IDEAS home Printed from https://ideas.repec.org/a/eee/lauspo/v99y2020ics0264837719316540.html
   My bibliography  Save this article

A mass appraisal assessment study using machine learning based on multiple regression and random forest

Author

Listed:
  • Yilmazer, Seckin
  • Kocaman, Sultan

Abstract

Mass appraisal is a complex matter because it depends on several categorical and continuously changing or constant parameters. In addition, development of new assessment approaches for mass appraisal of real estate properties in highly complex urban environments is desirable. The advancements in geospatial technologies and machine learning algorithms open up new horizons. For this reason, the purpose of the present study is to compare one conventional stepwise linear multiple regression (MRA) and one more automated machine learning approach, random forest (RF), for mass appraisal in an urban residential area where commercial properties are also available. A part of Mamak District, Ankara, Turkey is selected as the study area since the property values are diverse and representative. Additionally, the district has a complex and developing urban structure. The data employed in the study were compiled under a cadastral modernization project of General Directorate of the Land Registry and Cadastre of Turkey (GDLRC) and were based on the reports of licensed experts (∼50 %), court reports (∼20 %), field surveys, or a combined analysis of all. Consequently, the data used in the study has a high level of confidence. The initial set of parameters used in both methods reflect the most frequently observed characteristics of the real estate properties in the study area that are also effective on the values. The stepwise MRA required manual adjustments of the final parameter set by the expert, whereas RF eliminated unusable parameters fully automatically. The method performance was assessed by using a subset of the training data as a random test. According to the accuracy assessment results, the RF (Adjusted R² 0.734; the total variance explained from the model) slightly outperforms the MRA (Adjusted R² 0.696) where the optimal parameters were set by the human expert. Finally, the results exhibited are promising for quick assessment of mass appraisal and a comprehensive discussion is presented in the study.

Suggested Citation

  • Yilmazer, Seckin & Kocaman, Sultan, 2020. "A mass appraisal assessment study using machine learning based on multiple regression and random forest," Land Use Policy, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:lauspo:v:99:y:2020:i:c:s0264837719316540
    DOI: 10.1016/j.landusepol.2020.104889
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264837719316540
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.landusepol.2020.104889?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. 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.
    2. 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.
    3. Ke Li & Nan Yu & Pengfei Li & Shimin Song & Yalei Wu & Yang Li & Meng Liu, 2017. "Multi-label spacecraft electrical signal classification method based on DBN and random forest," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-19, May.
    4. Gang-Zhi Fan & Seow Eng Ong & Hian Chye Koh, 2006. "Determinants of House Price: A Decision Tree Approach," Urban Studies, Urban Studies Journal Limited, vol. 43(12), pages 2301-2315, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mònica González-Carrasco & Silvana Aciar & Ferran Casas & Xavier Oriol & Ramon Fabregat & Sara Malo, 2024. "A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(1), pages 25-47, October.
    2. Igor Ilin & Mikhail Laskin & Irina Logacheva & Askar Sarygulov & Andrea Tick, 2022. "Land Plots Evaluation for Agriculture and Green Energy Projects: How to Overcome the Conflict Using Mathematics," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    3. Nyamekye, Clement & Kwofie, Samuel & Ghansah, Benjamin & Agyapong, Emmanuel & Boamah, Linda Appiah, 2020. "Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality," Land Use Policy, Elsevier, vol. 99(C).
    4. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    5. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    6. Ching-Hsue Cheng & Ming-Chi Tsai, 2022. "An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices," Land, MDPI, vol. 11(8), pages 1-17, July.
    7. Unel, Fatma Bunyan & Yalpir, Sukran, 2023. "Sustainable tax system design for use of mass real estate appraisal in land management," Land Use Policy, Elsevier, vol. 131(C).
    8. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez, 2022. "Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times," Land, MDPI, vol. 11(11), pages 1-32, November.
    9. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).

    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. 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.
    2. 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.
    3. David Ling & Milena Petrova, 2008. "Avoiding Taxes at Any Cost: The Economics of Tax-Deferred Real Estate Exchanges," The Journal of Real Estate Finance and Economics, Springer, vol. 36(4), pages 367-404, May.
    4. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    5. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    6. Glumac, Brano & Herrera-Gomez, Marcos & Licheron, Julien, 2019. "A hedonic urban land price index," Land Use Policy, Elsevier, vol. 81(C), pages 802-812.
    7. Christian L. Redfearn, 2005. "The Cost of Imposing Monocentricity: Uncovering the Dynamics of Emerging Centrality in Post-Socialist Krakow's Land Markets," Working Paper 8586, USC Lusk Center for Real Estate.
    8. Cupal Martin & Sedlačík Marek & Michálek Jaroslav, 2019. "The Assessment of a Building’s insurable Value using Multivariate Statistics: The Case of the Czech Republic," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 81-96, September.
    9. Julia Koschinsky & Nancy Lozano-Gracia & Gianfranco Piras, 2012. "The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates," Journal of Geographical Systems, Springer, vol. 14(3), pages 319-356, July.
    10. Liu, Lu & Wang, Qiuyun & Zhang, Anquan, 2019. "The impact of housing price on non-housing consumption of the Chinese households: A general equilibrium analysis," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 152-164.
    11. Neil T Coffee & Tony Lockwood & Peter Rossini & Theo Niyonsenga & Stanley McGreal, 2020. "Composition and context drivers of residential property location value as a socioeconomic status measure," Environment and Planning B, , vol. 47(5), pages 790-807, June.
    12. Sebastian Gnat & Mariusz Doszyn, 2020. "Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1230-1245.
    13. Jin Hu & Xuelei Xiong & Yuanyuan Cai & Feng Yuan, 2020. "The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    14. Fuerst, Franz & McAllister, Patrick, 2008. "Green Noise or Green Value? Measuring the Price Effects of Environmental Certification in Commercial Buildings," MPRA Paper 11446, University Library of Munich, Germany, revised Sep 2008.
    15. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    16. Clapp, John M. & Wang, Yazhen, 2006. "Defining neighborhood boundaries: Are census tracts obsolete?," Journal of Urban Economics, Elsevier, vol. 59(2), pages 259-284, March.
    17. John M. Clapp & Jeffrey P. Cohen & Thies Lindenthal, 2023. "Are Estimates of Rapid Growth in Urban Land Values an Artifact of the Land Residual Model?," The Journal of Real Estate Finance and Economics, Springer, vol. 66(2), pages 373-421, February.
    18. Guijarro Francisco, 2021. "A Mean-Variance Optimization Approach for Residential Real Estate Valuation," Real Estate Management and Valuation, Sciendo, vol. 29(3), pages 13-28, September.
    19. David Dale-Johnson & Christian L. Redfearn & W. Jan Brzeski, 2004. "From Central Planning to Centrality: Krakow's Land Prices after Poland's Big Bang," Working Paper 8593, USC Lusk Center for Real Estate.
    20. Roland Füss & Jan A. Koller & Alois Weigand, 2021. "Determining Land Values from Residential Rents," Land, MDPI, vol. 10(4), pages 1-29, March.

    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:eee:lauspo:v:99:y:2020:i:c:s0264837719316540. 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: Joice Jiang (email available below). General contact details of provider: https://www.journals.elsevier.com/land-use-policy .

    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.