The Robustness of Hedonic Price Estimation: Urban Air Quality

Graves, Philip E.; Murdoch, James; Thayer, Mark; Waldman, Donald M.

doi:10.2307/3146246

Cited by 165 publications

(62 citation statements)

References 8 publications

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“…It has been argued that, as a demand side variable, this runs counter to the underlying theory that suggests that the estimated coefficients should represent the equilibrium value determined in the implicit market for each variable. Following several previous studies, however, the variable is used here as the best available proxy for neighbourhood quality rather than as a measure of demand (Graves et al, 1988;Palmquist, 1984). In these circumstances, the coefficient acts as an estimate of the implicit value of neighbourhood status.…”

Section: Resultsmentioning

confidence: 99%

Defining spatial housing submarkets: Exploring the case for expert delineated boundaries

Keskin

Watkins

2016

Urban Studies

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Although there are numerous reasons for real estate analysts to construct spatial housing submarkets, there is little clarity about how this might best be done in practice. The existing literature offers a variety of techniques including those based on principal components analysis, cluster analysis and a range of other statistical procedures. This paper asks whether, given their market expertise and their role in disseminating information, shaping search patterns and informing bid formation, real estate agents might offer an effective but less data intensive method of submarket construction. The empirical research is based on an experiment that compares the predictive of different sets of submarket boundaries constructed by using either standard statistical methods or through consultation with real estate agents and other market analysts. The analysis draws on housing transactions data from Istanbul, Turkey. While the results do not demonstrate the outright superiority of any single method, they do suggest that expert-defined boundaries tend to perform at least as well as alternative construction techniques. Importantly, the results suggest that agent-based methods for delineating submarket boundaries might be used with a degree of confidence by real estate analysts and planners in market contexts where rich micro-datasets are not readily available. This has been one of the constraints internationally on wider adoption of submarket boundaries as an analytical tool.

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Section: Resultsmentioning

confidence: 99%

Defining spatial housing submarkets: Exploring the case for expert delineated boundaries

Keskin

Watkins

2016

Urban Studies

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“…Although there is no general consensus on a preferred "best" functional form, the semi-log form was chosen because it is widely used and is an accepted practice in the hedonic literature (Palmquist 1991;Graves, et al 1988;and Cropper, Deck, and McConnell 1988).…”

Section: Empirical Estimation and Resultsmentioning

confidence: 99%

Are Eco‐Labels Valuable? Evidence From the Apparel Industry

Nimon

Beghin

1999

American J Agri Economics

176

122

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Environmentally friendly labels, or eco-labels, have begun appearing on products in the United States and Europe as a manifestation of industry efforts to be perceived as environment-friendly. This study investigates the extent of market valuation of the environmental claims made by apparel manufacturers. Disciplines ABSTRACTUsing U.S. apparel catalogue data, we estimate hedonic price functions to identify market valuation of environmental attributes of apparel goods. We identify a significant and robust premium for the organic fibers embodied in the apparel goods. We also find a discount for the "no-dye" label. We do not, however, find any evidence of a premium for environment-friendly dyes. We further investigate the pricing behavior of apparel suppliers for potential heterogenous pricing of the organic-fiber attribute and find no evidence of different premia across firms.

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“…environmental attribute) on house prices. For example, there is a number of studies that have applied the hedonic price model in quantifying the effects of noise (Mieszkowski and Saper, 1978;Damm et al, 1980;Uyeno et al 1993) and air pollution on house prices (Ridker and Henning, 1982;Graves et al, 1988).…”

Section: Hedonic Price Theorymentioning

confidence: 99%

House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

Limsombunc¹,

Gan²,

Lee³

2004

American J. of Applied Sciences

204

143

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The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.

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The Robustness of Hedonic Price Estimation: Urban Air Quality

Cited by 165 publications

References 8 publications

Defining spatial housing submarkets: Exploring the case for expert delineated boundaries

Defining spatial housing submarkets: Exploring the case for expert delineated boundaries

Are Eco‐Labels Valuable? Evidence From the Apparel Industry

House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

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