Hedonic Property Value Studies of Transportation Noise: Aircraft & Road Traffic

1. Hedonic Property Value Studies of Transportation Noise: Aircraft & Road Traffic Jon P. Nelson Department of Economics Pennsylvania State University Workshop on Regulation of Airport Noise ECORE, December 10, 2007 ULB, Brussels

2. Introduction – Objectives of the Survey • Discuss methods used in recent hedonic price studies of airport noise. • Five issues for methodology & econometrics • Compare hedonic price methods to stated preference methods as a means of valuing noise damages. • Brief summary of stated preference methods & results • Summary of recent aircraft noise damage values. – Compare with earlier meta-analyses (Nelson 2004) and other estimates (Navrud 2002, etc.)

3. Outline of Presentation • Hedonic price (HP) model – basic concepts & output • Five issues: • Extent of the market or market segmentation • Spatial linkages & econometrics • Housing market adjustments and information (“dynamics”) • Noise measurement & annoyance indices • Advantages & limitations of the HP model • Stated preference (SP) survey studies • Summary of three studies applied to airport noise • Summary of empirical estimates of noise damages • Compare to earlier results & discuss benefit transfer issues

4. Hedonic Price Model – Basic Concepts • Products are “bundles” of characteristics or attributes. • Markets impute implicit prices to each characteristic – Hedonic Price • Historical antecedents: Hass 1922, Waugh 1928, Court 1941, Griliches 1971, Rosen 1974. Empirical work on housing – Ridker & Henning 1967. • Examples: • Automobile is a combination of engine size & type, weight, styling, etc. • Housing is bundle of structural, location & environment attributes, measured as amenities or disamenities • Econometric methods are used to “unbundled” the market price. • First-stage estimation obtains the marginal hedonic price function (typically non-linear for environmental attributes) for each attribute • Second-stage estimation obtains an (inverse) market demand function for an attribute or willingness-to-pay (WTP) schedule

5. HP Model and Property Values • Revealed Preference Methods – housing & rental markets are (weakly) complementary to nuisance avoidance & mitigation. • Absent an explicit market, indirect methods are required to value damages & individual willingness-to-pay to avoid damages • If houses with different noise levels were valued the same, relocation of individuals would establish a noise-discount gradient • Estimate: PV = F (S, L, Noise Exposure) • PV = property values, S=structural attributes, L=locational attributes • ln(PV) = a + b(S) + c(L) + d(N) + , where • Noise Depreciation Index (NDI) as summary (Walters 1975) • NDI = Pct. change in PV for a decibel (dB) change in noise exposure, e.g., a dB change in the Day-Night Sound Level (DNL or Ldn) • NDI = d 100 = Marginal WTP for localized change in noise exposure

6. Noise Depreciation Index • Consider two identical houses: • One located close to a busy airport (60-65 DNL zone) & a comparable house located in an ambient noise area (50-55 DNL zone) • 10 dB difference is a doubling of perceived loudness (log scale) • Suppose that: • Noisy house is valued on the real estate market at US$180,000 and the quiet house is valued at $200,000, so capitalized discount is $2000 per dB • NDI = ($2000/$200,000)  100 = 1% per dB per property • Data requirements for HP model: • Sample of real estate values and associated characteristics (living space, number of bathrooms, measures of access to work, noise index, etc., etc.) • Nelson (2004)– meta-analysis of 33 NDI estimates for 23 airports: • Wt. mean NDI of 0.59% per dB (std. dev. = 0.04), median = 0.67%, and a wt. meta-regression estimate of 0.67% (std. error = 0.20). Weights are inverse std. errors of individual NDIs. Meta-analysis based one “best estimate” NDI per study • Moderator variables – mean property value (income proxy), sample size, & dummies for accessibility, linear model*, country *, census data, year

7. Housing Market Segmentation • What is the appropriate market size for HP analysis? • Do households choose over the entire market? • Basic problems: hedonic price function is non-linear & noise has to vary • Today – large metro datasets & GIS methods • Day et al. (2007), 10900 obs. for Birmingham, UK (submarkets by ethnicity, age, wealth, size of property, location) • Homogeneity Tests (Chow, Tiao-Goldberger, etc.) • Ex. 1: Baranzini & Ramirez (Geneva) • Private sector rents: NDI = 0.66% per dB (std. error skipped hereafter) • Public sector rents: NDI = 0.79% • Background noise level = 50 dB for Lden (skipped hereafter) • Ex. 2: Day et al, Bateman et al. (cluster analysis) • Glasgow: NDI = 0.40% (4 submarkets; only one significant) • Birmingham: NDI = 1.60% and 0.63% (8 submarkets; two significant)

8. Spatial Econometrics • How does the NDI change as more spatial linkages are incorporated? • Residuals in HP models are (positively) spatially-correlated due to common attributes and/or omitted spatial variables or endogeneity • Results in biased standard errors and/or biased coefficient estimates • Spatial-lag (SLD) and spatial-error dependence (SED) models • GMM estimator. Weighted neighbor matrix for regressors and/or residuals (distance-decay weighting by Tobler’s first law of geography). • Ex.1: Salvi (Zurich); SLD + SED • NDI = 0.75% per dB (close to existing estimates) • Ex. 2: Cohen & Coughlin (Atlanta); SLD + SED • NDI = 1.4 to 2.1%, but based on only 19 properties out of 508 obs. • Airport accessibility enhances property values

9. Housing Market Adjustments (“Dynamics”) • How does the NDI change in the face of new or better information? • Suppose that housing choices are affected by imperfections in the housing market due to limited and/or misleading information about housing attributes, such as noise levels. (Do people error in only one direction?) • Do general housing market conditions matter? • It might be that the noise discount is eliminated by “irrational exuberance,” but HP studies are now available for four decades & many areas • Ex. 1: Jud & Winkler (Greensboro–Winston Salem, NC) • Extensive newspaper coverage of an expanded air-cargo hub (Fed Ex) • Properties close to the airport sold at 0.2% discount prior to & 9.4% after the news. Market did adjust, but perhaps more than actual noise change • Ex. 2: Pope (Raleigh-Durham, NC) • Using state full-disclosure law, R-D imposed a program of informing prospective buyers about noise levels (binding on sellers & agents) • NDI was 0.25% before the program & 0.39% after (+55%)

10. Alternative Noise & Annoyance Indices • Past HP studies of airports rely on a cumulative (average) noise indices, such as Ldn, Lden, & Leq, expressed in 5-dB increments. • Which noise measure is most useful for policy decisions? • Break the index into component parts (e.g., number of events, time above 75 dB; nighttime noise level, etc.) • Ex. 1: Levesque (Winnipeg); NDI = 1.30% • Measure Ldn at each property using a noise simulation model • Ex. 2: On-going California study; NDI = 0.74 to 0.92% • Use dummy variables for each noise contour • Ex. 3: Cohen & Coughlin (Atlanta); NDI = 0.74 to 0.91% • Use the noise exposure data and existing Schultz-curve studies to estimate a percent highly-annoyed index for each property. • Ex. 4: Baranzini et al. (Geneva) for traffic noise– construct (1) actual Ldn; (2) perceived Ldn; & (3) perceived annoyance. Survey respondents tend to overest. actual noise levels, especially at lower levels.

11. Advantages & Limitations of the HP Model • Advantages: • Uses market behavior where individuals voluntarily make actual exchange decisions using money & real resources; • Not subject to numerous survey biases; • Damage values have been obtained for a large number of airports & are reasonably robust over space & time; • WTP values can be calculated using an appropriate discount rate; • Housing markets sort individuals according to noise sensitivity, which is itself a socially efficient means of limiting noise damages. • Limitations: • Not entirely sure what is being perceived & valued (annoyance, health effects, visual, safety, air pollution, costs of moving, etc.); • Choice bundle is complex, e.g., access, so specification matters; • Housing market information or conditions may matter.

12. Stated Preference (SP) Methods • Survey approach to valuing public goods • Using a constructed market, respondents are asked to accept (or reject) hypothetical changes at given price • Obtained result is a WTP (or WTA) value or function for a given scenario • Many variations depending on: • No. of choice dimensions in the scenario • Type of payment vehicle (tax, energy price, etc.) • Hundreds of survey studies exist (Carson’s bibliography has 5000 entries), but relatively few for noise exposure, especially aircraft • Harder to elicit values for intangible nuisance compared to values associated with “tangible” goods, such as green space or cleaner water

13. Examples of Survey Studies of Aircraft Noise • Ex. 1 – Feitelson et al. (Dallas- Ft. Worth) • How much would you be willing to pay for a house or apartment if located in a quiet area, rather than close to the airport or under the flightpath? • NDI = 1.5% for houses; NDI = 0.9% for apartments; sharp rise past 70 dB • Ex. 2 – Frankel (Chicago) – not in references (see Nelson 2004) • Survey of real estate agents and appraisers – asked to estimate the pct. discount that an average property is diminished by aircraft noise • For 60-70 dB, NDI = 0.64% to 0.71%; 70-77.5 dB, NDI = 1.36% to 1.56% • Ex. 3 – Wardman & Bristow (Manchester, Lyon, Bucharest) • Noise evaluated along with nine other quality of life variables & local tax. Noise as number of movements per hour (20, 30), categorical noise levels (type of plane), & time (weekday, weekend, daytime, evening, night). • Time of Day results: Weekday (6pm-10pm), 4.25 cents per movement (Manchester), 7.65 cents (Lyon), and 0.95 cents (Bucharest). Sunday, 6.94 cents (Manchester), 2.94 cents (Lyon?), and 1.31 cents (Bucharest).

14. SP Advantages & Limitations • Advantages: • Very flexible, context can be controlled; • Ex ante and ex post policy changes can be valued; • Strong link with preferences, in theory. • Limitations: • Results are not very robust; • Choice surveys are subject to several well-known biases, such as Hypothetical bias (protest responses, zeros, DK), Strategic bias (free-rider problem), Embedding/Scope bias (WTP should be size dep.), Sample selection bias (SP estimate can be more or less than HP). • Are SP estimates greater than HP estimates for WTP? • Carson et al. (Land Economics 1996) – meta-analysis for 83 studies and 555 estimates; the SP/HP ratio is about 0.62 (so WTP for SP < HP) • Gen (GA Tech diss., 2004) – meta-analysis for 337 SP and 252 HP estimates; SP/HP ratio is about 0.44 (so WTP for SP < HP) • Three SP & HP studies for noise – all possible results (sample selection?)

15. Conclusions • Noise discount has probably risen some over time (positive income elasticity): • Airport noise – mean NDI = 0.92%, median = 0.74; Nelson (2004) = 0.67% • Traffic noise – mean NDI = 0.57%, median = 0.54%; Bertrand (1997) = 0.64; Nelson (1982) = 0.40%. • Three major applications: • Cost-benefit analyses of specific noise mitigation and abatement projects • Total social-cost evaluations of different transportation modes (“full-cost”) • Models of alternative policy instruments (noise and congestion taxes) • Benefit transfer issues: • General problem in environmental economics is the use of a WTP value for a given study area (or mode) for policy evaluation for another location • Both unit value transfers and function transfer are possible • This paper and my earlier meta-analysis provide data for such transfers for all three types of applications

16. THANK YOU • QUESTIONS?