Spatial hedonic modelling adjusted for preferential sampling

Lucia Paci, Alan E. Gelfand, and María Asunción Beamonte, Pilar Gargallo and Manuel Salvador

Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 1, 169-192

Abstract: 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.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1111/rssa.12489

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:183:y:2020:i:1:p:169-192

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().