Modeling of apartment prices in a colombian context from a machine learning approach with stable-important attributes

Jorge Iván Pérez-Rave, Favián González-Echavarría, Juan Carlos Correa-Morales

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

Translated title of the contributionModelización de precios de apartamentos en un contexto colombiano desde un enfoque machine learning con atributos estables-importantes
Original languageEnglish
Pages (from-to)63-72
Number of pages10
JournalDYNA (Colombia)
Volume87
Issue number212
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The author; licensee Universidad Nacional de Colombia.

All Science Journal Classification (ASJC) codes

  • General Engineering

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