Exploratory Data Analysis on Unaffordable Housing Problem

Exploratory Data Analysis on Unaffordable Housing Problem: Predicting a Sample of Amsterdam’s Private Market Rental Prices using Hierarchical Bayesian Models

Kalenter, Deniz (TU Delft Technology, Policy and Management)

Cunningham, Scott (mentor)
de Wolff, Herman (graduation committee)

Delft University of Technology

Engineering and Policy Analysis

2019-02-20

The “affordability of housing” is generally defined as affordable housing for those with median household income (Eurostat, 2018). If not addressed effectively by policymakers, the unaffordable housing gap is expected to affect 1.6 billion people around the world by the year 2025 (McKinsey, 2014). The unaffordable housing in Amsterdam specifically harms the financial and social well-being of the residents of Amsterdam. Therefore, this research aims to grant a contribution to the field by using the Bayesian modelling methods on private rental market prices to reduce unaffordable housing issue. To investigate the issue, the researcher analyses the literature on urban economic models, the use of models in policy making and, collects data from the national database and online rental housing agencies. With the use of hierarchical Bayesian modelling and exploration tools, the relations between house features and local characteristics are explored and two price prediction models are built by using local house features such as size, bedroom number, distance to city centre and district category. The researcher finds several demand profiles and detected a strong negative correlation between rental prices and some industrial and business locations, which might provide an opportunity for city planners to combine the development of these locations with house supply injections to create more affordable housing. Moreover, from the two prediction models, the first model investigated the average district expensiveness better than conventional metrics by increasing its prediction accuracy and ability to quantify uncertainty. Furthermore, the second model categorized the Amsterdam districts according to the preference profiles obtained by model parameters to find most suitable districts for middle-income households. In both models, the location parameter is found to have the highest impact on rent prices. The research provides informative demand profile findings and a descriptive plan on housing supply injections which can be useful for policy makers. Moreover, the policymakers can benefit from the use of advanced model techniques to better assess the spatial housing market according to the city needs. Furthermore, the research evaluates the effect of local factors on rent prices in order to customize development plans to meet the citizen’s needs more robustly. Lastly, besides benefiting from policymaker’s improved actions, middle-income tenants themselves can also use the research findings to make more informed affordable housing decisions.

http://resolver.tudelft.nl/uuid:fd2d25ee-cf5e-48dd-91d4-b801f89c8784

Student theses

master thesis

© 2019 Deniz Kalenter