Tid: 24 maj 2017 kl 16.00-17.00.Seminarierummet 3418, Institutionen för matematik, KTH, Lindstedtsvägen 25, plan 7. Karta!
Föredragshållare: Otto Ryden
Titel: Statistical learning procedures for analysis of residential property price indexes
Abstract Residential Price Property Indexes (RPPIs) are used to study the price development of residential property over time. Modeling and analysing an RPPI is not straightforward due to residential property being a heterogeneous good. This thesis focuses on analysing the properties of the two most conventional hedonic index modeling approaches, the hedonic time dummy method and the hedonic imputation method. These two methods are analysed with statistical learning procedures from a regression perspective, specically, ordinary least squares regression, and a number of more advanced regression approaches, Huber regression, lasso regression, ridge regression and principal component regression. The analysis is based on the data from 56 000 apartment transactions in Stockholm during the period 2013-2016 and results in several models of a RPPI. These suggested models are then validated using both qualitative and quantitative methods, specically a bootstrap re-sampling to perform analyses of an empirical condence interval for the index values and a mean squared errors analysis of the dierent index periods. Main results of this thesis show that the hedonic time dummy index methodology produces indexes with smaller variances and more robust indexes for smaller datasets. It is further shown that modeling of RPPIs with robust regression generally results in a more stable index that is less aected by outliers in the underlying transaction data. This type of robust regression strategy is therefore recommended for a commercial implementation of an RPPI.
|Sidansvarig: Filip Lindskog