IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v8y2025i1p12-d1578012.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/8/1/12/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/8/1/12/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    2. Süreyya Özöğür Akyüz & Birsen Eygi Erdogan & Özlem Yıldız & Pınar Karadayı Ataş, 2023. "A Novel Hybrid House Price Prediction Model," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1215-1232, October.
    3. Pablo Picardo, 2019. "Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo," Documentos de trabajo 2019002, Banco Central del Uruguay.
    4. Laure Casanova Enault & Martin Bocquet & Guilhem Boulay, 2023. "Who owns France? Uncovering the structure of property ownership for a better understanding of the socio-spatial distribution of wealth [Qui détient la France ? Révéler la structure de la propriété ," Post-Print hal-04187490, HAL.
    5. Debrunner, Gabriela & Kaufmann, David, 2023. "Land valuation in densifying cities: The negotiation process between institutional landowners and municipal planning authorities," Land Use Policy, Elsevier, vol. 132(C).
    6. Juan-Gabriel Gonzalez-Morales & Marina Checa-Olivas & Rafael Cano-Guervos, 2021. "Impact of Evictions and Tourist Apartments on the Residential Rental Market in Spain," Sustainability, MDPI, vol. 13(13), pages 1-13, July.
    7. Cupal Martin & Sedlačík Marek & Michálek Jaroslav, 2019. "The Assessment of a Building’s insurable Value using Multivariate Statistics: The Case of the Czech Republic," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 81-96, September.
    8. Liu, Lu & Wang, Qiuyun & Zhang, Anquan, 2019. "The impact of housing price on non-housing consumption of the Chinese households: A general equilibrium analysis," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 152-164.
    9. Maier, Gunther, 2011. "Immobilienbewertung - theoretische Konzepte und praktische Anwendungen," SRE-Discussion Papers 2011/03, WU Vienna University of Economics and Business.
    10. Shanaka Herath, 2011. "Does it matter how far you live from the city centre? A study of apartment values in Vienna," ERES eres2011_97, European Real Estate Society (ERES).
    11. Jin Hu & Xuelei Xiong & Yuanyuan Cai & Feng Yuan, 2020. "The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    12. Guijarro Francisco, 2021. "A Mean-Variance Optimization Approach for Residential Real Estate Valuation," Real Estate Management and Valuation, Sciendo, vol. 29(3), pages 13-28, September.
    13. Aveline-Dubach, Natacha, 2022. "The financialization of rental housing in Tokyo," Land Use Policy, Elsevier, vol. 112(C).
    14. Xufeng Jiang & Zelu Jia & Lefei Li & Tianhong Zhao, 2022. "Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    15. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    16. Elżbieta Jasińska & Edward Preweda, 2021. "Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Kraków," Sustainability, MDPI, vol. 13(16), pages 1-25, August.
    17. Kelly D. Edmiston, 2012. "Nonprofit housing investment and local area home values," Economic Review, Federal Reserve Bank of Kansas City, vol. 97(Q I), pages 67-96.
    18. Gao, Qishuo & Shi, Vivien & Pettit, Christopher & Han, Hoon, 2022. "Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia," Land Use Policy, Elsevier, vol. 123(C).
    19. Wolfgang Brunauer & Stefan Lang & Wolfgang Feilmayr, 2013. "Hybrid multilevel STAR models for hedonic house prices," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 33(2), pages 151-172, October.
    20. Martin Jurkovic & Dirk Schiereck, 2023. "Internationale Büroimmobilien-Investoren: Preisprämien aufgrund von Selektionsverzerrungen? [International office investors: price premiums due to “selection bias”?]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 9(1), pages 1-29, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:12-:d:1578012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.