IDEAS home Printed from https://ideas.repec.org/a/kap/jrefec/v50y2015i2p270-287.html
   My bibliography  Save this article

Determinants of Housing Prices in Hong Kong: A Box-Cox Quantile Regression Approach

Author

Listed:
  • Hyung-Gun Kim
  • Kwong-Chin Hung
  • Sung Park

Abstract

This paper analyzes the determinants of housing prices in Hong Kong by using property transaction data of condominium units from Taikoo Shing, one of the largest real estate properties in Hong Kong. We use a hedonic pricing model for the empirical analysis and estimate the model by using the Box-Cox quantile regression method. The empirical results show that this method provides a more comprehensive description of housing price determinants. Housing prices and characteristics have a nonlinear relationship, and this relationship varies across all quantiles. In addition, the response of housing prices to various housing characteristics varies across quantiles. For example, an increase in the size of the gross floor area is more valued at higher quantiles. Other variables have differential effects on housing prices across the distribution of housing prices. We also perform a simple simulation for model predictability and show that our model outperforms other models which have been frequently used in the previous studies. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Hyung-Gun Kim & Kwong-Chin Hung & Sung Park, 2015. "Determinants of Housing Prices in Hong Kong: A Box-Cox Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 50(2), pages 270-287, February.
    • Handle: RePEc:kap:jrefec:v:50:y:2015:i:2:p:270-287
    DOI: 10.1007/s11146-014-9456-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11146-014-9456-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11146-014-9456-1?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. Ivar Ekeland & James J. Heckman & Lars Nesheim, 2004. "Identification and Estimation of Hedonic Models," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages 60-109, February.
    2. Linneman, Peter, 1980. "Some empirical results on the nature of the hedonic price function for the urban housing market," Journal of Urban Economics, Elsevier, vol. 8(1), pages 47-68, July.
    3. Velma Zahirovic-Herbert & Swarn Chatterjee, 2012. "Historic Preservation and Residential Property Values: Evidence from Quantile Regression," Urban Studies, Urban Studies Journal Limited, vol. 49(2), pages 369-382, February.
    4. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    5. Kaufmann, Daniel & Quigley, John M., 1987. "The consumption benefits of investment in infrastructure : The evaluation of sites-and-services programs in underdeveloped countries," Journal of Development Economics, Elsevier, vol. 25(2), pages 263-284, April.
    6. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    7. Ivar Ekeland & James J. Heckman & Lars Nesheim, 2002. "Identifying Hedonic Models," American Economic Review, American Economic Association, vol. 92(2), pages 304-309, May.
    8. repec:dau:papers:123456789/6486 is not listed on IDEAS
    9. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    11. Daniel J. Henderson & Christopher F. Parmeter & Subal C. Kumbhakar, 2007. "Nonparametric estimation of a hedonic price function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 695-699.
    12. Stephen Mak & Lennon Choy & Winky Ho, 2010. "Quantile Regression Estimates of Hong Kong Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 47(11), pages 2461-2472, October.
    13. Harry Haupt & Joachim Schnurbus & Rolf Tschernig, 2010. "On nonparametric estimation of a hedonic price function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 894-901.
    14. Helen X.H. Bao & Alan T.K. Wan, 2004. "On the Use of Spline Smoothing in Estimating Hedonic Housing Price Models: Empirical Evidence Using Hong Kong Data," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(3), pages 487-507, September.
    15. Mok, Henry M K & Chan, Patrick P K & Cho, Yiu-sun, 1995. "A Hedonic Price Model for Private Properties in Hong Kong," The Journal of Real Estate Finance and Economics, Springer, vol. 10(1), pages 37-48, January.
    16. Bernd Fitzenberger & Ralf A. Wilke & Xuan Zhang, 2010. "Implementing Box-Cox Quantile Regression," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 158-181, April.
    17. Buchinsky, Moshe, 1995. "Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963-1987," Journal of Econometrics, Elsevier, vol. 65(1), pages 109-154, January.
    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. Ming Li & Guojun Zhang & Yunliang Chen & Chunshan Zhou, 2019. "Evaluation of Residential Housing Prices on the Internet: Data Pitfalls," Complexity, Hindawi, vol. 2019, pages 1-15, February.
    2. Alan T. K. Wan & Shangyu Xie & Yong Zhou, 2017. "A varying coefficient approach to estimating hedonic housing price functions and their quantiles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1979-1999, August.
    3. Wilmar Alexander Cabrera-Rodríguez & Juan Sebastián Mariño-Montaña & Carlos Andrés Quicazán-Moreno, 2019. "Modelos hedónicos con efectos espaciales: una aproximación al cálculo de índices de precios de vivienda para Bogotá," Borradores de Economia 1072, Banco de la Republica de Colombia.
    4. Antonio Nesticò & Marianna La Marca, 2020. "Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    5. Heiko Kirchhain & Jan Mutl & Joachim Zietz, 2020. "The Impact of Exogenous Shocks on House Prices: the Case of the Volkswagen Emissions Scandal," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 587-610, May.
    6. 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.

    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. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, Institute of Labor Economics (IZA).
    2. 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.
    3. Heiko Kirchhain & Jan Mutl & Joachim Zietz, 2020. "The Impact of Exogenous Shocks on House Prices: the Case of the Volkswagen Emissions Scandal," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 587-610, May.
    4. Nicodemo, Catia & Raya, Josep Maria, 2012. "Change in the distribution of house prices across Spanish cities," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 739-748.
    5. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    6. Fritsch, Markus & Haupt, Harry & Ng, Pin T., 2016. "Urban house price surfaces near a World Heritage Site: Modeling conditional price and spatial heterogeneity," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 260-275.
    7. Alan T. K. Wan & Shangyu Xie & Yong Zhou, 2017. "A varying coefficient approach to estimating hedonic housing price functions and their quantiles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1979-1999, August.
    8. Gaetano Lisi, 2013. "On the Functional Form of the Hedonic Price Function: A Matching-theoretic Model and Empirical Evidence," International Real Estate Review, Global Social Science Institute, vol. 16(2), pages 189-207.
    9. Zahirovich-Herbert, Velma & Gibler, Karen M., 2014. "The effect of new residential construction on housing prices," Journal of Housing Economics, Elsevier, vol. 26(C), pages 1-18.
    10. Marcelo Cajias & Philipp Freudenreich & Anna Heller & Wolfgang Schaefers, 2018. "Censored Quantile Regressions and the Determinants of Real Estate Liquidity," ERES eres2018_203, European Real Estate Society (ERES).
    11. Carlo Fezzi & Ian Bateman, 2015. "The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(1), pages 57-92.
    12. Nana Cui & Hengyu Gu & Tiyan Shen & Changchun Feng, 2018. "The Impact of Micro-Level Influencing Factors on Home Value: A Housing Price-Rent Comparison," Sustainability, MDPI, vol. 10(12), pages 1-23, November.
    13. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    14. Marcelo Cajias & Philipp Freudenreich & Anna Freudenreich, 2020. "Exploring the determinants of real estate liquidity from an alternative perspective: censored quantile regression in real estate research," Journal of Business Economics, Springer, vol. 90(7), pages 1057-1086, August.
    15. Carlos Felipe Balcázar & Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2017. "Rent‐Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 881-898, December.
    16. Lisi, Gaetano, 2012. "On the theoretical derivation of a functional form for the hedonic price function," MPRA Paper 37066, University Library of Munich, Germany.
    17. Ming-Yi Huang, 2023. "Analyzing the effects of green building on housing prices: case study of Kaohsiung, Taiwan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1205-1235, February.
    18. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.
    19. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    20. Brunes, Fredrik & Hermansson, Cecilia & Song, Han-Suck & Wilhelmsson, Mats, 2016. "NIMBYs for the rich and YIMBYs for the poor: Analyzing the property price effects of infill development," Working Paper Series 16/2, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.

    More about this item

    Keywords

    Housing price; Hedonic price function; Box-Cox quantile regression; Model comparison; R31; C21; C29;
    All these keywords.

    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

    Statistics

    Access and download statistics

    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:50:y:2015:i:2:p:270-287. 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.