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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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    9. 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.
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    12. 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.
    13. Pablo Picardo, 2019. "Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo," Documentos de trabajo 2019002, Banco Central del Uruguay.
    14. 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.
    15. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.

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