A Longitudinal Study of Land Price Determinants

Author/s: Yosep Cho, Changro Lee

Date Published: 17/12/2025

Published in: Volume 30 - 2025 Issue 3 (pages 153 - 169)

Abstract

The market for land is inherently fluid and constantly changing; this necessitates a land tax assessment system that is amenable to continuous revision and capable of keeping pace with those changes. This study examines temporal changes in land price determinants from a property valuation perspective to enhance land tax policies. Using a mixed-effects model, land lots in Gangwon province, South Korea were longitudinally analyzed over a period of 20 years. The findings reveal increased demand for certain types of land lots, and stable or decreased demand for others. Prices for lots adjoining wide access roads, such as those with 25m and 18m widths, increased by approximately 200%. Similarly, prices for bag-shaped lots surged by roughly 250%. Demand for level and moderate-slope lots also grew stronger compared to steep-slope lots. Conversely, the preference for industrial-use lots decreased with prices declining by approximately 58%. Finally, the price gap between hilly land and forests became nearly negligible. One significant reason for an existing tax assessment system to become outdated is that the local authorities find it hard to identify and measure the gradual changes in land price determinants in the land market. The approach adopted in this study is expected to help local authorities enhance their land tax policies by updating their tax assessment systems in a timely manner, thereby improving horizontal equity in land taxation.

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Keywords

Horizontal Equity - Land Price Determinants - Land Tax Policy - Mixed-Effects Model - South Korea - Tax Assessment System

References

  • Abel, G., & Elliott, M. N. (2019). Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models. BMJ quality & safety, 28(12), 1032-1038.
  • Adam, S., Hodge, L., Phillips, D., & Xu, X. (2020). Revaluation and reform: bringing council tax in England into the 21st century (No. R168). IFS Report.
  • Asami, Y., & Niwa, Y. (2008). Typical lots for detached houses in residential blocks and lot shape analysis. Regional Science and Urban Economics, 38(5), 424-437.
  • Belloc, F., Maruotti, A., & Petrella, L. (2011). How individual characteristics affect university students drop-out: a semiparametric mixed-effects model for an Italian case study. Journal of applied Statistics, 38(10), 2225-2239.
  • Berry, C. R. (2021). Reassessing the property tax. Available at SSRN: https://ssrn.com/abstract=3800536.
  • Burford, B., & Rosenthal-Stott, H. E. (2017). First and second year medical students identify and self-stereotype more as doctors than as students: a questionnaire study. BMC Medical Education, 17, 1-9.
  • Capozza, D. R., & Helsley, R. W. (2017). The fundamentals of land prices and urban growth. In The Economics of Land Use (pp. 183-194). Routledge.
  • Chong, F. (2020). Housing price, mortgage interest rate and immigration. Real Estate Management and Valuation, 28(3), 36-44.
  • Cochrane, C., Ba, D., Klerman, E. B., & Hilaire, M. A. S. (2021). An ensemble mixed effects model of sleep loss and performance. Journal of Theoretical Biology, 509, 110497.
  • Cunnings, I., & Finlayson, I. (2015). Mixed effects modeling and longitudinal data analysis. In Advancing quantitative methods in second language research (pp. 159-181). Routledge.
  • French, N. (2003). The RICS valuation and appraisal standards. Journal of Property Investment & Finance, 21(6), 495-501.
  • Gilderbloom, J. I., Hanka, M. J., & Ambrosius, J. D. (2012). Without bias? Government policy that creates fair and equitable property tax assessments. The American Review of Public Administration, 42(5), 591-605.
  • Glaeser, E. L., Gottlieb, J. D., & Gyourko, J. (2012). Can cheap credit explain the housing boom?. In Housing and the financial crisis (pp. 301-359). University of Chicago Press.
  • Gloudemans, R., & Almy, R. (2011). Fundamentals of mass appraisal. International Association of Assessing Officers, Kansas City: MO.
  • Grimes, A., & Liang, Y. (2009). Spatial determinants of land prices: Does Auckland’s metropolitan urban limit have an effect? Applied Spatial Analysis and Policy, 2, 23-45.
  • Heisig, J. P., & Schaeffer, M. (2019). Why you should always include a random slope for the lower-level variable involved in a cross-level interaction. European Sociological Review, 35(2), 258-279.
  • Hughes, C., Sayce, S., Shepherd, E., & Wyatt, P. (2020). Implementing a land value tax: Considerations on moving from theory to practice. Land Use Policy, 94, 104494.
  • IAAO. (2017). Standards on mass appraisal of real property. International Association of Assessing Officers, Kansas City: MO.
  • Iwasaki, Y. (2021). Relationship between Rate of Vacant Houses and Rate of Houses below Exemption Point of Fixed Asset Tax in Japan. Urban and Regional Planning Review, 8, 186-200.
  • Jordà, Ò., Schularick, M., & Taylor, A. M. (2015). Betting the house. Journal of international economics, 96, S2-S18.
  • Khoda Bakhshi, A., & Ahmed, M. M. (2023). Does random slope hierarchical modeling always outperform random intercept counterpart? Accounting for unobserved heterogeneity in a real-time empirical analysis of critical crash occurrence. Journal of Transportation Safety & Security, 15(2), 177-214.
  • KOSIS. (2022). Statistics of local taxes in 2021. Korean Statistical Information Service, Daejeon City.
  • KOSIS. (2023). Populations and households in 2022. Korean Statistical Information Service, Daejeon City.
  • KROLL. (2022). Property Tax Snapshot: Mechanics and Trends. Kroll Inc., New York: NY.
  • Krupa, O. (2014). Housing crisis and vertical equity of the property tax in a market value–based assessment system. Public Finance Review, 42(5), 555-581.
  • Leamer, E. E. (2015). Housing really is the business cycle: what survives the lessons of 2008–09?. Journal of Money, Credit and Banking, 47(S1), 43-50.
  • Lee, C., & Park, K. (2014). Incorporating Subjective Priors into Mass Appraisal Modeling. The Korea Spatial Planning Review, 81, 67-89.
  • Lim, G. C., & Tsiaplias, S. (2018). Interest rates, local housing markets and house price over‐reactions. Economic Record, 94, 33-48.
  • Lin, L., Liu, Y., & Peng, C. L. (2023). Luxury tax and price changes: evidence from the Taiwan housing market. Journal of Housing and the Built Environment, 38(3), 1431-1455.
  • Lin, S. H., & Hsieh, J. C. (2021). Is property taxation useful for the regulation of residential market? Reflections on Taiwanese experience. Journal of Housing and the Built Environment, 36(1), 303-324.
  • Ma, J. H. (2023). A study on the role of publicly announced prices and taxation requirements in property tax. Tax Research, 23(4), 279-314.
  • Mandel, F., Ghosh, R. P., & Barnett, I. (2023). Neural networks for clustered and longitudinal data using mixed effects models. Biometrics, 79(2), 711-721.
  • Masci, C., Ieva, F., & Paganoni, A. M. (2022). Semiparametric multinomial mixed-effects models: A university students profiling tool. The Annals of Applied Statistics, 16(3), 1608-1632.
  • McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press.
  • Pellagatti, M., Masci, C., Ieva, F., & Paganoni, A. M. (2021). Generalized mixed‐effects random forest: A flexible approach to predict university student dropout. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14(3), 241-257.
  • Piazzesi, M., & Schneider, M. (2016). Housing and macroeconomics. Handbook of macroeconomics, 2, 1547-1640.
  • Pinheiro, J. C., & Bates, D. M. (2000). Linear mixed-effects models: basic concepts and examples. Mixed-effects models in S and S-Plus, 3-56.
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). Sage.
  • Runner, G., MA, F., Horton, J. E., Harkey, D. L., & Yee, B. T. (2016). State Assessment Manual. California State Board of Equalization.
  • Sutton, G. D., Mihaljek, D., & Subelyte, A. (2017). Interest rates and house prices in the United States and around the world.
  • Tosh, D. S., & Rayburn, W. B. (2004). Uniform Standards of Professional Appraisal Practice: Applying the Standards. Dearborn Real Estate.
  • Wen, H., & Goodman, A. C. (2013). Relationship between urban land price and housing price: Evidence from 21 provincial capitals in China. Habitat International, 40, 9-17.
  • Winter, B. (2018). A Very Basic Tutorial for Performing Linear Mixed Effects Analyses: Tutorial 2. Merced, CA: University of California.
  • Wu, H., & Zhang, J. T. (2006). Nonparametric regression methods for longitudinal data analysis: mixed-effects modeling approaches. John Wiley & Sons.
  • Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R (Vol. 574, p. 574). New York: springer.