U.S. metropolitan house price dynamics

Elias Oikarinen; Steven C. Bourassa; Martin Hoesli; Janne Engblom

doi:10.1016/j.jue.2018.03.001

U.S. metropolitan house price dynamics

Elias Oikarinen, Steven C. Bourassa, Martin Hoesli, Janne Engblom

Accountancy & Finance, Accountancy

Research output: Contribution to journal › Article › peer-review

27 Citations (Scopus)

6 Downloads (Pure)

Abstract

Using data for 70 U.S. metropolitan areas, this study explores spatial heterogeneity in house price dynamics. We use recent advances in panel econometrics that allow for spatial heterogeneity, cross-sectional dependence, and non-stationary but cointegrated data. We test for spatial differences and analyze the relationship between the price elasticity of housing supply and the
income elasticity of prices, as well as bubble size and duration. The long-term elasticity of house prices with respect to aggregate personal income averages 0.81, but varies considerably across metropolitan areas. The long-term income elasticity generally is greater in the more supplyinelastic metropolitan areas, and we show that bubble size and duration are inversely related to supply elasticity. Also short-term momentum and reversion dynamics show substantial spatial
heterogeneity.

Original language	English
Pages (from-to)	54-69
Number of pages	16
Journal	Journal of Urban Economics
Volume	105
Early online date	9 Mar 2018
DOIs	https://doi.org/10.1016/j.jue.2018.03.001
Publication status	Published - May 2018

Bibliographical note

For helpful comments, we thank Dan McMillen and other participants at the 2017 FSU-UF Critical Issues in Real Estate Symposium, two anonymous referees, and the editor. We thank Sam Khater for providing the CoreLogic house price indexes, Albert Saiz for giving supply elasticity data, and Andrew VanValin for preparing the maps.
Funding: This work was supported by the OP-Pohjola Group Research Foundation and the Academy of Finland (first author; grant number 268310)

Keywords

house prices
dynamics
panel data
spatial heterogeneity
spatial dependence
bubbles

UN SDGs

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

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Accepted Manuscript
© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 1.05 MBLicence: CC BY-NC-ND

Martin Hoesli
- Business School, Accountancy & Finance, Accountancy - Chair in Accountancy
- Business School, Centre for Real Estate Research (CRER)
Person: Academic

Cite this

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title = "U.S. metropolitan house price dynamics",

abstract = "Using data for 70 U.S. metropolitan areas, this study explores spatial heterogeneity in house price dynamics. We use recent advances in panel econometrics that allow for spatial heterogeneity, cross-sectional dependence, and non-stationary but cointegrated data. We test for spatial differences and analyze the relationship between the price elasticity of housing supply and theincome elasticity of prices, as well as bubble size and duration. The long-term elasticity of house prices with respect to aggregate personal income averages 0.81, but varies considerably across metropolitan areas. The long-term income elasticity generally is greater in the more supplyinelastic metropolitan areas, and we show that bubble size and duration are inversely related to supply elasticity. Also short-term momentum and reversion dynamics show substantial spatialheterogeneity.",

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AU - Oikarinen, Elias

AU - Bourassa, Steven C.

AU - Hoesli, Martin

AU - Engblom, Janne

N1 - For helpful comments, we thank Dan McMillen and other participants at the 2017 FSU-UF Critical Issues in Real Estate Symposium, two anonymous referees, and the editor. We thank Sam Khater for providing the CoreLogic house price indexes, Albert Saiz for giving supply elasticity data, and Andrew VanValin for preparing the maps. Funding: This work was supported by the OP-Pohjola Group Research Foundation and the Academy of Finland (first author; grant number 268310)

PY - 2018/5

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N2 - Using data for 70 U.S. metropolitan areas, this study explores spatial heterogeneity in house price dynamics. We use recent advances in panel econometrics that allow for spatial heterogeneity, cross-sectional dependence, and non-stationary but cointegrated data. We test for spatial differences and analyze the relationship between the price elasticity of housing supply and theincome elasticity of prices, as well as bubble size and duration. The long-term elasticity of house prices with respect to aggregate personal income averages 0.81, but varies considerably across metropolitan areas. The long-term income elasticity generally is greater in the more supplyinelastic metropolitan areas, and we show that bubble size and duration are inversely related to supply elasticity. Also short-term momentum and reversion dynamics show substantial spatialheterogeneity.

AB - Using data for 70 U.S. metropolitan areas, this study explores spatial heterogeneity in house price dynamics. We use recent advances in panel econometrics that allow for spatial heterogeneity, cross-sectional dependence, and non-stationary but cointegrated data. We test for spatial differences and analyze the relationship between the price elasticity of housing supply and theincome elasticity of prices, as well as bubble size and duration. The long-term elasticity of house prices with respect to aggregate personal income averages 0.81, but varies considerably across metropolitan areas. The long-term income elasticity generally is greater in the more supplyinelastic metropolitan areas, and we show that bubble size and duration are inversely related to supply elasticity. Also short-term momentum and reversion dynamics show substantial spatialheterogeneity.

KW - house prices

KW - dynamics

KW - panel data

KW - spatial heterogeneity

KW - spatial dependence

KW - bubbles

U2 - 10.1016/j.jue.2018.03.001

DO - 10.1016/j.jue.2018.03.001

M3 - Article

SN - 0094-1190

VL - 105

SP - 54

EP - 69

JO - Journal of Urban Economics

JF - Journal of Urban Economics

ER -

U.S. metropolitan house price dynamics

Abstract

Bibliographical note

Keywords

UN SDGs

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Martin Hoesli

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