Is Flood Risk Capitalized into Real Estate Market Value? A Mahalanobis-Metric Matching Approach to the Housing Market in Gyeonggi, South Korea

• Main Authors: Eunah Jung, Heeyeun Yoon
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Sustainability
• Subjects: natural disaster; housing price; random-intercept multilevel model; Mahalanobis-metric matching model
• Online Access: https://www.mdpi.com/2071-1050/10/11/4008

In this study, we investigate how far away and for how long past flooding affected single-family housing values in Gyeonggi, South Korea. In order to empirically explore the geographic and temporal extent of the effects, we adopt two analytical methods: random-intercept multilevel modeling and Mahalanobis-metric matching modeling. The analytical results suggest that the geographic extent of the discount effect of a flooding disaster is within 300 m from an inundated area. Market values of housing located 0⁻100, 100⁻200, and 200⁻300 m from inundated areas were lower by 11.0%, 7.4%, and 6.3%, respectively, than counterparts in the control group. The effect lasted only for 12 months after the disaster and then disappeared. During the first month, 1⁻3 months, and 3⁻6 months after a flood, housing units in the disaster-influenced area (within 300 m of the inundated area) were worth, on average, 57.6%, 49.2%, and 45.9% less than control units, respectively. Also, within the following 6 months, the discount effects were reduced to 33.2%. On the other hand, the results showed no statistically significant effects on market values more than 12 months after the disaster. By providing insights into how people perceive and respond to natural hazards, this research provides practical lessons for establishing sustainable disaster management and urban resilience strategies.