We are seeking a Stockholm-based Master’s student in Statistics, Economics, Mathematics, Engineering, or related field to undertake a degree project on Price Discrimination in the Rental Marketplace. This project aims to explore the impact of adaptive service fees on the rental market. The project will involve segmentation analysis, adaptive fee modeling, and impact assessment for tenants and landlords. The successful candidate will collaborate closely with our data science, product, and finance teams during the Spring semester.

What you can expect from us

Writing a master thesis is fun but for many the last thing they do before graduating. We want you to have a suitable scope and technical support to be able to write and finish your degree.

🎓 Previous Master’s Theses

At Qasa, we’ve had a strong history of collaborating with Master’s students on impactful projects. Here are some of the recent theses completed at Qasa:

• Automated Home Review: Eric Dahlgren developed a system to review home ads before they are published. Using XGBoost and SHAP, an interpretable anomaly detection model was built to automate ad reviews. The model not only scores ads but also provides human-readable explanations to assist decision-making. You can read the thesis here: Enhancement of an Ad Reviewal Process through Interpretable Anomaly Detecting Machine Learning Models
• Rent Prediction: Vasigaran Senthilkumar focused on improving house rental price predictions by incorporating external features such as walk scores and interest rates. The work also introduced methods for handling uncertainty in predictions, including generating prediction intervals and managing uncertain rent data to enhance model adaptability and generalization. You can read the thesis here: Enhancing House Rental Price Prediction Models for the Swedish Market
• Recommendation Systems: Julia Byström created and A/B tested (in production!) two new algorithms to improve tenant rankings for landlords in our “Find tenant” feature. The algorithms were rigorously tested and deployed in production, contributing significantly to our recommendation systems. You can read the thesis here: Probabilistic Weighting and Deferred Acceptance in Reciprocal Recommendations.

What could work on

• Segmentation analysis: Develop models to analyze different tenant and landlord segments based on factors such as rental history, property characteristics, and regional demand.
• Fee impact modeling: Investigate the relationship between service fee structures and property features, tenant preferences, and market conditions.
• Impact assessment: Analyze how adaptive fees could influence tenant costs and landlord decisions, aiming to balance personalization with fairness.
  
• Testing and Validation: Design simulation or A/B tests to evaluate how users react to varying service fees. 

You could be a great fit if you

• Are experienced or interested in learning Python, SQL, and Excel in the context of real estate data.
• Have theoretical understanding of price discrimination and price sensitivity analysis.
• Have a passion for data-driven decision-making, pricing models, or market dynamics.
• Excel at applying theoretical research to solve practical industry challenges.
• Can communicate complex ideas clearly to both technical and non-technical stakeholders.

What we offer

• An opportunity to work on a high-impact project addressing real-world challenges in the long-term rental market.
• Mentorship and guidance from experienced data scientists, developers, and product/financial experts.
• A collaborative, innovative, and fun working environment.
• Potential future employment opportunities at Qasa.