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

Francisco Louzada, Kleython José Coriolano Cavalcanti de Lacerda, Paulo Henrique Ferreira () and Naomy Duarte Gomes
Additional contact information
Francisco Louzada: Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trab. São Carlense, 400-Centro, São Carlos 13566-590, Brazil
Kleython José Coriolano Cavalcanti de Lacerda: School of Medicine, University of São Paulo, Av. Bandeirantes, 3900, Monte Alegre, Ribeirão Preto 14049-900, Brazil
Paulo Henrique Ferreira: Department os Statistics, Federal University of Bahia, Avenida Milton Santos s/n, Campus de Ondina, Salvador 40170-110, Brazil
Naomy Duarte Gomes: Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trab. São Carlense, 400-Centro, São Carlos 13566-590, Brazil

Stats, 2025, vol. 8, issue 1, 1-17

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.

Keywords: machine learning; statistics; artificial intelligence; real estate leasing; rental price (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/8/1/12/pdf (application/pdf)
https://www.mdpi.com/2571-905X/8/1/12/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:8:y:2025:i:1:p:12-:d:1578012

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().