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Spatial hedonic modelling adjusted for preferential sampling
Author(s) -
Paci Lucia,
Gelfand Alan E.,
Beamonte and María Asunción,
Gargallo Pilar,
Salvador Manuel
Publication year - 2020
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12489
Subject(s) - unobservable , econometrics , point (geometry) , property (philosophy) , sampling (signal processing) , sample (material) , set (abstract data type) , simple random sample , statistics , point process , computer science , point pattern analysis , mathematics , common spatial pattern , population , philosophy , chemistry , geometry , demography , epistemology , filter (signal processing) , chromatography , sociology , computer vision , programming language
Summary Hedonic models are widely used to predict selling prices of properties. Originally, they were proposed as simple spatial regressions, i.e. a spatially referenced response regressed on spatially referenced predictors. Subsequently, spatial random effects were introduced to serve as surrogates for unmeasured or unobservable predictors and were shown to provide better out‐of‐sample prediction. However, what has been ignored in the literature is the fact that the locations (and times) of the sales are random and, in fact, are an observation of a random point pattern. Here, we first consider whether there is stochastic dependence between the point pattern of locations and the set of responses. If so, a second question is whether incorporating a log‐intensity for the point pattern of locations in the hedonic modelling enables improvement in the prediction of selling price. We connect this problem to what is referred to as preferential sampling. Through model comparison we illuminate the role of the point pattern data in the prediction of selling price. Using two different years of property sales from Zaragoza, Spain, we employ both the full database as well as an intentionally biased subset to elaborate this story.