The prices of detached houses in Sweden have in recent decades shown a price development that has been record high. The purpose of this study is to estimate whether there is a significant relationship between different economic variables for detached house prices in Sweden's municipalities. The thesis was carried out through an economic multiple regression analysis with panel data where “fixed effect” was included. The study covers 289 municipalities in Sweden during the period 2001–2019 and the total number of observations is 5491. The study is carried out in the form of two analyzes where the variables supply ratio, income, unemployment, interest, population, tax and debt are included. In the first analysis in 2001–2019, debt is excluded because there is no basis and lack of data. In the second analysis in 2010–2019, the purpose is mainly to estimate the relationship between price as the dependent variable and debt as the independent variable. The study is based on the first analysis to answer the research question: Factors that affect the price development for detached houses in Sweden.The result of the thesis shows that income is the variable with the most estimated correlation and impact on housing prices relative to the other independent variables. The result also shows an expected positive relationship between the variable population and the detached house prices, as well as an expected negative relationship between the variables supply ratio, unemployment, mortgage interest, tax and debt on detached house prices. The study contributes to an increased understanding of how different economic factors affect the relationships for detached houseprices in Sweden's municipalities during the period 2001–2019. The study includes observations over a long period of time with a data base where there is no loss of observations. The validity of this study is based on the study's implementation and theoretical basis with authentic references.

En studie gjord av den internationella mäklarfirman Knight Frank placerar den svenska bostadsmarknaden på en fjärde plats i rankingen över världens hetaste bostadsmarknader. Många kommuner i Sverige har upplevt en kraftig prisutveckling på bostäder de senaste åren och det är därför viktigt att studera den svenska bostadsmarknaden och dess priser. Syftet med denna studie är således att undersöka vilka faktorer det är som påverkar prisutvecklingen på småhus i Sveriges kommuner. Studien är en tvärsnittsstudie över 288 kommuner där förändringen i bostadspriserna studeras under åren 1996 till 2015. Studien har utförts med hjälp av en statistisk metod i form av en regressionsanalys som har delats upp i två perioder: 1996 till 2005 och 2006 till 2015. Den beroende variabeln är prisutvecklingen på bostäder och de förklarande variablerna är utbudet av bostäder, förvärvsinkomst, arbetslöshet, befolkning och kommunalskatt. Regressionsanalysen visar att befolkningsökningen är den variabel som är signifikant med prisutvecklingen under den första perioden och alla förklarande variabler utom kommunalskatten ärsignifikanta under den andra perioden. Genom den deskriptiva statistiken kan vi även se ett samband mellan utbud av bostäder per invånare och prisutvecklingen på bostäder. / According to a study by the international real estate firm Knight Frank, the Swedish housing market is in a fourth place in the rankings of the world's hottest housing markets. Many municipalities in Sweden have experienced a strong price trend in housing during recent years. Therefore, it is important to study the Swedish housing market and its prices. The purpose of the study is to examine which factors affect the increase in house prices in Sweden's municipalities. We have made a cross-sectional study of 288 municipalities and studying the change in housing prices from year 1996 to 2015. The study was conducted using a statistical method in form of a regression analysis that has been divided into two time periods: 1996 to 2005 and 2006 to 2015. The dependent variable is the change in housing prices and the explanatory variables are the supply of housing, professional income, unemployment rate, population rate and municipal taxes. The regression analysis show that population growth is the variable that is significant to the price trend during the first period. All the explanatory variables except municipal taxes is significant during the second period. Through the descriptive statistics, we can also see a connection between supply of housing per capita and increases in house prices.