Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets

Joshua Hall, Donald J. Lacombe (), Amir Neto () and James Young ()
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Donald J. Lacombe: Texas Tech University
Amir Neto: Florida Gulf Coast University
James Young: University of Washington

Journal of Economics and Finance, 2022, vol. 46, issue 2, No 6, 360-373

Abstract: Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality.

Keywords: Spatial econometrics; SLX model; Bayesian methods; Spatial hierarchical models (search for similar items in EconPapers)
JEL-codes: C11 C31 I29 R31 R38 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12197-021-09568-2

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