Airport Noise in Atlanta: Economic Consequences and Determinants. Jeffrey P. Cohen, Ph.D. Associate Professor of Economics. University of Hartford

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1 Airport Noise in Atlanta: Economic Consequences and Determinants Jeffrey P. Cohen, Ph.D. Associate Professor of Economics University of Hartford Cletus C. Coughlin, Ph.D. Vice President and Deputy Director of Research Federal Reserve Bank of St. Louis Acknowledgements and Disclaimer: The authors thank Lesli Ott for excellent research assistance and the Atlanta Department of Aviation for noise contour data. We thank meeting participants at the Federal Reserve System Committee on Regional Economic Analysis, especially Anil Kumar, for their comments. The views expressed are those of the authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.

2 Airport Noise: Economists consider this an Environmental DISAMENITY or Externality Environmental Externality: an uncompensated cost (or damage) that is imposed upon individuals Damage from noise can affect health, as well as property values Questions: What are the impacts of airport noise on housing prices? Are some groups of residents faced with greater noise than others? How can greater sustainability be achieved (i.e., how society get airport noise to be lowered)?

3 Spatial Hedonic Models of Airport Noise, Proximity, and Housing Prices Jeffrey P. Cohen 1 Cletus C. Coughlin 2 1 Associate Professor of Economics, University of Hartford Scholar-In-Residence, New York University ( ) professorjeffrey@gmail.com 2 Deputy Director of Research and Vice President Federal Reserve Bank of St. Louis coughlin@stls.frb.org

4 Issues Are spatial econometric techniques valuable in an analysis of the impact of airport-related noise on housing prices? What is the best way to incorporate spatial effects in hedonic housing price studies? o Spatial autoregression o Spatial autocorrelation o Both?

5 Background Location, location, location o Here, location near airport can have positive and negative impacts on housing prices Exclusion of spatial considerations: biased estimates of parameters and their statistical significance plus errors in interpreting diagnostic statistics To date no study that looks at airport noise and housing prices using spatial econometrics Recent use of spatial econometrics in other hedonic studies: o Bowen, Mikelbank, and Prestegaard (2001) o Kim, Phipps, and Anselin (2003)

6 Background Federal Register (2000) o Annoyance: the adverse psychological response to noise 12 percent of people subjected to a DNL of 65 decibels report that they are highly annoyed 3 percent are highly annoyed when subjected to a DNL of 55 decibels 40 percent are highly annoyed at a DNL of 75 decibels Nelson (2004): Since 1979 federal agencies have regarded land subjected to DNLs ranging from: o 65 to 74 decibels as normally incompatible with residential use, less than 65 decibels as normally compatible with residential land use

7 Airport Noise, Proximity, and Housing Prices Standard finding is that airport noise reduces housing prices: o McMillen (2004): 9% reduction for houses near Chicago O Hare for 65 db or more Over time, noise levels around O Hare have decreased o Espey and Lopez (2000): 2% reduction for houses near Reno Cannon for 65 db or more o Lipscomb (2003): no effect for houses in College Park, GA, which is near Atlanta Hartsfield-Jackson Proximity: must control for proximity to accurately measure effect of noise Proximity tends to have a positive effect: o Tomkins, Topham, Twomey, and Ward (1998): Manchester o McMillen (2004): O Hare o Lipscomb (2003): Hartsfield-Jackson

8 Figure 1 The Location of Hartsfield Jackson Atlanta International Airport.! DeKalb Fulton ^ Clayton Clayton County DeKalb County Fulton County! Downtown ^ Airport

9 . Figure 2 The Location of Houses in the Sample 0.5 mile buffer zone 65 db noise zone 70 db noise zone 75 db noise zone Hartsfield Atlanta Intl Airport Houses Runways

10 Our Data and Model Data Sales prices and housing characteristics for single family houses in Atlanta for the year 2003 Data were purchased by the Federal Reserve Bank of St. Louis from First American Real Estate Sevices Airport noise data: from the Atlanta Department of Aviation

11 Dependent Variable Log of housing sales price, 2003 (508 observation total) Independent Variables Noise dummies (in decibels): given by contour maps for 2003 for the neighborhoods surrounding the airport o One dummy for 65 db, another dummy for 70 db Detailed housing characteristics such as: o Number of bedrooms (dummies) o Number of baths (dummies) o Number of fireplaces o Number of acres of land o Number of stories o We use ArcView (GIS Software) to calculate the distance between each property address and the airport o Weighted average of the log of housing sale price for all homes that sold in 2003 near the airport

12 Table 1 contains summary information on our data

13 Table 1: Summary Statistics Count Perecentage House sales in the buffer zone 2003 Contours House sales in 65 db zone 2003 Contours House sales in 70 db zone 2003 Contours House sales in Atlanta House sales in College Park House sales in Conley House sales in East Point House sales in Forest Park House sales in Hapeville story or more stories or less bedrooms bedrooms bedrooms or more bedrooms bathroom bathrooms or more bathrooms or 1 fireplace or more fireplaces Number of observations 508 Mean Range Price $128,442 $32,278-$460,500 Distance Acres

14 Anticipated Signs on Regression Coefficients We expect that: Age of dwelling should be negatively related to housing price All of the housing characteristics variables should be related positively to housing prices Higher prices of nearby homes should lead to higher housing prices Greater distance from the airport should result in lower housing prices, due to less convenient access to jobs at the airport and air transportation service We expect that greater noise exposure should lead to lower housing prices

15 Spatial Models Motivation for Incorporating Spatial Econometrics Analagous to time series model, except focus is on geographic space o Can be useful in analyzing urban problems Spatial Autocorrelation Omitted variables that vary spatially o Here, one is soundproofing of homes near airport o Since the 1980s, the local government authority has: Insulated approximately 10,150 structures at a cost of $174.5 million Relocated residents and acquired 2720 structures costing $171 million o But, unable to obtain data on specific homes that were soundproofed Implications of ignoring spatial autocorrelation when present o Inefficient parameter estimates that lead to invalid hypothesis testing

16 Spatial Autoregressive Model Individual house price may depend on prices of nearby houses Implications of ignoring spatial autoregressive variable when it should be included in the model: o Biased parameter estimates

17 Models Standard Model Ordinary Least Squares Y = β + ε (1) Spatial Error Model Y i = i β + ε i (2) ε i = λ j W i,j ε j + µ i Spatial Autoregressive Model Y i = ρ j W i,j Y j + i β + ε i (3) General Spatial Model Y i = ρ j W i,j Y j + i β + ε i (4) ε i = λ j W i,j ε j + µ i

18 Spatial Weights For both spatial errors and spatial autoregressive models W i,j = 1 dist i,j where dist i,j = distance between house i and house j Lower dist i,j implies greater influence of house j on house i

19 Tests for Preferred Model OLS vs. SEM Tests for presence of spatial autocorrelation (λ) Statistic P-Value Moran I test Likelihood Ratio test Wald test SEM preferred

20 Tests for Preferred Model cont. SEM vs. GSM Tests for presence of spatial autoregression (ρ) Statistic P-Value Likelihood Ratio test Wald test GSM preferred

21 Estimation of GSM (A)Generalized Moments vs. (B)Maximum Likelihood (B): Assumes normality of OLS error terms (A): Assumes OLS error terms are i.i.d. with zero mean, constant variance We tested the OLS error terms for normality using Jarque-Bera test Rejected the null hypothesis of normal error terms Generalized moments is the appropriate estimation technique

22 Additional advantage of using Generalized Moments to estimate λ It has been proven (by Kelejian and Prucha 1999) that the parameter estimate will be consistent In large samples we should see: o The expected value of λ equal to the true value (λ is unbiased in large samples) o The variance of the parameter estimate for λ approach zero

23 Procedure for Generalized Moments Estimation Approach developed by Kelejian and Prucha (1998) First, rewrite the GSM equation in matrix notation as: Y = ρwy + β + ε ε = λw ε + µ Step 1 Estimate the GSM using 2SLS, with W as instruments for WY Why 2SLS? o Since Y is correlated with ε, it follows that WY is correlate with ε o This violates a basic assumption that our explanatory variables are uncorrelated with the errors o Since is correlated with Y but not with residuals, it can be shown that W is correlated with WY, and also it follows that W is not correlated with residuals

24 Procedure for Generalized Moments Estimation Step 2 Retrieve the residuals, and use them in the Kelejian and Prucha (1998) Generalized Moments procedure to get estimates for λ and σ 2 Step 3 Do a spatial Cochrane-Orcutt transformation of the original equation based on the parameter estimate that was obtained from Step 2 (this is analogous to the approach for time series autocorrelation)

25 Procedure for Spatial Cochrane-Orcutt Transformation Our model is: Y = ρwy + β + ε where ε = λw ε + µ Substituting gives us: Y = ρwy + β + λw ε + µ (**) So, transform the model: Since, ε = Y ρwy β Then, λw ε = λwy λρwwy λw β Plugging in for λw ε in equation (**) and rearranging gives us: Y λwy = ρ(wy λwwy ) + ( λw )β + µ

26 Procedure for Spatial Cochrane-Orcutt Transformation Or, since we have parameter estimates for λ Y * = ρwy * + *β + µ (***) where Y * Y λwy WY * (WY λwwy ) * λw This leaves us with a good error term, µ (that is, one not subject to spatial autocorrelation) Step 4 Estimate this transformed equation (***) by 2SLS, using W as an instrument for WY * We ve resolved the problem of spatial autocorrelation in our model!

27 Table 2 Estimation Results (t-stats below coefficient value) Variable GSM/GM Constant 5.94** (3.13) DB (-1.31) DB ** (-2.80) Beds3d 0.09** (2.33) Beds4d 0.08 (1.10) Beds5d 0.26** (2.30) Baths2d 0.12** (2.93) Baths3d 0.36** (5.38) Fire2d 0.17* (1.77) Storiesd 0.21** (3.16) Log Acres 0.09** (2.67) Log Distance -0.15** (-2.21) ρ 0.54** (3.55) λ -0.09** n/a R R σ * denotes significance at the 10% (two-tailed) level ** denotes significance at the 5% (two-tailed) level

28 Interpreting the 70 DNL Coefficient in Percentage Terms The percentage impact of additional noise on housing prices = e β = 20.8% Based on the approach of Halvorsen and Palmquist (1980 AER) This implies that the average house in the 70 DNL zone sold for 20.8% less than the average house in the buffer zone, ceteris paribus

29 Test For Joint Significance of Noise Variables Performed a joint significance test for the two noise variables Rejected the null hypothesis that they are jointly zero

30 Interpretation of Spatial Parameters ρ > 0 o Neighborhood effects: Holding all else constant, when your neighbors housing prices are higher, your house price rises. λ < 0 o When your neighbors error terms rise (due to unobservable variables rising, for example), your house s error term falls (negative shock), leading to a fall in your home s price. o Holding constant neighborhood effects and other variables, increases in your neighbors soundproofing (for example) makes your home relatively less desirable, leading to lower price of your home.

31 Are spatial effects present? o YES Conclusion What is the preferred specification? o GSM/GM spatial error with a spatially lagged dependent variable, using Generalized Moments approach Negligible effect on prices for houses located in the 65 DNL noise contour Houses located in 70 DNL noise contour sell for significantly less o But 65 DNL and 70 DNL noise contours are jointly significant We have tested for and incorporated spatial effects to avoid potential econometric problems

32 Other Work in Progress Impact of ports on manufacturing, retail trade, and textile industry costs Use panel data to estimate production function: states over time (one paper), California counties over time (second paper). Incorporate fixed effects, spatial, and time series econometrics techniques Preliminatry Findings o First Paper: productive factors are drawn away from states that are neighboring ports, leading to higher production costs in the neighboring states. o Second paper: Neighbor ports effect is positive in counties with no ports; negative in counties with large ports that have neighbors with smaller ports.

33 Other Work in Progress Impact of hospital clusters on individual hospital costs Is there evidence of agglomeration economies (cost reduction from clustering) that can be caused by: o Labor market pooling o Knowledge sharing Panel data sample of 90 hospitals over 6 years Incorporate fixed effects and variable for measure of agglomeration (distance-weighted average of other hospitals employment) Preliminary findings: o Evidence of agglomeration increased employment in nearby hospitals lower a particular hospital s costs

34 Location of Hospitals in Washington State

35 Alternative Angle: What are the determinants of noise? Sobotta et al (2007): Found that additional Hispanic population led to greater noise exposure. We estimate an Ordered Probit Model, using an approach called Locally Weighted Regressions: Noise is the dependent variable 3 noise categories. How does a change in each variable affect the probability of being in the buffer zone? Enables us to come up with separate parameter estimates for each house sold For some variables, such as % of population Hispanic in the Census Block Group where the house is located: some houses have negative coefficients, others have positive ones.

36 y<65 y=65 y=70 When an increases, two possible outcomes: 1) distribution shifts right prob(y<65) falls, 2) distribution shifts left prob(y<65) rises

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38 Possible solutions to airport noise problem to achieve greater sustainability: 1. Additional Soundproofing 2. Restrict Flights (such as is done in DCA after certain times of day/night) 3. Relocation of Residents (as done in St. Louis before recent airport expansion) 4. Tax airlines for noise based on amount of noise generated 5. Compensate residents who moved in when there was less noise