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Do Spatial Characteristics Affect Housing Prices in Korea? : Evidence from Bayesian Spatial Models

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  • KWON, Heeeun
  • HWANG, Beom Seuk

Abstract

This paper employs a Bayesian conditional autoregressive model to geographically analyze housing prices in Seoul, Korea from a demographic perspective. Spatial dependence patterns are detected between 424 administrative districts in Seoul, and the parameter estimation will be implemented via a Bayesian approach. We confirm that the proposed model with spatial heterogeneity presents superior performance than the other common spatial regression models. We also demonstrate that the proposed model offers the flexibility to resent various global spatial autocorrelation, and that the model adequately captures the model variablesʼ effect on housing prices.

Suggested Citation

  • KWON, Heeeun & HWANG, Beom Seuk, 2023. "Do Spatial Characteristics Affect Housing Prices in Korea? : Evidence from Bayesian Spatial Models," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 64(2), pages 109-124, December.
    • Handle: RePEc:hit:hitjec:v:64:y:2023:i:2:p:109-124
    DOI: 10.15057/hje.2023006
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    1. Zhang, Yanbing & Hua, Xiuping & Zhao, Liang, 2012. "Exploring determinants of housing prices: A case study of Chinese experience in 1999–2010," Economic Modelling, Elsevier, vol. 29(6), pages 2349-2361.
    2. Jean Dubé & Diègo Legros, 2013. "A spatio-temporal measure of spatial dependence: An example using real estate data," Papers in Regional Science, Wiley Blackwell, vol. 92(1), pages 19-30, March.
    3. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
    4. Liu Hongyu & Yun W. Park & Zheng Siqi, 2002. "The Interaction between Housing Investment and Economic Growth in China," International Real Estate Review, Global Social Science Institute, vol. 5(1), pages 40-60.
    5. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    6. Lee, Duncan, 2013. "CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i13).
    7. Mr. Ashvin Ahuja & Lillian Cheung & Gaofeng Han & Mr. Nathan Porter & Wenlang Zhang, 2010. "Are House Prices Rising Too Fast in China?," IMF Working Papers 2010/274, International Monetary Fund.
    8. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    9. Kelejian, Harry H. & Robinson, Dennis P., 1998. "A suggested test for spatial autocorrelation and/or heteroskedasticity and corresponding Monte Carlo results," Regional Science and Urban Economics, Elsevier, vol. 28(4), pages 389-417, July.
    10. Kanghyun Oh & Sol Kim & Jaejun Yoon & Sangki Ahn & Donghwee Kwon, 2017. "Impact of population aging on the housing market (in Korean)," Working Papers 2017-25, Economic Research Institute, Bank of Korea.
    11. A. F. Militino & M. D. Ugarte & L. García-Reinaldos, 2004. "Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 193-209, September.
    12. Ashvin Ahuja & Lillian Cheung & Gaofeng Han & Nathan Porter & Wenlang Zhang, 2010. "Are House Prices Rising Too Fast in China?," Working Papers 1008, Hong Kong Monetary Authority.
    13. Anselin, Luc, 1988. "A test for spatial autocorrelation in seemingly unrelated regressions," Economics Letters, Elsevier, vol. 28(4), pages 335-341.
    14. Bradford Case & John Clapp & Robin Dubin & Mauricio Rodriguez, 2004. "Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 167-191, September.
    15. G. Donald Jud & Dan T. Winkler, 2002. "The Dynamics of Metropolitan Housing Prices," Journal of Real Estate Research, American Real Estate Society, vol. 23(1/2), pages 29-46.
    16. Liang, Qi & Cao, Hua, 2007. "Property prices and bank lending in China," Journal of Asian Economics, Elsevier, vol. 18(1), pages 63-75, February.
    17. Victor Oliveira, 2012. "Bayesian analysis of conditional autoregressive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 107-133, February.
    18. Reihaneh Entezari & Patrick E. Brown & Jeffrey S. Rosenthal, 2020. "Bayesian spatial analysis of hardwood tree counts in forests via MCMC," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
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    More about this item

    Keywords

    Bayesian inference; conditional autoregressive model; Markov chain Monte Carlo (MCMC); spatial dependence;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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