Your SlideShare is downloading. ×
0
Economic Valuation of Noise Pollution from the Suvarnabhumi Airport   Using Home Value under Hedonic Pricing Method Prepar...
Contents Introduction and Background 1 Hedonic Pricing Method 2 Methodology 3 Results and Conclusion 4
Objectives <ul><li>To construct a model used for estimating house values or prices  </li></ul><ul><li>To examine the impli...
Scope of the study <ul><li>Houses around Suvarnabhumi Airport </li></ul><ul><li>   30 km radius :   Bangkok (Ladkrabang, ...
<ul><li>Bangpli Samutprakarn </li></ul><ul><li>since 28 th  September 2006 </li></ul><ul><li>32 km 2  (4 km x 8 km) or 20,...
Airport Noise <ul><li>NEF or “Noise Exposure Forecast”:  See Noise Contour </li></ul><ul><li>NEF>40   high impact > 80 dB...
 
Noise Problem
Noise Problem <ul><li>Living Places </li></ul>dB(A) # Houses NEF >40  >80 49 NEF 35 – 40  75 - 80   596 NEF 30 – 35  65 - ...
 
 
Hedonic Pricing Method “ Revealed Preference” Hedonic Property Value Model Valuation through prices of properties, houses ...
Property Value Model <ul><li>The Hedonic Price Function: </li></ul><ul><li>House Price =   P(Z) = f(Attributes, Community,...
Property Value Model Welfare Change (Non-marginal) Demand Function Hedonic Price Function Data Welfare Measurement 2 nd  S...
Methodology Model Regression Data 1.Types - Cross-section Data - during Q1of 2008 - around Suvarnabhumi 2.Sources - organi...
Data <ul><li>No data from  Department of Land and the Treasury Department </li></ul><ul><li>Available sources </li></ul><u...
 
ระยะห่าง จากรั้วท่าอากาศยาน จาก  Runway หมู่บ้านร่มสุข วิลเลจ 4 2 3.4 หมู่บ้านร่มฤดี 2.2 3.6 หมู่บ้านสราญวงศ์ 2.4 3.8 หมู่...
 
 
 
 
 
 
 
 
 
 
 
Data <ul><li>44 observations, 10 variables </li></ul><ul><li>Prices of Houses </li></ul><ul><li>   Market Price (second h...
Data <ul><li>Community and Neighborhood Variables   </li></ul><ul><li>   No data for each home area e.g. crime, local ave...
Noise level <ul><li>Living Place: </li></ul>Nakarin Garden  65.3  dB(A) Romsuk Village 70.0  dB(A) Houses on Onnuch Road 7...
Variables Descriptions Definition Units Expected Sign P Sale price Baht N/A LOT Total land area Square Wa + AREA Total liv...
Descriptive Statistics Mean Median Max Min Std.Dev P 4040909 3860000 12790000 820000 2784737.3 LOT 68.64 57 287 15 56.85 A...
Results <ul><li>Full Model:  P = f(LOT,AREA,BATH,BED,FLOOR,CAR,DIS,D1,D2) </li></ul><ul><li>ln(P) = 13.6475 - 0.0017LOT + ...
Results
Results <ul><li>Other tests: </li></ul><ul><li>1. Normality Test   Pass </li></ul><ul><li>2. Heteroscedasticity   Pass <...
Results
Correlation Matrix
Results <ul><li>Six Models to be compared </li></ul><ul><li>1. Excluded variables: AREA, BED </li></ul><ul><li>2. Excluded...
Results Note: 1. “*” and “**” denote 5% and 10% level of significance respectively 2. the variable with wrong sign is “BED...
Results <ul><li>The selected model is “model #4”: </li></ul>ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.182...
Results
Results <ul><li>Other tests: </li></ul><ul><li>1. Normality Test   Pass </li></ul><ul><li>2. Heteroscedasticity   Pass <...
Results
Marginal Price <ul><li>for semi-log model </li></ul><ul><li>ln(P) =   0  +   i Z i </li></ul><ul><li>(1/P)  (  P/  Z...
Marginal Price <ul><li>Marginal Price: P(Z i )   =   i  P   </li></ul><ul><li>1. P(LOT)     =  0.0087*4040909.09  =  3522...
Interpretation <ul><li>1. Inside and outside noise contour (D1) </li></ul><ul><li>(D1 = 1 if inside, = 0 if outside) </li>...
Interpretation <ul><li>2. Single house and Townhouse (D2) </li></ul><ul><li>(D2 = 1 if townhouse, = 0 if single house) </l...
Interpretation <ul><li>3. Lot size (LOT) </li></ul><ul><li>3.1 from P(LOT)   = 35220.56  </li></ul><ul><li>   the value o...
Interpretation <ul><li>4. No.of floors (FLOOR) </li></ul><ul><li>4.1 from P(FLOOR)   = 1241003.59 </li></ul><ul><li>   th...
Interpretation <ul><li>5. No.of bathrooms (BATH) </li></ul><ul><li>5.1 from P(BATH)   = 1089631.14  </li></ul><ul><li>   ...
Interpretation <ul><li>6. No.of bedrooms (BED) </li></ul><ul><li>6.1 from P(BED)   = -737328.52  </li></ul><ul><li>   the...
Conclusion 1 The model used for house pricing in this stydy is 2 Noise problem from Suvarnabhumi airport can be  reflected...
Further Studies <ul><li>Collecting more observations  </li></ul><ul><li>Add more independent variables </li></ul><ul><li>-...
Thank You !
Upcoming SlideShare
Loading in...5
×

Noise pollution suvarnabhumi airport

1,036

Published on

Published in: Business, Travel
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,036
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
14
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Noise pollution suvarnabhumi airport"

  1. 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. 2. Contents Introduction and Background 1 Hedonic Pricing Method 2 Methodology 3 Results and Conclusion 4
  3. 3. Objectives <ul><li>To construct a model used for estimating house values or prices </li></ul><ul><li>To examine the implicit prices of factors affecting to house prices especially noise pollution from Suvarnabhumi airport </li></ul>
  4. 4. Scope of the study <ul><li>Houses around Suvarnabhumi Airport </li></ul><ul><li> 30 km radius : Bangkok (Ladkrabang, Suanluang, </li></ul><ul><li>Prawet, Minburi, Romklao) & Samutprakarn (Bangpli) </li></ul><ul><li>Focus on Noise Problem </li></ul><ul><li> inside and outside “noise contour” </li></ul><ul><li>Hedonic Pricing Method </li></ul>
  5. 5. <ul><li>Bangpli Samutprakarn </li></ul><ul><li>since 28 th September 2006 </li></ul><ul><li>32 km 2 (4 km x 8 km) or 20,000 rai </li></ul><ul><li>2 runways (60 m. wide, 4,000 m. and 3700 m. long) </li></ul><ul><li>Max # of Flights = 46 flights/hr = 700 flights/day </li></ul>Suvarnabhumi Airport
  6. 6. Airport Noise <ul><li>NEF or “Noise Exposure Forecast”: See Noise Contour </li></ul><ul><li>NEF>40  high impact > 80 dB(A) </li></ul><ul><li>NEF 35-40  medium impact 75 - 80 dB(A) </li></ul><ul><li>NEF 30-35  low impact 65 - 75 dB(A) </li></ul><ul><li>Victims: </li></ul><ul><li>Living Places  Romruedee Village, Ladkrabang Garden </li></ul><ul><li>Schools  Krirk University, KMITL </li></ul><ul><li>Temples  Wat Ladkrabang, Wat BangChalong </li></ul><ul><li>Hospitals  Sirinthorn Hospital, Ladkrabang Hospital </li></ul>
  7. 8. Noise Problem
  8. 9. Noise Problem <ul><li>Living Places </li></ul>dB(A) # Houses NEF >40 >80 49 NEF 35 – 40 75 - 80 596 NEF 30 – 35 65 - 75 1731
  9. 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
  10. 13. Property Value Model <ul><li>The Hedonic Price Function: </li></ul><ul><li>House Price = P(Z) = f(Attributes, Community, Environmental) </li></ul><ul><li>Use “ housing prices ” to estimate the value of </li></ul><ul><li>- Environmental quality e.g. noise, air pollution </li></ul><ul><li>- Housing attributes e.g. bathroom, swimming pool </li></ul><ul><li>- Community characteristics e.g. crime, quality of school </li></ul>
  11. 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
  12. 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
  13. 16. Data <ul><li>No data from Department of Land and the Treasury Department </li></ul><ul><li>Available sources </li></ul><ul><li> Websites: </li></ul><ul><li> - http://www.thaihomeonline.com </li></ul><ul><li>- http://classified.sanook.com </li></ul><ul><li> - http://www.ban4u.com </li></ul><ul><li>- http://www.pantipmarket.com </li></ul><ul><li> Books: Talad Ban ( ตลาดบ้าน ) , Arkarn Lae Teedin ( ( อาคารและที่ดิน ) </li></ul><ul><li> Phone Interview </li></ul>
  14. 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
  15. 30. Data <ul><li>44 observations, 10 variables </li></ul><ul><li>Prices of Houses </li></ul><ul><li> Market Price (second hand) </li></ul><ul><li> Single house and Townhouse around Suvarnabhumi airport </li></ul><ul><li>House’s Attributes </li></ul><ul><li> Common variables: area, lot size, #floors, #bathrooms, </li></ul><ul><li>#bedrooms, parking space, distance to the airport </li></ul><ul><li> Other variables to be excluded: swimming pool, hospital, </li></ul><ul><li>police station, shop, sport club, school </li></ul>
  16. 31. Data <ul><li>Community and Neighborhood Variables </li></ul><ul><li> No data for each home area e.g. crime, local average income </li></ul><ul><li>Pollution Variables </li></ul><ul><li> For each house, no data about pollution level </li></ul><ul><li> e.g. noise in dB(A), dust level (pm10) </li></ul><ul><li> use distance between a house and the airport to be a proxy </li></ul><ul><li> use dummy variable to distinguish the houses </li></ul><ul><li> i.e. inside and outside the noise contour </li></ul>
  17. 32. Noise level <ul><li>Living Place: </li></ul>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)
  18. 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 -
  19. 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
  20. 35. Results <ul><li>Full Model: P = f(LOT,AREA,BATH,BED,FLOOR,CAR,DIS,D1,D2) </li></ul><ul><li>ln(P) = 13.6475 - 0.0017LOT + 0.0017AREA + 0.2378BATH - 0.0616BED </li></ul><ul><li> (48.86)* (-0.415) (2.350)* (2.071)* (-0.727) </li></ul><ul><li> </li></ul><ul><li> + 0.1055FLOOR + 0.1974CAR + 0.0103DIST - 0.190D1 - 0.1379D2 </li></ul><ul><li>(0.833) (2.191)* (1.149) (-1.927)** (-1.420) </li></ul><ul><li>Adj.R 2 = 0.8839 </li></ul><ul><li>F-Stat = 37.377 </li></ul><ul><li>Note: “*” and “**” denote 5% and 10% level of significance respectively </li></ul>
  21. 36. Results
  22. 37. Results <ul><li>Other tests: </li></ul><ul><li>1. Normality Test  Pass </li></ul><ul><li>2. Heteroscedasticity  Pass </li></ul><ul><li>3. Serial Correlation  Pass (see DW) </li></ul><ul><li>4. Multicollinearity  Fail (see correlation matrix) </li></ul><ul><li>Correction: </li></ul><ul><li>Drop some variables and compare among models with corrections </li></ul>
  23. 38. Results
  24. 39. Correlation Matrix
  25. 40. Results <ul><li>Six Models to be compared </li></ul><ul><li>1. Excluded variables: AREA, BED </li></ul><ul><li>2. Excluded variables: AREA, CAR </li></ul><ul><li>3. Excluded variables: LOT, CAR </li></ul><ul><li>4. Excluded variables: AREA, CAR, DIS </li></ul><ul><li>5. Excluded variables: AREA, BED, DIS </li></ul><ul><li>6. Excluded variables: LOT, CAR, DIS </li></ul>
  26. 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
  27. 42. Results <ul><li>The selected model is “model #4”: </li></ul>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
  28. 43. Results
  29. 44. Results <ul><li>Other tests: </li></ul><ul><li>1. Normality Test  Pass </li></ul><ul><li>2. Heteroscedasticity  Pass </li></ul><ul><li>3. Serial Correlation  Pass </li></ul><ul><li>4. Multicollinearity  Pass </li></ul><ul><li> Hedonic Price Function (from 1 st stage Hedonic) is </li></ul>ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.190D1– 0.1379D2
  30. 45. Results
  31. 46. Marginal Price <ul><li>for semi-log model </li></ul><ul><li>ln(P) =  0 +  i Z i </li></ul><ul><li>(1/P)  (  P/  Z i ) =  i </li></ul><ul><li> (  P/P)/  Z i =  i </li></ul><ul><li>or  P/  Z i =  i P  marginal price of Z i or P(Z i ) </li></ul><ul><li> </li></ul>
  32. 47. Marginal Price <ul><li>Marginal Price: P(Z i ) =  i P </li></ul><ul><li>1. P(LOT) = 0.0087*4040909.09 = 35220.56 </li></ul><ul><li>2. P(FLOOR) = 0.3071*4040909.09 = 1241003.59 </li></ul><ul><li>3. P(BATH) = 0.2697*4040909.09 = 1089631.14 </li></ul><ul><li>4. P(BED) = -0.1825*4040909.09 = -737328.52 </li></ul><ul><li>5. P(D1) = -0.2731*4040909.09 = -1103370.23 </li></ul><ul><li>6. P(D2) = -0.1878*4040909.09 = -758878.69 </li></ul><ul><li>Note: Average P = 4040909.09 has been used in the estimation of marginal prices </li></ul>ln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.2731D1– 0.1878D2
  33. 48. Interpretation <ul><li>1. Inside and outside noise contour (D1) </li></ul><ul><li>(D1 = 1 if inside, = 0 if outside) </li></ul><ul><li>1.1 from P(D1) = -1103370.23 </li></ul><ul><li> the price of house which is outside noise contour is 1103370 baht </li></ul><ul><li> higher than the one which is inside noise contour </li></ul><ul><li>1.2 ln(P out ) - ln(P in ) = 0.273050  ln(P out / P in ) = 0.273050 </li></ul><ul><li>  P out / P in = e 0.27305 = 1.314 </li></ul><ul><li> the price of house which is outside noise contour is 1.314 times </li></ul><ul><li> (or 31.4% greater than) the one which is inside noise contour </li></ul>
  34. 49. Interpretation <ul><li>2. Single house and Townhouse (D2) </li></ul><ul><li>(D2 = 1 if townhouse, = 0 if single house) </li></ul><ul><li>2.1 from P(D2) = -758878.69 </li></ul><ul><li> the price of single house is 758879 baht higher than the price </li></ul><ul><li> of a townhouse </li></ul><ul><li>2.2 ln(P single ) - ln(P town ) = 0.187799  ln(P single / P town ) = 0.187799 </li></ul><ul><li>  P single / P town = e 0.187799 = 1.207 </li></ul><ul><li> the price of a single house is 1.207 times (or 20.7% greater than) the price of a townhouse </li></ul>
  35. 50. Interpretation <ul><li>3. Lot size (LOT) </li></ul><ul><li>3.1 from P(LOT) = 35220.56 </li></ul><ul><li> the value of lot size is about 35220.56 baht per 1 square Wa </li></ul><ul><li>3.2 (  P/P)/  LOT =  i = 0.008716 </li></ul><ul><li> when lot size increases 1 square Wa, the price of a house will </li></ul><ul><li> increase by 0.8716% </li></ul>
  36. 51. Interpretation <ul><li>4. No.of floors (FLOOR) </li></ul><ul><li>4.1 from P(FLOOR) = 1241003.59 </li></ul><ul><li> the value of one additional floor is about 1241003.59 baht </li></ul><ul><li>4.2 (  P/P)/  FLOOR =  i = 0.307110 </li></ul><ul><li> when there is one additional floor, the price of a house will </li></ul><ul><li> increase by 30.71% </li></ul>
  37. 52. Interpretation <ul><li>5. No.of bathrooms (BATH) </li></ul><ul><li>5.1 from P(BATH) = 1089631.14 </li></ul><ul><li> the value of a bathroom is about 1089631.14 baht </li></ul><ul><li>5.2 (  P/P)/  BATH =  i = 0.269650 </li></ul><ul><li> when there is one additional bathroom, the price of a house will </li></ul><ul><li> increase by 26.97% </li></ul>
  38. 53. Interpretation <ul><li>6. No.of bedrooms (BED) </li></ul><ul><li>6.1 from P(BED) = -737328.52 </li></ul><ul><li> the value of a bedroom is about -737328.52 baht </li></ul><ul><li>6.2 (  P/P)/  BED =  i = -0.187799 </li></ul><ul><li> when there is one additional bathroom, the price of a house will </li></ul><ul><li> decrease by 18.78% </li></ul>
  39. 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
  40. 55. Further Studies <ul><li>Collecting more observations </li></ul><ul><li>Add more independent variables </li></ul><ul><li>- pollution level e.g. dB(A), PM10 </li></ul><ul><li>- community and neighborhood variables e.g. crime, income </li></ul><ul><li>Estimate demand (2 nd Stage Hedonic) </li></ul><ul><li>- to measure welfare (in case of non-marginal) </li></ul><ul><li>- study in several area (Market segmentation) </li></ul>
  41. 56. Thank You !
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×