The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan

Ti-Ching Peng ()
Additional contact information
Ti-Ching Peng: National Taipei University

Review of Regional Research: Jahrbuch für Regionalwissenschaft, 2021, vol. 41, issue 1, No 1, 32 pages

Abstract: Abstract 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 machine-learning 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.

Keywords: Natural hazard; Japan; Property prices; Land prices; Machine-learning algorithms (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10037-020-00148-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jahrfr:v:41:y:2021:i:1:d:10.1007_s10037-020-00148-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10037

DOI: 10.1007/s10037-020-00148-1

Access Statistics for this article

Review of Regional Research: Jahrbuch für Regionalwissenschaft is currently edited by Thomas Brenner and Georg Hirte

More articles in Review of Regional Research: Jahrbuch für Regionalwissenschaft from Springer, Gesellschaft für Regionalforschung (GfR) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().