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Land Use Impacts of Bus Rapid Transit: The Boston Silver Line PowerPoint Presentation
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Land Use Impacts of Bus Rapid Transit: The Boston Silver Line
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  1. Land Use Impacts of Bus Rapid Transit: The Boston Silver Line Victoria Perk, Senior Research Associate National Bus Rapid Transit Institute Center for Urban Transportation Research University of South Florida, Tampa Steven Reader, Ph.D., Associate Professor Department of Geography, Environment, and Planning University of South Florida, Tampa GIS in Public Transportation Conference September 14, 2011 St. Petersburg, Florida

  2. Research Objective • Can bus rapid transit (BRT) impact surrounding land uses and property values in a similar way as light rail transit (LRT)? • Issue of permanence of services & facilities

  3. What is Bus Rapid Transit? BRT is an enhanced bus system that operates on bus lanes or other transitways in order to combine the flexibility of buses with the efficiency of rail. BRT operates at faster speeds, provides greater service reliability and increased customer convenience. BRT uses a combination of advanced technologies, infrastructure and operational investments that provide significantly better service than traditional bus service. Source: Federal Transit Administration

  4. BRT Elements Integration of Elements

  5. BRT in the U.S.

  6. Previous Work • Before 2009, no recent quantitative modeling studies on property value impacts of BRT in the U.S. • Previous studies address impacts of rail modes on property values • Isolate effect of distance from transit (either right-of-way, stations, or both) • Typical results find positive impacts on property values from nearby rail transit, but magnitudes are relatively small

  7. Hypothesis & Method • We hoped to find statistically significant, positive impacts on surrounding property values from BRT, with magnitudes approaching those found for rail transit modes. • Estimate the impacts of BRT on surrounding property values using hedonic regression models • Estimate the variation in property values due to proximity to BRT stations • Isolate the effect of distance to nearest BRT station from all other (measurable) factors that determine property values

  8. First Application • Pittsburgh Martin Luther King, Jr. East Busway • Hedonic regression model, statistically significant results, using data within ½-mile of BRT stations • Moving from 101 to 100 feet from a station increases market value of single-family home by $19.00 • Moving from 1,001 to 1,000 feet from a station increases market value of a single-family home by $2.73 • Next application: Boston Silver Line

  9. Boston Rapid Transit

  10. Boston Silver Line • Branded as part of MBTA’s rapid transit system • Low-floor 60 ft. CNG vehicles • Exclusive bus lanes • 10-minute peak frequency • 15-minute off-peak frequency • Real-time passenger information • Transit signal priority • Phase I Washington Street opened July 2002 • Phase II Waterfront opened December 2004 • Proposed Phase III to connect the two

  11. Boston Silver Line

  12. Boston Silver Line Washington Street Corridor • As the first phase of the Silver Line, this corridor was selected for research • Replaced MBTA Route 49 • Two routes operate along the corridor: SL4 & SL5 • Provide two options into Downtown Boston • 14 stations • Approximately 15,500 daily boardings

  13. Data • Parcel data from City of Boston Assessing Department, 2003-2009 • Sales transactions of condominium units from the City of Boston, 2000-2009 • U.S. Census data • Data set constructed using GIS • Used only parcels located within one quarter-mile of the Washington Street corridor • Data set contains approximately 5100 sales transactions from 2000 to 2009

  14. Data Sources Property Appraiser Database Sales Data GIS Parcel Layer - 2007

  15. Use of GIS in the Project – Data Matching Identification of “Condominium Main” Parcel using Point-in-Polygon (spatial join) b/w sales data “points” and GIS Parcel Layer 2007, clipped to ¼ buffer around BRT Line Use of Table “Relates” to relate b/w Condominium Main ID from sales data and multiple years of Property Appraiser Data, for identification of specific condominium unit ID’s and characteristics thru time.

  16. Use of GIS in the Project – Network Analysis Condominium property parcels generalized to centroids Shortest-path road network distances calculated in ArcGIS Network Analyst based on above centroids, ESRI StreetMap database for 2008, and BRT stations as destinations

  17. Parcels within ¼ Mile Buffer of the Boston Silver Line BRT indicating Condominium Parcels with Sales 2000-2009

  18. Condominium Parcels within ¼ mile of Silver Line with Sales 2000-2009 (n=563)

  19. Condominium Parcels within ¼ mile of Silver Line with Sales 2000-2009 Built after 1997, by Year Built (n=37)

  20. Number of Condominium Sales (2000-2009) within ¼ mile of Boston Silver Line BRT by Type of Parcel

  21. Condominium Parcels within ¼ mile of Silver Line with 40+ Sales 2000-2009 Built after 1997, by Year Built (n=11)

  22. Median Price Per Square Foot by Year for All Condominium Sales within ¼ mile of Boston Silver Line BRT

  23. Variables • Dependent variable: sale price per square foot • Key independent variable: network distance of parcel to nearest BRT station • Other variables • Property characteristics • Neighborhood characteristics • Local housing price index

  24. Results

  25. Results, cont’d • Hedonic regression models using sales from years 2000, 2001, and 2002 indicate that price per square foot was higher for condos further away from the corridor, but this relationship was not statistically significant. • Beginning in 2003, after the opening of the Silver Line Washington Street, similar models begin to show a statistically significant inverse relationship between distance to a BRT station and sale price per square foot.

  26. Results, cont’d • 2007 sales indicate a premium of $0.18 per square foot for each foot closer to a BRT station • 2009 sales indicate a premium of $0.11 per square foot for each foot closer to a BRT station (although this result is only significant at the 90% level) • These relationships exhibit decreasing marginal effects, meaning the effect diminishes farther from the stations

  27. Conclusion • With recent research on BRT in Pittsburgh and the Boston Silver Line, we are beginning to show that proximity to BRT stations can have a positive effect on residential property values and sale prices. • These effects are very similar to those shown in the literature for LRT

  28. A report on this research will be available later in the Fall Additional work will begin on further analyzing the Boston data, as well as data from other cities with BRT such as Cleveland Work underway this year to update information on local, regional, and state policies and plans related to transit and development For more information please visit www.nbrti.org Upcoming Work