Land Use Impacts of New Bus and Subway Services

Transcription

1 Land Use Impacts of New Bus and Subway Services Jeffrey M. Casello Clarence Woudsma University of Waterloo Transportation Research Board Best Practices: Coordination of Transit, Regional Transportation Planning and Land Use Denver, CO 27 August 2007 Presentation Outline I. Introduction and Research Motivation II. Literature Review / Research Method III. Region of Waterloo Case Study IV. Toronto Sheppard Line Case Study V. Data Analysis VI. Results and Discussion 1

2 I. Research Motivation Transit Planning Process: Planning: defining local system objectives Evaluation: mode selection Mode selection: identifying candidate modes Goals and Objectives Physical, socioeconomic, environmental Define system requirements Performance criteria, weights Comparative analysis and selection Alt. I Alt. II Alt. III Regular bus Streetcar BRT LRT AGT LRRT Right of way category Technology Capacity / operating costs Preferred transit system Metro I. Research Motivation Mode Selection Long-term impacts Impacts on region Urban form, mobility, character Performance (speed, reliability image, etc.) Frequency of service Modal characteristics Right of Way Technology Vehicle / TU / line capacity Land use patterns Environmental performance Investment costs: Infrast. & fleet Operating cost Passenger attraction Impacts on passengers Life cycle costs Impacts on operator 2

3 I. Research Motivation Cities: choice largely between Regular Bus, BRT, Rail Mode System characteristics: Investment costs Operating costs Flexibility System image Passenger attraction Land use impacts Very high Low Regular Bus Very low Sensitive to demand Variable Limited Bus Rapid Transit Medium Sensitive to demand Moderate Good Good Moderate High Less sensitive to demand Low Excellent Strong Strong Rail Transit II. Literature Review 3

4 II. Literature Review II. Literature Review 4

5 II. Literature Review / Research Method Research Method: For Express Bus Service (ixpress) and Subway (Sheppard Line), analyze property values adjacent to infrastructure compared to areas away from transit; For both cases analyze changes in property values; Isolate infrastructure related change by control area comparisons; Analysis of some external variables in Toronto case; Determine relative magnitude of property value change as surrogate for image and land use impacts. III. Waterloo Case Study 5

6 III. Waterloo Case Study Test and Control Areas: N 200 m Legend Boundary of Test Area Boundary of Control Area IXpress Bus Stop IXpress Route III. Waterloo Case Study Waterloo location: 118 parcels in test group; 111 parcels in control group; Average property value Average change % Max change Min change Standard deviation Average change (residential) Average change (commercial & industrial) Test Area $200, Control Area $182,

7 III. Waterloo Case Study Test and Control Areas: N 500 m Legend Boundary of Test Area Boundary of Control Area IXpress Bus Stop IXpress Route III. Waterloo Case Study Cambridge: 148 parcels in test group; 130 parcels in control group; Average property value Average change % Max change Min change Standard deviation Average change (residential) Average change (commercial / industrial) Test Area $258, Control Area $413,

8 IV. Toronto Case Study Test Station Map Not To Scale IV. Toronto Case Study 1000 m 1000 m N 8

9 IV. Toronto Case Study Toronto Data: 1175 parcels in test group; 1130 parcels in control group; Average Property Value Average change % Max change Min change Standard deviation Average change (residential) Average change (commercial / industrial) Test Area $617, Control Area $447, IV. Toronto Case Study Controlling for External Impacts: Aesthetics; Demographics: ethnicities / race; School qualities; Property tax / development charges. 9

10 V. Data Analysis Pertinent Questions: Are property values higher adjacent to the transit lines than in areas away from the transit services? Do properties adjacent to the transit lines grow in value more rapidly? Do the findings hold true for commercial and residential subsets of the data? Is there evidence that the transit technology (rail v. bus) impacts the property values or their rate of change? V. Data Analysis Transit proximity associated with higher housing values? Test areas (with Transit) consistently higher Cambridge market related explanation Residential Property Values average property values (thousands of $) Waterloo Cambridge Toronto Test (transit) Control % Difference 9.6%** -7.3%* 50.6%* df Commercial Property Values Waterloo Cambridge Toronto Test (transit) Control % Difference 103.6%* -45.8%* 389.0%* df * Statistically Signifcant at 95% ** Statistically Signifcant at 90% 10

11 V. Data Analysis Magnitude of Change in Residential Property Values: Area Waterloo Test Waterloo Control Cambridge Test Cambridge Control Toronto Test Toronto Control Mean Change in Value $85,900 $54,300 $51,800 $69,500 $89,200 $51,600 Baseline change $57,500 $57,500 $57,500 $57,500 $83,700 $83,700 Difference $28,400 -$3,200 -$5,700 $12,000 $5,500 -$32,100 V. Data Analysis Transit proximity contributes to greater increases in property values over time Change in Residential Property Values average property values (thousands of $) Waterloo % Difference df Test %* 86 Control ** 42.3%* 86 Cambridge % Difference df Test ** 37.1%* 130 Control %* 130 Toronto % Difference df Test %* 1111 Control ** 22.8%* 1111 Regional average difference in property values over time frame used in matched pairs t-test - Waterloo/Cambridge 57.5 (thousands $) - Toronto 83.7 * Statistically Signifcant avg. difference at 95% ** Difference less than regional avg. difference 11

12 VI. Results and Discussions We can conclude: For two of our three areas, proximity to transit resulted in statistically significant higher property values. Greater evidence of impact for commercial properties. For two of our three areas, residential property values adjacent to transit increased faster than the control areas; For two of three areas, growth rates exceeded regional growth rates in a statistically significant way. 12