Work detail

Real estate price modelling with a focus on location attributes

Author: Mgr. Ondřej Charvát
Year: 2020 - summer
Leaders: Mgr. Petr Polák MSc. Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 88
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/213877/
Abstract: The thesis introduces several methods of real estate price modelling suitable either for
prediction of the housing prices or for exploring the relationships between the price and its
determinants. We compared the conventional linear regression approach to the tree-based
methods of machine learning. The comparison analysis on the dataset of 28 019 apartments in
Prague suggests that regression trees (especially the Random forest) yield a higher accuracy in
the price prediction. Another objective was to examine the effects of location attributes
(especially its accessibility and environmental quality) on the prices of nearby apartments. To
address the spatial interactions in the geographical data, we employed three spatially conscious
models to achieve more reliable results. The local analysis performed with the geographically
weighted regression confirmed the presence of spatial heterogeneity and described the price
effects relative to the location. In some areas, an increase of 100 meters in distance from the
nearest metro station and the nearest park are associated with a decrease in the apartment prices
by 644 CZK/m2
and 916 CZK/m2
, respectively. These findings are especially important for the
apartments near the stations of the new metro line, which is currently in construction.

Partners

Deloitte

Sponsors

CRIF
McKinsey
Patria Finance