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Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective

Author

Listed:
  • Francisco Louzada

    (Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trab. São Carlense, 400-Centro, São Carlos 13566-590, Brazil
    These authors contributed equally to this work.)

  • Kleython José Coriolano Cavalcanti de Lacerda

    (School of Medicine, University of São Paulo, Av. Bandeirantes, 3900, Monte Alegre, Ribeirão Preto 14049-900, Brazil
    These authors contributed equally to this work.)

  • Paulo Henrique Ferreira

    (Department os Statistics, Federal University of Bahia, Avenida Milton Santos s/n, Campus de Ondina, Salvador 40170-110, Brazil
    These authors contributed equally to this work.)

  • Naomy Duarte Gomes

    (Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trab. São Carlense, 400-Centro, São Carlos 13566-590, Brazil
    These authors contributed equally to this work.)

Abstract

The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics.

Suggested Citation

  • Francisco Louzada & Kleython José Coriolano Cavalcanti de Lacerda & Paulo Henrique Ferreira & Naomy Duarte Gomes, 2025. "Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective," Stats, MDPI, vol. 8(1), pages 1-17, January.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:12-:d:1578012
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    References listed on IDEAS

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    1. Thierry Theurillat & Patrick Rérat & Olivier Crevoisier, 2015. "The real estate markets: Players, institutions and territories," Urban Studies, Urban Studies Journal Limited, vol. 52(8), pages 1414-1433, June.
    2. Gang-Zhi Fan & Seow Eng Ong & Hian Chye Koh, 2006. "Determinants of House Price: A Decision Tree Approach," Urban Studies, Urban Studies Journal Limited, vol. 43(12), pages 2301-2315, November.
    3. Shanaka Herath & Gunther Maier, 2010. "The Hedonic Price Method in Real Estate and Housing Market Research: A Review of the Literature," SRE-Disc sre-disc-2010_03, Institute for Multilevel Governance and Development, Department of Socioeconomics, Vienna University of Economics and Business.
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