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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. Introduction – Objectives of the Survey.

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

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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


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.)


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


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


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


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


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)


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


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%)


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.


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.


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


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).


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?)


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


THANK YOU

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