Economic Valuation of Noise Pollution from the Suvarnabhumi AirportEconomic Valuation of Noise Pollution from the Suvarnabhumi Airport Using Home Value under Hedonic Pricing MethodUsing Home Value under Hedonic Pricing Method
Prepared by Pisit Puapan and Pat PattanarangsunPrepared by Pisit Puapan and Pat Pattanarangsun
ContentsContents
Introduction and Background1
Hedonic Pricing Method2
Methodology3
Results and Conclusion4
ObjectivesObjectives
• 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
Scope of the studyScope 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
• Bangpli Samutprakarn• since 28th September 2006• 32 km2 (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 AirportSuvarnabhumi Airport
Airport NoiseAirport 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 GardenSchools Krirk University, KMITLTemples Wat Ladkrabang, Wat BangChalongHospitals Sirinthorn Hospital, Ladkrabang Hospital
Noise ProblemNoise Problem
Noise ProblemNoise Problem
dB(A) # Houses
NEF >40 >80 49NEF 35 – 40 75 - 80
596
NEF 30 – 35 65 - 75 1731
• Living Places
Hedonic Property Value Model
Valuation through prices of properties,
houses and land
HPMHPM
Hedonic Wage Model
Valuation through wages of workers
Hedonic Pricing MethodHedonic Pricing Method
“Revealed Preference”
Property Value ModelProperty 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
Welfare Change (Non-marginal)
Demand Function
Hedonic Price Function
Data
Welfare Measurement
2nd Stage Hedonic
1st Stage Hedonic
Data CollectionData Collection
Property Value ModelProperty Value Model
MethodologyMethodology
Model
Regression
DataData
1.Types - Cross-section Data - during Q1of 2008 - around Suvarnabhumi
2.Sources - organizations - websites - books - phone interview
(1st 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 - R2
DataData
• 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 (ตลาดบ้�าน), Arkarn Lae Teedin ((อาคารและที่��ด�น)
Phone Interview
ระยะห่�าง
จากร�วท่�าอากาศยาน จาก Runway
ห่มู่��บ้�านร�มู่สุ�ข ว�ลเลจ4 2 3.4
ห่มู่��บ้�านร�มู่ฤดี� 2.2 3.6
ห่มู่��บ้�านสุราญวงศ� 2.4 3.8
ห่มู่��บ้�านพาราไดีซ์� การ�เดี�น 6.8 8.2
ห่มู่��บ้�านนคร�นท่ร� การ�เดี�น 6.4 7.8
ห่มู่��บ้�านพนาสุนธิ์�% 3 7.6 9
ห่มู่��บ้�านศ�ร�นท่รา 5.6 7
ห่มู่��บ้�านวฒนา 5.2 6.6
ห่มู่��บ้�านร� �งก�จการ�เดี�นโฮมู่ 5 6.4
ห่มู่��บ้�านไตฮ��เพลสุ 3 4.4
ห่มู่��บ้�านลาดีกระบ้งการ�เดี�น 0.4 1.8
ห่มู่��บ้�านมู่ณสุ�น� 0.2 2.8
ห่มู่��บ้�านแฮปป-� เพลสุ 4.8 6.2
ห่มู่��บ้�านประภาวรรณโฮมู่ 2 10 11.4
ห่มู่��บ้�านเคห่ะนคร 2 0.4 1.8
ห่มู่��บ้�านร� �งก�จว�ลล�า 4 2.2 3.6
ห่มู่��บ้�านร� �งก�จว�ลล�า 5 2 3.4
ห่มู่��บ้�านร� �งก�จว�ลล�า 9 1.6 3
ห่มู่��บ้�านจ�ลมู่าศว�ลลา 0.2 1.6
ห่มู่��บ้�านสุ�ท่ธิ์าท่ร 2.6 5.2
DataData
• 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
DataData
• 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
Noise levelNoise 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)
Variables DescriptionsVariables DescriptionsDefinition 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 -
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
Descriptive StatisticsDescriptive Statistics
ResultsResults
• 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.R2 = 0.8839F-Stat = 37.377
Note: “*” and “**” denote 5% and 10% level of significance respectively
ResultsResults
ResultsResults
• Other tests:1. Normality Test Pass2. Heteroscedasticity Pass3. Serial Correlation Pass (see DW)4. Multicollinearity Fail (see correlation matrix)
• Correction:Drop some variables and compare among models with corrections
ResultsResults
0
1
2
3
4
5
6
7
8
-0.4 -0.2 -0.0 0.2 0.4
Series: ResidualsSample 1 44Observations 44
Mean -7.36e-16Median -0.016962Maximum 0.414774Minimum -0.543988Std. Dev. 0.220651Skewness -0.019328Kurtosis 2.545039
Jarque-Bera 0.382220Probability 0.826042
Correlation MatrixCorrelation Matrix
ResultsResults
• 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
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
ResultsResults
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
ResultsResults
• 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. R2 = 0.8647F-Stat = 46.805
Note: “*” and “**” denote 5% and 10% level of significance respectively
ResultsResults
ResultsResults
• Other tests:1. Normality Test Pass2. Heteroscedasticity Pass3. Serial Correlation Pass4. Multicollinearity Pass
Hedonic Price Function (from 1st stage Hedonic) isln(P) = 13.8262 + 0.0087LOT + 0.3071FLOOR + 0.2697BATH – 0.1825BED – 0.190D1– 0.1379D2
ResultsResults
0
1
2
3
4
5
6
-0.4 -0.2 -0.0 0.2 0.4
Series: ResidualsSample 1 44Observations 44
Mean -1.93e-15Median 0.002960Maximum 0.522445Minimum -0.415909Std. Dev. 0.248485Skewness 0.200434Kurtosis 2.281225
Jarque-Bera 1.241778Probability 0.537466
Marginal PriceMarginal Price
• for semi-log model
ln(P) = 0 + iZi
(1/P)(P/Zi) = i
(P/P)/Zi = i
or P/Zi = i P marginal price of Zi or P(Zi)
Marginal PriceMarginal Price
• Marginal Price: P(Zi) = 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
InterpretationInterpretation1. 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(Pout) - ln(Pin) = 0.273050 ln(Pout / Pin) = 0.273050
Pout / Pin = e0.27305 = 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
InterpretationInterpretation
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(Psingle) - ln(Ptown) = 0.187799 ln(Psingle / Ptown) = 0.187799
Psingle / Ptown = e0.187799 = 1.207
the price of a single house is 1.207 times (or 20.7% greater than) the price of a townhouse
InterpretationInterpretation
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%
InterpretationInterpretation
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%
InterpretationInterpretation
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%
InterpretationInterpretation
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%
ConclusionConclusion
11The model used for house pricing in this stydy is
22Noise problem from Suvarnabhumi airport can be reflected from a difference between prices of houses inside and outside noise contour 1103370 Baht
33Other attributes which may not be valued directly or easily can be determined by the calculation of marginal prices from hedonic price function in the 1st stage.
ln(P) = 13.82 + 0.009LOT + 0.31FLOOR + 0.27BATH – 0.18BED – 0.27D1– 0.19D2
Further StudiesFurther 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 (2nd Stage Hedonic)
- to measure welfare (in case of non-marginal)- study in several area (Market segmentation)