IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v49y2017i51p5183-5199.html
   My bibliography  Save this article

Impacts of transportation infrastructure on single-family property values

Author

Listed:
  • Arturo Bujanda
  • Thomas M. Fullerton

Abstract

New or long-standing public infrastructure such as highways, airports, and ports of entry (POE) can increase adjacent property values generating a value premium for private developers and adjacent property owners. States and local governments aim to determine the geographic footprint and anticipate the economic value created by transportation infrastructure proximity and accessibility since it represents an opportunity to capture some infrastructure costs. Hence, it is desirable to understand the degree of correlation between transportation infrastructure proximity and changes in real property values in a spatial context particularly when defining economic development zones where transportation investments are planned and where governments expect to recover some of the infrastructure cost from increases in real property values. This research applies geographically weighted regression (GWR) analysis to determine the geographic footprint and quantify the impacts of transportation infrastructure proximity and accessibility on real property values in El Paso, Texas using a 2013 cross-sectional data set. The presence of spatial nonstationarity and heterogeneity confirms that transportation infrastructure proximity and accessibility might generate premiums on real property values, but that such premiums are not always positive and are occasionally negative. GWR shows that benefits from a transportation facility can be capitalized by non-adjacent parcels. Finally, GWR maps can help better policy development by estimating how much value is added by infrastructure proximity and accessibility throughout particular locations.

Suggested Citation

  • Arturo Bujanda & Thomas M. Fullerton, 2017. "Impacts of transportation infrastructure on single-family property values," Applied Economics, Taylor & Francis Journals, vol. 49(51), pages 5183-5199, November.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:51:p:5183-5199
    DOI: 10.1080/00036846.2017.1302064
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2017.1302064
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2017.1302064?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dubin, Robin A, 1988. "Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms," The Review of Economics and Statistics, MIT Press, vol. 70(3), pages 466-474, August.
    2. Efthymiou, D. & Antoniou, C., 2015. "Investigating the impact of recession on transportation cost capitalization: a spatial analysis," Journal of Transport Geography, Elsevier, vol. 42(C), pages 1-9.
    3. Getis, Arthur, 2007. "Reflections on spatial autocorrelation," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 491-496, July.
    4. Luc Anselin & Nancy Lozano-Gracia, 2009. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 5-34, Springer.
    5. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    6. Efthymiou, D. & Antoniou, C., 2013. "How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 1-22.
    7. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    8. Christopher Bitter & Gordon Mulligan & Sandy Dall’erba, 2007. "Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method," Journal of Geographical Systems, Springer, vol. 9(1), pages 7-27, April.
    9. Danlin Yu & Yehua Dennis Wei & Changshan Wu, 2007. "Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI," Environment and Planning B, , vol. 34(6), pages 1085-1102, December.
    10. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2013. "A Generalized Spatial Panel Data Model with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 650-685, August.
    11. Karen Fierro & Thomas Fullerton & K. Donjuan-Callejo, 2009. "Housing Attribute Preferences in a Northern Mexico Metropolitan Economy," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 37(2), pages 159-172, June.
    12. Sharada Vadali, 2008. "Toll roads and economic development: exploring effects on property values," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(3), pages 591-620, September.
    13. A. Stewart Fotheringham & Martin Charlton & Chris Brunsdon, 1997. "Measuring Spatial Variations in Relationships with Geographically Weighted Regression," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 4, pages 60-82, Springer.
    14. George E. Peterson, 2008. "Unlocking Land Values to Finance Urban Infrastructure : Land-Based Financing Options for Cities," World Bank Publications - Reports 10599, The World Bank Group.
    15. Thomas Fullerton & Emmanuel Villalobos, 2011. "Street Widths, International Ports of Entry and Border Region Housing Values," Journal of Economic Issues, Taylor & Francis Journals, vol. 45(2), pages 493-510.
    16. George E. Peterson, 2008. "Unlocking Land Values to Finance Urban Infrastructure," World Bank Publications - Books, The World Bank Group, number 6552.
    17. Antonio Páez & Fei Long & Steven Farber, 2008. "Moving Window Approaches for Hedonic Price Estimation: An Empirical Comparison of Modelling Techniques," Urban Studies, Urban Studies Journal Limited, vol. 45(8), pages 1565-1581, July.
    18. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    19. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    20. Zhang, Ming & Wang, Lanlan, 2013. "The impacts of mass transit on land development in China: The case of Beijing," Research in Transportation Economics, Elsevier, vol. 40(1), pages 124-133.
    21. Koenker, Roger, 1981. "A note on studentizing a test for heteroscedasticity," Journal of Econometrics, Elsevier, vol. 17(1), pages 107-112, September.
    22. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thomas M. Fullerton & Arturo Bujanda, 2018. "Commercial property values in a border metropolitan economy," Asia-Pacific Journal of Regional Science, Springer, vol. 2(2), pages 337-360, August.
    2. Marco Locurcio & Pierluigi Morano & Francesco Tajani & Felicia Di Liddo, 2020. "An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context," Sustainability, MDPI, vol. 12(14), pages 1-29, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas M. Fullerton & Arturo Bujanda, 2018. "Commercial property values in a border metropolitan economy," Asia-Pacific Journal of Regional Science, Springer, vol. 2(2), pages 337-360, August.
    2. Mitra, Suman K. & Saphores, Jean-Daniel M., 2016. "The value of transportation accessibility in a least developed country city – The case of Rajshahi City, Bangladesh," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 184-200.
    3. Comber, Sam & Arribas-Bel, Dani, 2017. "“Waiting on the train”: The anticipatory (causal) effects of Crossrail in Ealing," Journal of Transport Geography, Elsevier, vol. 64(C), pages 13-22.
    4. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    5. Jorge Luis Casanova Ferrando, 2019. "The Airbnb Effect on theRental Market: the Case of Madrid," Studies on the Spanish Economy eee2019-34, FEDEA.
    6. Eilers, Lea, 2016. "Spatial Dependence in Apartment Offering Prices in Hamburg," VfS Annual Conference 2016 (Augsburg): Demographic Change 145639, Verein für Socialpolitik / German Economic Association.
    7. Simlai, Prodosh, 2014. "Estimation of variance of housing prices using spatial conditional heteroskedasticity (SARCH) model with an application to Boston housing price data," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 17-30.
    8. Chica-Olmo, Jorge & Cano-Guervos, Rafael, 2020. "Does my house have a premium or discount in relation to my neighbors? A regression-kriging approach," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    9. LE GALLO, Julie, 2000. "Econométrie spatiale 2 -Hétérogénéité spatiale," LATEC - Document de travail - Economie (1991-2003) 2001-01, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    10. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    11. GLUMAC Brano & HERRERA-GOMEZ Marcos & LICHERON Julien, 2018. "A residential land price index for Luxembourg: Dealing with the spatial dimension," LISER Working Paper Series 2018-07, Luxembourg Institute of Socio-Economic Research (LISER).
    12. Kim, Jinwon & Jang, Seongsoo & Kang, Sanghoon & Kim, SeungHyun (James), 2020. "Why are hotel room prices different? Exploring spatially varying relationships between room price and hotel attributes," Journal of Business Research, Elsevier, vol. 107(C), pages 118-129.
    13. Olaru, Doina & Mulley, Corinne & Smith, Brett & Ma, Liang, 2017. "Policy-led selection of the most appropriate empirical model to estimate hedonic prices in the residential market," Journal of Transport Geography, Elsevier, vol. 62(C), pages 213-228.
    14. Bodo E. Steiner, 2004. "Australian wines in the British wine market: A hedonic price analysis," Agribusiness, John Wiley & Sons, Ltd., vol. 20(3), pages 287-307.
    15. Glumac, Brano & Herrera-Gomez, Marcos & Licheron, Julien, 2019. "A hedonic urban land price index," Land Use Policy, Elsevier, vol. 81(C), pages 802-812.
    16. Julie Bulteau & Thierry Feuillet & Rémy Le Boennec, 2018. "Spatial Heterogeneity of Sustainable Transportation Offer Values: A Comparative Analysis of Nantes Urban and Periurban/Rural Areas (France)," Post-Print hal-04362203, HAL.
    17. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    18. Julie Le Gallo, 2000. "Spatial econometrics (2, Spatial heterogeneity) [Econométrie spatiale (2, Hétérogénéité spatiale)]," Working Papers hal-01526969, HAL.
    19. Dawid Siwicki, 2021. "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers 2021-05, Faculty of Economic Sciences, University of Warsaw.
    20. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:49:y:2017:i:51:p:5183-5199. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.