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Regressionsanalys av faktorer som påverkar skogsfastighetspriser i Sverige
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2015 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

I denna studie utformas en prediktionsmodell för försäljningspriser på skogsfastigheter i Sverige. Syftet är att ge marknadsaktörer ett verktyg för att bedöma till vilket pris skogsfastigheter i Sverige förväntas säljas.

Modellen bygger på multipel linjär regressionsanalys av skogsfastigheter sålda av fastighetsförmedlaren Areal mellan 2012 och 2014. De förklarande faktorerna som ingår i modellen är geografiskt läge, virkesförråd, bonitet, befolkningstäthet och huggningsklasser.

Modellen lyckas prediktera försäljningspriset med en förklaringsgrad på 90.0 procent, vilket är tillräckligt högt för att målet ska anses vara uppfyllt. Denna studie har utöver prediktionsmodellen också funnit intressanta strukturella samband.

Abstract [en]

In this study, a prediction model for selling prices of forest properties in Sweden is constructed. The purpose is to give the market operators a tool for estimating the expected selling prices of properties.

The model is based on multiple linear regression analysis of forest properties sold by the real estate company Areal between 2012 and 2014. The explanatory factors used in the model are geographical position, standing stock of timber, standing volume fertility, population density and cutting classes.

The model succeeds in predicting the selling price with a coefficient of determination at 90.0 percent, which is high enough for the aim to be considered fulfilled. Beyond the prediction model, this study has also found interesting structural relations.

Place, publisher, year, edition, pages
2015. , 34 p.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-168028OAI: oai:DiVA.org:kth-168028DiVA: diva2:813986
Supervisors
Available from: 2015-05-25 Created: 2015-05-25 Last updated: 2015-05-25Bibliographically approved

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