A House Price Modeling Based on Clustering and Kriging: The Medellín Case

In this study, house prices are modeled using a mixed two-stage model for mass appraisal employing valuations of second-hand housing units conducted in Medellín, Colombia. In the first stage, submarkets of houses that share non-spatial attributes are created using clustering; in the second stage, th...

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Bibliographic Details
Published in:The Review of regional studies 2022-01, Vol.52 (3), p.321-343
Main Authors: Villada-Medina, Hernán D., Rendón-García, Juan F., Ramírez-Dolores, César A., Alcalá, Gerardo
Format: Article
Language:English
Subjects:
Artificial intelligence
Cities
Clustering
Developing countries
Housing prices
LDCs
Literature reviews
Machine learning
Sales
Variables
Online Access:Get full text
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Summary:In this study, house prices are modeled using a mixed two-stage model for mass appraisal employing valuations of second-hand housing units conducted in Medellín, Colombia. In the first stage, submarkets of houses that share non-spatial attributes are created using clustering; in the second stage, the spatial dependency is incorporated into the house price estimation using kriging. The best results were obtained when the sample was divided into three submarkets using property area and age as the classification criterion and later applying a Matérn kriging model to submarket 1, a spherical kriging model to submarket 2, and a circular kriging model to submarket 3. These results may provide further guidance to enhance mass appraisal practice in other Latin American cities as well as potentially other cities in developing countries.
ISSN:0048-749X
1553-0892
DOI:10.52324/001c.66198