Detection and prediction of house price bubbles: evidence from a new city

Hanwool Jang; Kwangwon Ahn; Dongshin Kim; Yena Song

doi:10.1007/978-3-319-93713-7_76

Detection and prediction of house price bubbles: evidence from a new city

Hanwool Jang, Kwangwon Ahn, Dongshin Kim, Yena Song^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Computational Science – ICCS 2018
Editors	Yong Shi, Haohuan Fu, Yingjie Tian, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Jack Dongarra, Peter M. A. Sloot
Publisher	Springer Nature
Pages	782–795
Number of pages	14
Volume	10862
ISBN (Electronic)	9783319937137
ISBN (Print)	9783319937120
DOIs	https://doi.org/10.1007/978-3-319-93713-7_76
Publication status	Published - 12 Jun 2018
Externally published	Yes
Event	18th Annual International Conference on Computational Science - Wuxi, China Duration: 11 Jun 2018 → 13 Jun 2018 https://www.iccs-meeting.org/iccs2019/iccs-2018/ (Link to conference website)

Publication series

Name	Lecture Notes in Computer Science
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th Annual International Conference on Computational Science
Abbreviated title	ICCS 2018
Country/Territory	China
City	Wuxi
Period	11/06/18 → 13/06/18
Internet address	https://www.iccs-meeting.org/iccs2019/iccs-2018/ (Link to conference website)

Keywords

Newly developed city
Real estate bubble
Complex system

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1007/978-3-319-93713-7_76

Cite this

Jang, H., Ahn, K., Kim, D., & Song, Y. (2018). Detection and prediction of house price bubbles: evidence from a new city. In Y. Shi, H. Fu, Y. Tian, V. V. Krzhizhanovskaya, M. H. Lees, J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2018 (Vol. 10862, pp. 782–795). (Lecture Notes in Computer Science). Springer Nature. https://doi.org/10.1007/978-3-319-93713-7_76

Jang, Hanwool ; Ahn, Kwangwon ; Kim, Dongshin et al. / Detection and prediction of house price bubbles: evidence from a new city. Computational Science – ICCS 2018. editor / Yong Shi ; Haohuan Fu ; Yingjie Tian ; Valeria V. Krzhizhanovskaya ; Michael Harold Lees ; Jack Dongarra ; Peter M. A. Sloot. Vol. 10862 Springer Nature, 2018. pp. 782–795 (Lecture Notes in Computer Science).

@inproceedings{ae576a2c84ff44758c6ca6ef60ad5e06,

title = "Detection and prediction of house price bubbles: evidence from a new city",

abstract = "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.",

keywords = "Newly developed city, Real estate bubble, Complex system",

author = "Hanwool Jang and Kwangwon Ahn and Dongshin Kim and Yena Song",

year = "2018",

month = jun,

day = "12",

doi = "10.1007/978-3-319-93713-7_76",

language = "English",

isbn = "9783319937120",

volume = "10862",

series = "Lecture Notes in Computer Science",

publisher = "Springer Nature",

pages = "782–795",

editor = "Yong Shi and Haohuan Fu and Yingjie Tian and Krzhizhanovskaya, {Valeria V.} and Lees, {Michael Harold} and Jack Dongarra and Sloot, {Peter M. A.}",

booktitle = "Computational Science – ICCS 2018",

address = "United States",

note = "18th Annual International Conference on Computational Science, ICCS 2018 ; Conference date: 11-06-2018 Through 13-06-2018",

url = "https://www.iccs-meeting.org/iccs2019/iccs-2018/",

}

Jang, H, Ahn, K, Kim, D & Song, Y 2018, Detection and prediction of house price bubbles: evidence from a new city. in Y Shi, H Fu, Y Tian, VV Krzhizhanovskaya, MH Lees, J Dongarra & PMA Sloot (eds), Computational Science – ICCS 2018. vol. 10862, Lecture Notes in Computer Science, Springer Nature, pp. 782–795, 18th Annual International Conference on Computational Science, Wuxi, China, 11/06/18. https://doi.org/10.1007/978-3-319-93713-7_76

Detection and prediction of house price bubbles: evidence from a new city. / Jang, Hanwool; Ahn, Kwangwon; Kim, Dongshin et al.
Computational Science – ICCS 2018. ed. / Yong Shi; Haohuan Fu; Yingjie Tian; Valeria V. Krzhizhanovskaya; Michael Harold Lees; Jack Dongarra; Peter M. A. Sloot. Vol. 10862 Springer Nature, 2018. p. 782–795 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Detection and prediction of house price bubbles: evidence from a new city

AU - Jang, Hanwool

AU - Ahn, Kwangwon

AU - Kim, Dongshin

AU - Song, Yena

PY - 2018/6/12

Y1 - 2018/6/12

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.

KW - Newly developed city

KW - Real estate bubble

KW - Complex system

U2 - 10.1007/978-3-319-93713-7_76

DO - 10.1007/978-3-319-93713-7_76

M3 - Conference contribution

SN - 9783319937120

VL - 10862

T3 - Lecture Notes in Computer Science

SP - 782

EP - 795

BT - Computational Science – ICCS 2018

A2 - Shi, Yong

A2 - Fu, Haohuan

A2 - Tian, Yingjie

A2 - Krzhizhanovskaya, Valeria V.

A2 - Lees, Michael Harold

A2 - Dongarra, Jack

A2 - Sloot, Peter M. A.

PB - Springer Nature

T2 - 18th Annual International Conference on Computational Science

Y2 - 11 June 2018 through 13 June 2018

ER -

Detection and prediction of house price bubbles: evidence from a new city

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

Fingerprint

Cite this