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Determinants of House Price: A Decision Tree Approach

Author

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  • Gang-Zhi Fan

    (Research Institute of Economics & Management, Southwestern University of Finance and Economics, 55 Guanghua Cun Street, Chengdu, Sichuan 610074, China, gzfan@swufe.edu.cn, rstfg@nus.edu.sg, Department of Real Estate, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566)

  • Seow Eng Ong

    (Department of Real Estate, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, rstongse@nus.edu.sg)

  • Hian Chye Koh

    (School of Business, SIM University, Clementi Road, Singapore 599491, hckoh@unisim.edu.sg)

Abstract

The hedonic-based regression approach has been utilised extensively to investigate th relationship between house prices and housing characteristics. However, this approach is subject t criticisms arising from potential problems relating to fundamental model assumptions an estimation such as the identification of supply and demand, market disequilibrium, the selectio of independent variables, the choice of functional form of hedonic equation and marke segmentation. This study introduces and utilises an alternative approach-the decision tre approach, which is an important statistical pattern recognition tool. Using the Singapore resal public housing market as a case study, the article demonstrates the usefulness of this techniqu in examining the relationship between house prices and housing characteristics, identifying th significant determinants of housing prices and predicting housing prices. The built tree show that homebuyers are more concerned about the basic housing characteristics of two- and three room flats or four-room flats such as floor area, model type and flat age. However, homebuyer of five-room flats pay more attention to floor level in addition to the basic housin characteristics. In addition, homebuyers of executive apartments are less concerned about basi quantitative characteristics and have higher housing consumption expectations and pay mor attention to 'quality' and service characteristics such as recreational facilities and the livin environment.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:urbstu:v:43:y:2006:i:12:p:2301-2315
    DOI: 10.1080/00420980600990928
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    3. Ansgar Belke & Jonas Keil, 2018. "Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 24(1), pages 25-45, February.
    4. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    5. 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).
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    8. Renigier-Biłozor Małgorzata & Wiśniewski Radosław, 2012. "The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe," Folia Oeconomica Stetinensia, Sciendo, vol. 12(2), pages 103-125, December.
    9. Ogryzek Marek & Wisniewski Radoslaw & Kauko Tom, 2018. "On Spatial Management Practices: Revisiting the "Optimal" Use of Urban Land," Real Estate Management and Valuation, Sciendo, vol. 26(3), pages 24-34, September.
    10. Süreyya Özöğür Akyüz & Birsen Eygi Erdogan & Özlem Yıldız & Pınar Karadayı Ataş, 2023. "A Novel Hybrid House Price Prediction Model," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1215-1232, October.
    11. Xufeng Jiang & Zelu Jia & Lefei Li & Tianhong Zhao, 2022. "Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    12. Elena B. Pokryshevskaya & Evgeny A. Antipov, 2011. "Applying a CART-based approach for the diagnostics of mass appraisal models," Economics Bulletin, AccessEcon, vol. 31(3), pages 2521-2528.
    13. Renigier-Biłozor, Małgorzata & Janowski, Artur & Walacik, Marek & Chmielewska, Aneta, 2022. "Modern challenges of property market analysis- homogeneous areas determination," Land Use Policy, Elsevier, vol. 119(C).
    14. Reyes-Bueno, Fabián & García-Samaniego, Juan Manuel & Sánchez-Rodríguez, Aminael, 2018. "Large-scale simultaneous market segment definition and mass appraisal using decision tree learning for fiscal purposes," Land Use Policy, Elsevier, vol. 79(C), pages 116-122.
    15. Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
    16. 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.
    17. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
    18. Mateusz Tomal & Marco Helbich, 2022. "The private rental housing market before and during the COVID-19 pandemic: A submarket analysis in Cracow, Poland," Environment and Planning B, , vol. 49(6), pages 1646-1662, July.
    19. Francesco Riccioli & Roberto Fratini & Fabio Boncinelli, 2021. "The Impacts in Real Estate of Landscape Values: Evidence from Tuscany (Italy)," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    20. Tekin Mert & Sari Irem Ucal, 2022. "Real Estate Market Price Prediction Model of Istanbul," Real Estate Management and Valuation, Sciendo, vol. 30(4), pages 1-16, December.
    21. 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.
    22. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
    23. 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.
    24. 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.

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