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#9937. The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan
| September 2026 | publication date |
| Proposal available till | 25-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Social Sciences (miscellaneous); Geography, Planning and Development; Economics and Econometrics; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №9937.1   | Contract №9937.2 | Contract №9937.3 | Contract №9937.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
The occurrence of 6.7-magnitude Hokkaido Eastern Iburi Earthquake on 6th September 20XX 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 machine-learning algorithms to examine whether the unexpected hazard shock (i.e. 20XX 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.
Keywords:
Japan; Land prices; Machine-learning algorithms; Natural hazard; Property prices
Contacts :
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3 place - free (for sale)
4 place - free (for sale)
Abstract:
The occurrence of 6.7-magnitude Hokkaido Eastern Iburi Earthquake on 6th September 20XX 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 machine-learning algorithms to examine whether the unexpected hazard shock (i.e. 20XX 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.
Keywords:
Japan; Land prices; Machine-learning algorithms; Natural hazard; Property prices
Contacts :
