Spatial dependence in hedonic property models: Do different corrections for spatial dependence result in economically significant differences in estimated implicit prices?

Julie M Mueller, John B. Loomis

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

While data used in hedonic property models are inherently spatial in nature, to date the majority of past regression analyses have used OLS models that overlook possible spatial dependence in the data when estimating implicit prices for environmental hazards. This paper explicitly addresses spatial dependence in a hedonic property model. We use robust testing procedures to determine the existence and type of spatial dependence in our OLS Model. After identifying the nature of the spatial dependence, OLS estimates of the implicit price of wildfire risk are compared to implicit prices obtained using a spatial error model with three different spatial weighting matrices. Spatially corrected estimates of implicit prices are found to be nearly the same as those obtained using OLS. Our results indicate that the inefficiency of OLS in the presence of spatially correlated errors may not always be economically significant, suggesting nonspatial hedonic property models may provide results useful for policy analysis, and spatial and nonspatial hedonic property models might be pooled in meta-analysis.

Original languageEnglish (US)
Pages (from-to)212-231
Number of pages20
JournalJournal of Agricultural and Resource Economics
Volume33
Issue number2
StatePublished - Aug 2008

Fingerprint

environmental hazards
policy analysis
environmental hazard
meta-analysis
wildfires
price
Spatial dependence
Implicit price
wildfire
matrix
testing
Inefficiency
Hazard
Weighting
Meta-analysis
Testing
Policy analysis
Wildfire

Keywords

  • Forest fires
  • Hedonic property models
  • Spatial econometrics

ASJC Scopus subject areas

  • Aquatic Science

Cite this

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title = "Spatial dependence in hedonic property models: Do different corrections for spatial dependence result in economically significant differences in estimated implicit prices?",
abstract = "While data used in hedonic property models are inherently spatial in nature, to date the majority of past regression analyses have used OLS models that overlook possible spatial dependence in the data when estimating implicit prices for environmental hazards. This paper explicitly addresses spatial dependence in a hedonic property model. We use robust testing procedures to determine the existence and type of spatial dependence in our OLS Model. After identifying the nature of the spatial dependence, OLS estimates of the implicit price of wildfire risk are compared to implicit prices obtained using a spatial error model with three different spatial weighting matrices. Spatially corrected estimates of implicit prices are found to be nearly the same as those obtained using OLS. Our results indicate that the inefficiency of OLS in the presence of spatially correlated errors may not always be economically significant, suggesting nonspatial hedonic property models may provide results useful for policy analysis, and spatial and nonspatial hedonic property models might be pooled in meta-analysis.",
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AB - While data used in hedonic property models are inherently spatial in nature, to date the majority of past regression analyses have used OLS models that overlook possible spatial dependence in the data when estimating implicit prices for environmental hazards. This paper explicitly addresses spatial dependence in a hedonic property model. We use robust testing procedures to determine the existence and type of spatial dependence in our OLS Model. After identifying the nature of the spatial dependence, OLS estimates of the implicit price of wildfire risk are compared to implicit prices obtained using a spatial error model with three different spatial weighting matrices. Spatially corrected estimates of implicit prices are found to be nearly the same as those obtained using OLS. Our results indicate that the inefficiency of OLS in the presence of spatially correlated errors may not always be economically significant, suggesting nonspatial hedonic property models may provide results useful for policy analysis, and spatial and nonspatial hedonic property models might be pooled in meta-analysis.

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