IDEAS home Printed from https://ideas.repec.org/p/ptu/wpaper/w202506.html
   My bibliography  Save this paper

Determinants of Residential Property Prices in Portugal: A Nonparametric Quantile Approach

Author

Listed:
  • Fernando Cascão
  • Katja Neugebauer

Abstract

This paper investigates the determinants of house prices in Portugal, where nominal prices have doubled over the past decade. Using asking prices for residential properties from 2018 to 2022, we compare the accuracy of several machine learning techniques and select gradient tree boosting as the optimal model for analyzing the complex dynamics of the housing market. Structural property characteristics and neighborhood factors – such as proximity to amenities, demographics, and household financial standing – emerge as significant determinants, with their influence varying across price quantiles and districts. Furthermore, we introduce prediction intervals using conformalized quantile regression, providing a more robust measure of estimation uncertainty.

Suggested Citation

  • Fernando Cascão & Katja Neugebauer, 2025. "Determinants of Residential Property Prices in Portugal: A Nonparametric Quantile Approach," Working Papers w202506, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w202506
    as

    Download full text from publisher

    File URL: https://www.bportugal.pt/sites/default/files/documents/2025-04/WP202506.pdf
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

    Statistics

    Access and download statistics

    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:ptu:wpaper:w202506. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: DEE-NTD (email available below). General contact details of provider: https://edirc.repec.org/data/bdpgvpt.html .

    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.