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Constructing Residential Price Property Indices Using Robust and Shrinkage Regression Modelling
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Konstruktion av Prisindex För Småhus Genom Tillämpning av Robusta och Shrinkage Regressionsmetoder (Swedish)
Abstract [en]

This thesis intends to construct and compare multiple Residential Price Property Indices (RPPI) with the aim to express the price development of houses in Stockholm county from January 2013 to September 2018. The index method used is the hedonic time dummy variable method. Different methods of imputation of missing data will be applied and new variables will be derived from the available data in order to develop various regression models. Observations judged as not part of the index's target population will be excluded to improve the quality of the training data. The indices will be computed by fitting the final model with OLS regression (as a benchmark), Huber regression, Tukey regression, Ridge regression as well as least-angle regression. Lastly, the obtained indices will be assessed by analyzing different measures of performance when included in \textit{Booli}'s valuation engine. The main result of this thesis is that a specific regression model is produced and that it is concluded that Huber regression slightly outperforms the other methods.

 

Abstract [sv]

Denna uppsats ämnar att konstruera och jämföra flera prisindex för hus med syftet att beskriva prisutvecklingen i Stockholms län från januari 2013 till september 2018. Indexmetoden som tillämpas är den hedoniska time dummy variabel metoden. Olika tillvägagångssätt för imputering av saknade värden används och nya variabler härleds för att skapa diverse regressionsmodeller. Observationer som ej anses representera indexets målgrupp utesluts för att således förbättra kvalitén på träningsdatan. Indexen beräknas genom att passa den slutgiltiga modellen med OLS regression (som ett riktmärke), Huber regression, Tukey regression, Ridge regression samt least-angle regression. Avslutningsvis utvärderas de erhållna prisindexen genom att analysera dess prestanda när de implementeras i \textit{Boolis} värderingsmotor. Huvudresultatet av denna uppsats är att en specifik regressionsmodell tas fram och att det fastslås att Huber regression aningen överträffar de 

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:077
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252555OAI: oai:DiVA.org:kth-252555DiVA, id: diva2:1319781
External cooperation
Booli Search Technologies AB
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-03 Last updated: 2019-06-04Bibliographically approved

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