The impact of luxury housing on neighborhood housing prices: an application of the spatial difference-in-differences method
Abstract
This study investigated the spatial spillover effects of luxury housing during and after construction, in regards to increases in housing prices in neighboring areas as well as the spatial dependence of neighboring housing. This study focused on already completed luxury housing in Taipei, Taiwan. First, the nearest-neighbor matching approach of propensity score matching was used to overcome the problem of data heterogeneity. The difference-in-differences (DD) method and spatial econometrics were used for analysis. The empirical results indicated that the spatial error model had the best goodness of fit. This indicated that housing prices increased by 13.0% during construction of luxury housing nearby. This indicated that housing prices increased by 5.8% after the construction of luxury housing nearby. The empirical results showed that the ongoing and completed construction of luxury housing had spillover effects on housing prices. The effect of ongoing construction of luxury housing was particularly large in scope, indicating its role as a predictor of psychological reaction in the market.
Keyword : luxury housing, propensity score matching, difference-in-differences, spatial dependence, spatial lag model, spatial error model
How to Cite
Liang, C.-M., Lee, C.-C., Lin, Y.-H., Yu, Z., & Yeh, W.-C. (2020). The impact of luxury housing on neighborhood housing prices: an application of the spatial difference-in-differences method. International Journal of Strategic Property Management, 24(6), 456-473. https://doi.org/10.3846/ijspm.2020.13649
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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https://doi.org/10.1002/jae.2377
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https://doi.org/10.1068/a36103
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https://doi.org/10.1023/A:1007744706720
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Chen, F. C. (2011). Research on positioning design of luxury housing – a case study in Taipei Dazhi [Unpublished master thesis]. National Taiwan University of Science and Technology, Taipei City, Taiwan.
Chou, E. Y. (2012). The effects of inside-family vertical relationships on BIRG and CORF [Unpublished master thesis]. National Taipei University of Education, Taipei City, Taiwan.
Cordera, R., Chiarazzo, E., Ottomanelli, M., dell’Olio, L., & Ibeas, A. (2019). The impact of undesirable externalities on residential property values: spatial regressive models and an empirical study, Transport Policy, 80, 177–187.
https://doi.org/10.1016/j.tranpol.2018.04.010
Dempsey, J. A., & Plantinga, A. J. (2013). How well do urban growth boundaries contain development? Results for Oregon using a difference-in-difference estimator. Regional Science and Urban Economics, 43(6), 996–1007.
https://doi.org/10.1016/j.regsciurbeco.2013.10.002
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Dubé, J., Thériault, M., & Des Rosiers, F. (2013). Commuter rail accessibility and house values: the case of the Montreal South Shore, Canada, 1992–2009. Transportation Research Part A:
Policy and Practice, 54, 49–66.
https://doi.org/10.1016/j.tra.2013.07.015
Dubin, R. A. (1998). Predicting house prices using multiple listings data. Journal of Real Estate Finance and Economics, 17(1), 35–48. https://doi.org/10.1023/A:1007751112669
Dubin, R., Pace, R. K., & Thibodeau, T. G. (1999). Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 17(1), 79–95.
https://doi.org/10.1023/A:1008690521599
Ellen, I. G., Schill, M. H., Susin, S., & Schwartz, A. E. (2001). Building homes reviving neighborhoods: spillover from subsidized construction of owner-occupied housing in New York City. Journal of Housing Research, 12(2), 185–216.
François, D. R., Marius, T., Yan, K., & Paul, V. (2002). Landscaping and house values: an empirical investigation. Journal of Real Estate Research, 23(1/2), 139–162.
Frew, J., & Judd, G. D. (2003). Estimating the value of apartment buildings. Journal of Real Estate Research, 25(1), 77–86.
Geltner, D. M., Miller, N. G., Clayton, J., & Eichholtz, P. (2010). Commercial real estate analysis and investments. South-Western Pub.
Gibbons, S., Machin, S., & Sliva, O. (2013). Valuing school quality using boundary discontinuities. Journal of Urban Economics, 75, 15–28. https://doi.org/10.1016/j.jue.2012.11.001
Gibbons, S., Overman, H. G., & Patacchini, E. (2015). Spatial methods. In G. Duranton, J. V. Henderson, & W. C. Strange (Eds.), Handbook of regional and urban economics (Vol. 5, Ch. 3, pp. 115–168). https://doi.org/10.1016/B978-0-444-59517-1.00003-9
Glasock, J., Chiuling, L., & Raymond, W. S. (2000). Further evidence on the integration of REIT, bond, and stock returns. Journal of Real Estate Finance and Economics, 20(2), 177–194. https://doi.org/10.1023/A:1007877321475
Heil, O., & Helsen, K. (2001). Toward an understanding of price wars: their nature and how they erupt. International Journal of Research in Marketing, 18(1−2), 83–98. https://doi.org/10.1016/S0167-8116(01)00033-7
Hill, R. C., Griffiths, W. E., & Lim, G. C. (2012). Principles of econometrics (4nd ed.). John Wiley and Sons, Inc.
Ho, Y. F. (2011). Preliminary study on an appraisal model for luxury house via the structural equation model [Unpublished master thesis]. Chang Jung Christian University, Tainan City, Taiwan.
Hong, D. Y., & Lin, C. C. (1999). A study on the impact of subway system and road width on the housing prices of Taipei. Journal of Housing Studies, 8, 47–67.
Hsu, H. J. (2005). The investment project evaluation model for the luxury mansions in Taichung City [Unpublished master thesis]. Chaoyang University of Technology, Taichung City, Taiwan.
Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401.
https://doi.org/10.1080/13658810802672469
Imbens, G. W. (2015). Matching methods in practice: three examples. The Journal of Human Resources, 50(2), 373–419. https://doi.org/10.3368/jhr.50.2.373
Karl, G. L., & Gareth, T. (2005). Parcel size, location and commercial land values. Journal of Real Estate Research, 27(3), 343–354.
Kavetsos, G. (2012). The impact of the London Olympics announcement on property prices. Urban Studies, 49(7), 1453–1470. https://doi.org/10.1177/0042098011415436
Kuan, P. Y., & Lee, D. Y. (2008). Effects of cram schooling on math performance: a counterfactual analysis. Taiwanese Journal of Sociology, 41, 97–148.
Lee, C. C., Liang, C. M., Tung, C. H., & Lu, Y. J. (2018). The impact of luxury housing on neighborhood housing prices in Taipei City. Asian Economic and Financial Review, 8(10), 1211–1225.
https://doi.org/10.18488/journal.aefr.2018.810.1211.1225
Lee, C. C., Chang, W. L., & Wu, Y. L. (2014). The impact of the crime rate and star schools on house prices: an analysis of spatial dependence. International Journal of Economics and Finance, 6(1), 99–113.
https://doi.org/10.1108/IJHMA-10-2015-0065
Liang, J. H. (2009). The spillover effect of refurbishment on housing price (Executive Yuan Project Reports No. NSC 98-2410H-034-025). National Science Council.
Lin, C. C., & Ma, Y. C. (2012). An application of Bayesian inference in the real estate market-a case study of Taipei collective housing. Journal of Housing Studies, 21(1), 1–18.
Lin, J. H., & Jou, S. C. (2005). High-income housing and their building blocks in Taipei: a case study of luxurious condominiums in Xinyi center. Journal of Geographical Science, 40, 17–43.
Lin, R. H. (2004). Taipei Xinyi Plan mansions. Architect, 8, 76–83.
Lin, S. J. (2004). The marginal willingness-to-pay of star public elementary and junior high school districts in Taipei City. Journal of Housing Studies, 13(1), 15–34.
Lin, T. Y. (2012). The relation between a monetary policy and housing prices (Research Proposal Report of the National Science Council). Executive Yuan.
Malpezzi, S., Ozanne, L., & Thibodeau, T. G. (1987). Microeconomic estimates of housing depreciation. Land Economics, 63(4), 372–385. https://doi.org/10.2307/3146294
Martins-Filho, C., & Bin, O. (2005). Estimation of hedonic price functions via additive nonparametric regression. Empirical and Economics, 30(1), 99–114.
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