Publications-Periodical Articles
Article View/Open
Publication Export
Google ScholarTM
NCCU Library
Citation Infomation
Related Publications in TAIR
| Title | An empirical method for decomposing the contributions of land and building to housing value |
| Creator | 林士淵 Lin, Shih-Yuan Pan, Kuan-Lun;Teng, Hsiao Jung;Cheng, Yu En |
| Contributor | 地政系 |
| Key Words | Land Value; Building Value; Housing Value; Apportionment Theory; Artificial Neural Network |
| Date | 2021-01 |
| Date Issued | 18-May-2022 16:06:32 (UTC+8) |
| Summary | 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. |
| Relation | International Real Estate Review, 24(3), 385-403 |
| Type | article |
| dc.contributor | 地政系 | - |
| dc.creator (作者) | 林士淵 | - |
| dc.creator (作者) | Lin, Shih-Yuan | - |
| dc.creator (作者) | Pan, Kuan-Lun;Teng, Hsiao Jung;Cheng, Yu En | - |
| dc.date (日期) | 2021-01 | - |
| dc.date.accessioned | 18-May-2022 16:06:32 (UTC+8) | - |
| dc.date.available | 18-May-2022 16:06:32 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-May-2022 16:06:32 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/140103 | - |
| dc.description.abstract (摘要) | 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. | - |
| dc.format.extent | 124 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | International Real Estate Review, 24(3), 385-403 | - |
| dc.subject (關鍵詞) | Land Value; Building Value; Housing Value; Apportionment Theory; Artificial Neural Network | - |
| dc.title (題名) | An empirical method for decomposing the contributions of land and building to housing value | - |
| dc.type (資料類型) | article | - |