Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy
This work addresses the problem of subjective, costly, and unfair real estate appraisals for local governments and stakeholders in developing countries, offering a data-driven alternative.
This paper proposes a data-driven real estate pricing model using machine learning to estimate property prices, aiming to reduce human bias. The model was tested with 178,865 flat listings from Bogotá collected between 2016 and 2020, demonstrating robust and accurate price estimations.
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 Bogotá, 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.