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Economic Valuation of Noise Pollution from the Suvarnabhumi Airport   Using Home Value under Hedonic Pricing Method Prepared by Pisit Puapan and Pat Pattanarangsun
Contents Introduction and Background 1 Hedonic Pricing Method 2 Methodology 3 Results and Conclusion 4
Objectives ,[object Object],[object Object]
Scope of the study ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Suvarnabhumi Airport
Airport Noise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Noise Problem
Noise Problem ,[object Object],dB(A) # Houses NEF >40  >80 49 NEF 35 – 40  75 - 80   596 NEF 30 – 35  65 - 75 1731
 
 
Hedonic Pricing Method “ Revealed Preference” Hedonic Property Value Model Valuation through prices of properties, houses and land HPM Hedonic Wage Model Valuation through wages of workers
Property Value Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Property Value Model Welfare Change (Non-marginal) Demand Function Hedonic Price Function Data Welfare Measurement 2 nd  Stage Hedonic 1 st  Stage Hedonic Data Collection
Methodology Model Regression Data 1.Types - Cross-section Data - during Q1of 2008 - around Suvarnabhumi 2.Sources - organizations - websites - books  - phone interview (  1 st  Stage ) 1. Dependent Var. - prices of houses 2.Independent Var. - attributes - envi variables  - community   variables 3.Functional Form - Semi Log (Log-lin) 1.Estimation Method - OLS by EViews  2.Tests - Classical Assumptions  for OLS (CLRM) 3.Model comparison - signs - t-Stat - R 2
Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
ระยะห่าง จากรั้วท่าอากาศยาน จาก  Runway หมู่บ้านร่มสุข วิลเลจ 4 2 3.4 หมู่บ้านร่มฤดี 2.2 3.6 หมู่บ้านสราญวงศ์ 2.4 3.8 หมู่บ้านพาราไดซ์ การ์เด้น 6.8 8.2 หมู่บ้านนครินทร์ การ์เด้น 6.4 7.8 หมู่บ้านพนาสนธิ์  3 7.6 9 หมู่บ้านศิรินทรา 5.6 7 หมู่บ้านวัฒนา 5.2 6.6 หมู่บ้านรุ่งกิจการ์เด้นโฮม 5 6.4 หมู่บ้านไตฮี้เพลส 3 4.4 หมู่บ้านลาดกระบังการ์เด้น 0.4 1.8 หมู่บ้านมณสินี 0.2 2.8 หมู่บ้านแฮปปี้เพลส 4.8 6.2 หมู่บ้านประภาวรรณโฮม  2 10 11.4 หมู่บ้านเคหะนคร  2 0.4 1.8 หมู่บ้านรุ่งกิจวิลล่า  4 2.2 3.6 หมู่บ้านรุ่งกิจวิลล่า  5 2 3.4 หมู่บ้านรุ่งกิจวิลล่า  9 1.6 3 หมู่บ้านจุลมาศวิลลา 0.2 1.6 หมู่บ้านสุทธาทร 2.6 5.2
 
 
 
 
 
 
 
 
 
 
 
Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Noise level ,[object Object],Nakarin Garden  65.3  dB(A) Romsuk Village 70.0  dB(A) Houses on Onnuch Road 73.2  dB(A) Thana Place 55.8  dB(A)
Variables Descriptions Definition Units Expected Sign P Sale price Baht N/A LOT Total land area Square Wa + AREA Total living space Square Meters + FLOOR Number of floors floors + BATH Number of Bathrooms rooms + BED Number of Bedrooms rooms + CAR Garage space cars + DIS Distance to Suvarnabhumi airport kilometers +/- D1 1 if located in noise contour, 0 if not 0/1 - D2 1 if townhouse, 0 if single house 0/1 -
Descriptive Statistics Mean Median Max Min Std.Dev P 4040909 3860000 12790000 820000 2784737.3 LOT 68.64 57 287 15 56.85 AREA 335.23 288 1148 50 240.05 FLOOR 1.977 2 3 1 0.46 BATH 2.114 2 4 1 0.75 BED 2.591 2 6 2 1.00 CAR 1.477 2 4 0 0.95 DIS 12.682 12 32 7 4.89 D1 No. of “0” = 28 and No. of “1” = 16 D2 No. of “0” = 30 and No. of “1” = 14
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results
Correlation Matrix
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results Note: 1. “*” and “**” denote 5% and 10% level of significance respectively 2. the variable with wrong sign is “BED” for all cases in which variable “BED” is significant       LOT  *  *  *  * AREA  *  * FLOOR  *  *   *  *  BATH   *  *  *   * BED  *  *  *  ** CAR  *  * DIS    D1  *  *  *  *  *  * D2  **  **  **  **  **  ** # independent variables 7 7 7 6 6 6 - sig at    = 5% (10%) 4 (5) 5 (6) 4 (5) 5(6) 4(5) 3(5) - sig & correct sign  4 (5) 4 (5) 3(4) 4(5) 4(5) 3(4) Adjusted R-squared 0.8645 0.8632 0.8730 0.8647 0.8674 0.8721
Results ,[object Object],ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED   (53.568)*  (5.157)*  (3.135)*  (2.376)*  (-2.527)*     - 0.2731D1 - 0.1878D2 (-2.891)*  (-1.819)** Adj.   R 2  = 0.8647 F-Stat = 46.805 Note: “*” and “**” denote 5% and 10% level of significance respectively
Results
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.190D1– 0.1379D2
Results
Marginal Price ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Marginal Price ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.2731D1– 0.1878D2
Interpretation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interpretation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interpretation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interpretation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interpretation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interpretation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion 1 The model used for house pricing in this stydy is 2 Noise problem from Suvarnabhumi airport can be  reflected from a difference between prices of houses  inside and outside noise contour    1103370 Baht 3 Other attributes which may not be valued directly or easily can be determined by the calculation of marginal prices from hedonic price function in the 1 st  stage. ln(P) = 13.82 + 0.009LOT + 0.31FLOOR + 0.27BATH – 0.18BED – 0.27D1– 0.19D2
Further Studies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank You !

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Noise pollution suvarnabhumi airport

  • 1. Economic Valuation of Noise Pollution from the Suvarnabhumi Airport Using Home Value under Hedonic Pricing Method Prepared by Pisit Puapan and Pat Pattanarangsun
  • 2. Contents Introduction and Background 1 Hedonic Pricing Method 2 Methodology 3 Results and Conclusion 4
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.  
  • 9.
  • 10.  
  • 11.  
  • 12. Hedonic Pricing Method “ Revealed Preference” Hedonic Property Value Model Valuation through prices of properties, houses and land HPM Hedonic Wage Model Valuation through wages of workers
  • 13.
  • 14. Property Value Model Welfare Change (Non-marginal) Demand Function Hedonic Price Function Data Welfare Measurement 2 nd Stage Hedonic 1 st Stage Hedonic Data Collection
  • 15. Methodology Model Regression Data 1.Types - Cross-section Data - during Q1of 2008 - around Suvarnabhumi 2.Sources - organizations - websites - books - phone interview (  1 st Stage ) 1. Dependent Var. - prices of houses 2.Independent Var. - attributes - envi variables - community variables 3.Functional Form - Semi Log (Log-lin) 1.Estimation Method - OLS by EViews 2.Tests - Classical Assumptions for OLS (CLRM) 3.Model comparison - signs - t-Stat - R 2
  • 16.
  • 17.  
  • 18. ระยะห่าง จากรั้วท่าอากาศยาน จาก Runway หมู่บ้านร่มสุข วิลเลจ 4 2 3.4 หมู่บ้านร่มฤดี 2.2 3.6 หมู่บ้านสราญวงศ์ 2.4 3.8 หมู่บ้านพาราไดซ์ การ์เด้น 6.8 8.2 หมู่บ้านนครินทร์ การ์เด้น 6.4 7.8 หมู่บ้านพนาสนธิ์ 3 7.6 9 หมู่บ้านศิรินทรา 5.6 7 หมู่บ้านวัฒนา 5.2 6.6 หมู่บ้านรุ่งกิจการ์เด้นโฮม 5 6.4 หมู่บ้านไตฮี้เพลส 3 4.4 หมู่บ้านลาดกระบังการ์เด้น 0.4 1.8 หมู่บ้านมณสินี 0.2 2.8 หมู่บ้านแฮปปี้เพลส 4.8 6.2 หมู่บ้านประภาวรรณโฮม 2 10 11.4 หมู่บ้านเคหะนคร 2 0.4 1.8 หมู่บ้านรุ่งกิจวิลล่า 4 2.2 3.6 หมู่บ้านรุ่งกิจวิลล่า 5 2 3.4 หมู่บ้านรุ่งกิจวิลล่า 9 1.6 3 หมู่บ้านจุลมาศวิลลา 0.2 1.6 หมู่บ้านสุทธาทร 2.6 5.2
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  • 33. Variables Descriptions Definition Units Expected Sign P Sale price Baht N/A LOT Total land area Square Wa + AREA Total living space Square Meters + FLOOR Number of floors floors + BATH Number of Bathrooms rooms + BED Number of Bedrooms rooms + CAR Garage space cars + DIS Distance to Suvarnabhumi airport kilometers +/- D1 1 if located in noise contour, 0 if not 0/1 - D2 1 if townhouse, 0 if single house 0/1 -
  • 34. Descriptive Statistics Mean Median Max Min Std.Dev P 4040909 3860000 12790000 820000 2784737.3 LOT 68.64 57 287 15 56.85 AREA 335.23 288 1148 50 240.05 FLOOR 1.977 2 3 1 0.46 BATH 2.114 2 4 1 0.75 BED 2.591 2 6 2 1.00 CAR 1.477 2 4 0 0.95 DIS 12.682 12 32 7 4.89 D1 No. of “0” = 28 and No. of “1” = 16 D2 No. of “0” = 30 and No. of “1” = 14
  • 35.
  • 37.
  • 40.
  • 41. Results Note: 1. “*” and “**” denote 5% and 10% level of significance respectively 2. the variable with wrong sign is “BED” for all cases in which variable “BED” is significant       LOT  *  *  *  * AREA  *  * FLOOR  *  *   *  *  BATH   *  *  *   * BED  *  *  *  ** CAR  *  * DIS    D1  *  *  *  *  *  * D2  **  **  **  **  **  ** # independent variables 7 7 7 6 6 6 - sig at  = 5% (10%) 4 (5) 5 (6) 4 (5) 5(6) 4(5) 3(5) - sig & correct sign 4 (5) 4 (5) 3(4) 4(5) 4(5) 3(4) Adjusted R-squared 0.8645 0.8632 0.8730 0.8647 0.8674 0.8721
  • 42.
  • 44.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54. Conclusion 1 The model used for house pricing in this stydy is 2 Noise problem from Suvarnabhumi airport can be reflected from a difference between prices of houses inside and outside noise contour  1103370 Baht 3 Other attributes which may not be valued directly or easily can be determined by the calculation of marginal prices from hedonic price function in the 1 st stage. ln(P) = 13.82 + 0.009LOT + 0.31FLOOR + 0.27BATH – 0.18BED – 0.27D1– 0.19D2
  • 55.