Final Report2

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The Impact of the Light Rail System on Single Family Property Values in Mecklenburg County, NC, from 1997 to 2008

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Final Report2

  1. 1. PROJECT DEFENSE THE IMPACT OF A LIGHT RAIL SYSTEM (EXISTING BLUE LINE) ON SINGLE FAMILY PROPERTY VALUES IN MECKLENBURG COUNTY, NC, FROM 1997 TO 2008 SisiYan 2009 Master of Arts in Geography University of North Carolina at Charlotte Department of Geography and Earth Science Committee Members: Dr. Eric Delmelle, Dr. Mike Duncan and Dr. Harrison Campbell
  2. 2. Content Table •Introduction •Literature Review •Research Design & Hypotheses Introduction •Study Area & Data •Method •Results & Discussion •Conclusion & Future Study
  3. 3.  1984---the Charlotte- Mecklenburg Planning commission made its first recommendation;  1998, Dec---tax voted, the planning for the South Corridor to Pineville commenced;  2005, Feb26--- groundbreaking;  2007, November 24--- Opened
  4. 4. Research Questions:  how much is the property value change as it proximity to rail transit in Charlotte area?  Was there such impact during plan time? How have this relationship changed over time?
  5. 5. Content Table •Introduction •Literature Review •Research Design & Location Theory and Hypotheses Transit Capitalization •Study Area & Data Hedonic Price Studies •Method •Results & Discussion Empirical Studies on •Conclusion & Future Transit Impact Study
  6. 6. Literature Review Location Theory  Von Thünen 1826 (land use theory) Different land use will be adopted accordingly in order to maximize the overall profits. land that is closer to the market place will bear less transportation costs and therefore has higher value.  Alonso 1964, Muth 1969:(bid for rent) higher land value appears in a shorter distance to center and this rent gradient will decline nonlinearly as distance to center increases. “ good accessibility results in higher property values “
  7. 7. Hedonic Price Model Knaap (1998) summarized:  Property character: the size, age and quality of any structure, etc;  the location character: distance to CBD, transit and other amenities.  the neighborhood character: median household income and crime rate, etc. Sale _price =f (Pr, H, L, N)
  8. 8. Empirical Studies  Light rail in Portland, Oregon (Lewis-Workman and Brod, 1997) on average, property values increase by $75 for every 100 feet closer to the station  Metrorail in Miami, FL (Gatzlaff and Smith, 1993) weak evidence that there was any major effect on residential values because of the rail  Rapid Transit in Chicago (McMillen and McDonald, 2004) the housing market anticipated the opening of the line and house prices have been affected by proximity to the stations six years before its construction
  9. 9. Content Table •Introduction •Literature Review •Research Design & Hypotheses Research Design •Study Area & Data •Method Research Hypotheses •Results & Discussion •Conclusion & Future Study
  10. 10. Study Time Frame T1 T2 T3- T4 • Pre- • Planning • Rail • Rail Planning period; Construction Operation period; From 1999 period; period; From 1997 to 2004 • From 2005 to After to 1998 2007 Nov.1st 2007 till July 2008
  11. 11. Research Hypothesis T4 T3 T2 T1
  12. 12. Content Table •Introduction •Literature Review •Research Design & Charlotte Hypotheses •Study Area & Data Light Rail Station Area •Method •Results & Discussion Data Sources •Conclusion & Future Study
  13. 13. Charlotte  Since the 1980s, Charlotte has been one of the nation’s fastest growing urban areas. Between 1980 and 2005, Charlotte grew from the 47th to the 20th most populated city in the United States (Charlotte Chamber).  Due to the development of the banking industry, Charlotte became a financial city attracting many new businesses
  14. 14. LYNX Rail
  15. 15. Data Sources  Mecklenburg County & UNC Charlotte Urban Institute  Charlotte Area Transit (CATS)  Federal Housing Finance Agency  US census  other secondary data generated by Geographical information technology (GIS).
  16. 16. Table 3 Descriptive Statistics Minimum Maximum Mean Std. Deviation sales_pric 10000.00 992000.00 197997.82 146058.84 age 1.00 108.00 46.49 21.31 heatedarea 480.00 7003.00 1722.25 743.16 height 1.00 3.00 1.32 0.48 NUM_fire 0.00 4.00 0.67 0.49 qality_building 0.00 4.00 1.47 0.89 fullbaths 1.00 6.00 1.69 0.71 bedrooms 1.00 9.00 3.12 0.64 units 1.00 2.00 1.00 0.05 lnheatarea 6.17 8.85 7.38 0.38 lnnetdis 6.27 9.93 8.42 0.53 t1lnnetdis 0.00 9.93 2.11 3.67 t2lnnetdis 0.00 9.92 3.39 4.14 t3lnnetdis 0.00 9.93 1.72 3.40 t4lnnetdis 0.00 9.93 1.20 2.95 Valid N 6381 Note: t(i) Lnnetdisrepresents the ln_net_dis (in feet) at t(i) (i=1,2,3,4) time period
  17. 17. Content Table •Introduction •Literature Review •Research Design & Hypotheses Methodology •Study Area & Data •Methodology •Results & Discussion •Conclusion & Future Study
  18. 18. Methods:  hedonic regression model for four time periods: Model 1: Sale _price =f (Pr, H, N(i)) Model 2: Sale _price =f (Pr, H, BG(i)) Specify model: Ln(ad_sale_ price) =β0+βi * hi +βj * ln_net_distance+βk * Dumk + εi Where, dependent variable is the natural logarithm of the adjusted sales price; hiis a vector of asset-specific characteristics of the properties; ln_net_distance is the logarithm of proximity variable; Dumkis spatial dummy variables; βistands for the coefficients of each independent variable;
  19. 19. Spatial Dependence Neighborhood Boundary Moran’ s I 0.85 Block group Boundary 0.8 0.75 Moran's I t1 0.7 t2 t3 0.65 t4 0.6 0.55 300 500 600 650 700 800 900 1100 2000 Threshold Distance (feet)
  20. 20. Variables Discussion  Sales value vs. assessed value  Network distance vs. Straight-line Distance;  Variable List
  21. 21. Data Transformations  Ln_ad_price (HPI)  Ln_net_dis  Ln_heatedarea  Age2
  22. 22. Content Table •Introduction •Literature Review •Research Design & Hypotheses Models’ Results •Study Area & Data •Method Light Rail Impact •Results & Discussion •Conclusion & Future Study
  23. 23. Model1. HPR with neighborhood dummy variables: Model 1 regression coefficients for four time periods T1 T2 T3 T4 Notes: * insignificant at p < 0.05 Variable Coefficient Coefficient Coefficient Coefficient (since most of the variables are (constant) 7.028 6.502 7.248 7.796 significant in this table, for a Property characteristics better distinguish, I chose using * to age -0.003* 0.002* -0.002* -0.005 represent insignificant variables) agesqr 4.24E-05 1.06E-05* 5.99E-05 7.93E-05 height 0.152 0.083 0.076 0.091 Fule_None -0.762 0.327* 0.063* -0.718 AC-Central 0.061 0.097 0.100 0.117 Building_Grade 0.040 0.034 0.059 0.041 Num_Fire 0.114 0.092 0.074 0.016* ln_Heatedarea 0.490 0.538 0.443 0.502 Rail Impact Ln_Net_Dis 0.129 0.147 0.153 0.054* Neighborhood Dummy Variables York Road -0.729 -0.712 -0.960 -1.038 Wilmore -0.920 -0.611 -0.185 0.036* Dilworth 0.233 0.351 0.467 0.483 Starmount Forest -0.491 -0.609 -0.716 -0.912 Sterling -0.159 -0.202 -0.323 -0.317 Montclaire South -0.238 -0.355 -0.394 -0.594 Yorkmount -0.588 -0.601 -0.793 -0.779 See other neighborhoods in appendix table R2 0.746 0.750 0.781 0.829
  24. 24. Model2. HPR with block group dummy variables: T1 T2 T3 T4 Model 2 regressions coefficients for Variable Coefficient Coefficient Coefficient Coefficient four time periods (constant) 8.196 7.406 8.123 8.249 Notes: * insignificant at p < 0.05 Property characteristics (since most of the variables are age -0.004 -0.001* -0.003* -0.006 significant in this table, for a agesqr 4.37E-05 2.59E-05* 4.16E-05 8.21E-05 better distinguish, I chose using * to height 0.125 0.062 0.076 0.053* represent insignificant variables) Fule_None -0.796 0.302* 0.032* -0.794 AC-Central 0.045 0.080 0.090 0.101 Building_Grade 0.034 0.027 0.032 0.059 Num_Fire 0.089 0.064 0.057 0.004* ln_Heatedarea 0.337 0.392 0.338 0.455 Rail Impact Ln_Net_Dis 0.123 0.169 0.148 0.052* Sample of Block Group Dummy Variables First Ward blkg3 -0.110* 0.603 0.708 0.441 YorkRoad blkg26 -0.637 -0.622 -0.937 -1.078 Dilworth blkg19 0.558 0.720 0.881 0.578 Dilworth blkg20 0.325 0.492 0.597 0.434 Dilworth blkg23 0.772 0.923 0.849 0.638 Sterling blkg32 -0.506 -0.488 -0.669 -0.961 Yorkmount blkg28 -0.528 -0.552 -0.750 -0.781 See other block dummy variables in appendix table R2 0.779 0.786 0.811 0.837
  25. 25. Models T1 T2 T3 T4 Models Comparisons R2 M1 0.746 0.750 0.781 0.829 M2 0.779 0.790 0.811 0.837 M1 0.742 0.748 0.777 0.824 Adjusted R2 M2 0.773 0.783 0.805 0.83 M1 0.167 0.185 0.238 0.064 Moran’s I M2 0.097 0.110 0.167 0.021
  26. 26. Light Rail Impact Notes: * insignificant at p < 0.05 Models T1 T2 T3 T4 M1 0.129 0.147 0.153 0.054* Z Test Ln_Net_Dist M2 0.123 0.169 0.148 0.052* T1-2 T2-3 T1-3 T3-4 T1-4 T2-4 Z/neighbor -0.56 -0.19 -0.68 2.59 1.97 2.64 Z/blkgrp -1.29 0.60 -0.64 2.25 1.64 2.95
  27. 27. Content Table •Introduction •Literature Review •Research Design & Conclusions Hypotheses •Study Area & Data Future Studies •Method Suggestions •Results & Discussion •Conclusion & Future Study
  28. 28. Conclusions  Contradictory to many studies, single family housing value in Charlotte area tend to increase value as distance to rail increases  Comparing across four time periods, pre- planning, planning, construction and operation, rail operation diminish the proximity disadvantage that appears at the station area
  29. 29. Future Studies  Apply model to other available property types such as multiple family and commercial  Analyze the impact of rail when the line is completed  Integrate spatially-explicit regression models such as geographical weighted regression  Local patterns in residuals  Divide study time period according to station plan time
  30. 30. Acknowledgements  Thanks for Eric’s advice from Idaho to Charlotte  Thanks for Mike’s great help and guidance through this study  Thanks for Harry’s support  Thanks for Tom Ludden’s data support  Thanks for Paul McDaniel's great tolerance during editing my ‘professional’ Chine- glishwriting  Thanks for Amos’s Coding support  Thanks you all for coming today
  31. 31. Questions and Comments?
  32. 32. References Selected:  Al-Mosaind, M.A., Dueker, K.J., Strathman, J.G. (1993), "Light rail transit stations and property values: a hedonic price approach", Transportation Research Record, No.1400, pp.90-4.  Alonso, W. (1964). Location and land use: Toward a general theory of land rent. Cambridge, MA: Harvard University Press.  Bajic V (1983). The effects of a new subway line on housing prices in metropolitan Toronto. Urban Studies 20: 147–158.  Duncan, Michael (2007) The Conditional Nature of Rail Transit Capitalization in San Diego, California. Dissertation No. D07-003
  33. 33. Variables Description Data Sources Justification PROPERTY VALUE (dependent variable) Amount($) for which the single family the sales price generally reveals the property was sold during the study time the Property Ownership Land Records value of the property. (Bowes and Ln_ad_Price period. Dollar values are adjusted to the third Information System (POLARIS) Ihlanfeldt, 2001; Voith,1993;Al- quarter of 2005 based on HPI(Housing Price Federal Housing Finance Agency Mosaind et al,1993) Index). RAIL PROXIMITY semi-log of network distance(in feet) to the real access distance.(Duncan, 2007; Ln_Netdis Calculated using GIS nearest rail station Landis et al.1995) PROPERTY CHARACTERISTICS age of the structure(in year) 2008 age may affect the price of the Age POLAIRS substract building year building. squared age may capture the Age2 squared age POLAIRS nonlinear relationship between age and price (Coulson, 2008) semi-log of heated area(in square feet) of ln_HeatedArea POLAIRS same as above the property Fullbaths number of bathroom in the unit POLAIRS same as above Bedroom number of bedroom in the unit POLAIRS same as above Actype (Ac01, Ac02, Primary type of air conditioning system POLAIRS same as above Ac03, Ac04,) used (4 categories of AC) the quality of the structure(below average Qality_bui POLAIRS same as above to excellent, 1-5) UNITS Number of living units in the structure POLAIRS same as above HEATEDFUEL (Fuel01, Primary type of fuel used for heating (5 POLAIRS same as above 02, 03, 04, 05, ) categories of Fueltypes) HEIGH story height POLAIRS same as above NUM_FIRE number of fireplace POLARIS same as above
  34. 34. LOCATIONAL & NEIGHBORHOOD CHARACTERISTICS (based on two scales) Consider the whether or not the property is neighborhood boundary City of Charlotte F(i) within a neighborhood as dummy variables to Quality of life study and GIS i(0,1,6,900,etc) control for loccation and neighborhood characters Consider the block whether or not the property is group boundary as Dum(i) within a block group i(0- US Census and GIS dummy variables to 34,etc) control for location and neighborhood characters
  35. 35. Table 4 Price Statistics for four time periods Note: ad_price is the adjusted price that is calculated by House Price Index. Time_Preiod avg_ad_price min_ad_price max_ad_price N t1 197,950 13,422 1,133,820 1,592 t2 206,720 10,527 1,007,040 2,568 t3 213,300 15,000 990,000 1,308 t4 227,840 13,849 845,585 913

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