The property "Löjtnanten 1" is situated in Västra Skogen, Solna, and is owned by the Solna municipality. The question at issue answered in this study is "Is it profitable to convert the multistory car park "Löjtnanten 1" to a dwelling-­‐house?". Several matters are addressed to answer that question, amongst other things the demand for condominiums in the area, the effect of competing supply, the acceptable price per square meter from a market point of view, and how the issue of parking may be resolved. The study has been made with support from JM AB, a developer of housing and residential areas in the Nordic region.  As described in the vision, the project will increase the attractiveness of the area by the refurbishment of the nearby centre and commercial premises. The area is one of the development areas in the Master Plan for Solna, "ÖP 2006". The market analysis performed shows that the project is feasible if the turnover of residents in the applicable areas exceeds 5 percent. The project is also economically viable at a price level of SEK 38 000 – 42 000 per square meter. The project calculation shows a limited profit and presumes that the municipality price the land at a break-­‐even level factoring in the long term positive effects provided by a refurbishment of the nearby centre. / Fastigheten Löjtnanten 1 ligger belägen i Västra Skogen, Solna, och ägs av Solna kommun. Frågeställningen som besvaras i arbetet är ”Är det ekonomiskt lönsamt att omvandla parkeringshuset Löjtnanten 1 till bostäder?”. För att svara på den frågan har marknadsområdet undersökts för projektet, vidare har storleken på efterfrågan för nyproducerade bostadsrätter i området skattats liksom effekten av konkurrerande utbud. Arbetet belyser också vilket kvadratmeterpris som marknaden är beredd att betala samt hur parkeringsfrågan kan lösas. Studien är genomförd med stöd av JM AB, projektutvecklare av bostäder och bostadsområden. Enligt visionen i arbetet skulle projektet öka attraktiviteten i området genom en samtidig upprustning av närförortscentrumet och de kommersiella lokalerna. I Solna översiktsplan, ÖP 2006, anses området vara ett av utvecklingsområdena i Solna kommun. Den genomförda marknadsanalysen visar att projektet kan genomföras om omsättningen i det befintliga bostadsbeståndet inom marknadsområdet är högre än 5%. Projektet är ekonomiskt genomförbart vid en prisnivå inom intervallet 38 000 – 42 000 kr/kvadratmeter boyta. Projektkalkylen visar på en begränsad vinst och förutsätter att kommunen sätter markpriset till en ”break--‐even nivå”samt beaktar långsiktiga positiva effekter av ett upprustat närförortscentrum.

This thesis aims to understand the determinants of car ownership in the Swedish regions containing the largest cities: Skåne, Stockholm, and Västra Götaland. This is done by performing a fixed effects regression analysis as well as creating and comparing different predictive models. Both socioeconomic and spatial factors are looked at. The data used in the study is on a Demographic Statistic Zone level for the years 2016-2021. The data consists of approximately 90 variables that are narrowed down to 12 variables based on the level of existing multicollinearity, which are used in the final models. The results from the fixed effects regression show that variables such as population density, age, income, house owning type, and house type are the main influencers on car ownership. These results are similar for the specific regions; however, some differences are discovered, pointing out the disadvantages in creating a generalized model. The results of the predictive models shows that a Long Short-Term Memory model performs better than Random Forrest Regression and OLS, however the performance of the latter two models is considered satisfying enough making them superior as they are easier to interpret and more established within the industry. The region-specific predictive models perform equally well as the ones created from all the data. In conclusion, it can be said that the determinants of car ownership that are mentioned align well with the previous studies made and are considered reliable. Regarding which predictive model to use OLS should be considered sufficient even if more complex methods perform better.