Selected peer-reviewed research

Research in spatial econometrics, machine learning, and real estate.

Selected work by Jacob Dearmon and Tony E. Smith develops practical methods for spatial modeling, scalable Gaussian process regression, and residential property analysis.

Publications

Selected Dearmon–Smith scholarship.

Each DOI link opens the publisher record for authoritative citation information and publication access.

2025Journal article

A Local Gaussian Process Regression Approach to Mass Appraisal of Residential Properties

Jacob Dearmon and Tony E. Smith

The Journal of Real Estate Finance and Economics71(4), 703–721 · First published online in 2024

A local Gaussian process framework for residential mass appraisal and comparable-property selection.

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2021Journal article

A hierarchical approach to scalable Gaussian process regression for spatial data

Jacob Dearmon and Tony E. Smith

Journal of Spatial Econometrics2(1), Article 7

A hierarchical approximation that makes Gaussian process analysis practical for larger spatial datasets.

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2016Journal article

Gaussian Process Regression and Bayesian Model Averaging: An Alternative Approach to Modeling Spatial Phenomena

Jacob Dearmon and Tony E. Smith

Geographical Analysis48(1), 82–111 · First published online in 2015

An approach combining Gaussian process regression and Bayesian model averaging for spatial analysis.

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2016Book chapter

Local Marginal Analysis of Spatial Data: A Gaussian Process Regression Approach with Bayesian Model and Kernel Averaging

Jacob Dearmon and Tony E. Smith

Spatial Econometrics: Qualitative and Limited Dependent VariablesAdvances in Econometrics, Vol. 37, 297–342

A framework for estimating and interpreting local effects with Gaussian process regression and model averaging.

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