Application of Hedonic Regression Model and Spatial Durbin Model (SDM) in Land Price Assessment for Paddy Land in the Urbanization Area of Hue City
Main Article Content
Abstract
Urbanization causes rapid fluctuations in rice paddy land prices, leading to risks of inaccurate valuation in compensation, land-use conversion, and sustainable land management. This study compares the Hedonic model (OLS) and the Spatial Durbin Model (SDM) in valuing rice paddy land in Thuy Thanh commune (Hue city) based on a survey of 454 land parcels. The results of the OLS model (adjusted R² = 0.796) show that significant influencing factors include: project information (strongest influence), enterprise compensation policies, soil fertility, and yield. Meanwhile, the SDM model (Pseudo R² = 0.8667) shows that in addition to the impact of factors within the land parcel, there is also a spillover effect from the surrounding area, confirming the role of spatial autocorrelation in land valuation in urbanizing areas. Overall, the SDM model yields more suitable results when it comes to reflecting spatial impacts, thereby providing a scientific basis for improving the valuation of rice land in a scientific and equitable manner.
Keywords:
Hedonic, SDM, valuation, paddy land, urbanization.
References
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[15] J. E. Reynolds, D. L. Tower, Factors Affecting Rural Land Prices in An Urbanizing Area, Review of Regional Studies, Vol. 8, No. 3, 1978, pp. 23-34, https://doi.org/10.52324/001c.10181.
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[18] A. Embaye, B. Abidoye, S. Chan, Machine Learning and Spatial Econometrics in Property Valuation: A Comparative Study, Journal of Real Estate Research, Vol.40, No.4, 2018, pp. 567-590.
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[24] I. S. Jang, The Impact of Plan Characteristics of Residential Land Development Project on Inner Land Price Change, Journal of the Korea Academia-Industrial Cooperation Society, Vol 17, No. 8, 2016, pp. 698-705, https://doi.org/10.5762/KAIS.2016.17.8.698.
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[28] D. A. Son, H. T. Truong, Land Price and Determination of Land Price in Compensation When The State Recovery Land, Journal of Economics, Law and Management, Vol. 7 No.1, 2023, pp. 135-143, https://doi.org/10.32508/stdjelm.v7iS1.1255.
[29] M. Agosta, E. Schimmenti, C. Di Franco, Agricultural Land Markets: A Systematic Literature Review on The Factors Affecting Land Prices, Land, Vol. 14, No. 5, 2025, pp. 978, https://doi.org/10.3390/land14050978.
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[32] L. L. Cam, Maximum Likelihood: An Introduction, International Statistical Review, Vol. 58, No. 2, 1990, pp. 153-171, https://doi.org/10.2307/1403464.
[33] A. B. Golgher, P. R.Voss, How to Interpret The Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects, Spatial Demography, Vol. 4, No. 3, 2016, pp 175-205, https://doi.org/10.1007/s40980-015-0016-y.
[34] N. Bui, L. Wen, B. Sharp, House Prices and Flood Risk Exposure: An Integration of Hedonic Property Model and Spatial Econometric Analysis, The Journal of Real Estate Finance and Economics, Vol. 69, No. 1, 2024, pp. 100-131, https://doi.org/10.1007/s11146-022-09930-z.
[35] Y. S. Chung, D. Seo, J. Kim, Price Determinants and GIS Analysis of The Housing Market in Vietnam: The Cases of Ho Chi Minh City and Hanoi, Sustainability, Vol. 10, No. 12, 2018,
pp. 4720, https://doi.org/10.3390/su10124720.