noise pollution suvarnabhumi airport

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

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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)