<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This study discusses the impact of the mass rapid transit (MRT) system’s construction and operation on neighborhood housing prices. Estimations were conducted by considering the spatial dependence of housing prices and using a difference-in-difference (DD) method that incorporated the spatial lag model and spatial error model. The empirical results indicated that the coefficient of the interaction variable of the MRT’s range of influence and commencement time of construction was -0.079, reaching the 10% significance level. Hence, when MRT construction began, housing prices in the experimental group decreased by 7.9% as compared to the control group. A possible reason for these findings is that the “dark age” of urban traffic, air pollution, and noise brought about by MRT construction negatively affected housing prices. With regard to MRT operations, the coefficient of the interaction variable of the MRT’s influence range and service commencement time did not reach the level of significance, indicating that housing prices within the MRT’s influence range were not significantly affected when the MRT’s service began. This might be due to the fact that the psychological expectations of the increase in housing prices had already been realized during the planning, announcement, and construction phases.</p></abstract>

This study discusses the impact of the mass rapid transit (MRT) system’s construction and operation on neighborhood housing prices. Estimations were conducted by considering the spatial dependence of housing prices and using a difference-in-difference (DD) method that incorporated the spatial lag model and spatial error model. The empirical results indicated that the coefficient of the interaction variable of the MRT’s range of influence and commencement time of construction was -0.079, reaching the 10% significance level. Hence, when MRT construction began, housing prices in the experimental group decreased by 7.9% as compared to the control group. A possible reason for these findings is that the “dark age” of urban traffic, air pollution, and noise brought about by MRT construction negatively affected housing prices. With regard to MRT operations, the coefficient of the interaction variable of the MRT’s influence range and service commencement time did not reach the level of significance, indicating that housing prices within the MRT’s influence range were not significantly affected when the MRT’s service began. This might be due to the fact that the psychological expectations of the increase in housing prices had already been realized during the planning, announcement, and construction phases.

The Impact of a Mass Rapid Transit System on Neighborhood Housing Prices: An Application of Difference-In-Difference and Spatial Econometrics

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This study discusses the impact of the mass rapid transit (MRT) system’s construction and operation on neighborhood housing prices. Estimations were conducted...

The Impact of a Mass Rapid Transit System on Neighborhood Housing Prices: An Application of Difference-In-Difference and Spatial Econometrics

Online veröffentlicht: 12. März 2020

Seitenbereich: 28 - 40

DOI: https://doi.org/10.2478/remav-2020-0003

Schlüsselwörter
housing prices, mass rapid transit, geographic information system, difference-in-difference, spatial error model, spatial lag model

© 2020 Lee Chun-Chang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Impact of a Mass Rapid Transit System on Neighborhood Housing Prices: An Application of Difference-In-Difference and Spatial Econometrics

Online veröffentlicht: 12. März 2020

Seitenbereich: 28 - 40

DOI: https://doi.org/10.2478/remav-2020-0003

Schlüsselwörterhousing prices, mass rapid transit, geographic information system, difference-in-difference, spatial error model, spatial lag model

© 2020 Lee Chun-Chang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
housing prices, mass rapid transit, geographic information system, difference-in-difference, spatial error model, spatial lag model