Skip to main content

Advertisement

SpringerLink
Log in

Estimating the Effects of Light Rail Transit (LRT) System on Residential Property Values Using Geographically Weighted Regression (GWR)

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

This study assesses the effect of light rail transit system (LRT) on residential property values in Greater Kuala Lumpur, Malaysia, where traffic congestion has been a major issue since the mid 1990s. A relatively new technique namely Geographically Weighted Regression (GWR) is employed to estimate the increased value of land in the form of residential property values as a result of improved accessibility owing to the construction of the LRT systems. Using the Kelana Jaya LRT Line, located in Greater Kuala Lumpur, Malaysia as a case study, this paper reveals that the improvement of accessibility to employment and other amenities provided by the LRT system added premiums on residential property values but with spatial variation over geographical area indicates that the existence of the LRT systems may have a positive effect on residential property values in some areas but negative in others. The use of the GWR in this study is identified as a better approach to investigate the effect of the LRT system on residential property values since it has the capability to produce meaningful results by revealing spatially varying relationship.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Greater Kuala Lumpur is defined as an area covered by 10 municipalities surrounding Kuala Lumpur Metropolitan Area with land area of 2,793.27 km2.

  2. Focus variables are those variables of particular interest, and it may vary from study to study. For example, proximity to rail transit stations for those studies that focuses on the effect of rail transit systems on residential property prices.

  3. Free variables are those variables that are known to affect residential property prices, though are of no special interest in the study.

  4. The following criteria were implemented for the purpose of sales transactions data cleaning; non-year 2005 transactions, non-residential property use, incomplete information and suspected error in data entry.

  5. The programme was written based on Avenue programming language of ArcView by Dan Paterson from the US and it was made accessible to the public.

  6. Converted at official exchange rate: 3.80 Malaysia Ringgits (MYR) = 1.00 USD.

References

  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov and F. Csaki (Eds.), Second international symposium on information theory. Budapest: Academiai Kiado.

  • Alonso, W. (1964). Location and land use: Towards a general theory of land rent. Cambridge, MA: Harvard University Press.

    Book  Google Scholar 

  • Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer Academic Publisher.

    Book  Google Scholar 

  • Armstrong, R. (1994). Impacts of commuter rail service as reflected in single-family residential property values. Transportation Research Record, 1466, 88–98.

    Google Scholar 

  • Banister, D. & Berechman, Y. (2001). Transport investment and the promotion of economic growth. Journal of Transport Geography, 9(3), 209-218.

    Article  Google Scholar 

  • Barker, W. G. (1998). Bus service and real estate values. Paper presented at the 68th Annual Meeting of the Institute of Transportation Engineers. Toronto, August. http://www.ite.org/Membersonly/annualmeeting/1998/AHA98B25.PDF. Accessed 23 Nov 2013.

  • Benjamin, J. D., & Sirmans, G. S. (1994). Mass transportation, apartment rent and property values. Journal of Real Estate Research, 12(1), 1–8.

    Google Scholar 

  • Boarnet, M. G., & Chalermpong, S. (2001). New highways, house prices, and urban development: a case study of toll roads in orange county, Ca. Housing Policy Debate, 12(3), 575–605.

    Article  Google Scholar 

  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1996). Geographically weighted regression: a method for exploring spatial non-stationarity. Geographical Analysis, 28, 281–289.

    Article  Google Scholar 

  • Casetti, E. (1972). Generating models by the expansion method: application to geographical research. Geographical Analysis, 4, 81–91.

    Article  Google Scholar 

  • Cervero, R., & Duncan, M. (2002). Land value impacts of rail transit services in Los Angeles County. Report prepared for the Urban Land Institute. Washington, D.C.: National Association of Realtors.

    Google Scholar 

  • Cervero, R., Ferrell, C. and Murphy, S. (2002). Transit-oriented development and joint development in the United States: A literature review. TCRP Project H-27, Research Results Digest October 2002-Number 52, [online], Available: http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rrd_52.pdf. Accessed 14 September 2013.

  • Chen, H., Rufolo, A., & Dueker, K. J. (1997). Measuring the impacts of light rail systems on single family home values: A hedonic approach with GIS application. Portland, Oregon: Portland State University. Discussion paper 97–3.

    Google Scholar 

  • Chesterton, P. L. C. (2000). Property market scoping report. Prepared for the jubilee line extension impact study unit. Impact study unit. London: University of Westminster. Working Paper No. 32.

    Google Scholar 

  • Crespo, R., & Grêt-Regamey, A. (2013). Local hedonic house-price modelling for urban planners: advantages of using local regression techniques. Environment and Planning B: Planning and Design, 40, 664–682.

    Article  Google Scholar 

  • Cropper, M. L., Deck, L. B., & McConnell, K. E. (1988). On the choice of functional form for hedonic price functions. The Review of Economics and Statistics, 70(4), 668–675.

    Article  Google Scholar 

  • Debrezion, G., Pels, E. & Rietveld, P. (2006). The impact of railway stations on residential and commercial property value: An empirical analysis of the Dutch housing markets. http://papers.tinbergen.nl/06031.pdf. Accessed 19 Jan 2013.

  • Diaz, R. B. (1999). Impacts of rail transit on property values. Paper presented at the Commuter Rail/Rapid Transit Conference Sponsored by the American Public Transportation Association. Toronto, Ontario, May. http://www.rtdfastracks.com/media/uploads/nm/impacts_of_rail_transit_on_property_values.pdf.

  • Du, H., & Mulley, C. (2006). Relationship between transport accessibility and land value: local model approach with geographically weighted regression. Transportation Research Record, 1977(1), 197–205.

    Article  Google Scholar 

  • Du, H., & Mulley, C. (2012). Understanding spatial variations in the impact of accessibility on land value using geographically weighted regression. Journal of Transport and Land Use, 5(2), 46–59.

    Google Scholar 

  • Dueker, K. J. & Bianco, M. J. (1999). Light rail transit impacts in Portland: The first ten years. Transportation Research Record, 1685, 171-180.

    Article  Google Scholar 

  • Forrest, D., Glen, J., Grime, J., & Ward, R. (1996). House price changes in greater Manchester 1990–1993 and the impact of metro link. Journal of Transport Economics and Policy, 30(1), 15–29.

    Google Scholar 

  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30, 1905–1927.

    Article  Google Scholar 

  • Fotheringham, A. S., Brundson, C., & Charlton, M. E. (2002). Geographically weighted regression: The analysis of spatially varying relationships. West Sussex: John Wiley and Sons Ltd.

    Google Scholar 

  • Garrod, G., & Willis, K. (1992). Amenity value of forests in Great Britain and its impact on the internal rate of return from forestry. Forestry, 65(3), 331–346.

    Article  Google Scholar 

  • Gatzlaff, D., & Smith, M. (1993). The impact of the Miami metro rail on the value of residences near station locations. Land Economics, 69(1), 54–66.

    Article  Google Scholar 

  • Goldstein, H. (1987). Multilevel models in educational and social research. London: Charles Griffith.

    Google Scholar 

  • Goodwin, R. E., & Lewis, C. A. (1997). Land value assessment near bus transit facilities: a case study of selected transit centres in Houston. Texas: Southwest Region University Transportation Centre, Texas Southern University.

    Google Scholar 

  • Henneberry, J. (1998). Transport investment and house prices. Journal of Property Valuation and Investment, 16(2), 144–158.

    Article  Google Scholar 

  • Hess, D. B., & Almeida, T. M. (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.

    Article  Google Scholar 

  • Hewitt, C. M., & Hewitt, W. E. (2012). The effect of proximity to urban rail on housing prices in Ottawa. Journal of Public Transportation, 15(4), 43–65.

    Google Scholar 

  • Jones, K., & Bullen, N. (1993). A multilevel analysis of the variations in domestic property prices: Southern England 1980–1987. Urban Studies, 30(8), 1409–1426.

    Article  Google Scholar 

  • Jones, K., & Bullen, N. (1994). Contextual models of urban house prices: a comparison of fixed-and-random-coefficient models developed by expansion. Economic Geography, 70(3), 252–272.

    Article  Google Scholar 

  • Knapp, G. J., Ding, C. & Hopkins, L. D. (2001). Do plans matter? The effects of light rail plans on land values in station areas. Journal of Planning Education and Research, 21(1), 32–39.

    Article  Google Scholar 

  • Landis, J., Guhathakurta, S., Huang, W., & Zhang, M. (1995). Rail transit investments, real estate values and land use change: A comparative analysis of five California rail transit systems. UCTC working paper No. 285. Berkeley: University of California Transportation Centre.

    Google Scholar 

  • Leishman, C., Costello, G., Rowley, S., & Watkins, C. (2013). The predictive performance of multilevel models of housing Sub-markets: a comparative analysis. Urban Studies, 50(6), 1201–1220.

    Article  Google Scholar 

  • LeSage, J. (1998). Spatial econometrics, lecture notes, Department of Economics, University of Toledo. http://www.spatial-econometrics.com/html/wbook.pdf. Accessed 11 Dec 2013.

  • Lewis-Workman, S., & Brod, D. (1997). Measuring the neighbourhood benefits of rail transit accessibility. Report no. 97–1371. Washington, D.C: Transportation Research Board.

    Google Scholar 

  • Medda, F. (2012). Land value capture for transport accessibility: a review. Journal of Transport Geography, 25, 154–161.

    Article  Google Scholar 

  • Mennis, J. (2006). Mapping the results of geographically weighted regression. The Journal of Cartographic, 43(2), 171–179.

    Article  Google Scholar 

  • Mills, E. S. (1972). Urban economics. Illinois: Scott, Foresman and Company, Glenview.

    Google Scholar 

  • Mohamad, J., & Kiggundu, A. T. (2007). ‘The rise of the private car in Kuala Lumpur: Assessing the policy option’. IATSS RESEARCH, 31(1), 69–77.

  • Mulley, C. (2014). Accessibility and land value uplift: identifying spatial variations in the accessibility impacts of a bus transitway. Urban Studies, 51(8), 1707–1724.

    Article  Google Scholar 

  • Muth, R. F. (1969). Cities and housing. Chicago: Chicago University Press.

    Google Scholar 

  • Nelson, A. (1992). Effects of elevated heavy rail transit stations on house prices with respect to neighbourhood income. Transportation Research Record, 1359, 127–132.

    Google Scholar 

  • Orford, S. (1999). Valuing the built environment: GIS and house price analysis. Aldershot: Ashgate Publishing Ltd.

    Google Scholar 

  • O’Sullivan, S. and Morrall, J. (1996). ‘Walking distance to and from light rail transit stations’. Transportation Research Record, 15(38), 19–26.

  • Palmquist, R. B. (1984). Estimating the demand for the characteristics of housing. Review of Economics and Statistics, 66, 394–404.

    Article  Google Scholar 

  • Redman, L., Friman, M., Garling, T., & Hartig, T. (2013). Quality attributes of public transport that attract car users: a research review. Transport Policy, 25, 119–127.

    Article  Google Scholar 

  • Rodriguez, D., & Targa, F. (2004). Value of accessibility to Bogota’s bus rapid transit system. Transport Reviews, 24(5), 586–610.

    Google Scholar 

  • Ryan, S. (1999). ‘Property values and transportation facilities: Finding the transportation-land use connection’. Journal of Planning Literature, 13(4), 412–427.

  • Smith, J., Gihring, T., & Litman, T. (2006). Financing transit systems through value capture: an annotated bibliography. American Journal of Economics and Sociology, 65(3), 751–786.

    Article  Google Scholar 

  • Smith, J., Gihring, T. & Litman, T. (2009). Financing Transit Systems through Value Capture: An Annotated Bibliography, Victoria Transport Policy Institute. http://www.vtpi.org/smith.pdf. Accessed 21 April 2009

  • Tu, Y. (2000). The local housing submarket structure and its properties. Urban Studies, 34(2), 745–767.

    Google Scholar 

  • Tyrväinen, L. & Miettinen, A. (2000). Property prices and urban forest amenities. Journal of Environmental Economics and Management, 39, 205–23.

  • Unterman, R. K. (1984). Accommodating the Pedestrian: Adapting Towns and Neighborhoods for Walking and Biking. Florence, Kentucky: Van Nostrand Reinhold.

  • van der Krabben, E., & Needham, B. (2008). Land readjustment for value capturing: a new planning tool for urban development. Town Planning Review, 79(6), 651–672.

    Article  Google Scholar 

  • Voith, R. (1991). Transportation, sorting and house values. American Real Estate and Urban Economics Association Journal, 19(2), 117–37.

    Article  Google Scholar 

  • Yan, S., Delmelle, E., & Duncan, M. (2012). The impact of a new light rail system on single-family property values in charlotte, North Carolina. Journal of Transport and Land Use, 5(2), 60–67.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography and Environment, Faculty of Human Sciences, Sultan Idris Education University, 35900, Tanjong Malim, Perak, Malaysia

    Mohd Faris Dziauddin

  2. School of Architecture Planning and Landscape, Claremont Tower, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

    Neil Powe

  3. Built Environment, Northumbria University, Ellison Building, Ellison Place, Newcastle upon Tyne, NE1 8ST, UK

    Seraphim Alvanides

Authors
  1. Mohd Faris Dziauddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neil Powe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seraphim Alvanides
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohd Faris Dziauddin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dziauddin, M.F., Powe, N. & Alvanides, S. Estimating the Effects of Light Rail Transit (LRT) System on Residential Property Values Using Geographically Weighted Regression (GWR). Appl. Spatial Analysis 8, 1–25 (2015). https://doi.org/10.1007/s12061-014-9117-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-014-9117-z

Keywords

Search

Navigation