No Plane, Big Gain: Airport Noise and Residential Property Values in the Reno-Sparks Area

Transcription

1 No Plane, Big Gain: Airport Noise and Residential Property Values in the Reno-Sparks Area Hilary Kaufman Molly Espey Presented at Western Agricultural Economics Association 1997 Annual Meeting July 13-16, 1997 Reno/Sparks, Nevada

2 No Plane, Big Gain: Airport Noise and Residential Property Values in the Reno-Sparks Area Hilary Kaufman Department of Geography University of Denver Denver, Colorado and Molly Espey Department of Applied Economics and Statistics University of Nevada Reno, Nevada

3 Abstract No Plane, Big Gain: Airport Noise and Residential Property Values in the Reno- Sparks Area The hedonic price method is used to explore the relationship between residential property values and airport noise in the Reno-Sparks area. Empirical results suggest there is a statistically significant negative relationship between airport noise and residential property values, but that the disamenity value diminishes as distance from the airport increases.

4 Introduction The Reno-Sparks (Nevada) metropolitan area has been one of the fastest growing areas in the United States for more than the past decade. This growth in population has been accompanied by growth in Reno s popularity as a vacation site, leading to a rapid increase in the number of flights at the Reno-Tahoe International Airport, especially in the past few years. In fact, there was a 16% increase in the number of passengers between 1995 and 1996 alone and airport officials anticipate a new terminal will be needed within five years (Reno Gazette Journal, 1/1/97, 1E). While the increase in flights has contributed to economic growth in the area, plans for further airport expansion have created controversy in neighborhoods affected by airport noise and physical expansion of the airport. The Reno-Tahoe International Airport is located in the southeast section of the city of Reno and is nearly surrounded by residential communities. In 1990, the Washoe County Airport Authority conducted a Federal Aviation Regulation (F.A.R) part 150 Noise Compatibility Study for the Reno Cannon International Airport. (The name of the airport was subsequently changed to Reno Tahoe International Airport.) The area studied covered approximately 11 miles in a north-south direction and 3.9 miles in an east-west direction. In total, an area of roughly 42.9 square miles was covered, including significant portions of the City of Reno, Sparks, and unincorporated Washoe County. The purpose of the study was to identify current noise exposure conditions, forecast future conditions if noise continued unabated, as well as to provide a summary of proposed noise abatement 1

5 measures and forecasts of future conditions should noise abatement measures be implemented. This study did not, however, attempt to quantify the impact of airport noise on residential housing values. This is an issue of obvious concern to all homeowners in the area, but especially to those who did not foresee such growth when they bought their homes. This study uses the hedonic pricing technique to determine the impact of both airport noise and proximity to the airport on residential property values in the Reno- Sparks area. First, previous hedonic studies of airport noise are reviewed, then the data and model used in this study are discussed. Results of this study indicate that a one decibel increase in airport noise reduces property value by about 0.3%, slightly less than the 0.4 to 1.1% per decibel range Nelson (1980) found in his survey of airport noise studies. Background The hedonic pricing technique, as applied to housing, is based on the idea that the value of a house is a function of the value of the individual attributes that comprise the house, for example, square footage, number of bedrooms or bathrooms, or proximity to schools or parks. The price of a house (P H ) can be written as: P H = f (S i1,..., S ij, N i1,..., N ik, Q i1,..., Q im ), where, S j, N k, and Q m indicate vectors of structural, neighborhood and environmental variables, respectively. This equation represents the hedonic (implicit price) function for 2

6 housing. The implicit price of any characteristic, for example, N k, a neighborhood variable, can be estimated as: P h / N k = P Nk (N k ) This partial derivative gives the change in expenditures on housing that is required to obtain a house with one more unit of N k, ceteris paribus. If the value of the partial derivative is positive then the attribute is an amenity, if the value is negative then the attribute is a disamenity such as air pollution or airport noise. Since a household maximizes its utility by moving along its marginal price schedule until it reaches a point where its marginal willingness to pay for a certain characteristic equals the marginal implicit price of the characteristic, the implicit price function is defined as a locus of individual equilibrium marginal willingness to pay curves (Freeman 1979). Many prior studies that deal specifically with analyzing the relationship between airport noise and residential property values have been conducted. Nelson (1978,1980), in addition to reviewing the methodology of the hedonic price technique and validating its application to the problem of aircraft noise, also conducted a survey of prior airport noiseproperty value studies and found noise discounts ranging from 0.4% to 1.1% per decibel. O Byrne et al. (1985) addressed concerns raised over the comparability of results from two data sources, specifically census data versus individual sales data in a study of residential property adjacent to the Atlanta International Airport and concluded that the noise discount over time is independent of the data used in the study. Uyeno, Hamilton and Biggs (1993) provided an extension of the hedonic model into multiple unit residential 3

7 and concluded that airport noise also has a significant negative effect on the property value of condominiums. Data Data for this study was compiled from individual sales data available for Washoe County taken from Metroscan Software. A random sample of single family residential, owner occupied houses was drawn from census tracts 0017, 0018, 0019, 2101, 2102, 2203, 2204, 2205, 0028, 2902, The majority of these census tracts lie within the City of Reno. Parcels with housing values less than $50,000 were determined to be uninhabitable, hence were excluded from the sample. Parcels beyond the noise buffer zone were also excluded. The total sample used in this estimation consisted of 1,596 with 124 observations from 1991 sales, 280 from 1992, 390 from 1993, 432 from 1994, and 370 from The data for each observation consisted of the housing sales price (price), the quality of the house, the year the structure was built, the size of the lot in square feet (lot), the size of the house in square feet (sqft), the garage type associated with the house, the number of bedrooms in the house (bed), the number of bathrooms in the house (bath), the existence of fireplaces in the house (fire), the noise L dn associated with that parcel (noise), distance from the airport (distance), and the year in which the house was purchased. The quality of the house is broken down into various sub categories consisting of low quality, low\fair quality, fair quality, fair\average quality, average quality, 4

8 average\good quality, and good quality. Garage type is also broken down into various sub categories. The four garage types are attached, built-in, carport and detached. Both the quality ratings and garage types are converted into qualitative dummy variables taking on a value of one if the attribute was present and zero otherwise. Fire is transformed into a qualitative variable taking on a value of one if a fireplace was present and zero otherwise. A qualitative dummy variable is also constructed for the year of housing purchase to account for inflationary changes between different years, as well as other differences between years such as the interest rate. Dummy variables for the census tract the property is located within are included in the hedonic regression to capture the increase or decrease in property value of living in one census tract as opposed to another. Table 1 shows census tract information for median household income, percent of population in each tract with a college degree where college degree includes Associates, Bachelors, Graduate or Professional degrees, percent of tract that is white, not including white Hispanics, and percent of census tract population unemployed. This information is derived from the 1990 United States Census of Population and Housing, Summary Tape. The day-night average sound level, denoted as L dn, is defined as the energy mean sound level during a 24 hour period with a 10 db penalty for noises occurring during the hours of 10:00 p.m. to 7:00 a.m. The L dn was used in the F.A.R. part 150 study because it correlates with degrees of human resopnse such as annoyance, communication interference, and hearing loss. It has been found that L dn greater than 83 db will cause a 5

9 measurable hearing loss over a period of years, and therefore the maximum permissible yearly outdoor average sound level is considered to be 80 L dn (Nelson 1978). For L dn levels of 65 or over, other transportation or urban noise sources are likely to be quite small comparatively, and aircraft noise will be perceived as a significant intrusion. The FAA, as well as HUD, defines areas exposed to L dn levels of 65 or over as incompatible for residential housing uses. The data on noise used in this study is from the Noise Exposure Maps furnished by the Washoe County Airport Authority for each year. These maps show the noise contours for the L dn 65, L dn 70, and L dn 75 areas. The areas that fall into these contours represent areas where the intrusion from airport noise is the dominant source of noise. Individual properties used in this analysis were coded with a L dn noise decibel level according to the noise exposure contour they fell into on the noise exposure map that corresponded to the year the property was purchased. To achieve exact coding, the yearly noise exposure maps were scanned into MapInfo and layered on top of a base street map of Washoe County. Once the maps were joined, the parcel I.D. address was used to relate individual records with their exact street location. A one mile buffer was created outside of the 65 L dn noise contour and labeled as 60 L dn to allow for a control group of housing in the sample. Finally, the effect on housing value of proximity to the airport is measured through the use of two variables: a distance variable that measures distance from the airport, and an interaction variable defined as airport noise level multiplied by the distance from the 6

10 airport. A positive sign on the interaction coefficient would indicate that a given increase in noise level would have a smaller negative impact on the value of houses further from the airport than on those closer to the airport. Model and Empirical Results The hedonic price of housing is estimated as: n LnP=b 0 +b N Noise+b I Interact+Sb i LnX i +u, i=1 where, Ln P is the log of the price of the house, b 0 is a constant term, Noise is airport noise measured in L dn, Interact is airport noise multiplied by distance from the airport, X i is the ith non-noise variable, and u is a stochastic error term. The dummy variables included in the hedonic regression model can be viewed as deviations from the excluded dummy variable in each individual category. The antilog of the coefficients on dummy variables minus one can be interpreted as the percentage change in property value that occurs from having a house with that specific characteristic as opposed to a house with the characteristic that was excluded (Halvorsen and Palmquist 1981). For example, the antilog of the coefficient on the variable low minus one can be interpreted as the percentage change in property value that occurs from having a house of low quality, the specific characteristic, as opposed to a house of fair/average quality, the excluded characteristic. 7

11 The results from the model estimated with and without the interaction term are shown in Table 2. These models were corrected for heteroscedasticity using the White estimator (1980). The R 2 for these models (0.854 and 0.855) indicate a good fit with the data. It should be noted that the variable measuring distance from the airport was not included because it did not add significant information to the model which included airport noise and the interaction term 1. The estimated hedonic regression in model 1 (which does not include the interaction term) suggests a significant relationship between airport noise and property value at the 10% level. Specifically, the noise coefficient implies that for a one decibel increase in airport noise, there is approximately a 0.29% reduction in property value. (For example, a $100,000 house would have a noise discount of $2900 if located at 70 L dn instead of 60 L dn.) This result is slightly smaller than results found in prior studies which indicate that the marginal implicit price per decibel of noise avoided is usually between 0.4% to 1.1% per decibel (Nelson 1980). The excluded qualitative dummy variables are fair/average housing quality, attached garage, 1994 sales year, and census tract 2102, the tract with the highest income, percentage white, and percentage with a college degree, and close to the lowest unemployment rate. The variables measuring the quality of housing all exhibit their a priori expected signs, as do the variables measuring the implicit price of an older house, 1 Adding distance raised the log likelihood function from to The c 2 for this test is 0.6, well below the critical value of

12 housing square footage, lot square footage, sales year, and census tract. The results also suggest that there is no statistically significant change in property value for an additional bedroom but this may be due to the strong correlation between the bedroom and bathroom variables rather than true lack of significance of an additional bedroom. Adding the interaction term raises the log likelihood value from to , clearly statistically significant, with no significant changes in any of the other coefficient estimates. With the addition of the interaction term, the marginal implicit value of noise is: LnPrice/ Noise = b N +b I D As distance from the airport increases, the noise discount decreases and the reduction in property value for a one decibel change in airport noise becomes statistically insignificant 2. Stated alternatively, the disamenity value associated with a one decibel increase in airport noise diminishes as the distance a property is located from the airport increases. For example, a $100,000 house located a half mile from the airport and in the 70 L dn as opposed to the 60 L dn experiences a noise discount of $3000, while a $100,000 house located at least two miles from the airport and in the 70 L dn as opposed to the 60 L dn experiences a noise discount of $2100. The results also show that being incrementally further from the airport, measured by the change in property value with respect to distance from the airport, has less value in quieter noise zones. For example, the distance premium for a $100,000 property located two miles away as opposed to a mile away within the 70 2 The variance of the marginal implicit price of noise is calculated following the basic rules for adding two random variables. 9

13 L dn is $4200 compared to $3900 for a property in the 65 L dn. Conclusions Both amenity and disamenity values become capitalized in the value of houses. The hedonic pricing technique is used in this study to determine the negative impact that airport noise and proximity to the airport have on residential property values in Reno and Sparks, Nevada. While past studies have calculated a noise discount for residential properties, they have calculated it without consideration of the effect of the distance between the property and the airport. This analysis incorporates distance from the airport into the model and finds that the noise discount is not only affected by the prevalent noise level where the property is located, but by the distance of the property to the airport as well. Specifically, the results from this study show that if airport noise is looked at by itself, then there is a significant reduction in property value of approximately 0.3% from a one decibel increase in airport noise. However, when the combined effect of airport noise and distance from the airport is considered, the results of this study indicate that the reduction in property value caused by increased airport noise diminishes as houses are located further away from the airport. The results also show that for properties located more than two miles away, the reduction in property value caused by airport noise is not statistically significant. Information about the impact of airports on residential property value can be valuable, especially to officials associated with airports experiencing increasing flights or interested in physical expansion. When such changes are anticipated, the negative value 10

14 will be capitalized into property values. However, such growth may not have been anticipated at the time of purchase and the homeowner may therefore be negatively impacted by such changes. This study does not account for such expectations, but it does provide new information for local airport authorities interested in expansion and for property owners negatively impacted by such expansion. Census Tract Table 1: Comparison of Census Tract Socioeconomic Data Median Household Income Percent Unemployed Percent White Percent with College Degree Tract 17 $26, Tract 18 $17, Tract 19 $22, Tract 2101 $24, Tract 2102 $36, Tract 2203 $20, Tract 2204 $25, Tract 2205 $33, Tract 28 $30, Tract 2902 $36, Tract 30 $30,

15 variable Table 2: Results from Hedonic Price Regressions Model 1 Model 2 coefficient t-statistic coefficient t-statistic Log Lotsqft Log Sqft Log Age Low Low\Fair Fair Average Average\Good Good Built-in Garage Carport Detached Garage Fire Bed Bath Year Year Year Year Tract Tract Tract Tract Tract Tract Tract Tract Tract Tract

16 Noise Interact Constant Adjusted R Log Likelihood References Freeman, A. Myrick III (1979). The Hedonic Price Approach to Measuring Demand for Neighborhood Characteristics, in David Segal, ed., The Economics of Neighborhood. N.Y.: Academic Press, Inc. Nelson, Jon P. (1980). Airports and Property Values: A survey of recent evidence, Journal of Transport Economics and Policy 14: Nelson, Jon P. (1978). Economic Analysis of Transportation Noise Abatement, Cambridge, Mass: Ballinger Publishing Company. Halverson, Robert and Raymond Palmquist (1981). "The Interpretation of Dummy Variables in Semilogarithmic Equations", American Economic Review, 70(3): O Byrne, Patricia Habuda, Jon P. Nelson and Joseph J. Seneca (1985). Housing Values, Census Estimates, Disequilibrium, and the Environmental Cost of Airport Noise: A Case Study of Atlanta, Journal of Environmental Economics and Management 12: Uyeno, Dean, Stanley W. Hamilton and Andrew J. G. Biggs (1993). Density of Residential Land Use and the Impact of Airport Noise, Journal of Transport Economics and Policy (January): White, H. (1980). "A Heteroscedasiticity -Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity", Econometrica, 48: