Asuntojen hintatrendien tilastollinen mallinnus ja visualisointi
Mika Honkanen - Published 1.2.2018 - updated 27.3.2018
"Kannattaakokauppa.fi visualises apartment price trends by postal code regions in Finland between years 2005 and 2014. With the help of Bayesian probabilistic modelling, we can get much more reliable estimates of the real price and trend values than by looking at individual data points. The results are provided in a nice interactive visualisation that allows the exploration of prices and trends across the country. The service also provides predictions for year 2016. Details of the data and statistical modelling can be found from the links below. Link to the work http://kannattaakokauppa.fi/ Category Public Services Open data sources Apartment price data from Statistics Finland: http://www.stat.fi/til/ashi/index.html Paavo - Open data by postal code area: https://www.stat.fi/tup/paavo/index_en.html Data accessed with rOpenGov R packages pxweb and gisfin: http://ropengov.github.io/projects/ Postal code regions as map data from Duukkis: http://www.palomaki.info/apps/pnro/ Contact person and other team members Juuso Parkkinen, Janne Sinkkonen, Johan Himberg, Janne Hietamäki, Janne Aukia, Siru Kallström, Jaakko Särelä audience All citizens interested in apartment price trends. Also data scientists and students can learn from the open analysis. comparison Regional apartment prices are currently reported annually as simple lists of the top and bottom regions. Such lists are based on very noisy values, especially in regions with very few transactions per year, and can thus lead to incorrect conclusions. This is an example where statistical modelling can help to provide more reliable conclusions. future We would like to include more postal code level data to better explain the price trends. For example, demographics data from Paavo could be integrated into the model, so we could both provide better estimates and also identify which demographic factors are related to apartment prices.