published_or_final_version / Housing Management / Master / Master of Housing Management

he master’s thesis is focused on the comparison of real estate markets in the Czech Republic, Slovakia and Austria and theirs trends since 1989. The first chapters are aimed at the general terminology that relates to the issue of real estate markets. Thesis is also aimed at comparision of properties evaluation’s methods in those countries and explains the concept of property in the country’s law. Next chapters are focused on comparision of the residential markets and analyse reachability of housing for residents according to their income as well as mortgage availability. Last chapter discuss about the convenience of investment to residential properties in terms of investment’s return.

Fastighetsvärderingar har historiskt sett utförts av mäklare eller experter på området. Med den växande mängden verktyg på internet för värderingar uppstår frågan hur väl verktygen presterar och vad som kan göras för att förbättra dem. Moderna metoder utgår ifrån försäljningsstatistik av liknande objekt när en värdering görs med tekniska verktyg. Detta med hjälp av olika former av metadata, bland annat storlek, läge och byggnations år. Den här studien utforskar möjligheten att använda interiört skick som variabel i värderingar av lägenheter genom att träna Convolutional Neural Networks för att klassificera for lägenheter i Stockholm, samt undersöka sambandet mellan det interiöra skicket och den felterms om Boolis varderingsalgoritm ger upphov till. Klassificeringsmodellerna tränades på insamlad data för skick av lägenhetsägare samt tillhörande visningsbilder 200 stycken bilder som erhållits av Booli. Studien visar ett statistiskt signifikant samband mellan interiört skick och feltermen från värderingar. Värderingar på lägenheter av högt skick tenderar i genomsnitt att vara 3% för låga, och 3% för höga för lägenheter av lågt skick. Resultaten indikerar att interiör som variabel kan användas för att reducera felet i Boolis varderingsalgoritm. Dock lyckades ej experimentet med att reducera feltermen i någon större utsträckning i detta arbete. / Housing evaluations has historically been made in person by real estate agents or other experts. With growing online tools for evaluations, the question arises how well they perform, and what can be done to improve them. Modern approaches use sales data for similar housing when evaluating a certain house or apartment, with variables mainly being different forms of metadata such as living area, location and year or construction. This study explores the possibility to use the interior condition as a variable in housing evaluations by training Convolutional Neural Networks to classify the condition of kitchens and bathrooms for apartments in Stockholm, Sweden and testing the relationship between said conditions and the error of Booli’s evaluation algorithm. The classification models were trained on crowd sourced data of the condition and the advertisement images for 200 images provided by Booli. The study finds that a statistically significant relationship exists between interior condition and the evaluation error, and the evaluations of apartments tends to be 3% too small on high condition apartments, while being on average 3% too large for low condition apartments. The results of the study indicates that including interior as a variable might reduce the error of Booli’s evaluation algorithm. However, the experiment for doing so in this study failed to do so in any sizeable manner.

The purpose of this study is to explore how emancipated youth, who are clients of the aftercare ILP (Independent Living Program) and transitional housing program with Cameron Hill, evaluate those services.