TY - GEN
T1 - Detection and Prediction of House Price Bubbles
T2 - 18th International Conference on Computational Science, ICCS 2018
AU - Jang, Hanwool
AU - Ahn, Kwangwon
AU - Kim, Dongshin
AU - Song, Yena
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In the early stages of growth of a city, housing market fundamentals are uncertain. This could attract speculative investors as well as actual housing demand. Sejong is a recently built administrative city in South Korea. Most government departments and public agencies have moved into it, while others are in the process of moving or plan to do so. In Sejong, a drastic escalation in house prices has been noted over the last few years, but at the same time, the number of vacant housing units has increased. Using the present value model, lease-price ratio, and log-periodic power law, this study examines the bubbles in the Sejong housing market. The analysis results indicate that (i) there are significant house price bubbles, (ii) the bubbles are driven by speculative investment, and (iii) the bubbles are likely to burst earlier here than in other cities. The approach in this study can be applied to identifying pricing bubbles in other cities.
AB - In the early stages of growth of a city, housing market fundamentals are uncertain. This could attract speculative investors as well as actual housing demand. Sejong is a recently built administrative city in South Korea. Most government departments and public agencies have moved into it, while others are in the process of moving or plan to do so. In Sejong, a drastic escalation in house prices has been noted over the last few years, but at the same time, the number of vacant housing units has increased. Using the present value model, lease-price ratio, and log-periodic power law, this study examines the bubbles in the Sejong housing market. The analysis results indicate that (i) there are significant house price bubbles, (ii) the bubbles are driven by speculative investment, and (iii) the bubbles are likely to burst earlier here than in other cities. The approach in this study can be applied to identifying pricing bubbles in other cities.
UR - http://www.scopus.com/inward/record.url?scp=85049024678&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-93713-7_76
DO - 10.1007/978-3-319-93713-7_76
M3 - Conference contribution
AN - SCOPUS:85049024678
SN - 9783319937120
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 782
EP - 795
BT - Computational Science – ICCS 2018 - 18th International Conference, Proceedings
A2 - Dongarra, Jack
A2 - Fu, Haohuan
A2 - Krzhizhanovskaya, Valeria V.
A2 - Lees, Michael Harold
A2 - Sloot, Peter M.
A2 - Shi, Yong
A2 - Tian, Yingjie
PB - Springer Verlag
Y2 - 11 June 2018 through 13 June 2018
ER -