An Empirical Method for Decomposing the Contributions of Land and Building Values to Housing Value

Kuan-Lun Pan (), Hsiao Jung Teng (), Shih-Yuan Lin () and Yu En Cheng ()
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Kuan-Lun Pan: National Taiwan University
Hsiao Jung Teng: Anfu Co., Ltd
Shih-Yuan Lin: National Chengchi University
Yu En Cheng: Anfu Co., Ltd

International Real Estate Review, 2021, vol. 24, issue 3, 385-403

Abstract: This paper develops an empirical method that uses two separate housing related components to estimate housing value: land and building. The artificial neural network (ANN) technique is used to iteratively solve for two hedonic models simultaneously by minimizing the difference in the observed total value and the sum of the estimated land and building values. This method enables one to objectively separate housing value into land and building components. Using actual sales transaction data from Taipei City, we estimate the land value as a share of the total housing value. The results show that the land value accounts for a higher share with older properties. The share of the land value of low-rise buildings tends to be higher than that of high-rise buildings. The share of the land value can deviate by 20 percentage points between more or less expensive housing communities within Taipei City.

Keywords: Land Value; Building Value; Housing Value; Apportionment Theory; Artificial Neural Network (search for similar items in EconPapers)
Date: 2021
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