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Prediction of residential real estate selling prices using neural networks
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Uppskattning av bostadspriser med neurala nätverk (Swedish)
Abstract [en]

With the rising housing prices of the last 20 years, the appraisal of real estate has become more difficult. Underlined by the large differences between listing and selling prices, the valuation process brings a level of uncertainty. With the advances within the field of machine learning in recent years, attempts have been made to apply these techniques to the real estate market. This thesis investigates the potential of using neural networks to predict selling prices of apartments in Stockholm, based on apartment parameters. Networks are trained to either make an improved valuation, based on a listing price, or make a new valuation of an apartment. The results are promising, and in line with contemporary findings; however, the worst-case performance of the models could make them unsuitable for many purposes.

Abstract [sv]

Med stigande bostadspriser under de senaste tjugo åren har fastighetsvärdering blivit en svårare uppgift. Värderingsprocesssen medför en grad av osäkerhet, märkbar på de stora skillnaderna mellan utgångs- och försäljningspriser. Efter stora framsteg inom maskininlärning de senaste åren har försök gjorts att applicera dessa tekniker på bostadsmarknaden. Den här studien utforskar möjligheterna att använda neurala nätverk för att uppskatta försäljningspriser av lägenheter i Stockholm, baserat på lägenhetsparametrar. Nätverken tränas för att antingen göra en förbättrad värdering utifrån utgångspriset, eller för att göra en ny värdering av en lägenhet. Resultat visar på potential, och är i linje med liknande försök, men värstafallsprestandan kan göra modellen olämplig att använda för många syften.

Place, publisher, year, edition, pages
2019. , p. 37
Series
TRITA-EECS-EX ; 2019:70
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-249637OAI: oai:DiVA.org:kth-249637DiVA, id: diva2:1304961
Supervisors
Examiners
Available from: 2019-05-15 Created: 2019-04-15 Last updated: 2019-05-15Bibliographically approved

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