Hedonic Pricing Models: A Selective and Applied Review

By S. Malpezzi. 2008

768 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/201994500e9750c32238e64e7d9aba1ac9d0aa17

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Citations in the corpus

The Effect of Sustainable Urban Planning and Slum Disamenity on The Value of Neighboring Residential Property: Application of The Hedonic Pricing Model in Rent Price Appraisal (2019, background): “The partial derivative from the dependent variable rent with respect to essential variables gives the marginal willingness to pay for an additional unit from the attributes listed in the function, which results in an implicit measurable value of each characteristic [11,35,74].”
Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou (2007, background): “The selection of the eliciting equation form might affect the accuracy of estimated results (Garrod and Willis, 1999; Malpezzi, 2003).”
Modelling transport and real-estate values interactions in urban systems (2012, methodology): “This technique has become the standard econometric tool for estimating the determinant factors on the prices of heterogeneous goods such as property values (Malpezzi, 2008).”
Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction (2020, methodology): “Specifically, we natural logarithm transform the price and area variables as one usually does in a hedonic model (Malpezzi, 2008; Wheeler et al., 2014).”
Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria (2014, methodology): “Thus, a household implicitly chooses a set of different goods and services by selecting a specific object (Sheppard, 1997; Malpezzi, 2003).”
Modelling hedonic residential rents for land use and transport simulation while considering spatial effects (2010, background): “…helps minimize the problem of heteroscedasticity and mitigates the impact of nonlinear relationships between market price and the explanatory variables (Malpezzi 2003). erefore, hedonic pricing equations are typically estimated using either linear or semilogarithmic models (Sirmans et al. 2005, 4).”
Determinants of house prices in Turkey: Hedonic regression versus artificial neural network (2009, methodology): “Pagourtzi et al. (2003) classified these methods into two categories: traditional and advanced.”
Spatial effects of accessibility to parks on housing prices in Shenzhen, China (2017, background): “Malpezzi (2003) highlighted the following advantages of the semi-logarithmic functional form: (1) the implicit price of a housing attribute is related to the quantity of the other housing attributes; (2) the estimated coefficients can be interpreted in terms of semi-elasticity; (3) this form can…”
Wind Energy Facilities and Residential Properties: The Effect of Proximity and View on Sales Prices (2011, background): “As is common in the literature (Malpezzi, 2003; Sirmans et al., 2005b; Simons and Saginor, 2006), a semi-log functional form is used where the dependent variable, the (natural log of) sales price (P), is measured in inflation-adjusted (1996) dollars.”
Cities in Developing Countries: Fueled by Rural–Urban Migration, Lacking in Tenure Security, and Short of Affordable Housing (2015, methodology): “They address the concerns about functional-form bias in hedonic valuation methods raised by Malpezzi (2003), using a variant of a three-stage estimation procedure developed in Bajari and Kahn (2005).”
A HOUSE PRICE VALUATION BASED ON THE RANDOM FOREST APPROACH: THE MASS APPRAISAL OF RESIDENTIAL PROPERTY IN SOUTH KOREA (2020, background): “The model depends on the assumption that the effects from each attribute are separable and constant, which implies a separable preference, perfect competition, market equilibrium, and an integrated market (see Chau & Chin, 2003; Malpezzi, 2002; Sheppard, 1999).”