| 期刊詳細資料 Journal detailed information |
| 作者(Author) | Ti-Ching Peng* |
| 是否為通作者(Is Corresponding Author?) | Yes |
| 篇名(Article title) | The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan |
| 期刊名(Journal name) | Review of Regional Research |
| 國際期刊(International Journal) | Econlit |
| 中文摘要(Abstrct) | |
| ABSTRCT | The occurrence of 6.7-magnitude Hokkaido Eastern Iburi Earthquake on 6th September 2018 made local residents in Japan realise the underneath hazardous threat to their own safety and residential dwellings. Japan has faced various natural disasters for a long time, and Government chose to unveil these hazard data in details
to the public to raise every one’s alertness. Despite their long-term awareness of hazards, this short-term unexpected event should have impact on local real estate markets.
Given the transaction data of properties and lands, this study used machinelearning algorithms to examine whether the unexpected hazard shock (i.e. 2018 Hokkaido Earthquake) or the observed local hazard information in geographic areas (i.e. long-term evaluation of each real estate) would be capitalised into the prices of residential properties and lands in the case of Hokkaido, Japan. It is assumed that the difference in the characteristics of disaster notification would alter individuals’ risk perception and thus the evaluation of properties; compared to uncertain, infrequent occurrence of earthquakes in the unknown future (i.e. beyond the scope of objective estimation), the release of long-term hazardous information (tied to each property
or land), which is more perceivable in near future, is more likely to be capitalised into real estate.
It is found that housing/land attributes are still the key features to real estate values in Hokkaido. In comparison with short-term unexpected hazard shock (i.e. 2018 Earthquake), the long-term hazard threats are more influential to prices of properties and lands. This is possibly due to people’s awareness of the hazard conditions of local communities while deciding where to reside. |
| 中文關鍵字(Keyword) | |
| KEYWORD | Natural hazard · Japan · Property prices · Land prices · Machinelearning algorithms |
| 卷期(Volume No) | Vol. 41 No. 1 |
| 頁數(Page number) | pp. 1~32 |
| 年份(Year) | 2021 |
| 語言(Language) | 英文 English |