Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy

TL;DR

This paper presents a data-driven machine learning model for real estate pricing that reduces human bias, improves accuracy, and promotes fairness by utilizing large online listing datasets, demonstrated on Bogotá flats.

Contribution

It introduces a novel machine learning-based pricing model that leverages large datasets to enhance accuracy and fairness in real estate valuation, addressing limitations of traditional methods.

Findings

Model is robust and accurate in estimating prices

Large dataset improves fairness and reduces bias

Case study on Bogotá flats validates approach

Abstract

In many countries, real estate appraisal is based on conventional methods that rely on appraisers' abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogot\'a, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.