An Extension of the Hedonic Approach for Estimating the Value of Quiet

McMillan, Melville; Reid, Bradford G.; Gillen, David

doi:10.2307/3146034

Cited by 48 publications

(25 citation statements)

References 21 publications

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“…Other applications have involved the estimation of the benefits of environmental improvements (Freeman, 1979;Blomquist and Worley, 1981;Harrison and Rubinfeld, 1978;and McMillan et al, 1980). While the hedonic technique is an acceptable method for accommodating attribute differences in a house price determination model, it is generally unrealistic to deal with the housing market in any geographical area as a single unit.…”

Section: Hedonic Price Theorymentioning

confidence: 99%

“…Hedonic price theory has been applied to valuation of agricultural commodities (Brorsen et al, 1984;Ethridge and Davis, 1982;Wilson, 1984), residential amenities (Blomquist and Worley, 1981;McMillan et al,1980;Witte et al, 1979;and Milon et al, 1984,) and wildlife related recreation resources (Pope and Stoll, 1985;Livengood, 1983;Pope et al, 1984;and Messonnier and Luzar, 1990). Other applications have involved the estimation of the benefits of environmental improvements (Freeman, 1979;Blomquist and Worley, 1981;Harrison and Rubinfeld, 1978;and McMillan et al, 1980).…”

Section: Hedonic Price Theorymentioning

confidence: 99%

“…The amount or presence of attributes associated with the commodities defines a set of implicit or "hedonic" prices (Rosen, 1974). The marginal implicit values of the attributes are obtained by differentiating the hedonic price function with respect to each attribute (McMillan et al, 1980). The advantage of the hedonic methods is that they control for the characteristics of properties, thus allowing the analyst to distinguish the impact of changing sample composition from actual property appreciation (Calhoun, 2001).…”

Section: Hedonic Price Theorymentioning

confidence: 99%

“…This does not allow for the identification of the demand schedule for the attribute in question and also ignores the possibility that demand for the attribute may be a function of its level as well as the level of other attributes. In the case of non-linear specifications, the first derivative of the hedonic price function with respect to the specified attribute yields the implicit marginal price of the attribute (McMillan et al, 1980).…”

Section: Data and Proceduresmentioning

confidence: 99%

See 3 more Smart Citations

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

Limsombunc¹,

Gan²,

Lee³

2004

American J. of Applied Sciences

207

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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Section: Hedonic Price Theorymentioning

confidence: 99%

Section: Hedonic Price Theorymentioning

confidence: 99%

Section: Hedonic Price Theorymentioning

confidence: 99%

Section: Data and Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

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

Limsombunc¹,

Gan²,

Lee³

2004

American J. of Applied Sciences

207

143

View full text Add to dashboard Cite

show abstract

“…For instance, from equation (10), the implicit marginal price associated with the explanatory variables expressed in logs (eg. the log of the harvest of roe deer) is computed 5 as (Hussain et al, 2010, McMillan et al, 1980, Stewart, 2005:…”

Section: Implicit Pricesmentioning

confidence: 99%

An Empirical Analysis of Hunting Lease Pricing and Value of Game in Sweden

Mensah¹,

Elofsson²

2017

Land Economics

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Hunting generates considerable benefits to hunters, while simultaneously causing damages to agriculture and forestry. The aim of this paper is to estimate hunting values for multiple hunted species, by disentangling the role of wildlife harvesting opportunities from other factors which affect hunting lease prices. We examine the determinants of hunting lease prices in Sweden using both spatial and non-spatial econometric techniques. Our analysis confirms considerable hunting values for large ungulates such as moose and fallow deer. Results also suggest that hunters prefer to have a diverse set of ungulate species on their hunting ground. Moreover, the study reveals the presence of spatial spillovers in lease prices, implying that landowners have little scope for exerting monopoly power on the lease market. It also indicate that proximity to urban centers, income, size of the hunting field, and congestion cost are key drivers of hunting lease prices.3

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