Title: Urban Data Streams and Machine Learning: A Case of Swiss Real Estate Market,
Abstract: In this paper, we show how using publicly available data streams and machine
learning algorithms one can develop practical data driven services with no
input from domain experts as a form of prior knowledge. We report the initial
steps toward development of a real estate portal in Switzerland. Based on
continuous web crawling of publicly available real estate advertisements and
using building data from Open Street Map, we developed a system, where we
roughly estimate the rental and sale price indexes of 1.7 million buildings
across the country. In addition to these rough estimates, we developed a web
based API for accurate automated valuation of rental prices of individual
properties and spatial sensitivity analysis of rental market. We tested several
established function approximation methods against the test data to check the
quality of the rental price estimations and based on our experiments, Random
Forest gives very reasonable results with the median absolute relative error of
6.57 percent, which is comparable with the state of the art in the industry. We
argue that while recently there have been successful cases of real estate
portals, which are based on Big Data, majority of the existing solutions are
expensive, limited to certain users and mostly with non-transparent underlying
systems. As an alternative we discuss, how using the crawled data sets and
other open data sets provided from different institutes it is easily possible
to develop data driven services for spatial and temporal sensitivity analysis
in the real estate market to be used for different stakeholders. We believe
that this kind of digital literacy can disrupt many other existing business
concepts across many domains.