A Statistical Learning Approach to Land Valuation: Optimizing the Use of External Information

David Albouy and Minchul Shin

No 22-38, Working Papers from Federal Reserve Bank of Philadelphia

Abstract: We develop a statistical learning model to estimate the value of vacant land for any parcel, regardless of improvements. Rooted in economic theory, the model optimizes how to combine common improved property sales with rare, but more informative, vacant land sales. It estimates how land values change with geography and other features and determines how much information either vacant or improved sales provide to nearby areas through spatial correlation. For most census tracts, incorporating improved sales often doubles the certainty of land value estimates.

Keywords: land values; hierarchical modeling; spatial data; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C11 C43 R1 R3 (search for similar items in EconPapers)
Pages: 38
Date: 2022-11-14
New Economics Papers: this item is included in nep-agr, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Journal Article: A statistical learning approach to land valuation: Optimizing the use of external information (2022)
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DOI: 10.21799/frbp.wp.2022.38

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