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Estimating environment impacts on housing prices
Author(s) -
Montero JoséMaría,
FernándezAvilés Gema,
Mínguez Román
Publication year - 2017
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2453
Subject(s) - econometrics , hedonic pricing , measure (data warehouse) , order (exchange) , economics , spatial analysis , autocorrelation , willingness to pay , hedonic regression , parametric statistics , set (abstract data type) , house price , statistics , mathematics , computer science , microeconomics , finance , database , programming language
Housing is understood to be a necessity good, whereas the environment is still viewed as a luxury good, which implies that environmental factors significantly impact on housing prices. This impact is usually measured via aspatial linear hedonic models, but this article goes further, extending those traditional analyses to incorporate spatial autocorrelation, spatial heterogeneity, and nonlinearity. A set of competing parametric and semiparametric spatial models, some of which are new proposals, are estimated in order to measure the impact of the environment on such prices. The percentage of residents who declare that the neighborhood has serious pollution problems is used as a (subjective) environmental factor. One of the reasons why this measure was chosen is because the hedonic method only captures people's willingness to pay for perceived rather than measured differences in environmental attributes. We use a massive database containing the price and characteristics of 10,512 homes in Madrid (Q1 2010). The results obtained suggest that the environment has a significant impact on housing prices; however, when the model includes a drift and/or areal variables, these components absorb a substantial part of the environmental impact.