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Hybrid predictions of the homogenous properties’ market value with the use of ann [Prognozowanie wartości rynkowej jednorodnych nieruchomości hybrydowym modelem z wykorzystaniem sztucznych sieci neuronowych]

Anysz, H.; Podwórna, M.; Ibadov, N.; Lennerts, K.; Dikarev, K.


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.

Verlagsausgabe §
DOI: 10.5445/IR/1000136953
Veröffentlicht am 06.09.2021
DOI: 10.24425/ace.2021.136474
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technologie und Management im Baubetrieb (TMB)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1230-2945
KITopen-ID: 1000136953
Erschienen in Archives of Civil Engineering
Band 67
Heft 1
Seiten 285-301
Schlagwörter real estate valuation, ANN, comparative approach, machine learning, hybrid models
Nachgewiesen in Dimensions
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