Faculty Publications

A Non–Stationary Non–Gaussian Hedonic Spatial Model For House Selling Prices

Document Type

Article

Keywords

Bayesian analysis, Geostatistics, Hedonic models, Hoglot, Localized externality, Spatial correlation

Journal/Book/Conference Title

Communications in Statistics: Simulation and Computation

Abstract

This work proposes a hedonic random field model to describe house selling prices from 2000 to 2005 in Cedar Falls, Iowa. This real estate market presents two distinctive features that are not well described by traditional stationary Gaussian random field models: (a) the city has, on its periphery, a hoglot that acts as an externality, affecting both the mean and variance of the selling prices, and (b) the distribution of house selling prices display heavy tails, even after the distance to the hoglot and house–specific covariates are accounted for in the mean structure of the model. A non–stationary and non–Gaussian random field model is constructed by multiplying two independent Gaussian random fields tailored to model the probabilistic features displayed by the Cedar Falls dataset. A Markov chain Monte Carlo algorithm that uses data augmentation is employed to fit the proposed model.

Department

Department of Mathematics

Original Publication Date

1-1-2019

DOI of published version

10.1080/03610918.2019.1704418

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Language

en

Link to Full Text

Comments?

Share

COinS