Analysis of Airbnb Pricing in Lisbon - A Machine Learning Approach - Exploring the Impact of Host Attributes, Property Features and Geographical Factors

Aug 6, 2026

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The thesis examines the determinants of Airbnb pricing in Lisbon by applying multiple machine learning models, including Linear Regression, Gradient Boosting, and Random Forest. Using data sourced from the Inside Airbnb website across Lisbon listings, the study evaluates how host attributes, property features, and geographical characteristics shape pricing strategies. Results highlight lodging capacity, host response rates, and property factors such as bathroom count and listing type as significant price drivers. Advanced models better capture complex non-linear relationships, supporting predictive modeling for hosts and policymakers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages65

Reading Time2 h 44 min

AccessFree to download

Introduction

Context

Motivation

Research Gap and Objectives

Structure

Literature review

Airbnb

Analysis of Airbnb Price

Research Gaps and Objectives

Data and Methodology

CRISP-DM

Data Collection and Business Understanding

MGI Master Degree Program in Information Management Analysis of Airbnb Pricing in Lisbon – A Machine Learning Approach Exploring the Impact of Host Attributes, Property Features and Geographical Factors Sadik Amin Bhadurali Project Work presented as partial requirement for obtaining the Master Degree Program in Information Management NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Analysis of Airbnb Pricing in Lisbon – A Machine Learning Approach Exploring the Impact of Host Attributes, Property Features and Geographical Factors By Sadik Amin Bhadurali Project Work presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence Supervisor: Bruno Jardim, PhD, NOVA Information Management School July 2024 STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School. Lisbon, 10th July 2024 ACKNOWLEDGEMENTS I would like to express my sincere gratitude to all those who have supported and contributed to the completion of this thesis. First and foremost, I would like to thank my supervisor, Dr. Bruno Jardim, for his invaluable guidance, continuous support and constructive feedback throughout this research, which were instrumental in shaping this work. I am deeply grateful to my family for their unwavering support and encouragement. To my parents, who have always believed in me and provided me with the opportunity to pursue my dreams, and tomy siblings, for their constant motivation and love. I would also like to acknowledge my friends and classmates for their shared knowledge and moral support. The discussions and collaborations have enriched my learning experience and made this journey enjoyable. ABSTRACT The socioeconomic impact of Short-Term Rentals has been a hot topic of discussion in recent years across the Portuguese territory. This thesis investigates the determinants of Airbnb pricing in Lisbon using a variety of machine learning models, including Linear Regression, Gradient Boosting and Random Forest. The study aims to understand how different factors such as host attributes, property features and geographical characteristics influence the pricing strategies of Airbnb listings. Data was sourced from the Inside Airbnb website, encompassing a comprehensive range of listings across Lisbon. Key findings indicate that lodging capacity, host response rates, property features such as the number of bathrooms and the type of listing significantly impact prices. Advanced models like Gradient Boosting and Random Forest demonstrated superior performance in capturing complex, non-linear relationships, highlighting the importance of machine learning in predictive modeling. This research provides valuable insights for Airbnb hosts to optimize pricing strategies and for policymakers to understand the impact of Short-Term rentals on the local housing market. Future work should consider incorporating more dynamic data sources, advanced modeling techniques and broader geographical analyses to enhance the understanding of Airbnb pricing dynamics. KEYWORDS Airbnb; Machine Learning; Price Determinants; Property Features; Lisbon Metropolitan Area Sustainable Development Goals (SGD): INDEX 1. Introduction .................................................................................................................. 1 1.1. Context .........................................

FAQs

Which machine learning models are used to analyze Airbnb pricing in Lisbon?

The study uses Linear Regression, Gradient Boosting, and Random Forest. These models are compared to assess their ability to capture pricing determinants.

What data source supports the analysis of Airbnb listings?

The research uses data sourced from the Inside Airbnb website, covering a comprehensive set of listings across Lisbon.

Which factors most significantly influence Airbnb prices according to the findings?

Key findings point to lodging capacity, host response rates, and property features such as the number of bathrooms and the type of listing as significant drivers of price.

Aug 6, 2026

Upload by Aurora