This page contains the scientific work of others
The topic of gentrification has been widely discussed and is found in many publications.The issue is now not only on how to find solid indicators for when and where gentrification is happening but also on how to define gentrification uniformly. These challenges have been around since the beginning of the phenomenon. For example, Galster G. and Peacock S. tried to find different indicators, to identify gentrification during the period of 1970-1980 in Philadelphia, including "[...] proportion college-educated, real incomes and real property values." And they found very significant differences in areas getting classified as gentrified or not, depending on these different indicators.
In another attempt to come up with a more fundamental and simplistic way to quantify the gentrification process, Friedrichs J. focused mainly on the replacing of lower economic status households with high economic status ones, which is what we plan to look at. He also talked about the phase model with its invasion and succession cycle where the initial lower status population gets step by step replaced by pioneers who upgrade the neighborhood and then in term have to make way for the high status group of the actual gentrifiers. This model is also used by Blasius J. in his paper. Blasius J. also talks about two different market models, called the "value gap" and the "rent gap".
There have also been many papers written about the gentrification in Zurich which is our area of interest. Very dominant in this field is Corinna Heye of the Departement of Geography at the University of Zurich. She published a number of papers having the processes of gentrification or reurbanisation and the previous (and ongoing) suburbanization as the main topic. In the paper by Heye C. and Odermatt A. they focus on moving statistics and try to identify areas, where low status households move away from the area, while high status households move into it, which is very similar to the approach by Friedrichs J. We want to combine the moving statistics of Heye with the economic status discussed previously.
In another approach Heye C. and Leuthold H. have a look on Zurichs quarters, as well as Zurichs municipalities form 1990-2000 and quantify the status and lifestyle change in these areas, to identify those who are gentrified. A very similar approach is chosen in the report of Statisitk Stadt Zürich which is based on the dissertation of Heye and evaluate the change in financial status and in grade of individualization as key factors for the description of gentrified quarters in Zurich. They detect a movement of the "A-Stadt" phenomenon described by Frey R. L. towards the agglomeration areas.
A more recent paper by Craviolini C. et al. features the quarter of the Langstrasse in the context of gentrification. The chosen area is quite interesting, since they observe, that the Langstrasse quarter is undergoing gentrification but in an earlier stage than some of the previously evaluated quarters. They found that a newly renovated or newly built apartment complex has a significantly higher chance to attract high status tenants than apartments in other quarters that have undergone or are undergoing gentrification.
A more brute force approach than the ones we have seen, which have been based on models focusing on deductive reasoning, is the paper by Ahmari R. et al. which uses machine learning, specifically random forests to extract gentrified areas.
In order for us to get a good estimation of the crucial parameter of the economic status we have to combine factors like wealth and income into an index for this economic status. There are several papers that include such a task such as the ones of Radner D.B., Header B. et al. and Berkley H. which compared different combinations of wealth and income for an economic well-being factor. Additionally, there is also the paper of Vyas S. and Kumaranayake L. which construct socio-economic status indices with principal component analysis.