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Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
Noise pollution suvarnabhumi airport
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Noise pollution suvarnabhumi airport

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  • 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. Objectives
    • To construct a model used for estimating house values or prices
    • To examine the implicit prices of factors affecting to house prices especially noise pollution from Suvarnabhumi airport
  • 4. Scope of the study
    • Houses around Suvarnabhumi Airport
    •  30 km radius : Bangkok (Ladkrabang, Suanluang,
    • Prawet, Minburi, Romklao) & Samutprakarn (Bangpli)
    • Focus on Noise Problem
    •  inside and outside “noise contour”
    • Hedonic Pricing Method
  • 5.
    • Bangpli Samutprakarn
    • since 28 th September 2006
    • 32 km 2 (4 km x 8 km) or 20,000 rai
    • 2 runways (60 m. wide, 4,000 m. and 3700 m. long)
    • Max # of Flights = 46 flights/hr = 700 flights/day
    Suvarnabhumi Airport
  • 6. Airport Noise
    • NEF or “Noise Exposure Forecast”: See Noise Contour
    • NEF>40  high impact > 80 dB(A)
    • NEF 35-40  medium impact 75 - 80 dB(A)
    • NEF 30-35  low impact 65 - 75 dB(A)
    • Victims:
    • Living Places  Romruedee Village, Ladkrabang Garden
    • Schools  Krirk University, KMITL
    • Temples  Wat Ladkrabang, Wat BangChalong
    • Hospitals  Sirinthorn Hospital, Ladkrabang Hospital
  • 7.  
  • 8. Noise Problem
  • 9. Noise Problem
    • Living Places
    dB(A) # Houses NEF >40 >80 49 NEF 35 – 40 75 - 80 596 NEF 30 – 35 65 - 75 1731
  • 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. Property Value Model
    • The Hedonic Price Function:
    • House Price = P(Z) = f(Attributes, Community, Environmental)
    • Use “ housing prices ” to estimate the value of
    • - Environmental quality e.g. noise, air pollution
    • - Housing attributes e.g. bathroom, swimming pool
    • - Community characteristics e.g. crime, quality of school
  • 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. Data
    • No data from Department of Land and the Treasury Department
    • Available sources
    •  Websites:
    • - http://www.thaihomeonline.com
    • - http://classified.sanook.com
    • - http://www.ban4u.com
    • - http://www.pantipmarket.com
    •  Books: Talad Ban ( ตลาดบ้าน ) , Arkarn Lae Teedin ( ( อาคารและที่ดิน )
    •  Phone Interview
  • 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
  • 19.  
  • 20.  
  • 21.  
  • 22.  
  • 23.  
  • 24.  
  • 25.  
  • 26.  
  • 27.  
  • 28.  
  • 29.  
  • 30. Data
    • 44 observations, 10 variables
    • Prices of Houses
    •  Market Price (second hand)
    •  Single house and Townhouse around Suvarnabhumi airport
    • House’s Attributes
    •  Common variables: area, lot size, #floors, #bathrooms,
    • #bedrooms, parking space, distance to the airport
    •  Other variables to be excluded: swimming pool, hospital,
    • police station, shop, sport club, school
  • 31. Data
    • Community and Neighborhood Variables
    •  No data for each home area e.g. crime, local average income
    • Pollution Variables
    •  For each house, no data about pollution level
    • e.g. noise in dB(A), dust level (pm10)
    •  use distance between a house and the airport to be a proxy
    •  use dummy variable to distinguish the houses
    • i.e. inside and outside the noise contour
  • 32. Noise level
    • Living Place:
    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)
  • 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. Results
    • Full Model: P = f(LOT,AREA,BATH,BED,FLOOR,CAR,DIS,D1,D2)
    • ln(P) = 13.6475 - 0.0017LOT + 0.0017AREA + 0.2378BATH - 0.0616BED
    • (48.86)* (-0.415) (2.350)* (2.071)* (-0.727)
    • + 0.1055FLOOR + 0.1974CAR + 0.0103DIST - 0.190D1 - 0.1379D2
    • (0.833) (2.191)* (1.149) (-1.927)** (-1.420)
    • Adj.R 2 = 0.8839
    • F-Stat = 37.377
    • Note: “*” and “**” denote 5% and 10% level of significance respectively
  • 36. Results
  • 37. Results
    • Other tests:
    • 1. Normality Test  Pass
    • 2. Heteroscedasticity  Pass
    • 3. Serial Correlation  Pass (see DW)
    • 4. Multicollinearity  Fail (see correlation matrix)
    • Correction:
    • Drop some variables and compare among models with corrections
  • 38. Results
  • 39. Correlation Matrix
  • 40. Results
    • Six Models to be compared
    • 1. Excluded variables: AREA, BED
    • 2. Excluded variables: AREA, CAR
    • 3. Excluded variables: LOT, CAR
    • 4. Excluded variables: AREA, CAR, DIS
    • 5. Excluded variables: AREA, BED, DIS
    • 6. Excluded variables: LOT, CAR, DIS
  • 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. Results
    • The selected model is “model #4”:
    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
  • 43. Results
  • 44. Results
    • Other tests:
    • 1. Normality Test  Pass
    • 2. Heteroscedasticity  Pass
    • 3. Serial Correlation  Pass
    • 4. Multicollinearity  Pass
    •  Hedonic Price Function (from 1 st stage Hedonic) is
    ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.190D1– 0.1379D2
  • 45. Results
  • 46. Marginal Price
    • for semi-log model
    • ln(P) =  0 +  i Z i
    • (1/P)  (  P/  Z i ) =  i
    • (  P/P)/  Z i =  i
    • or  P/  Z i =  i P  marginal price of Z i or P(Z i )
  • 47. Marginal Price
    • Marginal Price: P(Z i ) =  i P
    • 1. P(LOT) = 0.0087*4040909.09 = 35220.56
    • 2. P(FLOOR) = 0.3071*4040909.09 = 1241003.59
    • 3. P(BATH) = 0.2697*4040909.09 = 1089631.14
    • 4. P(BED) = -0.1825*4040909.09 = -737328.52
    • 5. P(D1) = -0.2731*4040909.09 = -1103370.23
    • 6. P(D2) = -0.1878*4040909.09 = -758878.69
    • Note: Average P = 4040909.09 has been used in the estimation of marginal prices
    ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.2731D1– 0.1878D2
  • 48. Interpretation
    • 1. Inside and outside noise contour (D1)
    • (D1 = 1 if inside, = 0 if outside)
    • 1.1 from P(D1) = -1103370.23
    •  the price of house which is outside noise contour is 1103370 baht
    • higher than the one which is inside noise contour
    • 1.2 ln(P out ) - ln(P in ) = 0.273050  ln(P out / P in ) = 0.273050
    •  P out / P in = e 0.27305 = 1.314
    •  the price of house which is outside noise contour is 1.314 times
    • (or 31.4% greater than) the one which is inside noise contour
  • 49. Interpretation
    • 2. Single house and Townhouse (D2)
    • (D2 = 1 if townhouse, = 0 if single house)
    • 2.1 from P(D2) = -758878.69
    •  the price of single house is 758879 baht higher than the price
    • of a townhouse
    • 2.2 ln(P single ) - ln(P town ) = 0.187799  ln(P single / P town ) = 0.187799
    •  P single / P town = e 0.187799 = 1.207
    •  the price of a single house is 1.207 times (or 20.7% greater than) the price of a townhouse
  • 50. Interpretation
    • 3. Lot size (LOT)
    • 3.1 from P(LOT) = 35220.56
    •  the value of lot size is about 35220.56 baht per 1 square Wa
    • 3.2 (  P/P)/  LOT =  i = 0.008716
    •  when lot size increases 1 square Wa, the price of a house will
    • increase by 0.8716%
  • 51. Interpretation
    • 4. No.of floors (FLOOR)
    • 4.1 from P(FLOOR) = 1241003.59
    •  the value of one additional floor is about 1241003.59 baht
    • 4.2 (  P/P)/  FLOOR =  i = 0.307110
    •  when there is one additional floor, the price of a house will
    • increase by 30.71%
  • 52. Interpretation
    • 5. No.of bathrooms (BATH)
    • 5.1 from P(BATH) = 1089631.14
    •  the value of a bathroom is about 1089631.14 baht
    • 5.2 (  P/P)/  BATH =  i = 0.269650
    •  when there is one additional bathroom, the price of a house will
    • increase by 26.97%
  • 53. Interpretation
    • 6. No.of bedrooms (BED)
    • 6.1 from P(BED) = -737328.52
    •  the value of a bedroom is about -737328.52 baht
    • 6.2 (  P/P)/  BED =  i = -0.187799
    •  when there is one additional bathroom, the price of a house will
    • decrease by 18.78%
  • 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. Further Studies
    • Collecting more observations
    • Add more independent variables
    • - pollution level e.g. dB(A), PM10
    • - community and neighborhood variables e.g. crime, income
    • Estimate demand (2 nd Stage Hedonic)
    • - to measure welfare (in case of non-marginal)
    • - study in several area (Market segmentation)
  • 56. Thank You !

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