Spatiotemporal effects of proximity to metro extension on housing price dynamics in Manhattan, New York City
Keywords:New York City, Transit, Urban transportation, housing prices, regional economic development pattern, Economic Development
Investment in public transportation such as a metro line extension is often capitalized partially into housing values due to the spatiotemporal effects. Using housing transaction data from 2014 to 2019, this paper studies the Second Avenue Subway or Q-line extension in New York’s City’s Manhattan borough. Multiple metro station catchment areas were investigated using spatial autocorrelation-corrected hedonic pricing models to capture the variation of housing price dynamics. The results indicate that properties in closer proximity to the Q-line extension received higher price discounts. The effect varied by occupancy type and building form: condominiums experienced the highest price discount, while walk-up and elevator co-ops experienced a price premium. After controlling for location variations, we observed price discounts on the westside and price premiums on the eastside of the Q-line. Residential properties within 150 m west to the Q-line extension received the highest price discount post operation, while on the eastside, properties in the same proximity received the highest price premium. The anticipation effect varies by distance to metro extension stations, both before and after the operation of metro line extension. We discuss the disruption of metro construction on the housing market depending on housing type, location variation, and changes over time.
Agostini, C. A., & Palmucci, G. A. (2008). The anticipated capitalization effect of a new metro line on housing prices. Fiscal Studies, 29(2), 233–256.
Alonso, W. (1964). Location and land use: Toward a general theory of land rents. Cambridge, MA: Harvard University Press.
Anselin, L. & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In A. Ullah (Ed.), Handbook of applied economic statistics (pp. 237–290). Boca Raton, FL: CRC Press.
Atkinson-Palombo, C. (2010). Comparing the capitalization benefits of light-rail transit and overlay zoning for single-family houses and condos by neighborhood type in metropolitan Phoenix, Arizona. Urban Studies, 47(11), 2409–2426.
Cao, K., Diao, M., & Wu, B. (2019). A big data–based geographically weighted regression model for public housing prices: A case study in Singapore. Annals of the American Association of Geographers, 109(1), 173–186.
Cervero, R. (2006). Effects of light and commuter rail transit on land prices: Experiences in San Diego County. Berkeley, CA: University of California Transportation Center.
Comber, S. & Arribas-Bel, D. (2017). Waiting on the train: The anticipatory (causal) effects of cross rail in Ealing. Journal of Transport Geography, 64, 13–22.
City of New York. (2019). Building classification. https://www1.nyc.gov/assets/finance/jump/hlpbldgcode.html
Dai, X., Xin B., & Min X. (2016). The influence of Beijing rail transfer stations on surrounding housing prices. Habitat International, 55, 79–88.
Debrezion, G., Pels, E., & Rietveld, P. (2011). The impact of rail transport on real estate prices: An empirical analysis of the Dutch housing market. Urban Studies, 48(5), 997–1015.
Deng, L. & Chen, J. (2019). Market development, state intervention, and the dynamics of new housing investment in China. Journal of Urban Affairs, 41(2), 223–247.
Department of Finance. (2019). New York City. https://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page
Devaux, N., Dube, J. & Apparicio, P. (2017). Anticipation and post-construction impact of a metro extension on residential values: The case of Laval (Canada), 1995-2013. Journal of Transport Geography, 62, 8–19.
Diao, M. (2015). Selectivity, spatial autocorrelation and the valuation of transit accessibility. Urban Studies, 52(1), 159–77.
Diao, M., Leonard, D., & Sing, T. F. (2017). Spatial-difference-in-differences models for impact of new mass rapid transit line on private housing values. Regional Science and Urban Economics, 67, 64–77.
Duncan, M. (2008). Comparing rail transit capitalization benefits for single-family and condominium units in San Diego, California. Transportation Research Record: Journal of the Transportation Research Board, 2067(1), 120–30.
Dube, J., Legros, D., & Devaux, N. (2018). From bus to tramway: Is there an economic impact of substituting a rapid mass transit system? An empirical investigation accounting for anticipation effect. Transportation Research Part A: Policy and Practice, 110, 73–87.
Dube, J., Thériault, M., & Des Rosiers, F. (2013). Commuter rail accessibility and house values: The case of the Montreal South Shore, Canada, 1992–2009. Transportation Research Part A: Policy and Practice, 54, 49–66.
Gordon, B. L., &Winkler, D. T. (2019). New house premiums, market conditions, and the decision to purchase a new versus existing house. Journal of Real Estate Research, 41(3), 379–410.
Guan, C, & Peiser, R. (2018). Accessibility, urban form, and property value: A study of Pudong, Shanghai. Journal of Transport and Land Use, 11(1), 1057–1080.
Guan, C., & Rowe, P. (2021). China’s urban block structures: A comparative study in three cities across different territories. Socio-Ecological Practice Research, 3, 37–53.
Harjunen, O. (2018). Essays in urban economics and housing market capitalization. Aalto University Press, 119, 13.
Hess, B. & Almeida, T. (2007). Impact of proximity to light rail rapid transit on station-area property values in Buffalo, New York. Urban Studies, 44(5/6), 1041–1068.
Hiseh, L., & Noonan, D. (2018). The closer the better? Examining support for a large urban redevelopment project in Atlanta. Journal of Urban Affairs, 40(2), 246–260.
Kaneko, Y., Nakagawa, T., Phun, V., & Kato, H. (2019). Impacts of urban railway investment on regional economies: Evidence from Tokyo using spatial difference-in-differences analysis. Transportation Research Record: Journal of the Transportation Research Board, 2673(10), 129–140. https://doi.org/10.1177/0361198119846098
Kay, A., Noland, R., & DiPetrillo, S. (2014). Residential property valuations near transit stations with transit-oriented development. Journal of Transport Geography, 39(C), 131–140.
Kim, K., & Lahr, M. L. (2014). The impact of Hudson‐Bergen light rail on residential property appreciation. Regional Science, S1, 79–97.
Kopczewska, K., and Lewandowska, L. (2018). The price for subway access: Spatial econometric modelling of office rental rates in London. Urban Geography, 39(10), 1528–554. https://doi.org/10.1080/02723638.2018.1481601
Landis, J., Guhathukurta, S., Huang, W., Zhang, M., & Fukuji, B. (1995). Rail transit investments, real estate values, and land use change: A comparative analysis of five California rail transit systems (Research Report No. 48). Berkeley, CA: Institute of Urban and Regional Studies, University of California.
Lesage, J., & Pace, K. (2009). Introduction to spatial econometrics. Boca Raton, FL: CRC press.
Lewis-Workman, S., & D. Brod. (1997). Measuring the neighborhood benefits of rail transit accessibility. Transportation Research Record: Journal of the Transportation Research Board, 1576, 147–153.
Lin, J. Y., Chen, T. L. & Han, Q. Z. (2018). Simulating and predicting the impacts of light rail transit systems on urban land use by using cellular automata: A case study of Dongguan, China. Sustainability, 10(4), 1293.
Liou, F. M., Yang, S. Y., Chen, B. & Hsieh, W. P. (2016). The effects of mass rapid transit station on the house prices in Taipei: The hierarchical linear model of individual growth. Pacific Rim Property Research Journal, 22, 3–16.
Martínez, L., & Viegas, J. (2009). Effects of transportation accessibility on residential property values. Transportation Research Record: Journal of the Transportation Research Board, 2115(1), 127–137.
McDonald, J. F., & Osuji, C. I. (1995). The effect of anticipated transportation improvement on residential land values. Regional Science & Urban Economics, 25(3), 261–278.
McMillen, D. P. (2006). Testing for monocentricity. In R. J. Arnott, & D. P. McMillen (Eds.), A companion to urban economics (pp. 128–140). Oxford, UK; Blackwell.
Miller, N, Sah, V., & Sklar, M. K. (2018). Estimating property condition effect on residential property value: Evidence from U.S. home sales data. Journal of Real Estate Research, 40(2), 179–198.
Pan, Q., Pan H., et al. (2014). Effects of rail transit on residential property values. Transportation Research Record: Journal of the Transportation Research Board, 2453(1), 118–127.
Rodriguez, D., & Mojica, C. (2009). Capitalization of BRT network expansions effects into prices of non-expansion areas. Transportation Research Part A: Policy and Practice, 43(5), 560–571.
Spears, S., Boarnet, M. G., & Houston, D. (2017). Driving reduction after the introduction of light rail transit: Evidence from an experimental-control group evaluation of the Los Angeles Expo Line. Urban Studies, 54(12), 2780–2799.
Valente, J., Wu, S., Gelfand, A., & Sirmans, C. F. (2005). Apartment rent prediction using spatial modeling. Journal of Real Estate Research, 27(1), 105–136.
Wang, Y. M., Feng, S. W., Deng, Z. W. & Cheng, S. Y. (2016). Transit premium and rent segmentation: A spatial quantile hedonic analysis of Shanghai Metro. Transport Policy, 51, 61–69.
Xu, T., Zhang, M., Aditjandra, P. (2016). The impact of urban rail transit on commercial property value: New evidence from Wuhan, China. Transportation Research Part A: Policy and Practice 91, 223-235.https://doi.org/10.1016/j.tra.2016.06.026
Zhang, X., Liu, X. X., Hang, J. Q., Yao, D. B., & Shi, G. P. (2016). Do urban rail transit facilities affect housing prices? Evidence from China. Sustainability, 8(4), 380.
Zhong, H., & Li, W. (2016). Rail transit investment and property values: An old tale retold. Transport Policy, 51, 33–48.
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