Hybrid Predictions of the Homogenous Properties’ Market Value with the Use of Ann
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Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
Wrocław University of Science and Technology, Faculty of Civil Engineering , Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland
Karlsruhe Institute of Technology, Institute of Technology and Management in Construction, Gotthard-Franz-Street 3, 76131 Karlsruhe, Germany
Prydniprovska State Academy of Civil Engineering and Architecture, Department of Construction Technology, 24a, Chernyshevskogo St., Dnipro, 49005, Ukraine
Submission date: 2020-07-31
Final revision date: 2020-11-17
Acceptance date: 2020-11-17
Publication date: 2021-03-31
Archives of Civil Engineering 2021;67(1):285-301
The homogenous properties – as flats are – have the set of key features that characterizes them. The area of a flat, the number of rooms and storey number where it is located, the technical state of a building, and the state of the vicinity of the blocks of flats assessed. The database comprises 222 flats with their transaction prices on the secondary estate market. The analysed flats are located in a certain quarter of Wrocław city in Poland. The database is large enough to apply machine learning for successful price predictions. Their close locations significantly lower the influence of clients’ assessments of the attractiveness of the location on the flat’s price. The hybrid approach is applied, where classifying precedes the solution of the regression problem. Dependently on the class of flats, the mean absolute percentage error achieved through the calculations presented in the article varies from 4,4 % to 7,8 %. In the classes of flats where the number of cases doesn’t allow for machine predicting, multivariate linear regression is applied. The reliable use of machine learning tools has proved that the automated valuation of homogenous types of properties can produce price predictions with the error low enough for real applications.
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