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The Trade-offs between Population Density and Households' Transportation-housing Costs

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Haizhong Wang, Assistant Professor, Civil & Construction Engineering, Oregon State University

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The Trade-offs between Population Density and Households' Transportation-housing Costs

  1. 1. Presented  by       Haizhong  Wang,  Ph.D.   Assistant  Professor   Contributions  from  Matthew  Palm  and  Rachel  Vogt   Oregon  State  University  (OSU)   Corvallis,  OR   Oct.  23st,  2015   The  Trade-­‐Offs  between  Popula5on  Density  and   Household’s  Transporta5on-­‐Housing  Costs  
  2. 2. Paper  I:    Matthew  Palm,  Brian  Gregor,  Haizhong  Wang.   The  Trade-­‐Offs  between  Popula5on  Density  and  Household’s   Transporta5on-­‐Housing  Costs,  Transport  policy,  Volume  36,   November  2014,  Pages  160-­‐172,   http://www.sciencedirect.com/science/article/pii/ S0967070X14001504.       Paper  II:  Rachel  Vogt,  Haizhong  Wang.     Exploring  the  Rela5onship  between  Bicycle  Level  of  Traffic  Stress   and  Reported  Bicycle  Crashes,  Working  paper  for  Accident   Analysis  and  Prevention.      
  3. 3. PART  I:  DENSITY  PROMOTING   SMART  GROWTH  
  4. 4. De#initions   – Depended  variable:  “Housing  Costs”-­‐-­‐The   amount  a  household  spends  on  housing.     – For  Home  owners:  Mortgage  +  utilities  +   insurance  +  taxes   – For  Renters:  Rent  +  insurance  +  utilities    
  5. 5. The  Motivation   •  Integration  of  Land   Use  (housing)  and   Transport  planning:   •  CA:  SB  375   •  OR:  SB  100   •  MRCOG:  Proposed   BRT  lines   •  US:  HUD’s  TOD   Investment     http://agbeat.com/housing-­‐news/interactive-­‐housing-­‐ transportation-­‐affordability-­‐maps/  
  6. 6. The  Method,  Data,  and  Variables:   •  Data:  from  Public  Use  Micro-­‐  Sample  (PUMS)  data   •  Limitations:  only  units  in  urbanized  Public  Use  Micro  Sample   Areas  (PUMAs)  in  22  most  metropolitan  states   Room s   Bedro oms  HH   Num  of   Vehicles   Perso ns   Unit   Age   Units   in   Struct ure   Fixed   Route   Transit   Commut er   PUMA  SOV   Average   Commute  Times   Househ old   Income   PUM A   Mean   Inco me   PUMA   Income   Dispers ion   MSA   populati on   MSA   Dumm ies   State   Dum mies   Reside nts’   Tenur e   Area/Region   Variables   Occupant   Variables   Unit  Variables   race   PUMA   Density   +  
  7. 7. The  Method,  Data,  and  Variables     •  Questions:   –  Does  density  correlate  with  housing  costs?   –  Does  proximity  to  transit  use  correlate  with  housing  costs?   –  Do  PUMA  SOV  commute  times  correlate  with  housing  costs?   •  Model  Format:   –  Log-­‐Log:  results  produce  an  elasticity  of  rent  with   respect  to  density,  transit,  commute  times.   Model  Disaggregation:   •  Two  models  predicting  Gross  Rent:  one  for  Single   Family  Renters  ,one  for  Apartments   •  Two  models  predicting  Single  family  home  owner   costs:  one  for  housing  unit  value,  one  for  monthly   mortgage  payment  
  8. 8. Tracts  versus  PUMAs   http://www2.census.gov/geo/maps/dc10map/tract/ st06_ca/c06075_san_francisco/DC10CT_C06075_004.pdf  
  9. 9. SOV  Mode  Share     By  Density   PUMA  Density   PUMA  Density   Transit  Mode   Share   By  Density   PUMA  Density   Walk/Bike  Mode   Share   By  Density  
  10. 10. Label   State-­‐ Indicator   Model   Runs   PUMA  Pop.   Density   Fixed  Route   Commuter   Avg  SOV   Commute  Time   MSA  Size   R-­‐Squared   MSA-­‐ Indicator   Model   Runs     PUMA  Pop.   Density   Fixed  Route   Commuter   Avg  SOV   Commute  Time   R-­‐Squared   N-­‐Observations   Multi-­‐ Family   Rent   Beta   .05***   .07***   -­‐.05*   .03***   .38   .06***   .08***   .12**   .36   504,371   1015   Single   Family   Rent   Beta   .04***   .03***   -­‐.06**   .04***   .42   .05***   .04***   -­‐.01   .41   267,483   1015   Single   Family   Mortgage   (Owned   <1  yr)   Beta   .05***   .04***   -­‐.20**   .08***   .44   .06***   .06***   -­‐.29**   .48   108,229   1010   Single   Family  Unit   Value   (Owned  <1   yr)   Beta   .04***   .08***   -­‐.45**   .11***   .53   .08***   .07***   -­‐.45***   .49   116,050   1010  Not  Shown:    Indicator  variables,  other  independent  control  
  11. 11. MSA-­‐Specific  Elas5ci5es*   *full  results  available  upon  request   Label   MSA-­‐ Indicator   Model   Runs   PUMA  Pop.  Density   Fixed  Route   Commuter   Avg  SOV  Commute   Time   R-­‐Squared   MSA-­‐ Indicator   Model   Runs     PUMA  Pop.  Density   Fixed  Route   Commuter   Avg  SOV  Commute   Time   R-­‐Squared   New   York   City   Beta   .11***   -­‐.02***   -­‐.02   .22   San   Diego   -­‐.46***   .39***   .03   .26   San   Francisco   Beta   .04***   -­‐.02   -­‐.21**   .27   Seattle   .03***   .03*   -­‐.19***   .41   Los   Angeles   Beta   .07***   .03   -­‐.44***   .26   Indianapol is   .01   .21   -­‐.68***   .42   Charlotte   Beta   .05***   N/A   .15**   .38   .   Houston   .03***   N/A   -­‐.91***   .37  
  12. 12. Takeaways(1)   •  Housing  costs  may  be  very  inelastic  with  respect  to   density   •  Impacts  of  “density”  on  “affordability”  often  cited  may  in   fact  be  the  impacts  of  density  and  regional  economies  of   scale  as  opposed  to  density  promoting  policies  alone  like   in#ill  development.   In#ill  in  San   Francisco   In#ill  in  Corvallis  
  13. 13. Takeaways  (2)   •  The  “transit  premium”  can  be  captured  just  by   identifying  units  with  FRT  commuters.   –  Our  elasticities  of  .03  to  .07  are  going  to  be   underestimates.   •  PUMS  data  useful  in  measuring  affordable  housing  goals   over  time   http://www.valleymetro.org/metro_projects_planning/transit_oriented_development  
  14. 14. Takeaways  (3)   •  Home  owners  in  neighborhoods  with  lower  SOV   commute  times  pay  higher  mortgages   –  This  is  especially  true  and  more  signi#icant  in  the   sunbelt.       Source:  http://www.city-­‐data.com/forum/general-­‐u-­‐s/843161-­‐best-­‐city-­‐southwest-­‐3.html
  15. 15. Policy  Implications   •  “Density”  itself  is  not  a  major   driver  of  prices   –  Certain  types  of  policies  intended   to  promote  density  may  increase   housing  in  the  medium  term   (0-­‐30  years),  particularly  urban   growth  boundaries.   –  Other  policies  may  yield  less   negative  impacts   •  Construction  of  Fixed  Route   Transit  systems  may  signi#icantly   change  affordability  along  routes   –  This  has  not  been  proven  or   explored  for  Bus  Rapid  Transit,   however.     Source:  ttp://www.mercurynews.com/business/ci_24857532/san-­‐ francisco-­‐tech-­‐bus-­‐protests-­‐deal-­‐charge-­‐fees  
  16. 16. PART  II:  BICYCLE  SAFETY    
  17. 17. Bicycle  Usage  and  Safety  
  18. 18. Mo5va5on  
  19. 19. 2007-­‐2011  Oregon  Crash  Summary   Source:  Chris  Monsere  
  20. 20. Bicycle  Level  of  Traf#ic  Stress  (BLTS)   •  1,  2,  3,  4—based  on  four  “types”  of  bicyclists   • LTS  1:  Anybody  would  bike  on  it,  (Your  younger  children,  nieces  and  nephews)   • LTS  2:    For  your  basic  adult  cyclist,  (friends  of  people  in  this  room  not  as  ‘fanatical’  about   biking  as  we  are)   • LTS  3  or  4:    For  Advanced  Cyclists,  (probably  most  people  in  this  room…)    
  21. 21. Level  of  Traf#ic  Stress   21
  22. 22. Case  Study  Locations  
  23. 23. Number  of  Collisions  on  LTS  Road   Segments  by  City    
  24. 24. Severity  of  Collision  by  City    
  25. 25. Collision  Roadway  AQributes    
  26. 26. Collision  Environment  AQributes    
  27. 27. Collisions  on  LTS  Road  Segments  by  Severity    
  28. 28. GIS  Visual  Analysis  
  29. 29. City  of  Nashua  Strava  Data  
  30. 30. List  of  Variables  
  31. 31. Linear  Regression  Model  
  32. 32. Model  Coefficients  
  33. 33. Possible  Takeways   •  First,  there  is  likely  a  relaAonship  between  LTS  4  and  fatal   bicycle  collisions  and  secondly,  LTS  2  may  have  a  larger  impact   on  collision  severity  than  previously  thought.     •  As  expected,  ‘Injury’  type  collisions  are  the  most  common  in  all   four  ciAes  accounAng  for  about  88%  of  all  collision  types.     •  More  collisions  occurred  on  Mondays  and  Fridays  (17%)  than   any  other  day  and  the  fewest  collisions  occurred  on  Sunday   (7%).    More  collisions  occurred  during  peak  hours  (69%).    
  34. 34. Possible  Takeways   •  AddiAonally,  roadway  segments  without  bike  lanes,  speed  limits  of   30  mph  to  35  mph,  2-­‐4  lanes,  more  than  10,000  AADT,  and  parking   were  more  likely  to  have  “injury”  type  collisions.       •  The  results  of  the  simple  linear  regression  model  found  that  normal   road  condiAons,  dry  road  surfaces,  peak  hours,  intersecAons,  LTS  2,   ‘Injury’  type  collisions,  and  ‘Property  Damage  Only’  type  collisions   are  more  likely  to  involve  a  bicyclist,  while  two-­‐way  traffic,  dark   with  streetlight  on,  and  weekdays  were  less  likely  to  involve  a   bicyclist.       •  These  results  are  generally  consistent  with  the  literature  and  the   visual  maps,  although  they  are  difficult  to  compare  because  the   visual  maps  do  not  show  Ame  variables  or  road  and  surface   condiAons.  
  35. 35. Special  thanks  to:     Oregon  Department  of  Transporta5on       New  Hampshire  Bike  Walk  Alliance     New  Hampshire  Department  of  Transporta5on  
  36. 36. Collision  Loca5on  by  LTS    
  37. 37. Road  Alignment  by  LTS    
  38. 38. Road  Condi5on  by  LTS    
  39. 39. Road  Design  by  LTS    
  40. 40. Traffic  Control  by  LTS    
  41. 41. Surface  Condi5on  by  LTS    
  42. 42. Ligh5ng  Condi5on  by  LTS    
  43. 43. Weather  by  LTS    
  44. 44. Day  of  Collision  by  LTS    

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