Evaluating the Impact of Image Features on Airbnb Price Predictions - A Machine Learning Approach to Hedonic Pricing

Aug 3, 2026

This thesis explores how image features affect the predictive performance of hedonic pricing models for Airbnb listings. It combines machine learning for prediction with image quality, color feature engineering, and black-box interpretation techniques to examine both influence and interactions. A novel dataset scraped in 2023 from Amsterdam provides up-to-date insights. After running 10 models, XGBoost achieves the highest predictive accuracy. Image features improve model behavior, though the gain is not statistically significant, offering practical value for hospitality, real estate, advertising, and ML research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages59

Reading Time2 h 29 min

AccessFree to download

Abstract

Acknowledgements

1. Introduction

2. Literature Review

3. Theory

3.1 Machine Learning

3.2 Transfer Learning

3.3 Performance Metrics

3.4 Interpretable Machine Learning Methods

4. Dataset

4.1 Cleaning and Pre-processing

4.2 Image Data

5. Models

5.1 Tuning Hyperparameters

5.2 Model Results

5.3 Interpreting the Effect of Image Features

Norwegian School of Economics Bergen, Spring 2023 Evaluating the Impact of Image Features on Airbnb Price Predictions A Machine Learning Approach to Hedonic Pricing Sjur Gobeil Garcia Supervisor: Mateusz Mysliwski Master thesis, MSc in Economics and Business Administration, Major: Business Analytics NORWEGIAN SCHOOL OF ECONOMICS This thesis was written as a part of the Master of Science in Economics and Business Administration at NHH. Please note that neither the institution nor the examiners are responsible − through the approval of this thesis − for the theories and methods used, or results and conclusions drawn in this work. Abstract This thesis explores the influence of image features on the predictive performance of hedonic price models for Airbnb listings. By integrating machine learning methods, image quality features, colour features, and black-box model interpretation methods, the study demonstrates the value of these components in the field of property price prediction. This thesis utilizes a novel dataset scraped in 2023 from Amsterdam which offers updated insights into the role of image features in Airbnb pricing. After deploying 10 different machine learning models, the XGBoost model yields the best predictive accuracy based on several performance metrics. Although the enhancement in predictive performance of the XGBoost model by inclusion of image features was not statistically significant, these features showed non-negligible influences and interactions in the decision-making process of the model. These findings imply a potential role of image features in refining property price models, providing valuable insights for the stakeholders in the fields of hospitality, real estate, advertising, and machine learning research. Acknowledgements I would like to express my gratitude to my supervisor, Mateusz Mysliwski, who has assisted me throughout this process. Mateusz’s extensive knowledge and insightful perspectives have been instrumental in shaping this research work. His readiness to answer my queries, provide detailed explanations, and offer constructive criticism has been invaluable to me. Contents Contents ABSTRACT...........................................................................................................................................2 ACKNOWLEDGEMENTS .................................................................................................................2 CONTENTS ..........................................................................................................................................3 1. INTRODUCTION ......................................................................................................................5 2. LITERATURE REVIEW ..........................................................................................................7 3. THEORY...................................................................................................................................10 3.1 MACHINE LEARNING .............................................................................................................. 10 3.2 TRANSFER LEARNING ............................................................................................................ 11 3.3 PERFORMANCE METRICS........................................................................................................ 12 3.4 INTERPRETABLE MACHINE LEARNING METHODS .................................................................. 13 3.4.1 Permutation Feature Importance................................................................................ 13 3.4.2 Accumulated Local Effects.......................................................................................... 14 3.4.3 H-statistic .................................................................................................................... 15 3.4.4 Shapley Values ...............................

FAQs

What is the main research question of the thesis?

The thesis investigates how image features influence the predictive performance of hedonic price models for Airbnb listings.

Which machine learning model achieved the best predictive accuracy?

After deploying 10 models, the XGBoost model delivered the best predictive accuracy according to multiple performance metrics.

Are the improvements from adding image features statistically significant?

The enhancement in predictive performance from including image features was not statistically significant, although the features still showed non-negligible influences and interactions in the model’s decision process.

Aug 3, 2026

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