Incorporating Interior Property Images for Predicting Housing Values
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2024
Författare
Gortzak, Adrian
Ulusoy, Nedim Can
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The property valuation process for the real estate market is essential for predicting
a fair market value. This process is traditionally carried out by brokers, including
inspecting and assessing the subject property to find comparable sales for comparative
market analysis (CMA). Meanwhile, an automated valuation model (AVM) can
help achieve an autonomous version of this process, which speeds up the process but
lacks some of the inputs that a manual assessment provides. AVMs have difficulty
considering more subjective architectural qualities, such as beauty, stability, and utility,
due to the difficulty of quantifying these aspects objectively. New advancements
in Visual Transformers (ViT), self-supervised learning and Contrastive Language-
Image Pre-training (CLIP) technologies have shown favourable improvements in the
field of computer vision. Therefore, this study explores the potential improvements
of these new techniques within the visual feature extraction task to enhance the
AVMs from interior images. By applying ViTs as binary classifiers, clusters, and
textual descriptions matching, we aim to enrich the feature extraction process for
a property valuation model in the region of Uppsala County, Sweden. Our findings
show modest enhancements in the AVM’s performance, which align with prior
studies, but also highlight that these new technologies can extract more detailed features
compared to previous methods. Furthermore, they demonstrate the potential
for these technologies to capture more comprehensible architectural qualities from
images, which could significantly assist brokers in the valuation process.
Beskrivning
Ämne/nyckelord
Computer Vision, Transformers, Feature Extraction, Machine Learning, Deep Learning, Real Estate, Automated Valuation Models, Architectural Qualities.