With an increased population, many housing markets are experiencing a shortage, but the ability to resolve this through building new houses is limited. Demand in a housing market is caused not only by households without a house but also by those who are unsatisfied with their current housing. In the Netherlands, the housing market is unique because many houses are part of the social sector, a sector in which housing corporations decide the regulations on how houses are allocated. The question is whether different regulations can reduce how many households are unsatisfied with their current housing, thus reducing the total demand. Agent-based modelling (ABM) has been used to better understand various aspects of housing markets. It has not yet been used to model the Dutch social sector. In this thesis, a descriptive agent-based model of the Dutch housing market is created, with a focus on the effect of regulations on the demand of individual households. The main challenge of creating such a model is to create an abstraction that accurately captures the system of interest. The experiments show that the current model is limited due to some of the design choices. The results create insight into the impact of regulations and provides various directions for further research. The thesis provides a description of the Dutch housing market and a basis for agent-based models of this market.

As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time. After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.