A Machine Learning Approach to Delineating Neighborhoods from Geocoded Appraisal Data

Authors

Rao Hamza Ali, Chapman UniversityFollow
Josh Graves, CoreLogicFollow
Stanley Wu, CoreLogicFollow
Jenny Lee, CoreLogicFollow
Erik Linstead, Chapman UniversityFollow

Document Type

Article

Publication Date

7-17-2020

Abstract

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information that can be extracted from property appraisals. This paper demonstrates the potential of using only the distance between subjects and their comparable properties, identified in an appraisal, to delineate neighborhoods that are composed of properties with similar prices and features. Using spatial filters, we first identify regions with the most appraisal activity, and through the application of a spatial clustering algorithm, generate neighborhoods composed of properties sharing similar characteristics. Through an application of bootstrapped linear regression, we find that delineating neighborhoods using geolocation of subjects and comparable properties explains more variation in a property’s features, such as valuation, square footage, and price per square foot, than ZIP codes or Census tracts. We also discuss the ability of the neighborhoods to grow and shrink over the years, due to shifts in each housing submarket.

Comments

This article was originally published in International Journal of Geo-Information, volume 9, in 2020. https://doi.org/10.3390/ijgi9070451

Recommended Citation

Ali, R.H.; Graves, J.; Wu, S.; Lee, J.; Linstead, E. A machine learning approach to delineating neighborhoods from geocoded appraisal data. ISPRS Int. J. Geo-Inf. 2020, 9, 451. https://doi.org/10.3390/ijgi9070451

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The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.