market," Management Science, INFORMS, vol. 61(9), pages 2220-2240, September.
    2. Alexander N. Bogin & Jessica Shui, 2020. "Correction to: Appraisal Accuracy and Automated Valuation Models in Rural Areas," The Journal of Real Estate Finance and Economics, Springer, vol. 61(4), pages 730-731, November.
    3. Jozef Zurada & Alan S. Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, American Real Estate Society, vol. 33(3), pages 349-388.
    4. Shiller, Robert J & Weiss, Allan N, 1999. "Evaluating Real Estate Valuation Systems," The Journal of Real Estate Finance and Economics, Springer, vol. 18(2), pages 147-161, March.
    5. Phang, Sock-Yong, 2004. "House prices and aggregate consumption: do they move together? Evidence from Singapore," Journal of Housing Economics, Elsevier, vol. 13(2), pages 101-119, June.
    6. Clapp, John M, 2003. "A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation," The Journal of Real Estate Finance and Economics, Springer, vol. 27(3), pages 303-320, November.
    7. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    8. Jian Guan & Jozef Zurada & Alan S. Levitan, 2008. "An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment," Journal of Real Estate Research, American Real Estate Society, vol. 30(4), pages 395-422.
    9. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, American Real Estate Society, vol. 22(3), pages 313-336.
    10. K. C. Lam & C. Y. Yu & K. Y. Lam, 2008. "An Artificial Neural Network and Entropy Model for Residential Property Price Forecasting in Hong Kong," Journal of Property Research, Taylor & Francis Journals, vol. 25(4), pages 321-342, November.
    11. Sing, Tien-Foo & Tsai, I-Chun & Chen, Ming-Chi, 2006. "Price dynamics in public and private housing markets in Singapore," Journal of Housing Economics, Elsevier, vol. 15(4), pages 305-320, December.
    12. Alexander N. Bogin & Jessica Shui, 2020. "Appraisal Accuracy and Automated Valuation Models in Rural Areas," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 40-52, February.
    13. Geltner, David Michael, 1991. "Smoothing in Appraisal-Based Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 4(3), pages 327-345, September.
    14. Karl E. Case & Robert J. Shiller, 1990. "Forecasting Prices and Excess Returns in the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 18(3), pages 253-273, September.
    15. David Geltner, 1989. "Estimating Real Estate's Systematic Risk from Aggregate Level Appraisal‐Based Returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 17(4), pages 463-481, December.
    16. Elaine M. Worzala & Margarita Lenk & Ana Silva, 1995. "An Exploration of Neural Networks and Its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 10(2), pages 185-202.
    17. Quan, Daniel C & Quigley, John M, 1991. "Price Formation and the Appraisal Function in Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 4(2), pages 127-146, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    2. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    3. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    4. David Geltner & Bryan D. MacGregor & Gregory M. Schwann, 2003. "Appraisal Smoothing and Price Discovery in Real Estate Markets," Urban Studies, Urban Studies Journal Limited, vol. 40(5-6), pages 1047-1064, May.
    5. Jose Torres-Pruñonosa & Pablo García-Estévez & Camilo Prado-Román, 2021. "Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing," Mathematics, MDPI, vol. 9(7), pages 1-16, April.
    6. Robert Edelstein & Daniel Quan, 2006. "How Does Appraisal Smoothing Bias Real Estate Returns Measurement?," The Journal of Real Estate Finance and Economics, Springer, vol. 32(1), pages 41-60, February.
    7. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    8. Deng, Yongheng & McMillen, Daniel P. & Sing, Tien Foo, 2014. "Matching indices for thinly-traded commercial real estate in Singapore," Regional Science and Urban Economics, Elsevier, vol. 47(C), pages 86-98.
    9. George H. Lentz & Ko Wang, 1998. "Residential Appraisal and the Lending Process: A Survey of Issues," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 11-40.
    10. Jean‐Christophe Delfim & Martin Hoesli, 2021. "Robust desmoothed real estate returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(1), pages 75-105, March.
    11. David M. Geltner, 1993. "Estimating Market Values from Appraised Values without Assuming an Efficient Market," Journal of Real Estate Research, American Real Estate Society, vol. 8(3), pages 325-346.
    12. William Cheung & Lewen Guo & Yuichiro Kawaguchi, 2021. "Automated valuation model for residential rental markets: evidence from Japan," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-34, December.
    13. Mehmet Emin Tabar & Aziz Sisman & Yasemin Sisman, 2023. "A Real Estate Appraisal Model with Artificial Neural Networks and Fuzzy Logic: A Local Case Study of Samsun City," International Real Estate Review, Global Social Science Institute, vol. 26(4), pages 565-581.
    14. Carini Manuela & Ciuna Marina & De Ruggiero Manuela & Salvo Francesca & Simonotti Marco, 2017. "Repeat Assessed Values Model for Housing Price Index," Real Estate Management and Valuation, Sciendo, vol. 25(4), pages 25-39, December.
    15. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
    16. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
    17. Paul Calem & Jeanna Kenney & Lauren Lambie‐Hanson & Leonard Nakamura, 2021. "Appraising home purchase appraisals," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(S1), pages 134-168, March.
    18. Coën, Alain & Lefebvre, Benoit & Simon, Arnaud, 2018. "International money supply and real estate risk premium: The case of the London office market," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 120-140.
    19. Erwan Quintin & Morris Davis, 2014. "Default When House Prices are Uncertain," 2014 Meeting Papers 246, Society for Economic Dynamics.
    20. Alain Chaney & Martin Hoesli, 2015. "Transaction-Based and Appraisal-Based Capitalization Rate Determinants," International Real Estate Review, Global Social Science Institute, vol. 18(1), pages 1-43.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jrefec:v:65:y:2022:i:4:d:10.1007_s11146-021-09861-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.