Housing has major implications for individual's climate footprint - housing and transport account for more than half of the CO2-emissions from Norwegian households. Measures to reduce the negative climate impacts from housing include high-density city development and downsizing of dwellings. The project addresses challenges of compact living, i.e., high-density living in restricted space arrangements. A key goal is to increase knowledge on how to make compact living attractive for people in different life stages. 

Urban development is a central topic in the climate policy debate. The major cities in Norway have signed a Zero-Growth Goal with the Government which means that any growth in passenger transport shall be absorbed by public transport, cycling and walking. To achieve this goal, densification and real estate developments in close proximity to city centers and/or public transportation hubs are key strategies. 

Research shows that urban living has many qualities that sparsely populated areas cannot offer. However, people’s perceptions of liveability in compact urban environments differ, and some consider inner-city living inappropriate to specific groups, especially households with children, since the urban environment does not offer the same qualities that people often relate to life in suburbia. Also, spatial norms may impede the acceptance of urban living and downsizing. Research has shown that home buyers’ willingness to decrease dwelling size is negotiated against norms of what is considered a good home. This indicates that perceptions of small living spaces must be changed if downsizing is to become an adequate alternative. This may be a particular challenge in Norway since the average living space per inhabitant in the Nordic countries is well above the European norm. Thus, a main challenge is to create functional and appealing homes in restricted space arrangements. Sharing and collaborative consumption are key terms in this respect. 
Overall, the project aims to contribute to knowledge on how to create attractive homes and living environments in urban settings that facilitate climate-friendly city development. Themes that may be addressed in the project include (not exhaustive):
-    Environment and sustainability
-    Willingness-to-pay
-    Consumer/housing preferences
-    Attitudes, motivations, norms
-    Sharing economy, collaborative consumption
-    Financing models, green mortgages

If you have any questions, contact Jon Martin Denstadli: jon.m.denstadli@ntnu.no

Research project Big data in real estate finance intends to use automated valuation models (AVM) and Explainable AI (XAI) related to the housing market.

In Norway, we have large amounts of data available on housing transactions and a thriving proptech environment. With the help of various machine learning techniques, the data on the housing market can be used to predict the value of housing through automated valuation models (AVM). These estimates have a wide range of applications related to banking, ibuy, buying and selling housing, tax and property development. The models can also be used in research to improve the understanding of how the housing market works and which properties of a home the home buyers value.

Modern AVMs make use of large amounts of data and machine learning. While when producing the results, one uses XAI, including SHAP and LIME. This project allows you to help develop the forefront of a field that is developing rapidly. In addition to knowledge in finance, to participate in this project you should have knowledge of programming in e.g. R or Python.

If you have any questions, contact Are Oust: are.oust@ntnu.no

Examples of work we have done within the project:

https://link.springer.com/article/10.1365/s41056-022-00065-z

https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3035876

https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2673533

https://www.tandfonline.com/doi/pdf/10.1080/09599916.2022.2070525?needAccess=true&role=button

https://www.sciencedirect.com/science/article/pii/S016604621830303X