View Item 
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        JavaScript is disabled for your browser. Some features of this site may not work without it.

        Browse

        All of UU Student Theses RepositoryBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

        Influence of population demographics on real estate prices in Zuid-Holland

        Thumbnail
        View/Open
        Final Thesis Vic Bensdorp.pdf (3.230Mb)
        Publication date
        2021
        Author
        Bensdorp, V.R.J.
        Metadata
        Show full item record
        Summary
        Because the Dutch population is aging, a change in housing preferences can be expected. According to the lifecycle theory older people have accumulated more wealth, and therefore should be able to spend more on their housing. This thesis aims to model the real estate market between 2009 and 2016 in Zuid-Holland, comparing the results of linear regression to random forest regression while trying to incorporate the development of local population change in people over 65. Data from the Dutch national real estate broker association (NVM) is being used, enriched with publicly available neighborhood statistics. Analyses have been performed for each year, for both models. Results show the relations of structural, locational, and neighborhood variables on the recorded transaction price per square meter. From these results, conclusions have been drawn on the effectiveness of the linear regression in relation to the forest regression, as well as the performance and impact of the inclusion of age dynamics into hedonic price modeling. It was shown that the inclusion of age dynamics added a very slight value to the adjusted R-squared, however this variable was not consistently significant. Furthermore, the random forest regression has shown to consistently outperform the linear regression.
        URI
        https://studenttheses.uu.nl/handle/20.500.12932/39928
        Collections
        • Theses
        Utrecht university logo